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� GLACIOLOGICAL
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� DATA
ISSN 0149-1776
SNOW WATCH '85
March 1986
World Data Center A
for
Glaciology
�now and Ic �
l c
WORLD DATA CENTER A
National Academy of Sciences
2101 Constitution Avenue, NW
Washington, D.C. 20418 USA
World Data Center A consists of the Coordination Office
and the following eight Subcenters:
GLACIOLOGY (Snow and Ice)
World Data Center A: Glaciology
(Snow and Ice)
Cooperative lnst. for Research in
Environmental Sciences
University of Colorado
Boulder, Colorado 80309 USA
Telephone: (303) 492·5171
MARINE GEOLOGY AND GEOPHYSIC S
(Gravity, Magnetics, Bathymetry,
Seismic Profiles, Marine Sediment,
and Rock Analyses):
World Data Center A for Marine
Geology and Geophysics
NOAA, EJGC3
325 Broadway
Boulder, Colorado 80303 USA
Telephone: (303) 497·6487
METEOROLOGY (and Nuclear Radiation)
World Data Center A: Meteorology
National Climatic Data Center
NOAA,EJCC
Federal Building
Asheville, North Carolina 28801 USA
Telephone: (704) 259-0682
OCEANOGRAPHY
World Data Center A: Oceanography
National Oceanographic Data Center
NOAA, E/OC
2001 Wisconsin Avenue, NW
Page Bldg. 1, Am. 414
Washington, D.C. 20235 USA
Telephone: (202) 634-7510
COORDINATION OFFICE
World Data Center A
National Academy of Sciences
2101 Constitution Avenue, NW
Washington, D.C. 20418 USA
[Telephone: (202) 334·3359]
'
.......
ROCKETS AND SATELL ITES
World Data Center A: Rockets and
Satellites
Goddard Space Flight Center
Code 601
Greenbelt, Maryland 20771 USA
Telephone: (301) 344·6695
ROTATION OF THE EARTH
World Data Center A: Rotation
of the Earth
U.S. Naval Observatory
Washington, D.C. 20390 USA
Telephone: (202) 653-1529
SOLAR-TERREST RI AL PHYS IC S (Solar and
Interplanetary Phenomena, Ionospheric
Phenomena, Flare-Associated Events,
Geomagnetic Variations, Aurora,
Cosmic Rays, Airglow):
World Data Center A
for Solar-Terrestrial Physics
NOAA, EJGC2
325 Broadway
Boulder, Colorado 80303 USA
Telephone: (303) 497-6323
SOLID·EARTH GEOPHYSIC S (Seismology,
Tsunamis, Gravimetry, Earth Tides,
Recent Movements of the Earth's
Crust, Magnetic Measurements,
Paleomagnetism and Archeomagnetism,
Volcanology, Geothermics):
World Data Center A
for Solid-Earth Geophysics
NOAA, E/GC1
325 Broadway
Boulder, Colorado 80303 USA
Telephone: (303) 497·6521
World Data Centers conduct international exchange of geophysical observations in accordance with the principles set forth
by the International Council of Scientific Unions. WDC·A is established in the United States under the auspices of the National
Academy of Sciences. Communications regarding data interchange matters in general and World Data Center A as a whole
should be addressed to World Data Center A, Coordination Office (see address above). Inquiries and communications concern·
ing data in specific disciplines should be addressed to the appropriate subcenter listed above.
GLACIOLOGICAL
DATA
REPORT GD-18
SNOW WATCH '85
Edited by
G. Kukla
Lamont-Doherty Geological Observatory
Columbia University
Palisades, :\Tew York, U.S.A.
A. Hecht
Climate Dynamics Program
National Science Foundation
Washington, D.C., U.S.A.
-· R.G. Barry
National Snow and Ice Data Center
Cooperative Institute for Research
in Environmental Sciences
University of Colorado
Boulder, Colorado, U.S.A.
D. Wiesnet
Satellite Hydrology Associates
Vienna, Virginia, U.S.A.
Published by:
WORLD DATA CENTER FOR GLACIOLOGY
[SNOW AND ICE]
Cooperative Institute for Research in Environmental Sciences
University of Colorado
8oulder, Colorado 80309 U.S.A.
Operated for:
U.S. Department of Commerce
National Oceanic and Atmospheric Administration
National Environmental Satellite, Data, and Information Service
Boulder, Colorado 80303 U.S.A.
March 1986
DESCRIPTION OF WORLD DATA CENTERS 1
woe-A: Glaciology <Snow and Ice> Is one of three International data centers serving the field of gla-
ciology under the guidance of the International Council of Scientific lkllons Panel of World Data
Centers• It Is part of the World Data Center System created by the scientific community In order to
promote worldwide exchange and dissemination of geophysical Information and data. WDC-A endeavors to
be promptly responsive to Inquiries from the sclentlflc community, and to provide data and biblio-
graphic services In exchange for copies of publications or data by the participating scientists•
t. The addresses of the three WDCs for Glaciology and of a related Permanent Service are:
World Data Center A
University of Colorado
Campus Box 449
Boulder, Colorado, 80309 U.S.A.
World Data Centre C
Scott Polar Research Institute
Lensfleld Road
Cambridge, CB2 lER, England
2. Subject Matter
World Data Center B
Molodezhnaya .3
Moscow 117 296, USSR
Permanent Service on the Fluctuations
of Glaciers
Swiss Federal Institute of Technology
CH-8092 Zurich, Switzerland
WDCs will collect, store, and disseminate Information and data on Glaciology as follows:
Studies of snow and Ice, Including seasonal snow, glaciers, sea, river, or lake Ice, seasonal or
perennial Ice In the ground, extraterrestrial Ice and frost.
Material dealing with the occurrence, properties, processes, and effects of snow and Ice, and
techniques of observlng,and analyzing these occurrences, processs, properties, and effects, and
Ice physics.
Material concerning the effects of present day and snow and Tee should be limited to those In
which the Information on Ice Itself, or the effect of snow and Ice on the physical environment,
make up an appreciable portion of the material.
Treatment of snow and Ice masses of the historic or geologic past, or paleoclimatic chronologies
will be limited to those containing data or techniques which are applicable to existing snow and
Ice.
!International Council of Scientific Unions. Panel on World Data Centers. (1979) Guide to Internat-
Ional Data Exchange Through the World Data Centres. 4th ed. Washington, o.c. 113p.
+rhe lowest level of data useful to other prospective users.
This guide for Glaciology was prepared by the International Commission on Snow and Ice (ICSI> and was
approved by the International Association of Hydrological Sciences (IAHS> In 1978.
iii
3. Description and Form of Data Presentation
3·1 Genera I· woes •collect, store . and are prepared to dT ssem I nate raw, analyzed, and pub IT shed
data, Including photographs. woes can advise researchers and Institutions on preferred formats
for such data submissions• Data dealing with any subject matter listed In (2) above will be
accepted· Researchers shou I d be aware that the woes are prepared to organIze and store data
which may be too detailed or bulky for Inclusion Tn published works. It Is understood that such
data which are submitted to the woes will be made available according to guidelines set down by
the ICSU Panel on woes Tn thts Guide to International Data Exchange. Such material will be
available to researchers as copies from the woe at cost, or If It Is not practicable to copy the
mater! al, It can be consulted at the woe. In all cases the person receiving the data will be
expected to respect the usual rights, Including acknowledgement, of the original Investigator•
3.2 Fluctuations of Glaciers. The Permanent Service Ts responsible for reoetvtng data on the fluctu~
atlons of glaciers. The types of data which should be sent to the Permanent Service are detailed
In UNESCO/lASH (1969>*• These data should be sent through National Qlrrespondents In ttne to be
Included In the regular reports of the Permanent Service every four years (1964-68, 1968-72,
etc•>• Publications of the Permanent Service are also available through the Woes.
3.3 Inventory of Perennial Snow and Ice Masses. A Temporary Technical Secretariat <TTS> was recently
established for the completion of this IHD project at the SWISS Federal Institute of Technology
Tn Zurich. Relevant data, preferable Tn the desired format**, can be sent directly to the TTS
or to the World Data Centers for forwarding to the TTS.
3.4 other International Programs. The Wort d Data Centers are equipped to expedite the exchange of
data for ongoing projects such as those of the International Hydrological Project (especially the
studies of combined heat, Ice and water balances at selected glacier basins***>, the Interna-
tional Antarctic Glaciological Project <IAGP>, and G-'eenland Ice Sheet Project (GISP>, etc.~ and
for other developing projects In the ftel d of snow and Ice.
4. Transmission of Data to the Centers
In order that the woes may serve as data and Information centers, researchers and Institutions
are encouraged:
4.1 To send Woes raw or analyzed data Tn the form of tables, computer tapes, photographs, etc., and
reprints of all published papers and public reports which contain glaciological data or data
analysts as described under heading <2>; one copy should be sent to each woe or, alternatively,
three copies to one WDC for dTstrtbutTon to the other woes.
4•2 To notify WDCs of changes In operations Involving International glaciological projects, Including
termination of previously existing stations or major experiments, commencement of new experle-
ments, and Important changes In mode of operation.
*UNESCO/lASH (1969) Variations of Existing Glaciers. A Guide to International Practices for their
Measurement.
**UNESCO/lASH <1970a) Perennial Ice and Snow Masses. A Guide for Compilation and Assemblage of Data
for a World Inventory; and
***UNESCO/lASH <1970b) Combined Heat, Ice and Water Balances at Selected Glacier Basins. A Guide for
Compilation and Assemblage of Data for Glacier Mass Balance Measurements; and
UNESCO/lASH <1973) Combined Heat, Ice and Water Balances at Selected Glacier Basins•
Part II, SPecifications, Standards and Data Exchange.
iv
FOREWORD
This issue of Glaciological Data reports on the results of a workshop on
snow cover and its role is the climate system, specifically as it may relate
to potential C02-induced climatic changes. The workshop, held in October 1985
at the University of Maryland, College Park, was a succesor to SNOW WATCH 1980
(Glaciological Data Report GD-11). Apart from the presentation of scientific
papers, the 40 participants :from 4 countries divided into three working groups
to disucss questions of data bases, the role of snow cover in the climate
system and the treatment of snow cover processes in climate models. The
recommendations from these groups, included here, will be forwarded to the
sponsors of the meeting -the World Meterological Organization, the Carbon
Dioxide Research Division of the u.s. Department of Energy, the Office of Cli-
·mate Dynamices of the National Science Foundation, the National Environmental
Satellite, Data, and Information Service of the National Ocean and Atmospheric
Administration and the International Commission on Snow and Ice. The wider
dissemination of these results through our mailing list will, we hope, in-
crease awareness of the importance of these issues in the climatological and
glaciological communities.
We are pleased to acknowledge the support provided through the u.s.
Department of Energy, Carbon Dioxide Research Division, by agreement
DE-FG02-85ER60366 to Dr. G. Kukla, for the publication costs of this issue.
v
R.G. Barry
Director
World Data Center-A for
Glaciology (Snow & Ice)
PREFACE
SNOWWATCH 1985: Workshop on C02/Snow Interaction
The growth and decay of Pleistocene ice sheets demonstrated how
changeable the earth's climate is and how important is the role which snow
plays in the climate system. Because rapidly increasing concentrations of
many greenhouse gases in the atmosphere are expected to result in a major
world wide warming, ·it is important to know to what degree changes in snow
cover will amplify the C02 impact. To address this problem, researchers from
several countries gathered on October 28-30, 1985, at the University of
Maryland, to discuss existing knowledge on the potential role of snow in the
C02-induced climate change and to identify problems in need of further
research.
The workshop was sponsored by the World Meteorological Organization, the
Carbon Dioxide Research Division of the Department of Energy, the Office of
Climate Dynamics of the National Sciences Foundation, the National
Environmental Satellite, Data, and Information Service of the National Oceanic
and Atmospheric Administration and the International Commission on Snow and
Ice of the International Association of Hydrological Sciences. Funding for
the workshop was provided by the u.s. Department of Energy, Carbon Dioxide
Research Division by agreement DE-FG02-85ER60366 and by grant ATM-83-18676
from the Office of Climate Dynamics of the National Science Foundation.
The workshop was a follow-up to an earlier SNOW WATCH conference held in
Washington D.C. in 1980 (Glaciological Data Report GD-11, 1981). The first
two days were dedicated to individual presentations which are included in this
volume. On the third day the participants, divided into three working groups,
discussed and drafted recommendations of actions considered to be of highest
priority.
vii
Organizing Committee
G. Kukla
R.G. Barry
D.R. Wiesnet
A.D. Hecht
CONTENTS
Page
FOREWORD • . . . . . . . . . . . . . . . . . . . v
PREFACE . . . . . . . . . . . . . . . . . vii
SNOW WATCH '85
(University of Maryland, 28-30 OCTOBER 1'85)
RECOMMENDATIONS . . . . . . . . . . . . . . . . . . . . 1
PARTICIPANTS • • • . . . . . . . . . . . . . . . 19
Climate !.pact of Snow
Snow Cover, Cyclogenesis and Cyclone Trajectories -J. Walsh • • 23
The Relationship between Snow Cover and Atmospheric Thermal and
Circulation Anomalies ~K.F. Dewey and R. Heim, Jr. • • • • 37
Relationships between Snow Cover and Temperature in the Lower
Troposphere, General Circulation in East Asia and Precipitation
in China-z. Zhao and:S. Wang • • • • • • • • • • • • • • 55
Progression of Regional Snow Melt -~D.A. Robinson . . . . . . . . .
Soot from Arctic Haze: Radiation Effects on the Arctic Snowpack
v·-S .G. Warren and~A.D. Clarke •••••••••••••••
Ground Station Data
The Snow Cover Record in Eurasia -'J. Foster • . . . .
Distribution of Snow Cover in China ~P. Li . . . . . . . . . .
Snow Surveying in Canada -B. Goodison • . . . . . . .
Satellite Data Bases
. .
Snow Cover in Real Time Climate Monitoring --C.F. Ropelewski . . . .
Northern Hemisphere Snow and Ice Chart of NOAA/NESDIS
-T. Baldwin ••••• . . . . . . .
NOAA Satellite-Derived Snow Cover Data Base:
Past, Present and Future -YM. Matson •••••••••• . . .
Joint Ice Center Global Sea Ice Digital Data -C.E. Gross
ix
63
73
79
89
97
105
109
115
125
Remote Sensing
Snow Cover Data: v Status and Future Prospects -R.G. Barry • • • • • 127
Comparison of Northern Hemisphere Snow Cover Data Sets
-vA. Robock and~. Scialdone ••• • • • • • • • . . .
Influence of Snow Structure Variability on Global Snow Depth
Measurement Using Microwave Radiometry -vD.K. Hall
Retrieval of Snow Water Equivalent from Nimbus-7 SMMR Data
v-M. Hallikainen and P. Jolma •••••••••••••
Nimbus-7 SMMR Snow Cover Data -~.T.c. Chang • . . . . .
Snow Cover Monitoring using Microwave Radiometry -~N. Grody
Remote Sensing of Snow Properties in Mountainous Terrain
_v' J. Dozier • • • • • • • • • • • • • • • • • . .
Remote Sensing of Snow Cover over the Carpathian Watersheds
v-H. Grumazescu • • • • • • • • • • • • • • • • • • •
Snow Cover 'MOdeling
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
Effects of Snow Cover and Tropical Forcing on Mid-Latitude Monthly
141
161
173
181
189
193
205
Mean Circulation -VA. Robock andvJ.W. Tauss •••••••••• 207
Parameterization of Snow Albedo for Climate Models
v-·s. Marshall andvS.G. Warren •••••• • • . . . . . . . 215
Modelling of a Seasonal Snowcover _vE.M. Morris • . . 225
Characteristics of Seasonal Snow Cover as Simulated by GFDL Climate
Models -vA• Broccoli • • • • • • • • • • • • • • • • • • • • • 241
C02~Induced Changes in Seasonal Snow Cover Simulated by the OSU
Coupled Atmosphere-Ocean General Circulation Model
-M•v'Schlesinger • • • • • • • • • • • • • • • • • • • • • 249
ACRONYMS and ABBREVIATIONS • . . . . . . . . . . . . . . . . . . . . 271
NOTES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273
X
Kukla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) SNOW WATCH '85.
Proceedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College Park, MD. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice), Glaciological Data, Report GD-18, p.1-17.
SNOW WATCH '85
RECOMMENDATIONS
The participants divided into three working groups to discuss the actions
deemed to be of the highest urgency in specific areas. These groups addressed
the following topics:
I. Data bases
II. Snow-climate interactions
III. Modeling and simulation of snow covers
Some of the tasks were assigned high priority recommendations of more
than one working group.
Data Bases: Working Group I
Edited by B.E. Goodison.
Members: R.G. Barry, A. Chang, J. Foster, J. Gavin, D. Hall, P. Li, M.
Matson, D. Robinson, c. Ropelewski; D. Wiesnet.
Background .
Most, it not all, snow cover data (notably extent, depth, water equiva-
lent and albedo) are collected by agencies for purposes other than climatol-
ogy. In addressing the question of C02/ snow interaction the snow data base
is one area which requires special attention if research on co2-induced cli-
mate change is to progress. The working group focused on data needs, new
sources of data, and recommended action required to ensure a data base useful
to the climate modeling community. It is recognized that an efficient method
of integrating remotely sensed data with conventional ground data must be
found if sufficiently accurate information on snow is to be obtained on a glo-
bal scale. Because methods of acquiring the data were addressed in the pre-
sented papers, a review will not be presented here. However, the important
question of data management will be given special attention since it is
crucial to successful use of the data, whatever their origin.
Satellite data are particularly suited to address this global scale of
study while at the regional scale integration of ground and satellite derived
snow cover data is required.
1
Integration of Conventional and Remotely Sensed Data
Depth of ~now on the ground is measured daily at several thousand meteo-
rological stations world-wide. Reports of several hundred of these stations
are currently collected by u.s. National Weather Service/National Meteorologi-
cal Center (NWS/NMC). The National Oceanic and Atmospheric Adminstration/Na-
tional Environmental Satellite, Data and Information Service (NOAA/NESDIS)
currently produces satellite derived charts of snow cover in the Northern
Hemisphere. The NOAA/Navy Joint Ice Center produces charts of the sea ice ex-
tent in both hemispheres. Automated and interactive digital and chart pro-
ducts showing snow cover extent on land and ice in the two hemispheres based
on Advanced Microwave Sounding Unit (AMSU) and 1.6 Jll1l.sensor returns are plan-
ned to be produced by NOAA starting in 1990.
Recommendations
1. Integrate conventional and remotely sensed data to create more com-
plete snow cover data sets. Surface station data sets could be used
to refine and cross-check the validity of satellite-derived pro-
ducts. The accuracy and completeness of merged data sets will be
dependent on the quality and density of data sources (see Data Man-
agement).
2. Produce the merged data sets, for both the Northern and Southern
Hemispheres, at daily (instead of current weekly) intervals.
Archival of the satellite products and the conventional data could
be performed separately, but in compatible formats. NOAA/NESDIS
should produce the merged data set.
Regional ~ata Bases
Many of the C02-related changes of snow cover will be in middle latitudes
and the effects are expected to be localized. In order to provide a base for
impact assessment analysis of both practical and economic significance, re-
gional data bases of snow cover must be created.
Recommendations
1. Identify key regions for monitoring changes based on the results of
model sensitivity studies as well as on agricultrual and hydro-
logical impact studies.
2. Assemble longer term data bases (50-100 years) on snow extent,
depth, duration and water equivalent on a regional scale. Such re-
cords can be used to monitor changes over time and to improve cur-
rent estimates of variability.
3. Use proxy data to the fullest extent. Permafrost records could
serve as a useful indicator of long-term temperature change. Other
meteorological variables should be coupled with snow information to
produce climate indices.
2
Microwave Data
On-going research indicates that passive microwave radiometry, which can
image day and night through most clouds, has utility for mapping snow cover
extent, depth, and water equivalent and can indicate the presence of wet snow
on regional or continental scales. Dry snow can be distinguished from snow-
free land, thus allowing identification of the snowline. Dry snow and melting
snow have very different signals, their differentiation is possible by contin-
uously monitoring the snow throughout the accumulation and depletion periods.
It is feasible to develop algorithms to estimate snow depth in specific re-
gions. On a global scale, it appears feasible to distingish broad categories
of snow depth which may be useful for studies which require information on the
presistence of snow cover over broad regions (cf. Kunzi et al. 1982; Hall,
1986).
In order to improve existing algorithms, research must be continued to
resolve the following problems which affect the snow depth/brightness tempera-
ture relationship:
a) Effect of snow structure -further modeling is required to look at
the effect of ice lenses and layers and snow metamorphism (fresh vs.
old);
b) Effect of vegetation type -the presence of dense coniferous forests
is a major factor adversely affecting the snow depth/TB
relationship;
c) Effect of steep topography.
Snow covered area measurements have proven to be beneficial in hydrolog-
ical, agricultural, and climatological applications. Sat~llite microwave
radiometry is promising in being able to determine snow depth/water equivalent
under all weather conditions and during day and night.
Recommendations:
1. Exploit all potential information from new satellite sensors, espe-
cially passive and active microwave. Routine data collection, ar-
chiving, data mangement and funding for such activities must be
ensured. These data bases should be developed from the time opera-
tional data acquisition begins.
2. Conduct additional analysis of the effect of regional snow structure
and snow depth determination, particularly in coniferous boreal
forests.
3. Support continued refinement and development of snow depth/water
equivalent algorithms derived from passive microwave data. The u.s.
High Plains and Canadian prairies are a desirable test area. A near
continuous data record, covering a wide range of snow conditions,
should be compiled. The data set should include passive microwave
measurements (e.g. SMMR), visible and infrared data, ground measure-
ments of snow depth, water equivalent, snow state and structure.
3
4. Operate SMMR (NIMBUS-7) and SSM/I (DMSP) sensors simultaneously for
~ period during the first winter season to compare the calibration
of the two sensors • ..
5. Support research programs for validation of satellite derived snow
cover parameters using ground truth.
6. Combine NOAA ground station and visible data with microwave data for
snow cover mapping, particularly when the snowpack is wet or less
than 5 em deep when it is difficult to resolve the snow bounda~y, or
where the shortwave albedo is of interest.
Data Management
Data management is one of the most crucial aspects in meeting users'
needs for snow data. Data management comprises the assembly, quality control,
archiving, cataloging, and distribution of data. Considerable progress has
been made in some areas of snow cover data management since Snow Watch 1980
was held. The following accomplishments are noted:
a) Digitization and archival of satellite-derived Northern Hemisphere
snow cover charts have been implemented and will continue.
b) An experimental seven year set of Southern Hemisphere snow cover
charts was produced, digitized, and archived.
c) Plans for improved passive microwave and snow/cloud discrimination
measurements from future operational satellites have been approved
for the late 1980s-early 1990s.
d) NOAA-NESDIS weekly snow charts now omit the previously used arbi-
trary reflectance categories and include dates the snowline was ob-
served.
Areas where no progress in data management has been made include:
a) International exchange of snow cover data collected at ground sta-
tions. The Eurasian data are particularly needed.
b) Mandatory global exchange on the Global Telecommunications System
(GTS) of precipitation and snow depth data from SYNOP reports.
Global exchange of these regional data are not prohibited, and could
be organized. In addition reporting procedures differ between
regions and require standarization.
c) The absence of unified archives on snow depth and water equivalent
for climate model verification.
d) The failure to address user needs for ready access to manageable
data sets (ground and satellite-derived) on snow cover properties.
e) The failure to differentiate modeled and observed input in the glo-
bal snow analysis of the Air Force Global Weather Central (AFGWC).
f) The failure to assure appropriate data quality and homogeneity
·checks of the operational snow charts.
4
While noting the progress since recommendations were first made in 1980,
it is felt that there still remain key needs which should be addressed at this
time.
Recommendations
1. To help clarify snow cover/atmospheric interactions, archives of
daily temporal resolution are required, with area-averaged snow
cover properties on scales appcopriate to numerical models. These
properties are the snow cover presence or absence, melting/non-
melting, depth, water equivalent and short-wave albedo. Target ac-
curacies should be to WMO standards.
2. Urge WMO, through the Commissions on Climate, Agricultural Meteoro-
logy, Hydrology, Basic Systems and Instruments and Methods of Ob-
servation to standardize the obervation and reporting of precipita-
tion and snow-depth data and to implement mandatory global exchange
of these data.
3. Provide relevant terrain and land surface type information routinely
with snow cover data.
4. Continue current NESDIS snow cover maps and digital archives.
Automated and interactive display and analysis systems are required
to facilitate the interpretation procedures.
5. Check data quality and homogeneity of the NOAA/NESDIS snow charts
and apply needed corrections applied.
6. Following algorithm development and testing, routine production of
snow cover products is required from the anticipated DMSP and N-Ross
SSM/I sensor, NOAA AMSU sensors, and the NOAA 1.6 ~~ sensor. Radi-
ance data in gridded formats should also be retained. Processing
procedures and algorithms should be fully documented in the data
files.
7. Initiate pilot studies for developing merged snow cover data pro-
ducts from different sensors and platforms, and conventional ground
data.
8. Routinely extract conventional ground station reports of snow depth
and new snowfall/precipitation from the GTS data streams and ar-
chived through collaboration of NMC and the National Snow and Ice
Data Center (NSIDC).
9. An inventory of snow depth data sets should be made by NCDC and
other national data centers. Such snow depth data sets should be
archived, documented and made available to the user community
through the appropriate national/world data centers.
10. Ensure adequate long-term continuing funding must be ensured for
data collection, archiving and distribution.
5
Su~ary
There is ~ need for development of integrated climatological data bases
of snow cover if questions related to COz/snow interaction are to be answered.
Continued collection of relevant snow data from ground, airborne and satellite
observations must be ensured. Conventional ground observations of daily
snow depth must continue to be observed and reported at all synoptic meteoro-
logical stations. Observation of daily snow depth at designated climate sta-
tions is recommended. Automatic snow depth sensors should be developed and
used at stations being automated to ensure data continuity. Tests of data
compatibility with changes in methods of observation are implicitly assumed.
Current and future satellite products must be merged with conventional
data on regional and global scales compatible with those used by climate
modelers. Creation of snow cover data sets compiled from all existing ground,
airborne, and satellite systems for validation of climate models and for use
in regional impact studies in climate sensitive areas is highly recommended.
The most important element is an efficient data management system that will
ensure collection, archiving, quality control and distribution of snow cover
data in a format that will meet user needs.
6
Snow-Climate Interactions: Working Group II
Edited by J. Walsh, G. Kukla.
Members: K. Dewey, G. Kukla, A. Robock, J. Walsh, z. Zhao.
The following recommendations pertain to snow cover and its interactions
with the atmosphere over timescales ranging from days to years. The longer of
these timescales provides the more appropriate context for the consideration
of COz-induced climate changes. However, the role of snow cover in COz-induced
climate changes may be viewed as a composite result of processes involving
cryosphere-atmosphere interactions over seasons, months, and even the shorter
timescales of individual synoptic events.
Snow cover may also play a major role in climatic fluctuations that are
independent of COz. Short-term climatic variability, for example, represents
the major scientific challenge in seasonal and monthly forecasts. To the ex-
tent that these forecasts may be improved by consideration of the earth's
lower boundary, snow cover is directly relevant to the World Climate Program
(WCP) objective of extending the range and accuracy of atmospheric predic-
tions.
Detection of Trends
Detection of signiftcant snow trends, whether COz-induced or not, re-
quires the analysis of data-derived indices of snow cover. Insofar as snow
cover is quite sensitive to air temperature, snow can be a useful indicator of
surface temperature changes. Because the albedo~temperature feedback may
amplify perturbations of snow cover and because the areal distribution of snow
in station-sparse regions is more amenable than air temperature to remote
measurement, snow data may provide the earliest warning of an incipient cli-
matic change. Trend detection is severely hampered, however, by inhomogene-
ities in time series of data, and by the sampling errors inherent in rela-
tively short records and by the very high natural variability of snow occur-
rences. Appropriate recommendations are made in Working Group I.
Recommendations
1. Hemispheric/regional snow indices derived from satellite data pro-
vide the most representative measures of snow cover, but high pri-
ority must be given to test the compatibility of past, present and
future satellite-derived products depicting snow coverage and
brightness. Only then can any conclusions be made on secular varia-
tions present in the record.
2. Surface station records for longer periods () 50 years) should be
examined for trends and low-frequency fluctuations. The optimal
strategy will likely involve the choice of a network of ((100)
"benchmark" stations having homogeneous records minimally affected
by urbanization and by changes of locations, instrumentation, etc•
Emphasis should be placed on climatic "key regions" in the Snow
Transition Zone, which undergoes a large seasonal migration. We
recommend that the WMO appoint a working group of data experts to
address the selection of stations (regions) and appropriate para-
meters to observe (snow depth, deviation, depth threshold, etc.).
7
Analysis of persistence
Effective utilization of snow cover in long-range forecasting re-
quires intormation about the characteristics of snow persistence. It
might be expected, for example, that anomalies of snow will tend to per-
sist for longer times if the depth and water content are large. Despite
the availability of a substantial data base, a thorough documentation-of
the regional and large scale persistence of snow cover has not been made.
Recommendation
Snow cover persistence should be evaluated quantitatively as a func-
tion of variables on which it may depend: e.g. depth, water content,
radiative fluxes, other elements of energy balance, etc. Satellite
imagery will provide the mest appropriate data base for computations
of large-scale persistence. However, an evaluation of the depth-
dependence will require a consolidation of satellite data and sta-
tion reports. Attention should also be given to the choice of the
parameter for which the persistence is evaluated. Candidate para-
meters include coverage by snow of a minimum depth (e.g., 2.5 em, 5
em, 10 em), a minimum areal fraction, and a minimum brightness, etc.
Snow/atmosphere interactions on synoptic scale.
Local effects of snow cover on surface air temperature have been well
established (Dewey, 1977; Kukla, 1981). Through its effects on the distribu-
tion of diabatic heating, static stability and low-level baroclinicity, snow
cover might also be expected to influence the intensity and locations of
synoptic-scale low and high pressure systems. Suggestive results in this re-
gard have been accumulating from data analyses over the past several decades
(Lamb, 1955; Namias, 1962; Heim and Dewey, 1984; Walsh and Ross, 1986).
To the extent that snow persists and creates feedbacks, the short-range
(synoptic) influences will be relevant to forecasts for the weekly, monthly or
seasonal time scales.
Recommendation
The magnitude and the regional and seasonal dependences of synoptic-
scale influences of snow cover need to be demonstrated more rigor-
husly. Because snow cover is also a response to large-scale and
synoptic-scale circulation features, careful analysis strategies are
required for unambiguous isolation of the snow cover influences on
the evolution of synoptic systems.
Snow/atmosphere interactions on global scale
In view of the relatively small levels of skill shown by current long-
range forecasts (e.g., Nicholls, 1980; Gilman, 1983), even small increments of
skill resulting from consideration of snow effects are potentially valuable.
8
Recommendations
1. The most appropriate time frame for the detection and utilization of
a snow-atmosphere "signal" needs to be established. The strength of
snow-atmosphere interactions should therefore be delineated in the
context of the temporal resolution of the observational data. Data
will have to be stratified in more detail than by calendar month.
2. Snow cover influences on atmospheric variability (and on correspond-
ing long-range forecasts) are likely to depend strongly on the re-
gime of the large-scale atmospheric circulation. Objective strat-
egies are needed for extracting this regime-dependence from the
available data.
3. Simple climate models incorporating anomalous diabatic heating offer
economical advantages in the study of snow cover influences, espe-
cially when used in conjunction with observational data (e.g.,
Robock and Tauss, 1986). Application of these models t.o larger
samples of cases should be pursued, particularly in regard to the
determination of the "regime dependence" of snow influences.
4. The extent to which the skill of numerical model forecasts depends
on land surface boundary conditions (e.g., snow cover) needs to be
established. Model runs incorporating prescribed and/or computed
snow anomalies over periods of a week to a.season are needed to
place snow cover into the framework of other model sensitivities
(e.g., SST, sea ice, initial conditions). This task deserves high
priority in view of the impending use of dynamical models as long-
range forecasting tools.
Snow-hydrology link
Snow is a precipitation in solid form. The variable occurence of snow,
though temperature dependent, is mainly the result of precipitation fluctua-
tions. To date very few studies have addressed the relationship between wet
and dry weather anomalies and snow cover.
The association between snow anomalies and soil/atmosphere anomalies of
the months or seasons subsequent to the snow melt has been noted in GCM model
results (Yeh et al., 1984; Schlesinger, 1985) and in speculative arguments
based on observed fluctuations of seasonal temperature and precipitation
(Namias; 1964; Lamb, 1972, p. 390; Obukhov, et al., 1984). The potential
economic impacts of the drier continental summers suggested by C02-sensitivity
experiments has recently added to the interest in this association. However,
the existence of a soil moisture "link" between winter snow cover and
spring/summer temperature or precipitation has not been demonstrated conclu-
sively with observational data. It is also unclear whether such a link, if it
exists, is strong enough to be useful in the diagnosis or prediction of spring
and summer climate anomalies. In the absence of observational data on soil
moisture, data-based analyses have tended to rely heavily on computed soil
moisture indices, none of which include snow cover in their formulation.
9
Recommendation
1. The dependence of snow cover anomalies on fluctuations of precipita-
tion~ changes in the source areas of moisture and on the shifts of
circulation patterns should be analyzed.
2. High priority should be given to the inclusion of snow cover in the
formulation of hydrological models utilizing obervational data. The
formulations should then be used to quantify surface-atmosphere
associations through the months and seasons subsequent to the snow
melt. The validity of the snow/hydrology formulations in data-based
models and in GCM's should be verified by comparisons with each
other, with streamflow data, and with the small amounts of available
data derived from direct measurements of soil moisture.
Snow-related feedbacks
Cause-and~effect relationships involving snow cover, air temperature and
the atmsopheric circulation are difficult to unravel because of the complex
feedbacks and because of the frequent dependences on controlling factors ex-
ternal to the region of investigation. Thus the association between these
variables can generally not be interpreted in straightforward manner. Even in
GCM model results, the contribution of·snow cover to biases in the simulated
temperature fields is unclear (Broccoli, 1986).
In the scenario of nuclear winter, the snow-albedo-temperature feedback
is complicated by the effects of "dirty snow", i.e., the deposition of soot
from smoke clouds on the snow. Recent model experiments suggest that the soot
will not alter the model forecasts for the initial stages of nuclear winter,
when insolation is so small that the surface albedo is relatively unimportant.
However, the role of soot in reducing the snow-albedo feedback in later
stages, or in less extreme cases as for Arctic haze conditions, is uncertain.
Recommendations
1. Controlled model experiments are needed to assess the relative im-
portance of snow cover feedbacks and other factors in the associa-
tions between snow cover, air temperature, and the atmospheric cir-
culation. Parallel simulations with prescribed and interactive snow
cover can help clarify the importance of the snow cover feedback and
its potential significance over time scales ranging from those of
long-range forecasting to those of C02-induced climatic changes.
2. The parameterization of soot-induced albedo modifications merits
further attention in modeling of climatic change over the seasonal,
interannual or longertimescales.
10
Modeling and Simulation of Snow Covers: Working Group III.
Edited by M. Schlesinger, G. Kukla.
Members: A. Broccoli, L. Morris, A. Robock, M. Schlesinger, s. Warren.
Background
Snow can affect the weather on time scales of days and weeks, and can in-
fluence the climate on time scales of months, seasons, and years. Ori short
weather time scales, snow can produce a very cold, shallow layer of air near
the ground because of its high albedo and emissivity. The temperature con-
trast between snow-covered and snow-free surfaces can produce enhanced baro-
clinicity which can generate more-intense mid-latitude cyclones and more snow
in a positive feedback loop. The snow boundary can also be a location for the
synoptic storm track. Air travelling over an extensive snow field is cooled
and can reduce temperatures downstream by cold-air advection. However, linear
atmospheric models suggest that the atmosphere can react to produce warm ad-
vection and high surface pressure downstream of the snow to counteract the
cooling. This can be important in short-term weather forecasting. Negative
snow anomalies can produce the opposite effects and are therefore also import-
ant.
On the longer climatic time scales, changes in snow cover can influence
the surface albedo producing feedbacks. Increased snow cover raises the al-
bedo and produces less absorption of solar energy, cooler temperatures, and
more snow. Decreased snow cover lowers the albedo and produces more absorp-
tion of solar energy, warmer temperatures, and less snow. In both cases· the
initial change in snow cover is amplified by the response of the climate
system. Early climate models suggested that this positive feedback was so
strong that it could result in an ice-covered earth for even a very small re-
duction of the solar constant. More recent climate models show that the early
models overestimated the magnitude of the snow and ice surface albedo feed-
back. Climate model simulations of co2-induced climatic change have shown
that changes in the areal extent of snow and sea ice amplify the global warm-
ing by a factor of 1.1 to 1.4. Perhaps of even greater impact is the influ-
ence ·of a C02-induced change in snow cover on the surface hydrology. Several
general circulation model simulations (see Schlesinger and Mitchell, 1985 and
Schlesinger, 1985) have shown a considerable desiccation of the soil in the
mid~latitude agriculturally-productive areas in the Northern Hemisphere. This
summer drying occurred in part due to the earlier spring melting of the sea-
sonal snowpack in the co2-enriched world. Finally, a simulation of C02-induced
climatic-change with an atmosphere-ocean general circulation model
(Schlesinger, 1986) shows an increase in the snowpack in high-altitude loca-
tions, particularly in Antarctica. Thus, a change in the accumulation rate of
snow in Antarctica may be an indicator of a C02-induced climatic change.
The influence of snow on the weather and climate can
lyzing observations of the extent and properties of snow,
simulations with mathematical weather and climate models.
servational and modeling approaches are considered in the
along with recommendations for their improvement.
11
be studied by ana-
and by numerical
These combined ob-
next two sections
Model Development
Most GCMs predict the mass of snow ori th.e Earth's surface from a snow
mass budget eqtiation that includes the processes of snowfall, snow melt, and
sublimation. Generally, the snow layer is considered to have uniform proper-
~ies over its entire depth within a model gridbox, and the surface albedo is
taken to be a function of the depth of snow and the type of underlying sur-
face. GCMs calculate snow accumulation as the result of precipitation from
clouds. Snow ablation is treated as a result of above freezing temperature.
Although most of the snow cover in middle latitudes dissipates in below
freezing temperatures (Kuvaeva et al., 1967), on some occasions a snowpack can
survive air temperature substantially above freezing without appreciable melt.
On the south facing slopes a snow layer several inches thick will disappear in
a few days whereas in the nearby shaded regions of the forest floor, it will
remain for several additional weeks.
The intensity of solar radiation reaching the snow/ground interface, the
ground temperature arid albedo, solar angle and the proportion of the direct
diffuse radiation, surface roughness, redistribution of snow by wind, forma-
tion of ice crusts and the growth of surface frost, deposition of diamond
dust, rain events -all these are processes which influence the properties and
the duration of the snow, but which are not adequately accounted for in cli-
mate models.
The calculation of snowmelt requires solution of the surface energy bud-
get equation. Because the largest terms are the radiative fluxes, the calcu-
lation could be improved by incorporation of a more accurate physically-based
snow albedo parameterization. In addition to parameterizing the albedo of the
snow itself, the albedo of a mixed field of snow and vegetation must also be
represented.
On the polar ice sheets, especially Antarctica, other processes which are
not represented in the GCMs may also be important. A significant fraction of
the accumulation may be due to ice crystal formation in clear sky from a humid
layer at the top of the temperature inversion near the surface, and also due
to hoarfrost deposition directly to the surface. There is negligible snowmelt
in Antarctica, but as much as half of the annual accumulation has been esti-
mated to be lost by sublimation on the Antarctic slope, a region of persistent
katabatic winds. Representation of these processes in GCMs will require finer
vertical resolution near the surface.
Recommendations
1. Develop highly-detailed, physically-based (baseline) snow models.
The parameterizations of surface snow in GCMs should be developed
from, and compared with, the results from highly-detailed, physical-
ly-based (baseline) models of snow in which all the snow properties
are predicted.Improved equations for the parameters controlling the
transfer of mass and energy across the snow-atmosphere boundary,
such as the turbulent transfer coefficients and albedo, should be
developed in terms of these snow properties. This model development
12
will require detailed data from experimental sites in a range of
contrasting areas such as those of high and low radiation and
evaporation.
2. Develop subgrid-scale to grid-scale aggregation methods. Because a
GCM gridbox generally has dimensions of a few hundred kilometers, it
encompasses a wide variety of surface types and landforms. Conse-
quently, it is necessary to develop methods to aggregate the sub-
grid-scale information gained from the baseline models to the grid
scale.The baseline models should be developed at successively larger
scales and be validated using observed streamflow and snow cover
dat,a.
3. Develop grid-scale to subgrid-scale disaggregation methods. Because
a GCM obtains only a single gridbox-average value for each predicted
quantity, there is no variation of a quantity such as snowfall with-
in a gridbox. Consequently, it may be necessary to disaggregate the
GeM-predicted grid-scale quantities to the subgrid-scale to provide
the information required by the aggregation method described above.
Furthermore, disaggregation methods are required to transform the
grid-scale climatic information provided by GCMs to the smaller
scales of human endeavor so that climtic impact studies can be per-
formed.
Model Validation
Most GCMS predict snowfall and the surface snow mass. A complete valida-
tion of the models must therefore include an ass.essment of their ability to r-
eproduce both the climatological mean distribution and the interannual vari-
ability of snow cover. At present, the most suitable snow cover record for
validation of GCMs is the NOAA satellite-derived snow cover data base. This
data base has been used to a limited extent in model validation.
Several weather forecasting models use climatological snow cover and do
not, therefore, include the interaction between the snow and the atmosphere.
Other forecast models predict the snow, but it is not known how well·
The observed water equivalent of snow is required to validate the surface
snow mass simulated by GCMs. (See Recommendations of Working Group I). Be-
cause satellite observations of the water equivalent of snow are not yet
available, a climatology can be constructed now based only on surface observa-
tions. Such observations have been made locally for Europe, North America and
elsewhere, and are archived in synoptic reports.
Recommendations
1. Define the snow-identification threshold in satellite charts. The
definition of the snow-identification thereshold in satellite ob-
servation is needed so that the snow cover simulated by GCMs can be
correctly compared with the observed snow cover. The definition of
this threshold must include its dependence on the surface type such
as boreal forest or tundra.
13
2. Evaluate weather forecasts with interactive snow. Snow cover pre-
dicted by numerical weather forecasting models on short (1 to 3
days), medium (3 to 10 days), and long (10 to 30 days) ranges should
be compared with the observed snow cover.
3. Develop a climatology of the water equivalent of snow and use it for
validation of climatic models. An international effort is needed to
obtain these observations. The surface snow observ~tions would also
be useful for comparison with the satellite snow observations.
4. Develop an observationally-based surface albedo climatology for val-
idation of the models. Most albedo climatologies are based on a
variety of snow albedo parameterizations. Comparison of an observa-
tionally-based albedo climatology with the albedos simulated by GCMs
is needed.
Model Applications
Numerical model simulations and experiments can be useful in determining
the role of snow in weather and climte. Very few numerical experiments have
been performed to date, and many more will be needed to arrive at a definitive
understanding of the importance of snow. The importance of snow for weather
and climate can be evaluated by performing sensitivity studies in which the
snow cover is prescribed to be different from that of a control simulation.
The response of the atmosphere to the change in snow cover on several time
scales can be determined and compared with the atmosphere's natural variabil-
ity to discriminate the signal from the noise. The statistically-significant
component of the atmospheric response can then be analyzed to determine the
influence of the snow anomaly.
The earliest GCM simulation of the annual cycle of C02-induced climatic
change by Manabe and Stouffer (1980) showed a considerable desiccation of the
soil in the mid-latitude agriculturally-productive land areas in the Northern
Hemisphere. This summer drying occured in part due to the earlier spring
melting of the seasonal snowpack in the co2-enriched world. More recent GCM
studies (Hansen et al., 1984 and Washington and Meehl, 1984) with similar
fixed-depth ocean models do not simulate this summer drying, while the study
of Schlesinger (1986) with a coupled atmosphere-ocean qcM does and also indi-
cates a linkage with the seasonal snowmelt.
An analysis of the surface albedo/temperature feedback, fsA, in the
radiative convective model (RCM) simulation of Wang and Stone (1980 gives a
value of fsA = 0.2 to 0.3 which, acting alone, would amplify the global
warming by 20 to 40 percent (Schlesinger, 1985). However, the estimate of fsA
in the recent GCM study of Hansen et al. (1984) yielded a value of less than
0.1.
Recommendations
1. Sensitivity studies of the atmospheric response to snow should per-
formed and/or continued.
14
2. Further analysis of the snow abledo feedback in co2-induced climatic
simulations change should be undertaken to clarify the value of the
feedback and to determine the individual contributions of snow and
sea ice. These analyses should include not only the method of in-
serting the GCM-simulated changes into a compatible RCM, but also
the utilization of the GCM evolution from the 1xC02 equilibrium cli-
mate to the 2xC02 equilibrium.
3. Since the existence of a summer drying is a C02-induced climatic
change with potentially significant agricultural impact, it is
essential that the extant GCM simulations be analyzed further to
understand the hydTological changes and the contributions thereto by
changes in the seasonal snowcover.
4. Because the simulation of C02-induced climate change with a coupled
atmosphere-ocean GCM (Schlesinger, 1986) indicates a substantial
increase in the permanent snowpack in the interior of Antarctica,
with a corresponding decrease along the Antarctic coast, the
stability of Antarctic ice shelves to C02 induced increases in
Antarctic snow accumulation should be analyzed.
References
Broccoli, A.J. (1986) Characteristics of seasonal snow cover as simulated by
GFDL climate models. World Data Center A for Glaciology [Snow and Ice].
Glaciological Data. Report GD-18, Snow Watch '85, p.241-248.
Dewey, K.F. (1977) Daily maximum and minimum temperature forecasts and the
influence of snow cover. Monthly Weather Review, 105, p.1594-1597.
Gilman, D.L. (1983) Predicting the weather for the long term. Weatherwise,
36, p.290-297.
Hall, D.K. (1986) Influence of snow structure variability on global snow
depth measurement using microwave radiometry. World Data Center A for
~laciology [Snow and Ice]. Glaciological Data. Report GD-18, Snow Watch
'85, p.161-171.
Hansen, J.; Lacis, A.; Rind, D; Russell, G.; Stone, P.; Fung, I.; Ruedy, R.;
Lerner, J. (1984) Climate sensitivity: Analysis of feedback mechanisms.
(In: Climate Processes and Climate Sensitivity, Maurice Ewing Series, 5,
~sen, J.E.; Takahashi, T., Eds., American Geophysical Union, Washing-
ton, DC, p.130-163.)
Heim, R., Jr.; Dewey, K.F. (1984) Circulation patterns and temperature
fields associated with extensive snow cover on the North American contin-
ent. Physical Geography, 4, p.66-85.
Kukla, G •• (1981) Snow covers and climate. World Data Center A for Glaci-
ology [Snow and Ice]. Glaciological Data. Report GD-11, Snow Watch
1980, p.27-39.
15
Kunzi, K.F.; Patil, s.; Rott, H. (1982) Snow cover parameters retrieved from
NIMBUS 7 scanning multichannel microwave radiomeeter (SMMR) data. IEEE
Translation of Geoscience and Remote Sensing, GE-20, p.452-467 •
•
Kuvaeva, G.M.; Sulakvelidze, G.K; Chitadze, V.S.; Chotorlishvili, L.S.;
El'mesov, A.M. (1967) Physical Properties of snow cover of the greater
Caucasus. Moscow, Nauka. (Published for the u.s. Department of
Agriculture, Forest Service and the National Science Foundation,
Washington, D.C. by the Indian National Scientific Documentation Centre,
New Delhi. Translated from Russian.)
Lamb, H.H. (1955) Two-way relationship between snow or ice limit and 1000-500
mb thickness in the overlying atmosphere. Quarterly Journal Royal Meteo-
rlogical Society, 81, p.172-189.
Lamb, H.H. (1972) Climate: Present, Past and Future. Vol. 1. Methuen and
Co, London, 613p.
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an increase of C02 concentration in the atmosphere. Journal of Geo-
physical Research, 85, p.5529-5554.
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behavior. (In: Symposium on Numerical Weather Predction. Proceedings.
Tokyo. Meteorlogical Society, p.615-627.)
Namias, J. (1964) Seasonal persistence and recurrence of blocking during
1958-1960. Tellus, 16, p.394-407.
Nicholls, N. (1980) Extended and long range forecasting. Review of Geo-
physics and Space Physics, 18, p.771-788.
Obukhov, A.M.; Kurganskii, M.V.; Tatarskaya, M.S. (1984) Dynamic conditions
on the origin of droughts and other large-scale weather anomalies.
Soviet Meteorology and Hydrology, 10, p.1-8.
Robock, A.; Scialdone, J. (1986) A comparison of northern hemisphere snow
cover data sets. World Data Center A for Glaciology [Snow and Ice].
Glaciological Data. Report GD-18 Snow Watch '85, p.141-160.
Robock, A.; Tauss, J.W. (1985) Anomalous snow cover effects on the monthly
mean circulation using a steady-state climate model. World Data Center A
for Glaciology [Snow and Ice]. Glaciological Data. Report GD-18 Snow
Watch '85, p.207-214.
Schlesinger, M.E. (1986) Co2-induced changes in seasonal snow cover simulated
by the OSU coupled atmosphere-ocean general circulation model. World
Data Center A for Glaciology [Snow and Ice]. Glaciological Data. Report
GD-18, Snow Watch '85, p.249-270.
16
Schlesinger, M.E. (In press) Feedback analysis of results from eriergy balance
and radiative-convective models. (In: The Potential Climatic Effects of
Increasing Carbon Dioxide, MacCracken, M.C.; Luther, F.J., eds., u.s.
Department of Energy [in press])
Schlesinger, M.E.; Mitchell, J.F.B. (In press) Model projections of
equilibrium climate response to increased C02• (In: The Potential
Climatic Effects of Increasing Carbon Dioxide, MacCracken, M.C.; Luther,
F.M, eds., u.s. Department of Energy [in press].)
Walsh, J.E.; Ross, B. (1986) Influence of snow cover on cyclonic events.
World Data Center A for Glaciology [Snow and Ice]. Glaciological Data.
Report GD-18, Snow Watch '85, p.23-35.
Wang, w.-c.; Stone, P.H. (1980) Effect of ice-albedo on global sensitivity in
a one-dimensional radiative-convective climate model. Journal of the
Atmospheric Sciences, 37, p.545-552.
Washington, W.M.; Meehl, G.A. (1984) Seasonal cycle experiment on the climate
sensitivity due to a doubling of C02 with an atmospheric general circula-
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Monthly Weather Review, 112, p.474-490.
17
PARTICIPANTS
Robert Atkins
National Climate Program Office
NOAA
6020 Executive Blvd
Rockville, MD 20852
Thomas Baldwin
Satellite Analysis Branch Rm 401
World Weather Building
Camp Springs, MD 20233
Roger Barry
World Data Center A for Glaciology
CIRES
University of Colorado
Boulder, CO 80309
Anthony Broccoli
Geophysical Fluid Dynamics
Laboratory/NOAA
P.o. Box 308
Princeton, NJ
Jerry Brown
08542
Arctic Res. & Policy Staff
Division of Polar Programs
National Science Foundation
1800 G St. NW, Rm 620
Washington, DC 20550
Al Chang
NASA/Goddard Space Flight Center
Code 624
Greenbelt, MD 20771
Antony Clarke*
Hawaii Institute of Geophysics
University of Hawaii
Honolulu, HI 96822
Kenneth Dewey
311 Avery Lab
University of Nebraska
Lincoln, NE 68588-0135
19
Jeff Dozier
Dept. of Geography
University of California
Santa Barbara, California
James Foster
93106
NASA/Goddard Space Flight Center
Code 624
Greenbelt, MD 20771
Joyce Gavin
Lamont-Doherty Geological Obs.
Palisades, NY 10964
Barry Goodison
Canadian Climate Centre
Atmsopheric Environment Service
4905 Dufferin Street
Downsview, Ontario, M3H 5T4
Canada
Norman Grody
NOAA/NESDIS
Suitland, MD 20233
Charles Gross
Navy/NOAA Joint Ice Center
4301 Suitland Rd.
Washington D.C. 20390
Horia Grumazescu *
Lab. Remote Sensing
Institute of Meteorology and Hydrology
Sos. Bucuresti-Ploiesti 97
Bucharest, Romania
Dorothy Hall
NASA/Goddard Space Flight Center
Code 624
Greenbelt, MD 20771
Martti Hallikainen *
Helsinki University of Technology
Radio· Laboratory
Otakaari SA, 02150 Espoo
Finland
William Klein
Dept. of Meteorology
University of Maryland
College Park, MD 20742
George Kukla
Lamont-Doherty Geological Obs.
Palisades, NY 10964
Helmut Landsberg
Dept. of Meteorology
University of Maryland
College Park, MD 20742
Peiji Li
Lanzhou Institute of Glaciology and
Cryopedology
Academia Sinica
Lanzhou, China
Susan Marshall
Geography Department
University of Colorado
Boulder, CO 80309
Michael Matson
NOAA/NESDIS
World Weather Building, Rm 510
Washington, DC 20233
Kingtse Mo
NASA/Goddard Space Flight Center
Code 611
Greenbelt, MD 20771
Elizabeth M. Morris
Institute of Hydrology
Crowmarsh Gifford
Wallingford
Oxfordshire, OX10 8BB, U.K.
Jerome Namias *
Scripps Institution of Oceanography
A-024 La Jolla, California 92093
Al Rango
U.S. Dept. of Agriculture
Beltsville, MD
Mike Riches
u.s. Dept. of Energy
Office of Energy Research ER 12
Washington D.C. 20545
Dave Robinson
Lamont-Doherty Geological Obs.
Palisades, NY 10964
Alan Robock
Cooperative Institute of
Climate Studies
Dept. of Meteorology
University of Maryland
College Park, MD 20742
Chester Ropelewski
Climate Analysis Center W/MNC52
Washington, D.C. 20233
Greg Scharfen
CIRES
University of Colorado
Campus Box 449
Boulder, CO 80309
John Scialdone
Cooperative Institue of Climate
Studies
Dept. of Meteorology
University of Maryland
College Park, Maryland 20742
Michael Schlesinger
Dept. of Atmospheric Sciences
Oregon State University
Corvallis, OR ·97331
Mark Serreze
Lamont-Doherty Geological Obs.
Palisades, NY 10964
Jagadish Shukla
Laboratory for Atmospheric Sciences
NASA/Goddard Space Flight Center
Greenbelt, MD 20771
Honnappa Siddelingaiah
Dept. of Meteorology
University of Maryland
College Park, MD 20742
James Tauss
Cooperative Institute of Climate
Studies
Dept. of Meteorology
University of Maryland
College Park, MD 20742
20
Hassan Virji
Office of Climate Dynamics
National Science Foundation
1800 G Street NW
Washingto~, D.C. 20550
Sastri Vemury
IFAORS/STC
7474 Greenway Center Drive
Suite 580
Greenbelt, MD 20770
John Walsh
Dept. of Atmospheric Sciences
University of Illinois
1101 w. Springfield Ave.
Urbana, Illinois 61801
Note: *Contributed by Mail
Shao-wu Wang*
Beijing University
Beijing, China
Stephen Warren
Dept. of Atmospheric Sciences AK-40
University of Washington
Seattle, WA 98195
Donald Wiesnet e
Satellite Hydrology Associates
601 McKinley St., NE
Vienna, VA 22180
Jay Winston
Dept. of Meteorology
University of Maryland
College Park, MD 20742
21
Kukla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) SNOW WATCH '85.
Proeeedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College Park, MD. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice), Glaciological Data, Report GD-18, p.23-35.
Snow Cover, Cyclogenesis and Cyclone Trajectories
J. E. Walsh
B. Ross
Department of Atmospheric Sciences
University of Illinois
Urbana, Illinois, U.S.A.
Abstract
Samples of 75-150 cyclogenetic events in eastern North Amer-
ica, the North Atlantic and the North~ Pacific are obtained from
daily data for thirty winters (1951-1980). The large-scale dis-
tribution of snow or sea ice cover is used to composite the errors
of forecasts derived from persistence, from a barotropic model,
and from an analog system. The results are consistent with the
notion that extensive snow/ ice cover contributes to stronger cy-
clogenesis and to southward displacements of storm tracks along
the East Coast of North America and in the marginal ice zone of
the North Atlantic. The apparent signal is statistically signifi-
cant in these two regions, although the significance is greater in
the sea level pressure data than in 500 mb data. No corresponding
signal is found in the North Pacific.
Controlled experiments with the NCAR Community Forecast Model
are performed to determine the response· of a more sophisticated
model to extremes of snow and ice in eastern North America and the
North Atlantic. While patterns similar to the data-based results
are found, the response in the model pressure fields is weaker and
farther north of the snow/ice edge than in the corresponcUng re-
sults from the barotropic model and persistence forecasts.
In discussions of the influences of snow cover and sea ice on the at-
mosphere, it is convenient to distinguish the local effects from potential
synoptic-scale effects. The primary local effect is a suppression of the
low-level air temperature over timescales of days (Dewey, 1977; Kukla, 1981)
to months (Wagner, 1973; Namias, 1985). In support of non-local influences
of snow cover, Namias (1962) has argued that the enhancement of baroclini-
city by an unusually extensive snow cover favors stronger cyclogenesis and
23
more meridional storm tracks along the east coast . of the United States.
Dickson and Namias ( 1976) showed that the more northerly "Alberta Clipper"
sto-rm track is more common during winters when the eastern United States is
w·armer than normal, while Atlantic coastal storms are more common during
cold winters. Carleton (1985) has recently shown that sea ice variability
is associated with significant differences in the frequencies of various
types of vortices detected in satellite imagery. Corresponding associations
between vortex types and the continental snowline were not found to be sig-
nificant, although Carleton's study included only two years of data from the
late 1970's. The notion that sea ice fluctuations influence cyclone trajec-
tories has been popular since the work by Wiese ( 1924) over a half century
ago. However, a determination of the role played by snow or sea ice in
synoptip-scale -fluctuations is difficult because an anomalous distribution
of show or_ ice is largely the result of the large-scale circulation and its
associated storm tracks.
In this work we attempt to evaluate quantitatively the influence of
snow cover and sea ice on cyclone events over data samples much larger than
the two...,year periods used by Namias (1962) and Carleton (1985). Data on
both surface variables (continental .snow cover, sea ice) are used to evalu-
ate the systematic errors in short-range atmospheric forecasts composi ted
over cas~s of heavy and light snow or ice. The atmospheric forecasts are
obtained from a simple numerical model, from persistence, and from an analog
system. The use of large samples of events permits objective stratifica-
ti<ons of cases which are otherwise similar except for the distribution of
snow cover or sea ice. The procedure is an attempt to perform "controlled
experiments'' with observational data, thereby producing observationally-
derived counterparts of conclusions obtained from large-scale model sensi-
tivity tests.
The· snow cover data for the United States are the latitudinal extents
of l-:inch . snow coverage at seven longitudes: 1 00 ow, 95 ow, •.. , 70 ow. The
data were digitized from the Weekly Weather and Crop Bulletin for the years
1947-82 and are described by Walsh et al. ( 1982)-.--The specific index used
here is the mean of the 1-inch extent over the seven longitudes.
_The_ sea_ ide indices are the ice-covered areas in the North Atlantic
(60-75°N, , 20°E.,.40°W) and the North Pacific (50-65°N, 160°E-:-160°W). The
ice-covered areas were obtained from the digital sea ice dataset described
by Walsh and Johnson (1979).
Daily grids of 500 mb height and sea level pressure for the Northern
Hemisphere were obtained from the National Center for Atmospheric Research
(NCAR). The former were in the 47 x 51 NMC grid format, while the latter
were in a 5° x 5° latitude-longitude format.
Data for each January from the 1948-1980 period were used for the North
American. snow cover experiments (the daily 500 mb grids for 1956-1958 were
24
not available, nor were the daily grids of sea level pressure for 1948-
1950). Data for 17 winters (January-March, 1961-1977) were used for the sea
ice experiments. The use of a shorter period for the sea ice experiments
was dictated by considerations of the ice data reliability, which was con-
siderably higher in the 1960's and 1970's than in the pre-satellite era.
Methodology
The following strategy was used to obtain ensembles of forecast errors
in two categories of cases: those with large and those with small areal
coverage by snow or ice in ·the region of interest. The forecasts were ob-
tained by several techniques, none of which utilized information about the
state of the surface. Thus the systematic differences between the errors of
the forecasts in cases with heavy and light snow (ice) may be consequences
of contrasting surface states.
At the core of each regional experiment is a "target" case, or a cy-
clonic event characterized by rapid intensification and by motion generally
parallel to the snow (ice~ boundary. The target case for each region con-
tained a low pressure center which deepened by 20-40 mb in 24 hours during
the appropriate month or season (January or winter). The 500mb grid of the
day prior to the rapid intensiffcation of the target case was then used to
obtain a poor-or the 100 most similar cases from the collection of daily 500
mb grids for the same month or season. The selection was based on a ranking
of the daily grids according to their pattern correlations with the target
case. However, a grid was omitted from the ranking if it did not contain a
maximum of vorticity advection within a 9 x 9 subset of NMC grid points
located in the appropriate region.
For each of the 100 cases, 500 mb forecasts to 48 hours were then com-
puted by three methods: (1) a one-level barotropic vorticity model solved
on the NMC grid, (2) simple persistence (also used to obtain a corresponding
forecast of sea level pressure), and (3) analog evolution. The analog was
sel~cted from all other days in the month or season under investigation and
was::chosen on the basis of the minimal root-mean-square difference over the
app~opriate region •
. The 100 cases were then grouped into terciles according to the areal
· -cov~rage of snow (ice), and the errors in the 24-and 48-hour forecasts were
composited over each tercile of cases: heavy snow (ice), near-normal snow
·(ice) and light snow (ice). The differences in the errors of the heavy and
light composites were then examined for physical plausibility and tested for
statistical significance.
Resu.lts
Figure 1 shows the boundaries of the snow/ice data regions for the
three study areas. Also shown are the sea-level intensities and 24-hour
trajectories of the cyclonic systems in the respective target cases.
25
Figure 1. The boundaries of the zones for which snow and ice indices were
computed. Also shown are 24-hour trajectories and intensities (mb-900) of
low pressure centers in target cases.
26
Eastern North America
As a background for the analysis of forecast errors, Figure 2 shows the
mean absolute errors of the three types of forecasts for the 100 cases based
ort the United States snow cover. Lower and upper "bounds" are indicateid by
the closeness of fit to the best 0-hour analog and by the differences be-
tween randomly selected January grids. It is apparent from Figur~ 2 that
the forecasts of each type are nearly, but not quite, saturated with error
by 48 hours, and that all three types of forecasts show comparable errors at
48 hours. At 24 hours, the errors associated with the barotropic model_are
slightly smaller than those associated with persistence or the analog fore-
casts. The similarity of the error growth rates of the three forecasts is
consistent with the finding that conclusions concerning the apparent, snow
influence are similar no matter which type of forecast is emplqyed.
Figure 3 shows the 500 mb m~ps depicting the target case for the U.S.
snow cover experiment. Strong deepening and modest eastward (-15°) mover.nent
of the 500 mb trough are evident in the 24 hours following the initial time,
12Z January 25, 1978. ~The associated surface low pressure system d.eepens
from 998 mb to 960 mb and moves approximately 1200 km northeastward (Figure
1).
As expected with forecasts from a one-level model, the barotropic.model
forecasts for both the heavy-snow and the light-snow composites contain
positive errors (i.e., forecast> observed 500mb heights) in the vic~nity
of the amplifying trough over eastern North America. However, in the cases·
with above-normal snow cover, the average errors are larger by as much ·as
70 m at 48 hours (Figure 4). The location of the largest differences over
New England and the Canadian Maritime Provinces indicates that the intensi-
fication and/or eastward movement of the 500 mb trough is more seriously
underpredicted under conditions of heavy snow. This pattern of errors is
consistent with the findings of Namias (1962) for Februaries of two individ-
ual years, 1959 and 1960.
It should be noted that the differences in Figure 4 are not statisti-
cally significant at the 5% level, even at the centers of the major positive
and negative lobes. The corresponding plots for the 500 mb persistence and
analog forecasts were qualitatively very similar to those in Figure 4' and
also failed significance tests at the 5% level.
Figure 5 shows the 24-and 48-hour composite difference errors ("heavy-
light") for the persistence forecasts of sea level pressure. The 24-hour
maximum of +9.8 mb over New England moves northeastward by 48 hours to the
Canadian Maritime Provinces, where it has increased to +14.4mb. The com-
posites for the "heavy" and "light" cases show that the +14.4 mb maximum
receives comparable contributions from differential intensification-(strong-
er with heavy snow) and the systematic differences between the resultant
trajectories in the heavy and light cases. As indicated in Figure 5, the
sea level pressure differences are statistically significant over large
areas near the maximum differences.
27
E ..
c:t .. ..
CD
• .,
.c::::l
ca
N
00 = ca
CD
E
1111 absal111 error
Barotropic Analog
Persistence
Analog Persistence .
Barotropic
Analog
O·bour 24-hour 48-hour
Figure 2. Mean absolute errors (m) of the various forecasts.
RandoM
grids
Figure 3.
26, 1978.
isotherms.
[a) 12Z Jan. 25, 1978
SOD-MILLIBAR HEIGHT CONTOURS
AT 7:00 A.M .• E.S.T. ,,.. ,,...
lbJ 12Z Jan. 26. 1978
500 mb maps for the North American "target" case of January 25-
Solid lines are contours of 500 mb height, dashed lines are
29
• • • .,--,
I '. .. -..
' I ' ·25 . \ . , . ,_,
• • •
• •
•
• •
•
•
48-hr Barotropic
heavy· light
Figure 4. composite differences of errors in 48-hour barotropic model
forecasts of 500 mb geopotential height (m). Contoured values represent
ans for cases with "heavy" North American snow cover minus means for ~=ses with "light" North American snow cover. Signs thus correspond to
"heavy" snow cases.
30
0
0
0
-6
6 -7·0 . \ -3
--·-'· '
\.,. -I \
-3, ' . I I
0 ' ' ~ . ' .... _.
' \
' ' .,
\ . I
\ I ""
laJ 24-hr
• 0
0
SLP Persistence
lbl 48-hr
SLP Persistence
Figure 5. Composite differences of (a) 24-hour and (b) 48-hour errors in
persistence forecasts of sea level pressure (mb). Contoured values repre-
sent means for cases with "heavy" North American snow cover minus means
for cases with "light" North American snow cover. Signs thus correspond
to "heavy" snow cases. Values inside thin dashed line are statistically
significant at the 951 level.
31
North Atlantic and North Pacific
The North Atlantic cases were selected according to their correlation
with. the target case of March 18, 1967, when a surface cyclone moving north-
eastward through the Denmark Strait deepened by 22 mb in the subsequent 24
hours. Figure 6 shows the persistence-derived sea level pressure difference
fields corresponding to Figure 5. Maximum differences of +8.3 and +11.8mb
are found north of Great Britain. While the pattern of differences is quite
similar to that in the North American case, the largest differences are
somewhat farther southeast relative to the North Atlantic storm track and
ice edge. Local significance (at the 5% level) is achieved over 12.1% of
the total domain at 48 hours, but over only 5.8% at 24 hours.
The corresponding difference fields for the North Pacific, based on the
target case of March 25-26, 1962, contained only small values (~7mb) and no
areas of statistical significance near the ice edge. The apparent absence
of a signal attributable to sea ice in the North Pacific is most likely a
consequence of the shorter longitudinal (and latitudinal) extent of sea ice
fluctuations relative to those in the North Atlantic (Walsh and Johnson,
1979).
General circulation model results
Sets of 7-day global forecast experiments were also performed with the
NCAR Community Forecast Model (CFM) in order to determine the model's sensi-
tivity to snow cover in eastern North America and sea ice in the North At-
lantic. The NCAR CFM is a global spectral model and is described in detail
by Williamson (1983). Forecasts with heavy and light snow (ice) were ini-
tialized with data for days in which observational data showed that strong
cyclone development ensued in the respective regions: January 25, 1978 for
the North American experiment and January 12, 1980 for the North Atlantic
experiment. The prescribed extremes of snow (ice) corresponded to the enve-
lope of the fluctuations observed during the length of the records used in
the data-based experiments described above.
While the details of the CFM results will not be presented here, the
major differences between the model-based and data-based results will be
summarized. In general, there was little evidence of stronger cyclone in-
tensification in the simulations with heavy snow (ice). The "heavy-light"
differences of sea level pressure and 500 mb height were noticeably smaller
than in the data-based results. The largest 48-hour pressure differences
were.+7.3 mb in the North Atlan~ic simulations but only ±2.5 mb in the North
American simulations, while the corresponding 500 mb differences were -17
and -18 m. Because these maxima occurred in regions of strong gradients in
the torecast fields, they are indicative of relatively small shifts in the
locations of the major centers. These shifts tended to be sufficiently small
that displacements of cyclone trajectories were visually detectable only in
the later portions of th~ 7-day period, i.e., after the skill of the fore-
cast had largely disappeared. The model results also showed a tendency for
32
hJ 24·hr
SLP Persisten~e~ ~ .
. . . .
0'-
r:
lbJ 48-hr
SLP
-.
/.'
Figure 6. As in Figure 5, but for cases with "heavy" and "light" North
Atlantic sea ice.
33
higher pressures (and heights) in the upstream ridge behind the developing
cyclonic systems.
It should be noted, however, that the model results are quite sensitive
·to -the parameters used in the surface flux formulation, e.g., the drag coef-
ficient, the surface albedo, emissivity, etc. For this reason the model-
derived conclusions are somewhat more tentative and problematic than the
data-based results described earlier.
Conclusion
The results described here point to a detectable influence of North
American snow cover and North Atlantic sea ice on cyclone intensification
and/or trajectories in the vicinity of the snow/ice boundary. The signals
are more apparent in the sea level pressure data than in the 500 mb data.
No corresponding influence was detectable in the North Pacific. The most
severe limitation in the experimental strategy appears to be the sensitivity
of the composite difference fields to the choice of the target case. Experi-
ments with alternative target grids produced difference fields which, in
some cases, were only qualitatively similar to those obtained using the
original targets. Nevertheless, the data-based conclusions for all the re-
gions are less susceptible to the strong parametric sensitivities of the CFM
simulations.
References
Carleton, A.M. ( 1985) Synoptic cryosphere-atmosphere interactions in the
Northern Hemisphere from DMSP image analysis. International Journal
of Remote Sensing 1 _ v.6(1), p.239-261.
Dewey, K.F. (1977) Daily maximum and minimum temperature forecasts and the
influence of snow cover. Monthly Weather Review, v.105(12), p.1594-
1597.
Dickson, R.R.; Namias, J. (1976) North American influences on the circula-
tion and climate of the North Atlantic sector. Monthly Weather Review,
v.104(10), p.1255-1265.
Kukla, G., (1981) Snow covers and climate. Snow Watch 1980, Glaciological
Data, GD-11, World Data Center A for Glaciology, Boulder, CO, p.27-40.
Namias, J.(1985) Some empirical evidence for the influence of snow cover on
temperature and precipitation. Monthly Weather Review, v.113(9),
p.1542-1553.
Namias, J. ( 1962) Influences of abnormal heat sources and sinks on atmos-
pheric behavior. Proceedings of an International Symposium on Numeri-
cal Weather Prediction held at Tokyo, 1960, Meteorological Society of
Japan, p.615-627.
34
wagner, A. J. ( 1973) The influences of average snow depth on monthly mean
temperature anomaly. Monthly Weather Review, v.101(8), p.624-626.
walsh, J.E.; Tucek, D.R.; Peterson, M.R. (1982) Seasonal snow cover and
short-term climatic fluctuations over the United States. Monthly Wea-
ther Review, v.110(10), p.1474-1485.
Walsh, J.E.; Johnson, C.M. (1979) An analysis of arctic sea ice fluctua-
tions, 1953-1977. Journal of Physical Oceanography, v.9(3), p.580-591.
Wiese, W. von (1924) Polareis und atmospharische schwankungen. Geographisca
Annaler, v.6, p.273-299.
Williamson, D.L. (1983) Description of NCAR Community Climate Model (CCMOB).
NCAR Technical Note NCAR/TN-21 O+STR, National Center for Atmospheric
Research, Boulder, CO, 88 pp.
35
Kukla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) SNOW WATCH '85.
Proceedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College Park, MD. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice), Glaciplogical Data, Report GD-18, p.37-53.
The Relationship between Snow Cover and Atmospheric
Thermal and Circulation Anomalies
Kenneth F. Dewey
Richard Heim, Jr.
Climatology Program
Department of Geography
University of Nebraska
Lincoln, Nebraska U.S.A.
Abstract
WeEkly snow CO\Ter areas, derived fran the NG\AINESS N:>rthem Hanisphere
Digitized Snow and Ice Cover rata Base, ~ correlated with weekly tE!~J~.I2rature
ananalies across the United States as ~1 as 500-mb geopotential heights,
7()(}-,]D geopotential heights, and sea level pressure across N:>rth hrica. The
correlations were canputed for snow c011er across the entire North hnerlcan
continent as well as the western and eastern Udted States for the winters
1966-67 through 1982-83. 'Ibis geographic partitio~ allmied for an evalua-
tion of llilat influ:mce regional snow cover ~t have on the lar~ scale circ-
ulation. Negative and positive lag correlations were also canputed to deter-
mine the nagpitude and direction of influ:mce (snow cover to atomphere or the
reveme). The winter sea90ns were divided into two grwps (8 years each) based
upon the average ammt of snow cover for each winter season. The cyclonic
stonn tracks for the wintem with extensive snow c011er were than canparai to
the winters with least snow cover.
The Climatological/Meteorological Significance of Snow Cover
The importance of snow cover goes beyond the obvious effects that it has
on human day-to-day activities. Matson et al. (1979), for example, stressed
that snow cover is a significant climatic index and reflects the dynamic na-
ture of climate. Namias (1960) has indicated that, because average snowfall
amounts are related to the average mid-tropospheric prevailing wind patterns,
year-to-year variations in snowfall are manifestations of short-period cli-
matic fluctuations.
In addition to. the climatic significance of snow cover, there are also
meteorological implications behind variations in snow cover. Wagner (1973)
investigated the statistical relationship between monthly snowfall and temper-
ature anomalies at fifteen stations located near, or just north of, the mean
mid-winter snow cover limit in the United States. The results of his study
indicated that average monthly snow depth or anomaly exerts a significant in-
fluence on monthly temperature anomalies. Namias (1964, 1978b) also related
the influence of snow cover through a study of seasonal temperature anomalies
37
and western U.S. snow cover. One of the most significant results of that re-
search effort was the suggestion that persistence of cold (or warm) springs
into the following season could be associated with the lingering effects of
snow (or lack of snow) in the spring. Kukla and Gavin (1981) compared depar-
tures of hemispheric total snow and ice cover with departures of zonal mean
monthly temperatures. Their statistical results indicated that more extensive
snow cover appears to be associated with lower temperatures in the proximity
of the snow line, but with higher temperatures well north of it. They further
concluded that a snow cover-temperature relationship may exist on a hemispher-
ic scale, but they could not determine if the temperature anomaly induces the
departure in snow cover or vice versa. Dewey (1977) demonstrated the signi-
ficance of the inclusion of snow cover anomalies in the MOS forecasts of daily
maxiumum/minimum temperatures.
One of the most intriguing areas of research has been the study of the
interactions between snow cover and various aspects of the atmospheric circul-
ation. Much of the premier work in this area has been conducted by Namias
(1962, 1963a, 1963b, 1966, 1978a, and 1985). In his research, Namias has
described the positive feedback that occurs when an extensive snow cover be-
comes established over portions of the North American continent. This feed-
back is started by some forcing mechanism (such as sea-surface temperatures in
the North Pacific) which then in turn affects the upper level circulation re-
sulting in a trough being positioned· downstream over eastern North America.
The Polar anticyclones continue to be directed by the airflow within this
trough and draw upon the vast moisture source of the Gulf and the Atlantic
Ocean. The snow cover continues to spread southward resulting in spatially
increased negative thermal anomalies over the snow covered region. The in-
creasing temperature contrast between these colder temperatures and the warmer
maritime air masses to the south and east enhances the east coast baroclini-
city and intensifies the polar anticyclones advecting through this region.
With stronger than normal cyclonic activity, polar air advection is amplified
resulting in even further southward penetration of snow cover. Namias sug-
gests that this positive feedback loop will remain in effect until the snow
cover disappears with the rising spring temperatures or until influences on
the atmospheric circulation become overwhelming. Dickson and Namias (1976)
further demo•nstrated this link between intensified cyclogenesis and increased
snow cover in the southeastern u.s. It should also be pointed out that Lamb
(1972) demonstrated this positive feedback with applications to Eurasia. A
more recent evaluation of the interactions between snow cover and atmospheric
circulation was conducted by Heim and Dewey (1984) utilizing the satellite-
derived data base of snow cover.
Research Methodology
Prior to the advent of meteorological satellites, the monitoring of the
areal extent of continental snow cover was limited to point measurements and
extrapolations between these points (Matson et ale, 1979). Whereas the amount
of snow cover data in the midlatitudes and surrounding population centers was
sufficient, snow cover data were very limited for the higher latitude, moun-
tainous, or sparsely populated regions. However, since 1966, a spatially and
temporally continuous snow cover data archive has been growing through satel-
lite imagery analysis under the direction of NOAA/NESDIS. This historical ar-
chive of "weekly" snow cover maps were digitized by Dewey and Heim (1982) and
are being digitized on a "current time" basis now by NOAA/NESDIS. It is this
38
digitized snow cover data archive which was utilized in the research presented
in this paper.
It is the purpose of this research effort to analyze this unique data ar-
chive in an attempt to determine the relationships that exist between snow
cover and (1) atmospheric thermal anomalies and (2) atmospheric circulation
anomalies. Weekly North American, as well as regional u.s. snow cover areas
were spatially correlated to regional u.s. temperature anomalies (using con-
current and +1, -1 week lagged temperature data). The same North American and
regional u.s. snow cover areas were statistically correlated to 500 mb an~
700 mb geopotential heights as well as sea level pressure data (using concur-
rent and +1, -1 week lagged·height and pressure data). As a last step in this
research project, the frequency of North American cyclonic activity during ex-
tensive winter snow covers was compared to the cyclonic activity during those
winters with less extensive snow covers.
Research Results
Snow Cover vs. Temperature
Weekly temperature anomaly data for the continental United States were
obtained from the Weekly Weather and Crop Bulletin series. It is significant
to note that the temperature anomaly dates corresponded exactly to the snow
cover weeks. Figure 1 illustrates the grid pattern for North America and the
various snow cover regions. Figure 2 illustrates the thermal anomaly regions.
The correlation coefficients for snow cover and temperature anomalies at no
lag are listed in table 1. All correlations that are significant at the 0.01
level are indicated with an asterisk (*)·
Table 1. Correlation coefficients for snow cover vs. concurrent temperatures.
Weekly Snow Cover Area
WEEKLY
TEMPERATURE North NW sw NC sc NE SE
ANOMALY America u.s. u.s. u.s. u.s. u.s. u.s.
u.s. Average -.552* -.152 -.302* -.279* -.372* -.481* -.221*
NW U.S. -.350* -.254* -.316* -.337* -.284* -.436* -.125
sw u.s. -.141 -.009 -.260* -.251* -.299* -.403* -.119
NC U.S. -.545* -.233* -.299* -.296* -.338* -.486* -.168
sc u.s. -.471* -.078 -.286* -.246* -.402* -.451* -.297*
NE u.s. -.452* -.055 -.122 -.085 -.205* -.229* -.123
SE u.s. -.329* -.002 -.060 -.035 -.126 -.105 -.143
Significant at the 0.01 level.
39
..
Ill
I 1,. : ..=PI"". ,...-V "/'
•-t-: I~ l't
b.'! 10_\ 1\.' ,.
' ~ J: ).;_ ,/ : r...:~
IJ.. i LLZ I"? A. r'\.: :..c '
~ •/! ;. ~-IK fK' •'jl'l,\-'.\~~ '\.~
r:o·~ ; ~ ~ .. "\ f'..
iJI
·: -~ ' """' \ I'-'
[l(""t_ I : 1:)!. '!" 1.;:
h»o )' I II
H+H~~~T I ·~ V~ '\
.,
. I\.
,•
H-\HI:t"'lH-t-~.-.."'illl;· "'i-+H_-!, H'+'i-·. SE Ll
110 ...
.......
. )H , . ..: .
··-:::-: Zo .•: • .-~. •
I V ·II ·, '•c.'.
·=···II··~.;. 1"1•,.. "·.··=·"'··==.'\
Grid pattern for North America with
regions.
... 10
r----
-~------.. J ---l NC ') ., . . ' . l-•, 1--------: -----, ..... \
--~1 .. _ •,.
: I ,_ \------: ··of-------\~ . .... ___ .. j :
;:.:::::l--------1----
/ : sc I
' I '
I' ·--I : '"'~---..~
-----.J r-· ,,
• ..
Figure 2. Thermal anomaly regions used in this study.
40
The highest correlations exist between North American snow cover area (as
'()pposed to the regional snow covers) arid, the various thermal anomalies. The
correlation between North American snow cover and the u.s. average thermal
anomaly ranks the highest followed by, in decreasing order, the North Central,
South Central, Northeast, Northwest, Southeast, and Southwest temperature
anomalies. This hierarchy is a manifestation of the positive feedback out-
lined by Namias (1962, 1963a, 1963b, 1966, and 1978a). The cyclonic storms
that produce extensive continental snow cover are followed by cold polar
anticyclones which move out of Canada and are responsible for the negative
temperature anomalies in the eastern half of the United States. The mean sea-
sonal snow boundary passes through the NC,SC, and NE regions, hence the en-
largment of the snow bounda~ will be mostnoticeable in these regions. It
could be assumed that the lack of a significant relationship for the SW region
is due its position upstream from the general region experiencing the Namias
positive feedback. The Rocky Mountains also provide an effective barrier to
thee cold Canadian air masses. The highest regional snow cover correlations
exist for the NE United States, again emphasizing the significant influence
that the snow margin has on thermal anomalies.
The +1 and -1 week thermally lagged data which were correlated to the
snow cover areas are listed in tables 2 and 3. An examination of the lagged
correlations reveals that of a possible 49 correlations, there were only 9
significant positively lagged correlation coefficients, yet there were 28
significant negetively lagged correlations. The inference to be derived from
these two correlation coefficient matrices is that there seems to be a larger
influence of temperature on subsequent snow cover than snow cover on subse-
quent temperatures. It is also interesting to note that there were 32 signif-
icant concurrent correlations which is only four more than the 28 significant
negatively lagged correlations. The largest significant negative and posi-
tively lagged correlation coefficients were, respectively, for the NC and NE
regions. This would suggest that the largest influence of cold temperatures
on subsequent snow cover occurs across the region of primary winter cold air
advection and the largest influence of snow cover on subsequent temperatures
occurs in the northeastern United States. The colder NE temperatures may be
the result of a shifting of the,polar front and cyclonic activity to a further
.east and south location.
Snow Cover vs. 500 mb Geopotential Heights
Weekly 500 mb geopotential height data were generated from the daily
500 mb data and correlated with the weekly snow cover area data. Concurrent
and lagged correlations were computed for two geographic regions (the South-
eastern states and the southern Hudson Bay region) and the gradient between
those two locations. This gradient was chosen to measure the strength of the
500mb zonal westerlies over the eastern United States. The statistical re-
sults are illustrated in tables 4, S, and 6. Only one correlation is signif-
icant (and only marginally) indicating a lack of a relationship between snow
cover and the 500 mb geopotential heights.
41
Table 2. Correlation coefficients for snow cover vs. temperature at +1 week.
Weekly Snow Cover Area
WEEKLY .
TEMPERATURE North NW sw NC sc NE SE
ANOMALY America u.s. u.s. u.s. u.s. u.s. u.s.
U.S. Average -.262* -.060 -.059 -.099 -.114 -.231* -.056*
NW U.S. -.075 -.091 -.ooo -.071 -.Oll -.131 +.016
sw u.s. +.122 +.145 +.044 +.Oll +.061 -.043 +.128
NC U.S. -.257* -.135 -.050 -.109 -.105 -.217* -.074
sc u.s. -.210* -.on -.096* -.105* -.132 -.213* -.065
NE u.s. -.333* -.064 -.072 -.084 -.135 -.215* -.094
SE u.s. -.250* -.020 -.049 -.046 -.139 -.139 -.081
*Significant at the 0.01 level.
Table 3. Correlation coefficients for snow cover vs. temperature at -1 week.
Weekly Snow Cover Area
WEEKLY
TEMPERATURE North NW sw NC sc NE SE
ANOMALY America u.s. u.s. u.s. u.s. u.s. u.s.
u.s. Average -.344* -.078 -.290* -.231* -.270* -.275* -.175
NW u.s. -.243* -.164 -.310* -.279* -.167 -.258* -.100
sw u.s. -.086 +.024 -.240* -.219* -.144 -.252* -.003
NC u.s. -.351* -.148 -.273* -.224* -.224* -.273* -.125
sc u.s. -.251* +.022 -.276* -.208* -.281* -.248* -.188
NE u.s. -.272* -.012 -.097 -.050 -.191 -.100* -.169
SE u.s. -.209* +.023 -.082 -.051 -.160 -.078 -.146
*Significant at the 0.01 level.
42
Table 4. Correlation coefficients for snow cover
vs. concurrent 500 mb data.
WEEKLY Weekly Snow Cover Area
500 MB
DATA North America Eastern u.s.
s. Hudson
Bay -o.043 +0.037
SE U.S. -o.253* +0.074
Gradient -0.117 +0.013
*Significant at the 0.01 level.
Table 5. Correlation coefficients for snow cover
vs. 500 mb data at +1 week lag.
WEEKLY Weekly Snow Cover Area
500MB
DATA North America Eastern u.s.
s. Hudson
Bay -o.l42 -0.149
SE U.S. -o.l85 -o.066
Gradient +0.004 +0.085
Table 6. Correlation coefficients for snow cover
vs. 500 mb data at -1 week lag.
WEEKLY Weekly Snow Cover Area
500 MB
DATA North America Eastern u.s.
S. Hudson
Bay -0.022 +0.076
SE U.S. -0.175 +0.066
Gradient -0.088 -0.022
43
North Ameri~an Snow Cover vs. 700 mb Geopotential Heights
Weekly 190 mb geopotential height data were generated from the daily
700 mb data and correlated (concurrently and lagged + and - 1 week) with the
weekly snow cover area data. The statistical results are presented in figures
3, 4, and 5. Snow cover area demonstrates good statistical relationships wi~h
700 mb heights for both concurrent and negative lags. There appears to be no
relationship between snow cover and subsequent (positively lagged) 700 mb
heights. Significant patterns appear in the concurrent and negatively lagged
correlations. In both cases, extensive snow cover is associated with higher
than normal 700 mb heights across the northern portion of the continent and
with lower than normal heights across the southern portion of the.continent.
It would appear that extensive snow cover is related to a southward shift in
the location of the mean 700 mb trough and an amplified ridge shifting north-
ward out of the Pacific and into the Arctic.
The winter of 1978-79 is an ideal example of the flow pattern associated
with extensive North American snow cover. North American snow cover exceeded
the mean values for every week during this season and large portions of the
continental u.s. experienced bedlow normal temperatures from December through
February. The 700 mb circulation for each winter month was characterized by
high latitude blocking, with the west~rlies displaced south of their normal
position over North America in January (Dickson, 1979; Taubensee, 1979; and
Wagner, 1979).
Snow Cover area vs. Sea Level Pressure
The research of Dickson and Namias (1976) indicates that a greater fre-
quency of cyclones occurs along the Atlantic coast of North America, and cy-
clone frequency decreases in the region of the Icelandic low, during winter
seasons with enhanced snow cover in the eastern United States.Consequently,
the greater frequency of cyclones passing a given point should result in a
lower mean sea level pressure in that area. The sea level pressure data for
this study were taken from the National Meteorological Center's 1200Z Northern
Hemisphere synoptic map and they were then rendered into weekly averages to be
statistically correlated with the weekly snow cover data. Six geographic re-
gions were examined in order to evaluate any regional influence that might
exist between snow cover and surface pressure.
Tables 7, 8, and 9 illustrate the tabular correlations for snow cover and
concurrent +1 and -1 week surface pressure. Only two correlations are statis-
tically significant: North American snow cover vs. the Icelandic low pressure
index on both a concurrent and a -1 week lag basis. This direct relationship
indicates that extensive North American snow cover is associated with a weaker
than normal Icelandic low between Greeland and Iceland. Unfortunately, as in
the evaluation of the other pressure levels, the infuence seems to be one
direction (pressure pattern to snow cover and not the reverse).
44
Figure 3. North American snow cover vs. concurrent 700 mb heights.
Figure 4. North American snow cover vs. 700 mb heights at +1 week lag.
Figure 5. North American snow cover vs. 700mb heights at -1 week lag.
Table 7. Correlation coefficients for snow cover
vs. concurrent sea level pressure.
Weekly Snow Cover Area
SEA LEVEL
PRESSURE North Western Eastern
INDEX America u.s. u.s.
Icelandic
Low +0.237* +0.056 +0.059
Aleutian
Low -0.085 +0.112 +0.070
South Atl.
Coast -0.060 +0.019 -o.009
Middle Atl.
Coast -0.047 +0.062 +0.068
North Atl.
Coast -0.047 +0.028 +0.024
North Pac.
Coast +0.042 -0.125 +0.011
*Significant at the 0.01 level.
Table 8. Correlation coefficients for snow cover
vs. sea level pressure at +1 week lag.
Weekly Snow Cover Area
SEA LEVEL
PRESSURE North Western Eastern
INDEX America u.s. u.s.
Icelandic
Low +0.001 -0.036 -0.157
Aleutian
Low +0.018 +0.145 +0.148
South Atl.
Coast +0.009 -o.091 -0.030
Middle Atl.
Coast +0.032 +0.051 +0.054
North Atl.
Coast -0.014 +0.039 +0.024
North Pac.
Coast -0.081 -0.141 +0.062
48
Table 9. Correlation coefficients for snow cover
vs. sea level pressure at -1 week lag.
Weekly Snow Cover Area
SEA LEVEL
PRESSURE North Western Eastern
INDEX America u.s. u.s.
Icelandic
Low +0.202* +0.121 +0.099
Aleutian
Low -0.152 -0.008 +0.056
South Atl.
Coast -0.069 -0.073 -o.060
Middle Atl.
Coast -0.092 -0.018 +0.001
North Atl.
Coast .-0.083 -0.002 -0.007
North Pac.
Coast -0.050 -0.138 +0.006
*Significant at the 0.01 level.
Although caution should be exerted in inferring too much from these two
significant correlations, the following scenario can be formulated. A south-
ward shift in the location of the upper level trough normally located over
eastern Canada would result in an expanded North American snow cover. The
change in upper-level flow would redirect the paths taken by surface cyclones
away from the Iceland-Greenland area. The more extensive snow cover over the
eastern United States would enhance the surface temperature contrast along the
Atlantic coast, thereby intensifying cyclogenesis along the coast, which is in
agreement with the results of Dickson and Namias (1976).
An explanation can be offered as to why snow cover was poorly correlated
with variations in cyclonic activity (using sea level pressure). Mean sea
level pressure may be a poor index of cyclogenesis. Frequently during heavy
snow cover seasons, the intense cyclones are followed by large high pressure
systems. When the pressures are averaged to compute the weekly means, the
presence of the cyclones would be masked by the intense highs. Barry and
Perry (1973), in their discussion of mean pressure maps, have pointed out
that, while deep depressions frequently tend to move, such maps should be
examined with reference to charts of the frequency and tracks of cyclones and
anticyclones. As a last step then, the cyclonic paths of extensive and non-
extensive snow cover years were compared.
Cyclonic Activity During Extensive and Non-Extensive Snow Cover Years
The winter snow covers were averaged and divided into two equal groups,
extensive (above the median) snow cover years and non-extensive (below the
median) snow·cover years. Using the cyclonic paths as presented in Mariners
Weather Log, the frequency of cyclones passing through a 5 degree square grid-
ded network was computed.· The frequency of cyclonic activity for each grid
49
cell during the less extensive snow cover years was then subtracted from the
frequency of cyclones during the extensive snow cover years. The results are
illustrated i~ figure 6. It is significant to note that, as has been suggest-
ed in the literature, there is an increased amount of cyclonic activity across
the southern and southeastern states during extensive snow cover years. The
southward displacement of the cyclonic storm track is made even more evident
with the indication of decreased cyclonic activity across the northern states
during extensive snow cover years.
Summary and Conclusions
The purpose of this research project was to determine the relationships
that exist between snow cover and (1) atmospheric thermal anomalies and (2)
atmospheric circulation anomalies. Instead of using point observations of
snow cover, which are highly clustered into the midlatitudes as well as near
population centers, the newly created digitized archive of satellite-derived
snow cover observations was utilized. This satellite-based data archive ex-
tends back to fall 1966 and provides a spatially and temporally continuous
archive of snow cover. Weekly North American, as well as regional u.s. snow
cover areas, were spatially correlated to regional u.s. temperature anomalies
(using concurrent and +1, -1 week lagged temperature data). The same North
American and regional u.s. snow cover areas were statistically correlated to
500 mb and 700 mb geopotential heights as well as sea level pressure data
(using concurrent and +1, -1 week lagged height and pressure data). As a last
step in this research project, the frequency of North American cyclonic ac-
tivity during extensive winter snow covers was compared to the cyclonic activ-
ity during those winters with less extensive snow covers.
Correlating snow cover to temperature, the highest correlations exist be-
tween North American (as opposed to regional) snow cover and the regional
thermal anomalies. The regional thermal anomalies of most significance were
the entire United States, North and South Central, and northeast United
State.s. The concurrent and negatively lagged statistics produced an almost
equal number of significant correlations (32 and 28 of a possible 49) yet the
positively lagged data produced only 9 significant correlations. Therefore,
the influence of temperature on subsequent snow cover far exceeds the influ-
ence of snow cover on subsequent temperatures.
The 700 mb geopotential height field was much more significantly related
to snow cover than was the 500 mb geopotential height field. It was concluded
that there is no statistical or physical relationship between 500 mb height
anomalies and snow cover. Significant patterns appear in the concurrent and
negatively lagged correlations between 700 mb heights and snow cover, once
again emphasizing that the influence is from the atmosphere to snow cover and
not the reverse. It was concluded that extensive snow cover is associated
with higher than normal 700 mb heights across the northern portion of the con-
tinent and with lower than normal heights across the southern portion of the
continent.
· The only relationship that appears to exist between sea level pressure
(concurrent and -1 week lag) and snow cover is a reduced Icelandic low during
extensive snow cover years. The occurrence of disappointing corre·lations be-
tween sea level pressure and snow cover was judged to be a function of inten-
sified cyclonic activity being balanced by subsequent intensified advection of
50
Figure 6. The difference in the total number of cyclones (as calculated for 5° squares) between
winters with extensive snow cover and winters with reduced snow cover. Positive values indicate
that there is a greater amount of cyclonic activity during winters with extensive snow cover.
polar high pressure. Therefore, the cyclonic paths were then mapped to deter-
mine if there was an influence or relationship between snow cover and cyclonic
activity. It was demonstrated that during winters of extensive snow cover,
the cyclonic storm track was displaced south and eastward resulting in in-
creased cyclonic activity (as has been suggested in the literature} across the
southeastern and east coast regions of the United States. Unfortunately,
because seasonal data were utilized, it could not be determined whether exten-
sive snow cover caused the shift in the cyclonic paths or the shift in the
cyclonic activity caused the extensive snow cover.
References
Barry, R.G.; Perry, A.H. (1973} Synoptic Climatoloy: Methods and Applica-
tions. London, Methuen and Co., Ltd.
Dewey, K.F. (1977} Daily maximum and minimum temperature forecasts and the
influence of snow cover. Monthly Weather Review, v.lOS, p.1594~1597.
Dewey, K.F.; Heim, R. (1982} A digital archive of Northern Hemisphere snow
cover, November 1966 through December 1980. Bulletin of the American
Meteorological Society, v.lOS, p.l594-1597.
Dickson, R.R.; Namias, J. (1976} North American influence on the circulation
and climate of the North Atlantic sector. Monthly Weather Review, v.104,
p.1255-1265.
Dickson, R.R. (1979} Weather and circulation of February 1979: Near-record
cold over the northeast quarter of the country. Monthly Weather Review,
v.107, p.624-630.
Heim, R.; Dewey, K.F. (1984} Circulation patterns and temperature fields
associated with extensive snow cover on the North American continent.
Physical Geography, v.4, p.66-85.
Kukla, G.; Gavin, J. (1981} Cool autumns in the 1970's. Monthly Weather
Review, v.109, p.903-908.
Lamb, H.H. (1972} Climate: Present, Past and Future, vol. 1. London, Methuen
and Co., Ltd.
Matson, M.; Wiesnet, D.R.; Berg, C.P.; McClain, E.P. (1979} New data and new
products: The NOAA/NESS continental snow cover data base. (In: Fourth
Annual Climate Diagnostics Workshop. Proceedings. National Oceanic and
Atmospheric Administration, p.351-364.}
Namias, J. (1960} Snowfall over eastern United States: Factors leading to
its monthly an seasonal variations. Weatherwise, v.13, p.238-247.
Namias, J. (1962} Influence of abnormal surface heat sources and sinks on
atmospheric behavior. (In: International Symposium on Numerical Weather
Prediction, Tokyo, November 7-13, 1960. Proceedings. Meteorological
Society of Japan, p.615-627.}
52
Namias, J~ (1963a) Large-scale air-sea interactions over the North Pacific
from summer 1962 through the subsequent winter. Journal of Geophysical
Research, v.68, p.6171-6186.
Namias, J. (1963b) Surface-atmosphere interactions as fundamental causes of
drought and other climatic fluctuations. (In: Arid Zone Research XX,
Changes of Climate. Rome Symposium UNESCO and WMO. Proceedings,
p.345-359.)
Namias, J. (1964) A 5-year experiment in the preparation of seasonal out-
looks. Monthly Weather Review, v.92, p.449-464.
Namias, J. (1966) Large-scale air sea interactions as primary causes of
fluctuations in prevailing weather. Transactions of the New York Academy
of Sciences, s. II, v.29, p.l83-191.
Namias, J. (1978a) Multiple causes of the North American abnormal winter
1976-77. Monthly Weather Review, v.l06, p.279-295.
Namias, J. (1978b) Persistence of u.s. seasonal temperatures up to one year.
Monthly Weather Review, v.l06, p.l557-1567.
Namias, J. (1985) Some empirical evidence for the influence of snow cover on
temperature and precipitation. Monthly Weather Review, v.113,
p.1542-1553.
Taubensee, R.E. (1979) Weather and circulation of December 1978: Record and
near record cold in the West. Monthly Weather Review, v.107, p.354-360.
Wagner, A.J. (1973), Influence of average snow depth on monthly mean tempera-
ture anomaly. Monthly Weather Review, v.101, p.624-626.
Wagner, A.J. (1979) Weather and circulation of January 1979: Widespread
record cold with heavy snowfall in the Midwest. Monthly Weather Review,
v.107, p.499-506.
53
Proceedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College Park, MD. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice), Glaciological Data, Report GD-18, p.55-61.
Relationships between Snow Cover and Temperature in the
Lower Troposphere, General Circulation in East Asia
and Precipitation in China
Zhao Zong-ci
Wang Shao-wu
Department of Geophysics
Peking University, China
Abstract
Relationships between sn<X~T cover ani tenperat:ure in ~ lower troposphere,
general clrculat:f.on at 500 ni> in East Asia ani preclpitat:f.on in <lrl.na liilei'e
analyzed. It is slnwn that tlere are obvious negative correl.atiom between
snow cover in the N:>rthern Hemisplere in winter ani tenperature in tle follow-
ing Sllllll!r. Significant negative relationships between sea ice in SUilller ani
tenperature in tle biglEr latitudes are noticed. Saai cover in Eurasia in
winter influences tle general circulation in East Asia am rainfall in China in
tle following spring ani 8\llller. Wban tlere is mre snow cover in Eurasia in
winter, drooghts appear in Northam China in ~ following spring ani in tle
Yangtze River valley of China in tle following 8\llller•
·Introduction
It was noticed that snow cover and sea ice are important elements when
climatic changes in the earth-atmosphere system are studied.
The purpose of this paper is to investigate the relationships between
snow cover and temperature in the lower troposphere, general circulation in
East Asia and precipitation in China.
Data
Areas of snow cover in the Northern Hemisphere in winter (Dec.-Feb.) were
from Wiesnet and Matson (1979) and Matson and Varnadore (1981). North
American and Eurasian seasonal averages of snow cover in winter (Dec.-Feb.)
were given by Kukla and Gavin (1979) and Kukla et al. (1982). Sea ice was
from Zakalof and Stlokuna (1978).
Characteristic values of the general circulation at 500 mb in East Asia
and precipitation and temperature in China were offered by the long-term fore-
casting office of the Chinese Meteorological Administration. Drought and flood
data for China were shown by Wang and Zhao (1981) and Zhao and Wang (1984) •
. Temperature in the lower troposphere was taken from Wang and Zhao, (1984).
55
Due to the limited amount of snow cover data, time series for data were
taken from 1967 to 1982. Long-term averages were computed over this time in-
terval.
correlation between snow cover, sea ice and temperature in the lower tropo-
sphere.
Cross lag correlation coefficients between areas of snow cover in the
Northern Hemisphere in winter, Wiesnet and Matson (1979) and Matson and
Varnadore (1981), (Dec.-Feb.) and zonal mean air temperature along each lati-
tude (10-90°N) in the lower parts of the troposphere (1000 mb-500 mb) from
November to December of the following year were computed (figure 1). It was
found that there are more obvious negative correlations with the following
summer half-year {May-Oct.) than the same season (Dec.-Feb.), especially at
60°N (June-Sept.). The curves of snow cover in winter and average air temper-
ature at 60°N in June-September are given in figure 2b. Winter of 1966-67 of
figure 2 is represented by 1967. It is noticed from figure 2b that the rela-
tionship is strong. Temperature in the middle latitudes of the Northern Hemi-
sphere in summer has a close relation to snow cover in winter. When there is
more snow cover in the Northern Hemisphere in winter, lower temperature at the
middle latitudes of the Northern Hemisphere in the following summer appear and
vice versa.
11 i~
80
70
co
~0
40
50
10
0 10
t.$l ?o.s1 ~ ~ -0.51
Fig. 1 Cross correlation between snow cover in the Northern Hemisphere in
winter and average air temperature along each latitude. (Positive and nega-
tive correlation whih received the 95% level of confidence are shown by a9
and ~ here).
56
·c
10 6 km!
-o.s 6. ~
-1.0 6.6
6. a
·c 7 .o
1.0 7.2
o.s
0
-o.s 10 6 J.:m!
-1.0
1951 1955 19£0 19CS 1910 year
Fig. 2. Curves of Arctic ice {a), snow cover (b) in the Northern Hemisphere
and air temperature in summer.
Cross lag correlation coefficients between areas of Arctic ice in July
and August and zonal mean air temperature along each latitude (10°N-90°N) in
the lower parts of the troposphere (1000 mb-700 mb) from November to December
of the following year for 1951-1976 were computed {figure 3). There are sig-
nificant negative correlations between Arctic ice and air temperature in the
higher latitudes at the same season and prior seasons. In other words, the
negative correlations continue from winter, spring, to summer. Fig. 2a shows
curves of areas of Arctic ice in July and August and averaged air temperature
at 70°N between May and August. It is interesting to note that the trend of
temperature change varies with Arctic ice. When there is more Arctic ice,
there is lower temperature and vice versa.
N II 12 3 10 II i] month
30 ~ ~~~' ~ ~ I ·~~ "?'/
I 0
+l ao ~~'~'~''~'-@ 1§2"''"''~ ~~~~ '~~~ ~ '~~ ~"'~ ~ ~~~,-'"D\_~~ ::;.:'! "' 70 ~~~-~~\)1 j ;;o '~~9 so ,() ~ ~' ~ 30 + ~"4__ ~ 2Q
'i
10 o I o • 1 1 1 ~~. _~0
I
€}) ?-O.lT t1l ~ -O.l7
Fig. 3. Cross lag correlations between Arctic ice in July and August and
zonal averaged air temperature along each latitude from the prior November to
December.
57
Relationships between snow cover and general circulation at 500 mb in East
Asia.
Major sfOoptic systems that influence rainfall and temperature in China
are the intensity and position of the subtropical high in the western Pacific,
of the trough in East Asia, intensity of the trough in India and Burma, height
of Tibet Plateau and the zonal circulation index in East Asia at 500 mb. They
are called "circulation characteristic values in East Asia". Relationships
between snow cover in Eurasia and North American in winter (Dec.-Feb.) and the
circulation characteristic values in East Asia at the same season and the fol-
lowing seasons were examined. It was found that there are obvious relations
between snow cover in Eurasia in winter and several circulation characteristic
values in East Asia in the following seasons. ·several exampls are shown in
figure 4. It. is interesting to note from figure 4 that when there is more
snow cover in Eurasia in winter, there is weaker intensity of the trough in
East Asia in March, and there is weaker zonal circulation (and stronger meri-
dional circulation) in East Asia in April and the southern extreme of the sub-
tropical high ridgeline in the western Pacific in July.
Relationships between snow cover and precipitation and temperature in China.
Relationships between snow cover in Eurasia and North America in winter
and the major weather phenomena in China at the same season and the following
seasons, such as droughts and floods, Bai-u, cold damage, typhoon, and so on,
were analyzed. Several obvious relationships are shown in figure 5. When
there is more snow cover in Eurasia in winter, there is dry weather in the
North of China in spring and in the Yangtze River valley of China in summer.
Conclusion
1. There are obvious negative correlations between snow cover in the
Northern Hemisphere in winter and temperature in the following
summer.
2.-· Temperature in the higher latitudes from the prior winter to summer
relates to the Arctic air in summer.
3. There are significant relationships between snow cover in Eurasia in
winter and several characteristic values of circulation at 500 mb in
East Asia in the following spring and summer.
4. There are obvious relationships between snow cover in Eurasia in
winter and droughts and floods in China in the following spring and
summer.
Due to the limited data on snow cover, these relationships will be
further examined in the future.
Acknowledgements
The suthors wish to acknowledge the excellent working conditions and com-
puter time given by the Climatic Research Institute, Oregon State University.
We would like to thank Mr. William McKie for improving the English in this
paper, and Ms. Naomi Zielinski for typing the manuscript.
58
30 ~\~ 28
/
26
20
00
200
150
100 ·-·· ..
26
24
20
Fig. 4.
: ' ..... ,. . . ·-------..--------T·--····--1--..
:. ·! . i .
---.. ·--·-· --··· -----··----------
Curves of snow cover in Eurasia in winter (unit: million sq. km) (a),
intensity of the trough in East Asia in March (b) (unit: 10*
geopotential meter), zonal circulation index in East Asia in April
(c) and the position of the subtropic high ridgeline in the western
Pacific in July (unit: latitude) (d).
59
(a)
(b)
(c)
(d) ...
1967 69 71 73 75 79 81 year
32 =._
30
28
26
nnn
8000
6000
4000
2000 1 ·-.
I
!\
·V-··-· ...
----
(a) ) ...
I
(b)
( c}
Fig. 5. Curves of (a) Eurasian seasonal averages of snow cover, Dec.-Feb.
(unit: million sq·. km)
(b) Grades of rainfall in the North of China in spring
(Mar.-May)
(c) Precipitation in the Yangtze River of China in summer
(Jun.-Aug.) (unit: mm).
60
References
Kukla, G.; Gavin, J. (1979) Snow and sea ice in 1978-1979. Fourth Annual
Climate Diagnostics Workshop. Proceedings.
Kukla, G.; Gavin, J.; Varnadore, M.; Ropelewski, C. (1982) Snow and varia-
tions of 1981-82. Seventh Annual Climate Diagnostics Workshop. Pro-
ceedings.
Matson, M.; Varnadore, M.S. (1981) The winter snow cover drought of 1980-81.
Sixth Annual Climate Diagnostics Workshop; New York, October 14-16,
1982. Proceedings.
Wang, S.; Zhao, Z. (1981) Droughts and Floods in China, 1470-1979, Climate
and History. Cambridge University Press.
Wang, s.; Zhao, Z. (1984) Investigation of temperature change in the lower
parts of troposphere in the Northern Hemisphere. Acta Meteorologica
Sinica, 42, p.283-245.
Wiesnet, D.R.; Matson, M. (1979) The satellite-derived northern hemisphere
snow cover record for the winter 1977-78. Monthly Weather Review,
v.107, p.928-933.
Zakalof, B.F.; Stlokuna, L.A. (1978) Changes in sea ice in Arctic Ocean.
Meteorology and Hydrology, 7.
Zhao, z.; Wang, S. (1984) Spring Droughts in Huabei of China and its Develop-
ment, Weather in North China. Peking University Press, 5.
61
Kukla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) SNOW WATCH '85.
Proceedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College Park, MD. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice), Glaciological Data, Report GD-18, p.63-72.
Abstract:
Progression of Regional Snow Melt
David A. Robinson
Lamoni-Doherty Geological Observatory
Columbia University
Palisades, New York, U.S.A.
Snow melt may be accurately monitored by observing time-related
variations of surface albedo. When snow is present, regional surface albedo
is primarily a function of 1) the physical properties of snow, 2) the fraction
of the snow covered surface which is unobstructed and 3) the amount of exposed
snowfree ground. The second is a function of the height and density of
vegetation or other objects protruding through the pack. The most accurate
means of obtaining data on regional to continental scales is through the
analysis of shortwave satellite imagery in an interactive manner on an image
processor by an observer familiar with the studied area.
Introduction:
As snow dissipates its strong insulating and atmospheric drying
capacities diminish. Its capacity to cool the atmosphere, which stems from
its high emissivity and high albedo, aiso decreases. This is primarily a
result of changes in the physical properties of snow, including snow depth,
increases in snow grain size, wetness and contaminants, and changes in the
amount of exposed snowfree ground.
Presently, the most accurate means of monitoring the state of a regional
snow cover is by observing its surface albedo in shortwave satellite imagery.
The utility of gro•.md station data in monitoring melt is limited because of
63
drifting, topography, local variations in initial snow depth and heat island
effects.
Shortwave image analysis is not without its problems. Clouds may obscure
the surface, particularly in the transitional zone between fully snow-covered
and snowfree terrain. AI bedo can not be directly measured from satellites
when skies are clear, due to atmospheric attenuation of solar radiation
reaching the surface and reflected from it, and due to the narrow-band
bi-directional nature of the sensors. For these reasons, it is necessary to
have ground-truth knowledge of different regions under a variety of snow-cover
conditions. Here we present albedo data gathered on the ground and from
aerial and satellite platforms to exemplify their utility in monitoring the
progression of melt and, also, to show how the albedo of different surfaces
varies with changing snow conditions.
Ground truth:
Measurements of broadband hemispheric albedo (0. 28-2.80 microns) were
made following a snowstorm in order to document melt conditions over surfaces
representative of broad regions of the middle latitudes. The study was
undertaken in southeastern New York and northern New Jersey following the
major East Coast storm of February 11-12, 1983. Albedo was monitored on the
ground and from a low-flying aircraft as a 50 em deep snowpack dissipated.
Data were collected during a three week period ·where daily high temperatures
were above freezing and no significant rain or snow fell until March 2.
Ground measurements of the snowpack were made at midday over a short
grassy field. Albedo of the pack fell by 0.29, from 0.83 to 0.54, between the
13th and 25th (fig. 1 ). While the snow was slushy on the. 25th, and some
portion of the reflected signal was from the underlying grass, no grass blades
had yet protruded through the pack. Thus, significant changes in regional
albedo occurred when snow continued to cover 100% of the surface. However, it
was observed that over all but extremely level and smooth surfaces some bare
ground began to be exposed prior to the albedo of most of the snowpack falling
below 0.70 (Feb. 21).
Time series of albedo were constructed over key middle latitude surfaces
as the snow dissipated (fig. 2). Data were gathered from wingtip-mounted
pyranometers flown at an altitude of approximately 200m.
Forests (lines B & C) masked much of the snow, thus initial albedo values
were low. They diminished in a quasi-linear fashion as the snow dissipated.
Shrubland (F) albedo initially dropped rapidly, in response to snowpack
settling and snow falling off of branches, and later slowed. The opposite
pattern was observed· over open fields and meadows (A,D,E) where albedos
initially were as much as eight times their snowfree values. Initial
decreases in albedo paralleled the snowpack values. Later, as bare ground-
became exposed, the melt accelerated· and albedo decreased more rapidly. · Over
residential sites (H) and particularly over industrial locations (G), albedo
decreased and snow disappeared more rapidly than over natural terrain.
64
E
~
.s=
0. ...
0
60
<10
30
20
;
~' I
I
I
10 / /
"'~
JO
A
_90
Figure 1. Midday albedo of a complete snowpack over a short grassy field at
the Lamont Observatory (41°N) from Feb. 13-25, 1983. Albedo is shown with
a diamond for 1-3 day old snow (open diamond: dry, Feb. 13 and 14),
squares 4-6 days old, triangles 7 days or more old. Solid symbols
indicate a wet snowpack. Datum for Feb. 20 is missing. Arrows point to
cases where global atmospheric transmissivity was below 40%. (~fter
Robinson, 1984)
The ground truth study also exemplified the difficulties in using station
spow depth data alone, even a dense network, when monitoring regional snow
4'icssipation. Albedos from 26 sites along the flight path on a day with 13 em
deep 3 day old snow (Feb. 10, 1983) were approximately 20% greater than the
identical sites on a day with 13 em deep 9 day old snow (Feb. 22, 1983) (fig.
3). Snow depth was aver~ged from a network of stations with a density of
approximately 1 per lOOkm • Skies were clear on both dates. The albedo of
the snowpack was 0. 78 on the lOth and 0. 60 on the 22nd. Only the industrial
site (cf. Figure 2 G), where snow of any depth remained for only a short time,
does not fit the relationship.
65
A
Feb Mar
Figure 2. Albedo (A) of major surface elements in southeastern New York and
northern New Jersey under winterlike snowfree (March 24, 1983) and a
variety of snow-cover (Feb. 14-March 3, 1983) conditions. The lettered
curves indicate locations which include: (A) dark soiled farmland,
(B) deciduous forest, (C) mixed coniferous forest, (D) cultivated field,
(E) grassy meadow, (F) shrubby grassland, (G) industrial and
(H) residential. (from Rooinson and Kukla, 1984)
66
.60
.0
If
N .40
N
<l: y= .83X -.0069
.20
A IOFeb
Figure 3. Snow depth-age-albedo relationships. The albedo of a site on Feb.
10, 1983 is plotted against the albedo of the identical site on Feb. 22,
1983. Both days had clear skies and identical snow depths at forest and
field test sites. Snow on the lOth was 3 days old and dry with an albedo
of 0.78. Snow on the 22nd was 10 days old and wet with an albedo of 0.60.
The regression equation has a correlation of 0.96. It does not include
point G which is an industrial site (cf. fig. 2, location G). (from
Robinson and Kukla, 1984)
Satellite observations:
To permit an assessment of regional snow melt from satellite imagery it
is first necessary to know the maximum regional albedo that may be exJected
u~der .deep fresh snow-cover conditions. This has b~en done in 1 x 1°
latitude/longitude cells over Northern Hemisphere seasonally snow covered
lands (£igw 4) (Robinson and Kukla, 1985). Data were obtained ~y image
processor ,analyses of clear-sky Defense Meteorological Satellite Program
(I!MSP) imagery. Scene brightness was converted to surface albedo by linear
interpolation between bright and dark snow-covered surfaces with known albedo.
A broad zonal distribution of forests masks the underlying snowpack in
Eurasia and North America. For instance, in Eurasia the areally averaged
albedo is 0.36 over the boreal forest zone between 60° and 65°N. It rises to
0.76 over the short grassy tundra further north.
67
0 30 60 90
I I I I I I I I I I I
eo-+ ~+ 1?.Y
-+
-1 I I I I I f I I •I I
120 150 180 150 120
I I I I I I I I I I I I I I I
+ + + +
+ +
f I I I I I I I I I I I I I I
90 60 30
I I I I I I I I I I
-eo
+ -60
~~~E~ .21-30 +
·31-40
...... 50
1!151-60
+ Elst-10
[]71-BO
I I 1 I I I I I I I
-40
Figure 4. Maximum surface albedo of Northern Hemisphere land with the
potential of developing seasonal snow cover. Cells, measuring 1° x 1°,
are marked in 0.10 increments. Comparisons with Figure Sa may be made in
south central Asia. (from Robinson and Kukla, 198S)
The ground truth study exemplified the fact that regional albedo with
snow present is frequently below its potential maximum. This is particularly
so where the climate is dry and snow cover shallow (eg. south central Asia) or
in the snow transition zone. In figure Sa the fully snow-covered steppe
(points 1-3) is considerably brighter than the forested hills to the northwest
(point 4), where the snow cover is masked by the forest canopy. In Figure Sb
the snow cover has melted over portions of the steppe and bare ground is
exposed (points 1 and 2). The dark pattern of roads and rails radiating from
cities (point 3) is more pronounced in b.
Thus, many of the same characteristics of snow-covered and partly
snow-covered lands observed on and near the ground are· recognized on clear-sky
shortwave satellite images. Analysis of this imagery in an interactive manner
on an image processor by an observer familiar with the studied area is quite
useful in identifying and quantifying this information. Figure 6 shows
brightness histograms measured from NOAA Very ~igh Resolution Radiometer
(VHRR) images over an approximately 100,000 km portion of central U.S.
farmland. Histogram 1 shows the area a year earlier, when it was covered with
over !Scm of fresh snow. There is a single peak at a high brightness value.
The February 1, 1977 histogram (2) quantifies the distribution of snow
over the region when an originally complete snow cover was 7 days old. Some
bare ground had begun to appear, giving the histogram a slightly bimodal
character. Compared to histogram I, the bright peak is shifted towards the
68
a 70E 80E
SSN-
-55
1 .... .. ,
3
SON-
70 80
b 70E 80E
SSN-
-55
SON-.
70 80
Figu re 5. A portion of a DMSP image covering south-central Asia on (a) Feb.
19, 1979 and (b) March 23, 1979, showing a decrease of surface albedo due to
prog ressing snow melt. (after Robinson and Kukla, 1985)
69
dark end, probably due to a combination of decreasing snow albedo and
subresolution patches of bare ground. Some 80% of the stations in the region
reported snow •cover on the 1st. At these stations the average depth was 10
em. In subsequent days (histograms 3-5), the amount of snowfree ground began
to exceed that of snow-covered ground. By the lOth the single dark peak on
the histogram (5) indicates that most surfaces were snowfree, however, some
subresolution snow patches were present, which broadens the peak and skews it
towards the right. On this date, 50% of the stations reported snow cover,
with the depth averaging 4 em.
10-
6-
2-
I
75
1
2/10/77
I
125
I
175
Figure 6. Brightness histograms with different snow-cover
centr~ U.S. farmland. Vertical axis shows the number of
(x 10 ) with a particular brightness (horizontal axis).
and Kukla, 1982)
70
conditions over
processor pixels
(after R<:>binson
Further evidence of the utility of satellite-derived time series in
monitoring regional snow melt is shown in figure 7. Brightness histogram
statistics from clear-sky Advanced VHRR imagery over tundra and forest in
Alaska are plotted for the springs of 1981 and 1980, respectively. Mean
brightnesz is expressed as surface albedo. Each region is approximately
50,000 km •
In both regions, decreases in brightness as snow melted were found to be
accompanied by simultaneous shifts in other histogram statistics. For
instance, standard deviation decreased and kurtosis and skewness rose as snow
disappeared in forested areas. These shifts indicate that the open fields,
lakes, rivers, etc. in this region were no longer brightly snow covered. Once
both the woodlands and the openings were snowfree, skewness fell to zero. In
the tundra, standard deviation rose, kurtosis fell and skewness became
negative once snowfree areas began to appear. Once dark snowfree areas
predominated, skewness became positive, standard deviation decreased and
kurtosis increased. When snowfree conditions were reached (around June 15),
relatively bright ice-covered water bodies resulted in skewness remaining
positive and the standard deviation staying high.
Snow dissipation can be successfully monitored by only observing changes
in skewness, kurtosis and standar& deviation. This is useful in cases where
ima!;;e quality or thin homogeneous clouds permit the surface to be seen but
prohibit accurate albedo estimates.
1
FORESTED
20
1-4 ,32 ... / .· ·./ .· /• \ .. · I \
+ f · .. ·· I \ ... '··I'······\"· I . ... . . ~ I
\ . ·. 0
10 20
A
1 10 20
M
TUNDRA
37 -76 _ •• ;::....,oo.."-l. ___ _ --··-...
4
20 1
A
' ' ' ' '
' ' ' ' . ' ····-3 .. ·· · .. ·· ...
10 20
M
2!
10 20
J
Figure 7. Response of brightness histogram standard deviation (-------),
skewness ( ••••••• ), kurtosis (----)and surface albedo (-•• -•• )to snow
melt in forest and tundra areas of Alaska in the springs of 1981 and 1980,
respectively. Horizontal scale shows dates between April 1 and June 23.
(from Robinson and Kukla, 1982)
71
Conclusions:
Accurate representation of snow cover and its impact on surface albedo is
important for the realistic performance of climate models used to project
climate perturbations due to increasing levels of co 2 • Continued monitoring
of snow melt is needed, as model results to date indicate that the initial
signs of a CO climate impact may be recognized in the marginal cryosphere.
Shortwave satellite imagery is the most accurate means of obtaining this data
on regional to continental scales, particularly if ~t is analysed in an
interactive manner on an image processor by an observer familiar with the
studied area.
References:
Robinson, D.A. (1984) Anthropogenic Impact On Winter Surface Albedo. Doctoral
thesis, Columbia Universtiy, 384pp.·
Robinson, D.A.; Kukla, G. (1982) Remotely sensed characteristics of snow
covered lands. in: (Keydel, W., ed.) 1982 IEEE International Geoscience
and Remote Sensing Symposium Digest, WA-1, 2.1-2.9.
Robinson, D.A.; Kukla, G. (1984) Albedo of a dissipating snow cover. Journal
of Climate and Applied Meteorology, 23, 1626-1634.
Robinson, D.A.; Kukla, G. (1985) Maximum surface albedo of seasonally
snow-covered lands in the Northern Hemisphere. Journal of Climate and ------~----------~------Applied Meteorology, 24, 402-411.
72
l{ukla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) SNOW WATCH '85.
proceedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College Park, MD. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice), Glaciological Data, Report GD-18, p.73-77.
Soot from Arctic Haze: Radiative Effects
on the Arctic Snowpack
Stephen G. Warren
Department of Atmospheric Sciences
University of Washington
Seattle, Washington, U.S.A.
Antony D. Clarke
Hawaii Institute of Geophysics
University of Hawaii
Honolulu, Hawaii, U.S.A.
The burning of fossil fuels adds,pot only co 2 to the atmosphere but
also particulates which are the products of incomplete combustion. The
small soot particles, which seem to be largely responsible for the
absorption of solar radiation in the Arctic haze, are eventually scavenged
from the atmosphere and are incorporated in the Arctic snowpack. Here
we examine the possible effects of these particulates on the snow albedo
and the surface radiation budget, using a model for radiative transfer
in snow.
The model (Wiscombe and Warren, 1980) was developed to explain the
reasons for the large observed variability of snow albedo. It showed
good agreement with available spectral measurements. The principal factor
controlling snow albedo is the grain size (Figure la of Marshall and
Warren, these proceedings), which normally increases with snow age due to
metamorphism.
Small amounts of absorptive impurities in snow can reduce the albedo
dramatically in spectral regions where the albedo is high (visible wave-
lengths). Warren and Wiscombe (1980) computed the radiative effects of
graphitic carbon ('soot') distributed uniformly through a snowpack (shown
in Figure ld of Marshall and Warren, these proceedings). Subsequent
experiments which measured both albedo and soot content (Grenfell et al,
1981) showed agreement with results of the radiation model to within the
experimental uncertainty, which was about a factor of 2 in soot content
(Sec. Hl of Warren, 1982).
Clarke and Noone (1985) collected snow samples from several locations
in the Arctic (but none in the Siberian sector), shown here in Figure 1.
They applied an optical method to the filter through which meltwater from
these samples had been passed, to determine the concentration of soot in
the snow. The mass fraction of soot ranged from about 5 x lo-9 to 5 x 10-8
(shaded area in Figure 2). Warren and Wiscombe (1985) then estimated the
effect on snow albedo due to these soot concentrations. For the energy
73
Figure 1. Map of Arcti~ sampling locations for the University of Washington atmospheric(~)
aircraft samples and snowpack (0) samples for 1983 .,. 1984.
(Figure 1 of Clarke and Noone, 1985).
0 ----..............
-0.01 ' ' '
-0.02 --spectrally ;=
0 averaged z
(/) -0.03 ----470 nm w wavelength a:
:;)
Q.
..... -0.04
0
0
0 -0.05 bJ m
...J
<(
:E -0.06
0 a: .....
1&.1 -0.07 Soot content of C) z Arctic snow samples <(
J: -0.08
winter-spring 1983, 1984 u
-0.09
-0.10
10
MASS FRACTION OF SOOT IN SNOW
Figure 2. Computed effects on snow albedo due to small mass fractions of
soot. Plotted is the change from the albedo values of pure snow, a 0 • The
spectrally-averaged changes are shown as solid lines. The dashed lines are
calculations at the wavelength where snow albedo is most sensitive to soot
content (470 nm wavelength). The reduction in spectrally-averaged albedo is
thus about half as large as the reduction at visible wavelengths. The
shaded region indicates the range of soot concentrations determined by
Clarke and Noone,(l985) in 12 samples of snowfall collected from Arctic
Canada, Alaska, Greenland and Svalbard during winter and spring 1983, 1984.
To ensure consistency between soot measurement and albedo calculation they
have been multiplied here by the factor 0.85 because Clarke and Noone
assumed a mass absorption coefficient kabs = 8.5 m2g-l for ambient soot
at 525 nm wavelength, whereas the Mie calculation for the soot parameters
used by Warren and Wiscombe (1985) gave kabs = 10.0 m2g-l. [Figure and
caption from Warren and Wiscombe (1985).]
75
budget of the snowpack, it is th~ spectrally-averaged albedo which is
important. The solid lines in Figure 2 show that the reduction in
spectrally-averaged albedo is in the range 0 to 0.035 • .
Clarke and Noone (1985) took 0.02 as a representative albedo reduction
from Figure 2 and computed the effect on the radiation budget in the
Arctic Ocean during spring and summer, using the solar radiation climato-
logy of Fletcher (1965). The effect on the earth-atmosphere radiation
budget is estimated to be +4.6 x 107 J m-2 for the period 1 February -
15 July for 75°N (sunlight is negligible before February, and snow is
mostly gone after mid-July). The positive sign means a ne~ gain by the
earth-atmosphere system. This compares to +8.2 x 107 J m-estimated
by Cess (1983) for the net effect of Arctic haze in the atmosphere on
absorption of solar radiation by the earth-atmosphere system at 75°N.
Cess's calculations were for clear sky conditions only. If clouds were
included, his estimate would be smaller.
Thus we conclude that Arctic-haze soot in the snowpack should have
an effect on the earth-atmosphere radiation budget comparable to that of
Arctic haze in the atmosphere. However, the effects we calculated are
based on soot concentnations measured in newly-fallen snow. The effect
of soot during the melting season will be greater than we calculate if
the soot particles tend to concentrate at the surface of melting snow, as
do micron-size dust particles (Higuchi and Nagoshi, 1977).
These effects on absorption of solar radiation do not take into
account any feedbacks. There is a positive feedback which would make the
effect larger, but which would require a climate model to estimate it:
The lower snow albedo could cause increased melting rates in summer, and
cause the snow to disappear sooner than usual, thus uncovering the lower-
albedo sea ice earlier in the season.
References
Cess, R.D. (1983) Arctic aerosols: model estimates of interactive
influences upon the surface-atmosphere clear-sky radiation budget.
Atmospheric Environment, v.l7(12), p.2555-2564.
Clarke, A.D.; Noone, K.J. (1985) Soot in the Arctic snowpack: a cause
for perturbations in radiative transfer. Atmospheric Environment,
v.l9, in press.
Fletcher, J.O. (1965) The Heat Budget of the Arctic Basin and Its
Relation to Climate. RAND Report R-444-PR, RAND Corp., Santa
Monica, CA.
Grenfell, T.C.; Perovich, D.K.; Ogren, J.A. (1981) Spectral albedos of
an alpine snowpack. Cold Regions Science and Technology, v.4,
p.l21-127.
76
Higuchi, K.; Nagoshi, A. (1977) Effect of particulate matter in surface
snow layers on the albedo of perennial snow patches. Isotopes and
Impurities in Snow and Ice, IAHS Publ. No. 118, p.95-97.
Marshall, s.; Warren, S. (1986) Parameterization of snow albedo for
climate models. These Proceedings.
warren, S.G. (1982) Optical properties of snow. Reviews of Geophysics
and Space Physics, v.20(1), p.67-89.
warren, S.G.; Wiscombe, W.J. (1980) A model for the spectral albedo of
snow, II. Snow containing atmospheric aerosols. Journal of the
Atmospheric Sciences, v.37(12), p.2734-2745.
Warren, S.G.; Wiscombe, W.J. (1985) Dirty snow after nuclear war. Nature,
v.313(6002), p.467-470.
Wiscombe, W.J.; Warren, S.G. (1980) A model for the spectral albedo of
snow, I. Pure snow. Journal of the Atmospheric Sciences, v.37 (12),
p.2712-2733.
77
Kukla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) SNOW WATCH '85.
Proceedings of the Workshop held 28-30 October 1985 at the ·University of
Maryland, College Park, MD. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice), Glaciological Data, Report GD-18, p.79-88.
The Snow Cover Record in Eurasia
James Foster
Hydrological Sciences Branch
National Aeronautics and Space Administration
Goddard Space Flight Center
Greenbelt, Maryland, U.S.A.
Abstract
Eurasia has considerably llDre of its surface area in nortlEm latitudes
than does North Arerica ani so its snow cover is !lDre extensive. Trenis in tlE
l'brthern Hemisph!re ani Eurasia are thus similar. As a result in any study of
hemispheric feedhlck m:cha.ni.sms involving snow cover, Eurasia has a greater
feedback potential than l'brth Auerica. Satellites have been used to map and
measure continental snow COlTer only since the mid-1960's. If snow cover data
can be retrieved fran pre-satellite clinatological records, an historical file
representing varying snow cover coniitiom may be used to get a lo~er estinate
of any treni or cycles tirl.ch may exist. ht attempt was made in this study to
le~hen the satellite sn<M cover record in Eurasia by reviewing p1st climato-
logical records in Europe and Asia ani by examining proxy indices such as Arctic
sea ice data. Also, the satellite snc:M cover record was investigated to
detennine if there is a correlation between the continental snow cover extent
ani tlE rumber of days of snow COlTer at several different geographic areas in
Eurasia where a winter snow cover does not always occur. Results indicate that
while there is a ratlEr weak associat:ion between sea ice extent ani continental
snow cover, an r?-of • 77 exists between the nean. mmber of snow cover days for
selected location; in the interior of Eurasia ani the continental snow cover
extent. It may be possible then to use the nean. mmber of snow cover days at
selected sites in Eurasia pr:lor to the aivent of satellite !lDnitori~
capabilities as an index of the continental snow covered area.
Introduction
The polar caps of Earth expand and contract in accordance with the dis-
tance from the sun and the amount of solar radiation received. The dramatic
increase in ice and snow cover from one season to another is bound to have a
considerable impact on climate (Kukla and Robinson, 1981). Prior to the
mid-1960's the monitoring of the areal extent of snow cover was limited to
point measurements and extrapolation between these often widely separated
points. But since November of 1966, continental and hemispheric snow cover
has been monitored on a weekly basis by satellites. Except for the summer
months, the proportionately larger variations in Eurasian snow cover dominate
79
the North American variations so that trends in the Northern Hemisphere and
Eurasia in autumn, winter, and spring are very similar. As a result in any
study of he~ispheric feedback mechanisms involving snow cover, Eurasia has a
greater feedback potential than North America (Matson and Wiesnet, 1981). As
the satellite snow cover record is extended temporally in years to come it
should provide a new understanding of global and regional climate. Addition-
ally, if snow cover data can be retrieved from past records, an historical
file representing varying snow cover conditions may be used to get a longer
look at any trends or cycles which may exist. ·
Snow cover data for North America collected since 1948 have been digi-
tized using data from surface stations to extend the satellite record provid-
ing over 35 years of snow cover information (Walsh et al., 1982). In this
study an effort was made to reconstruct the pre-satellite seasonal variations
in Eurasia snow extent by reviewing climatological publications and by examin-
ing proxy indices such as Arctic sea ice extent and the number of snow cover
days for different geographic locations in Europe and Asia.
Winter Snow Cover Regime
From December until March, Eurasia has more of its land covered by snow
than any other surface feature. By early December a stable snow cover is
formed in most years over all but the south central and southwestern areas of
the Soviet Union. Much of northern and western China and Mongolia is snow
covered at this time as are the mountain ranges of the Himalayas. In western
Europe, typically only Scandanavia and the highland areas surrounding the Alps·
and Carpathian Mountains are snow covered during December. Generally the snow
covered area reaches its greatest extent in late January or early February
when as much as 30 million square kilometers of the continent may be snow
covered. Almost all of Eurasia north of 40° and east of the Black Sea is
beneath a veneer of snow (Lydolph, 1977). During most years coastal areas of
western Europe are snow covered for only a few days at a time, but in some ex-
treme years a snow cover will persist for several weeks even in those coun-
tries which are usually thought of as having rather mild winters such as
England and France. By March the snow covered area begins its northward re-
treat.
Methodology
It is known that snow records gathered at stations of the climatological
network are often incomplete in time and space. In Asia the network is e$pe-
cially sparse, making delineation of the snowline particularly difficult. But
it was hoped that by assembling past records on snowfall and snow cover from
locations in Europe and Asia that more information on seasonal variations in
snow cover extent prior to the time of satellite observations could be de-
rived.
Climatological data have been examined in Great Britain as well as in
Scandanavia and other locations in western Europe. However, these records are
of little value without data for the Soviet Union, because of its vast size,
when assembling a snow cover .data s~t for the entire continent of Eurasia.
80
In order to find out .more about the pre-satellite snow extent in the
Soviet Union, a survey of the literature was performed which identified many
papers dealing with various aspects of snow. Most of these papers required
translation from Russian to English. For the most past, the papers deal with
seasonal dynamics of snow depth, snow density, snow cover distribution, and
snow cover conditions including maxiumum depth and mean dates for its estab-
lishment and disappearance (Bigl, 1984), (Bilello, 1984). But articles con-
cerned with seasonal variations in snow cover area or the annual maxiumum
limit of the snow boundary either for the Soviet Union as a whole or for
specific locales have not been found and, in fact, may not exist. The Soviet
Union's tremendous size and numerous autonomus provinces woudl likely discour-
age and perhaps be prohibitive to the compilation of snow cover data on a
monthly or even seaonal basis. In addition, throughout most of the Soviet
Union a snow cover is an expected occurrence and so its presence is less
likely to be noted in the literature then if it were a less frequently occurr-
ing phenomenon. Therefore, proxy indices have been examined to see if they
can be used in place of actual snow cover data. The proxy data which have
been looked at here include Arctic sea ice data and data on the number of snow
cover days for selected areas in Eurasia.
Since 1966, NOAA has prepared a weekly snow and ice boundary chart for
the Northern Hemisphere. This chart is prepared on a 1:50,000,000 polar-
stereographic base map centered on the North Pole. Satellite images from the
visible scanning radiometers on board the NOAA polar-orbiting satellites are
the main source of information used in making the charts. Secondary input
comes from the system of Geostationary Satellites (GOES) which even though
transfixed over the equator are capable of observing areas south of about 60°N
latitude. With the advent of the Very High Resolution Radiometer aboard the
NOAA series satellites since 1972, more sophisticated snow and ice charts have
been produced (Smigielski, 1980).
Problems identified in the production of the charts stem from variability
in orbit time, sensor differences on the different satellites, varying experi-
ence of the analysts, and lack of information on snow under persistent
clouds. Also the scale of the charts limits the precision of snowline posi-
tioning to about 20-30 km (Kukla, 1976).
The snow and ice charts show three levels of increasing improvement in
consistency and detail (1966-70, 1971-73, 1974-present) with the most recent
charts being more reliable (Kukla and Robinson, 1981). Himalayan snow cover
was not consistently mapped during the 1966-1974 period. For these reasons in
this study only satellite data from 1973 were used to calculate the number of
snow cover days.
Sea ice has been routinely monitored from satellites since 1972
(Kniskern, 1979), but records for sea ice extent exist back to the turn of the
century (Kelly, 1979). The variable year to year positions of the snow cover
and sea ice edge at their winter maxima and summer minima have often been used
as indicators of changes in global climate during colder climatic periods
(Lamb, 1972), such as the Little Ice Age in the eighteenth century. Ice cover
data from 1972 (table 1) was plotted against snow cover for Eurasia for the
same time period to determine if a reliable association between these data
sets could be found. If so, then perhaps the pre-satellite ice cover data
could be used as an indicator of continental snow cover.
81
In a similar manner the number of days with snow cover, as mapped from
satel.lite images for selected sites in Europe and Asia where a winter snow
cover does not always appear, was plotted against the continental winter snow
cover area. If a strong correlation exists between these data, then the num-
ber of snow cover days might be used to estimate the continental snow cover.
This would preclude the need to piece together snow cover data from hundreds
of meteorological stations, if such data are available, in order to derive the
pre-satellite continental snow cover area. Five different geographic areas
(f'igure 1) were chosen and the number of days with snow lying from December,
through February were tallied at these sites for the years 1973-1985 (table
1). The five areas are Scotland, the Crimea area north of the Black Sea, the
Turan Lowland of south central Soviet Union, the Tarim Basin of northwestern
China, and the Korean Peninsula (South Korea). Each of these areas is about
30° in longitude apart in non-mountainous areas, and in areas where a snow
cover exists generally for only a portion of the winter season.
The weekly snow and ice charts were used to determine the number of snow
cover days at a given site for each week during the winter. This was accomp-
lished by monitoring the position of the snowline relative to one of the
lected sites. If a site was south of the continental snowline, then the
se-
num-
her of snow cover days for that week was zero, and if the site was north of
the snowline, then the number of snow cover days was seven. If the site was
at the edge of the snow covered area, then the number of snow cover days
estimated to be four.
was
Table 1. Ice cover data plotted against snow cover data for Eurasia.
Average Dec""":Feb
Snow Cover Area
for Eurasia
(106 km2)
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
29.5
28.9
26.8
28.3
28.8
31.6
30.5
27.4
25.3
27.8
28.2
27.0
28.4
Number of Snow
Cover Days for Five
Geographic Sites*
in Eurasia
116
110
79
146
121
252
164
81
65
134
105
127
191
Number of Snow
Cover Days in the
Turan Lowlands
54
40
19
29
55
76
44
34
7
34
23
30
48
Maximum
Arctic Sea
Ice Extent
(105 km2)
147.9
139.1
146.7
150.0
152.0
150.7
157.0
147.8
145.2
150.7
151.3
141.8
149.6
*Five Geographic Sites are: Scotland
Crimea area north of the Black Sea
Turan Lowlands in the South Central USSR
Tarim Basin in northwestern China
South Korea
82
"
~1 ~
1. Perennial snow and ice on land
2. Regions regularly covered by winter snow
3. Regions occasionally covered by winter snow
.: 4. Regions devoid of seasonal snow
i !\e nulnbered lines depict duration of snow cover in months
Figure 1. Location of geographic sites where the number of snow cover days was determined from
satellite observations.
Results
Table 2 gives coefficient of correlation (r) and coefficient of determin-
ation (r2) val~es for the data in Table 1 for the years 1973-1985. It seems
that the Eurasian snow covered area is well related to the number of snow
cover days. The amount of variance in the snow cover area of Eurasia explain-
ed by the number of snow cover days at five different geographic sites is
fairly high (r -.78, r2 = .61) and was found to be significant at the .02
level. For each individual site the amount of variance in the Eurasian snow
cover explained by the number of snow cover days at that site ranged from a
high of .77 for the Turan Lowland to a low of .01 for the Korean Peninsula.
It appears that a simple linear regression using the number of snow cover days
for the Turan Lowland as the predictor variable and the continental snow cover
area as the criterion variable produces the best results.
Table 2. Coefficient of Correlation (r) and Determination (r2) for snow cover
in Eurasia versus number of snow cover days and Arctic sea ice'
maximum extent for the years 1973-1985.
Average Dec-Feb Snow Cover
Area in Eurasia
Average Dec-Feb Snow Cover
Area in Eurasia
Average Dec-Feb Snow Cover
Area in Eurasia
Average Dec-Feb Snow Cover
Area inEurasia
vs. Number of Snow
Cover Days in the
Turan Lowland
vs. Number of Snow
Cover Days for Five
Geographic Sites
vs. Arctic Sea Ice
Maximum Extent
vs. Number of Snow
Cover Days in the
Turan Lowland and
Arctic Sea Ice
Maximum Extent.
s = significant at the 98% level
r
.47
Multiple
r
.77
.61
.22
Multiple
r2
.81
In relating the Arctic sea ice maximum extent to the Eurasian snow cover,
the resulting r2 value of .22 was considerably less than for the number of
snow cover days. Multiple regression approaches using the number of snow
cover days and the Arctic sea ice maximum extent provided no marked improve-
ment in the relationship than when using only the number of snow cover days
84
(table 2). Even though some of the correlations shown in table 2 are statis-
tically significant at a high level, the interpretation of the correlations in
terms of cause and effect is by no means obvious. But it should be noted that
the data sets include a wide range of values for the 13 year period. The num-
ber of snow cover days as used in this study is intended to be a simple indi~
cator of the mean winter snow covered area for Eurasia and is not meant to re-
flect the snow cover extent at given locations across the continent.
The regression equation shown in figure 2 was used to estimate the
Eurasian snow cover for the observed years (1973-1985). The average differ-
ence between the observed and the estimated values is about 2.2 percent and as
can be seen in figure 3, the predicted values show trends comparable to the
observed values. However, the limits of prediction for any given year are
likely to be quite wide since so few data points were used. For example,
using the data for 1975 shows that when the number of snow cover days for the
December-February period is forty, then there is a 95 percent change that the
estimated snow covered area for Eurasia will be between 27.1 and 30.7 million
km2. The observed value is 28.9 million km2.
Discussion
Pronounced changes in snow cover and sea ice extent have occurred during
the first half of the twentieth century which according to Kelly (1979) and
Lamb and Morth (1978) is considered to be without a climatic parallel during
the last few hundred years. In order to extend the existing satellite data
base, the number of snow cover days at specified sites in Eurasia, where a
snow cover does not always occur, was used as an indicator of the continental
snow cover. As pointed out by the working group of the 1980 Snow Watch meet-
ing (World Data Center A Glaciology, 1981) there is a need for historical
series of snow cover and ice cover charts incorporating data from all avail-
able sources.
Determining if an area is actually snow covered and subsequent measure-
ment of snow covered areas have been problems which require a degree of sub-
jectivity not only from remote sensing platforms but from ground-based vantage
points as well. Mention has already been given about the problems identified
in the production of the snow and ice charts. But difficulty can also arise
when trying to decide at what point the ground near a meteorological station
is considered to be snow covered. For many stations, the ground is said to be
snow covered when half the ground area in the vicinity of the station is ob-
served lying in snow. In some areas, 2.5 em of snow must cover the ground at
7:00 a.m. local time for a snow covered day to occur (Jackson, 1978). How-
ever, there is no standard or convention that has been universally accepted.
Perhaps this is because the snow cover is frequently discontinuous. South
facing slopes may be snow free, whereas north facing slopes may remain fully
covered; drifting may expose bare ground in the lee of some obstacles and pile
up snow on the windward side of others; forested areas generally retain snow
longer than open areas; and snow lying over dark soil or rocks is likely to
melt faster than snow lying over unplowed ground. In some areas snow measur-
ing sites are located in areas not subject to interference by man, but in
other areas snow cover determinations may be made at airports. Also the
sparse network of reporting stations in much of central and eastern U.s.s.R.
and western and northern China requires much interpretation of where to draw
the snowline. For these-reasons, determining whether or not an area is snow
85
~
~
d 32
(/)
z
0 3 31
~
a: w 30 > 0 u
~ 29 0 z
(/)
aJ w 28 u.. u w
0 27 w
CJ
<( a:
~ 26
<(
z
<( en 2s
<( a:
EURASIAN SNOW COVER AREA VS
NUMBER OF SNOW COVER DAYS IN
CENTRAL EURASIA (1973-1985)
• •
•
•
Y = .0798 X + 25.3190
•
n = 13
r = .88
r2 = .77
SD=1.63
SE =0.81
~ 7 14 21 28 35 42 49 56 63 70
NUMBER OF SNOW COVER DAYS IN THE TURAN LOWLAND
77
Figure 2. Relationship between number of snow cover days for
the Turan lowlands and the Eurasian snow cover extent.
EURASIAN AVERAGE WINTER SNOW COVER
(DEC.-FEB.)
-----Observed
-----Predicted
32 -~ 31
~
d 20 en
z
0 29
:::::i
_J
~ 28 -0: 27 w > 0 u 26 ~
0 25 z en
24 1973 74 75 76 77 78 79 80 81 82 83 84
Figure 3. Relationship between the observed and predicted
values for Eurasian average winter snow cover.
86
85
covered and hence the location of the.snowline from ground based climatolog-
ical records is probably no more accurate than mapping the snow covered area
from space.
As demonstrated in this study, past records of the number of snow covered
days at specified sites seem to be useful indicators of continental snow
cover. The use of such an index requires much less time and resources than is
required to reconstruct the snowline from the hundreds of ground-based sta-
tions across Eurasia. But it is paramount that data are available for the
number of snow cover days in the interior of Eurasia, for the number of snow
cover days at interior.stations appears to be much more representative of
continental snow cover conditions than do data from coastal areas or northern
lands. This is reasonable since interior locations have a higher degree of
continentality and so are more likely to reflect large scale continental pat-
terns. During the winter months in Eurasia, the Siberian or Asiatic High has
become well developed as a result of the extreme surface cooling and the con-
stant feeding of fresh Arctic air into central Asia (Lydolph, 1977). This
high in most years is large enough to effectively prevent the incursion of
maritime air or air from other source regions into the continental interior.
~~ry and Conclusions
The polar caps on Earth wax and wane in response to seasonal changes, and
the interannual difference in the size of the polar caps is intimately linked
to global energy balance. Before the advent of satellites, it was practically
impossible to monitor snow cover on a continental basis. Efforts to recon-
struct the pre-satellite snow cover record from data for climatological sta-
tions across Eurasia have thus far proved futile. An attempt was made in this
study to utilize proxy indices in order to estimate snow cover extent. It was
found tha~ the number of snow c-over days, as derived from satellite observa-
tions for selected sites in areas of the interior of Eurasia where a snow
cover does not always occur, are well correlated with continental snow cover.
If pre-satellite data on the number of snow cover days for these interior
sites are available, then it may be possible to extend the snow cover record
and therefore get a longer look at any trends or cycles which may exist. This
is important because variations in snow cover extent and duration are being
studied as possible indicators of large scale changes in weather patterns. As
the snow cover record is extended in future years, it should help in providing
a better understanding of global and regional climate.
Bibliography
Bigl, S.R. (1984) Permafrost, seasonally frozen ground, snow cover and
vegetation in the USSR. u.s. Army. Cold Regions Research and Engineer-
ing Laboratory. CRREL Special Report, 84-36, 128p.
Bilello, M.A. (1984) Regional and seasonal variations in snow-cover density
in the USSR. u.s. Army. Cold Regions Research and Engineering Labora-
tory. CREEL Report 84-22, 70p.
Foster, J.L.; Owe, M.; Rango, A. (1983)
ships in North America and Eurasia.
Meteorology, v.22(3), p.460-469.
87
Snow cover and temperature relation-
Journal of Climate and Applied
Jackson, M.c. (1978) Snow cover in Great Britain. Weather, v.33, p.298-308.
Kelly, P.M. (1979) An Arctic sea ice data set, 1901-1956.
A for Glaciology [Snow and Ice]. Glaciological Data.
shop on Snow Cover and Sea Ice Data, p.101-106.
World Data Center
Report GD-5, Work-
Kniskern, F.E. (1979) Ice products derived from satellites at NOAA/NESS.
World Data Center A for Glaciology [Snow and Ice]. Glaciological Data.
Report GD-5, Workshop on Snow Cover and Sea Ice Data, p.27.
~ukla, G. (1976) Global variation of snow and ice extent. (In: Symposium ~
Meteorological Observations from Space: Their ContributiOn to the Fir~t
GARP Global Experiment, Proceedings. Boulder. National Center for Atmo-
spheric Research, p.11Q-115.)
Kukla, G.; Robinson, D. (1981) Climatic value of operational snow and ice
charts. World Data Center A for Glaciology [Snow and Ice]. Glaciolog-·
ical ~· Report GD-11 Snow Watch 1980, p.103-120.
Kukla, G. (1981) Snow covers and climate.
[Snow and Ice]. Glaciological Data.
and Sea Ice Data, p.27-40.
World Data Center A for Glaciology
Report GD-5, Workshop on Snow Cover
Lamb, H.H. (1972) Climate: Present, Past, and Future. London, Methuen and
Co., 613p.
Lamb, H.H.; Morth, H.T. (1978) Arctic ice, atmospheric circulation, and world
climate. Geographical Journal, v.144, p.l-22.
Lydolph, P.E. (1977) Climates of the Soviet Union. (In: World Survey of
Climatology, Vol. 7. Elsevier.
Matson, M.; Wiesnet, D. (1981) New data base for climate studies. Nature,
v.289(5797), p.l-6.
Smigielski, F. (1980) Northern Hemisphere snow and ice charts of NOAA/NESS.
World Data Center A for Glaciology [Snow and Ice]. Glaciological Data~
Report GD-11, Snow Watch 1980, p.59-62.
Walsh, J.E.; Tucek, D.R.; Peterson, M.R. (1982) Seasonal snow cover and
short-term climatic fluctuations over the United States. Monthly Weather
Review, v.llO, p.l474-1485.
World Data Center A for Glaciology [Snow and Ice] (1981) Snow Watch 1980.
Glaciological Data. Report GD-11, p.1-7.
88
Kukla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) SNOW WATCH '85.
Proceedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College Park, MD. Boulder, Colorado, World Data Center A for
Giacioiogy (Snow and Ice), Glaciological Data, Report GD-18, p.89-95.
Distribution of Snow Cover in China
Li Peiji
Lanzhou Institute of Glaciology and Geocryology
Academia Sinica
Lanzhou, China
Seasonal snow cover in Chi~ is highly variable from year
to year. As much as 9 million km has been occupied by snow at
some time during the past 35 years. The distribution, duration
and depth of snow have a large impact on agriculture and on the
supply of industrial and drinking water which is especially
~mportant in the western mountains and in the northernmost part
of the country. Snow on the Tibetan Plateau is suspected of
i nf 1 uenci ng the general circulation of the atmosphere and the
summer precipitation in Southeast Asia.
In order to determine the recent char act eri s tics of the
snow cover, the average number of days with snow on ground and
snow depth have been analysed from 1600-2300 stations from the
1951-80 interval. For the purpose of this study it is assumed
that glaciers are snow covered throughout the year. Information
on glacier dis t ri but ion was provided by maps based on aerial
•urveys and ERTS satellite imagery. Based on this data the snow
co'ver in China is divided into the following regions as shown in
Figure 1.
· (1) The area of permanent snow cover occupies about 50,000
km 2 and is limited to mountain glaciers in the west. (2) The
area of stable snow cover is defined as having a mean annual
number of 60 or more snow days and a standard deviation of less
than 0.4. In the northernmost parts of China snow may be present
for as many as 170 days. Regions of stable snow cover are
.important for agriculture because spring droughts are frequent
·in both the northern and western regions, with sno~ runoff being
an important source for irrigation.
(3) Unstable snow cover forms almost every winter with the
mean annual number of snow cover days varying between 10 and 60
a'n d an i n t era n n u a 1 v a r i a b i 1 i t y f rom 0 • 4-1 • 0 • The pre s en c e or
a'bs ence of snow cover during winter in this area is particularly
crucial for the growing of wheat. ( 4) The area of irregular
89
snow cover generally occurs in southeast China with the mean
ann u a 1 number of s now day s lf s s t han 1 0 • T o g e t her 2t he s e two
regions o~cupy 4.8 million km • Only 0.55 million km of China
is not affected by snow.
~Permanent
1§:§1 Stable
~Unstable
~Irregular
DN~·Snow
0 180 360 540 ~·
Snow depth in China is generally less than in other parts
of the world. The maximum snow depth in most of the country is
only about 20 em. However in the north of Xinjiang province in
both Altay and the Yi 1 i Valley the maxi mum snow depth reaches
between 80-90 em. This is also true for the eastern part of the
Tibetan Plateau. In the northeast of China the maximum depth is
40-50 em. The average daily depth is 25-35 em in Altay and in
the Yili Valley and 10-15 em in the northeast. Occasionally a
snow cover from 30-50 em deep is also formed in southeast China
in the lower reaches of the Yangtse river valley.
In most of China precipitation falls mainly during the
summer while snowfall peaks in the spring and autumn. Maximum
snow depth arid number of days with snow cover occurs in winter.
Figure 2 shows the average monthly precipitation, snowfall, snow
depth, number of days with snow cover and temperature for Harbin
located in Heilongjiang province in the northeast of China. The
seasonal distribution of these elements can be considered
typical for most of the country. Figure 3 shows the same data
for Pagri which is located on the Tibetan Plateau. The average
snow depth decreases from 10 em in October to less than 2 em in
December and increases thereafter to a second maxi mum during
January. The number of days with snow cover decreases from
90
October through December and then increases reaching a maximum
in March. The seasonal distribution of snow cover days at this
station appears to be approximately representative of the
plateau region •
.... • • • ~ .. • ~ • * .at:r 5I
4 8 10 II 1:1.
8 " , 12. 3 • 7
-20 D
z -10
j
.tlf' D
~
•• 10
<110
a1t ~ fJf Harbin
Figure 2: Mean monthly distribution of
meteorological elements for Harbin based on
the 1951-80 period. A = precipitation in
mm; B = average number of days with snow
cover (bar graph, scale on left) and
average depth of snow for days with snow
cover in em (solid line, scale on right); C
0 = snowfall in mm; D = temperature in C
91
--JAO ,u
0
j ~~----~--~~~~~--~~--~~~~~--~
l!r"
~ .. ,o.o
;a 1 cu~5" -t9 so)
Pagrl
~-·I 0' 1111-
Figure 3: Mean monthly distr{bption of meteorological
elements for Pagri based on the 1956-80 period. A-D
as described for Figure 2.
The surface air temperature in the Northern Hemisphere
fluctuated considerably in the last 100 years. To what degree
then does the surface air temperature and the seasonal snow cover
in ~hina .show any secular trend? During 106 years of instrumental
records, :the mean annual temperature in Shanghai oscil.lated
w.ithin-a range of 2. 3°C. As in the Northern Hemisphere record,
temperatures show a gradual increase from the 1920's to the
1940's followed by .a cooling into the 1960's. A long term record
of~snow cover iri Shanghai starts in 1882. It shows a greater
maximum snow depth and mor~ days with snow on the ground during
the 1951-70 interval than in 1921~40. No relationship can be seen
between annual precipitation and temperature (Figure 4).
92
1600
~ ~200
800
30
E 20 ()
10
• ::~ >-Cll
'0. ...
16
14
A .. . . . .· ... . . . ..... ·· .. --... ·. .· .... ~ . . . ·.
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B
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c ...._
· . . . . : ... · .. ·. . . .. . . .···. . . . ... ······· I " I
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0 ..
. . . . . ....... : ..... · .. .· :· :· ·. . . . : ............... . .... ··:·... . .·.· ... ···.:·· ......... ..
.80
Shanghai
Figure 4: Annual mean values from 1873
for temperature and precipitation and
from 1884 for snow cover. A •
precipitation in mm; B = maximum snow
cover in em; C = number of days with
0 snow cover; D = temperature in C.
Missing data for the 1940's for B and
c.
Shorter records of snow cover and temperature from various
regions are shown in Figures 5-7. Large year-to-year and regional
variations are evident. For example, Altay and Pagri located in
the west show abnormally heavy snow conditions and cold
temperatures during the winter of 1968/69 while Shenyang situated
in the northeast does not. The heaviest ice conditions in Qinghai
lake, also located in the west, occurred in 1967/68 and 1969/70.
93
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Figure
Figures 5,
meteorological
fro the 1950's
of snow cover
days with snow
Pagri
5 Figure 6
6 and 7: Mean annual values for
elements for AI t ay, Pagri and Shenyang
to 1980. A = snowfall in mm; B = number
days; C = mean depth of snow for those
' D • °C cover 1n em · = temperature 1n •
94
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50 60
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c
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50
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it ~
Shenyang
Figure 7
. .
..
. . · .. "' . .. ... .. . . ... . .. . . . . . · .... . .
:Jt * Beijing
. .. . . . .
40 60
~··· .. . . .. . . .
80
Figure 8: Mean winter temperature (D,J,F) for Beijing
in °C from 1841.
Mean winter temperatures were well
1935/36, 1944/4~, 1946/47, 1956/57,
The heaviest s~a ice in both Po Hai
in 1935/36, 1944/45 and in 1968/69.
95
below normal in Beijing in
and in 1967/68 (Figure 8).
and Hwang Hai bays occurred
K\lkla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) SNOW WATCH '85.
Proceedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College Park, MD. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice), Glaciological Data, Report GD-18, p.97-103.
ABSTRACT
Snow Surveying in Canada
B.E. Goodison
Canadian Climate Centre
Atm!)spheric Environmental Service
Downs view, Ontario, Canada
Snow cover data for Canada are available from ground, airborne or
satellite surveys. The conventional ground observations -daily snow depth
a't synoptic and climate stations and weekly, bi-weekly or monthly snow sur-
veys at over 1200 snow course sites -form the basic snow cover network. The
current and future availability of these data and some of the problems in
using the data are outlined. It is"not unreasonable to expect the number of
snow courses operating in Canada will decline in the future. Development of
~lternative methods, notably airborne gamma-ray surveys of water equivalent
and satellite determination of areal extent, and more recently, depth and
water equivalent, are necessary. The status of these methods are summarized.
These data are generally collected for hydrological rather than climatologi-
cal applications. Some questions related to their use in addressing the
question of C02/snow interaction are raised for further discussion.
INTRODUCTION
Snow cover data have traditionally come from surface observations of
snow depth and water equivalent, either from snow courses or meteorological
observations. Although ground based measurements have formed the basis of
the snow survey network, recent advances in remote sensing technology offer
the potential for significant changes in snow survey procedures in Canada. A
combination of ground based, airborne and satellite information, or ulti-
mately remote sensing methods alone, may be the most effective and efficient
means of snow sury_ey data collection to meet a variety of user needs.
Although snow surveys have a history in hydrology, many of the current uses
of these data are in other fields, climatology and the study of climate
change being one of the most notable.
It is important that all users of these data are aware of what data are
available, what changes may occur in data collection and what the outlook
for data continuity may be in the future. Such questions are particularly
relevant in the discussion of current and future studies on climate change
and particularly in the context of COz/snow interactions.
97
CANADIAN SNOW COVER NETWORK
a) Snow Depth Observations: . .
The most basic snow cover observations available are the daily snow
depth measurements made at 12GMT at about 225 Atmospheric Environment
Service (AES) synoptic weather stations. These data are available in real-
time on the GTS network and later from the AES national climate archive dn
Downsview, Ontario. In addition, since 1980 all climate stations have been
requested to measure snow depth daily, rather than just on the last day of
the month as was the earlier practice. These data are also available from
the digital archive.
Figure 1 is an example of a weekly national map of depth of snow on the
ground at 12GMT as published in Climatic Perspectives, a weekly bilingual
publication of the Canadian Climate Centre, AES, Downsview. The representa-
tiveness of these observations may be open to discussion, particularly in
non-uniform topographical regions such as the Rocky Mountains and this must
be considered in any analysis and subsequent use in climate models. However,
its availability on a weekly basis provides a regular monitoring of the
progression of the snow season.
A complement to the snow depth map is a monthly map of snow cover water
equivalent for Canada which is also published in Climatic Perspectives.
Water equivalent is a derived component of a water budget model which uses
DEPTH OF SNOW
ON THE GROUND
AT 12 GMT
FEBRUARY 18, 1185
em
Figure 1. National map of snow depth on the ground
98
daily temperature and precipitation observations from synoptic stations as
input (Johnstone and Louie, 1984). Like the snow depth map, it is ori.e tool
available for monitoring snow cover during the winter.
b) Snow Course Observations:
Table 1 outlines the growth of the snow course network by province from
1963-64 to 1982-83 and includes the ntunber of courses operated· by the
.Atmospheric Environment Service (AES) and the Water Resources Branch (WRB),
.the only agencies operating national snow survey programs. By 1973, 13.6% of
all courses were operated by WRB. The ntunber of WRB courses peaked in 1976,
when 175 courses were surveyed out of some 1200 across Canada, but by 1982~
83 the ntunber of stations reporting had dropped to 94. In 1984-85 another
s;ubstantial reduction by WRB resulted }n the retirement of all but 9 snow
courses in the Saint John River Basin and 13 courses in the Lake-of~the
.Woods watershed. In 1985-86 the Lake-of-the-Woods network will be eliminat-
ed. AES continues to operate 130 snow courses at principal observing sta-
tions across Canada, representing about 10% of the total network. All AES,
stations take observations twice monthly and many make weekly observations.
Table 1. Canadian Snow Cou~se N~twork
Province/Territory Network Size
1963-64 1972-73 1982-83
British Columbia 139 219 265
Alberta 34 64 54
Saskatchewan & Manitoba 149 258 228
Ontario 125 201 278
Quebec 104 192 183
New Brunswick 38 52 67
Nova Scoti.a 30 42 32
Prince Edward Island t 3 8 4
Newfoundland & Labrador 42 81 74
Yukon & Northwest Territories 10 40 106
TOTAL 674 1157 1291
Total operated by AES 11 128 130
Total operated by WRB 122 157 94
/
The development of a snow survey network and the frequency and accuracy
of the measurements mus.t be related to its purpose. The Canadian snow course
network was established to obtain an index of snow water equivalent over an
area, primarily for hydrological applications. Absolute estimates of ·areal
water equivalent can only be obtained after allowance is made for observa-
tional, instrtunental (eg. Farnes et al., 1983) and siting biases and the
sampling network has been specifically designed to reflect areal snow cover
variability (eg. Steppuhn and Dyck, 1974; Goodison, 1981) •-Unless one knows
99
the site characteristics of snow courses used in an analysis, it is diffi-
cult to develop climatological or synoptic maps of snow cover water equi-
valent.
However, by using known vegetative and physiographic parameters, objec-
tive techniques such as multiple regression, trend surface or proximity
analysis can then be tested for development of basin maps of snow distrib~
tion (Trivett and Waterman, 1980). Table 2 is one example of the difficulty
in making effective use of standard snow survey data for hydrological or
climatological purposes. The three snow courses are at three different sit.es
at Sioux Lookout, each with a different number of sampling points, operated
by three different agencies, and possibly using different equipment. Is
there a "right" or "wrong"? Each may, in fact, be correct for the land cover
and terrain characteristics that it represents. This example is one of the
practical problems which must be considered when preparing a snow cover data
base for climatological analyses related to the C02 question.
Table 2. Comparison of Snow Course Water Equivalent Measurements (mm)
Sioux Lookout 1975-1979
March AES 1 % Ontario Hydro % WRB %
(first week) (mm) Change (mm) Change (mm) 9hange
1975 183 102 130
1976 134 -26% 84 -18% 97 -25%
1977 117 -13% 99 +17% 112 +15%
1978 154 +32% 106 + 7% 112 0
1979 108 -30% 76 -28% 99 -12%
1 Means of surveys on March 1 and 8
Atmospheric Environment Service publishes Snow Cover Data, which is an
annual summary of snow course observations made by 19 agencies. No national
digital archive of these data currently exists, although individual agencies
may have their own data archived for their specific uses. The lack of an
easily accessible national digital archive has often hindered spatial and
temporal analyses of snow data. AES is currently investigating a change in
the method and format of publication in line with the proposal of Findlay
and Goodison (1979). A readily accessible digital archive is· necessary if
snow course data are to be used in any climatic change analysis.
c) Future of the Snow Cover Network
Snow depth observations at synoptic stations should continue. Since it
is a manual observation, there is expected to be a requirement for an inex-
pensive automatic snow depth sensor for use at stations being automated.
Such a sensor is currently being tested in Canada (Goodison et al., 1985).
100
In view of recent trends, it is not unreasonable to expect that the
number of snow courses operating in Canada will decline in the future. WRB
has almost eliminated its network of snow courses for a variety of reasons.
Agencies independently continue to operate snow courses across Canada,
usually to meet their own needs.
Networks are susceptible to the pressure of resource constraints; uses
and users of the data must be clearly identified in an effort to show the
value of snow cover data. Even though snow courses are primarily intended
for hydr.ological applications, if the data would be useful in addressing the
co2/snow interaction question, then this should be clearly indicated to
the operating agencies. One of the most important uses will be in the
"ground truthing" of new afrborne or satellite methods of snow survey. The
accuracy of snow data derived from satellite remote sensing will very much
be a function of the accuracy of the "ground truth" data against which it is
calibrated.
ALTERNATIVE SURVEY METHODS
One very viable alternate snow survey procedure is the airborne gamma
ray method. This technique has been used in Canada on an experimental basis
since 1972. In the last three years extensive surveys have been performed in
southern Saskatchewan, around the north shore of Lake Superior and in the
Saint John River Basin. An operational airborne snow survey program in
Canada is not yet available. If one were to be implemented and operated
regularly for many seasons over the same flight line network, the data might
be very useful for climatological analyses, particularly in climate sensi-
tive areas such as the Canadian prairies and the Great Lakes. Development of
a reliable data base would be required for the data to be useful in the
context of climate impact assessment.
Satellite remote sensing of snow cover is another method of snow survey
which would complement the existing ground and airborne systems. Currently
in Canada, only the areal extent of snow cover is operationally derived from
satellite data, and only for selected basins (eg. Waterman et al., 1980).
There is no centralized system for mapping every basin that might be ·requir-
ed by user agencies. On a continent-wide basis, Canadian users could use
snow cover products of NOAA/NESDIS.
A major challenge in the remote sensing of snow cover is to develop
algorithms to determine snow depth, water equivalent, areal extent and
liquid water content using sensors having all-weather capabilities. At the
moment, passive microwave data offer the greatest prospect for the deter-
mination of snow cover. Recent investigations (eg. Kunziet al., 1982; Chang
et al., 1982) have shown that there is a potential for using passive micro-
wave sensors to monitor snow cover, notably areal extent, depth, water equi-
valent and melt.
One of the objectives of the Canada/US Prairie Snow Cover Runoff Study
was to assess the applicability of passive microwave data for mapping snow
cover on the Canadian prairies. Details of the study are given in Good·ison
et al. (1984). Initial results have been very encouraging. Maps of snow
101
cover water equivalent derived from NIMBUS-7 SMMR data are now being pre-
pared for selected dates using test algorithms derived from airborne data
collected during the experiment.
Since passive microwave resolution from space in currently 30 km, the
technique is best suited to large, relatively homogeneous regions such as
the Prairies and the North. The spatial resolution provides a snow water
equivalent or depth distri'hution already "smoothed"; the siting problems
associated with conventional surveys are minimized. It must be considered as
a technique deserving further research and development. It offers what could
be a very useful method for collecting snow cover over large areas, often
lacking data from conventional networks. Such a data source would be
extremely useful in future analyses of snow cover/climate change linkages.
DISCUSSION
The question of COz/snow interactions might be viewed as a two-fold
one. First, there is the impact that snow cover has on the climate system;
secondly, there is the effect or impact that the climate system has on snow
cover. The data needs may be different for each question. For example, to
address the first question, climate models would be a viable approach. Does
the Canadian data base meet the needs of the climate modellers? Are grid-
square averages of snow depth adequate or would water equivalent estimates
and percent snow cover also be useful? What temporal and spatial resolutions
would be required? Are near real-time or archive data necessary? In practi-
cal terms, what accuracy levels of snow measurement would be considered
satisfactory to validate the output of a model or to test a climatic change
hypothesis? It would seem that a snow cover data set compiled from integra-
tion of conventional ground, airborne and satellite information would-be one
method of meeting the potential needs of modellers.
The study of the impact of changes in climate on snow cover would
require regional data of a higher resolution than that needed for analysis
of continental scale changes. Conventional ground and airborne data sets
would currently be most useful for this task, although satellite d~ta may
provide additional information in the future. Compilation of regional data
sets on different scales is a requirement if changes in regional snow cover
are to be identified and analyzed. Finally, how important is data continuity
in a region? Can we reduce our snow survey networks without affecting our
climate data base?
Answers to some of these questions could lead one to the argument that
there is a need for a more comprehensive and co-ordinated national snow
survey program in Canada. It should ensure the standardization of data col-
lection, analysis and dissemination procedures, while ensuring the introduc-
tion and integration of modern techniques. Such a program should be respon-
sive to the needs of the hydrological and climatological communities. It
should promote the development of the neces'sary expertise to apply know-
ledge, gained from basic research, to the analysis and interpretation of
snow data. Ultimately, however, users of snow data must clearly identify
their requirements.
102
REFERENCES
Chang, A.T.C., J.L. Foster, D.K. Hall, A. Rango and B.K. Hartline, 1982:
Snow Water Equivalent Estimation by Microwave Radiometry. Cold Regions
Science and Technology• Vol. 5, 259-267.
Farnes, P.E., B.E. Goodison, N.R. Peterson and R.P. Richards, 1983: Final
Report. Metrication of Manual Snow Sampling Equipment. Western SQow
Conference, Spokane, Washington, 106.pp.
Findlay, B.F.; Goodison, B.E. 1979: Archiving and mapping of Canadian snow
cover data. World Data Center A for Glaciology (Snow and Ice). Glaciol-
ogical Data. Report GD-5, 71-87.
Goodison, B.E., 1981: Compatibility of Canadian snowfall and snow cover
data. Water Resources Research, 17, 4, 893-900.
Goodison, B.E., A. Banga and R.A. Halliday, 1984: Canada -United States
Prairie snow cover runoff study. Can. Water Res. J., 9, 1, 99-107.
Goodison, B.E., B. Wilson and J. Metcalfe, 1985: An inexpensive remote snow
depth gauge. Proc. TECIMO-III. WMO Technical Conference on Instruments
and Methods of Observation, Ottawa. July 8-12, 1985, 111-116.
Kunzi, K.F., s. Pati1 and H. Rott, 1982: Snow Cover Parameters Retrieved
from NIMBUS-7 Scanning Multi-Channel Microwave Radiometer (SMMR) Data.
IEEE Trans. Geosc. Rem. Sens. GE-20(4), 453-467.
Johnstone, K.J. and P.Y.T. Louie, 1984: An operational water budget for
climate monitoring. Atmospheric Environment Service CCC Report No.
84-3. 52 PP•
Steppuhn, H. and G.E. Dyck, 1974: Estimating true basin snowcover. In,
Advanced Concepts and Techniques in the Study of Snow and ice Resour-
ces, National Academy of Sciences, Washington, D.C., 314-324.
Trivett, N.B.A. and S.E. Waterman, 1980: Evaluation of the spatial and
temporal distribution of snowpack parameters in the Saint John River
Basin. Proc. Eastern Snow Conf., 19-35.
Waterman, S.E., W.D. Hogg, A.J. Hanssen and V.L. Polavarapu, 1980: Computer
analysis of TIROS-N/NOAA-6 satellite data for operational snowcover
mapping. Proc. 6th Can. Symp. on Remote Sensing, Halifax, N.S. May
21-23, 1980, 435-442.
103
l{ultla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) SNOW WATCH '85.
Proceedings of the Workshop held 28-30 October 1985 at the University of
.Maryland, College Park, MD. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice), Glaciological Data, Report GD-18, p.l05-108.
Snow Cover in Real Time Climate Monitoring
Chester F. Ropelewski
Climate Analysis Center
National Meteorological Center
National Oceanic and Atmospheric Administration
Washington, D.C., U.S.A.
A number of empirical studies and numerical climate models have
indicated that the large scale areal extent of snow cover is a potentially
important parameter of the global climate system. Fluctuations in monthly
and seasonal continental scale snow cover, monitored both with surface
station data and remote satellite observations, have been linked to tempera-
ture and atmospheric circulation anomalies. In light of these studies the
~limate Analysis Cente~ has developed a program to monitor snow cover extent
in real time. The results of this analysis and other products are used
~~alitatively as aides to the interpretation and diagnosis of anomalies in
ihe monthly temperature, precipitation, and circulation features. Time
series of Northern Hemispheric and continental scale snow cover extent and
standardized anomaly are also produced monthly. These plots, e. g., Figs 1,
2, and 3, provide useful tools for: placing current snow cover fluctuations
into historical context, estimating the magnitude of typical snow cover
fluctuations, and identifying longer term trends in the data.
(!)
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1973 1974 1975 1976 1977 1979 1979 1990 19BI 1992 1993 1984 1995
AREA COVERED BY SNOW NORTHERN HEMISPHERE NOVC*l
64
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Fig 1 a. Time series of Northern Hemisphere snow cover area C10 6 km 2 ). The
heavy horizontal line corresponds to the period mean (1973-1985) November
snow cover area. The (*) designates the November snow cover area for each
year. Snow cover area was underestimated before 1973 and thus not plotted.
Operational time series are produced and updated monthly.
105
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NORMALIZED SNOW AREA NORTHERN HEMISPHERE NOV!•l
3
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Fig 1b. Time series of standardized Northern Hemisphere snow cover area. The
means and standard deviations for the normalization are computed over the
1973-present-month period. The (*) designates the standardized departures
for November of each year. Operational time series are produced and updated
monthly.
1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985
AREA COVERED BY SNOW IN NORTH AMERICA NOV!•l
Fig 2a. Same as 1a except for North America.
106
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NORMALIZED SNOW AREA IN NORTH AMERICA NOVI•l
Fig 2b. Same as 1b except for North America,
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AREA COVERED BY SNOW IN EURASIA NOVI•l
Fig 3a. Same as 1a except for Eurasia.
107
4
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NORMALIZED SNOW AREA IN EURASIA
Fig 3b. Same as lb except for Eurasia,
108
4
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Kukla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) . SNOW WATCH '85.
Proceedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College Park, MD;. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice), Glaciological Data, Report ~-18, p.109-113.
Northern Hemisphere Snow and Ice Chart ofNOAA/NESDIS
Thomas E. Baldwin
National Environmental Satellite, Data, and Information Service
National Oceanic and Atmospheric Administration
Camp Springs, Maryland, U.S.A.
Abstract
Since 1966, tba Satellite Analysis Brareh (SAB) of tle National
Environoontal Satellite, D=lta, and Infol'DBtion Service (l£SDIS) has preparEd a
weekly snow ani ice boundary chart for tba librtlern Hemisphere. 'Ibis chart is
preparEd on a 1:50,000,000 polar steorographic base map centerEd on the librth
Pole.
Data Sources
The primary sources of information used in the preparation of this chart
(figure 1) are satellite images from the visible scanning radiometers of the
National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satel-
lite systems. Secondary input comes from the visible scanning radiometers of
the Geostationary Satellite (GOES) systems over the North American continent
south of 60°N, and occasionally from the Meteorological Satellite (METEOSAT)
system.
The specifications of currently used sensors are shown below:
Satellite Camera and Sensor Resolution (km)
NOAA-9
GOES-6
VHRR*
VISSR**
*VHRR -Very High Resolution Radiometer
**VISSR -Visible Scanning Radiometer
1.0 -4.0
1.0 -7.4
Wavelength (mm)
0.58 -0.68
0.55 -0.75
Presently the polar-orbiting satellite crosses the equator northbound at
approximately 1500 local standard time.
109
Philosophy and Purpose
Polar-ofbiting satellites are the only source of a complete look at the
polar areas of the earth, since their orbits cross near the poles approxi-
mately every two hours with 12 or 13 orbits a day of useful visible data.
This visible imagery can then be analyzed to detect the snow and ice fields
and the difference in reflectivity of the snow and ice. By analyzing these
areas over a one week period, areas of cloud cover over any particular area of
snow and ice cover can be kept to a minimum to allow a cloud free look at
these regions. This chart can then be useful as a measure of the extent of
snow and ice for any weekly period during the year and it can also be compared
to previous years for climatic studies. '
Procedure
Each Monday the analyst, a satellite meteorologist, collects all the NOAA
visible data received iri the past week ( 12 or 13 orbits per day) and. deciphers
snow and ice cover on the pictures and makes appropriate updates to the pre-
vious week's chart. Over North America, additional higher resolution data
from GOES are used. Therefore, each segment of the chart shows the latest
cloud-free satellite observation of the world. If an area is cloud covered
for several days, the analysis of snow cover for that area will be several
days old. If the area remains cloudy for an entire week, the previous week's
analysis is used. The chart is then finalized and sent out.
Guidelines and Suggestions
To distinguish snow or ice from cloud cover, the analyst looks for geo-
graphical features, such as forested areas or rivers and lakes (figure 2,3).
Valley fog on morning Polar-orbiter passes and mountain top clouds or cumulus
clouds on afternoon passes are obstacles to a clear view of snowcover (figure
4). Areas of uniform brightness that persist for several days can aid in dis-
tinguishing snow from clouds.
A solid line indicates a definite snow boundary while a dotted line indi-
cates an icy boundary. A dashed line indicates "patchy" snowcover, for ex-
ample, mountain top snow in late spring. Snow or ice free areas enclosed by a
snow or ice field are labeled 0, for open, snow and ice areas are then
stippled.
Digitizing the Snow and Ice Chart for Archives
An 89 x 89 square polar stereographic map is placed on top of the final-
ized weekly snow and ice chart. Along the southern border of the snow line,
each square is looked at to determine if at least 50 percent of the square is
covered by snow according to the analysis. This includes squares with more
than 50 percent coverage if it is snow/patchy snow mixed. Oceans and lakes
are automatically eliminated in the program. The purpose is to show the area
of snow cover, so a box is included if the immediate area totals one box, even
if no individual box equals 50 percent coverage.
The chart is then digitized and a program is run to put data on a per-
manently mounted dist. A second program is run to produce a microfilm record
of the analysis.
110
¢ Open
Analysis prepared by
NESDIS/Synoptic Analysis
Branch.
B•sed on GOES, J.leteosat
~ :-ruAA-9 Satellite Imasery
/
Figure 1: An example of the NESDIS Northern Hemisphere weekly
Snow and Ice boundary chart.
111
c~.o~
· .... 1 . /' ;_.'~
I ..._,_ ...
,;: -.
Figure 2: Dendritic pattern of snow-
cover on mountains.
Figure 3: Snowcover appears brighter in
un forested areas.
Figure 4: Cumulus clouds obscure
mountain tops.
112
Figure 5: Using two days' data for a complete analysis.
113
Kukla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) SNOW WATCH '85.
Proceedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College Park, MD. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice),Glaciological Data, Report GD-18, p.ll5-124.
The NOAA Satellite-Derived Snow Cover Data Base:
Past, Present, and Future
Michael Matson
National Environmental Satellite, Data, and Information Service
National Oceanic and Atmospheric Administration
Washington, D.C., U.S.A.
The NOAA Northern Hemisphere satellite-derived snow cover
data base now includes 20 years of data from 1966-1985. A time
line of the history, use, and future of this data base can be
divided into four periods: the Age of Darkness, the Age of
Discovery, the Age of Enlightenment, and the Age of Advancement.
This paper reviews the highlights of each of these periods with
the goal of providing the reader a perspective on the evolution
of the NOAA snow cover data base.
The Age of Darkness occurred prior to 1974, the year Kukla
and Kukla (1974) published the first paper about the NOAA
satellite snow cover data. Although the satellite-derived snow
cover maps had been in existence since 1966, they were not used
as a data set until the publication of the Kukla and Kukla paper.
Before 1974, Northern Hemisphere snow cover data sets were
derived from point measurements taken at reporting stations.
These data sets suffer from a lack of spatial and temporal
continuity, especially in remote and mountainous areas. One of
the better attempts at assimilating this ground data into a
Northern Hemisphere snow cover data set was done by Dickson and
Posey (1967). They prepared maps depicting the probability of
snow cover of one inch or more in depth at the end of each month
from September through May for the Northern Hemisphere. An
example of one of these maps is shown in figure 1.
The Age of Discovery began in 1974 with the publication of
the previously mentioned paper by Kukla and Kukla. This paper
brought to the attention of the scientific community the
existence of the NOAA Northern Hemisphere Weekly Snow and Ice
Cover Chart produced since 1966 (figure 2). For the first time,
a snow cover data· set existed having spatial and temporal
continuity. The Age of Discovery was marked by manual analysis
of the data set and the creation of climatologies and time series
based on the data set. Few, if any quality control checks were
done on the data set during this period, although the Himalayan
area was recognized as an area of unreliable analysis.
115
The year 1980 marked the beginning of the Age of
Enlightenment. The highlight of this period was the creation of
a digitized data set of the NOAA Northern Hemisphere Weekly Snow
and Ice Cover Chart (Dewey and Heim, 1981)·. The charts have been
operationally digitized since the creation of the original
digitized data set. The digitized data has been used to create
monthly mean maps (figure 3), monthly frequency maps (figure 4),
and monthly anomaly maps (figure 5). Climatologies of monthly
snow cover frequency have also been created for the 1967-1981
period (figure 6). A Southern Hemisphere digitized snow cover
data set was also created for the 1974-1980 period (Dewey and
Heim, 1983). Although the data set showed variability in
South American snow cover (figure 7), the data set was not
continued because of the small snow covered areas involved when
compared to the Northern Hemisphere snow cover. The Age of
Enlightenment is also marked by a careful examination of the
limits of the data base. Kukla and Robinson (1979, 1981),
Dickson (198'4 >, and Kukla and Gavin < 1983 > have shown problems in
the analyses of the charts with persistent clouds, low quality in
the early satellite imagery, and poor snow recognition in heavily
forested and low illumination scenes. These problems must be
considered when using the digitized data base.
Starting in 1990, NOAA will launch the NOAA-K,L,M series of
polar-orbiting satellites which will include a 1.6 micrometer
channel on the Advanced Very High Resolution Radiometer (AVHRR),
and the Advanced Microwave Sounding Unit (AMSU), which will have
several channels suitable for snow cover detection. The AVHRR
1.6 micrometer channel will enable users to discriminate snow
from clouds and the AMSU data will allow snow cover detection
through clouds. These new developments in snow cover detection
mark the beginning of the Age of Advancement. Using these new
instruments, NOAA plans to create automated snow cover analyses
for the Northern and Southern Hemisphere. Hopefully, this will
finally eliminate the biases inherent in the manual analysis of
continental snow cover.
References
Dewey, K.F.; Heim, R., Jr. (1981) Satellite observations of
variations in Northern Hemisphere seasonal snow cover.
NOAA Technical Report NESS 87, u.s. Department of Commerce,
Washington, D.C., 83pp.
Dewey, K.F.i Heim, R., Jr. (1983) Satellite observations of
variations in Southern Hemisphere snow cover. NOAA
Technical Report NESDIS 1, u.s. Department of Commerce,
Washington, D.C., 20pp.
Dickson, R.R. (1984) Eurasian snow cover versus Indian monsoon
rainfall. Journal of Climate and Applied Meteorology, v.23,
p.l71-173.
116
Dickson, R.R.J Posey, J. (1967) Maps of snow-cover probability
for the Northern Hemisphere, Monthly Weather Review, v.95,
p.347-353.
Kukla, G.J Gavin, J. (1983) Recent fluctuations of Northern
Hemisphere snow cover in autumn. (In: Eighth Annual Climate
Diagnostics Workshop, Proceedings held at Downsview,
Ontario, Canada, p.289-296).
Kukla, G.J Kukla, H.J. (1974> Increased surface albedo in the
Northern Hemisphere, Science, v.l8, p.239-253.
Kukla, G1 Robinson, D. (1979) Accuracy of snow and ice
monitoring. (In: Crane, R.G., ed. Glaciological Data Report
GD-5. University of Colorado, Boulder, Colorado, p.91-98).
Kukla, G1 Robinson, D. (1981> Climatic value of operational snow
and ice monitoring. (In: Kukla, G; Hecht, A; Wiesnet, D.,
eds. Glaciological Data Report GD-11. University of
Colorado, Boulder, Colorado, p.l03-119).
117
Figure 1. Northern Hemisphere snow cover probability map for
January 31 based on ground data. From Dickson and Posey (1967).
118
Figure 2.
Chart for
I
NORTHERN HEMISPHERE ,
AVERAGE SNOW AND ICE
BOUNDARIES
DECEMBER 29,1974 to
JANUARY 4,1975 ·><
1.--Lowest Reflectivity
2.--Moaerate Reflectivity
3.--Hi~est Reflectivity
----Snow oooolce
Based on NOAA -4
Satellite Analysis
' /
NOAA Northern Hemisphere weekly Snow and Ice Cover
the period December 19, 1974 to January 4, 1975.
119
NOAA/NESS MARCH 1985 MONTHLY MEAN SNOW COVER
Figure 3. Digitized montply mean Northern Hemisphere snow cover
map for March 1985.
120
NOAA/NESS MARCH 1985 FREQUENCY OF SNOW COVER
Figure 4. Digitized monthly Northern Hemisphere snow cover
frequency map for March 1985. The numbers represent how many
weeks snow cover was on the ground for the area.
121
NOAA/NESS MARCH 1985 MONTHLY MEAN SNOW ANOMALY
Figure 5. Digitized monthly Northern Hemisphere snow cover
anomaly map for March 1985. A comparison is made between the
monthly 50% snow cover line and a satellite-based 15-year 50%
snow cover line. Areas of excess snow cover are shown by a "+"
and areas of deficit snow cover are shown by a "-" ·
122
100=
91 -99=
&1-90=8
71-80=7
1-70=6
s -60: s
1-1 0=
15 YEAR APRIL SNOW COVER FREQUENCY MAP
Figure 6. A digitized satellite-based 15-year snow cover
frequency map for April. The alphanumeric coding represents snow
cover frequency expressed as a percentage of 100. The boxed area
is calculated as a separate map due to bad data in the early
years of the data set.
123
.
~
::,;:: .
0
(j)
0
0
D .
0
D ......
X
II
IJ...I
0::
II
a: w
>
0
L•
~
0 z
(/)
1 9 7! c ~c.J P'i Ll ·= E R" (-.. (?1 N I I ---' I ~· V'l ._) . .) u
SCJUTH RHERICR
7-YERR t~ERN
12-
10
:'.lc'
*?F. *
*
B *
6 t
I
4:
2
* ,'~ IT
::L*
0 I I I I I I I I I I I I I I I I I I I I I I I I 1-TTTTTITl-r-rTTITT-rrn
7 10 16 19 22 25 28 31 liO
Figure 7. Satellite-based snow cover areas for South America
during 1977 compared to a 7-year satellite-based climatology.
The week numbers begin with the first week in January.
124
Co ._,.:,
:Kukla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) SNOW WATCH '85.
Proceedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College Park, MD. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice), Glaciologl.cal Data, ReportGD-18, p.125.
Joint Ice Center Global Sea Ice Digital Data
Charles E. Gross
U.S. Navy /National Oceanic and Atmospheric Administration
Joint Ice Center
Suitland, Maryland, U.S.A.
Digital sea ice data for Arctic and Antarctic regions are now being
distributed through the National Snow and Ice Data Center, Boulder,
Colorado. These data were digitized and gridded from the weekly sea ice
charts produced by the U.S. Navy-National Oceanic and Atmospheric
Administration Joint Ice Center (JIC). The gridded data are spaced at no
greater than 15 nm intervals on an evenly divisible latitude-longitude
geographic grid. Sea ice concentrations, ice type, surface features and
other related information are coded using the proposed WMO standard SIGRID
(sea ice grid) system.
The weekly gridded sea ice data were digitized from weekly operational
ice charts produced by the JIC and its predecessor, the u.s. Navy Fleet
Weather Facility. The original charts were produced on a scale of 1:11.6
million for the Arctic and 1:16 million for the Antarctic •. Charts were
plotted on an azimuthal ~quidistant projection with the point of tangency
at the poles. The smallest homogeneous area resolved by the mapping system
is less than 4 km under typical conditions and 40 km under the worst.
Boundries are accurate to less than 20 km under typical conditions.
Data sets available include Antarctic data for January 1973 through
December 1982, Eastern Arctic data (90 W-90 E) and Arctic West (90 E-90 W)
for January 1972 through through December 1982. Annual updates for each
data set are anticipated, normally in mid-summer for the previous calender
year. Details of the digital format may be found in Proposed Format for
Gridded Sea Ice Information, T.Thompson,1981, 27p; report prepared for the
World Climate Program,WMO. Copies are available from the National Snow and
Ice Data Center.
125
l.{ukla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) SNOW WATCH '85.
;proceedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College Park, MD. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice), Glaciological Data, Report GD-18, p.127-139.
Snow Cover Data: Status and Future Prospects
Roger G. Barry
National Snow and Ice Data Center
Cooperative Institute for Research in 'Environmental Sciences
University of Colorado
Boulder, Colorado, U.S.A.
Abstract
SncM cover data are collected by a ~ of riat:fonal neteorol.ogical ani
hydrological agencies ani globally since the mi.d-1960; by satellite renm:e sen-
sing. There are major problems of :i.J:lcanplt:lbility between the various types of
grour:d neasureoent even within a single cOt.m.try. Satellite infoiDBtion up to
now refets only to snow cover extent ani it is limited by cloudiness ani reso-
' lution. 'lbare is also uncertainty as to at paraneter (snow depth, effective
integrated sb>rt-wave reflectivity, etc.) is of 11Dst relevance for climate
studies as canpared with hydrological prediction. A survey is given of the
present statllS of snow cover data ani its availability for N>rth · Amarica ani
Fm"opean COt.m.tries.
New satellite systEDB will bring rapid changes ani opp>rturl.ties. '1he
feasibility of sn<Jii cover ~ with 11llltifrEquancy passive micmeve radi-
oueter data can be exploited with NASA, NW, and IMSP sensots by the late
1900s. In addition, NOM polar orbitets will carry a 1.5-1.6 mic~ter sensor
capable of Sl'IW"'Cloud discriml.na.tion. Coordinated planning to ensure JDaJdDun
use of these syst:eos is urgently needed.
Introduction
Snow cover data are collected in different countries by a wide range of
national agencies to serve a variety of purposes. Meteorological and hydro-
logical services are most commonly responsible for ground measurements of snow
cover, but in the United States certain data are also co.llected by the Soil
Conservation Service and the Forest Service of the u.s. Department of Agricul-
ture. There are, consequently, many problems of incompatibility between the
various types of measurement even within a single country.
This paper reviews the requirements for snow cover data and the avail-
ability of data sets for selected European countries and for North America.
Following this assessment of ground observations, the present status of, and
future prospects for, satellite-based data on snow cover are discussed.
127
Data Requirements
Informat~n on snow is required by several types of user. For example,
near-real time (operational) information is needed for hydrological forecasts
of runoff and flood risk, avalanche warnings, and for urban highway, and air-
port snow clearance agencies. Retrospective data are required for climate re-
search and for design studies including questions such as snow loads on struc-
tures, snow cover frequency for development of ski centers, and risk statis-
tics for insurance companies, etc. The data requirements for each use
category differ considerably with respect to the specific variable and the
needed space -time coverage.
The variables that are important for meteorological and hydrological re-
search are listed in Table 1. They are defined with respect to the study of
the global hydrological cycle from space (National Research Council, 198S).
Earlier, more restrictive definitions were proposed for purposes of snow and
ice research (NASA, 1979). For example, area coverage for operational needs
was specified with a minimum accuracy of S percent (desired accuracy 1 per-
cent) with a spatial resolution of 10 km (desired 1 km) and time interval of 7
days (desired 3 days). Even for climate purposes, the accuracy required was S
percent. Water equivalent was requested for climate and operational purposes
with an accuracy of 3 cm/cm2 (1 cm/cm2 desired). Simpler requirements for
snow cover monitoring, identified by working groups of the World Climate Pro-
gramme (World Meteorological Organization, 1981), are shown in Table 2. At
present, only snow cover extent is mapped routinely, at weekly intervals, and
this is limited to the northern hemisphere.
Table 1. Minimum observational requirements for snow cover.
(After National Research Council, 198S)
Variable Observation Accuracl Resolution Freguency
(Percent) (km) (weeks)
Extent Percent of area 10% so 1
Thickness Area coverage 10% so 2
Density Area coverage 10% so 2
Water equivalent Area coverage 10% so 2
Grain size Area coverage 10% so 2
Albedo Area coverage 4% 100 2
128
Table 2. World Climate Program requirements for global snow cover
monitoring. (Modified from World Meteorological Organiz-
ation, 1981)
Snow Cover Type of Resolution Accuracy Duration* Priority Observational
Variable Observation Time Space Methods
Extent Tenths of 3d 100 lan 10% permanent 1 Visible & IR;
area covered square passive
microwave
Albedo Average of -3d 100 lan -+0.03 several 2 Multispectral -area square years radiometry
Water Average over Winter 100 lan 10% several 2 Passive
equivalent area maxi-square years microwave?
mum
* "Permanent" indicates the need for indefinite mnitoring. "Several years" indicates
that, after a period of research, a reduced mnitoring effort may suffice.
Existing Modes of Data Collection
Data on snow cover are collected in a number of ways. Figure 1 identi-
fies the separate data streams for satellite and aircraft remote sensing meas-
urements which provide regional information on snow extent and to a lower
level of accuracy on the snow water equivalent. Surface observations fall in-
to two broad categories: meteorological reports of snow depth and snowfall,
and snow course or related site measurements of snow water equivalent. The
international synoptic weather code has provision for daily reports of depth
of snow on the ground whenever at least half of the ground in the vicinity of
the station is snow covered. These data, as well as 6-and 24-hourly reports
of precipitation are collected through the Global Telecommunications System
(GTS) by the three World Meteorological Centers (Washington, D.C., Moscow and
Melbourne). Unfortunately, these reports are archived in high volume synoptic
data files. At the present time, convenient accessible sets of these global
records do not exist.
There are differences in national practices concerning snow data collect-
ed for hydrological purposes. In Canada and the United States, various agen-
cies (see Table 3) collect data on snowpack water equivalent from measurements
of depth and density at snow courses (Goodison, 1981). Typically, these ob-
servations are made at the end of each month, sometimes twice-monthly, through
the winter. For the western states, snowpack data and meteorological vari-
ables are now collected from more than 500 remote automatic sites, through the
snow telemetry (SNOTEL) network. Data are retrieved via ionospheric meteor
burst telemetry on a daily, or more frequent basis by interrogation of the
stations (Barton and Burke, 1979).
129
RAW
DATA
LEVEL
1
DATA
LEVEL
2A
DATA
LEVEL
2B
DATA
LEVEL
3
DATA
SNOW COVER DATA STREAMS
SURFACE OBSERVATIONS
S-;OW JME-;~;R-;{c;"Gl"c:L
COURSES OBSERVATIONS
TABULATED SYNOPTIC
REPORTS I CODED
OBSERVATIONS
SA'rELLITE REMOTE
SENSING
VISIBLE,IR,MICROWAVE2
POLAR ORBITING MET.
SATELLITES, GEOS,
LANDSAT
LARGE SCALE : REGIONAL _____ _J. ___ _
SNOW COVER/DEPTH/ALBEDO
CHARTS AND TABLES
DIGITIZATION
AIRCRAFT REMOTEl
SENSING
VISIBLE,IR,MICROWAVE,
GAMMA-RAY
MERGED SNOW-TERRAIN/VEGETATION-
ATMOSPHERE DATA SE'rS
1 Primarily in a research mode, or operational at the local/regional scale.
2 The microwave is used primarily in a research mode.
Figure 1. Snow cover data streams.
In Canada, snow courses are operated by provincial and federal govern-
ments. The network has been reduced considerably in recent years, due to
costs. Similarly, many meteorological stations, in northern Canada especi-
ally, are being converted to automatic stations necessitating changes in mea-
;surement procedures and introducing inhomogeneities into station records (see
Goodison, this volume).
Data for North America
For both Canada (Canada, Atmospheric Environment Service, 1961 onwards)
and the United States (by state), snow course data are published in full.
However, these data are not-currently available in computer-compatible format.
The representativeness of snow course observations is dependent on the land
use characteristics, but such effects are not routinely incorporated into
spatial analyses (Goodison, 1981). Goodison also showed that if precipitation
gauge measurements are corrected, especially for wind effects on catch, these
records are then generally compatible with stick observations for seasonal and
storm totals.
Table 3 shows that, in addition to the synoptic and snow course data,
there are various other data sets with regional coverage in the United States,
as well as the map of snow cover prepared for the Weekly Weather and Crop Bul-
letin (U.S. Department of Agriculture). This map product has been published
weekly since 1935 based on station reports for 0900 Local Standard Time on
Monday. Until recently it showed station reports as well as selected depth
contours; since December 1983, only station reports of snow depth have been
shown on the map.
In the western United States, the u.s. Forest Service collected snow
cover and climate data as part of its former research and forecasting program
on avalanches. Data for between 10 and 30 years for up to 60 stations are now
available at the National Snow and Ice Data Center (NSIDC). The extensive
data collected for operational purposes since 1979 by the SNOTEL network of
the Soil Conservation Service, u.s. Department of Agriculture (Table 3) have
not yet been made available for climate and other research by secondary users.
The National Weather Service has carried out an operational program using
aircraft measurements of surface gamma radiation emissions to assess snow
water equivalent since 1980 (Peck et al., 1980). Data are now available in
the NSIDC for five seasons from 800 flight lines over 16 states and three
Canadian provinces. They include location, percent snow cover, snow water
equivalent, soil moisture and normalized over-snow gamma radiation•
Ground-based observations to calibrate the aircraft data and assess the
accuracy of the technique continue to be collected in various locations with
surface cover types ranging from bare soil or grassland to heavily forested
areas.
131
Table 3. Snow data for North America.
Source ·. ; .Area Stations Variables FrEquency Period
SCS/USDA 11 W'n States 1600 Depth, Monthly 1910s/30s ->
Calif. Dept. California >400 Water Content (Winter/Spri~) 193o->
Water Bes. (Snow Coorses)
SCS/USDA W'n States )500 Water Content >Day (1979) ->
Stm'EL PrEclp., TEmp.
USFS W'n States 5o-60 Depth, Dally 1950s/60s ->
Westwide (Mountaim) Snowfall
Netwotk
NOM USA Map Depth Contours, Weekly 1935 ->
Weather Point Values (Momay) (1949-81.
am Corp digitized by
Bul~tin Walsh et al. 198l)
Minnesota Minnesota Map Depth Contours Weekly 1977/78 ->
State
Cl.inatology
Office
NOM USA/Qmada 800 flight-Cover, > 1/m. 198>-1985
ms (Gaoma lines Water Content
Radiation)
NO!\A Co-USA 5,000 Depth DaUy 18!n-198l
operative (saDe preclp.)
Observers
AES. Qmada 1800 Depth Monthly 1955-79
(ani (snow Water Content
Provinces) courses)
J. Walsh Qmada 140 Depth, Snowfall Monthly 1950s/6(B-)
Data for Some European Countries
There are extensive snow cover records for stations in many European
countries {Table 4). In some cases these date back to the beginning of the
century or earlier. The agencies responsible for these data differ between
countries. In Austria, for example, it is the Hydrographic Bureau. In many
others it is the national meteorological service. In Switzerland, the Swiss
Meteorological Agency maintains one network and the Snow and Avalanche Re-
search Institute (EISLF), Davos, collects supplementary mountain snow data
from mountain stations.
132
Table 4. Snow cover data for some European countries.
Source Area I . Stat.:Wns Variables Fretuency Period
Osterr. Austria 760 Depth Sn<7.olfall Dally 1901 ->
Hydrograph
Dienst. ..
Deutscher Fed. Rep. 75 Depth Dally 1961 ->
Wetteniienst Getmao,y Snc:Mall, Water
Content (3/TNeek)
Meteorol. Gt. Britain 200 Days with Monthly 1947 ->
Office Snow Cover,
Snow Fall
Meteorol. Gt. Bri ta:fn 20 Depth Daily 1947 ->
OffiCe
SMHI Sweden 412 Depth Daily 1931->
SMA Switzerlan:l I 52 Depth New Snow Daily 1959/60 ->
EISLF Swiss Alps 45 Depth ,New Snow Daily 1949/59 ->
Most national agencies publish statistical summaries on a station basis.
An example for Austria is given in taple ~· This shows annual values of the
-dates of first and last snow cover as well as dates of stable snow cover, the
number of days of snow cover so defined, days of new snowfall, the total sea-
sonal snowfall, the amounts and dates of maximum snow depth and of maximum new
~now~all. In addition to station summary data, there are also maps of average
snow depths on a monthly basis and average number of days of snow cover (e.g.
Pershagen, 1969, for Sweden; Simojoki, 1967, and Lavila, 1972, for Finland).
Although such statistics and maps are widely available (Caspar, 1962;
Schuepp et al., 1980, for example), there have been few detailed assessments
of their usefulness for climatological purposes. There are obvious limita-
tions to the value of indices such as the duration of snow cover due to the
problem of definition. Figure 2 for Sodankyla, Finland, illustrates the
interannual variability of snow cover duration. Duration, as defined by ear-
liest and latest snow cover, is influenced greatly by isolated weather events;
likewise, in some cases (1936, 1938 and 1942) so would be a measure of the
"stable" snow cover. For Zurich, Uttinger (1963) analyzed temperature, pre-
cipitation, snowfall and snowcover data for winters 1880/81 -1960/61. On a
yearly basis, days with snowcover were only weakly correlated with snowfall
freque.ncy and air temperature, although a general relationship was more appar-
ent for decadal averages of snowcover duration. Differences were also found
in the monthly temperature-snowfall frequency-snow cover relationships between
early and late winter months at Zurich. Uttinger showed also that there was
considerable variability at central European stations in the timing of maxima
and minima of snow cover duration from the 1880s to 1950s with quite different
trends at several of the stations in this region with long records. These few
isolated results point to the need for further investigation of snow cover as a
climate variable.
133
Table 5. An example of snow cover data from Austria, mean and extreme
values. (Beitraege zur Hydrographie Oesterreichs, Nr. 46, 1983)
Schrreeverhaltnisse mit Normalzahlen und Extremwerten
..
; Schnee W1nter-Zahl der Tage
Summe GrtiBte Grb8te J der
l Zeit· bedec"ung de eke Neu Schnee-Neuschnee• m•t ; ,., (b) ICt"or"lee hbhe ' raum hOhen htihe
Beo•"n-Enae Beginn-Ende • ta Neu em em Datum Datum achnee em
Nr.: 73 OBERLEUT ASCH Hohe: 1130 m u.A.
Mst. Nr. · 101303 LEUTASCHER ACHE NZ.: 485cm
1970/71 03 10 28 04. 22 11. 12.04 166 142 53 459 125 27.02. 51 23.10
1971172 10 11 13 OS 20.11. 2403. 142 125 37 281 64 1502. 27 2704
1972173 21 10 09 05. 12 11 09.05 193 179 66 715 136 10.03. 39 2210
1973!7-t 1910 13 06 22 10. 13.04. 187 17-t 59 534 134 0802. 5.t 2911
1974/75 29 09 02 06 2110. 30.04 20S 192 79 742 126 3001 .s 3012
1975/761 11 10 23 OS. 1611. 13 04. 163 1.t9 51 287 71 2501. 32 22 11
1976177 08 11 27 04. 2011.2704. 162 159 63 550 96 18 01. 48 15 04
1977178 03 10 24 06. 1.t.11. 29 04 172 167 70 554 113 2002 35 2003
1978179 18 10 18 06 26 11. 09 05. 168 165 67 453 98 0804 25 0604
1979'80 22 09 12 05 03.11. 1205 193 191 81 790 124 0504 .tO 0302
Mittelwerte
191o:11 1
-1979180 15 10 23 05 1111. 24 04. 175 164 63 537 109 40
1930131
-1959160 24 10 01 05 22 11. 1104 155 140i 53 485 126
1900!01
143! ·1979180: 26 10 06 05 23 11. 14 04 156 54 .t85 118
E t t ffuttes••• £,,.,.,,~ ::1• -...!"tiP.U'..-•"•
X remwer e 11)11 ..... Eon:•ont-z .. ~···"'•'·.,··
22 09 27 04 2110 24.03 1.t2 1251 371 281 64 1502 25 0604
1970/71 1979 1977 1974 1972 1972 19721 19721 1972 1972 1979
-1979!80 1011 2406 2611 12.05 205 192 81 I 790 136 1003 5.t 29 11
Future Products
For climate research and monitoring there is a clear need for consistent
global snow cover data on a daily basis. At present, the weekly satellite-
derived products are limited by problems of persistent cloud cover in some
areas, and by interpretation difficulties in mountains and in areas where
vegetation or land use cause small-scale heterogeneity and masking effects.
The surface data are limited in terms of uncertain spatial representativeness
and the difficulty and cost of data access.
New satellite sensor systems that are planned to be launched over the
1986-1990 time frame will provide opportunities for significant improvements
in snow cover data products. Analysis of data from an experimental 1.5 -1.6
micrometer sensor on the Defense Meteorological Program Satellite, flown in
1979, shows that this channel allows useful discrimination of snow cover and
cloud (Scharfen and Anderson, 1982; Crane and Anderson, 1984). A similar
AVHRR sensor will be flown on the NOAA polar orbiters in 1989 and daytime data
134
YEAR
1931
1935
1940
1945
1950
1955
SODANKYLA
.X
15
-
311
-
--
-
XI
15 301
xn
15 311
-
----==+~===+====+=====~
N
15 301·
-
Y.
15
-
-
-
31
-
1931
1935
1940
1945
1950
1955
1960 1960
Figure 2. Time plot of seasonal snow cover, 1931-60 at Sodankyla, Finland (after Lavila, 1972).
from this channel can be combined with visible and infrared channel data to
differentiate cloud from snow cover in mountain areas or wherever cloud cover
exhibits persistence, especially over snow covered ground.
Research with multifrequency passive microwave data from the Nimbus 7
SMMR.has demonstrated a valuable capability to map snow cover with 25 km res-.
olution even in the presence of cloud (unless it has a high liquid water con-
tent) •.. Some information on snow pack water equivalent can be obtained, and
discrimination of thawing and dry snow·is also feasible (Kunzi et al., 1982;
Fo~ter et al., 1984; Chang, this volume). Further work to validate the
alg~~ithms is needed, although the possibility of this is still limited by
the. meager data base of surface observations. Nevertheless, it is antici-
pated .that the routine availability of such satellite-derived products will
itself stimulate verification assessments for a range of geographical envi-
ronments and "snow cover climates" (e.g. Hall, this volume).
Multifrequency passive microwave data will be collected by the Special
Sensor Microwave Imager (SSM/I) .on a DMSP satellite due to be launched in
mid-1986. The National Snow and Ice Data Center has been supported by NASA
Ocean Sciences program to develop a Cryospheric Data Management System (CDMS)
for sea ice products on a VAX (VMS)-750 computer system, employing software
specially developed by the Pilot Ocean Data System (PODS) at the Jet Propul-
sion Laboratory, Pasadena. The Navy-NOAA Joint Ice Center will use opera-
tional sea ice products, produced by the Fleet Numerical Oceanographic Center
using an interim algorithm whereas the NSIDC products will employ the NASA
Science Working Group algorithm (Weaver and Barry, 1985). It is anticipated
that separate funding may be secured to incorporate snow cover algorithms
into the software in order to demonstrate prototype snow cover products.
The SSM/I snow and ice products will fill the gap between the data from
SMM!t, which was launched on Nimbus 7 in 1978 and has already far exceec;led its
design life time, and the data expected from NOAA polar orbiters to be equip-
ped with an Advanced Microwave Sounding Unit (AMSU) in 1990 (Yates, 1985;
Grody, this volume).
A question remaining to be addressed is how the various new data sets
(visible, infrared and passive microwave) will be integrated into consistent
snow cover products. In the near term, it may be preferable to provide all of
theindependently-derived products. Determination of the optimal procedures
for·· c.ombining them into conventional snow cover variables ( cf. Table 1) will
be a task for future research. It is, for example, unclear whether/how to in-
tegrate surface point observations with spatially-averaged satellite pixel
data. Ultimately, however, the preparation of a selected range of variables
for permanent archiving may be decided through a working group. The raw sci-
entific data (radiances, brightness temperatures etc.) should also be archived
in suitable gridded formats.
Summary
The researcher requiring snow cover data for global climate or hydrologi-
cal studies is considerably hampered at present. The meteorological data on
snow cover have been summarized by national agencies in most countries for
average monthly conditions at individual stations and in map form, but the
variables provided differ from country-to-country. Archives of daily/weekly
136
station or gridded data are largely non-existent in conveniently accessible
form• Data availability is also seriously proscribed by cost considerations•
Weekly snow cover maps have been produced for the Northern Hemisphere
from satellite data by manual techniques since 1966 and are now available in
digital form. Some discrepancies exist between these products and station
data, particularly as a result of the compositing of weekly information.
Microwave data products, demonstrated as a research capability, will eventual-
ly provide a valuable all-weather information source, although their resolu-
tion is only about 25 km (12.5 km for the snow boundary). The advent of new
sensor systems over the 1986-1990 time frame offers new opportunities, pro-
vided that ad~quate financial resources are available for production and ar-
chiving of the data sets and for research to test and validate the algorithms
and products. Snow cover data and research have been largely neglected by
various funding agencies because, outside of the polar regions, the topic does
not fall centrally within the purview of any single discipline or agency.
Moreover, the accessibility of much satellite and other data is a major
hindrance to researchers. The importance of resolving this problem has
recently been recognized by the Space Science Board, Committee on Earth Sci-
.ences (National Research Council, 1985, p.142).
Acknowledgement
This paper was supported in part by the Department of Energy, Carbon
Dioxide Research Division under contract DE-AC02-83ER60106.
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Caspar, w. {1962) Die Schneedecke in der Bundesrepublik Deutschland. Deutsche
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1.38
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Comparison of N orther,n He~.isphere Snow Cover Data Sets
Alan Robock
, John Scialdone
Cooperative Institute for Climate Studies
Department of"Meteorology
University of Maryland
College Park, Maryland, U.S.A.
ABSTRACT
Four Northern Hemisphere snow cover data sets are compared
on a weekly basis for the 25-month period, July 1981 through July
1983. The data sets are the NOAA/NESDIS Weekly Snow and Ice
Chart, the Composite Minimum Brightness (CMB) Chart, the United
States Weekly Weather and Crop Bulletin (data only for North
America), and Air Force data. The NOAA/NESDIS Chart is produced
through the use of photo-interpretation of visible· satellite
imagery and ground observations. The United States Crop Bulletin
is also done manually, using only ground observations. The CMB
Chart and the Air Force data are both produced using automated
processes, the first by way of visible satellite imagery and the
second by way of ground observations, climatology, satellite
observations, and persistence. Since the NOAA/NESDIS Chart is
the only standard and complete data set dating back to the
mid-1960's, it is used as the basis for the study. The main
emphasis of this paper is a comparison of the CMB and the
NOAA/NESDIS Chart.
The CMB frequently overestimated snow cover, especially the
southward extent of the main Arctic snow boundary and areas far
from the snow boundary which were not present on the NOAA/NESDIS
Chart. On numerous occasions, the outline of mountain ranges was
either distorted or totally missed by the CMB. The CMB also
underestimated snow cover, especially in densely populated
forested areas. Other regions of underestimation by the CMB can
be attributed to the bias factor of the NOAA/NESDIS Chart (the
NOAA/NESDIS Chart uses the latest snow cover information while
the CMB is composited over a week). The United States Crop
Bulletin agreed fairly well with the NOAA/NESDIS Chart east of
~e Rockies, but differed to the west due to the sparse network
of ground observation stations. The Air Force data also
overestimated snow cover when compared to the NOAA/NESDIS Chart.
141
1. Introduction
Snow cover is an important climate parameter. In this paper
we compare f~ur snow cover data sets to see how well they portray
the weekly average Northern Hemisphere snow cover. The data sets
include input from both satellite and ground based observations.
A primary emphasis is on the evaluation of the Composite Minimum
Brightness (CMB) technique.
In this paper, four Northern Hemisphere snow cover data sets
are compared on a weekly basis over a 25-month period from July
1981 through July 1983. These include the NOAA/NESDIS vJeekly
Snow and Ice Chart, the Composite Minimum Brightness Chart, the
u.s. Weekly Weather and Crop Bulletin (CROP), and Air Force data.
The NOAA/NESDIS Chart is produced through the use of photo-
interpretation of visible satellite imagery and ground
observations. The u.s. Crop Bulletin is also done manually,
using only ground observations. The CMB Chart and the Air Force
data are both produced using automated processes, the first using
visible satellite imagery and the second using ground
observations, climatology, satellite observations, and
persistence. The main emphasis of this project focuses on the
evaluation of the CMB chart for use as an "automatic snow cover
detection" system. Since the NOAA/NESDIS Chart is the only
standard and complete data set, it was used as verification.
2. Data
Figs. 1-4 display examples of each data set for the same
weekly period. Fig. 1 is a NOAA/NESDIS Heekly Snow and Ice Chart
representing the week of Jan. 11 to Jan. 17, 1982. It reveals
the areal extent of the snow cover as well as three categories of
brightness determined by the human analyst. Fig. 2 is an actual
CMB chart for Jan. 11 to Jan. 18, 1982. This finalized chart
shows bright areas representing snow or clouds that persisted
during the entire 7-day period. Fig. 3 is a snow cover map taken
from the U.S. Heekly vJeather and Crop Bullet in for Jan. 18, 19 8 2.
This map only covers the United States and southern Canada while
showing the actual snow depth values at 1200 GMT, Monday, extent
of the snow cover,as well as the boundary for the previous week.
An Air Force snow cover map for Jan. 17, 1982 can be seen in Fig.
4. The NCAR graphics package allowed us to display the extent of
the snow cover in digital form. Since our only concern was the
southern most extent of the snow fields, it was not necessary to
access grid points in the northern latitudes.
3. Results
Over the 2-year study period, 108 weekly maps were produced
142
ll.IZ:t:Jl=\r.J. 11 _;, 1•u· -.-..;.;_.,:.~
l. taut, !!i!l!'loct.1Te
2. lb:le~at8l:' R.e.!"lac'tiTe
J. }"'.o~t. ~e.t"lec:~!.Te
'· Open
}.naly-:lb p,.epared b7
NESS/Synoptic J.n~lr•1~
S•cticn.
Sued on A/4 AA "'T 1 ..l
GO€S S .. l.l\;t~ !""•vw _,
e.t ..... ,_
Figure 1. NOAA/NESDIS Weekly Snow and Ice Chart for
Jan. 11-17, 1982.
143
.....
Figure 2. Composite Min~ Brightness Chart for Jan. 11-18, 1982
144
Figure 3. u.s. Weekly Weather and Crop Bulletin Snow Cover
Map for Jan. 18, 1982.
145
Figure Force Snow Air
146
'-~ i~~--~----1982.
Jan. 17' for
to compare the four data sets. In this report, six comparison
maps are shown as examples of the similarities and differences
(Figs. 5-10). Five problems became apparent when the NOAA snow
cover was compared to the CMB snow cover: 1) The CMB extended
~he main Arctic snow boundary farther south than the NOAA Arctic
snow line; This condition appeared in the nor~h Atlantic and
pacific Oceans as well as the Arctic, and land areas in Asia and
Europe 1 2) Persistent bright areas far from the main Arctic snow
Boundary occurred quite frequently in southeast and west Asia1 3)
The CMB ~lso had trouble with most major mountain regions in the
Northern Hemisphere, especially the Himalayas in central Asia.
often the southern part w9s missed, the whole range was shifted
to the east or persistent white areas covered the entire outline.
The Rockies of North America posed problems for the CMB except in
the winter months, along with the Pyrenees of Spain which went
constantly undetected. The CMB handled the Caucasus and Elburz
mountains of west Asia fairly well and the Alps were picked up
repeatedly; 4) Forested areas of Asia and Japan appeared very
dark when snow covered, resulting in underestimation of the snow
fields by the CMB; 5) In some areas which were not forested, the
c~m still underestimated snow cover with respect to NOAA due to
the fact that the Cf'fB shows the minimum for the week while NOAA
uses the most recent data with a bias toward the end of the week.
CROP agreed fairly well with NOAA east of the Rockies but
underestimated snow cover to the west. The AF data overestimated
w~en compared to NOAA, especially in North America, Asia and
Europe.
4. Discussion of Results
The bright areas associated with the first two problems are
interpreted to be persistent cloudiness. The dominant storm
track apparently coincided with the snow boundary. This may have
been due to a feedback, where the snow/no snow delineation
induced storm formation or paths and the storms produced the
snow, but cannot be determined here. Persistent cloudiness also
existed frequently in southeast Asia and both oceans where,
climatologically, snow cannot occur, while other areas of Asia
and Europe were apparently detected as being snow covered by the
CMB.
The detection of snow cover in mountain regimes proved to be
very ·difficult using the CMB technique. This problem was
produced by positioning errors. Each day, the polar orbiting
satellites cover different strips of the earth's surface and the
location that they cover must be calculated from imperfect
knowledge of the orbits. Furthermore, grid points have a finite
size and do not cover exactly the same location on each day,
shifting about as orbits vary. The result is that a grid point
which includes a mountain and is bright on one day, may be
147
slightly to the side of the mountain on the following day and
appear dark. If this only happens on one day during the
compositin~ period, the minimum brightness will be low and the
snow will not appear on the chart. Except for the Himalayas, the
other mountain ranges mentioned have locally dense forests below
their crests which decreases the surface brightness when snow
covered. Also, circulation surrounding the mountainous area may
be oriented in such a way to produce orographic cloudiness for an
extended period of time, and this was quite evident in the
Himalayas.
The CMB technique also experienced difficulties in
perceiving snow cover in heavily forested landscapes. McClain
and Baker (1969} found very low surface brightness associated
with snow-covered forested regions of North America. Robeck and
Kaiser (1985} found planetary albedos of forested areas were
significantly lower than farming and grazing lands when snow-
covered. Kukla and Brown (1982} found similar results observing
surface brightness of various surface types. Robinson and Kukla
(1985} computed zonal averages of surface albedo of Northern
Hemisphere lands under maximum snow cover and found low values in
Eurasia and North America between 45°N and 65°N.
The last problem deals with NOAA's bias toward the end of
the week. The NOAA/NESDIS vleekly Snow and Ice Chart is
compounded over a weeks' period from Monday thru Sunday, however,
it incorporates only the most up-to-date information in its final
outline, i.e. only data from the end the week are used. In the
CMB technique only minimum values of surface brightness are
retained. Essentially, each technique serves a different
purpose, The CMB technique displays the minimum amount of snow
cover while NOAA reveals more than the minimum, especially if
snow is increasing toward the end of the week.
CROP agreed fairly well with NOAA east of the Rockies but
underestimated snow cover to the west du~ to the sparse network
of ground observation stations. The AF data, like the CMB,
overestimated snow cover when compared to NOAA but did however,
reveal the probable sea ice/open water delineation in the Arctic
Ocean during the winter. This is made possible thru the use of
the joint NOAA/NAVY ice charts.
Table 1 displays a quantitative comparison between NOAA an'd
the other data sets as a function of area difference. Each
category represents a problem that was found on the weekly
comparison maps and is rated separately on a scale of 0 (No
Difference} to 5 (Maximum Difference}. Occasionally, CMB data
were not available during the 2-year period so that the
corresponding week was labeled as "MISSING". After each weekly
map from July 1981 to July 1983 was rated, several quantities
were calculated. We first computed the average difference for
154
Table 1.
Area Difference for NOAA vs. Other Data Sets
values: 0-No Difference
5-Maximum Difference
(Each category is rated separately)
CMB(l); Persistent clouds obstructing the main snow boundary
CMB(2): Persistent clouds far from the main snow boundary
CMB(3): Inconsistent detection in mountain regions
CMB(4): Underestimation in forested landscapes
CMB(5): Underestimation due to NOAA's end of the week bias
CROP(EAST): NOAA vs. CROP agreement east of the Rockies
CROP(WEST): NOAA vs. CROP agreement in the west
AF: Overestimation of AF vs. NOAA
DATE
1981
7/6~7/12
7/13-7/19
7/20-7/26
7/27-8/2
8/3-8/9
. 8/10-8/16
8/17-8/23
8/24-8/30
8/31-9/6
. 9/7-9/13
9/14-9/20
9/21-9/27
9/28-10/4
10/5-10/11
10/12-10/18
10/19-10/25
10/26-11/1
11/2-11/8
11/9-11/15
11/16-11/22
11/23-11/29
11/30-12/6
12/7-12/13
12/14-12/20
12/21-12/27
1982
12728-1/3
1
3
3
1
2
2
3
4
4
4
5
5
5
4
4
3
4
4
3
3
4
3
2
3
2
C M B
2 3 4
2 3 3
0 2 3
2 4 3
2 4 3
M I S S I N G
2 4 3
3 4 3
2 3 2
3 2 2
3 2 5
3 2 1
4 3 1
4 3 1
3 2 1
3 3 2
3 3 3
2 4 2
1 5 2
1 3 2
1 2 3
2 3 3
2 3 2
1 4 3
2 2 2
1 2 2
M I S S I N G
155
5
2
2
1
2
2
1
1
0
1
1
0
1
0
0
2
2
2
2
2
3
2
3
2
3
C R 0 P
EAST WEST
2
1
1
2
3
3
3
3
2
2
AF
4
4
4
5
5
3
C M B C R 0 p
DATE 1 2 3 4 5 EAST WEST AF
1/4-1/10 2 1 2 1 3 2 2
1/11-1/17. 2 0 2 2 2 2 3 3
1/18-1/24 2 1 2 2 4 1 4
1/25-1/31 2 1 3 2 2 2 3
2/1-2/7 2 1 2 3 2 2 2
2/8-2/14 2 2 2 2 2 5 4 3
2/15-2/21 3 1 2 2 3 3 3
2/22-2/28 2 1 2 2 3 2 4
3/1-3/7 2 1 2 2 3 4 3
3/8-3/14 2 1 3 2 2 3 5 4
3/15-3/21 2 1 3 2 3 3 4
3/22-3/28 2 0 2 2 2 2 3
3/29-4/4 2 1 3 2 2
4/5-4/11 2 1 3 4 3
4/12-4/18 2 2 3 3 3
4/19-4/25 2 2 3 3 4 5
4/26-5/2 1 0 3 3 2
5/3-5/9 1 0 3 2 1
5/10-5/16 1 3 3 2 2 4
5/17-5/23 1 4 3 2 1 4
5/24-5/30 1 2 2 3 0
5/31-6/6 2 2 3 3 2
6/7-6/13 1 3 2 2 4
6/14-6/20 1 4 3 2 2 5
6/21-6/27 M I S s I N G
6/28-7/4 1 5 4 1 1
7/5-7/11 2 4 2 1 2
7/12-7/18 2 5 3 1 2
7/19-7/25 M I S s I N G
7/26-8/1 2 1 2 2 1 4
8/2-8/8 2 5 3 1 2
8/9-8/15 2 5 3 1 2
8/16-8/22 4 5 3 1 1
8/23-8/29 3 5 2 1 3
8/30-9/5 M I s s I N G
9/6-9/12 4 5 2 2 1
9/13-9/19 3 4 4 2 3
9/20-9/26 M I S s I N G
9/27-10/3 4 4 3 3 2
10/4-10/10 4 5 4 3 1
10/11-10/17 3 5 4 2 2
10/18-10/24 5 5 4 2 3
10/25-10/31 3 5 3 2 3
11/1-11/7 4 5 4 2 3
11/8-11/14 3 5 2 2 2
11/15-11/21 4 4 5 3 3 5
11/22-11/28 4 2 3 1 2
11/28-12/5 3 3 3 1 2 3
156
C M B C R 0 p
DATE 1 2 3 4 5 EAST WEST AF
12/6-12/12 5 3 3 2 3 5 3
12/13-12/19 5 3 3 2 2 3 4 4
12/20-12/26 4 0 2 1 3 2 1
1983
12727-1/2 4 2 2 1 1 2 2
1/3-1/9 4 3 2 1 1 2 3 4
1/10--1/16 5 4 3 1 3 5 3
1/17-1/23 3 0 2 1 2 2 2
1/24-1/30 5 1 3 2 2 3 3 3
1/31-2/6 4 0 3 1 2 3 4
2/7-2/13 4 2 4 2 3 1 3
2/14-2/20 3 1 2 1 3 1 3
2/21-2/27 5 1 4 2 3 3 3 3
2/28-3/6 3 2 3 1 2 2 3
3/7-3/13 3 3 4 3 2 3 4
3/14-3/20 4 3 3 2 2 2 4 3
3/21-3/27 4 2 2 2 3 3 3
3/28-4/3 3 2 3 1 2
4/4-4/10 5 3 3 2 2 4
4/11-4/17 2 5 3 2 1
4/18-4/24 3 5 4 4 2
4/25-5/1 2 5 4 3 1
5/2-5/8 2 4 3 3 3
5/9-5/15 5 4 4 4 2 5
5/16-5/22 4 4 3 4 2
5/23-5/29 2 5 4 4 2
5/30-6/5 3 4 3 2 2
6/6-6/12 2 5 3 2 3
6/13-6/19 2 5 4 4 3 5
6/20-6/26 2 5 3 4 4
6/27-7/3 2 5 3 3 2
7/4-7/10 2 4 3 3 2 5
7/11-7/17 1 5 3 3 2
7/18-7/24 2 5 2 3 2
7/25-7/31 2 5 3 3 2
157
c M B C R 0 p
DATE 1 2 3 4 5 EAST WEST AF
AVG DIFF 2.9 2.8 2.9 2.2 2.1 2.5 3.1 4.0
AVG DIFF ( % ) 58 56 58 44 42 50 62 80
RELATIVE AREA
OF LEVEL 5
DIFF (%) 80 40 10 30 5 10* 5* 100
RELATIVE AREA
OF LEVEL 5
DIFF
( 10 km 8.8 4.4 1.1 3.3 .55 1.1 .55 11
AVG AREA
DIFF
( 10 km 5.1 2.5 .64 1.5 .23 .55 .34 8.8
*note that differences are zero in Eurasia because data set
only covers North America.
158
each category, then we converted the average difference to
percent values. The Air Force data showed the largest average
difference, 80% of the maximum difference while CROP in the west
was next highest at 62%. The CMB problems of clouds and
mountains ranged from 56% to 58%. Then we compared the area of
level 5 difference for each category to level 5 areas for the Air
Force data. We calculated a typical level 5 area difference for
the Air Force data and proceeded to calculate area difference
values for the other categories in millions of square kilometers.
The CMB's cloud problem showed the largest area difference next
to the Air Force data. After computing the average area
difference for each category, the Air Force displayed the highest
value at 8.8 million sq. km. while cloud problems 1 and 2
followed at 5.1 million sq. km. and 2.5 million sq. km.,
respectively. If we add up the CMB average area differences
(categories 1-5), the CMB area difference is larger than the Air
Force area difference, 10 million sq. km. to 8.8 million sq. km.
5. Conclusions
Several differences arose between CMB and NOAA in analyzing
two consecutive years of data. On many occasions, the CMB Arctic
snow boundary stretched farther south than the NOAA Arctic snow
boundary, leading ~s to interpret this as persistent cloudiness.
This condition also occured quite often far from the snow
boundary. Snow cover in mountainous regions throughout the
Northern Hemisphere went undetected or partially detected many
times while forested regions caused underestimation by the CMB.
Due to the nature of their techniques, the CMB occasionally
showed less snow cover than NOAA. The U.S. Crop Bulletin and
NOAA showed significant differences which should be expected
since CROP does not use satellite imagery. The Air Force data
overestimated snow cover, like the CMB, but did however reveal
the probable sea ice/open water delineation in the Arctic during
the winter months.
For research purposes, we can recommend only the weekly NOAA
data set for a consistent indication of snow cover. Although
this data set also has its problems, including effects of
persistent cloudiness, forests and broken satellites, and hence
missing data, it is still by far the best source. The CMB
process introduces too many errors to be used without additional
information from surface observations or human interpretation of
individual high-resolution imagery. The CROP data set does
provide accurate coverage in regions with a dense surface
observing network and without mountains. The AF data set,
although daily incorporating large amounts of surface data, is an
undocumented operational product. It contains large errors and
is unsuitable for research purposes.
159
The ideal snow cover data set of the future will include the
best aspects of each of the current data sets. Surface
observations will supplement satellite images with the ~MB
technique perhaps helping to remove clouds. New technologies,
including near-infrared snow detection channels on future DMSP
and NOAA satellites and microwave techniques (Robinson, et al.,
1984), also show promise in producing better snow cover data
sets.
Acknowledgements: He are grateful to the following people: Mike
Matson and Tom Baldwin for supplying the NOAA/NESDIS data and
advising us where necessary, Bruce H. Needham of the Satellite
Data Service Division of NOAA for providing us access to the CMB
data, John Walsh for providing copies of the U.S. \'7eekly Weather
and Crop Bulletin, David Lee for the Air Force data, Joyce Gavin
for comments and advice on snow cover information in general, Max
Woods for drafting all snow cover comparison maps, and Robe,rt G.
Ellingson for continued support and encouragement. We are also
much obliged for computer time supplied by NASA/GLA. This study
was supported by NOAA Grant NA84AA-H-00026 and NSF Grant
ATM-8213184.
REFERENCES
Kukla, G., and J. Brown, 1982: Impact of snow on surface
brightness. EOS, ~, 576-578.
McClain, E. P., and D. R. Baker, 1969: Experimental large-scale
snow and ice mapping with . composite minimum b~ightness
charts. ESSA Technical Memorandum NESCTM-12, u.s. Dept. of
Commerce-;--25pp.
Robinson, D., and G. Kukla, 1985: Maximum surface albedo of
seasonally snow-covered lands in the northern hemisphere.
J. Climate~· Meteor.,~, 402-411.
Robinson, D., K. Kunzi, G. Kukla, and H. Rott, 1984: Comparative
utility of microwave and shortwave satellite data for all-·
weather charting of snow cover. Nature, 312, 434-435.
Robock, A., and D. Kaiser, 1985: Satellite-observed reflectance
of snow and clouds.~·~·~., 113, 2023-2029.
160
Kukla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) SNOW WATCH '85.
Proceedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College Park, MD. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice), Glaciological Data, Report GD-18, p.l61-171.
Influence of Snow Structure Variability on Global Snow Depth
Measurement using Microwave Radiometry
Dorothy K. Hall
Hydrological Sciences Branch
National Aeronautics and Space Administration
Goddard Space Flight Center
Greenbelt, Maryland, U.S.A.
ABSTRACT
The high albedo of snow and its sometimes transient nature can have a
large impact on the receipt of solar energy and thus on global circulation.
In addition, snow depth and liquid water content of snow must be known
for assessment of the global water balance. Passive microwave sensors
are useful for measuring the snow extent through cloudcover and darkness,
and snow depth can be estimated under certain snow and terrain conditions.
However, the ability to measure snow depth using microwave radiometry on
a global scale, in part, depends upon knowledge of the effects of snow
structure on the microwave emission from snow. It has been observed that,
even with no increase in snow depth, the microwave emissivity decreases
throughout the winter in many snowcovered areas. This appears to be
related to increasing depth hoar thickness through time. Utilization of a
two layer radiative transfer model which is used to simulate the microwave
emission from a snowpack has enabled a comparison of calculated data points
with observations. The observational data consist of a time series of
Scanning Multichannel Microwave Radiometer (SMMR) data of the Arctic
Coastal Plain of Alaska during the period from January through March 1980.
The snowpack on the Arctic Coastal Plain of Alaska is known to develop a
depth hoar layer each year as a result of a large temperature gradient in
the snowpack which causes crystal sizes at the base of the snowpack to
161
increase throuyh time. Crystals can grow up to 10 mm in size although
the average crystal size in the lower or depth ho_ar layer is considerably
less than JO mm. Using the model, the crystal diameters in the upper
and lower layers of the snowpack were set at 0.50 and 1.40 mm respective-ly.
These sizes were determined from a previous study. In the first simulation,
the depth hoar layer thickness was assumed to be a constant 10 em from
January through March 1980 with the tot a 1 snow depth varying as determined
from climatological data. When the model results were correlated with
the SMMR data, usiny 15 data points, the coefficient of correlation was
R = 0.30. For the second simulation, all parameters remained the same
as in the first simulation except that the depth hoar layer thickness
was increased by 0.50 em per week to simulate the reported increasing
thickness of the depth hoar 1 ayer as the winter progresses. In this
case, the simulated and observed data points matched quite well with a
coefficient of correlation of R = 0.85 significant at the 0.01 level.
Results thus show that the presence and variability of the depth hoar
layer can have a significant effect on the microwave emission and that
changing snow structure must be considered when measuring snow depth
using a time series of data.
Introduction
The high albedo of snow and its sometimes transient nature can have
a large impact on the receipt of solar energy and thus on global circula-
tion. In addition, the depth and liquid water content of snow must be
known for assessment of the global water balance. Passive microwave
sensors are useful for measuring the snow extent through cloudcover and
darkness, and snow depth can be estimated under certain snow and terrain
conditions. However, the ability to measure snow depth using microwave
radiometry on a global scale, in part, depends upon knowledge of the
effects of snow structure on the microwave emission from snow. In this
paper, the effect of changing snow structure through time is addressed for
the snowpack on the Arctic Coastal Plain of Alaska.
Snow Structure on the Arctic Coastal Plain of Alaska
Local and regional energy balance processes influence the structure
of a snowpack. Snowpack structure can change through time as a result of
air temperature changes, wind, precipitation type and quantity and length
of time that snow is on the ground. When snow remains on the ground for a
substantial portion of the winter, metamorphism at the base of the snowpack
can result in the formation of a 1 ayer comprised of 1 arge, 1 oosely-bonded
crystals. This is known as the depth hoar layer. Depth hoar is common in
snowpacks throughout the world and is well developed in portions of Alaska •.
A steep, negative temperature gradient occurs in the snowpack of the
Arctic Coastal Plain. Early snow accumulation reduces upward soil heat
flux (Santeford, 1979). Even though the air temperatures can be lower than
-45°C, snow/soil interface temperatures may be as high as -5°C producing
a 40°C difference in temperature between the snow/air and snow/soil
interfaces. This temperature gradient produces inequalities in the degree
162
of air saturation and the diffusion rate of water vapor in the snowpack
(Marbouty, 1980). Associated ~ressure gradients cause water vapor to
diffuse from warmer to colder parts of a snowpack (Langham, l~:J81). This
leads to constructive metamorphism iQ.which snow crystal size increases
through time as crystals grow from one side of existing snow crystals in a
direction that is opposite to the vapor pressure gradient. Faster crystal
growth occurs in the warmer (lower) layers of a snewpack (Colbeck, 1982).
Depth hoar crystals can grow up to 10 mm in diameter (Benson et al., 1975).
On the Arctic Coastal Plain of Alaska, low snow accumulation, very
cold air temperatures and strong winds contribute to the snowpack struc-
ture. A relatively thin, continuous snow cover forms each year. High
average snowpack densities result from re-distribution of the snow by wind
causing the crystals in the u~per layers of the snowpack to be closely-
packed, rounded and abraded.
Recently, a two-1 ayer radiative transfer model developed by Chang et
al., (1976), was employed to study the effect of the depth hoar layer on
microwave emission from snow (Hall et al., in press). It was found that
the microwave brightness tem~erature (Ts) decreased with increasiny depth
hoar layer thickness (Figure 1). The rate of increase in Ts was shown to
lower with increasing thickness of the depth hoar layer. In other words,
when the de~th hoar layer was first formed, there was the greatest decrease
in Ts (Figure 1).
In this paper, we employ the same basic model to calculate a time
series of Tss to compare with observed Tss from the Arctic Coastal Plain of
Alaska using the 37 GHz sensor on the Scanning Multichannel Microwave
Radiometer (SMMR). Results show that the observed and modeled Tss corre-
late well when the depth hoar layer thickness is increased through time in
the model snowpack.
Previous Work
Satellite data are useful for analyzing snow conditions on regional
and global scales, and microwave sensors are able to acquire data through
cloudcover and darkness. Previous studies have shown that there is an
inverse relationship between snow depth and microwave TB as measured by
passive microwave sensors at specified wavelengths in dry snow (Foster et
al., 1984). The 37 GHz (0.81 em wavelenyth) sensor on the Nimbus-7 SMMR
has been shown to be particularly useful for analyzing internal properties
of snowpacks es~ecially when the horizontally polarized data are used (Hall
et al., 1984). Some characteristics of the Nimbus-7 SMMR are shown in
Table 1.
For temperatures generally encountered on Earth, the emitted intensity
of microwave radiation is expressed as Ts in degrees Kelvin and follows the
Rayleigh-Jeans approximation which shows that the radiance from a blackbody
163
240
Snowpaek depth 30 em
230
Crystal diameter:
Top layer 0.50 mm
220 Bottom layer 1.40 mm -~
0
w 210 a:
~
~ a: 200 w a..
~ w
I-190
(/)
(/) w z 180 I-
I
(.9
a: 170 co
160
150 l--_ ____JL_ _ ___J, __ __._ __ ........J... __ __,
0 5 10 15 20
BOTTOM LAYER THICKNESS (em)
Figure l. Calculated 37 GHz (horizontal polarization) brightness
temperatures for dry snow cover over frozen ground showing the
effect of changing the thickness of the depth hoar layer (from
Hall et al., in press).
164
is proportional to its temperature:
Ts = ETse-T + Tl + (1 -E)T2e-T + (1 -E)Tspe-2T ( 1 )
where E is the emissivity of the surface, Ts is the sensible temperature of
the surface, T is the total atmospheric opacity, T1 is the upward emitted
radiance contribution of the atmosphere, T2 is the total downward (emitted
and reflected) atmospheric brightness temperature, and Tsp is the avera~e
temperature of free space (Gloersen and Barath, 1977).
Table 1. Some Characteristics of the SMMR (after Gloersen
and ~arath, 1977)
Wavelength (em) 0.81 1.43 1.66 2.80 4.54
Frequency (GHz) 37.00 21.00 18.00 10.69 6.60
Spatial resolution (km) 30 60 60 97.5 156
Temperature resolution
trms (°K) (per IFOV) 1.5 1.5 1.2 0.9 0.9
Antenna beam width (degree) 0.8 1.4 1. 6 2.6 4.2
Calculations have shown that the grain or crystal size is a dominant
factor influencing the microwave emission of dry snow and polar firn.
Additional calculations using a microscopic scattering model for snow have
shown that the scattering of 37 GHz radiation from a snowpack is dependent
strongly upon the grain size of the snow particles (Chang et al., 1982;
Hall et al., in press). Larger grain sizes within the snowpack allow
greater incidence of scattering of microwave radiation as the grain size
approaches or surpasses the size of the wavelength. The relatively large
crystals characterizing the depth hoar layer would tend to increase
scattering of the upwelling radiation relative to scattering through a
snowpack (with a similar thickness) which is lacking a depth hoar layer.
Radiative Transfer Model
The intensity of microwave radiation emitted from a snowpack depends
on the physical temperature, yrain size, density and the underlying surface
conditions of the snowpack. By knowing these parameters, the radiation
emerging from a snowpack can be calculated by solviny the radiative
transfer equation. The radiative transfer e~uation for an axially sym-
metric inhomogeneous medium can be written in the form of an inteyro-
differential equation
165
dl(X,J.I)
1J ---------= -o(x)I(x,J.I)+o(x){[l-w(x)]B(x)
dx
1
+ l/2w(x) J P(x,lJ,lJ 1 )I(x,lJ•)dlJ 1
}
-1
where the radiation intensity I(x,J.I) is at a depth x and traveling in
the direction towards increasing x, making an angle whose cosine is 1J with
the normal (Chang et al., 1982). The functions o(x), w(x), B(x) and
P(x, J.l, 1J 1
) are prescribed functions of their arguments. They are
the extinction per unit length, the single scattering albedo, and the
source and phase functions, respectively. Instead of working with depth
x, one generally works with a dimensionless depth variable called optical
depth T (Chang and Choudhury, 1978):
dT = o(x)dx
(2)
(3)
For the snowpack on the Arctic Coastal Plain of Alaska, the air
temperature as determined from the meteorological station located at Umiat,
Alaska is used as a guide to infer the snow temperature, and the crystal
sizes in the upper and lower layers of the model snowpack were determined
from previous work by Hall et al. (in press) in which the crystal size in
the upper layer was assumed to be 0.50 mm and in the lower layer 1.40 mm
in diameter. The thickness of the depth hoar or lower layer was assumed
to be 10 em during the period 5-10 January 1980 which was the period during
which Tss were averaged to determine the average Ts from SMMR data.
Snow depth in the model varied with reported snow depths from the meteoro-
logical station at Umiat, Alaska.
The average snowpack temperature is obviously higher than the reported
air temperature. However, without in-situ measurements the average snow-
pack temperature is not known. Thus, for the purpose of this study, the
first point shown on Figure 2 was fitted to the observed Ts by adjusting
the average snow temperature until the simulated Ts matched the observed
Ts. A 25°K increase (relative to reported average air temperature for
each 5 day study period) was added in each case and used as an average
snow temperature of the model snowpack.
Results of the Model as Compared to Observations
Brightness temperatures were calculated for 15 data points correspond-
ing to the 15 time periods for which SMMR data were averaged in 5 day
periods between 1 January and 30 March 1980 (Julian days 1-89).
When the depth hoar thickness remained at 10 em for the study period,
the correlation between modeled and observed Tss was low, R = 0.30. How-
ever, when the thickness of the depth hoar or lower layer of the model
166
1-'
0\ ......
200
~ -SMMR,(OBSERVED)
0
w 190 MODEL (SIMULATED) a: ~--..
:::>
I-
<( a: 180 w a..
~ w
I-
en 170 en w z
I-:::c
(!) 160 a:
[D
150
5 11 17 23 29 35 41 47 53 59 65 71 77 83 89
START DAY (JULIAN) OF SMMR OVERPASS
Figure 2. Observed and simulated brightness temperatures for the snowpack on the
Arctic Coastal Plain of Alaska -January through March 1980.
snowpack was allowed to increase by 0.50 em per week, the correlation
between modeled and observed TB was quite high, where R = 0.85 significant
attheO.Ollevel.
The TB for the period beginning on day 41 that was originally deter-
mined from the model was anomalously high -186oK. This period was
unusually warm where ai~ temperatures (which were used to infer snow temp-
eratures in the model snowpack) averaged -8.5°C for the 5 day period.
This was well above the average of the previous (-26.50°C) and subsequent
(-30.88°C) SMMR overpass periods. Because it is unlikely that this
unusually high air temperature caused an equivalent warming of the average
temperature of the snowpack, that case was re-calculated by changing the
average,snowpack temperature to a value determined from averaging the air
temperatures of that period, the previous period and the subsequent period.
The adjusted air temperature then becomes -22.0°C and the TB becomes 177°K
(Figure 2). The correlation between the modeled and observed TBs increases
toR= D.88, significant at the 0.01 level, as seen in Figure 3.
Discussion
lt:has been obse~ved that the thickness of the depth·hoar layer may
increas~ through time as long as a strong negative temperature gradient is
maintained in a snowpack (Giddings and LaChappelle, 1962). For example, in
central Alaska, Trabant and Benson (1972) reported that the depth 'hoar
1 ayer increased in thickness throughout the winter .and comprised a 1 most the
entire snowpack by the end of the winter. Thus it is reasonable to include
an incre.ase in depth boar thickness t.i'irough time in the present study, as
it is known from ni~teorological records that a strong negative temperature
gradient was maintained in the snowpack during the winter of 1980 on the
Arctic Coastal Plain of Alaska. ·
Conclusion
Many of the snow covered areas on the Earth have depth hoar. It is
espect~lly common in areas where the snowpack lasts long enough to allow
depth hoar crystals to grow. Thus depth hoar is an important type of
snowpa:tk structure that has to be taken into account when estimating global
snow depth from mi crowa,ve data because the large crystals which. character-
ize a depth hoar layer cause increased scattering of microwave radiation
and t~u~a lower TB than would be expected for a snowpack of similar
depth without depth hoar. Furthermore~ ft appears that this type of snow
structtiJre can be modeled quite successfully using radiative transfer model-
ing.
However, it is important to understand that, before the microwave
emission(from snow can be successfully modeled, one must have prior know-
ledge about the snowpack structure. This is necessary for a reliable
estimate 'of snow depth to be possible.
168·
~
0
CD t-
o w 180 ~ w
(/) m
0 170
160
150 160
R = 0.88
R2 = 0.78
STANDARD ERROR = 3.533
200
SIMULATED T8 °K
figure 3. Correla~ion-of model (simulated) T8s and SMMR r8s using an adjustment
for day 41 as explained in text.
References
Benson, C. s. and D. c. Trabant (1973) Field measurements on the flux of
water vapor through dry snow. The Role of Snow and Ice in Hydrology,
UNESCQ .. WMO-IASH, p. 291-298.
Benson, c., B. Holmgren, R. Timmer, G. Weller and s. Parrish (1975)
Observations on the Seasonal Snow Cover and Radiation Climate at
Prudhoe Bay, Alaska, during 1972. In Ecological Investigations of the
Tundra Biome in the Prudhoe Bay Region, Alaska (J. Brown, ed.),
Biological Papers of the University of Alaska Special Report Number 2,
October, 1975, p. 13-50.
Chang, A. T. C. and B. J. Choudhury (1978) Microwave Emission from Polar
Firn. NASA Technical Paper 1212.
Chang, A. T. C., P. Gloersen, T. Schmugge, T. T. Wilheit and H. J. Zwally,
(1976) Microwave emission from snow and glacier ice. Journal of
Glaciology, V. 16, p. 23-39.
Chang, A. T. C., J. L. Foster, D. K. Hall, A. Rango and B. K. Hartline,
{1982) Snow water equivalent estimation by microwave radiometry.
Cold Regions Science and Technology, V. 5, p. 259-267.
Colbeck, S. C. (1982) An overview of seasonal snow metamorphism. Reviews
of Geophysics and Space Physics, V. 20, p. 45-61.
Foster, J. L., D. K. Hall, A. T. C. Chang and A. Rango (1984) An overview
of passive microwave snow research and results. Reviews of Geophysics
and Space Physics, V. 22, p. 195-208.
Giddings, J. C. and E. LaChapelle (1962) The formation rate of depth hoar.
Journal of Geophysical Research, V. 67, p. 2377-2383.
Gloersen, P. and F. Barath (1977) A Scanning Multichannel Microwave Radi-
ometer for Nimbus-G and Seasat-A. IEEE Journal of Oceanic Engineer-
J!lll, V. OE-2, p. 172-178.
>··
·Hall, D. K., J. L. Foster and A. T. C. Chang (1984) Nimbus-7 SMMR polari-
zation responses to snow depth in ~he mid-western U.S. Nordic
Hydrology, V. 15, p. l -8.
Hall, D. K., A. T. C. Chang and J. L. Foster (in press): Detection of the
Depth Hoar Layer in the Snowpack of the Arctic Coastal Plain of Alaska
using Satellite data.
Langham~ E. J. (1981) Physics and properties of snowcover. (In: D. M.
Gray and D. H. Male, eds., Handbook of Snow, Pergamon Press, New York,
p. 275-337. )
170
Marbouty, D. (1980) An experimental study of temperature-gradient meta-
morphism. Journal of Glaciology, V. 23, p. 303~312.
NOAA {1979-1983) Climatological Data of Alaska. National Oceanic and
Atmospheric Administration, National Climatic Data Center, Asheville,
NC. "
Santeford, H. S. (1979) Snow soil interactions in interior Alaska.
Proceedings of the Worksho oh Modelin of Snow Cover Runoff, (s~ C.
Colbeck and M. Ray, editors , 26-28 September 1978, Hanover, NH, p'.
311-318.
Trabant, D. and C.S. Benson {1972) Field Experiments on the Development of
Depth Hoar. Geological Society of America Memoir 135, p. 309-322.
171
Kukla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) SNOW WATCH '85.
Proceedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College' Park, MD. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice), Glaciological Data, Report GD-18, p.173-179.
1. Introduction
Retrieval of Snow Water Equivalent from
Nimbus-7 SMMR Data
M. Hallikainen
P. Jolma
Department of Electrical Engineering
Helsinki University of Technology
Espoo, Finland
In microwave radiometry of snow cover, the intensity of microwave radiation
emitted by snow and the underlying ground is measured. The result is expressed
as a brightness temperature. The effect of snow cover to the brightness
temperature depends on several snow parameters. Even a small amount of liquid
water makes the snow a high-loss material. In that case, no radiation from the
bottom of the snow cover reaches the air, and the brightness temperature does
not depend on the thickness and the water equivalent of the snow cover. If the
snow is dry, the microwave radiation from the ground is scattered by snow
particles, while the snow itself emits practically no radiation. At
frequencies above 20 GHz, the scattering is so strong that it tends to
decrease the brightness temperature, depending on the average snow particle
size. Since the amount of scatterers increases with increasing snow thickness,
the brightness temperature of snow-covered ground can be used to retrieve the
water equivalent of dry snow cover (Rango et al., 1979).
Satellite microwave radiometer data have been used
retrieve the water equivalent of snow cover both on
al., 1982) and on a regional basis (Hallikainen,
several factors that have an effect to the retrieval
2. Water equivalent algorithms
to develop algorithms to
a global basis (Kunzi et
1984a). In this paper,
accuracy, are examined.
Since scattering by dry snow particles tends to decrease the
temperature from the value observed before the first snowfall,
quantity related to the snow water equivalent is of the form
brightness
a simple
.!lT(Weq) = F(Weq) -F(Weq=O) ( 1)
where F(Weq) = observed quantity for water equivalent Weq• and F(Weq=O) = observed quantity for water equivalent Weq =
free terrain).
0 (snow-
173
F may be either a brightness temperature at a single frequency, the difference
between the brightness temperatures at two frequencies, or a more complicated
function derived from the observed brightness temperatures. In studies
employing data from the Nimbus-7 satellite (measurement angle 50 degrees from
vertical), the brightness temperature difference between 18 GHz and 37 GHz,
horizontal polarization, has been used. For that case, ~T is
~T(Weq) = (T18H(Weq) -T37H(Weq)) -(T18H(Weq=O) -T37H(Weq=O)) (2 )
where T18H ; brightness temperature at 18 GHz, horizontal polarization,
and T37H ; brightness temperature at 37 GHz, horizontal polarization.
Using a brightness temperature difference or a more complicated function for
F has been found to give a higher retrieval accuracy than using a single-
frequency value. An obvious reason for this is that the second frequency helps
to partly eliminate the effects of snow and ground temperature.
The value of F(Weq;O) for each antenna footprint should be defined in winter-
like conditions. The state (frozen or thawed) and the temperature of ground
should be the same as in winter beneath the snow cover. The use of F(Weq;O) is
necessary in areas where several land-cover categories (farmlands, torests,
etc.) exist. In homogeneous areas where the brightness temperature is
practically a constant before the first snowfall, the use of F(Weq;O) may not
be necessary.
The relation between the observed ~T and the snow water equivalent can be
established experimentally for the area under examination. The relation
depends on the measurement frequencies and polarization (definition of ~T) and
on the properties of snow cover and the underlying ground. Theoretical
brightness temperature calculations may be used to determine suitable
frequencies for the algorithm. However, the effect of vegetation canopies to
the microwave temperature of snow-covered terrain cannot yet be solved
theoretically. ·
Figure 1 shows the change of the brightness temperature due to snow cover in a
100 km x 100 km test area in Southern Finland for Winter 1979-80. Nimbus-7
data at 10.7 GHz, 18 GHz, and 37 GHz are shown along with some functions
derived from those brightness temperatures. Also shown are the daily minimum
and maximum air temperatures, precipitation, and manually measured snow water
equivalent (compiled by the National Board of Waters). The surface type
composition of the test area is: coniferous forests 84 %, lakes 14 %, and bogs
1 %. It is obvious from Figure 1 that any water equivalent algorithm for
Finland should include the 37 GHz channel.
Figure 2 shows the correlation between the manually measured water equivalent
values (from water equivalent maps, compiled by the National Board of Waters)
and Nimbus-7-derived brightness temperature functions for Winter 1979-80. Data
for Southern Finland (77 % coniferous forests, 10 % lakes, 10 % bogs, and 3 %
farmland, area approximately 200,000 square kilometers) were used, including
the results for both dry and wet snow. The results show that the brightness
temperature difference between 18 GHz and 37 GHz gives the best correlation.
174
The vertical polarization is slightly better than the horizontal polarization.
At present, the accuracy of the retrieved water equivalent values does not
meet the value of 10 mm, specified in the NASA climate plan. In order to
increase the accuracy, several factors have to be considered, including the
effects of snow particle size, snow and ground temperature, and land-cover
categories.
3. Effect of snow particle ~ize
The results from both measurements and theoretical calculations suggest that,
in addition to the water equivalent, the brightness temperature of snow-
covered ground depends substantially on the average snow particle size. The
~heoretical results in Figure 3 show that the brightness temperature
difference between 18 GHz and 37 GHz saturates around Weq = 100 mm for a grain
size of 1 mm. Since both the snow particle size and the water equivalent of
snow cover usually increase with time, the microwave response to snow does not
follow any of the curves with a fixed snow particle diameter d in Figure 3.
Rather, it may start by following the curve for d = 0.5 mm for small values of
Weq and finally reach the curve for d = 1.25 mm after several melt-freeze
cycles.
Figure 3 suggests that for fine-grained snow, another channel with a frequency
higher than 37 GHz is needed. For snow with an average particle size above
1.25 mm, the use of the brightness temperature difference between 18 GHz and
10 GHz may be a good choice.
4. Effect of snow and ground temperature
Due to the low dielectric loss of dry snow, radiometers operating below
approximately 20 GHz detect the changes in the temperature of ground. This is
shown in Figure 1, where the brightness temperatures at 10.7 GHz and 18 GHz
follow the pattern of the air temperature. The air temperature pattern can be
seen even in the 37 GHz results. Of course, the changes in the brightness
temperatures are much smaller than those in the air temperature.
Since dry snow acts as an efficient thermal insulator, the changes in the
temperature of ground surface are small, depending on the thickness of the
snow cover. The variation of the brightness temperature is larger than
expected from the variatio~s of the soil temperature alone. This is explained
by the dielectric behavior of soils as a function of temperature. In the
temperature range of 0 OC to -5 OC, the amount of liquid water in soils
depends strongly on the temperature. If the temperature decreases even by one
degree centigrade, the fraction of liquid water decreases substantially and
the dielectric properties of soils change correspondingly (Hallikainen et al.,
1984b). The dependence of the soil dielectric properties on the liquid water
content increases with decreasing frequency. Hence, even the use of the
brightness temperature difference between 18 GHz and 37 GHz cannot totally
remove the effects of the air temperature. This is illustrated in Figure 4,
where the brightness ~emperature difference increases with decreasing air
temperature. In order to remove the effect of snow water equivalent to the
175
resu 1 ts in Figure 4, on 1 y the reso 1 uti on ce 11 s with Weq between 50 mm and 75
mm were considered.
5. Effect of melt-freeze cycles
When the seasonal snow cover starts to melt, days are often warm and nights
are cold. Th1s causes melt-freeze cycles to occur in the topmost snow layers,
resulting in a larger average snow particle size and, consequently, in a lower
brightness temperature at frequencies above approximately 20 GHz. Hence, the
brightness temperature may be very low at night (dry snow) and high in the
daytime (wet snow surface). These variations can be interpreted as being due
to the start of the melting period.
The low brightness temperature due to melt-freeze cycles may erroneously be
interpreted as an increase of the water equivalent value. As illustrated in
Figure 5, the microwave response to dry snow at night may increase even when
Weq is decreasing day by day.
6. Effect of annual variations
The stratigraphy of seasonal snow cover may vary from winter to winter, due to
the local weather conditions of each winter. This causes the microwave
response to snow water equivalent to vary correspondingly. Figure 5 shows that
this is indeed the case for Winters 1978-79 through 1981-82. Winters 1978-79
and 1979-80 were "normal" winters, while in Winters 1980-81 and 1981-82 the
maximum snow water equivalent in Finland was exceptionally high. Especially in
1980-81, frequent snowfalls kept the average snow particle size small in the
topmost snow layers, resulting in a low microwave response.
7. Effect of land-cover categories
Since more than two thirds of Finland is forested, the average microwave
response to snow water equivalent can be expected to be fairly small. However,
in open areas (farmlands, etc.) the response should be substantially larger,
In a previous study, the microwave response to snow water equivalent was
indeed observed to depend considerably on the surface type (Hallikainen,
1984a). Hence, in order to use the water equivalent algorithm given in
Equations (1) and (2) in Finland, the effect of land-cover categories must be
included. This can be done by assuming the microwave response within each
antenna footprint to be a linear,combination of individual responses, weighted
by the fraction of each land-cover category,
b.T(Weq) =L f; b.T;(Weq) (3)
where f; = fraction of surface type i
liT; = response to water equivalent for surface type i.
·"'
By accounting for various surface types within each antenna footprint, the
accuracy of snow water equivalent .retrieval from satellite microwave
radiometer measurements was found to increase substantially (Hallikainen,
1984a).
176
8. References
Hallikainen, M. (1984a) Retrieval of snow water equivalent from Nimbus-7 SMMR
data: effect of land-cover categories and weather conditions. IEEE
Journal of Oceanic Engineering, v. OE-9(5), p.372-376. --
Hallikainen, M.; Ulaby, F.T.; Dobson; M.C.; El-Rayes, M. (1984b) Dielectric
measurements of soils in the 3-to 37-GHz band between -50 C and 23 C.
(In: IGARSS "84 Symposium, Proceedings, held at Strasbourg, 27-30 August
1984, ESA SP-215, p. 163-168.)
' Kunzi, K.; Patil, H.; Rott, H. (1982) Snow-cover parameters retrieved from
Nimbus-7 scanning multichannel microwave radiometer (SMMR) data. IEEE
Transactions on Geoscience and Remote Sensing, v. GE-20(4), p. 452~46g:--
Rango, A. et al. (1979) The utilization of spaceborne microwave radiometers
for monitoring snowpack properties. Nordic Hydrology, v. 10, p. 25-40.
~ ~P~ipi~lion I~: A :M : ~.J
1~~~ Snow Water Equivolen t lmmJ .Y ~
Figure 1. Comparison of AT (Equation 1) with meteorological data in a
100 km x 100 km test area in Southern Finland for Winter 1979-80 (Nimbus-7).
177
l Snow Water Equ;valent lmmJ J
1 !~r ~ ======~~=~~:~~~:::~:::~::;:::~~;;~=~===~1 -1~~ Average ~· ~ _20 Daily
Temperatur, ,
Figure 2. Correlation coefficient between brightness temperature functions (Nimbus-7,
Equation 1) and snow water equivalent in Southern Finland (200,000 sq. km) for Winter
1979-80. Correlation coefficients for each 5-day period and the whole Winter are shown.
120
100
80
:;;;
-~ 60
,.!"
·., 40 ... o-=-
Look Angle: 50"
Horizontal Polarization
Density of Snow: 0. 3 g/cm 3
-Plane Boundary
--Bottom Rough, RMS • 0.3
0. 75 mm
O.Smm
-200 300 100 200
Water Equivalent lmml
Figure 3. Theoretical brightness temperature difference between
18 GHz and 37 GHz for snow-covered terrain as a function of snow
water equivalent, with snow particle diameter d as a parameter.
"" 30
~ 20 ..
"' g 10
.r' d. -10
~ -20
.... -30
1979
Southern Finland
Weq = 50 to 75mm
Nighttime Data
T18H-T37H
Min. and. Max. Air
Temperature
in Jyviiskylo
~ -40~~~--~~---L--~~--~----------~
13 25 16 18 2 14 26
January February March
Date
Figure 4. Comparison of average ~T (Equation 2) in Southern Finland
with daily air temperatures in Jyvaskyla for Winter 1978-79 (Nimbus-7).
40.-----.------.~~--.------r----~
30
:= 20
1-
<l
10
250
Water Equivalent (mml
Figure 5. Average ~T response (Equation 2) to snow water equivalent
in Southern Finland for Winters 1978-79 through 1981-82 (Nimbus-7).
179
Kukla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) SNOW WATCH '85.
Proceedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College Park, MD. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice), Glaciological Data, Report GD-18, p.181-l87.
ABSTRACT
Nimbus-7 SMMR Snow Cover Data
A.T.C. Chang
Hydrological Sciences Branch
National Aeronautics and Space Administration
Goddard SpaC'e Flight Center
Greenbelt, Maryland, U.S.A.
Snow cover maps are presently produced routinely by NOAA/NESDIS and
by USAFGWC. Studies concluded that the gross features of the snow cover
are well represented; however the fine structure of the snow boundary is
greatly generalized. The NESDIS maps which rely on data from spaceborne
visible and infrared sensors sometimes miss large snow fields due to
persistent cloudiness, particularly in the fall when the snow areal
extent is rapidly changing. Microwave radiation penetrating through
clouds and snowpacks could provide additional information of snow fields.
The Nimbus-7 spacecraft which was launched on October 24, 1978, carried
onboard a five channel dual polarized Scanning Multichannel Microwave
Radiometer (SMMR). Based on theoretical calculations, a snow covered
area retrieval algorithm was developed. Global snow cover maps for the
northern hemisphere were derived from SMMR data for a period of five
years {1979-1983). Comparisons with NOAA/NESDIS and USAFGWC products
were conducted to evaluate and assess the accuracy of SMMR derived snow
maps. In general, these data sets compared well: the total snow covered
area derived from SMMR is usually about 5 percent less than for the other
two products. This is because passive microwave sensors cannot detect
snow less than 2.5 em depth due to the fact that the emission from the
underlying snow is not modified very much by emission or scattering by the
snowpack for a shallow snow cover.
INTRODUCTION
The use of remotely-acquired microwave data, in conjunction with essen-
tial ground measurements, will most likely lead to improved information
extraction regarding snowpack properties beyond that available by conven-
tional techniques. Visible and near-infrared data have recently come
into operational use for performing snowcovered area measurements. How-
ever, the data acquisition is hampered by cloudcover, sometimes at critical
181
times when a snowpack is ripe. Furthermore, information on water equi-
valent, free water content, and other snowpack properties germane to
accurate runoff predictions is not currently obtainable using visible and
near-infrared data alone because only surface and very near-surface
reflectance are detected.
Microwaves are mostly unaffected by clouds and can penetrate through
various snow depths depending on the wavelength. Hence, microwave sensors
are potentially capable of determining the internal snowpack properties
such as snow depth and snow water equivalent {Hall et al., 1978; Rango,
et al., 1979). However, operational use of remotely-collected microwave
data for snowpack analysis is not imminent because of complexities
involved in the data analysis. Snowpack and soil properties are highly
variable and their effects on microwave emission are still being explored.
Nevertheless, much work is being done to develop passive microwave tech-
niques {Edgerton et al., 1973; Schmugge et al., 1974; Chang et al., 1976;
Kong et al., 1979; Chang and Shiue, 1980; Matzler et al., 1980 and Stiles
and Ulaby, 1980) for analysis of snowpack properties. In this paper, an
attempt to retrieve snow parameters by using Nimbus-7 SMMR data is
reported.
MICROWAVE EMISSION FROM SNOW
Snow particles act as scattering centers for microwave radiation. Com-
putational results indicate that scattering from individual snow particles
within a snowpack can be the dominant modification factor of upwelling
emission in the case of dry snow {Figure 1 ). This type of radiation
upwelling through snow is governed by Mie scattering theories for which a
good description can be found in Chang et al. {1976). Microwave radiation
emanating from snow originates from a depth of~ 10-100 times the wave-
length used {Chang et al., 1976). However, when the snowpack thickness is
less than the microwave penetration, the underlying surface will contribute
to the brightness temperature (Ts) {Chang and Gloersen, 1975).
Using the multifrequency analysis approach, one can make inferences
regarding not only the thickness of the snowpack, but the moisture condi-
tions and the condition of the underlying soil {wet versus dry). The
shorter wavelengths, such as 0.8 em, sense near-surface temperature
and emissivity, and surface roughness. At the intermediate wavelengths,
1.4 and 1.7 em, the radiation is less affected by the surface and more
information is obtained on the characteristics of the mid-pack. All of
the above generalizations apply to the snow conditions encountered by
the various satellite observations for different regions of the world.
The presence of liquid water content in the snowpack completely changes
the observed microwave signatures {Chang and Shiue, 1980; Matzler et al.,
1980; and Stiles and Ulaby, 1980). A few percent of liquid water in snow
will cause a sharp increase in the brightness temperature {Chang and
182
250 6.6 AND 250 10.7 GHz 6.6 GHz
18 GHz 10.7 GHz
21 GHz
-~ -w 200 200 18GHz a:
:l ~ w 21 GHz
Q..
~ w t-
t)) en 150 150 w ..... z 00 t-37 GHz w :::c:
(.!) R ==0.5 MM -a: m
R ==0.3 MM
100 100
0 100 0 100
SNOW WATER EQUIVALENT (CM)
Figure 1. Computed brightness temperature vs. snow water equivalent for SMMR frequencies.
Gloersen, 1975). This is because the emission of individual snow particles
are increased when liquid water coats the crystal.
The condition of the ground beneath the snow will determine the intensity
of the radiation incident from below. Dry or frozen ground has a high
emissivity (~ 0.90-0.95) whereas unfrozen wet ground has a much lower
emissivity (~ 0.7). Knowledge of the condition of the ground underlying
the snow is important for the interpretation of observed brightness temp-
eratures and can generally be determined from the longer wavelength
observations. However, due to the small penetration depth of microwave
radiation into wet soil, typically l/4 wavelength, a wavelength of the order
of 20 em will be required.
SNOW RETRIEVAL ALGORITHM AND PRELIMINARY RESULTS
SMMR is a five frequency, dual polarized microwave radiometer which measures
the upwelling microwave radiation at 6.6, 10.7, 18.0, 21.0, and 37.0 GHz
while scanning 25° to either side of the spacecraft (approximately 780 km
swath width) with a constant incidence angle of approximately 50° with
respect to the Earth's surface. The spatial resolution varies from 25 km
for the 37 GHz to 150 km for the 6.6 GHz. Detailed descriptions of this
instrument can be found in Gloersen and Barath (1977). It was launched on
October 24, 1978, into a sun synchronous polar orbit with local noon/mid-
night equator crossing.
Kunzi et al. (1982) reported an algorithm to retrieve snow-cover parameters
using Nimbus-7 SMMR data. The brightness temperature gradient of 37 GHz
and 18 GHz were used to discriminate snow parameters. Based on the SMMR
data for 1978-1979 winter season, encouraging results were derived. Chang
et al. (1982) reported the snow parameter retrieval results based on a
theoretically derived algorithm.
After experimenting with SMMR data over several large open areas, the
Canadian Height Plains, u.s. Great Plains, Alaska and central Russia, the
algorithm has been refined. Since the brightness temperature decreases
with depth due to snow scattering, 37 GHz wavelength data which is sensi-
tive to scattering are used. However, the variation of the 37 GHz
brightness temperature is also affected by the snowpack temperature. By
using 18 GHz channel data, which is us~ally less affected by scattering, in
the retrieval algorithm the temperature effect is normalized. Figure 2
shows the relationship between the snow depth and brightness temperature
difference of 37 GHz and 18 GHz. Due to the inhomogeniety within the large
SMMR footprint (25 km x 25 km), derived snow depth less than 2.5 em was
assigned as no snow. The equation used to derive snow depth information is
SO= 1.59 * (Tl8H-T37H) em ( 1 )
The snow covered area is defined when retrieval snow depth is larger than
2.5 em. By using this algorithm, a five year data set derived for Nimbus-7
184
SNOW DEPTH= 1 .59 x (18H-37 H) em
-100 ~
w u z w a: w u. u.
0
w a:
::>
~ a: 50
w a...
~ w
I-
(/)
(/) w z
I-
J:
(.!)
a:
CD
0
SNOW DEPTH (em)
Figure 2. Brightness differences (Tl8H -T37H) vs. snow depth.
Figure 3. Nimbus-7 SMMR derived northern hemisphere snow depth map
February, 1982.
185
SMMR brightness has been produced. It covered the period of October, 1978
to October, 1983. Figure 3 shows a sample snow map of northern hemisphere
for February,·l982. In this map, the deep snow cover areas were shown with
brighter tone while shallow snow were shown with darker tone.
DISCUSSION
5 year SMMR derived northern hemisphere snow parameter maps have been
produced using one simple algorithm. In order to evaluate the quality of
this product, several steps were taken. First, the snow boundary lines of
SMMR products and NOAA products were compared. Normally, the SMMR derived
snow boundary line fell behind the NOAA snow line. This is because
microwave can penetrate through a shallow snowpack without registering
snow effect. However, because of this characteristic, when the microwave
signature indicates snow, the entire field of view is probably snow covered.
Over the mountainous area, the slope tends to modify the microwave signatures.
Although the sensitivity of microwave radiation with respect to snow depth
has changed, the snow boundary can still be delineated. For 1979 to 1983,
the boundary lines in the Great Plains and Rockies of u.s. compared favor-
ably (within 100 km) between SMMR and NOAA products for the months of
January to March. For the same time period, for eastern, mid-west and
western regions of the u.s., the differences were about 200 km. In the
western China and central Asia area, the comparisons gave large differences
in the result. Due to limited "ground truth" data, the relative accuracies
are still being assessed.
To evaluate the quality of snow water equivalent, several areas were chosen
for comparison. The results from the Canadian Height Plains and the Russia
steppe area, which are uniform and homogeneous, are usually good with
correlation coefficients R = 0.85. The results from forested area and
mountainous area are different. Since the surface features masked out the
microwave snow signatures, a different algorithm will be required to retrieve
the snow parameters. An ongoing project which studies the Colorado River
basin area will suggest what algorithm is required to retrieve useful
information.
References
Chang, A.T.C.; Shiue, J.C. (1980) A comparative study of microwave radi-
ometer observations over snowfields with radiative transfer model
calculations. Remote Sensing of Environment, v.lo, p.215-229.
Chang, A.T.C.; Foster, J.L.; Hall, O.K.; Rango, A; Hartline, B.K. (1982)
Snow water equivalent determination by microwave radiometry. Cold
Regions Science and Technology, v.5, p.259-267.
186
Chang, T.C.; Gloersen, P. (1975) Microwave emission from dry and wet snow.
(In: 0 erational A lications of Satellite Snowcover Observations,
NASA SP-391, Washington, D.C., p.399-407.
Chang, T.C.; Gloersen, P.; Schmugge, T.; Wilheit, T.T.; Zwally, H.J. (1976)
Microwave emission from snow and glacier ice. Journal Glaciology,
v.l6{74), p.23-39.
Edgerton, A.T.; Ruskey, F.; Williams, D.; Stogryn, A.; Poe, G.; Meeks, D.;
Russell, 0. (1973) Microwave emission characteristics of natural
materials and the environment. Final Technical Report 9016R-8,
Microwave Systems, Aerojet-General Corporation, Azusa, California.
Gloersen, P; Barath, F.T. (1977) A Scanning Multichannel Microwave Radi-
ometer for Nimbus-G and Seasat-A. IEEE Journal of Oceanic Engineering,
v.2, p.l72-178.
Hall, O.K.; Chang, A.; Foster, J.L.; Rango, A.; Schmugge, T. (1978) Passive
microwave studies of snowpack properties. Proceedings of the 46th
Annual Western Snow Conference, Otter Rock, OR, p.33-39.
Kong, J.A.; Shin, R.; Shiue, J.C.; Tsang, L. (1979) Theory and experiment
for passive microwave remote sensing of snowpacks. Journal of
Geophysical Research, v.84, p.5669-5673.
Kunzi, K.F.; Patil, S.; Rott, H. (1982) Snow-cover parameters retrieved
from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR)
Data. IEEE Transactions of Geoscience and Remote Sensing, v.20,
p.452-467.
Matzler, C.; Schanda, E; Hofer, R.; Good, W. (1980) Microwave signatures
of the natural snow cover at Weissfluhjoch. NASA CP-2153, p.203-223,
available from NTIS, Springfield, Virginia.
Rango, A.; Chang, A.T.C.; Foster, J.L. (1979) The utilization of space-
borne microwave radiometers for monitoring snowpack properties.
Nordic Hydrology, v.lO, p.25-40.
Schmugge, T.; Wilheit, T.T.; Gloersen, P.; Meier, M.F.; Frank, D.;
Dirmhirn, I. (1974) Microwave signatures of snow and fresh water
ice. (In: Advanced Concepts and Techniques in the Study of Snow
and Ice Resources, National Academy of Sciences, Washington, D.C.,
p.551-562.
Stiles, W.H.; Ulaby, F.T. (1980) Microwave remote sensing of snowpacks.
NASA Contractor Report No. 3263, 404 p., available from NTIS,
Springfield, Virginia.
187
Kukla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) SNOW WATCH '85.
Proceedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College Park, MD. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice), Glaciological Data, Report GD-18, p.189-192.
Snow Cover Monitoring Using Microwave Radiometry
Norman C. Grody
National Environmental Satellite, Data, and Information Service
National Oceanic and Atmospheric Administration
Washington, D.C., U.S.A.
Abstract
MJltispectral satellite observations of the earth's surface ani abmsphere
have providEd infol'lllation on geophysical paranetem Wdch are important in
meteorology, b,ydrology, agriculture, ani oceanograpb,y. '!he primary advantage
of passive microwave neasurem:mts over toose ·:In tle visible ani infrarEd is
tleir ability to probe through clouds, with rain being tle mjor ~ce of
atterwation, alloo~ for all-watler observations. A technique is presentEd to
identify snow cover ani discriminate ~ a rumber of otler surface ani
at:mspleric paranetem basEd on neasurenents by tle N:f.mhls-7 Scanning
Mlltichamel Micromve Radianeter.
Snowmelt is the major component of the total annual water supply in large
parts of the world. Also, the amount of water stored as snow is important in
forecasting its effect on the water supply, flooding, irrigation and manage-
ment of agricultural commodities. As such, the major parameters required are
areal extent, water equivalent or depth, and liquid water content. Repetitive
coverage of the observations is required every 3-5 days with an all weather
measurement capability.
Visible and infrared satellite data have recently come into operational
use for snow area determinations. However, the data acquisition is hampered
by cloud cover, sometimes at critical times when a snowpack is ripe. Further-
more, information on water equivalent, free water content, and other prop-
erties pertinent to accurate runoff predictions are not currently available
using visible and infrared techniques. More complete information on snow
properties is available from microwave radiometers whose measurements are
nearly independent of clouds. The microwave emission from within a snow layer
can penetrate through various depths depending on the frequency and changes
dramatically between dry, refrozen snow, and melting snow (Hoffer and Matzler,
1980). The use of microwave data can therefore lead to an analysis of the
internal snowpack properties with an all weather capability.
189
Passive microwave imagery from space has been available since December
1972, when Nimbus-5 was launched carrying a 19.35 GHz microwave radiometer.
Additional data became available in June 1975 with the launch of Nimbus-6 with
a 37 GHz duai polarization radiometer. This instrument was succeeded by a
five-frequency, dual polarization Scanning Multichannel Microwave Radiometer
(SMMR) flown on Nimbus-7 in November 1978. The SMMR frequencies are at 6.6,
10.7, 18.0, 21.0 and 37.0 GHz. Studies have been and are currently being con-
ducted to utilize microwave radiometers for the determination of snow prop-
erties. The major drawback of these satellite sensors for snow hydrology is
the relatively poor spatial resoulution of about 25 km. However, results from
various studies (e.g., Kunzi et al., 1984) indicate that qualitative monitor-
ing of snowpack buildup and disappearence during the winter appears feasible.
Studies have also shown the use of microwave data for deriving information on
snow water equivalent for certain areas (Foster et al., 1984).
Snow produces a somewhat unusual microwave emissivity characteristic.
This is illustrated in Figure 1 which summarizes the frequency dependence of
emissivity for snow as well as other surfaces. A snowpack contains a collec-
tion of ice crystals or melting ice particles depending on the snow tempera-
ture. The emissivity of snow depends on the absorption and scattering due to
the particles. For dry snow the ice crystals scatter some of the upwelling
radiation out of the sensor's field of view. The scattering effect increases
with frequency so that the emissivity decreases as the frequency increases.
As shown in Figure 1, the emissivity characteristic of dry snow is different
from most other surfaces. The opposite slope of emissivity with frequency can
be used in distinguishing dry snow from other surfaces. A more general clas-
sification technique has been developed which utilizes both the magnitude as
well as the variation of emissivity with frequency to separate the different
surface types. These two quantities, the emissivity and its slope with fre-
quency, can be inferred using dual frequency microwave measurements. The
brightness temperature "difference" is proportional to the slope while the
"average" of the two measurements is related to the emissivity itself·
To illustrate the classification method, Figure 2 shows a scatter plot of
the average brightness temperature versus the brightness temperature differ-
ence using the 18 and 37 GHz (vertical polarization) SMMR channels. These
data were obtained from a January 1979 SMMR pass over the central u.s. and
contains areas of open water, wet and dry land, lake ice, rainfall and snow
cover. For details on the data set see the papers by Grody (1984) and Ferraro
et al., (1985). The diagram shows six "clusters" corresponding to the various
surface and atmospheric parameters. Note that both the average and difference
measurements are needed to separate the different geophysical paramete~s. Dry
snow is readily distinguished from all other features where the scatter within
the cluster is associated with variations in snow depth as well as other fac-
tors. Those observations which do not fall into a specific cluster are "mixed
pixels" and correspond to more than one geophysical parameter in the field of
view. An example of a mixed pixel is a SMMR observation which occurs along a
coastline. In this case, part of the field of view contains land, part con-
tains open water. Thus, the brightness tempeatures do not correspond to a
unique surface or atmospheric parameter.
190
)I
toe
Jol
)
Jol ., .,
Jol
I M
Fig. 1.
270 -~ .....
' 260 ....--.... -.....
(") ..... 1250
.,._>
+ C\1 -co '240 -' .....
~
........__... .230
E
:1 en :220
CD ...
:1 ..
al 210 ...
CD
Q.
E
{! 1200
(I)
(I)
CD c .. 1'0
~
~ ... m 180
-50
1 DRY SOIL, VEGETATIOH, MOIST SHOW
·' ttDI SEA ICE
.8 DRY SHOW a OLD SEA ICE
.7
NET SOIL a NET SHOW
.6
.5 -
-CA·UI WATER
1 2 3 4
J'RftUD«:Y <GHz>
Emissivity as a function of frequency for different surface types.
LEGEND: A 1 OBS, B 2 OBS, ETC.
Wet
B
A
A
A A
A
A A AAAAA A
A AAA
A B A
Open Water
-40 -30 -20 -10
I
r
I
I
I
I
10
Brightness Temperature Difference ( Tv (18) -Tv (37~ ) ( K )
Dry Snow
20
Fig. 2. Scatter plo~ of the average brightness temperature versus the
brightness temperaf_ure difference using the 18 and 37 GHz vertically polarized
SMMR channels for latitudes between 25° and 60°N on January 20, 1979.
191
The algorithm shown in Figure 2 has been applied to SMMR data to form a
classification image (Ferraro et al., 1985). Each geophysical paramet~r is
classified ~ requiring the brightness temperature measurements to lie within
one of the six clusters. To simplify the designation, the clusters are repre-
sented as four point polygons, each having a specific color assigned to the
pixels. Undefined pixels are indicated as white pixels. For a given color
the intensity is defined by the magnitude of the brightness temperature "dif-
ference", i.e., dark intensities represent small.differences, bright intensi-
ties large differences. These intensity variations yield insight to various
features, such as snow depth, soil moisture content or rain intensity.
This paper presents a method using the 18 and 37 GHz vertically polarized
brightness temperatures from the Nimbus-7 SMMR to classify six different geo-
physical parameters (dry snow, sea ice, dry land, flooded land, precipitation,
and open water). The use of multichannel techniques is necessary because
several surface and atmospheric features cannot be sep~rated by single channel
measurements. Finally, a technique of this type is quite attractive for pro-
ducing maps, of geophysical parameters on an operational basis, which can be
used as a screening tool to produce accurate quantitative algorithms.
References
Ferraro, R.R.; Grody, N.C.; Kogut, J.A. (1985) Classification of geophysical
parameters using passive microwave satellite measurements. (In: Inter-
national Geoscience and Remote Sensing Symposium (IGARSS' 85):-Amherst
Massachusetts, 7-9 October, Proceedings, p.919-924.)
Foster, J.L.; Hall, D.K.; Chang, A.T.c. (1984) An overview of passive micro-
wave snow research and results. Reviews of Geophysics and Space Physics,
22, p.195-207.
Grody, N.c •• (1984) Precipitation monitoring over land from satellites using
microwave radiometry. (In: International Geoscience and Remote Sensing
Symposium (IGARSS' 84) Strasbourg, France, 27-30 August, Proceedings,
p.417-423.)
Hoffer, R.; Matzler, C. (1980) Investigations of snow parameters by radi-
ometry in the 3 to 60 mm wavelength region. Journal of Geophysical !!=
search, 85, p.453-460.
Kunzi, K.F.; Patil, s.; Rott, H. (1984) Snow-cover parameters retrieved from
Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) data. Reviews
of Geophysics and Space Physics, 22, p.452-467.
192
Kukla, G; Barry, R.G.; Hecht, A.;· Wiesnet, D. eds. (1986) SNOW WATCH '85.
Proceedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College Park, MD. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice),_Glaciological Data, Report GD-18, p.193-203.
Remote Sensing of Snow Properties in Mountainous Terrain
Abstract
Jeff Dozier
University of California
Santa Barbara, California, U.S.A.
Spectral albedo measurements from satellite (e.g. Landsat Thematic Mapper) require that
spacecraft upwelling radiances be corrected for atmospheric absorption and scattering and
for local surface illumination. The lower boundary condition of the atmospheric radiative
transfer model varies with incidence angle, and the satellite data must be co-registered to
digital elevation data. Results from extensive radiative transfer calculations fortunately fall
into some simple statistical relationships, whereby the optical grain size of the snow and the
degradation of albedo resulting from contamination are estimated. One remaining problem
is that inaccuracies in the elevation data make precise registration with satellite data hard
to achieve.
Introduction
This paper addresses climate modeling on a smaller scale than that considered by most of the
other papers in this volume. Over a watershed we often want to estimate the energy transferred into
the snow pack, in order to compute water balances and estimate snow melt runoff. ·
Solution of the snow surface energy balance components at a well instrumented, possibly remote
micrometeorological site is feasible (Figure 1). With data on incoming and outgoing radiation in solar
and thermal wavelengths, low-level atmospheric profiles of air temperature, wind speed, and water
vapor density, and temperature profiles in the top layers of the snow pack, it is possible to compute the
amount of energy expended in melting snow, and thereby estimate the amount of water produced
(Anderson, 1976).
AQ = R+H+L~E+ G (1)
Here R is the net radiation at the snow surface, H and L 11 E are sensible and latent heat fluxes, and G
is the soil heat flux. IT AQ is positive, either the temperature of the snow pack increases, or, if it is
already at 0°0, a mass of snow M =A Q / L1 is melted. L 11 and L1 are the latent heats of vaporization
and fusion. Of the components in equation (1), the net radiation term R usually dominates, but the
issue is complicated by spectral and angular variation in the snow albedo p.
193
Figure 1. Measurement of snow pack energy balance parameters at a remote site, by
transmitting instrument output data to satellite. Station is located at an elevation of
3200m in Emerald Lake Basin, Sequoia National Park, California.
At a particular time the net radiation is sum of the solar and thermal components integrated over
wavelength >... If we simplify and assume the diffuse irradiance field is isotropic, the net radiation is
'.'' R = I [ JLo [1-p,(>..,JLo)]E,(>..) + [1-Pc~(>..)]Ec~(>..)] d>.. + I t:d(>..) [ Ec~(>..)-6 h;;~: ] d>.. (2 )
•olar thermal >.. ( e -1)
The first term represents the absorbed solal\irradiance, the second accounts for the longwave radiation
balance . p, and Pd are the direct and diffuse spectral albedoes. E, and Ed are the direct and diffuse
spectral irradiances; Ed exists both at solar and thermal wavelengths. f.c~ is the spectral hemispherical
emissivity, and the last expression in the second term accounts for the Planck emission by the surface at
temperature T ; h is the Planck c·onstant, k is the Boltzmann constant, and c is the speed of light . .
Our problem in analysis of surface climate hydrologic purposes is to evaluate equation (2) over a
melt season and over a drainage ~!}sin. In this paper we concentrate on the estimation of spectral
albedo p from measurements of satellite radiance above the atmosphere. Spectral albedo measurements
from satellite (e.g. Landsat Thematic Mapper) require that spacecraft upwelling radiances be corrected
.......
194
for atmospheric absorption and scattering and for local surface illumination. The lower boundary con-
dition of the atmospheric radiative transfer model used must vary with incidence angle, and the satellite
data must be registered to digital elevation data. The results from extensive radiative transfer calcula-
tions fortunately fall into some simple statistical relationships, whereby the optical grain size of the
snow and the degradation of albedo res\llting fr0m contamination can be estimated. One difficult
remaining problem is that precise registration of satellite and' elevation data is hard to achieve, mainly
because of inaccuracies in the elevation data. .,...
Figure 2. Snow reflectance in the Landsat Thematic Mapper bands, calculated for an illumi-
nation angle of 66° from a twostream approximation . Grain radii range from 50 p.m, very
fine new snow, to 2000p.m, coarse old melting or refrozen snow.
Landsat Thematic Mapper
The Thematic Mapper was first launched on July 16, 1982 aboard Landsat-4 . The second instru-
ment was launched on March 1 , 1984 aboard Landsat-5. The satellite is at a nominal altitude of 705 km
in a sun-synchronous orbit. Equator crossing time is between 09:30 at 10:00 local solar time, and the
orbit period is 100 minutes. The repeat period is 16 days.
Table 1 specifies the wavelength bands of the TM. For the solar portion of the electromagnetic
spectrum, the values of the "solar constant ," exoterrestrial solar radiation at the mean earth-sun dis-
tance integrated over the wavelength bands, are also given. In the last column of the table the sensor
saturation radiance is expressed as a percentage of the solar constant. If the product of the planetary
reflectanc e and the cosine of the solar zenith angle exceeds this value, the sensor will saturate in this
band.
195
T bl 1 L d t 5Th a e an sa-t' M ema 1c apper R d' t. Ch a wme nc a rae t . t' ens 1cs
wavelength radiances (W m-2 p,m-1 sr-1)
band range (i!_m) NED.L £min Lmu L.olo.T pet
TM1 0.45 0.52 0.63 -1.5 152.1 621 24
TM2 0.53 0.61 1.17 -2.8 296.8 540 55
TM3 0.62 0.69 0.80 -1.2 204.3 468 44
TM4 0.78 0.90 0.81 -1.5 206.2 320 64
TM5 1.57 1.78 0.108 -0.37 27.19 66.9 41
TM6 10.4 12.5 0.057 1.25 15.75 (thermal)
TM7 2.10 2.35 0.057 -0.15 14.38 23.9 60
Snow Reflectance in TM Bands
In the visible wavelengths (TM bands 1 and 2 especially) snow reflectance is insensitive to grain
size, but sensitive to modest amounts of absorbing impurities (Warren and Wiscombe, 1980). In the
near-infrared (TM band 4) snow reflectance is insensitive to absorbing imputities but sensitive to grain
size (Wiscombe and Warren, 1980). Figure 2 shows the spectral variation in reflectance of pure snow of
various grain radii over the TM bands. We note that in TM4 the relectance is sensitive enough to grain
radius to allow estimate of the "optical grain size" from reflectance measurements. For albedo esti-
mates from remote sensing, our aim is to estimate the important physical parameters, absorbing impuri-
ties and grain size, from spectral measurements in suitable wavelength bands. The spectral albedo or
band-integrated albedo can be calculated. The scheme should fit the parameterization under develop-
ment by Marshall and Warren {this volume).
In TM5 reflectance is sensitive to grain size only for very small radii. For most snow reflectance in
this band will be near zero. Both ice and water clouds, however, will be appreciably brighter than snow,
allowing for snow /cloud discrimination {Figure 3). Thermal data (TM6, Figure 4) cannot be used for
snow /cloud discrimination, because in mountainous areas the overlying clouds may be colder or warmer
than the snow.
Some details still need to be solved. In the radiative transfer approach (e.g. Wiscombe and War-
ren, 1980; Choudhury and Chang, 1981) snow reflectance is modeled as an ensemble of spheres. In real-
ity snow grains may not be spherical {Figure 5). While it is possible to calculate the radius or distribu-
tion of radii of equivalent spheres, by choosing sizes that cause the radiative transfer calculations to fit
the measurements, the question of how these equivalent sphere radii compare to the physical sizes and
shapes of the grains is still unanswered. We are currently trying to resolve this issue, by measuring the
spectral bidirectional reflectance-distribution function (BRDF) of snow at wavelengths from 0.3-3.0 p,m.
At the same time we are using the plane-section method {Perla, 1982; Perla and Dozier, 1984) to meas-
ure such grain properties as specific volume, specific surface area, mean intersection length, etc. Li's
{1982) model can be used to compute the radiative transfer solution to the BRDF for a specified distri-
bution of spherical radii.
Planetary Reflectance (Above the Atmosphere)
The satellite measures upwelling radiance Ll above the earth's atmosphere. We define "planetary
reflectance" Pp as
{3)
1rS0 is the exoterrestrial solar irradiance (W m-2 p,m-1), incident at angle cos-1p,0 • A simplified "two-
stream" atmospheric model {Meador and Weaver, 1980) is defined by the following pair of differential
196
Figure 3. Snow /cloud discrimination with TM band 5. The area is the southern Sierra
Nevada, with the Kern (south-flowing) and Kings · (west-flowing) River basins shown. The
clouds along the east side of the Sierra Nevada and over the White Mountains (northwest
corner) are clearly visible. Date is December 10, 1982.
equations:
dLt Lt £! -s -r/JJo --;;:; = ''11 -'12 -w o"f3e
dL! Lt £! -s -r/JJo --;;:; = '12 -'It + w o'14 e
(4)
(5)
The atmospheric layer is considered homogeneous if the "{-values are independent of optical depth
r. The choice of the "{-values is determined by the particular approximation to the scattering phase
function and the radiation intensity distribution. One constraint is that "{3 + "{4 = 1, because of energy
conservation. Meador and Weaver (1980) give expressions for 7 different twostream approximations.
For a single-layer atmosphere, the solution to equations ( 4) and (5) depends on the boundary con-
ditions. The usual top boundary condition in atmospheric radiative transfer problems is that there is no
diffuse irradiance at the top, i.e. £! = 0. At the bottom, however, the situation is not so simple. We
must allow for rugged terrain, such that the local illumination angle cos-1J.L, on a slope S is not the
same as cos-1J.L 0 . Moreover, part of the incident diffuse irradiance comes from reflection from adjacent
terrain. For some points, for example bottoms of valleys, the contribution from this source may be
large, much larger than the diffuse irradiance on an unobstructed horizontal surface.
197
Figure 4 . Snow /cloud discrimination is not feasible with TM band 6, the thermal band, b e-
cause the snow is not necessarily colder than the clouds . This is the same area shown in Fig-
ure 3. The coldest areas are the brightest white. Warmest areas are dark.
We define two "view factors" to account for the effect of adjacent terrain. Vd, the view factor for
diffuse irradiance , is the portion of the overlying hemisphere that is obscured by terrain, weighted for
the cosine of the angle of any radiation reflected or emitted from adjacent slopes toward the slope
whose radiation balance we want to calculate. Thus for an unobstructed horizontal point, Vd = 0, and
Vd < 1 always. In typical mountainous areas in the Sierra Nevada , we have found that the largest
values of Vd may exceed 0.4. It is computed from digital elevation data as follows. For each point in
the elevation grid we calculate the angle to the horizon for some set of azimuths (typically 8-16) that
cover the full circle of directions. For each azimuth ¢J , H; is the horizon angle, measured from horizon-
t a~. , (Some authors measure this angle from zenith . Beware!) For the point in question, S is the slope
and E is the exposure . We assume for generality that the local configuration can be approximated as a
bowl surrounding the point , with sides uniformly sloping to the horizon in each direction.
' Vd = f[cosS sinH;+sinS cosHq,cos(¢J:_E)]2 d ¢J (6)
271"
The view factor V, for dire~t irradiance is similar to Vd , except that the integrand for each direc-
tion ¢J must be weighted by the cosine of the illu~ination angle on the slope to the horizon, i .e. by
[Jt 0 cosHq,-siniJ0 sinHq,cos(¢J 0-¢J)], where this term is set to zero if it is negative . IJ 0 =cos-1Jto is the solar
zenith angle, and ¢J 0 is the solar azim.Y..th.
The optical depth of the atmosphere is r0 . The lower boundary condition for a surface with direct
albedo p,(Jt,) and diffuse albedo Pd is the sum of several terms:
'. '-....,
198
Figure 5 . Plane sections of snow. In the left section, from snow kept at -5°C for 6 months,
the grains are relatively spherical , but in the right section, from old kinetic growth metamor-
phism followed by formation of a sun crust, the grains are mostly concave. Grid spacing on
edges is 1 mm.
reflected direct irradiance
reflected diffuse irradiance
direct irradiance on adjacent
slopes that is reflected toward
point
diffuse irradiance on adjacent
slopes that is reflected toward
point
The sum of the right hand terms above must be multiplied by cosS to find the mean upwelling radiance
projected on a horizontal plane . Therefore the lower boundary condition is , after algebraic rearrange-
ment:
(7)
199
where
11, = P d P, (JLo) V, + P, (JL,) JL,
'1a = Pa [l-(1-pd)Va]
Simplified bottom boundary conditions can be implemented by modifying some of the above ter1111.
If the surface is Lambertian, set p, = p4 = p; then 17, = p(p V, + JL,). If the surface is fiat, set JL, =l'o and
cosS=l. If the surface is unobstructed by surrounding terrain, set V,= V4 =0; then TJ,=p,(JL,)I', and
17d =Pa·
Figure 6 . Digital elevation image of the 7 .5X7 .5 min Mt . Tom quadrangle , in the southern
Sierra Nevada .
Snow 'Properties from Planetary Reflectance
Planetary reflectance can be measured from satellite . Can we therefore infer snow properties from
satellite data? In the previous section, we described how planetary reflectance is a function of six vari-
' ables:
slope s (known)
solar zenith cosine JLo (known)
local illumination cosine JL, (known)
atmospheric optical depth To (unknown)
surface direct reflectance p, (unknown)
surfac~ diffuse reflectance Pa (unknown)
200
The values of p, and Pa are connected, in that each can be inferred from the optical grain radius
and amount of absorbing impurities in the snow. Similarly, the optical depths in various wavelengths
are functions of the water vapor and aerosol contents of the atmosphere. Unfortunately for a given
pixel ("picture element") the local values of S and therefore Jl., are not known. The spatial frequency
at which elevation varies is low; therefore errors in digital elevation data or misregistration of the eleva-
tion grid to the satellite data do not cause severe errors if we want to know the elevation of a given
pixel. Values of slope and exposure, however , vary at much higher spatial frequencies . Therefore errors
in digital elevation data or misregistration of the elevation grid to.,..the satellite data effectively elim-
inate the possibility of using information about local slope and local illumination angle for a given pixel.
Figure 6 shows a digital elevation grid for the Mt . To·m 7 .5X7 .5 min quadrangle. Figure 7 shows a calu-
lation of local values of Jl., computed from the data in Figure 6 and registered to a Thematic Mapper
image . Division of the satellite radiance data by local values of Jl., leads to obvious misregistration
effects, represented by the bright patches in Figure 7.
Figure 7. TM band 4 image divided by cosine of local illumination angle . The bright areas
are due to misregistration with the elevation grid.
An alternative approach is statistical. We can use combinations of satellite spectral bands to esti-
mate local values of reflectance, or of physical parameters that determine reflectance, without knowing
local values for the illumination angle. This approach is similar to that used to determine sea surface
temperatures from multispectral infrared measurements from the NOAA A VHRR (Deschamps and Phul-
pin, 1980; Bernstein, 1982; Strong and McClain, 1984).
The scheme then is to simulate planetary reflectance for a variety of atmospheric profiles, local
illumination angles, local slopes, snow grain sizes, and amounts of absorbing impurities. Some radiances
in the TMl data are too low to be snow , even in the shadow . even in the shadows. Similiarly, some of
201
the TM5 values are so high that the surface cannot be snow, even in full illumination . Examining the
results further, we find that a linear combination of bands can be used to statistically fit the radiances
to optical grain sizes (Figure 8).
. d" I [ TM3-TM4 TM2-TM4] gram ra ms = TM2 , TMS (Oq}
While the R 2 value of 0 .75 is not high, the results are encouraging in that at least rough estimates of
snow albedo are available from satellite, without requiring that the satellite images be registered to
high-resolution digital elevation data .
Figure 8. Linear combination of bands to estimate optical grain size of snow. Grain sizes in
this image are clustered around 100 p.m .
Conclusion
The Landsat Thematic Mapper has useful spectral bands for estimation of snow reflectance at fine
spatial resolution over mountainous terrain .'.Even though the quality of the satellite data exceeds that
of most available digital elevation data, the capabilities of the sensor can be used without requiring pre-
cise registration to the elevation grid.
References
Anderson, E. A., (1976) A point energy and mass balance model of a snow cover. NOAA Technical
Report NWS 19, p. 1-150, Washington, DC.
?Q2
Bernstein, R. L., (1982) Sea surface temperature estimation using the NOAA 6 satellite Advanced Very
High Resolution Radiometer. Journal of Geophysical Research, v. 87, p. 9455-9465.
Choudhury, B. and Chang, A. T. C., (1981) On the angular variation of solar reflectance of snow. Jour-
nal of Geophysical Research, v. 86, p. 465-472.
Deschamps, P. Y. and Phulpin, T., (1980) Atmospheric correction of infrared measurements of sea sur-
face temperature using channels at 3.7, 11, and 12Jtm. Boundary-Layer Meteorology, v. 18, p. 131-
143.
Li, Shusun, (1982) A model for the anisotropic reflectance of pure snow. M.A. Thesis, Discussion Paper
4, 61 p., Department of Geography, University of California, Santa Barbara, CA.
Meador, W. E. and Weaver, W. R., (1980) Two-stream approximations to radiative transfer in planetary
atmospheres: a unified description of existing methods and a new improvement. Journal of the
Atmospheric Sc£ences, v. 37, p. 630-643.
Perla, R., (1982) Preparation of section planes in snow specimens. Journal of Glaciology, v. 28, p. 199-
204.
Perla, R. and Dozier, J., (1984) Observations on snow structure. in Proceedings, Sixth International
Snow Science Workshop, v. 28, p. 182-187, Mountain-Rescue, Aspen, CO.
Strong, A. E. and McClain, E. P., (1984) Improved ocean surface temperatures from space -comparis-
ons with drifting buoys. Bulletin of the American Meteorological Society, v. 65, p. 139-142.
Thekaekara, M.P., (1970) The solar constant and the solar spectrum measured from a research aircraft.
NASA TR-R-351, p. 139-142, NASA Goddard Space Flight Center, Greenbelt, MD.
Warren, S. G. and Wiscombe, W. J., (1980) A model for the spectral albedo of snow, II, Snow containing
atmospheric aerosols. Journal of the Atmospheric Sciences, v. 37, p. 2734-2745.
Wiscombe, W. J. and Warren, S. G., (1980) A model for the spectral albedo of snow, I, Pure snow. Jour-
nal of the Atmospheric Sciences, v. 37, p. 2712-2733.
203
Kukla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) SNOW WATCH '85.
Proceedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College Park, MD. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice), Glaciological Data, Report GD-18, p.205-206.
Abstract
Remote Sensing of Snow Cover over the
Carpathian Watersheds
Horia Grumazescu
Laboratory of Remote Sensing
Institute of Meteorology and Hydrology
Bucharest, Romania
In 1980 the Institute of Meteorology and Hydrology started
the surveillance and assessment of snow cover in the Carpathian
basins using remote sensing techniques. The objective was. to
determine the seasonal water storage by obtaining timely input
for ~nowmelt-runoff models.
The project activity was preceded by experimental research
which was aimed at developing working procedures for the
processing and interpretation of remote sensing data needed for
hydrological assessment and forecasting. The development of the
operational working techniques took into account the following:
a) Complex morphometric, physiographic and hydrometeorologic
conditions of the Carpathian watershed on a relatively small
scale. The hydrologic regime is characterized by spring
floods linked to snowmelt. This situation requires the use
of large scale imagery which is provided by high resolution
recorders such as airborne photogrametric cameras and
multispectral sensors. Repetitive measurements such as taken
by MSS hand held recorders were also needed.
b) Photographic products and magnetic tapes in the visible and
thermal infrared bands are available from meteorological
geostationary and polar-orbiting satellites and are received
by a read-out station at the Institute. Data from the
Landsat satellites in visible and near infrared bands and
large scale black and white aerial photos are also
205
available. The heterogeneous character of the remotely
sensed ~ata as well as the large volume of information to be
processed and interpreted in a relatively short time (as in
the case of the operational charting of several Carpathian
watersheds with relatively short snow melt periods) called
for the use of efficient procedures for data processing and
analysis with high speed and accuracy.
Two systems for digital processing of the remotely sensed
data and for automated mapping of the hydrological targets have
been developed and are currently in use. One is used for
processing photographic satellite products and aerial recordings
(SADFAS) and another for reprocessing the analog and digital data
stored on magnetic tapes (SPID).
Emphasis has been given to the interactive procedures
utilizing ground truth in hydrologic calibration of the
reflectance levels recorded by the remote sensors.
At present, the operational snow cover assessment and
surveillance is carried out on a routine basis and the resulting
hydrologic information is transmitted to forecasting centers and
to electric power stations in real time.
206
Kukla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) SNOW WATCH '85.
Proceedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College Park, MD. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice), Glaciological Data, Report GD-18, p.207-214.
Effects of Snow Cover and Tropical Forcing
on Mid-Latitude Monthly Mean Circulation
Alan Robock
James W. Tauss
Cooperative Institute for Climate Studies
Department of Meteorology
University of Maryland
College Park, Maryland, U.S.A.
ABSTRACT
The effect of anomalous snow cover on the monthly mean
atmospheric circulation is studied, by incorporating such forcing
into the simple, linear, steady-state climate model of Opsteegh
and Mureau. Anomalous forcing fields of snow cover are created
for three months {October, January and April) in the snow season
of the winters of 1976 through 1982. Anomalous heating fields
are also imposed based on observed tropical anomalies of outgoing
longwave radiation. The monthly mean anomalous circulation
patterns are calculated for each forcing separately and for the
combined forcings. The outgoing longwave radiation has
previously been shown to produce circulation that has small but
positive correlations with the observed atmospheric circulation.
In this study, correlation coefficients calculated for various
regions in the Northern Hemisphere show that the addition of snow
cover as a forcing mechanism does not produce better simulations
of the monthly mean flow.
1. INTRODUCTION
The effect of anomalous snow cover has long been studied as
having a pronounced influence on the temperature of the overlying
atmosphere {Landsberg, et al., 1941; Namias, 1962; Wagner, 1973;
Dewey, 1977; Yeh, et al-:-; 1983; Foster, et.al., 1983) •. However,
little if any literature has described the incorporation of such
a forcing into a climate model to investigate the anomalous
atmospheric response. It is the purpose of this paper to study
the effects of such anomalous forcings when imposed on a monthly
mean basic state using a simple, linear, steady-state climate
model.
The model is described by Opsteegh and Mureau {1983). The
207
equations are linearized with respect to a fixed, zonally
averaged basic state. The equations for the time-mean anomalous
response to a time-mean forcing are then calculated. There are 15
levels in th~vertical and the model equations are formulated in
sigma coordinates. It is semi-spectral with gridpoints in the
meridional direction and wave components (waves 1-6 in these
experiments) in the zonal direction.
Since it is known that anomalous snow cover is not the only
cause of monthly mean circulation anomalies and it has been
suggested that anomalies of tropical atmospheric heating may
produce anomalous mid-latitude circulation (e.g. Horel and
Wallace, 1981), the model is forced with tropical heating alone,
snow cover alone and tropical heating and snow cover together. If
the results are good for snow cover alone, or if the tropical
results are improved by including snow cover, then it can be
concluded that this experiment suggests that snow cover is
important. Preliminary results were presented by Robock and
Tauss (1985). Essentially the same work is described by Robock
and Tauss (1986).
2. RESULTS
Digitized NOAA satellite-derived snow cover data (Dewey and
Heim, 1981) for 1967-1982 are used to create January, April and
October snow climatology maps for the Northern Hemisphere.
Because of some early data discrepancies over the Himalayas, the
snow climatology for that region only covers the years 1976-1982.
Subsequently, anomalous forcing fields are created for the period
1976-1982. To create 3-dimensional forcing fields, vertical
profiles of diabatic heating are parameterized in terms of the
anomalous heating.
Monthly anomalous Outgoing Longwave Radiation (OLR) fields
in the tropics are created for the years 1976-1982 inclusive and
used as a proxy for the anomalous monthly mean diabatic heating
(Arkin, 1983). (No OLR data were available for 1978.) It is
assumed that the profile of heating in the vertical has a maximum
at 400 mb and gradually decreases toward the surface and the
tropopause.
The first model runs contain "idealized" snow anomalies to
examine anomalous response of the atmosphere. 11 Idealized 11
anomalies are created by removing climatologically present snow
cover where it was found at least 50% to 100% of the time during
the month (negative snow anomaly) or by adding snow cover to
those areas exhibiting between 0% and 49% climatologically
present snow cover (positive snow anomaly). A reasonable vertical
profile of diabatic heat for a mid-latidude surface forcing such
as this is selected to be +l.OC/day for the negative (heat
source) or positive (heat sink) anomaly at the lowest model level
( 9 67 mb) •
208
The forcing field resulting from the idealized snow anomaly case
was first imposed upon the January climatological basic state.
The response was zonal in character. Strong surface high
pressure was seen just downstream of the forcing with weaker low
pressure upstream. At 300 rob a strong trough was seen just
downstream of the forcing with weaker high pressure upstream.
Similar responses were seen for April and October cases.
These results are similar in pattern to Egger (1977), who
used sea surface temperature fields as anomalous surface forcing.
Egger's model contained only two levels in the vertical. Since
very similar patterns are obtained with both-models, the increase
in vertical resolution seemed to offer little additional
information about the response.
The next series of experiments force the model with the
actual observed snow anomalies for the month of January (both
positive and negative). For these model runs, a vertical profile
of +2.0/+1.0/+0.5 C/day in the three lowest model layers was used
because it seemed to give a more realistic forcing. For January
1977, the observed positive height anomaly at 700 rob over Alaska
and Northern Europe agrees with the model. The broad negative
height anomaly observed over Siberia and the North Central
Pacific also appears in the model response. However, other
features are erroneous such as the model response of positive
height anomaly over Northeast China, the model's southern
displacement of the westerlies over Southern Europe and the lack
of the observed trough over the Eastern United States.
For January 1978, the positive height anomaly in Western
Canada, the broad negative height anomaly pattern stretching from
Siberia through the Central North Pacific and the positive height
anomaly over Northeast China is an erroneous model response and
the high and low height anomaly centers are reversed over the
Eastern United States and North Atlantic.
Because the model is linear, one expects the strongest
forcings to produce the strongest responses. The years with the
two largest snow cover anomalies of the years tested were January
1977 and 1978, which ranked 1st and 4th highest respectively
across the Northern Hemisphere (Wiesnet and Matson, 1979). Still
there are many regions where poor correlati9ns between model
response and observation appear to exist. This leads one to
tentatively conclude that either the effect of extreme anomalous
snow cover does not outweigh all the other causes of monthly mean
circulation· anomalies, or this model experiment does not
adequately simulate this effect.
The model was also forced with tropical OLR anomalies alone
and in combination with snow cover anomalies. Pattern Correlation
Coefficients (PCC) are calculated over four separate regions.
These are Northern Hemisphere (NH) (30 N-90 N), North America
only (NA), Eurasia only (EA), and North America and Eurasia
(Northern Hemisphere land)(LAND). The partitioning in this
209
fashion is justified because anomalous snow cover is a regional
phenomenon. Increasing correlation between model generated
geopotential fields and observed geopotential fields should be
seen over specific middle and high latitude regions downstream
from the forcing rather than over the entire hemisphere.
PCC's are calculated for two model levels, 850 rob and 300
rob. These are shown in Tables 1 and 2. Because snow cover is an
intense but shallow, low-level forcing, one should expect to see
higher correlations with observed geopotential fields at the
lower model level.
The results of the experiments calculating the correlation
coefficients indicate that snow cover seems to have less
influence on the atmospheric circulation than previously
expected. Poor correlations were noted in general over all four
regions when snow cover was the only forcing. However, in
approximately 70% of the cases tested, good correlations existed
between model generated and observed geopotential fields when OLR
was the only forcing. No distinct pattern emerged with regard to
improving the correlations when snow plus OLR were used as
forcings. No appreciable change was detected between the 850 rob
and 300 rob results although when good(bad) correlations existed
at 850 rob for a particular month and region, good(bad)
correlations also existed at 300 rob.
3. CONCLUSIONS
The expected higher correlations at 850 rob did not exist nor
did the anticipated increase in correlation appear for specific
land regions where snow cover would have a marked influence.
This, in addition to the fact that little or no improvement
occured when snow forcing was added onto OLR forcing only, leads
to the conclusion that the effect of snow cover is much less than
previously thought and/ or the many other variables not
parameterized in this model are much stronger factors than snow
cover.
Some of these factors associated with snow cover but not
parameterized in the model include no change in albedo with
latitude, no consideration of latent and sensible heat fluxes and
no consideration of the effects of transients. Furthermore,
enhanced baroclinicity and the associated vorticity forcing or
dissipation to accompany the imposed diabatic heating are not
included.
Future experiments will include the calculation of
correlation coefficients at the surface to see if the effect of a
very shallow forcing in the form of snow can be modelled.
210
TABLE 1. 850mb Pattern Correlation Coefficients
Month
Jan Apr Oct
1979
X
X
X
1980
X
X
X
X
X
X
X
X
X
X
X
Forcing
Tropical
Snow Tropical and Snow
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Region
NH NA EA
.05 -.23
• 25 • 45
.21 .45
-.15
-.14
-.21
.07
.07
.10
.12 -. 08
• 46 • 21
.33 .00
LAND
-.11
.34
.12
-.04
.27
.07
-.02 -.43 .23 .02
.14 .10 -.04 -.01
-.12 -.78
-.02 -.21
.29
.27
.23
.21
.23
.26
.23
.28
.04 -.29
• 28 .13
.21
.28
.42
.36
.13
.17
.36
.32
-----------------------------------------------------------~----
1981
X
1982
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
211
X
X
X
X
.42 -.42
. 30 • 37
.45 -.01
.02
-.16
.01
.05
.38
.21
-.30
.42
-. 28
.65
.62
.79
.35 -.02
-.04 .62
.13 -.17
.53
.40
.27
-. 49
.26
.27
.49
.40
.31
.23
.33
.38
.22
-.31
.21
.20
.45
.34
.28
.27
Tl\BLE 2. 300 mb Pattern Correlation Coefficients
Month Forcing Region
Jan Apr Oct Tropical NH NA EA LAND
Snow Tropical and Snow ----------------------------------------------------------------
1979
X
X
X
1980
X
X
1981
X
1982
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
I X
-.30
.18
.13
.12
.23
.28
-. 68
.38
.25
-.53
.03
-.13
.16 -.18
.34 .07
.36 .05
-.53
.20
.17
-.11
.11
.10
-.11 -.30 -.25 -.27
.oo -.29 .02 -.11
.13 -.39
-.05 -.43
.06
.38
.19
.61
.22 -.57
.19 -.27
.41
.46
.16
.38
.69
.39
.07
.18
.12
.50
.36
.25
-.13 -.49 -.16 -.25
.43
.50
.10
-.30
-.02
.14
• 39
.45
.60
.64
-.05
-.33
-.23
.48
.56
• 59
.62
• 59
.20
-. 39
.13
.34
.16
.31
.52
.61
.17
-. 29
.09
.39
.37
.44
-----------------------------~----------------------------------
X
X
X
X
212
.16
.10
.04
.60
.56
• 58
.40
.55
4. ACKNOWLEDGMENTS
This work was supported by NOAA grants NA81AA-H-00023 and
NA84AA-H-00026 and NSF grant ATM-8213184. Computer time was
provided by NASA/GLA.
5. REFERENCES
Arkin, Phillip A., 1983: An examination of
oscillation i~ the upper tropospheric
subtropical wind field. Ph. D. thesis,
Meteorology, University of Maryland.
the southern
tropical and
Department of
Dewey, Kenneth, 1977: Daily maximum and minimum temperature
forecasts and the influence of snow cover. Mon. weather
Rev., 105, 1594-01596.
Dewey, Kenneth and Richard Heim Jr., 1981: Satellite observations
of variations in northern hemispheric snow cover. NOAA Tech
Rep., NESS ~·
Egger, J., 1977: On the linear theory of the atmospheric response
to sea surface temperature anomalies. J. Atmos. Sci.,}!,
603-614.
Foster, James, Manfred Owe and Albert Rango, 1983: Snow cover and
temperature relationships in North America and Eurasia. J.
Clim. and~· Met.,~' 460-469.
Borel, J.D. and J.M. Wallace, 1981: Planetary-scale circulations
associated with the southern oscillation. Mon. weather
Rev., 109, 813-829.
Landsberg, Helmut, G.P. Cressman and H.K. Saylor, 1941: The
influence of a snow cover on air temperature. Proc. Central
Snow Conference, l' 45-48.
Namias, Jerome, 1962: Influences of abnormal surface heat sources
and sinks on atmospheric behavior. Proc. International
Symposium on Numerical Weather Predicti~ Tokyo, 615-627.
Opsteegh, J.D. and Robert Mureau, 1984: Description of a 15-layer
steady-state atmospheric model. Report SR-84-19, Department
of Meteorology, University of Maryland, 25 pp.
Robock, Alan and James w. Tauss, 1985: The effect of anomalous
snow cover on the general circulation of the atmosphere
using a simple climate model. Proc. Ninth Annual Climate
Diagnostics Workshop, NOAA, 231-2~
213
Robock, Alan and James w.
tropical forcing on
Proc. WMO workshop
MonthL¥ and seasonal
in press.
Tauss, 1986: Effects of snow cover and
mid-latitude monthly mean circulation.
on the Diagnosis and Prediction of
AtmosPfieric variations Over the Glob~
wagner, James, 1973: The influence
monthly mean temperature anomaly.
928-933.
of average snow depth on
Mon. Weather Rev., 101,
Wiesnet, Donald and Michael Matson, 1979: The satellite-derived
northern hemispheric snow cover record for the winter of
1977-78. Mon. Weather Rev., 107, 928-933.
Yeh, T.C., R.T. wetherald and S. Manabe, 1983: A model study of
the short-term climatic and hydrologic effects of sudden
snow cover removal. Mon. Weather Rev., 111, 1013-1024.
214
Kukla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) SNOW WATCH '85.
Proceedings of the Workshop held 28-30 October 1985 at the University of
,Maryland, College Park, MD. Boulder, Colorado, World Data Center. A for
G,laciology (Snow and Ice), Glaciological Data, Report GD-18, p.215-223.
Parameterization of Snow Albedo for Climate Models
Susan Marshall
Department of Geography
University of Colorado
Boulder, Colorado, U.S.A.
Stephen G. Warren
Dep~rtment of Atmospheric Sciences
University of Washington
Seattle, Washington, U.S.A.
Abstract
General circulat:lon. DDdels (<Dfs) find that the respmse of clinate to in-
creases in ~ is enb.an=ed by tiE snow-albedo-tempemture feeib!ck. '!he results
are very sensitive to the assUDB:l value of 8llOW' albedo. Snow albedo, J:rJwever,
is highly variable, and it is not calculated accurately in present-day <Dfs. We
would like to replace the current simple enpirical p3raneterizat:ions of SlOW al-
bEdo with a pqysically-based paraneterization lihlch is accurate yet efficient to
coopute.
~ approa.dl is to develq> simple functions 'fihid:l fit tiE spectrally-
averaged results of a detailed theoret:1cal. UDdel of tiE spectral albedo of snow
lirl.ch uses the delta4'.ddi:ogton netlnl for nultiple scatteri~ and Mie th:!ory for
si.qJle scatter!~. '!he spectrally-averaged snow albedo varies with snow grain
size, ~:Dlar zenith qle, 8llOW' cover thickness, uoderlyi~ surface albedo {for
thin snow), concentration of absorptive impurities in the snowpack, and cloud
optical thickness (becatse clouds alter the ~:Dlar spectnm at the surface).
'lbis netlnd divides the solar spectrum into the two broad lliavebatds caDDDnly
used in clinate tmdels: visible and near-infrared.
General circulation models (GCMs) find that the response of climate to
increases in C02 is enhanced by the snow-albedo-temperature feedback. The
results are sensitive to the assumed value of snow albedo. Snow albedo, how-
ever, is highly variable·, and it is not calculated accurately in present-day
GCMs. Most GCMs assign a single value to the albedo of an optically-thick
snow cover. These albedo values can range from 0.55 to 0.85,. and generally
remain constant with time until the snowpack decays to some critical depth,
then decrease as a function of the snow depth until the albedo reaches the
underlying surface albedo. Other GCMs allow the snow albedo to vary with
solar zenith angle, snowpack thickness, age of the snow layer, and latitude.
We would like to replace the current simple empirical parameterizations of
snow albedo with a physically-based parameterization which is accurate yet
efficient to compute.
215
Our approach is to develop simple functions which fit the spectrally-av-
eraged results·of a detailed theoretical model of the spectral albedo of snow
which uses the.delta-Eddington method for multiple scattering and Mie theory
for single scattering (Wiscombe and Warren, 1980, Warren and Wiscombe, 1980,
herefter WWI and WWII, respectively). This method assumes a homogeneous snow-
covered surface with no vegetation cover. The GCMs we surveyed (NCAR, GFDL,
OSU, GISS, GLAS, LMD, UKMO, and ECMWF) break the solar spectrum into at most
two parts. The break is either at 0.7 ~m or 0.9 ~m wavelength ) with one ex-
ception at 0.78 ~m). We therefore also break the solar spectrum into two
parts, "visible" and "near-infrared" (NIR). We will develop·· the parameteriza-
tions for the two regions split at 0.7 ~m. Then we expect that the functional
forms we develop can also be used when the break is at 0.9 ~m, so we will need
only to develop a second set of coefficients for those climate models which
break the spectrum at 0.9 ~m. In all examples given here, the separation be-
tween "visible" and "near-inrared" is at 0.7 ~m.
The spectrally-averaged snow albedo varies directly with snow grain
radius, r, effective solar zenith angle,8, snow cover thickness, h (g cm-2),
underlying surface albedo (for thin snow), ua, and concentration of absorp-
tive impurities (especially soot) in the snow layer, s; and indirectly with
cloud cover. Figure 1 shows the effects of snow grain size and zenith angle
on the spectral albedo of snow to be much greater in NIR wavelengths, while
the effects of finite snow thickness and soot concentration on the spectral
albedo are greater in the visible wavelengths. The similarity between the
spectral signatures of grain size and zenith angle, and those of snowpack
thickness and soot concentration, suggest that each pair of variables might _be
lumped together as one predictor.
The predictors of snow albedo will be effective grain size, r, effective
zenith angle, e, (which will depend on the diffuse direct ratio), impurity
content, s, (as an effective soot content), and snowpack thickness,
h (g cm-2). In the NIR there will be an additional predictor, the atmos-
pheric transmittance, t. This is needed because snow albedo varies greatly
with wavelength, and the absorption of solar radiation by clouds is also wave-
length-dependent. Clouds are more absorptive at the longer wavelengths
(Figure 2), where snow albedo is also lower {Figure la), so increasing cloud
optical thickness ~c causes the NIR snow albedo to increase (table 1).
Changes in spectrally-integrated snow albedo due to differential atmos-
pheric absorption (the visible albedo is insensitive to i'c·> will also be
caused by changes in water vapor optical depth, l'w, and the atmospheric path
(sec 90 , where 6 0 is the solar zenith angle). We hypothesize that the effects
C' W' and sec 0 can be lumped together, so that only one predictor, t, is
needed. This will be desirable not just for simplifying the parameterization,
but because not all GCMs compute ~, but they all obtain t.
216
I.O Cli~~TTT"'TT,..,.,"'TT'TT,.,-T"T'rT'liiJ
0.9
0.8
07
0.6
o.s
0.4
0.3
0.2
0.1
00 -.
02 0.4 0.6 08 1.0 1.2 1.4 1.6 IB 2.0 2.2 2~ 2.6 2B
WAVELENGTH ("m)
1.0 i=31~~~TT'If'irrt..,."T"TTT'~
0.9 E-~==---~
OB 1!"-'lt':=----.:
0.7
0.6~~~--------2mm 0 ~ Uquid Equivalent
OA
0.3
0.2 GRAIN RADIUS •2001'm
1.0 ~..-o;c:-T"TTTTTTTT'T"T"1,-,:-nn-rrn
0.9
0.8
0.7
0.6
o.s
0.4
0.3
02
0.1
0.0 CJ...JL1..J....L.J....Ll..J...l....Ll.J;;::t::LJ...Diji;i:[_~~o..J
I 0.2 04 0.6 08 1.0 1.2 1.4 1.6 IB 2.0 2.2 2.4 2.6 2;8
WAVELENGTH (I'm)
0.9 r=----.;;:~
0.8
0.7 1------.....
0.6
o.s
0.4r:----
0.3
0.2
0.1 .0.1
0.0 0.0 L...L~......L..L.L~'--L..L...L..L...J'--L.J-L....L...IL-L..J-L..L-Iblool
0.3 0.4 0.5 0.6 01 OB 0.9 1.0 1.1 1.2 1.3 1.4 1.5 0.3 O.S 0.7 0.9 1.1 1.3 ·I.S
WAVELENGTH (1'... WAVELENGTH (I'm)
Fig. 1 Plots of the effects on the spectral albedo of snow of (a) snow
grain size, (b) solar zentith angle, (c) snowpack thickness, and (d)
contamination by soot (from WWI and WWII).
Table 1. Visible and near-infrared snow albedos for clear and cloudy Arctic
sky conditions (using unusually thick cloud for Arctic; tc = 40).
Spectral
Interval
( m)
visible
(0.3-0.7)
visible
(0.3-0.7)
infrared
(0.7-3.0)
infrared
(0.7-3.0)
Sky
Condition
clear
cloud
clear
cloud
cosGa
0.40
0.40
0.40
0.40
217
Snow Albedo Solar Downflux
(W m-2)
0.98 226.5
0.97 171.7
0.64 195.3
0.78 60.6
:<
..:l•oo
4-1
~
"t:l
1-1
t'd
.--i
0
tf.l
.z
Fig. 2. Spectral distribution of incoming solar downflux {W m-2 pm-1)
for the Arctic summer under clear and cloudy (with an unusually thick
cloud; optical thickness = 40) sky conditions. These are calculated
using the ATRAD mode (Wiscombe ~ al., 1984).
Table 2 shows the breakdown of the parameterization from (a) the final
combination of visible and NIR albedos and downfluxes (Fvis' Fnir) into a
single value of the snow albedo, to {d) the list of input values needed from
the GCM to complete the parameterization. The parameterization will estimate
the visible and NIR (clear-sky) snow albedos as functions of three main para-
met~rs: effective grain size, r, effective soot, s, and an effective zenith
cosine, P • The effect of cloud cover on the NIR snow albedo may be estimated
using a value of the atmospheric transmittance, t.
The effective zenith cosine, ~' is a linear combination of the diffuse
and direct-beam fractions of the solar irradiance and the diffuse and
direct-beam zenith cosines (yd, ~0 ). This relationship holds because the snow
albedo is linear in for both the visible and NIR spectra. The fraction of
insolation at the surface that is diffuse (d) will be parameterized, possibly
as a combination of the atmospheric transmissivity and the direct-beam zenith
angle. Because the spectral signatures of the grain radius and of the .zenith
angle are similar in their effect on the snow albedo, the two variables may be
parameterized as one value, an effective grain size, r. Some properties of
the albedo which may be useful are that it is nearly linear in cos G0 , and
that it is nearly linear in r1/2 in the visible (Bohren and Barkstrom, 1974),
but not in the NIR (WWI, figure 9).
The problem remains that snow grain size is difficult to predict, which
is unfortunate since grain size is the most important variable controlling
snow albedo. We will have to use observational data to develop a crude para-
meterization of grain size, probably in terms of the snow age and its temper-
ature history, following the works of Anderson (1976).
218
Table 2. Parameterization for snow albedo~s.
ex tot
Fnir OCnir + Fvis OCvis
(a) =
(b) CXvis = f(r,s, 9)
de nir = f(r,s, e) (clear)
CXnir = f(r,s, 6 ,t)(cloudy)
Fnir + Fvis
(c) effective grain size r = f(age,temperature)
effective soot content s = f(s,h,ua)
effective zenith cosine ,.,.. .. cos e .. d f-ld + (1-d) f'b·
where f-d .. 0.65.
diffuse fraction d .. f( e 0' t)
atmo,spheric transmittance t = F (surface) I F (top of tlie
atmOsphere)
//
(d) Need from GCM:
temperature history, age of
do~~ward fluxes at surface,
solar zenith cosine )Mo
atmospheric transmittance t
snow thickness (g cm-2)
underlying albedo ua
impurity content (as soot)
snow
Fvis' Fnir
F(sfc)
= F(toa)·
s
Our work thus far has concentrated on how to lump together the soot con-
tent, snow thickness, and underlying albedo. Increasing soot concentrations
in the snowpack and decreasing snow thickness lower integrated snow albedos
( ~ vis• c;(nir) in a similar manner (figure 3), and might therefore be com-
bined into one parameter, an effective soot concentration, s. For each snow
albedo of one grain size and underlying surface albedo, there is associated
one value of snow thickness, h (g cm-2), and one value of soot concentration,
a (ppmw). Figure 4 shows the relationships between these values of soot and
snow thickness. The regression lines plotted for these figures are for sever-
al combinations of~s• rand ua. The fit of this relationship is improved by
allowing the coefficients of the regression line to vary as functions of ua.
Figure 5 shows the errors involved in using an effective soot to calcu-
late the integrated snow albedo. The parameterization shown in these figures
breaks down for the case of an underlying surface albedo greater than the
albedo of an optically-thick snow layer. A dirty snow layer covering a clean
snowpack would be one example of this situation. An approach based on the
underlying surface albedo, the snow thickness, and the snow grain radius, will
be used to interpolate the snow albedo for the special case when ua is greater
thano<.s•
219
..
0
U)
>
a
o.o-----w-__ _. __ ~.__. __ w-~ 1
10-10 10-
soot. (ppmw)
1.0
c
a· so.,.,
I
..
0
It
0.0~~_.--~--~--~--~~~
io-10 1o-1
Soot (ppmw)
1.0
d
..
0
It
0.~--~~--~--~--------~
10-5 103 Snow thickness (g cm-2)
Fig. 3. Relationships between integrated snow albedo (O(s) and soot
concentration in deep snow (a) and (c), and between integrated snow
albedo and thickness of pure snow (b) and (d) for the visible and
near-infrared wavebands, for three values of grain size, r •
220
..
Ul ..t >
Cl
-l
E
~ -..s
~
0
0 rn
VISIBLE
a •
-}
E
~ .e
~
0
0 rn •
b
JE+O JE+3
THICKNESS (. cm-2 )
Fig. 4 A thin snowpack of pure snow has an albedo which can be mimicked
by adding soot to a deep snowpack. These corresponding values of soot
and thickness (for a variety of grain sizes and underlying albedos) are
plotted as points here. The lines are least-squares fits, (a) visible
(b) near-infrared.
visible
•
a
....,
0
0
Ul
Ql
~ ....,
u
Ql
~
~
Ql .. ... ..t c::
Cl
near-infrared
b
o.o~~~~~~~~----~~ o.o~~~~~._~~--~--_.--
0.0 o.o
"nir• correct s,h avis• correct s,h
1.0 1.0
Fig. 5. Plots of integra~ed snow albedos using correct values of soot,
a, and snow thickness, h, compared to integrated snow albedos using an
"effective soot" calculation for (a) visible and (b) near-infrared
integrations.
221
The final diagram (figure 6) outlines the structure we foresee for our
parameterization. Task 1, the combination of snowpack thickness and concen-
tration of §OOt into one parameter, an "effective soot", is now being com-
pleted. Task 2 involves the actual parameterization of the integrated snow
albedos (visible and NIR) from given values of an effective grain radius, r,
effective soot concentration, s, and effective zenith cosine, ~ • The final
task will then be to research methods by which the effective grain size can be
estimated from data available to climate models. We are assuming that other
steps not labelled as "tasks" will be relatively straight-forward.
task
3
age,
temperature -
history
grain
size). r
Fvis
Fnir
I
task
1
I a tot I
thickness,
underlying albedo,
impurities
effective
soot, 8
task 2
L
avis ( r, 8 •I" )
Gnir ( r, 8 .,.. • t )
I
~I
Fig. 6. <>utline of research for parameterization of snow albedo.
This paper has presented completed work and outlined proposed tasks in
the parameterization of snow surface albedo. Since much of this work is in
the preliminary stages, the authors anticipate that changes will be made to
the method as outlined at the workshop.
222
References
Anderson, E.A. (1976) A Point Energy and Mass Balance Model of a Snow Cover.
U.S. National Oceanic and Atmospheric Administration, Silver Spring, MD.,
NWS 19, 150p.
Bohren, C.F.; Barkstrom, B.R. (1974) Theory of the optical properties of
snow. Journal of Geophysical Research, v.79(30), p.4527-4535.
Warren, S.G.; Wiscombe, W.J. {1980) A model for the spectral albedo of snow.
II. Snow containing atmQspheric aerosols. Journal of the Atmospheric
Sciences, v.37, p.2734-2745.
Wiscombe, W.J.; Warren, S.G. {1980) A model for the spectral albedo of snow.
I. Pure snow. Journal of the Atmospheric Sciences, v.37, p.2712-2733.
Wiscombe, W.J.; Welch, R.M.; Hall, W.D. (1984) The effects of very large
drops on cloud absorption. Part I: Parcel models. Journal of the Atmo-
spheric Sciences, v.41, p.1336-1355.
Acronyms
Atmospheric General Circulation Models
ECMWF
GFDL
GISS
GLAS
LMD
NCAR
osu
UKMO
European Centre for Medium Range Weather Forecasting
Bracknell, Berkshire, England.
Geophysical Fluid Dynamics Laboratory
Princeton, New Jersey
Goddard Institute for Space Studies
New York, New York.
Goddard Laboratory for Atmospheric Science
Greenbelt, Maryland.
Laboratorie Meteorologie Dynamique
Paris, France.
National Center for Atmospheric Research
Boulder, Colorado.
Oregon State University
Corvallis, Oregon.
United Kingdom Meteorological Office
Bracknell, Berkshire, England
223
Kukla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. ( 1986) SNOW WATCH '85.
Proceedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College Park, MD. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice), Glaciological Data, Report GD-18, p.225-2~0.
Abstract
Modelling a Seasonal Snow Cover
E. M. Morris
Institute of Hydrology
Wallingford, Oxfordshire, U.K.
This paper describes physics-based, distributed models for snow
processes and their potential use in assessing the effects of climatic
change produced by increased levels of C02 in the atmosphere. The
conservation and constitutive equations for snow treated as a three phase,
four component mixture are described and the simplifications made in the
various current distributed models explained. One particular model, the
SHE snow component, is used to estimate the sensitivity of predictions of
snowmelt rate to variations in meteorological inputs using field data from
a site in the Cairngorm Mountains of Scotland. This analysis indicates
that, for the expected levels of climatic variation, the change in
predicted snowmelt rates is of the same order as the uncertainty in these
rates arising from uncertainty in one of the parameters of the model, the
aerodynamic roughness length. However, field data suggests that it may
well be possible to specify this parameter more precisely. Given that this
can be achieved, the SHE snow routine, and other distributed models which
use the aerodynamic roughness or an equivalent parameter, should form a
useful component of general models for prediction of the effects of
increased C02.
Introduction
The problem of describing the interaction between snow cover and the
atmosphere concerns both hydrologists and climatologists. Both groups have
developed general models which, for the purposes of discussion, may be
described as consisting of two sub-models, for snow processes and for
processes in the atmosphere, together with initial conditions, extern~l
225
boundary conditions and equations describing transfers at the internal
snow/atmosphere boundary. However, perhaps not surprisingly, the
hydrologists have tended to simplify the atmospheric component of their
models, and conversely climatologists have often used simplified snowmelt
models. This paper describes some of the physics-based, distributed models
for snow processes developed by hydrologists which might form a useful part
of general models designed to allow the effect of changes in carbon dioxide
levels in the atmosphere to be predicted.
In order to use a model to investigate the effects of hypothetical
changes in inputs or system characteristics it is necessary to establish
(i) that the model produces good predictions for the present
situation
(ii) that the parameters of the model can he specified a priori for
conditions outside the range of those for which the model has
been calibrated and
(iii) that the uncertainty in model output is small compared to the
effect of the hypothetical changes to be investigated.
The first criterion can be met by many relatively simple snowmelt models.
Provided that output data are available for optimisation, indices linking
snowmelt and meteorological data may be defined. The problem is that these
parameters are found to be site and climate specific and therefore cannot
be expected to fulfill the second criterion listed above. Physics-based
models provide improved predictions for present day climates and, more
importantly, use parameters which we can hope to define accurately for new
situations without having to optimise the model. There are a series of
such models now in use, ranging from the classic Anderson energy budget
model (Anderson, 1968), which gives detailed equations for the
snow/atmosphere boundary conditions but considers only average properties
of the snow cover, to full distributed models (e.g. Morris, 1983) which
give detailed predictions of the variation of properties such as
temperature, density and water content within the snow.
226
One advantage of the full distributed physics-based models is that
more accurate calculations of the mass and energy transport across the
boundary between the snow and the atmosphere may be made. For example
albedo, and hence the magnitude of radiative transfers, depends on the
grain size, density and water content of the snow at the surface and within
the snowcover. Turbulent transfer of sensible heat and evaporation both
depend on the surface temperature and water vapour content. Comparison of
the performance of various models using the same field data sets shows that
distributed models provide the most accurate predictions provided that
sufficient input data are available to run them correctly (Morris, 1982).
Clearly the best chance of meeting the third criterion defined above is
obtained by use of the most accurate model possible, since there are no
restrictions on the range of hypothetical input data which may be provided
for the model.
Mixture Theory
The basis of the distributed model is the concept that snow can be
regarded as a mixture containing four constituents, ice, water, water
vapour and dry air. Each volume element of snow is assumed to contain all
four components dispersed within it. The first specific reference to the
application of mixture theory to snow was made by Male et al. (1973).
They made a dimensional analysis of the conservation equations for mass,
momentum and energy in the snow, treated as a four-component mixture, and
found 31 dimensionless groups, a telling indicator of the complexity of the
problem. Male et al. concluded that "the development of general
analytical, numerical or experimental models will prove impracticable" and
suggested that field studies of situations involving a restricted number of
parameters would prove to be the only tractable approach. Recently Kelly
et al. (1986) have looked again at the general mixture theory problem in an
attempt to define clearly the appropriate equations for a four-component
analysis of snow processes and derive a hierarchy of simplified equations
which may be used for particular cases. Since the distributed models used
by other workers fit in as special cases of the general four-component case
we begin with a brief outline of the general theory.
227
Let the subscripts i, w, v, a denote the components ice, water, water
vapour and air respectively. The continuity equation for constituent k may
be written
where t is time, Pk is the intrinsic density of component k, 9k_ the
porosity (volume per unit volume of snow), ~ the velocity and Mkj is
(1)
the mass of component k produced per unit time per unit volume of snow by a
phase change from component j. By definition, Mkj = -Mjk and Mkj = 0.
The porosities are related ny the equation
(2)
and the density of the snow is defined as
(3)
The equations for conservation of momentum are
' ( 4)
where ~ are the interaction body forces acting on constituent k per unit
mixture volume. By definition
E ~k = 0. (5)
k
~ are the intrinsic stresses for each component and gravity g = -g !
is assumed to be the only external body force. Note the term involving the
momentum of the source/sink terms Mkj• It may be assumed that the
interaction forces for the fluid components are given by the empirical
228
Darcy equations
Ps_~ = Pk grad~ -Pk ek g (~~-(1-0i) Vi) Kk --
(6)
where Pk is the intrinsic pressure and Kk is the hydraulic conductivity
of fluid component k. Empirical equations are required to define Kk•
The derivation of the· appropriate energy conservation equation, which
is not at all straightforward, has been discussed in detail by Kelly et
al. (1986). With the assumption that for all components, including the
solid phase, the specific internal energy is a function solely of
intrinsic density and temperature they derive the equation
oT E Pk~(cp)k (--+ ~· grad T) k ot -(7)
where T is the temperature of the mixture, (cp)k is the specific heat
at constant pressure of component k, K is the thermal conductivity of ice,
Ljk is the latent heat produced by transformation of unit mass of
component k to component j, SR are sources of radiant energy and Q
represents the stress working terms.
A series of constitutive equations are required to complete the
model. Given that the air and water vapour behave as perfect gases we may
begin by writing two equations of state
Pk
R
Pk-T
Mk
k v,a (8)
where Mk is the molecular weight of component k and R is the gas
constant. An empirical equation for the diffusion of water vapour in air
may be added to these
D
~v -~a =
0
<PvOv) oz (9)
229
where D is the diffusivity. The relation between the water content and
water pressure in the snow may he described by the characteristic equation
(10)
where ~a the air entry potential and a the pore size index are
constants. A further equation should describe the motion of the ice matrix
under the applied stress either in terms of the compressibility of the snow
(e.g. Navarre, 1975) or by setting ~i = 0.
For the remaining two equations required to complete the model there
is a choice of strategy. The snow can be regarded as a mixture in
thermodynamic equilibrium, with the proportions of each component
determined by equilibrium equations derived by minimising the Gibbs
function of the mixture for a given bulk temperature and pressure.
Alternatively, rate equations for the source/sink terms may he defined. ~rn
this case, which may well be a more realistic model of physical processes
in snow, the mixture is not necessarily in thermodynamic equilibrium.
The boundary conditions to be applied at the upper and lower
boundaries of the snow have been discussed by many authors. The precise
equations used depend on the level of input data available for a particular
application. Given the usual output from an automatic weather station the
mass and energy flux at the snow/atmosphere interface can be calculated so
long as the albedo and aerodynamic roughness length of the snow cover are
known. Thus these quantities commonly appear as parameters in
physics-based models. However, it is worth pointing out that both albedo
and aerodynamic roughness can be measured in the field_so they are not
necessarily unknown for all applications.
Simplified forms of the equations
So far no working model of snow processes using the four-component
equations has been produced. In this section the simplified equations
230
which form the basis of current models are described. We begin with the
assumptions which allow the momentum equation (4) to be simplified to the
familiar Darcy flow equations. These are
(i) that the stresses ~ are purely hyrlrostatic so that 9k= -pk !
(ii) that the inertia terms on the l.h.s. of the momentum equation
are negligible and
(iii) that the ice matrix is static so that ~i = 0.
Then equations (4) and (6) reduce to
(11)
which is recognisable as the Darcy flow equation with an extra term
involving the source/sink terms Mkj• This extra term is negligible for
normal values of Mkj•
If it is assumed that the ice and water phases are incompressible and
the air and water vapour behave as perfect gases the term Q in the energy
conservation equation may be written
Q (12)
where the summation convention for repeaterl indices is used in the last
term. Q is neglected in all current models. Some authors also neglect
SR but Navarre (1975), Colbeck (1979), Morris (1983) and Akan (1984) use
empirical equations for radiation extinction in snow.
A major difference between distributerl models arises in the choice of
simplified thermodyanmic equilibrium equations. Most authors make a
distinction between two types of mixture; colrl, dry snow, or ripe snow at
the melting point T0 •
231
For dry snow Sulakvelidze (1959), Yen (1962), Navarre (1975) and Obled and
Rosse (1977) set
• T < T 0 (13)
Thus the mixture has only three components and only one thermodynamic
equilibrium equation is required. The vapour pressure Pv is set equal to the
saturated vapour pressure over ice which, following Yen (1962), is given by the
empirical equation
Equation (9) is often replaced by an expression for the water vapour
velocity
Yv
Deff is an effective diffusivity which varies with lla•
For ripe snow with 8w > 0 the equation
T
(14)
(15)
(16)
is used by Colbeck (1972) and Navarre (1975) as the second thermodynamic
equilibrium equation. These authors also assume that the pressure of the
moist air i~ the snow is constant. A new variable, the capillary tension
(17)
is defined and equation (11) is simplified to
-Kw (grad(~) + Pw g !) (18) =-
p g
w
In this "single-phase approximation" the terms Miv' Mwvare considered to
232
be negligible and the model reduces to four equations (1), (10), (17), (18)
for the four variables ~, 9w, !w and Mwi•
Two authors, Morris (1983) and Akan (1984), have used a more complex
thermodynamic equilibrium equation derived by Colbeck (1975). Colbeck
derived the equation
(19)
by minimising the Gibbs function for a mixture of spherical ice grains of
diameter di with a low water content so that all three phases are in
contact (the "pendular" regime). Morris (1983) and Akan (1984) both assume
that the terms Miv, Mwv are negligible so their models are only valid
for low temperature gradients in the snow.
Sensitivity analysis
To illustrate the potential of physics-based distributed models a
sensitivity analysis has been carried out using the SHE snow process model,
Morris (1983), and field data from a site in the Cairngorm mountains of
Scotland. The site and the data have been described in detail by Morris
(1983). Figure (1) shows the measured and predicted snow depths for a
period of 297 hours in February/March 1979. The uncertainty in the
predicted snow depth arises from the uncertainty in the measurements of
initial snow density Ps = 500-550 kg m-3 and grain size di = 1-2 mm. A
constant aerodynamic roughness length of z 0 = 0.6 mm and a fixed lower
boundary temperature T(O, t) = -0.4°C have·· been used. The net long wave and
short wave fluxes have been calculated from the measured net and incoming
solar radiation using a fixed short-wave albedo of 0.9. A simple measure
of variation in the model output is given by the normalised root mean
square error function
(20)
I where Z is the mean of the measured values of snow depth, Zi, and Zi are
233
0.5 -------.....
SNOW DEPTH
/M 0.4
0.3
0.2
0.1
100 200
TIME/HOURS
300
Fig.l Measured • and predicted _ snow depths for a site in the
Cairngorm Mountains of Scotland. t=O at 0800 hours on 21
February 1979. The shaded area shows the uncertainty in the
predicted depths arising from uncertainty in the measured initial
conditions.
predicted values. The r.m.s. error in the output for the predictions shown
in Figure (1) ranges from F = 0.162 to F = 0.223.
The aerodynamic roughness length and lower boundary temperature, z 0
and T(O,t), were not measured continuously throughout the experiment.They
have therefore been treated as constant parameters in the model. Since
they are in fact likely to have been variable, some uncertainty in the
output will arise from uncertainty in these parameters. The effect of
varying the lower boundary temperature within the range cPc to -ZOe is of
the same order as the effect of measurement uncertainties in the initial
conditions. F varies from 0.162 to 0.2sq. However, as shown in Figure
(2), the effect of varying z 0 over the range of values from other field
sites reported in the literature (Chamberlain, 1983), Harding et al. 1985)
is significant. F reaches a value of 1.4 for the largest value reported,
234
z 0 = 10 mm. These results illustrate the importance of the correct
determination of turbulent transfers at the snow/atmosphere interface.
Clearly for the SHE model an expression specifying z 0 more precisely,
presumably in terms of the physical characteristics of the surface snow and
the meteorological conditions, would remove a major source of uncertainty
in the output.
Chamberlain (1983) has shown that roughness lengths for blowing snow
are related to friction velocities by Charnock's equation for the roughness
length of the sea. This suggests that it may be possible to relate z 0 to
measured wind speed. The value of z 0 = 0.6 mm which produces the best
predictions (minimum value of F) for the Cairngorm data is given by
1.4
1.2
1.0
F
0.8
0.6
0.4
0.2
0.0
10-5
Fig.2
10-4
RANGE OF Zo DETERMINED FROM FIELD OAT A
HARDING et al.( 1985) 20 min averages
HARDING et al.( 1985) Dally averages
CHAMBERLAIN ( 1983)
ZoiM
Variation of the r.m.s. error function, F, with
aerodynamic roughness z 0 •
235
Charnock's equation using a wind velocity of 11.5 m s-1 at 1.2 m and a
logarithmic wiQd v'elocity profile. Measured hourly-average values .:of wind
vel()city at an · anemometer height of 1.2 m. a't the Cairngorm site varied from
l • l 0.2 m s-to 11.8 m s~ .. over the experimental period. Given that the best
effective value for z0 over the whole period is likely to be biased
towards the value associated .with the highest winds when most turbulent
transfer takes place it appears that Charnock's equation would give a good
a priori estimate 'c)f aerodynamic roughness for the .Cairngor-m data. Clearly
this possibility needs further invest.igation.
In an experiment over stable, melting snow Harding et al• (1985) found
that values of z 0 averaged over 20 minute intervals varied from 2.5 10-4m
2 4 3 to 10-m and daily average values lay between 2 10.-m and 3.9 10-m with an
average value over the 15 day experimental period of 1.7 10-3m. This is a
1.4
1.2
0
Fig.3
2 3
Variation of F with increase of air temperature ~Ta and
associated change in net long wave radiation (a) incoming solar
radiation unchanged (b) incoming solar radiation reduced by SO%.
236
wide spread of values for one site but evidence was found that the value of
z 0 could be related to the ageing of the snow surface with low values
occurring after snowfalls. Thus there is reason to hope that further
research will allow z 0 values to be specified more precisely.
The effect of a change in input data is demonstrated in Figure (3).
Curve (a) shows the variation in F for an increase of 6T in the hourly air
temperature values input to the model. It is supposed that the net
long-wave radiation is given by a temperature dependent equation
(21)
and the albedo is allowed to vary according to the grain size and water
content of the surface snow
l
a = 0 •02 m -0.09 Sw(Z,t)
di(Z,t)i
(22)
This simple empirical equation could undoubtedly be improved but is
sufficient for the purpose of this sensitivity analysis. All other
meteorological inputs are held unchanged. Curve (b) shows the variation of
F if, in addition to the changes which produce curve (a), the solar
radiation input is reduced by 50% as a consequence of increased
cloudiness. These curves give a crude idea of the magnitude of the change
in output which would be produced at this site by the level of climatic
variation we might expect to be produced by increased C02 in the
atmosphere. The increase in air temperature produces increased melting
from turbulent transfer of sensible heat whereas the increased cloudiness
reduces melting from solar radiation. This is why curve (b) has a minimum.
237
Obviously these results can only be an estimate of the response at
this site, since a properly coupled general model for the atmosphere and
snow has not been used. However, they serve to reveal the existence of a .
problem which will almost certainly also arise with a general model. The
effect of the hypothetical change in climate is to change the meltrate of
the snowcover and hence raise the value of F. The maximum value of F • 1
is of the same order as the maximum value produced by uncertainty in z 0
as shown in Figure (2). Thus, unless some method of specifying z0 more
precisely is established criterion (iii) of the introduction cannot be
fulfilled. This problem is not restricted to distributed models which use
the aerodynamic roughness to calculate turbulent transfers. All models
have to specify the energy flux at the upper snow boundary using one or
more parameters and these, like z 0 , can only be known to a certain
precision. For any snow model, it is necessary to establish the true
uncertainty in the output before confident predictions about the effect of
hypothetical changes in climate can be made.
References
Akan, A.D. (1984) Simulation of runoff from snow-covered hillslopes.
Water Resources Research Vol.20 no.6 p.707-713.
Anderson, E.A. (1968) Development and testing of snow pack energy balance
equations. Water Resources Research Vol.4 no.l p.l9-39.
Chamberlain, A.C. (1983) Roughness length of sea, sand and snow. Boundary
Layer Meteorology Vol.25 no.4 p.405-410.
Colbeck, S.C. (1972) A theory of water percolation in snow. Journal of
Glaciology Vol.ll no.63 p.369-385.
Colbeck, S.C. (1975) Grain and bond growth in wet snow. In: Snow
Mechanics. International Association of Hydrological Sciences
Publication 114 p.Sl-61.
238
Colbeck, S.C. (1979) Effect of radiation penetration on snowmelt runoff
hydrographs. U.S. Army Cold Regions Research and Engineering Laboratory
Report 76-11, Hanover, N.H.
Harding, R.J., Calder, I.R., Moore, C.J. and Morris, E.M. (1985) A
comparison of techniques for the estimation of snow melt and evaporation
from a snow surface. Institute of Hydrology, Wallingford, 27pp.
Kelly, R.J., Morl~nd, L.W. and Morris, E.M. (1986) A mixture model for
melting snow. In Press. Proceedings of the 2nd International Assembly
of the International Association of Scientific Hydrology, Budapest,
1986.
Male, D.H., Norum, D.E. and Besant, R.W. (1973) A dimensional analysis of
heat and mass transfer in a snowpack. International Association of
Hydrological Sciences Publication 107 (Banff Symposium 1972 -The role
of snow and ice in hydrology) Vol.l p.258-290.
Morris, E.M. (1982) Sensitivity of the European Hydrological System snow
models. In: Hydrological aspects of alpine and high mountain areas.
International Association of Hydrological Sciences Publication 138
p.221-231.
Morris, E.M. (1983) Modelling the flow of mass and energy within a
snowpack for hydrological forecasting. Annals of Glaciology Vol.4
p.l98-203.
Navarre, J.P. (1975) Modele unidimensionnel d'evolution de la neige
deposee. Neiges et Avalanches (Grenoble), No.ll, p.109-27.
Obled, C. and Rosse, B. (1977) Mathematical models of a melting snowpack
at an index plot. Journal of Hydrology Vol.32 no.l/2 p.l39-163.
239
Sulakvelidze, G.K. (1959) Thermoconductivity equation for porous media
containing saturated vapour, water and ice. Bulletin of the Academy of
Sciences or the USSR. Geophysics Series Vol.2 p.284-287.
Yen, Y.C. (1962) Effective thermal coductivity of ventilated snow.
Journal of Geophysical Research Vol.67 p.1091-1098.
240
Kukla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) SNOW WATCH '85.
Proceedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College Park, MD. Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice), Glaciological Data, Report GD-18, p.241-248.
Characteristics of Seasonal Snow Cover
as Simulated by GFDL Climate Models
Anthony J. Broccoli
Geophysical Fluid Dynamics Laboratory
National Oceanic and Atmospheric Administration
Princeton, New Jersey, U.S.A.
Abstract
Two climate simllat:lDns wre P,erfotmed us~ an at:nDspb!ric genenU. circu-
lation mdel. developed at tba Geophysical Fluid Dynamics laboratory. The mdel.
employed for these s:lnulat:l.ons uses tiE spectral netOOd, in lirlch tiE lDrizon-
tal distributions of atonspb!ric variables are represented by a limited n.mDer
of splErical hanmnics. In this study, tiE seas:mally-varyil'@ distribution of
insolat:lDn at tiE top of tie at:nDspb!re li13S prescribed, alOI'@ with tba climatx>-
logical distributions of sea surface tenperature ani sea ice. The SOOJJ c~r
distributions prodtx::ed in t1Ese s:lmul.ations wre c<lllpU'ed with satellite ob;er-
vations. Both versions of tiE tlDdel generate SllCJW co~ very similar in extent
to the ob:lerved snow cover.
··A number of studies have suggested that the feedback mechanism involving
snow cover, albedo, and temperature is an important factor in climatic change
(e.g., Schneider and Dickinson, 1974). Thus, it is reasonable that the real-
istic treatment of snow cover may be quite important in studies of co2-induced
climate change using mathematical models of the earth's climate. The most
sophisticated of these models, the general circulation models (GCMs), are cap-
able of simulating snow cover and its interactions with the atmospheric circu-
lation. In such models, the proper representation of the snow-albedo-tempera-
ture feedback mechanism requires reasonable agreement between the simulated
snow cover and realtiy. This study compares the area and distribution of
Northern Hemisphere snow cover produced by a GCM with observational data from
satellites.
The GCM used in this study was developed by s. Manabe and his collabora-
tors at the Geophysical Fluid Dynamics Laboratory and is similar to that de-
scribed by Manabe et al. (1979) and Manabe and Hahn (1981). The model is
global with realistic geography and topography. Insolation at the top of the
atmosphere is prescribed as a function of season, but no diurnal variation is
included. Seasonally-varying sea surface temperature and sea ice cover are
prescribed based on climatological data from Reynolds (1982), Walsh (1978),
Zwally et al. (1983), and Alexander and Mobley (1976). Cloudiness is fixed
and depends only on latitude and height. The model uses a hydrologic budget
to predict soil moisture based on rainfall, snowmelt, evaporation, and runoff,
and computes snow cover based on snowfall, snowmelt, and sublimation.
For its dynamical computations, the model uses the spectral method, in
which the horizontal distribution of atmospheric variables is represented by a
limited number of spherical harmonics. The model's horizontal resolution is
241
determined by the number of spherical harmonics retained. This study uses
GCMs with two different horizontal resolutions; the low resolution version is
truncated at wavenumber 15 (corresponding grid size: 4.5° latitude x 7.5°
longitude) a'nd the high resolution version at wavenumber 30 (2.25° latitude x
3.75° longitud~). Nine finite-difference levels, extending from the surface
to approximately 25 mb, are used to represent the vertical distribution of the
atmospheric variables.
Both models are started from an initial state consisting of a dry, iso-
thermal atmosphere at rest. A relatively short period of integration is
required for the models to reach a quasi-equilibrium climate, since the sea
surface temperature distribution is prescribed. The models are further inte-
grated to provide data for analysis. This analysis period is nine model years
for the low resolution model and only one year for the high resolution version
due to its greater computational requirements.
The most comprehensive set of observations of Northern Hemisphere snow
cover available for comparison with the model is the NOAA satellite-derived
snow cover data base (Matson and Wiesnet, 1981). A climatology of the season-
al variation of Northern Hemisphere snow cover area based on this data set
has been published by Dewey and Heim (1981), and monthly maps of mean Northern
Hemisphere snow cover have been constructed by Robock (1980). Both of these
studies will be used as sources of observed snow cover data to which the model
snow cover distributions can be compared.
40
30
20
10
--.......
J F
' ' ..... .....
" '\
M
' ' ' '\ ', .
\
\
A
' ' ' \
' \
\
\
M
•
•
" ~
•
J
"I
I
I
I
I
I
I
• • •
• • • •
J A s 0
/
/
• I
I
I
N
• •
• . ...-
/ . /
/
/
/
/
D
6 2 Figure 1. Areal coverage of Northern Hemisphere snow cover (10 km ) from the low
resolution (solid line) and high resolution climate simulations. The observed
snow cover area from the climatology of Dewey and Heim (1981) is indicated by the
solid circles.
242
Figure 2. February mean snow cover: (left) low resolution model; (center) high resolution model; (right)
observed. Hatched areas represent mean snow cover water equivalent of 1 em or more from the climate model
simulations and the observed mean snow cover based on the analysis by Robock (1980).
Figure 3. As in Figure 2 except for May.
The seasonal variation of snow cover area produced by both models is
compared with observed data in Fig. 1. A model gridpoint is considered to be
snow-covered if the water equivalent of the snow on the ground averages at
least 1 em. Both the high and low resolution versions of the model simulate a
seasonal-variation quite similar to that-observed. The low resolution model
overestimates snow cover from November through April and underestimates it
from May through September. In contrast, the high resolution model systemati-
cally underestimates snow cover area in practically all seasons. In compar-
ing the two resolutions wi:th each other, the low resolution model has more
snow cover in all but summer.. Bo.th versions produce a spring retreat of snow
cover that is too rapid.
The comparison of the geographical distributions of snow cover produced
by each of the GCMs with observations is made at two different times during
the seasonal cycle. February is representative of the seasonal maximum of
snow cover, and May ' illustrates the spring retreat phase of the seasonal
cycle. Maps of snow cover from both resolution models are compared with the
observed snow cover maps of Robock (1980) in Figs. 2 and 3.
During February, the low resolution model simulates snow cover which is
slightly too extensive, while the high resolution version has snow cover area
well below that observed. Over North America, the low resolution simulation
is very close to reality, with the snowline at approximately 40°N. In the
90N
w 60
0 ::>
1-
i=
:5 30
~
0 R15
J F M A M J J A s 0 N D J
90N
w 60
0 ::>
1-
i=
:5 30
0 R30
J F M A M J J A s 0 N D J
Figure 4. Latitude-time distribution of the difference
between the climate model and observed zonal mean
surface air temperature (°C): (top) low resolution model;
(bottom) high resolution model. The observed data are
taken from the climatology of Crutcher and Meserve (1970).
The dashed lines represent the approximate southern limit
of snow cover from each model simulation.
245
high resolution model too little snow covers the eastern two-thirds of the
continent. In western and central Europe both models are very similar to the
observed snow cover, while a slight deficit of snow cover can be noted from
eastern Europe to the Caspian Sea region south of 50°N. In Asia an excessive
amount of snow·covers China between 30-42°N in the low resolution model. This
exce~s snow cover is the primary reason for the model's overestimation of
winter snow cover area. In the high resolution simulation, only a slight
excess of snow occurs in western China. The more patchy appearance of the
snow cover in the high resolution model results from the short averaging
period as compared with the low resolution case (one February versus nine
Februaries).
Turning to the May maps (Fig. 3), both models can be seen to underesti-
mate snow cover in the spring retreat season. In North America, the area east
of Hudson Bay is free of snow in the model simulations in contrast to the snow
cover observed in this area. In both models snow cover is also absent from
much of high latitude Eurasia from Scandinavia, along the coasts of the Barents
and Kara Seas, while obseivations show this area to be snow covered. Better
agreement occurs along the Arctic coast of Siberia east of 90°E.
In a model as complicated as a GCM, it can be difficult to ascertain the
causes of a particular deficiency in the model's climate simulation because of
the complex interactions that take place. In the case of snow cover, sorting
out cause and effect can be particularly difficult. Its existence depends on
factors such as temper~ture, precipitation, and solar radiation, but once pre-
sent snow cover can influence each of these factors. Despite this difficulty,
the strong association between temperature and snow cover may allow some
insight to be gained into the systematic errors in the simulation of snow
cover by studying the temperatures simulated by the model.
Figure 4 is a latitude-time plot of the difference between the simulated
and observed zonal mean surface air temperature over land. The observed tem-
peratures are taken from the Northern Hemisphere climatology of Crutcher and
Meserve (1970). Both models have a similar error pattern, with temperatures
too warm at high latitudes and too cold in middle latitudes. In the high
resolution model, the region of excessive warmth extends farther south than it
does in the low resolution version. This is consistent with the differences
in the snow cover simulated by the two models.
An approximate southern limit of snow cover (excluding regions of ele-
vated terrain) is indicated on each latitude-time plot by the heavy dashed
line. During the spring retreat of snow cover, both models are too warm at
the latitudes near the mean snow boundary. This is consistent with the too
rapid retreat of the snow cover in both models. During the autumn expansion
of snow cover, the snow boundary occupies latitudes at which the models' tem-
peratures are close to or slightly cooler than observed. Although cause and
effect cannot be distinguished, the consistent beh~vior of temperature and
snow cover suggests that the models treat the interaction between these cli-
matic variables in a reasonably realistic manner.
In summary, both models are quite successful in reproducing the seasonal
variation of snow cover area, with the low resolution version producing more
246
snow than the high resolution model. In both model simulations, the spring
retreat of snow cover occurs too rapidly. Errors in the simulation of surface
air temperature are consistent with those involving snow cover.
Future efforts to validate climate models should continue to consider
snow cover by virtue of its importance in the climate system. The avail-
ability of the NOAA satellite-derived snow cover data set in digital form
should allow more detailed comparisons of observed snow cover frequency and
variability with model simulations.
Acknowledgments
I am indebted to Suki Manabe for making his climate model available for
this study and providing a~vice and encouragement. He and Tom Del worth
contributed a number of useful suggestions that improved this manuscript.
Many thanks to Joyce Kennedy for typing the camera-ready copy, and to John
Conner and Phil Tunison and his staff for preparing the illustrations.
References
Alexander, R.C., Mobley, R.L. (1976) Monthly average sea-surface temperature
and ice-pack limits on a 1° global grid. Monthly Weather Review,
v.104(2), p.l43-148.
Crutcher, H.L.; Meserve, J.M. (1970) Selected
dew points for the Northern Hemisphere.
Report NAVAIR 50-IC-52.
level heights, temperatures, and
u.s. Naval Weather Service,
Dewey, K.F.; Heim, R., Jr. (1981) Satellite observations of variations in
Northern Hemisphere snow cover. U.S. Department of Commerce, NOAA
Technical Report NESS 87.
Manabe, s.; Hahn, D. G .• (1981) Simulation of atmospheric variabj.lity. Monthly
Weather Review, v.109(11), p.2260-2286.
Manabe, S.; Hahn, D.G.; Holloway, J.L., Jr. (1979) Climate simulations with
GFDL spectral models: effect of spectral truncation. (In: Gates, W.L.
ed •. Report of. the JOC Study Conference on Climate ModeiS:'" Performance,
Intercomparison and Sensitivity Studies. World Meteorological
Organization, GARP Publication Series No. 22, p.41-94.
Matson, M.; Wiesnet, D.R. (1981) New data base for climate studies. Nature,
v.289, p. 451-456.
Reynolds, R. W. (1982) A-monthly averaged climatology of sea surf ace tem-
perature. u.s. Department of Commerce. NOAA Technical Report NWS 31.
Robock, A. (1980) The seasonal cycle of snow cover, sea ice and surface
albedo. Monthly Weather Review, v.103(3), p.267-285.
Schneider, S.H.; Dickinson, R.E. (1974) Climate modeling. Reviews of
Geophysics and Space Physics, v.12(3), p.447-493.
247
Walsh, J.E. (1978) A data set on Northern Hemisphere sea ice extent, 1953-76.
World Data Center A for Glaciology (Snow and Ice). Glaciological Data,
Report GD-2, p. 49-51.
Zwally, H.M.; €omiso, J.C.; Parkinson, C.L.; Campbell, W.J.; Carsey, F.D.;
Gloersen, P. (1983) Antarctic sea ice 1973-1976: satellite passive
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248
Kukla, G; Barry, R.G.; Hecht, A.; Wiesnet, D. eds. (1986) SNOW WATCH '85.
Proceedings of the Workshop held 28-30 October 1985 at the University of
Maryland, College Park, MD. . Boulder, Colorado, World Data Center A for
Glaciology (Snow and Ice), Glaciological Data, Report GD-18, p.249-270.
CO 2-Induced Changes in Seasonal Snow Cover Simulated .
By The OSU Coupled Atmosphere-Ocean General Circulation Model
1. Introduction
Michael E. Schlesinger
Department of Atmospheric Sciences
and
Climatic Research Institute
Oregon State University
Corvallis, Oregon USA
If the Earth's atmosphere were composed of only its two major constitu-
ents, nitrogen (N2 , 78% by volume) and oxygen (02 , 21%), the Earth's surface
temperature would be close to the -18°C radiative-equilibrium value necessary
to balance the approximately 240 W m-2 of solar radiation absorbed by the
surface-atmosphere system. The fact that the Earth's surface temperature is
a life-supporting 15°C is a consequence of the greenhouse effect of the
atmosphere's minor constituents, mainly water vapor (H2 o, 0.2%) and carbon
dioxide (C~, 0.03%). Measurements taken at Mauna Loa, Hawaii show that the
co2 concentration has increased from 316 ppmv in 1959 to 342 ppmv in 1983
(Elliott et al., 1985), an 8% increase in 24 years. A variety of direct co2 measurements and indirect reconstructions indicate that the pre-industrial
co2 concentration during the period 1800 to 1850 was 270 ±10 ppmv (WMO,
1983). A study by Rotty (1983) reports that the C0 2 concentration increased
from 1860 to 1973 due to the nearly constant 4.6%/yr growth in the consump-
tion of fossil fuels (gas, oil, coal), and has continued to increase since
1973 due to the diminish~d 2.3%/yr growth in fossil fuel consumption. A
probablistic scenario analysis of the future usage of fossil fuels predicts
about an 80% chance that the 002 concentration will reach twice the pre-
industrial value by 2100 (Nordhaus and Yohe, 1983). Computer simulations of
the climate change induced by a doubling of the co2 concentration have been
made with a hierarchy of mathematical climate models and give a warming of
1.3 to 4.2°C in the global-mean surface air temperature (Schlesinger and
249
Mitchell, 1985). Since such a global warming represents about 25 to 100% of
that which is estimated to have occurred during the transition from the last
ice age to the present interglacial (Gates, 1976a, b~ Imbrie and Imbrie,
1979), there is considerable interest in the identification of a co2-induced
climatic change, and in the potential impacts of such a change on the spec-
trum of human endeavors.
One aspect of a co2 -induced change in climate is the change in the
amount of snow on the Earth's surface. It can be argued that the amount of
snow will decrease in response to the increase in temperature induced by
increased amounts of 002 in the Earth's atmosphere. On the other hand, it
can also be argued that the snow amount will increase as a result of there
being more water vapor available to form snow in the warmer, co2 -enriched
atmosphere. The actual change in the snow cover induced by increased co2 is
of potential importance because of the four roles that snow cover can play.
First, changes in snow cover can influence the surface aibedo to produce a
feedback. Decreased snow cover would lower the surface albedo, produce more
absorption of solar energy, and enhance the ·warming. Increased snow cover
would raise the surface albedo, produce less absorption of solar energy and
diminish the warming. Tnus, insofar as the co2 -induced warming is concerned,
decreased snow cover produces a positive feedback and increased snow cover
produces a negative feedback. Second, co2 -induced changes in snow cover can
have a significant impact on the surface hydrology. Two of the three most
recent simulations of 2xco2 -induced climate change performed with atmospheric
general circulation models (AGCMs) coupled to prescribed-depth oceanic mixed
layer models have shown a considerable desiccation of the soil in the mid-
latitude agriculturally-productive areas in the Northern Hemisphere (see
Schlesinger and Mitchell, 1985). This summer drying occurred in part due to
the earlier spring melting of the seasonal snowpack in the co2 -enriched
world. Third, co2 -induced changes in the Antarctic and Greenland snowpacks
can change the equilibria of the corresponding ice sheets and surrounding ice
shelves which could potentially affect sea level. Fourth, and lastly,
changes in the rate of snow accumulation induced by increased co2 can be
monitored to serve as one of the "fingerprint" quantities in a strategy to
detect 002 -induced climate change.
Because of these potentially important roles of changes in snow cover in
co2 -induced climate change, we report here results of two simulations per-
formed with the Oregon State University (OSU) atmospheric general circulation
model coupled to the OSU oceanic general circulation model. As these simula-
tions differed only in their prescribed constant 002 concentrations, their
differences can be interpreted as the climate change induced by increased
co2 • Selected results from these simulations have been presented and inter-
preted by Schlesinger et al. (1985) to obtain an estimate of the thermal
response time of the atmosphere, ocean and sea ice components of the climate
system. Here we shall focus attention on the simulated co2 -induced changes
in the surface snow cover and surface air temperatures.
In the following we briefly describe in section 2 the coupled
atmosphere/ocean general circulation model and the two simulations. Selected
results from the simulations are then presented in section 3. Finally,
250
conclusions regarding the importance of co2 -induced changes in snow cover are
described in section 4.
2. Description of the Model and Simulations
The atmospheric component of the coupled model is basically the same as
the atmospheric general circulation model described by Schlesinger and Gates
(1980, 1981), and documented by Ghan et al. (1982). This is a two-layer
primitive equation GCM formulated using normalized pressure (sigma, a) as the
vertical coordinate, with the top at 200 mb and surface orography as resolved
by a 4 degree by 5 degree latitude-longitude grid. The model predicts the
atmospheric velocity (wind), temperature, surface pressure and water vapor,
the surface temperature, s~ow mass, soil water and clouds, and includes both
the diurnal and seasonal variations of solar radiation.
The oceanic component of the coupled model is basically the same as that
described by Han (1984a, b). This is a six-layer primitive equation model of
the world ocean that includes realistic lateral and bottom topography as
resolved by the 4 degree by 5 degree latitude-longitude grid. In distinction
.from the oceanic GCM described by Han (1984a, b), however, the model version
used in the present coupled simulation has been extended to include the
Arctic Ocean. The model predicts the oceanic velocity (current), temperature
and salinity (under the constraint of a prescribed surface concentration),
and the formation and melting of sea ice.
The vertical structure of the coupled model is shown in Fig. 1 along
with the primary quantities predicted during the course of a simulation.
Here u and v are the horizontal components of the wind or current, T the
temperature, q the atmospheric water vapor mixing ratio, s the oceanic
salinity, and cr and w the atmospheric and oceanic vertical velocities,
respectively. The surface boundary condition for the atmosphere is o = 0 at
a = 1, while that for the ocean is w = 0 at z = 0. At the ocean bottom the
condition w = V•Vh is imposed where v is the horizontal velocity and h is the . . ocean depth, wh1le a = 0 at the top of the model atmosphere at a = 0 (200
mb) • Figure 2 shows the horizontal resolution of the modei and the
orographies of the continental surface and depths of layers 1-3, 4-5 and 6 of
the ocean model.
The coupling of the atmospheric and oceanic models is synchronous, that
is, both component models simulate the same period of time. The atmospheric
model is integrated forward in time one hour subject to the sea-surface
temperature and sea ice thickness fields predicted by the oceanic model, and
the latter is integrated forward in time one hour subject to the net surface
heat flux and surface wind stress fields calculated by the atmospheric model.
Two 20-year simulations have been performed with the coupled
atmosphere-ocean model that differ only in their prescribed co2 concentra-
tions. In the "reference" or "1xC~" simulation the co2 concentration was
taken to be constant in space and time and equal to 326 ppmv. In the
"experiment" or "2xco2 n simulation the concentration was doubled to 652
ppmv. Each simulation was started from the same initial condition. For the
251
0
1/4
E
~~ts --------------
w
~~$ --------------
4350~--------~·~·i~·~Vh~-------
Fig. 1. The vertical structure of the coupled model, together with the primary
dependent variables and boundary conditions in the atmosphere and
ocean. The atmospheric GCM uses normalized pressure (sigma,
a= (p-Pt>I<Ps-Pt>, where pis pressure, Pt the 200mb pressure
of the top of the model, and Ps the variable surface pressure) as ver-
tical coordinate and determines the horizontal velocity components u
and v, the temperature T, and the water vapor mixing ratio q at two
tropospheric levels a= 1/4 (p ~ 396mb) and a= 3/4 (p ~ 788mb), the . . vertical velocity a at a = 1/2 (p ~ 592mb), the temperature Ta and
water vapor mixing ratio qa of the surface air, the temperature Ts of
the earth's non-water surfaces, the soil water qs, the snow mass, and
the cloudiness. The z-coordinate oceanic GCM determines the horizontal
velocity components u and v, the temperature T and the salinity s at
six levels intermediate to those at which the oceanic vertical veloc-
ity w is determined. The boundary condition o = 0 is imposed at the
model top and at the earth's surface, while the conditions w = 0 and
w = V•Vh are imposed at the ocean surface and ocean bottom, respective-
ly, where V is the horizontal velocity and h is the ocean depth.
252
70N
50N
30N
ION
30S
50S
70S
Fig. 2. The global domain of the coufled model, showing the continental
outline and orography (in 10 m), and the oceanic depth resolved
by the model's 4 degree by 5 degree lai;itude-longitude grid. Here
the unshaded oceanic area is less than 750 m depth, the hatched
area is between 750 m and 2750 m depth, and the shaded area is
between 2750 m and 4350 m depth.
253
atmospheric component of the model the initial conditions were taken as those
on 1 November of year 1 of a 10-year atmospheric GCM integration that was
itself initialized from an earlier model simulation. For the oceanic
component of t~ model the intial conditions were taken as those on 1
November of year 9 of an 11-year oceanic GCM integration with prescribed
monthly atmospheric forcing that was itself initialized from an earlier
25-year simulationl with annually-averaged atmospheric forcing with initial
conditions from the observed ocean climatology of Levitus (1982). In the
following we shall identify the starting time for both the 1xco2 and 2xC02 simulations as 1 November of year 0. In this chronology each 20-year simula-
tion terminates at the end of 31 October of year 20.
3. Results
In this section we present surface air temperature and snow cover
results from the 1xco2 and 2xco2 simulations and their 2xC0 2 -1xco2 differ-
ences. First, we present the temporal evolution of the global mean tempera-
ture and hemispheric snow cover areas. Second, we display the annual cycles
averaged over the last ~ive years of the simulations for the 2xco2 -1xco2 differences in the zonal mean temperature and snow amount. Next, we exhibit
the 5-year average geographical distributions of the 2xco2 -1xC02 differences
in temperature and snow amount for December-January-February (DJF) and June-
July-August (JJA). Finally, we show plots for these seasons for each
hemisphere of the 2xco2 -1xco2 snow amount differences for each grid point
versus the altitude of the grid point.
a. Temporal evolution of the 1xCC? and 2xco2 simulations
The top panel of Fig. 3 shows the temporal evolution of the global mean
surface air temperature during the 20-year 1xC02 and 2xco2 simulations,
together with the observed normal annual cycle. This panel shows that
although the temperature of the 1xco2 simulation was initially colder than
the observed temperature throughout the entire annual cycle, the 1xco2 simu-
lation warmed with time such that there is relatively good agreement with the
observed temperatures during at least the last five simulated years. The top
panel of Fig. 3 also shows a similar warming with time of the 2xco2 simula-
tion such that it is warmer than the 1xco2 simulation throughout virtually
each annual cycle during the last five simulated years.
The temporal evolution of the difference between the global mean surface
air temperatures for the 1xco2 and 2xco2 simulations is shown in the bottom
panel of Fig. 3, together with the 12-month running mean superposed. This
panel shows an initiallJ rapid warming of the surface air temperature induced
.bY the doubled co2 concentration, followed by a more gradual warming to a
value of about 1.5°C averaged over the last five years of the simulations.
Using a one-dimensional model to represent the evolution of the global mean
warming of the atmosphere and ocean, Schlesinger et al. (1985) have estimated
1 This was erroneously stated to be a 40-year simulation in Schlesinger et
al. (1985).
254
u
0
n.e
1&.1
ta.e
' I
A
111
I
I
I
I
~\I 1\J'
I \'fl
I I v 'J
A
t~
~
1,
I I
I I
I I
I I
I : I
\V: ~~:
\) 'J
I
I
I
~~I ~~~II v v
I
I I
I ~~i ~~
1 tl I 'I
II ')
IJ
I
lt-J
I
11/1
\I
"
A
I I I I ~V!, ,y
l'il I·"
I I .. \1 " ,
tt.e;--,--~---r--.---.--.--~---r--,---r--.~~---r--,---r-~r-~---r--,---~
I 2 I 3 I 4 I S I & I 7 I 8 I II I 11 I 11 I 12 I 13 I 14 I IS I 16 I 17 I 18 I 19 I 28 I
Q)
H ::s
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Q)
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Q) 1.0
NH 0 Q)
Ult-l
Xlt-l
r-i ·ri
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Time, years
Fig. 3. The global mean surface air temperature (above) as simulated for
1xco2 (dashed line) and 2xco2 (solid line), and as observed
(dotted line, from Jenne, 1975), and the 2xco2 -1xco2 surface air
temperature difference (below) with the 12-month running mean
superposed (broken line).
255
that the asymptotic equilibrium warming of the surface air temperature is
2.8°C, and that the characteristic e-folding time required for the atmosphere
and upper ocean to reach 63% of their equilibrium warming is about 50 to 75
years. This long thermal response time is a result of the downward transport
of the C~-indaced surface warming into the interior of the ocean by the
oceanic general circulation.
The top panel of Fig. 4 depicts the evolution of the northern hemisphere
snow area for the 1xco2 and 2xco2 simulations, together with the observed
northern hemisphere snow area obtained by satellite observations from
November 1966 through .December 1980 (Dewey and Heim, 1981). This panel shows
that the model simulates the observed minimum snow area quite well, but simu-
lates its occurrence in July rather than in August each year. The model also
simulates the maximum snow area about one month earlier than is observed
(February) in 11 of the 20 years, and overestimates its magnitude by almost
30x106 krn2 every year. However, this overestimate by the model of the winter
snow area is not surprising because a model grid area is included in the
total snow area if it has any non-zero snow cover, regardless of how small,
while the satellite-observed snow area includes only those areas whose snow
cover is larger than some unknown threshold value. Thus, to validate the
surface snow cover simulated by GCMs, it is imperative that the snow-cover
threshold of the satellite observations be documented.
The top panel of Fig. 4 also shows that the northern hemisphere snow
area in the 2xco2 simulation evolves such that there is less snow cover
throughout most of the annual cycle than there is in the 1xco2 simulation.
This is more clearly .shown by the 2xc~-1xco2 snow area differences presented
in the lower panel of Fig. 4. Averaged over the last five years of the simu-
lation, the northern hemisphere snow area is 4.8x106 km2 smaller in the 2xco2 simulation than in the 1xco2 simulation. However, the minimum and maximum
northern hemisphere snow areas in the 2xco2 simulation occur at about the
same time of the year as their counterparts in the 1xco2 simulation.
The top panel of Fig. 5 shows the evolution of the southern hemisphere
snow area for the 1xco2 and 2xco2 simulations, together with two crude esti-
mates for the observed southern hemisphere snow area. The smaller estimate
(solid horizontal line) is simply the area of Antarctica, and thus presumes
that there is negligible snow on the southern hemisphere sea ice (and else-
where in the Southern Hemisphere) • The larger estimate (dotted line) pre-
sumes that the southern hemisphere sea ice is everywhere covered by snow~
thus, this estimate is taken as the area of Antarctica plus the area of the
southern hemisphere sea ice as reported by Zwally et al. (1983) based on
9-years of satellite microwave observations. This]panel shows that the
southern hemisphere snow areas in the 1xco2 and 2xco2 simulations decrease
with time from values that are about Sx106 km2 larger in the annual mean than
the larger estimate of the observed area (due to an overestimate of the
southern hemisphere sea ice by the oceanic general circulation model during
its 34-year "spinup" integration), to values that are somewhat larger in the
annual mean than the smaller estimate of the observed area. This decrease in
the simulated southern hemisphere snow area with time is due to the decrease
in the southern hemisphere sea ice area with time in both the 1xco2 and 2xco2
256
.... I I I I I I I I I I I I I I I I I I I I
I~ p f f n f f 1 f ~ ~ f
l :\ f f "
,,
f p R f r ~
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71.1
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• i ! i i \ 11 /I i i j1 I\ : \ 1\ 1\ n !\
:;, I \ I! II i \ I i
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: \ I \ ; I \ l \ ' I I ; I \ I I I \ i : ! I
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I 1 l I \ i I f I I I : I i \ ' I r i
I I '
I .
I
. I I i 0 i l
I
I l i i l i \ I I
I
I I I . I ! I I ! 0 .
I I
.
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;
I I I I
I I ; . I i
r I I I I [ I 1
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f I l I I ! 1
.
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1 r 2 I 3 I .. • 5 I 6 I 7 I 8 I !I I 11 I 111 I I I I I I I I 12 13 14 lS 16 17 18 1!1 21 I
l4 15 I& 17 II 19 2e
Time, years
Fig. 4. The northern hemisphere snow area {above) as simulated for 1xco2
{dashed line) and 2xco2 {solid line), and as observed {dotted
line, from Dewey and Heim, 1981), and the 2xC0 2 -1xC02 northern
hemisphere snow area difference {below) with the 12-month running
mean superposed {broken line).
257
~
....
HN
~ a ~~ ~
m~ 3t.t ~~
~
~ ~ m
~ ~ .... H H
~ m
£ ~ ~
0 ~ lt.t w m
·~ I 3 4
~ HN
~ a
~~ t.t ~ m~
~~ ••• ~
~ ~
~
~ u -s.t H ~
~ ~
~ H
~ ~ -··· ~~
0~
m~
~ N -3.t
0 m u ~ X H
~ m -4 ••
I N~ 8 g -s.t X m N
Time, years
Fig. 5. The southern hemisphere snow area (above) as simulated for 1xco2
(dashed line) and 2xco2 (solid line), and the 2xoo2 -1xco2 southern
hemisphere snow area difference (below) with the 12-month running
mean superposed (broken line). Two crude estimates of the observed
southern hemisphere snow area are presented in the upper panel. Th~
smaller estimate (solid horizontal line) is equal to the area of
Antarctica, and the larger estimate (dotted line) is equal to the
area of Antarctica plus the southern hemisphere sea ice area from
Zwally et al. (1983).
258
simulations. The southern hemisphere snow area decreases more rapidly with
time in the 2xco2 simulation than in the 1xco2 simulation. This is shown
more clearly in the bottom panel of Fig. 5 wherein the evolution of the
2xco2 -1xco2 differences in southern hemisphere snow area is presented.
Averaged over the last five years of the simulations, the co2 -induced
decrease in the snow area of the Southern Hemisphere is 2.2x106 km, or about
half the decrease in the snow area of the Northern Hemisphere.
Although the estimate by Schlesinger et al. (1985) of the characteristic
response time of the climate system indicates~hat the 1xco2 and 2xco2 simulations are not in equilibrium after only 20 years' integration, Figs.
3-5 show that the 2xco2 -1xco2 differences in the global mean surface air
temperature and hemispheric.snow areas are at least not changing rapidly
during the last five years of the simulations. It is therefore of interest
to examine the seasonal and geographical characteristics of the co2 -induced
changes in these quantities during the five-year time period extending over
years 16 to 20 of the simulations. Accordingly, in the following we present
these co2 -induced climate change characteristics based on averages taken over
the last five years of the standard and doubled co 2 simulations.
b. Five-year averaged co2 -induced climatic changes
The five-year averaged ~-induced changes in selected global and h~i
spheric quantities are presented in Table 1. This table shows that the
surface air temperature warmed somewhat more in the Northern Hemisphere than
in the Southern Hemisphere, most likely as a result of their being more land
in the boreal hemisphere than in the austral hemisphere. Table 1 also shows
that the sea ice area decreased about equally in both hemispheres as a result
of the co2 doubling. The decrease in snow area of the Southern Hemisphere is
Table 1. 2xco2 -1xco2 Differences in Selected Annual-Mean Global and Hemi-
spheric Mean and Total Quantities for Years 16-20.
Global N. Remis. s. Remis.
Quantity Mean Mean Mean
Surface Air Temperature ( OC) 1. 54 1. 74 1. 34
Snow Area (1 o6 km2) -7.04 -4.82 -2.21
Snow Mass (g cm-2) -0.34 -1.05 0.37
Snowfall ( 10-2 g cm-2 day-1 ) -0.14 -o .11 -0.11
Snowmelt < 1 o-2 g cm-2 day-1 ) -0.05 -0.07 -0.04
Sea Ice Area (106 km2) -3.86 -1.89 -1.97
Sea Ice Thickness (em) -2.56 -4.84 -0.87
a These are total, not mean, quantities.
259
only somewhat larger than the decrease in southern hemisphere sea ice area,
while the decrease in the northern hemisphere snow area is more than twice
the decrease in the sea ice area of the Southern Hemisphere. This shows that
the decrease in snow ar~a in the Southern Hemisphere occurs primarily as a
result of the dec~ase in the sea ice area, while the decrease in northern
hemisphere snow area occurs over the northern hemisphere continents as well
as a result of the decrease in sea ice extent in the Northern Hemisphere.
Table 1 further shows that the average snow mass decreases in the Northern
Hemisphere, but increases in the Southern Hemisphere. From the preceding, it
is apparent that this increased southern hemisphere snow mass must occur over
Antarctica. On the other hand, the average sea ice thickness decreases in
both hemispheres. Finally, Table 1 shows that both the average snowfall and
snowmelt decrease in both hemispheres as a result of the co2 increase, with
the decreases in the Northern Hemisphere being about 60% larger than those in
the Southern Hemisphere.
The annual cycle of the 2xC~ -1xco2 zonal mean surface air temperature
differences averaged over the last five years of the simulations is presented
in Fig. 6. This figure shows that the c~-induced warming is small in the
tropics throughout the year with a seasonal variation of less than 1°C. The
seasonal variation of the warming is larger in the polar regions with maximum
values of.about 5°C and 3°C in the Northern and Southern Hemispheres, respec-
tively. In the polar re9ions of both hemispheres the co2 -induced warming is
smaller in summer than in winter. In the Arctic the warming is maximum in
fall and· occurs at 78°N latitude, while in the Antarctic the warming is
maximum in summer and occurs at 68°S latitude. These features of the annual
cycle of ~-induced zonal mean surface air temperature changes are qualita-
tively similar to those obtained by the three most recent atmospheric GCM/
mixed layer ocean model.simulations of the equilibrium climate change.result-
ing from a co2 doubling (Schlesinger and Mitchell, 1985). In these simula-
~ions, as well as in the present coupled atmosphere-ocean .GCM simulation, the
maximum high latitude warming occurs where the sea ice has retreated poleward
in the 2xC~ simulation relative to that in the 1xco2 simulation.
Fig. 7 presents the annual cycle of the 2xC~ -1xco2 zonal niean ·snow mass
differences. Here it is seen that the snow mass in the Northern Hemisphere
decreased at latitudes north of about 30°N virtually throughout the year,
with a maximum decrease of 10 g cm-2 centered near 70°N. This decrease in
the northern hemisphere snow mass was already seen in Table 1 in terms of the
annual mean value. A decrease in the southern hemisphere zonal mean snow
mass is also exhibited in Fig. 7, but only equatorward of about 68°S lati-
tude. Poleward of this latitude the snow mass increases with maximum values
of 10 g cm-2 centered near 74°S and 15 g cm-2 over the interior of
Antarctica. It is this increase in the snow mass over Antarctica throughout
the year that gives rise to the increase in the annual mean southern hemi-
sphere snow mass that was already noted in Table 1.
The geographical distributions of the co2 -induced changes in the surface
air temperature averaged over the last five years of the simulations are
presented in Fig. 8 for December-January-February (DJF) and June-July-August
(JJA). This figure shows that there is an increase in the surface air
260
9011.1
GON
30N
ECU
30$
60S
905
.JI\N FEB MI\R 1\PR Ml\ V .JUN .JUL 1\UG SEP 0C T N0V DEC .JAN
Month
Fig. 6. The annual cycle averaged over the last five years of the
simulations of the 2xco2 -1xco2 zonal-mean surface air temperature
differences (°C) as a function of latitude. Light stipple shows
warming less than 1•c and heavy stipple shows warming greater than
2•c.
261
$ON
60N
30N
305
605
90S
............................... ................................ ...............................
. .. . .............................. .
• • • ••••••• 0 .................... 0 .. . . ............................... . . ............................... ..
.. ······························ ................................
• .. • • • • • • ........ 0 ............... . ... ····· ················ ······· ................................ -....... ----= ························ ························
............. ························ ······· ::::::::::::::·:::::::::::::::::::::g::::::::::=:::::::::::~:::::::::::~:·
~~;;;;;;;;;~;~;;;;;;;;;t~l~~l11llil~l1l1tjliiiiiiiiii;iii~iiiii~iiiiiiiii~iiiill1illiiiiiiiiiiiji1~ifijjjjjjjjjjj~iii~;;~i~;1;;;;;;;;;;;;;;;;;:;;;;;;
JAN FEB MAq APR MAY JUN JUL AUG SEP 0CT N0V DEC JAN
Month
Fig. 7. The annual cycle averaged over the last five years of the
simulations of the 2xco2 -1xco2 zonal-mean snow amount difference
(g cm-2 ) as a function of latitude. The zonal means exclude the
open ocean. Light stipple shows decreases in snow amount, and
heavy stipple shows increases greater than 10 g cm-2.
262
Fig. 8. The geographical distribution of the 2xco2 -1xco2 surface air
temperature differences (°C) for December-January-February (DJF,
above) and June-July-August (JJA, below) as averaged over the last
five years of the simulations. Light stipple shows cooling and
heavy stipple shows warming greater than 2°C. Contours shown are
-1, o, 1, 2, 4 and 6•c, and also -2, 10 and 1a•c (below).
263
temperature almost everywhere, with warming larger than 2•c over most of the
continents and less than 2•c over most of the ocean. The oceanic warming is
less than 1•c over much of the equatorial region during both seasons, and is
less than 1•c around the periphery of Antarctica in DJF and in the Arctic
during JJA. On thli'! other hand, warming in excess of 4•c is found around the-
periphery of Antarctica in JJA and near the northern boundaries of northern
hemisphere continents in DJF. As previously noted, these regions of maximum
warming are associated with the decrease in sea ice in the 2xco2 simulation
relative to the 1xco2 simulation. In general, the qualitative features of
the co2 -induced surface air temperature simulated by the coupled atmosphere-
ocean general circulation model are similar to the features simulated for a
co2 doubling by the three most recent atmospheric GCM/mixed layer ocean
models (Schlesinger and Mitchell, 1985).
Figure 9 presents the geographical distributions of the change in snow
mass averaged over years 16-20 of the 1xco2 and 2xco2 simulations for both
DJF and .JJA. This figure shows that, in contrast to the surface air tempera-
ture, the snow mass decreases and increases over large geographical areas
during both seasons. In particular, the snow mass increases in the interiors
of Greenland and Antarctica during both winter and summer. The increased
snow mass over Antarctica was already noted in the zonal mean snow mass
change (Fig. 7) and for the entire Southern Hemisphere (Table 1).
It is of interest to examine the relationship between the elevation of a
geographical location and the sign of its co2 -induced snow mass change. This
is done in Figs. 10 and 11 for the Northern and Southern Hemispheres, respec-
tively. In the Northern Hemisphere (Fig. 10) in winter there is no relation
between the altitude of a location and the sign of its snow mass change. In
summer, however, locations with altitudes below about 1500 m predominantly
have decreases in their snow mass, while higher-altitude locations have
increases as well as decreases in their snow mass. As previously noted,
these increases in snow mass occur over the Greenland interior. In the
Southern Hemisphere (Fig. 11), increases in the snow mass predominate during
both seasons and occur only at locations with altitudes larger than about 400
m. These high-altitude regions are located in the Antarctic interior as
shown in Fig. 9.
4.0 Summary and Conclusions
Two 20-year simulations have been performed with the osu coupled
atmosphere/ocean general circulation model that differ only in their co2 concentrations, one a 1xco2 simulation with 326 ppmv co2 , and the second a
2xC~ simulation with 652 ppnv co2 • Although neither simulation attained its
equilibrium during its 20-year period, it is useful to compare the simulated
C~-induced changes in the seasonal snow cover.
Averages taken over the last five years of the 1xco2 and 2xco2 simula-
tions show that there is a co2 -induced increase in the annual mean surface
air temperature in both hemispheres and a decrease in the snow area in each
hemisphere. Although the snow mass decreases in the Northern Hemisphere, it
increases in the Southern Hemisphere. Poleward of 30°N and equatorward of
264
,.,.
7111
5'"
3tll
1'"
CbO rt:J tes Q 3U
ses
Fig. 9. The geographical distribution of the 2xc~-1xco2 snow amount
differences (g cm-2 ) for DJF (above) and JJA (below) as averaged
over the last five years of the simulation. Stipple shows
decreases in the snow amount. Contours shown are 0, ±1, ±10, ±50
and ± 100 g cm-2 •
265
N
I Ei
0
IJ'I
1000
to a
sa
a
•I
-so
-too
-1000
•1000
1000
100
so
a
-I
-so
-sao i
. -toaa
-taaa
......... .. .. .. ..
.. .. .. .. .. .. ..
.. ..
• .. .. .. • . .. .. •
.. ..
.. -J .. • Ia .. ' .. 614 ..
0 toao 2000, 3000 4000 saoo
62
........ .. . .. .. ..
..
~~· • • .. .. ....
.. • ..
•• -1· • .. .. . .. .. 442 .
0 1000 2000 300a 4000 &oao
Altitude, m
Fig. 10. The 2xc~-1xco2 snow amount difference for each grid point in the
Northern Hemisphere versus the altitude of the grid point for DJF
(above) and JJA (below). The grid points where the snow amount
was zero for both the 1xco2 and 2xco2 simulations are not shown • ..
The number of grid points where the snow amount increased and
decreased are displayed in the upper and lower halves of each
panel, respectively.
266
N
IE!
0
IJ'I
Q) g
Q) ,..
Q) .... ....
·.-I
't!
+I
I
~ s::
Ul
N
~
.-1
I
N
0 ~
N
Fig. 11.
tODD
100
10
0
-I
-10
•100
·1000
•1000
1000
100
10
0
-I
·10
·100
·1000
·1000
0
II
II
~
• • .. -=--.. _.. . ..
11111111,..&1! ; '!:II II II ~ ... : .. :. ~ .... ~. :'.
Oo }1111'\o f:~llo • II
II II II .. .. .... ... .
II •
1000
II
II
II
II
" II 10 "
• • .r ... ..
.... ,. •••• rl' .. ..
2000 3000
II
11~11 • II,.,
IIIOJIJI ~ti -::.II 10 ~·,.=~ ill'..:;. "'! ~~ lJ.. II II~~~ .... .. " ..
II II,P ,(' II
..
II .. ..
II
II
II 10 J' "'
II ..................
... • II
II 10 ). II ~"!"
0 1000 2000 3000
Altitude, m
30&
138
4000 6000
308
~
161
4000 6000
As in Fig. 10, except for the Southern Hemisphere.
267
68°8 the snow mass decreases virtually throughout the year in response to the
c~ doubling, while the snow mass increases poleward of 68°S latitude
throughout the year. In contrast to the co2 -induced global warming of the
surface air temperature, the snow mass both decreases and increases over
large geographical areas during both December-January-February and June-
July-Au,gust •. · In ·winter there is no relation between the snow mass change in
the Northern Hemisphere and surface elevation. In summer, northern
hemisphere locations below 1500 m elevation predominantly have snow mass
decreases, while higher-altitude surfaces have both increases and decreases.
The increases in northern hemisphere snow mass occur over the Greenland
interior in both summer and winter. This simulated increase in the Greenland
snow accumulation rate for a warmer climate is consistent with the decrease
in the snow accumulation rate reconstructed at Dye, Greenland for the colder
climate of the last ice age (Dahl-Jensen and Johnson, 1986). In the Southern
Hemisphere, the snow mass increases during summer and winter in the interior
of Antarctica above the 400 m level and decreases around the Antarctic
coastline.
The simulated co2 -induced snow mass increase in the interiors of
Antarctica and Greenland suggests that the monitoring of the snow accumula-
tion rates in these locations might be of use in the identification of the
projected climatic change, and in the attribution of this change to the
increasing concentration of co2 and other trace gases. Furthermore, it is
likely that the simulated co 2 -induced changes in the Antarctic and Greenland
snowpacks can change the equilibria of the corresponding ice sheets and
surrounding ice shelves which could potentially affect sea level. This
possibility should be investigated through the use of suitable glaciological
models for Greenland and Antarctica.
ACKNOWLEDGEMENTS
I would like to thank Dean Vickers for programming assistance and
Dee Dee Reynolds for typing the manuscript. This research was supported by
the National Science Foundation and the u.s. Department of Energy under
grants ATM 8205992 and ATM 8511889.
268
REFERENCES
Dahl-Jensen, D., and S.J. Johnson, 1986: Paleotemperatures still exist in
the Greenland ice sheet. NatUPe, 320, 250-252.
Dewey, K.F., and R.H. Heim, 1981: Satellite observations of variations in
northern hemisphere seasonal snow cover. NOAA Technical Report NESS 87,
83 pp.
Elliott, W.P., L. Machta and C.D. Keeling, 1985: An estimate of the biotic
· contribution to the atmospheric co 2 increase based on direct
measurements at Mauna Loa Observatory. J. Geophys. Res., 90, 3741-3746.
Gates, W.L., 1976a: Modeling the ice-age climate. Soienoe, 191, 1138-1144.
Gates, W.L., 1976b: The numerical simulation of ice-age climate with a
global general circulation model. J. Atmos. SOi., 33, 1844-1873.
Ghan, S.J., J.W. Lingaas, M.E. Schlesinger, R.L. Mobley and W.L. Gates, 1982:
A documentation of the OSU two-level atmospheric general circulation
model. Report No. 35, Climatic Research Institute, Oregon State
University, corvallis, 395 pp.
Han, Y.-J., 1984a: A numerical world ocean general circulation model,
Part I. Basic design and barotropic experiment. D,yn. Atmos. Ooeans,
8" 107-140.
Han, Y.-J., 1984b: A numerical world ocean general circulation model,
Part II. A baroclinic experiment. D,yn. Atmos. ~eans, 8, 141-172.
Imbrie, J., and K.P. Imbrie, 1979: Ioe Ages, Sotving the Mystepy. Enslow
Publishers, Short Hills, NJ, 224 pp.
Jenne, R.L., 1975: Data sets for meteorological research. NCAR-TN/IA-111,
National Center for Atmospheric Research, Boulder, CO, 194 pp.
Levitus, s., 1982: Climatological Atlas of the World Ocean, NOAA
Professional Paper No. 13, u.s. Government Printing Office, Washington,
D.C., 173 pp.
Nordhaus, W.D., and G.W. Yohe, 1983: Future paths of energy and carbon
dioxide emissions. In Changing ctimate , National Academy of Sciences,
Washington, D.C., 87-153.
Rotty, R.M., 1983: Distribution of and changes in industrial carbon
dioxide production. J. Geophys. Res., 88, 1301-1308.
Schlesinger, M.E., and W.L. Gates, 1980: The January and July performance
of the OSU two-level atmospheric general circulation model. J. Atmos.
Soi., 37, 1914-1943.
269
Schlesinger, M.E., and W.L. Gates, 1981: Preliminary analysis of the mean
annual cycle and interannual variability simulated by the OSU two-level
atmospheric general circulation model. Report No. 23, Climatic Research
Institute, Oregon State University, Corvallis, 47 pp.
Schlesinger, M.E., W.L. Gates and Y.-J. Han, 1985: The role of the ocean in
co2-induced climate warming: Preliminary results from the OSU coupled
atmosphere-ocean GCM. In CoupLed Oaean-Atmosphere ModeLs, J.C.J.
Nihoul, Ed., Elsevier, Amsterdam, 447-478.
Schlesinger, M.E., and J.F.B. Mitchell, 1985: Model projections of equili-
brium climate response to increased C02. In The PotentiaL CLimatia
Effeats of Inareasing Carbon Dioxide, M.C. MacCracken and F.M. Luther,
Eds., U.S. Department of Energy (in press).
WMO, 1983: Report of the WMO (CAS) Meeting of Experts on the C02 Concentra-
tions from Pre-Industrial Times to I.G.Y. World Climate Programme,
WCP-53, WMO/ICSU, Geneva, 34 pp.
Zwally, H.J., J.C. Oomiso, C.L. Parkinson, W.J. Campbell, F.D. Carsey and
P. Gloersen, 1983: Antarctic sea ice cover 1973-1976 from satellite
passive microwave observations. NASA SP-459, National Aeronautics and
Space Administration, Washington, D.C., 170 pp.
270
AES
AMSU
AVHRR
CFM
CMB
CROP
CRREL
DMSP
EISLF
GCM
GOES
GTS
JIC
JPL
N-ROSS
NASA
NCAR
NESDIS
NIR
run
NMC
NOAA
NSIDC
NWS
OLR
PODS
RCM
SAB
scs
SMA
SMHI
SMMR
SNOTEL
SSM/I
SST
TM
USAFGWC -
USDA
USFS
VHRR
WCP
WMO
WRB
ACRONYMS AND ABBREVIATIONS
Atmospheric Environment Service (Canada)
Advanced Microwave Sounding Unit
Advanced Resolution Radiometer
Community .Forecast Model {NCAR)
Composite Minimum Brightness
Weekly Weather and Crop Bulletin (U.S.)
Cold Regions Research and Engineering Laboratory (U.S.)
Defense Meteorological Satellite Program (U.S.)
Snow and Avalanche Research Institute (Switzerland)
General Circulation Models
Geostationary satellites
Global Telecommunications System
Joint Ice Center (U.S.)
Jet Propulsion Laboratory
Naval -Remote Ocean Sensing Satellite
National Aeronautics and Space Administration (U.S.)
National Center for Atmospheric Research
National Environmental Satellite, Data, and Information Service
(U.S.)
Near-infrared
Nautical mile
National Meteorological Center (U.S.)
National Oceanic and Atmospheric Administration (U.S.)
National Snow and Ice Data Center
National Weather Service (U.S.)
Outgoing Longwave Radiation
Pilot Ocean Data System
Radiative -convective model
Satellite Analysis Branch (U.S.)
Soil Conservation Service
Switzerland Meteorological Agency
Swedish Meteorological and Hydrological Institute
Scanning Multichannel Microwave Radiometer
Snow telemetry network (U.S.)
Special Sensor Microwave imager
Sea surface temperature
Thematic Mapper
U.S. Air Force Global Weather Central
u.s. Department of Agriculture
u.s. Forest Service
Very High Resolution Radiometer
World Climate Programme (WMO)
World Meteorological Organization
Water Resources Branch {Canada)
271
NOTES
Meetings
International Association of Hydrological Sciences (IAHS) at the XIX General
Assembly of the International Union of Geodesy and Geophysics:
In response to an invitation from the Canadian National Committee for the
International Union of Geodesy and Geophysics and sponsored by the Na-
tional Research Council of Canada, the International Union of Geodesy and
Geophysics (IUGG) will hold the XIX General ssembly in Vancouver, British
Columbia, Canada, from 9-22 August 1987.
The principal aim of the IAHS General Assembly is to promote the advance-
ment of the hydrological sciences, to review the newest developments in a
few selected fields and also to outline new directions for future re-
search. In identifying the main themes for the XIX General Assembly an
attempt was made to select interdisciplinary topics which will ensure the
cross-fertilization of the work and ideas of the different Commissions of
the Association. The main themes include:
a) Climate/hydrology interactions with emphasis on the effects of
climate change;
b) Transport and movement of pollutants in surface and sub-surface
hydrological systems;
c) Snow and ice studies;
d) Environmentally sound methods of water management including
sediment studies.
The Assembly will be held on the campus of the University of British
Columbia. Lecture halls and meeting rooms will be available on campus
for symposia, workshops and business meetings.
Symposia and Workshops of particular interest to the snow and ice community
include:
Sl: Large Scale Effects of Seasonal Snow Cover. Convenor: Dr. B.E.
Goodison, Canadian Climate Center, Atmospheric Environment Service.
Objective: To focus on the developing state of knowledge and research
techniques related to snow/climate interactions in both polar
and mid-latitude regions, snowmelt effects in major
()2500km2) river basins and appropriate methods for acquiring
data pertinent to large-scale effects of seasonal snow cover.
S3: The Influence of Climate Changes and Climatic Variability on Hydro-
logical Regime and Water Resources. Convenor: Prof. S.I. Solomon,
Department of Civil Engineering, University of Waterloo, Waterloo,
Ontario.
273
Objective: To focus on the influence of climate changes and variability
on hydrological processes and water resource systems and to
define better these changes using developing research tech-
•niques, hydrological proxy d~ta and time series analysis.
S5: The Physical Basis of Ice Sheet Modelling. Convenor: Dr. E.D.
Waddington, Geophysics Program AK-50, University of Washington.
Objective: To focus on a complete description of ice sheet physics
(rheology, boundary conditions, and interactions with the
atmosphere, oceans, and mantle) in a form suitable for ice
sheet models, and to bring together a body of data of suffi-
cient size and quality to both calibrate and test increasing-
ly complex ice sheet models.
W5: River Ice. Convenor: Dr. K.S. Davar, Department of Civil Engineer-
ing, University of New Brunswick, Fredericton, N.B., Canada.
Objective: To assess recent progress in approaching problems of river
ice and especially focus on the important subject of ice jams
and regional flooding.
IAHS Symposia. Any scientist wishing to present a paper or poster at an IAHS
symposium should send an extended abstract in English or French (300-500
words) to the symposium Principal Convenor and a copy to:
Dr. G.J. Young
Inland Waters Directorate
Environment Canada
Ottawa, Ontario
Canada K1A OE7
Telephone: (819) 997-1487
Telex: 053-3188 Env HQ Hull
by 15 May 1986. Authors will be notified of acceptance/rejection by 15 August
1986 and will be given instructions on preparing papers at that time. Dead-
line for receipt of full papers including a short abstract preferably in
English and French (maximum 200 words) will be 30 November 1986. All IAHS
Symposia will be pre-published and available at the Assembly.
274
-----------------------------------------
Fifth International Conference on Permafrost, Trondheim, Norway, 2-5 August
1988.
The importance of cold regions is growing and thus the interest in the
cold regions science and enginee~ing. It is necessary to give scientists
and engineers an opportunity to meet regularly in order to report on pro-
gress made and to get impulses for further work.
The V International Conference on Permafrost is an important tool to
reach this goal. The Conference will take place in Trondheim, Norway,
2-5 August 1988. The arrangement will be under auspices of the Norwegian
Committee on Permafrost and organized by the Norwegian Institute of Tech-
nology.
' The first four International Conference on Permafrost were held at Purdue
University, USA (1963), Yatutsk, USSR (1973), Edmonton, Canada (1978) and
Fairbanks, Alaska (1983).
Tentative Items: The Conference Themes will be separated into permafrost
science and permafrost engineering, and will deal with
the following items:
SCIENCE ENGINEERING
Thermal aspects
Physics and chemistry
of frozen ground
Hydrology
Geocryology, past and present
Regional permafrost
Ecology of natural and
disturbed areas
Site investigations and
terrain analyses
Geothermal considerations
Geotechnical properties
Geotechnical engineering
Petroleum engineering
Municipal engineering
Mining engineering
If you are interested in being added to the mailing list for bulletins of the
V International Conference on Permafrost, send your request to:
V International Conference on Permafrost (VICOP)
Norwegian Road Research Laboratory
P.o. Box 6390 Etterstad
N-0604 OSLO 6
Norway
Telephone: + 47 2 466960 Telex: 71238 sregN
275
Recent Publications of WDC/NSIDC Staff Include:
Barry, R.G.; Crane, R.G.; Weaver, R.L.; Anderson, M.A. (1984) Sea-ice and
snow-cover data•availability, needs and problems. Annals of Glaciology,
45, p.9-15.
Barry, R.G.; Brennan, A.M. (1984) World Data Center-A for Glaciology
Antarctic-related activities, 1983-1984. Antarctic Journal of the United
States, v. 19(5), p.245-246.
Barry, R.G. (1985) Snow and ice data. (In: HEcht, A.D., Paleoclimate
Analysis and Modeling, New York, Wiley, p.259-290.
Barry, R.G.; Brennan, A.M. (1985) World Data Center-A for Glaciology:
functions and services. Antarctic Journal of the United States, v.20(1),
p.l4-16.
Weaver, R.L.; Barry, R.G. (1985) Cryospheric Data Management System for
Special Sensor Microwave Imager DMSP Data. (In: Oceans '85 Conference
Record, proceedings of the conference held November 12-14, 1985, San
Diego, CA, p.411-415.)
276 *U.S. GOVERNMENT PRINTING OFFICE: 1986-676-041:20008
GLACIOLOGICAL DA.TA SERIES
Glaciological Data, which supercedes Glaciological Notes, is published by the
World Data Center -A for Glaciology (Snow and Ice) several times per year. It con
tains bibliograph ies, invento ries, and survey reports relating to snow and ice
data, specially prepared by the Center, as well as invited articles and brief, un
solicited statements on data sets, data collection and storage, metho dology, and
terminology in glaciology. Contributions are edited, but not refereed or copy
righted. There is a $5 shelf stock charge for back copies.
'
Scientific Editor: Roge r G. Barry
Technical Editor: Ann M. Brennan
The following issues have been published to date:
GD-1,
GD-2,
GD-3,
GD-4,
GD-5,
GD-6,
GD-7,
GD-8,
GD-9,
GD-10,
GD-11,
GD-12,
GD-13,
GD-14,
GD-15,
GD-16,
GD-17,
GD-18,
Avalanches, 1977
Parts 1 and 2, Arctic Sea Ice, 1978
World Data Center Activities, 1978
Parts 1 and 2, Glaciological Field Stations, 197 9, Out of Print
Workshop on Snow Cover and Sea Ice Data, 1979
Snow Cover, 1979
Inv entory of Snow Cover and Sea Ice Data, 1979
Ice Cores, 1980, Out of Print
Great Lakes Ice, 1980, Out of Print
Glaciology in China, 1981
Snow Watch 1980, 1981
Glacial Hydrology, 1982
Workshop Proceedings: Radio Glaciology; Ice Sheet Modeling, 1982
Permafrost Bibliography, 1978-1982, 1983
Workshop on Antarctic Climate Data, 1984
Soviet Avalanche Research; Avalanche Bibliography Update;
1977 -1983, November 1984
Marginal Ice Zone Bibliography, July 1985
Snow Watch '85, April 1986
Contributions or correspondence should be addressed to:
World Data Center -A for Glaciology (Snow and Ice)
CIRES, Box 449
University of Colorado
Boulder, Colorado 80309
u.s.A.
Telephone (303) 492 -5171; FTS 32 0-531