Hostname: page-component-7b7567c8c-fvjsw Total loading time: 0 Render date: 2026-11-15T01:48:43.846Z Has data issue: false hasContentIssue false
  • English
  • Français

Cryospheric data for model validations: requirements andstatus

Published online by Cambridge University Press:  20 January 2017

R. G. Barry
R. G. Barry*
Affiliation:
(National Snow and Ice Data Center, Cooperative Institute for Research in Environmental Sciences, University of Colorado, Campus Box 449, Boulder, CO 80309-0449, U.S.A.
Rights & Permissions [Opens in a new window]

Abstract

The general status of cryospheric datasets required in climate model studiesis reviewed. Datasets are necessary as boundary conditions, and forvalidation purposes. The former application is decreasing as cryosphericvariables are increasingly being derived prognostically. By contrast, thescope of cryospheric parameters that can be validated is expanding.Cryospheric datasets suitable for validation studies are reported and areaswhere data are lacking are identified.

Information

Type
Research Article
Information
Annals of Glaciology , Volume 25 , 1997, pp. 371 - 375
Copyright
Copyright © International Glaciological Society 1997

Introduction

Numerical climate models require certain global data as boundary conditions. In the first phase of climate modelling using atmospheric general circulation models (ACGMs) in the 1970s, climate experiments generally involved a modern “control” run and a perturbation experiment, analyzing the model's equilibrium response to some change in boundary conditions or model physics. The latter might include a dry atmosphere, prescribed vs prognostic clouds, or varying lapse-rate parameterization. The former category included changing continental geography (Reference Barron, Sloan and HarrisonBarron and others, 1980), incorporating ice sheets, introducing a stratospheric volcanicaerosol layer, modifying sea-ice limits or land-surface cover, and albedo (Reference BarryBarry, 1975). First generation AGCMs were used by several groups to perform experiments with ice age boundary conditions (Reference Williams, Barry and WashingtonWilliams and others, 1974; Reference GatesGates, 1976; Reference Manabe and HahnManabe and Hahn, 1977). These studies involved the development of maps with changed continental boundaries, land and sea-ice extent, sea-surface temperatures and surface albedo. The CLIMAP project for the Last Glacial Maximum and the COHMAP project for conditions between 18 000 years BP and present, at 3000 year intervals, are important examples of such boundary-condition mapping programs for model input purposes. In parallel with these attempts at the reconstruction of specific paleo-climates, the effects on the atmosphere of imposed changes in the extent of snow cover (Reference WilliamsWilliams, 1975) and of sea ice (Reference Herman and JohnsonHerman and Johnson, 1978) were simulated.

The second generation of GCMs from the mid 1980s generally incorporated higher-resolution and improved model physics. They could treat atmosphere–ocean interactions, for example (Reference Meehl and TrenberthMeehl, 1992); the representation of ocean processes ranges from a simple “swamp” ocean, with no heat storage or ocean currents, to a “slab” mixed-layer ocean where sea-surface temperatures are determined from the surface-energy balance and heat storage, or a coupled ocean GCM that incorporates energy transfers and ocean dynamics (Reference Manabe, Spelman and StoufferManabe and others, 1992).

There is an extensive literature on the use of coupled atmospheric–swamp-ocean and slab-ocean mixed-layer models for climate sensitivity studies. This second phase of GCM developments also witnessed the beginning of transient experiments, for 100 years or more, in genuine climate-change studies of increasing greenhouse-gas concentrations. Second-generation model studies that address the cryosphere are primarily experiments to assess CO2 doubling sensitivity, where it rapidly becomes evident that thermodynamic and dynamic processes need to be treated, and that many variables, other than surface albedo, require parameterization (Reference Meehl and WashingtonMeehl and Washington, 1990). Specific studies of the importance of cryospheric parameterizations were also performed, particularly in view of the role of snow- and ice-albedo feedbacks in climate sensitivity to forcing (Reference Ingram, Wilson and MitchellIngram and others, 1989; Reference OglesbyOglesby, 1990; Reference Cohen and RindCohen and Rind. 1991).

The third generation of fully coupled GCMs are beginning to link the atmosphere–ocean–cryosphere–terrestrial biosphere in fully-coupled treatments (Reference Broccoli, Manabe and PeltierBroccoli and Manabe, 1993), and to incorporate biogeochemical transfers. Land-surface schemes are being intercompared (Reference Henderson-Sellers, Vang and DickinsonHenderson-Sellers and others, 1993), but so far similar work for the cryospheric variables is lacking. Nesting of high-resolution mesoscale models in an atmospheric GCM is increasingly used to address regional processes, including orographic precipitation (Reference Giorgi, Bates and NiemanGiorgi and others, 1993) and marginal sea-ice zone processes (Reference Walsh, Lynch, Chapman and MusgraveWalsh and others, 1993; Reference Lynch, Chapman, Walsh and WellerLynch and others, 1995).

A further development is the improved parameterization of variables such as snow and ice albedo in both process models (Reference Ebert and CurryEbert and Curry, 1993) and in GCMs (Reference BarryBarry, 1996), as well as the detailed treatment of ice dynamics in sea-ice models, including leads, ridging and ice drift (Reference Hibler, Flato and TrenberthHibler and Flato, 1992; Reference Mellor, Häkkinen, Johannessen, Muench and OverlandMellor and Häkkinen, 1994). Their are GCM sensitivity studies that update earlier analyses for the effects of leads (Reference VavrusVavrus, 1995), and sea-ice extent and thickness (Reference Rind, Healy, Parkinson and MartinsonRind and others, 1995). These illustrate the increasing need for cryospheric data and highlight the gaps in current archives (Table 1).

Table 1.

Global sea-ice variables and their availability

Data Requirements

Requirements for cryospheric data have recently begun to receive attention from several standpoints. In 1980, the Snow Watch Group identified the requirements for GCMs in terms of basic global maps of snow and ice extent, which until then were mostly in map form, or in the case of snow-depth climatology were thought to be unavailable (Reference HahnHahn, 1981). Reports of the World Climate Programme (Reference JenneJenne, 1982) set out basic data required for model verification and process studies, and these have been subsequently considerably refined and expanded (see Reference CraneCrane, 1993; WMO/Uncsco/UNEP/ICSU, 1995). A comparable effort focusing on modelling needs is desirable (see recommendations in Reference Barry, Goodison and LeDrewBarry and others, 1993), but is beyond the scope of this paper.

Cryospheric Datasets

The primary emphasis here is on hemispheric or global datasets that are available digitally and in gridded form, suitable for use in validation of GCM outputs. However, important gaps in information needed for process model studies and model parameterization are also discussed. Tables 1–3 summarize key cryospheric sets of interest to modellers for sea ice, snow cover and land ice, respectively. Before discussing these, it should be noted that a weekly Northern Hemisphere combined snow-cover and sea-ice extent product for 1978-95 is now available on CD-ROM from National Snow and Ice Data Center (NSIDC, 1996a). It incorporates the National Oceanic and Atmospheric Administration National Environmental Satellite Data Information Service (NOAA-NESDIS) weekly snow data, regridded to the Equal Area Special Sensor Microwave Imager (SSM/I) Earth (EASE) grid (Reference Armstrong and BrodzikArmstrong and Brodzik, 1995), combined with the Scanning Multichannel Microwave Radiometer (SSMR) and SSM/I-derived weekly averaged ice extent.

Table 2.

Global snow-caver variables and their availability

Table 3.

Global land-ice variables and their availability

Snow cover

Data on weekly snow-cover extent (see Table 2) are available for the Northern Hemisphere since 1966, based on satellite visible and infrared imagery, but more reliably since 1972 when the Advanced Very High Resolution Radiometer (AVHRR) was first launched. These data do not take account of snow on sea ice or Greenland. There is a limited dataset for South America for 1974–80, the only southern continent with significant snow cover. These NOAA-NESDIS products are, unfortunately, gridded to a polar Stereographic 89 × 89 square cell grid, so that the resolution is variable; also a grid is counted as snow covered if the fractional cover is ≥50%. New products on snow extent that are planned to be available in 1998 on a daily or weekly basis are described in Reference HallHall (1995). A passive-microwave-derived six day (five day) snow-extent product based on the SMMR for 1978–87 (SSM/I for 1987–present with 25 × 25 km gridcells is currently in preparation at NSIDC.

Snow water equivalent (SWE) can be estimated from passive-microwave data and an experimental mid-monthly dataset was generated for 1978–87 by Reference Chang, Foster, Hall, Powell and ChienChang and others (1990). The data have recently been compared with the output of six GCMs (Reference FosterFoster and others, 1996) based on Atmospheric Model Intercomparison Project standard control runs for 1979–88 (Reference GatesGates, 1992). However, Reference Tait and ArmstrongTait and Armstrong (1996) find systematic biases in products obtained with the linear algorithm developed by Reference Chang, Foster, Hall, Powell and ChienChang and others (1990) compared with station measurements. It is clear that spatio-temporal adjustments are necessary in such algorithms to rectify errors caused by topographic effects on SWE values, screening by forest canopies, and the occur-rence of depth hoar or melt on snow-grain surfaces. Nevertheless, the work of Reference GoodisonGoodison (1989) for the Canadian Prairies illustrates the potential of passive-microwave sensors, even for operational products. Reference Walker and GoodisonWalker and Goodison (1993) have also demonstrated that snowmelt signatures are readily detectable with passive-microwave data although this approach has not yet been implemented as a hemispheric analysis product.

For validation purposes, station data are often desirable, but until now these have been mostly climatological averages. Newly available regional datasets include CD-ROMs of daily snow depth at 284 stations in the Former Soviet Union (FSU) from the beginning of observations (NSIDC, 1994) and snow depths at North Pole drifting stations, 1937–38 and 1951–91 (NSIDC, 1996b). Additionally, 10 day snow-course surveys for more than 1300 stations across the FSU for 1966–90 have been received and are currently being quality checked prior to release.

Snow (and ice) albedo is an important model variable that is commonly parameterized. Approaches to this range from simplistic temperature dependencies (linear or step functions), to calculations that take account of snow age, fractional cover, vegetation masking depth, grain-size, solar zenith angle and spectral range. (see Reference BarryBarry, 1996). The AVHRR Polar Pathfinder plans to generate polar surface broadband albedo data products over the next two to three years (Polar Pathfinder Group, 1997). Research on the estimation of snow grain-size via a pixel mixing approach is being carried out using Airborne Visible and Infrared Imaging Spectroradiometer (AVIRIS) data over mountain watersheds (Reference Nolin and DozierNolin and Dozier, 1993) but wide-area implementation of this approach is unlikely in the near future.

Sea ice

Twenty-five years of data on sca-ice extent and concentration for both polar regions are available on a weekly basis since 1972–73 and daily since 1987 (Table 1). These have also been reformatted for use by the European Centre for Medium-Range Forecasta (ECMWF) model re-analysis program (Reference NomuraNomura, 1995). For the northern polar regions, monthly ice extent and concentration data exist since 1953, and these data will be improved retrospectively as 10 day Russian data for the summer period are incorporated. Since 1900 there are also monthly extent data, mainly for the North Atlantic sector, that are less reliable and more spatially heterogeneous.

Currently there are only limited climatological ice-thickness maps for the Arctic Ocean (Reference Bourke and McLarenBourke and McLaren, 1992), and no digilal archive. There are also extensive published thickness-frequency data for the Arctic and more limited publications for the Antarctic. An atlas of morphological characteristics in the Arctic Ocean exists in digital form (Reference RomanovRomanov, 1993), although information on snow cover on the ice is limited, and those are only point or limited-area data on melt-pond coverage. Melt onset dates can be estimated from passive-microwave signatures, and work is in progress in provide a dataset for 1978–present. There are also limited data on lead coverage (Reference Lindsay and RothrockLindsay and Rothrock, 1995).

Ice surface albedo has been mapped in the Arctic for 10 summer seasons using visible satellite images classified subjectively, as well through analyses of radiance data. However, averages for individual months are currently only within about ±0.10 (Reference Schweiger, Serreze and KeySchweiger and others. 1993). Techniques are available to estimate ice surface temperature from infrared radiance data and products for broadband surface albedo, ice surface temperature and a cloud mask will be generated by the AVHRR Polar Pathfinder for 1982–97 (Polar Pathfinder Group, 1997). In addition, the TIROS Operational Vertical Sounder (TOVS) Polar Pathfinder will provide daily and monthly gridded products of surface skin-temperature and boundary-layer parameters for 1979–97. The EOS Polar Exchange at the Sea Surface (POLES) project also plans to produce products of sea-ice statistics from MODIS and Radarsat data (Reference RothrockRothrock and others, 1995).

Land ice

Remarkably, the state of information on basic land-ice parameters is the least satisfactory (Table 3) . There is currently no digital archive of land-ice extent and elevation. Even inventory data on glaciers are unavailable for several countries with large ice areas, including Canada (Reference BarryBarry, 1995) and the Himalayan nations, although partial national archives exist. In other areas, such as China and South America, satellite mapping is in progress. Data on surface elevation for the Greenland and Antartic ice sheets poleward to 81.5 latitude are available from satellite radar altimetry collected by ERS-1 and ERS-2. In 1998 the Radarsat Antarctic Mapping Program will collect data for the first complete high-resolution digital map of all of Antarctica (Polar Pathfinder Group, 1997).

Future plans include the precision mapping of Greenland and Antarctica by the EOS Geoscience Laser Altimeter System (GLAS) in about 2002 and the mapping of selected target glaciers via the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), in conjunction with a proposed Global Land Ice Monitoring with Satellites (GLIMS) (Reference Kargel and KiefferKargel and Kieffer, 1995).

Data on general ground ice and frozen ground conditions are currently not available in digital form, although a digital version of a Northern Hemisphere map of perennially frozen ground (Reference Brown, Ferrians, Heginbottom and MelnikovBrown and others, in press) is in preparation. Under the auspices of the International Permafrost Association, a project to develop a Global Geo-cryological Database is currently in progress and a CD-ROM is planned for release in mid 1998 (Reference Barry, Heginbottom and BrownBarry and others, 1995).

Concluding Remarks

Data products characterizing many large-scale properties of the cryopshere, and suitable for the validation of GCM control experiments, are now available. Notable gaps exist for land-ice extent and frozen ground characteristics, although such products are planned, or are in preparation. In the case of the seasonally varying elements (snow cover and sea ice), time series of their extent are available individually, or combined. The weekly 25 × 25 km combined snow-cover and sea-ice dataset (17 years) for the Northern Hemisphere should be especially valuable for climate model validation. However, for snow depth (water equivalent) and ice thickness, even reliable climatologies remain to be developed. Intercomparisons of NOAA visible-based and NASA passive-microwave derived snow extent by Reference RobinsonRobinson (1997) and Armstrong (personal communication from R. L. Amstrong, 1996) show significant discrepancies. Whether these exhibit systematic spatial and temporal biases remains to be determined. Nevertheless, further improvement in this situation can be expected over the next few years as blended satellite and ground data on SWE are produced and newly released in situ ice-thickness measurements become available for the Arctic Ocean. It is in the area of derived parameters (such as albedo and snow grain-size) that much additional work remains to be done. There is also a significant need for readily accessible test data for process model validation in all areas of cryospheric research. An illustration of what is currenlly available is provided by the holdings of the National Science Foundation's Arctic System Science (ARCSS) Data Coordination Center at the National Snow and Ice Data Center (Reference McGinnis and CrossMcGinnis and Cross, 1997) and accessible via the World Wide Web.

Acknowledgements

Supported in part by University of Washington (POLES) subcontract to the University of Colorado. Thanks are due to L. Ryder for word processing.

References

Armstrong, R.L., and Brodzik, M.J. 1995. An earth-gridded SSM/I data set for cryospheric studies and global change monitoring. Adv. Space Res., 15(10), 155–163.Google Scholar
Barron, E.J., Sloan, J. L. and Harrison, C. G.A. 1980. Potential significance of land–sea distribution and surface albedo variations as a climatic forcing factor. 180 M.Y. to the present. Palaeogeogr., Palaeoclimatol., Palaeoecol., 30(1), 17–40.Google Scholar
Barry, R.G. 1975. Climate models in palaeoclimatic reconstruction, Palaeogeogr., Palaeoclimatol., Palaeoecol., 17(2), 123–137,Google Scholar
Barry, R.G. 1995. Observing systems and data sets related to the cryosphere in Canada: a contribution to planning for the Global Climate Observing System. Atmosphere–Ocean, 33(4), 771–807.Google Scholar
Barry, R.G. 1996. The parameterization of surface albedo for sea ice and its snow cover. Prog. Phys. Geogr., 20(1), 63–79.CrossRefGoogle Scholar
Barry, R.G., Goodison, B. E. and LeDrew, E.F. eds. 1993. Snow Watch '92. Detection Strategies for Snow and Ice: Proceedings, International Workshop on Snow and Lake Ice Cover and the Climate System, 29 March–1 April 1992, Niagara-on-the-Lake, Ontario. Glaciol. Data Rep. GD-25.)Google Scholar
Barry, R.G., Heginbottom, J. A. and Brown, J. 1995. Workshop on Permafrost Data Rescue and Access, 3–5 November 1994, Oslo, Norway. Glaciol. Data Rep. GD-28.Google Scholar
Bourke, R.H. and McLaren, A.S. 1992. Contour mapping of Arctic Basin ice draft and roughness parameters. J. Geophys. Res., 97(C11), 17,715–17,728.Google Scholar
Broccoli, A.J. and Manabe, S. 1993. Climate model studies of interactions between ice sheets and the atmosphere–ocean system. In Peltier, W.R., ed. Ice in the climate system. Berlin, etc., Springer-Verlag, 271–290. (NATO ASI Series I: Global Environmental Change 12.)CrossRefGoogle Scholar
Brown, J., Ferrians, O.J. Jr, Heginbottom, J. A. and Melnikov, E.S. eds. In press. Circumartic map of permafrost and ground ice conditions. Reston, VA, U.S. Geological Survey. (CP 45, Scale 1:10 million.)Google Scholar
Chang, A.T.C., Foster, J.L. Hall, D.K. Powell, H. W. and Chien, Y.L. 1990. Nimbus-7 derived global snow cover and snow depth data set: the pilot land data system. Greenbelt, MD, National Aeronautics and Space Administration. Goddard Spare Flihgt Center.Google Scholar
Cohen, J. and Rind, D. 1991. The effect of snow cover on the climate. J. Climate, 4(7), 689–706.2.0.CO;2>CrossRefGoogle Scholar
Crane, R.G. 1993. Workshop on cryospheric data rescue and access, Glaciology. Data GD-25, 271–294.Google Scholar
Ebert, E.E. and Curry, J.A. 1993. An intermediate one-dimensional thermodynamic sea ice model for investigating ice–atmosphere interactions, J. Geophys. Res., 98(C6), 10,085–10,109.CrossRefGoogle Scholar
Foster, J.L. and 9 others. 1996. Snow cover and snow mass intercomparisons of general circulation model and remotely sensed datasets. J. Climate, 9(2), 409–426.2.0.CO;2>CrossRefGoogle Scholar
Gates, W.L. 1976. Modeling the ice age climate. Science, 191(4232), 1138–1144.Google Scholar
Gates, W.L. 1992. AMIP: the Atmospheric Model Intercomparison Project. Bull, Am. Meterol. Soc., 73(12), 1962–1970.2.0.CO;2>CrossRefGoogle Scholar
Giorgi, F., Bates, G. T. and Nieman, S.J. 1993. The multiyear surface climatology of a regional atmospheric model over the western United States, J. Climate, 6(1), 75–85.2.0.CO;2>CrossRefGoogle Scholar
Goodison, B.E. 1989. Determination of areal snow water equivalent on the Canadian prairies using passive microwave satellite data. In International Geoscience and Remote Sensing Symposium (IGARSS). Quantitative remote sensing: an economic tool for the nineties. 12th Canadian Symposium on Remote Sensing, Vancouver, British Columbia, 10–14 July 1989. Proceedings. Vol. 3. New York, Institute of Electrical and Electronics Engineers, 1243–1246.Google Scholar
Hahn, D.G. 1981. Summary requirements of GCMS for observed snow and ice cover data. Glaciol. Data GD-11, 43–53.Google Scholar
Hall, D.K. 1995. First Moderate Resolution Imaging Spectroradiometer (MODIS) Snow and Ice Workshop. NASA-Conf. Publ. CP 3318.Google Scholar
Henderson-Sellers, A., Vang, A. -L. and Dickinson, R.E. 1993. The project for intercomparison of land-surface parameterization Schemes. Bull. Am. Meteorol. Soc., 74(7), 1335–1349.2.0.CO;2>CrossRefGoogle Scholar
Herman, G.F. and Johnson, W.T. 1978. The sensitivity of the general circulation to Arctic sea ice boundaries: a numerical experiment. Mon. Wealther Rev., 106(12), 1649–1664.Google Scholar
Hibler, W.D. III, and Flato, G.M. 1992. Sea ice models. In Trenberth, K. E., ed. Climate system modeling. Cambridge, Cambridge University Press, 413–436.Google Scholar
Ingram, W. J., Wilson, C. A. and Mitchell, J. F. B. 1989. Modeling climate change: an assessment of sea ice and surface albedo feedbacks. J. Geophys. Rev., 94(D6), 8609–8622.Google Scholar
Jenne, R. L. 1982. Planning guidance for the world climate data system. Geneva, World Meteorological Organization. World Climate Programme, (WCP 19.)Google Scholar
Kargel, J.S. and Kieffer, H.H. 1995. Opportunity for nearly comprehensive global glacier monitoring with ASTER. [Abstract.] EOS, 76(46), Fall Meeting Supplement, F91.Google Scholar
Lindsay, R.W. and Rothrock, D.A. 1995. Arctic sea ice leads from advanced very high resolution radiometer images. J. Geophys. Res., 100(C3), 4533–4544.CrossRefGoogle Scholar
Lynch, A.H., Chapman, W.L. Walsh, J. E. and Weller, G. 1995. Development of a regional climate model of the western Arctic. J. Climate, 8(6), 1555–1570.Google Scholar
Manabe, S. and Hahn, D.C. 1977. Simulation of the tropical climate of an ice age. J. Geophys. Res., 82(27), 3889–3911.CrossRefGoogle Scholar
Manabe, S.M. Spelman, J. and Stouffer, R.J. 1992. Transient reponses of a coupled ocean–atmospheric model to gradual changes of atmospheric CO2. Part II: Seasonal reponse. J. Climate, 5(2), 105–126.2.0.CO;2>CrossRefGoogle Scholar
McGinnis, D.L. and Cross, M.D. 1997. Arctic modeling data resources: the data archives at the ARCSS Data Coordination Center and the National Snow and Ice Data Center, U.S.A. Ann. Glaciol., 25 (see paper in this volume).Google Scholar
Meehl, G.A. 1992. Global coupled models: atmosphere, ocean, sea ice. In Trenberth, K. E., ed. Climate system modeling, Cambridge, Cambridge University Press, 555–581.Google Scholar
Meehl, G.A. and Washington, W.M. 1990. CO2 climate sensitivity and Snow–sea-ice–albedo parameterizations an atmospheric GCM coupled to a mixed-layer Ocean. Climatic Chnage, 16(3), 283–306.CrossRefGoogle Scholar
Mellor, G.L. and Häkkinen, S. 1994. A review of coupled ice–ocean models. In Johannessen, O. M., Muench, R. D. and Overland, J.E. eds. The polar oceans and their role in shaping the global environment: the Nansen Centennial volume. Washington, DC, American Geophysical Union, 21–31. (Geophysical Monograph 85.)Google Scholar
National Snow and Ice Data Center (NSIDC). 1994. Historical Sovier daily snow depth. Boulder, CO, University of Colorado. Cooperative Institute for Research in Environmental Sciences. National Snow and Ice Data Center.Google Scholar
National Snow and Ice Data Center (NSIDC). 1996a. Northern Hemisphere EASE-grid weekly snow cover and weekly extent. Vol. 1.0, 2.0. Boulder, CO, University of Colorado. Cooperative Institute for Research in Environmental Sciences. National Snow and Ice Data Center.Google Scholar
National Snow and Ice Data Center (NSIDC). 1996b. Arctic Ocean snow and meterological observations from drifting stations. Boulder, CO, University of Colorado. Cooperative Institute for Research in Environmental Sciences. National Snow and Ice Data Center.Google Scholar
Nolin, A.W. and Dozier, J. 1993. Estimating snow grain size using AVIRIS data. Remote Sensing Environ., 44(2–3), 231–238.Google Scholar
Nomura, A. 1995. Global sea uce concentration data set for use with the ECMWF reanalysis system. Reading, European Center for Medium-Range Weather Forecasts. Re-Analysis Project (ERA). (Technical Report 76.)Google Scholar
Oglesby, R.J. 1990. Sensitivity of glaciation to initial snow cover, CO2, snow albedo, and oceanic roughness in the NCAR CCM. Climate Dyn., 4(4), 219–235.Google Scholar
Polar Pathfinder Group. 1997. Satellite-derived data produced for the polar regions. EOS, 78(5), 52. (EOS Electronic Supplement 96149e.)Google Scholar
Rind, D., Healy, R., Parkinson, C. and Martinson, D. 1995. The role of sea ice in 2 × CO2 climate model sensitivity. Part I: The total influence of sea-ice thickness and extent. J. Climate, 8(3), 449–463.Google Scholar
Robinson, D.A. 1997. Hemisphere snow cover and surface albedo for model validation. Ann. Glaciol., 25 (see paper in this volume).Google Scholar
Romanov, I.P. 1993. Atlaksh: morfometricheski kharakteristiki l'da i snega v Arkticheskom basseyine (Atlas of morphometric characteristics of ice and snow in the Arctic Basin). St. Petersburg, Russia, privately published.Google Scholar
Rothrock, D.A. and others. 1995. Polar Exchange at the Sea Surface (POLES) progress report. Seattle, WA, University of Washington. (NASA Grant NAGW 2407.)Google Scholar
Schweiger, A.J., Serreze, M. C. and Key, J.R. 1993. Arctic sea ice albedo: a comparison of two satellite-derived data sets. Geophys. Res. Lett., 20(1), 41–44.Google Scholar
Tait, A. and Armstrong, R. 1996. Evaluation of SSMR satellite-derived snow depth using ground based observations. Int. J. Remote Sensing, 17(4), 657–665.CrossRefGoogle Scholar
Vavrus, S.J. 1995. The sensitivity of the Arctic climate ot leads in a coupled atmosphere–mixed-layer ocean model. J. Climate, 8(2), 158–171.2.0.CO;2>CrossRefGoogle Scholar
Walker, A.E. and Goodison, B.E. 1993. Discrimination of a wet snowcover using passice microwave satellite data. Ann. Glaciol., 17, 307–311.CrossRefGoogle Scholar
Walsh, J.E., Lynch, A., Chapman, W. and Musgrave, D. 1993. A regional model for studies of atmospheric–ice–ocean interaction in the western Arctic. Meteorol. Atmos. Phys., 51(3–4), 179–194.Google Scholar
Williams, J. 1975. The influence of snow cover on the atmospheric circulation and its role in climatic change: an analysis based on results from the NCAR global circulation model. J. Appl. Meterol., 14(2), 137–152.Google Scholar
Williams, J., Barry, R. G. and Washington, W.M. 1974. Simulation of the atmospheric circulation using the NCAR global circulation model with ice age boundary conditions. J. Appl. Meteorol., 13(3), 305–317.2.0.CO;2>CrossRefGoogle Scholar
WMO/Unesco/UNEP/ICSU. 1995. GCOS/GTOS plan for terrestrial climate-related observations: Version 1.0. GCOS 21. Geneva, World Meteorological Organization. (WMO TD 721, UNEP/EAP.TR/95-07.)Google Scholar
Figure 0

Table 1.Global sea-ice variables and their availability

Figure 1

Table 2.Global snow-caver variables and their availability

Figure 2

Table 3.Global land-ice variables and their availability