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David Pollard
,
Mary L. Batten
, and
Young-June Han

Abstract

Results are described for three simple numerical models of the upper ocean and sea ice with prescribed atmospheric forcing. The ability of each model version to simulate the observed sea surface temperature (SST) is assessed as a basis of comparison for future coupled experiments with atmospheric GCM'S.

lie upper-ocean model versions raw from a slab of fixed thickness to a variable-depth mixed layer above a variable exponential temperature gradient representing the seasonal thermocline. Sea-ice thickness is determined thermodynamically by local melting or accretion, and the effects of ice transport and leads are neglected. Each version is tested by integrating to equilibrium with horizontal advection neglected, using .monthly climatological atmospheric data for a selected north-south section in the mid-Pacific Ocean, and the results for different model versions are compared with each other and with available observations.

It is found that the fixed-slab version gives realistic sea-ice thickness and extent, and temperatures within 1–2°C of observed SST's over much of the mid-Pacific. However. a variable-depth mixed layer is required to maintain this level of accuracy for summer SST's north of 40°N, and also to produce the correct phases of the annual cycles of temperature at all extratropical latitudes. Mixed-layer depths in the latter version are somewhat too shallow in winter, but the overall seasonal pattern agrees with that observed.

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James Foster
,
Glen Liston
,
Randy Koster
,
Richard Essery
,
Helga Behr
,
Lydia Dumenil
,
Diana Verseghy
,
Starly Thompson
,
David Pollard
, and
Judah Cohen

Abstract

Confirmation of the ability of general circulation models (GCMs) to accurately represent snow cover and snow mass distributions is vital for climate studies. There must be a high degree of confidence that what is being predicted by the models is reliable, since realistic results cannot be assured unless they are tested against results from observed data or other available datasets. In this study, snow output from seven GCMs and passive-microwave snow data derived from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) are intercompared. National Oceanic and Atmospheric Administration satellite data are used as the standard of reference for snow extent observations and the U.S. Air Force snow depth climatology is used as the standard for snow mass. The reliability of the SMMR snow data needs to be verified, as well, because currently this is the only available dataset that allows for yearly and monthly variations in snow depth. [The GCMs employed in this investigation are the United Kingdom Meteorological Office, Hadley Centre GCM, the Max Planck Institute for Meteorology/University of Hamburg (ECHAM) GCM, the Canadian Climate Centre GCM, the National Center for Atmospheric Research (GENESIS) GCM, the Goddard Institute for Space Studies GCM, the Goddard Laboratory for Atmospheres GCM and the Goddard Coupled Climate Dynamics Group (AIRES) GCM.] Data for both North America and Eurasia are examined in an effort to assess the magnitude of spatial and temporal variations that exist between the standards of reference, the models, and the passive microwave data. Results indicate that both the models and SMMR represent seasonal and year-to-year snow distributions fairly well. The passive microwave data and several of the models, however, consistently underestimate snow mass, but other models overestimate the mass of snow on the ground. The models do a better job simulating winter and summer snow conditions than in the transition months. In general, the underestimation by SMMR is caused by absorption of microwave energy by vegetation. For the GCMs, differences between observed snow conditions can be ascribed to inaccuracies in simulating surface air temperatures and precipitation fields, especially during the spring and fall.

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