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Snow Cover and Snow Mass Intercomparisons of General Circulation Models and Remotely Sensed Datasets

James Foster*NASA, Goddard Space Flight Center, Greenbelt, Maryland

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Glen Liston*NASA, Goddard Space Flight Center, Greenbelt, Maryland

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Randy Koster*NASA, Goddard Space Flight Center, Greenbelt, Maryland

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Richard EsseryUnited Kingdom Meteorological Office, Hadley Climate Centre, Bracknell, England

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Helga BehrUniversity of Hamburg, Meteorology Institute, Hamburg, Germany

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Lydia DumenilUniversity of Hamburg, Meteorology Institute, Hamburg, Germany

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Diana VerseghyCanadian Climate Centre, Atmospheric Environment Service, Downsview, Ontario, Canada

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Starly Thompson&National Center for Atmospheric Research, Interdisciplinary Climate Systems, Boulder, Colorado

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David Pollard&National Center for Atmospheric Research, Interdisciplinary Climate Systems, Boulder, Colorado

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Judah Cohen**NASA, Goddard Institute for Space Studies, New York, New York

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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.

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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