2002 Annual

Thursday, 17 January 2002: 4:15 PM
Improving the Representation of Snow Processes in Global Climate Models
Zong-Liang Yang, University of Texas, Austin, TX; and G. Y. Niu
Snow cover is a key component of regional and global climate systems because of its highly reflective nature combined with its high emissivity, low thermal conductivity, low roughness length, and large surface coverage (snow can cover up to 40% of the Earth's land surface during the Northern Hemisphere winter). Snow cover is also a useful indicator of climate change because of its sensitivity to temperature. The increase in the active growing season across much of the Northern Hemisphere high latitudes in the past two decades is attributed to the earlier disappearance of spring snow cover. However, the present-day global climate models (GCMs) show a large disparity in the simulations of climate variables in the high latitudes during cold seasons. Therefore, developing a next-generation representation of snow processes in GCMs is crucial for accurate simulations of snow-related climate parameters and for effectively using GCMs in paleoclimate and global change studies.

Guided by our recent survey of 45 snow models used in climate modeling, weather forecasting, snow processes studies and basin-scale snowmelt modeling (see www.hwr.arizona.edu/~liang/snow.html), a physically-based multi-layer versatile integrator of snow atmosphere processes (VISA), has been developed for the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM3) Land Surface Model (LSM). The prognostic variables in the VISA model are snow surface albedo, surface and substrate temperatures, ice and liquid content, and snow density. The heat budget equations for both snow and soil are solved using one set of tri-diagonal matrix equations, which easily allows changes in the number of snow layers. The VISA model is validated with field data sets representing a wide range of land cover patterns and climate regimes. The model simulates the snow water equivalent (SWE), snow density, snow surface temperature and snowmelt more accurately than the original snow scheme in LSM, mainly due to the inclusion of thin surface snow layers, and the realistic consideration of water retention and densification processes. The performance of the VISA model in the NCAR CCM3 is assessed with global data sets of snow depth, precipitation and air temperature. Most noteworthy is that the VISA snow model has significantly reduced a warm bias in 2-m air temperature and a low bias in SWE over land areas between 45-75N during December-February, as seen in the original snow model. The VISA model also improves the simulations of diurnal temperature range (DTR) over the original snow model.

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