Thursday, 13 January 2005: 11:45 AM
Coupling and Analysis of CLM3 in MM5 to Improve Snow Simulations
To improve regional scale snow processes and related cold season hydroclimate simulations, the newly released Community Land Model version 3 (CLM3) developed by the National Center for Atmospheric Research (NCAR) was coupled into the Penn State-NCAR fifth generation Mesoscale Model version 3 (MM5). CLM3 physically describes the mass and heat transfer within the snowpack using 5 snow layers that include liquid water and solid ice. Interactions among the snow, soil, and vegetation are a function of the CLM3 mass and energy equations. A sophisticated surface albedo scheme is adopted to improve the surface energy balance simulations. Introduction of a maximum of 8 sub-cells within each CLM3 cell is shown to significantly improve the accuracy of the land surface characterization and the land surface-atmosphere water and energy flux exchanges. The coupled MM5-CLM3 model performance was evaluated at the Columbia River basin for the cold season using the high resolution NASA MODIS and SeaWiFS satellite data and ground observations from an automated Snowpack Telemetry system. The Telemetry data includes snow water equivalent, precipitation, and surface air temperature. The improved description of the snowpack and the more realistic albedo algorithm in CLM3 results in an improved surface albedo simulation within the coupled MM5-CLM3. Hence, the coupled model is able to better distribute the energy components on the land surface, and dramatically reduces the surface warm bias that was seen in the original version of MM5 with the NOAH land surface model. The resulting decrease in surface temperature leads to a more thermally stable atmospheric structure. Therefore, MM5-CLM3 suppresses exaggerated convective precipitation that was seen in the original MM5. The simulated runoff is in better agreement with observations due to the improved simulation of snowmelt. The coupling of the advanced CLM3 with MM5 significantly improves the regional hydroclimate and water resources predictability.