HR: 0800h
AN: C21C-1121 INVITED     [Abstracts]
TI: Multi-model estimates of Arctic land surface conditions
AU: * Lettenmaier, D P
EM: dennisl@u.washington.edu
AF: Dept. of Civil and Environmental Engineering, Box 352700 University of Washington, Seattle, WA 98105 United States
AU: Bohn, T J
EM: tbohn@hydro.washington.edu
AF: Dept. of Civil and Environmental Engineering, Box 352700 University of Washington, Seattle, WA 98105 United States
AU: Slater, A G
EM: aslater@cires.colorado.edu
AF: Cooperative Institute for Research in Environmental Sciences, 216 UCB University of Colorado, Boulder, CO 80309-0216 United States
AU: McCreight, J
EM: mccreigh@colorado.edu
AF: Cooperative Institute for Research in Environmental Sciences, 216 UCB University of Colorado, Boulder, CO 80309-0216 United States
AU: Serreze, M C
EM: serreze@kryos.colorado.edu
AF: Cooperative Institute for Research in Environmental Sciences, 216 UCB University of Colorado, Boulder, CO 80309-0216 United States
AB: Hydrologic processes in the Arctic terrestrial drainage system are thought to exert a significant influence on global climate. For example, the impact of freshwater fluxes into the Arctic ocean on the global thermohaline circulation and the positive atmospheric feedback exhibited by snow albedo have the potential to amplify the Arctic's response to global climate change. However, these processes are not well understood, in part due to the sparseness of observations of such variables as stream flow, soil moisture, soil temperature, snow water equivalent, and energy fluxes in Arctic regions. While these variables can be estimated with a single land surface model (LSM), the predictions are often subject to biases and errors in the input meteorological forcings and limited by the accuracy of the model physics. To reduce these errors, we have implemented an ensemble of five LSMs: VIC, NOAH, CHASM, CLM, and ECMWF, all of which have been used previously to simulate Arctic hydrology under the Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) Experiment 2e. The use of multiple models is facilitated by the Standard Interface Multi- Model Array (SIMMA), a framework that automates model execution, data processing, and translation of model inputs and outputs to a common format. Model predictions are combined via Bayesian model averaging to arrive at an optimum estimate of hydrological conditions. Here we compare multi-model estimates of snow cover and stream flow to observ ations and investigate the robustness of estimates of soil moisture and temperature and latent heat fluxes over time and space for the Arctic terrestrial drainage system from 1979 to 1999.
DE: 0704 Seasonally frozen ground
DE: 0736 Snow (1827, 1863)
DE: 1816 Estimation and forecasting
DE: 1818 Evapotranspiration
DE: 1860 Streamflow
SC: Cryosphere [C]
MN: Fall Meeting 2005