HR: 1340h
AN: C23A-1148    [Abstracts]
TI: Monte-Carlo Evaluation of Distributed Energy Balance Model Parameter Sensitivity. Implications for Multidecadal Mass Balance Simulations.
AU: * Anslow, F
EM: anslowf@onid.orst.edu
AF: Oregon State University, Department of Geosciences 104 Wilkinson Hall, Corvallis, OR 97331 United States
AU: Hostetler, S
EM: steve@coas.oregonstate.edu
AF: Oregon State University, Department of Geosciences 104 Wilkinson Hall, Corvallis, OR 97331 United States
AU: Clark, P
EM: clarkp@onid.orst.edu
AF: Oregon State University, Department of Geosciences 104 Wilkinson Hall, Corvallis, OR 97331 United States
AB: Estimating model uncertainty in a distributed glacier surface energy/mass balance model is difficult due to the large number of possible combinations of model parameter values. Here we address the issue of uncertainty in ablation submodels by examining model response to randomly selected values of seven key parameters (surface roughness lengths, precipitation lapse rates, snow grain growth, atmospheric transmissivity, and surrounding terrain albedo) over 10,000 model realizations. The parameter ranges were constrained to those typically reported in the literature, and the simulations were performed using input meteorological data collected during the 2004 ablation season at South Cascade Glacier, Washington. We used RMSE between the simulated mass-balance and the monthly mass-balance measurements made at 8 stakes distributed along the glacier center line as the metric for evaluating model performance. Persistence of the fitted parameters is assessed by comparison with measurements made during the 2005 ablation season. We are evaluating the robustness of the optimal parameter estimates over a multi-decadal time period (1959-2003 ) by comparing the observed mass balance record from South Cascade Glacier with energy balance model simulations conducted using output from a regional climate model. The climate model was run at 45 km grid spacing using the NCEP Reanalysis data as time-varying boundary conditions. The 100 "best-fit" parameter sets from the first stage of the analysis are applied to these longer-term runs to explore the effects parameter choice.
DE: 0720 Glaciers
DE: 0764 Energy balance
DE: 0798 Modeling
DE: 3307 Boundary layer processes
DE: 3309 Climatology (1616, 1620, 3305, 4215, 8408)
SC: SPA-Aeronomy [SA]
MN: Fall Meeting 2005