HR: 1330h
AN: H22B-0928    [PDF]
TI: Analyzing model uncertainty in predicted surface fluxes resulting from prescribed soil and vegetation parameters
AU: * Jankov, M
EM: mj@gi.alaska.edu
AF: University of Alaska Fairbanks, Geophysical Institute, 903 Koyukuk Drive, Fairbanks, AK 99775 United States
AU: Prochaka, L
AF: University of Alaska Fairbanks, Geophysical Institute, 903 Koyukuk Drive, Fairbanks, AK 99775 United States
AU: M\"olders, N
EM: molders@gi.alaska.edu
AF: University of Alaska Fairbanks, Geophysical Institute, 903 Koyukuk Drive, Fairbanks, AK 99775 United States
AB: The atmosphere and land-surface continuously interact, for which the surface affects current weather and climate. The biosphere-soil system plays an important role because it is the media in those interactions. The processes that describe those interactions are the exchange of momentuum, heat, water vapor, and matter. To include these processes at the soil-biosphere-atmosphere interface in atmospheric models they have to be parameterized. The different vegetation and soil types are realized by prescribed plant physiological and soil physical parameters (e.g. soil hydraulic conductivity, soil thermal conductivity, porosity, pore-size distribution index, leaf area index, albedo and emissivity of the foliage and soil, minimum stomatal resistance, canopy height, etc.) in these parameterizations. The parameters can vary even among the same soil or plant type. The order of magnitude of those variations can be as much as the mean values of the parameters themselves. In order to improve weather prediction the model uncertainty, caused by the necessity to prescribe parameters, has to be minimized. To asses the errors uncertainty analysis with respect to the prescribed parameters is carried out using the Gaussian Error Propagation method. We use the PennState/NCAR mesoscale meteorological model MM5 coupled with the Oregon State University land surface model (OSULSM) as the test-platform. The Gaussian Error Propagation technique provides error bars for the fluxes simulated by MM5. Moreover, the technique can point out which parameters contribute the most to the error, and should be replaced in future model development. Our preliminary results show that throughout the domain errors were at low or moderate levels. The highest errors predicted appear to be associated with scarcely vegetated, sandy clay loam areas and areas covered by ice and snow.
DE: 0315 Biosphere/atmosphere interactions
DE: 0325 Evolution of the atmosphere
DE: 1818 Evapotranspiration
DE: 1836 Hydrologic budget (1655)
SC: Hydrology [H]
MN: 2003 Fall Meeting