Monday, 11 January 2016
New Orleans Ernest N. Morial Convention Center
Global land surface hydrology and heat fluxes can be estimated by running a land surface model (LSM) driven by the atmospheric forcing data. Previous multi-model studies focused on the impact of LSM parameterizations on model results. Here we evaluate the sensitivity of the Community Land Model version 4.5 (CLM4.5) results to the atmospheric forcing data. Together with the model default global forcing dataset (CRUNCEP), three newly developed reanalysis-based surface meteorological data sets (i.e., MERRA, CFSR, and ERAI) with the precipitation adjusted by the Global Precipitation Climatology Project monthly product (GPCP v2.2) were used to drive CLM4.5. All four simulations were run at 0.5x0.5 degree grids from 1979-2009 with the identical initialization. The simulated monthly surface hydrology variables, fluxes, and their ensembles, along with the forcing data sets were then intercompared and evaluated against various observation-based data sets. While simulations based on the four forcing data sets are overall comparable, the simulated soil moisture and snow from newly constructed forcing datasets are closer to the observations than simulations from CRUNCEP. The multi-simulation ensembles are generally superior or comparable to the best individual simulations. Compared with the previous multi-LSM projects, this study reveals that the diversity of model parametrizations (rather than forcing data) plays a dominant role in the dispersion of model results. These estimates of global long-term land surface hydrology and heat fluxes are available for various applications in land surface process research.
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