Initialization of Soil Moisture in a Global Climate Model: A North American Case Study
Jeffrey P. Walker and Paul R. Houser
Accurate initialization and forecasting of land surface soil moisture in fully-coupled global climate models is critical for seasonal-to-interannual climatological and hydrological prediction, because of its feedback to precipitation and atmospheric circulations, through its control on partitioning of the available energy into latent and sensible heat exchange. To overcome such limitations in the NASA Seasonal-to-Interannual Prediction Project (NSIPP), a one-dimensional Kalman filter assimilation of near-surface soil moisture observations has been added to the Catchment-based Land Surface Model (CLSM) used by NSIPP.
In this paper, the CLSM is run off-line from the atmospheric and ocean simulation models, forced by bias corrected European Centre for Medium-range Weather Forecasts (ECMWF) re-analysis data. Near-surface soil moisture observations from the Scanning Multi-channel Microwave Radiometer (SMMR) satellite 6.6GHz (C-band) channel, covering the period 1979 to 1987, are assimilated to correct for soil moisture forecast errors resulting from incorrect initial conditions, inaccurate meteorological forcing data and an imperfect forecast model. The soil moisture estimates from the assimilation are compared with the limited number of ground-based point measurements of soil moisture; 19 stations in Illinois, 6 stations in Iowa and transect of 89 points in New Mexico.