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Abstract:
Forcing a land surface model (LSM) offline with realistic global fields of precipitation, radiation, and nearsurface
meteorology produces realistic fields (within the context of the LSM) of soil moisture, temperature, and
other land surface states. These fields can be used as initial conditions for precipitation and temperature forecasts
with an atmospheric general circulation model (AGCM). Their usefulness is tested in this regard by performing
retrospective 1-month forecasts (for May through September, 1979-93) with the NASA Global Modeling and
Assimilation Office (GMAO) seasonal prediction system. The 75 separate forecasts provide an adequate statistical
basis for quantifying improvements in forecast skill associated with land initialization.
Evaluation of skill is focused on the Great Plains of North America, a region with both a reliable land
initialization and an ability of soil moisture conditions to overwhelm atmospheric chaos in the evolution of the
meteorological fields. The land initialization does cause a small but statistically significant improvement in
precipitation and air temperature forecasts in this region. For precipitation, the increases in forecast skill appear
strongest in May through July, whereas for air temperature, they are largest in August and September. The joint
initialization of land and atmospheric variables is considered in a supplemental series of ensemble monthly
forecasts. Potential predictability from atmospheric initialization dominates over that from land initialization
during the first 2 weeks of the forecast, whereas during the final 2 weeks, the relative contributions from the
two sources are of the same order. Both land and atmospheric initialization contribute independently to the actual
skill of the monthly temperature forecast, with the greatest skill derived from the initialization of both. Land
initialization appears to contribute the most to monthly precipitation forecast skill.