Using observed spatial correlation structures of rainfall and temperature to improve the skill of subseasonal forecasts relying on land surface moisture initialization

Past studies with a seasonal forecast system have confirmed that the accurate initialization of land moisture reservoirs increases the skill of subseasonal precipitation and air temperature forecasts. Soil moisture initialization appears to affect forecasts mostly in the transition zones between humid and dry regions. In most other areas of the globe, the evaporation rates are either too small or too unconnected with variations in soil moisture to have a coordinated effect on rainfall and temperature variations in the presence of background atmospheric variability.

Such forecast systems, however, have strong and identifiable biases in the spatial structures of climate statistics. For example, rainfall structures in the NASA/Global Modeling and Assimilation Office (GMAO) seasonal forecast system are spatially too compact; the horizontal length scale of spatial rainfall correlation is smaller than that derived from observational datasets. By accounting for this bias, we may be able to extend the usefulness of the forecasts from “skillful” regions to regions where the model appears unaffected by land initialization. This talk will illustrate the differences between modeled and observed spatial correlation structures of rainfall and air temperature and will demonstrate an application of the observed structures to the forecast problem.