Impact of Land Surface Initialization on Seasonal Precipitation and Temperature Prediction

Abstract

The potential role of land initialization in seasonal forecasting is illustrated through ensembles of simulations with the NASA Seasonal-to-Interannual Prediction Project (NSIPP) model. For each boreal summer during 1997?2001, two 16-member ensembles of 3-month simulations were generated. The first, ?AMIP style? (Atmospheric Model Intercomparison Project) ensemble establishes the degree to which a perfect prediction of SSTs would contribute to the seasonal prediction of precipitation and temperature over continents. The second ensemble is identical to the first, except that the land surface is also initialized with ?realistic? soil moisture contents through the continuous prior application (within GCM simulations leading up to the start of the forecast period) of a daily observational precipitation dataset and the associated avoidance of model drift through the scaling of all surface prognostic variables. A comparison of the two ensembles shows that land initialization has a statistically significant impact on summertime precipitation over only a handful of continental regions. These regions agree, to first order, with those that satisfy three conditions: 1) a tendency toward large initial soil moisture anomalies, 2) a strong sensitivity of evaporation to soil moisture, and 3) a strong sensitivity of precipitation to evaporation. The impact on temperature prediction is more spatially extensive. The degree to which the initialization increases the skill of the forecasts is mixed, reflecting a critical need for the continued development of model parameterizations and data analysis strategies.