A heavy rain or a dry period can produce an anomaly in soil moisture, and the dissipation of this anomaly may take weeks to months. It is important to understand how land surface models (LSMs) used with atmospheric General Circulation Models simulate this soil moisture ``memory", since this memory may have profound implications for long term weather prediction through land-atmosphere feedback.

In order to understand better the effect of precipitation and net radiation on soil moisture memory, we forced both the NASA Seasonal-to-Interannual Prediction Project (NSIPP) Catchment LSM and the Mosaic LSM with a wide variety of idealized climates. The imposed climates had average monthly precipitations ranging from 15mm to 500mm and monthly net radiations (in terms of water equivalent) ranging from 20mm to 400mm, with consequent changes in near surface temperature and humidity. For an equivalent water holding capacity, the two models maximize memory in distinctly different climate regimes. Memory in the NSIPP Catchment LSM exceeds that in the Mosaic LSM when precipitation and net radiation are of the same order; otherwise, memory in the Mosaic LSM is larger.

The NSIPP Catchment and the Mosaic LSMs were also driven off-line, globally, for a period of 15 years (1979-93) with realistic atmospheric forcings. Global distributions of one-month-lagged autocorrelation of soil moisture for boreal summer were computed. An additional global run with the NSIPP Catchment LSM employing the Mosaic LSM's water holding capacities was also performed. These three global runs allow us to determine how intermodel differences in soil moisture memory are related to differences in physical parameterizations, such as those for evaporation and runoff production.