Assessing the influence of soil moisture on seasonal climate variability with AGCMs

Ensembles of boreal summer atmospheric simulations, spanning a 15-yr period (1979-93), are performed with the Action de Recherche Petite Echelle Grande Echelle (ARPEGE) climate model to investigate the possible influence of soil moisture (SM) on climate variability and predictability. All experiments are forced with observed sea surface temperatures. In addition to a control experiment using interactive SM boundary conditions, two sensitivity experiments are performed with a relaxation of total SM toward different monthly mean datasets: the ARPEGE climatology and the Global Soil Wetness Project climatology. Both sensitivity experiments indicate that damping the SM variability leads to a clear and robust reduction in low-level temperature variability over most areas in the Tropics and the summer extratropics. Variability in precipitation is not necessarily reduced because the effect of reduced evaporation variability can be offset by an increase in the mean precipitation. Such an increase is however very limited when ARPEGE is relaxed toward its own SM climatology. Changes in predictability are less homogeneous than changes in variability. For example, SM appears to be a source of predictability over North America, but not over India. Several reasons can be proposed for such a contrast in the regional reponses, including the stronger evaporation-precipitation feedback in the interior of the North American continent than over the Indian peninsula. Though generally limited in magnitude, some impacts found on potential predictability suggest that there is still some hope for improving dynamical seasonal forecasts in the summer midlatitudes. The study also indicates that the impact of SM on precipitation variability is not only model dependent, but also subject to the experiment design. Different strategies could be necessary to study the role of the land-atmosphere feedback at the synoptic and seasonal timescales, respectively.