Moreover, based on statistical analyses of multi-model ensembles, Orlowsky and Seneviratne evaluated the influences of both SST and soil moisture on summer rainfall over the European continent. They further suggested that taking into account the exact land-surface's memory tends to improve the climate predictability via improving the water cycle. reported that influence of SST anomalies on continental rainfall is limited, especially in the tropics including the Indian subcontinent. Although it was shown that sea surface temperature (SST) anomaly has longer memory than soil moisture, Koster et al. Hereafter, we use the term “memory” for soil moisture and “persistence” for precipitation and evapotranspiration cases. Since soil moisture is a storage term, it remembers previous atmospheric forcing which can therefore persist (e.g., in a form of precipitation) into the following days, weeks, or seasons. An important process of interaction between land-surface and atmosphere is through a feedback loop with land-surface evapotranspiration and precipitation. In the climate system, soil moisture is a slowly varying land surface component state variable which affects the near surface atmospheric variables (temperature, humidity, precipitation, etc.). However, in a diagnostic statistical sense, memory or persistence is the lag correlation. The strength of memory is therefore mainly attributed to the changes in the inherent processes. Memory is cognitively defined as the processes by which a system or object stores and recalls encoded information. This enhanced explained variance value in the western region reveals the potential usefulness of improved soil moisture initialization in subseasonal rainfall forecasting. Also, the meridionally averaged (20°N–30°N) variance of subsequent precipitation explained by soil-moisture rises from east to west. The analysis, which was performed for the Indian summer monsoon season (ISM), shows that simulated memory lengths (a) increase with soil depth, and (b) are longer in the western region than in the eastern region (14 and 9 days, respectively, at 34 cm soil layer depth). This provides an estimate of the internal variability of the (modeled) climate system. To prevent an external atmospheric forcing, the simulation was done with constant greenhouse gas concentrations. The RCM was driven by lateral boundary conditions derived from a preindustrial control run of the coupled global ocean-atmosphere model ECHAM5/MPIOM. Soil moisture memory over the Indian subcontinent was investigated on the basis of a 101 year long simulation with the regional climate model (RCM) COSMO-CLM (COSMO model in Climate Mode).
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