Abstract
As a vital land surface parameter, soil moisture influences climate through its impact on water and energy cycles. However, the effect of soil moisture on precipitation has been strongly debated. In this study, a new causal detection method, convergent cross mapping (CCM), was applied to explore the causality between soil moisture and precipitation over low- and mid- latitude regions in the Northern Hemisphere. CCM method generally identified a strong effect of soil moisture on precipitation. Specifically, the optimal effect of soil moisture on precipitation occurred with a lag of one month and clearly decreased after four months, suggesting that soil moisture has potentials to improve the accuracy of precipitation forecast at a sub-seasonal scale. In addition, as climate (i.e., aridity index) changed from dry to wet, the effect of soil moisture on precipitation first increased and then decreased with peaks in semi-arid and semi-humid areas. These findings statistically support the hypothesis that soil moisture impacts precipitation and also provide a reference for the design of climate prediction systems.
Highlights
Previous studies showed discrepancies on feedback signs and intensities of the impact of soil moisture on precipitation
A new approach known as convergent cross mapping (CCM) has been proposed to detect causality in dynamical systems based on empirical dynamics and Takens’ theorem[24]
This study focuses on low- and mid- latitude regions in the Northern Hemisphere
Summary
Previous studies showed discrepancies on feedback signs and intensities of the impact of soil moisture on precipitation. Due to the paucity of continuous observation data on soil moisture, previous studies are mainly based on climate models, such as the Atmospheric General Circulation Models (AGCMs)[14], the Consortium for Small-Scale Modeling Model in Climate Mode (CCLM)[15], the Weather Research and Forecasting (WRF) Model[16], and the regional climate model (CLM)[17]. Such analyses could be limited by parameter settings and assumed behavior in models, which influences the simulated responsiveness[18,19].
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