Empirical evidence of contrasting feedbacks between remotely sensed soil moisture and gauge-based precipitation in arid vs. humid areas of the United States

Land surface moisture content affects the partitioning of radiative energy into heat and moisture fluxes at the land surface, and thus can affect the state of the overlying atmosphere by supplying water vapor, inducing moist convection and lateral convergence, and growing the planetary boundary layer. However, it is difficult to determine how this influence may in turn affect the propensity for subsequent precipitation. Empirical studies have disagreed on the sign and statistical significance of the estimated influence of soil moisture on precipitation, and have suffered from the difficulty of distinguishing a causal effect from lagged correlations that arise from the autocorrelation, seasonality, and interannual variability of each signal. Modeling studies can more easily establish causality, but feedback behavior has been shown to vary widely among models. We apply Granger Causality (Granger (1969), Econometrica, 37: 424-438) to quantitatively diagnose the relationship between soil moisture state and the occurrence of subsequent precipitation, and implement it over the contiguous United States using remotely sensed soil moisture and gauge-based precipitation observations. This is accomplished through a generalized linear model (GLM), specifically a probit regression model, constructed to estimate the observed record of precipitation occurrence. Predictor variables included in the regression account for external atmospheric and climatic influences on precipitation, the prior occurrence of precipitation, and previous day soil moisture anomalies. We find a generally positive soil moisture-precipitation feedback in the western part of the United States and a negative feedback in the east. These findings suggest that processes linking soil moisture to precipitation triggering may differ in arid versus humid climate regimes, and that soil moisture state can play an important role in determining future weather conditions.