Functional data decomposition reveals unexpectedly strong soil moisture-precipitation coupling over the Great Plains

TL;DR

This study uses high-dimensional model representation to quantify soil moisture's influence on precipitation over the Great Plains, revealing a stronger coupling than previously estimated and highlighting the importance of higher-order land-atmosphere interactions.

Contribution

It introduces HDMR as a novel, effective method for decomposing land-atmosphere variable contributions, surpassing traditional correlation and regression techniques in accuracy.

Findings

Soil moisture explains up to 40% of summer precipitation variance.

First-order soil moisture effects can increase precipitation by up to 8 mm.

Higher-order interactions significantly influence precipitation variability.

Abstract

Soil moisture-precipitation coupling (SMPC) plays a critical role in Earth's water and energy cycles but remains difficult to quantify due to synoptic-scale variability and the complex interplay of land-atmosphere processes. Here, we apply high-dimensional model representation (HDMR) to functionally decompose the structural, correlative, and cooperative contributions of key land-atmosphere variables to precipitation. Benchmark tests confirm that HDMR overcomes limitations of commonly used correlation and regression approaches in isolating direct versus indirect effects. For example, analysis of gross primary productivity using a light-use-efficiency model shows that linear regression underestimates the temperature effect, while HDMR captures it accurately. Applying HDMR to CONUS404 reanalysis data reveals that morning soil moisture explains up to 40 percent of the variance in summertime afternoon precipitation over the Great Plains, more than double prior estimates. On days with afternoon rainfall (12-hour totals of 4.7-8.2 mm), first-order SM effects can boost precipitation by up to 8 mm under wet conditions, with an additional 3 mm from second-order interactions involving temperature and moisture. By capturing real-world co-variability and higher-order effects, HDMR provides a physically grounded, data-driven framework for diagnosing land-atmosphere coupling. These results underscore the need for more nuanced, interaction-aware data analysis methods in climate modeling and prediction.