🤖 AI Summary
Soil moisture–precipitation coupling (SMPC) has long been challenging to quantify accurately due to strong nonlinearity in land–atmosphere interactions and weather-scale variability. To address this, we introduce high-dimensional model representation (HDMR) — a functional decomposition technique — to climate variable analysis for the first time, applied to the CONUS404 reanalysis dataset. This enables physically interpretable separation of direct effects, synergistic (second-order) interactions, and higher-order couplings. Results reveal that morning soil moisture over the U.S. Great Plains explains 40% of the variance in afternoon precipitation during summer; on rainy days, the first-order soil moisture effect enhances precipitation by up to 8 mm, while the second-order temperature–humidity interaction contributes an additional 3 mm. Our approach overcomes the persistent underestimation of land–atmosphere feedbacks inherent in conventional methods, substantially improving both the accuracy of SMPC quantification and the mechanistic interpretability of underlying processes.
📝 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.