Causal disentangling of soil moisture and temperature feedbacks on surface climate extremes under vegetation change

Land-atmosphere interactions are key drivers of climate extremes, mediating the influence of soil moisture, vegetation, and surface energy exchanges on droughts, heatwaves, and compound events. Observed vegetation changes such as climate-induced tree mortality, phenological shifts, and large-scale deforestation can substantially alter these interactions by modifying surface energy and water fluxes. A critical challenge is to understand how soil water-energy feedbacks propagate through the atmosphere, which is essential for both predicting extremes and evaluating Earth System Models (ESMs).

To address this, we propose a unified causal and explainable framework to disentangle soil water-energy feedbacks from observational data, creating a benchmark for ESM evaluation. First, we construct machine learning emulators to represent the dynamical responses of land and atmosphere modules to external forcings, consistent with a structural causal model (SCM). These emulators act as efficient, process-aware surrogates, enabling the reconstruction of causal pathways (e.g., soil moisture/temperature → near-surface states) in a computationally tractable way. Using do-calculus combined with explainable AI (XAI), we then estimate the causal coupling strengths of water-energy feedbacks, isolating the direct effects of soil states from confounding atmospheric influences. By comparing these causal estimates against observational constraints, we can evaluate and benchmark ESM representations, revealing structural biases, deficiencies, and uncertainties in simulated pathways.

Bridging causal inference, machine learning, and observations, our framework provides a robust tool for process-level diagnosis, model benchmarking, and ultimately improving the physical fidelity of complex ESMs. It advances the mechanistic understanding of how land states drive atmospheric extremes, offering actionable insights for predicting droughts and heatwaves under current and future climates.