Identifying potential drivers of land-atmosphere coupling variation under climate change by explainable artificial intelligence

Land-atmosphere coupling (LAC) involves a variety of interactions between the land surface and the atmospheric boundary layer that are critical to are critical to understanding hydrological partitioning and cycling. As climate change continues to affect these interactions, identifying the specific drivers of LAC variability has become increasingly important. However, due to the complexity of the coupling mechanism, a quantitative understanding of the potential drivers is still lacking. Recently, deep learning has been considered as an effective approach to capture nonlinear relationships within the data, which provides a useful window into complex climatic processes. In this study, we will explore the LAC variability under climate change and its potential drivers by using Convolutional Long Short-term Memory (ConvLSTM) together with explainable AI techniques for attribution analysis. Specifically, the variability of the LAC, defined here as a two-legged index, is used as the modeling target, and variables representing meteorological forcing, land use, irrigation, soil properties, gross primary production, ecosystem respiration, and net ecosystem exchange are the inputs. Our analysis covers global land with a spatial resolution of 0.1° × 0.1° every one day during the period 1979–2019. Overall, the study demonstrates how interpretable machine learning would help us understand the complex dynamics of LAC under changing climatic conditions. We expect the results to facilitate the understanding of terrestrial hydroclimate interactions and hopefully provide multiple lines of evidence to support future water management.