Deseasonalized Attention for Scientific Discovery of Extreme and Compound Climate Events

作者：Buse Onay, Stefan Kollet · 年份：2026 · DOI：10.5194/egusphere-egu26-20642 · 研究领域：Seismology and Earthquake Studies、Scientific Computing and Data Management、Climate variability and models

The Earth system is characterized by complex, nonlinear interactions where the combination of multiple drivers can lead to extreme or compound events with significant impacts. While traditional statistical methods often struggle to capture these multivariate dependencies, deep learning models have emerged as powerful tools for forecasting hydro-climatic time series. However, their utility in Earth system science is currently limited by a lack of transparency. While ML/DL is useful in predicting extremes, the explainability of the physical mechanisms or compound drivers is limited. Furthermore, standard interpretability techniques applied to geophysical data are often misleading, as they tend to highlight dominant seasonal cycles rather than the dynamic, event-specific interactions that are crucial for scientific discovery. This research proposes a diagnostic framework that repurposes the internal decision-making process of an attention-based encoder-decoder LSTM as a hypothesis generation tool, specifically targeting the latent drivers of extreme and compound events, exemplified here by drought. Using multivariate Terrestrial System Modeling Platform simulation data, we trained an attention-based encoder-decoder LSTM where 14 climatological variables serve as both input features and prediction targets in round robin training experiments, generating a comprehensive 14×14 matrix of target-specific attention maps. To transition from predictive modeling to physical interpretation...