Deep Learning based reconstructions of the Atlantic Meridional Overturning Circulation confirm twenty-first century decline
Environmental Research Letters 20:6 (2025) 064036
Abstract:
Gaining knowledge of the past and present variations of the Atlantic Meridional Overturning Circulation (AMOC) is crucial for the development of accurate future climate projections. The short range covered by direct AMOC observations, inconsistent paleoclimate records, and scattered hydrographic data are insufficient to realistically reconstruct the AMOC strength since 1900. An AMOC proxy index based on sea surface temperatures suggests that the AMOC has declined by 15% since the late 19th century but this index received extensive scientific criticism. Here, we use a deep learning algorithm and climate model simulations to accurately reconstruct the AMOC strength between 20°N and 60°N since 1900. In contrast with the existing indices, our reconstructions are well in agreement with AMOC strength variations simulated by climate models and direct observations at 26.5°N. Our novel set of AMOC reconstructions contribute to a larger confidence in 21st century AMOC decline projections from climate models.
Characterizing uncertainty in deep convection triggering using explainable machine learning
Journal of the Atmospheric Sciences American Meteorological Society (2025)
Abstract:
Realistically representing deep atmospheric convection is important for accurate numerical weather and climate simulations. However, parameterizing where and when deep convection occurs (“triggering”) is a well-known source of model uncertainty. Most triggers parameterize convection deterministically, without considering the uncertainty in the convective state as a stochastic process. In this study, we develop a machine learning model, a random forest, that predicts the probability of deep convection, and then apply clustering of SHAP values, an explainable machine learning method, to characterize the uncertainty of convective events. The model uses observed large-scale atmospheric variables from the Atmospheric Radiation Measurement constrained variational analysis dataset over the Southern Great Plains, US. The analysis of feature importance shows which mechanisms driving convection are most important, with large-scale vertical velocity providing the highest predictive power for more certain, or easier to predict, convective events, followed by the dynamic generation rate of dilute convective available potential energy. Predictions of uncertain, or harder to predict, convective events instead rely more on other features such as precipitable water or low-level temperature. The model outperforms conventional convective triggers. This suggests that probabilistic machine learning models can be used as stochastic parameterizations to improve the occurrence of convection in weather and climate models in the future.Discovering convection biases in global km-scale climate models using computer vision
Copernicus Publications (2025)
Precipitation rate, convective diagnostics and spin-up compared across physics suites in the model uncertainty model intercomparison project (MUMIP)
Copernicus Publications (2025)
Unravelling the role of increased model resolution on surface temperature fields using explainable AI
Copernicus Publications (2025)