The Link between Gulf Stream Precipitation Extremes and European Blocking in General Circulation Models and the Role of Horizontal Resolution

Journal of Climate (2025)

Authors:

Kristian Strommen, Simon LL Michel, Hannah M Christensen

Abstract:

Past studies show that coupled model biases in European blocking and North Atlantic eddy-driven jet variability decrease as one increases the horizontal resolution in the atmospheric and oceanic model components, but it remains unclear if atmospheric or oceanic resolution plays the greater role, and why. Here, following recent work by Schemm et al., we leverage a large multi-model ensemble to show that a coupled model’s ability to simulate extreme Gulf Stream precipitation is tightly linked to its simulated frequency of European blocking and northern jet excursions. Furthermore, the reduced biases in blocking and jet variability are consistent with better resolved precipitation extrema in high-resolution models. Analysis supports a hypothesis that models which simulate more extreme precipitation can generate more strongly poleward propagating cyclones and more intense anticyclonic anomalies due to the stronger latent heat release occurring during extreme events. By contrast, typical North Atlantic SST biases are found to share only a weak or negligible relationship with blocking and jet biases. Finally, while previous studies have used a comparison between coupled models and models run with prescribed SSTs to argue for the role of ocean resolution, we emphasise here that models run with prescribed SSTs experience greatly reduced precipitation extremes due to their excessive thermal damping, making it unclear if such a comparison is meaningful. Instead, we speculate that most of the reduction in coupled model biases may actually be due to increased atmospheric resolution leading to better resolved convection.

Can Weather Patterns Contribute to Predicting Winter Flood Magnitudes Using Machine Learning?

(2025)

Authors:

Emma Ford, Manuela I Brunner, Hannah Christensen, Louise Slater

Deep Learning based reconstructions of the Atlantic Meridional Overturning Circulation confirm twenty-first century decline

Environmental Research Letters 20:6 (2025) 064036

Authors:

Simon LL Michel, Henk A Dijkstra, Francesco Guardamagna, Valérian Jacques-Dumas, René M van Westen, Anna S von der Heydt

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)

Authors:

Greta A Miller, Philip Stier, Hannah M Christensen

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.

Potential for equation discovery with AI in the climate sciences

Huntingford, C., Nicoll, A. J., Klein, C., and Ahmad, J. A.: Potential for equation discovery with AI in the climate sciences, Earth Syst. Dynam., 16, 475–495, https://doi.org/10.5194/esd-16-475-2025, 2025.

Authors:

Chris Huntingford, Andrew J. Nicoll, Cornelia Klein, and Jawairia A. Ahmad

Abstract:

Climate change and artificial intelligence (AI) are increasingly linked sciences, with AI already showing capability in identifying early precursors to extreme weather events. There are many AI methods, and a selection of the most appropriate maximizes additional understanding extractable for any dataset. However, most AI algorithms are statistically based, so even with careful splitting between data for training and testing, they arguably remain emulators. Emulators may make unreliable predictions when driven by out-of-sample forcing, of which climate change is an example, requiring understanding responses to atmospheric greenhouse gas (GHG) concentrations potentially much higher than for the present or recent past. The emerging AI technique of “equation discovery” also does not automatically guarantee good performance for new forcing regimes. However, equations rather than statistical emulators guide better system understanding, as more interpretable variables and parameters may yield informed judgements as to whether models are trusted under extrapolation. Furthermore, for many climate system attributes, descriptive equations are not yet fully available or may be unreliable, hindering the important development of Earth system models (ESMs), which remain the main tool for projecting environmental change as GHGs rise. Here, we argue for AI-driven equation discovery in climate research, given that its outputs are more amenable to linking to processes. As the foundation of ESMs is the numerical discretization of equations that describe climate components, equation discovery from datasets provides a format capable of direct inclusion into such models where system component representation is poor. We present three illustrative examples of how AI-led equation discovery may help generate new equations related to atmospheric convection, parameter derivation for existing equations of the terrestrial carbon cycle, and (additional to ESM improvement) the creation of simplified models of large-scale oceanic features to assess tipping point risks.