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.

How to Derive Skill from the Fractions Skill Score

Monthly Weather Review American Meteorological Society 153:6 (2025) 1021-1033

Authors:

Bobby Antonio, Laurence Aitchison

Abstract:

Abstract The fractions skill score (FSS) is a widely used metric for assessing forecast skill, with applications ranging from precipitation to volcanic ash forecasts. By evaluating the fraction of grid squares exceeding a threshold in a neighborhood, the intuition is that it can avoid the pitfalls of pixelwise comparisons and identify length scales at which a forecast has skill. The FSS is typically interpreted relative to a “useful” criterion, where a forecast is considered skillful if its score exceeds a simple reference score. However, the typical reference score used is problematic, since it is not derived in a way that provides obvious meaning, does not scale with neighborhood size, and may not be exceeded by forecasts that have skill. We, therefore, provide a new method to determine forecast skill from the FSS, by deriving an expression for the FSS achieved by a random forecast, which provides a more robust and meaningful reference score to compare with. Through illustrative examples, we show that this new method considerably changes the length scales at which a forecast would be regarded as skillful and reveals subtleties in how the FSS should be interpreted. Significance Statement Forecast verification metrics are crucial to assess accuracy and identify where forecasts can be improved. In this work, we investigate a popular verification metric, the fractions skill score, and derive a more robust method to decide if a forecast has sufficiently high skill. This new method significantly improves the quality of insights that can be drawn from this score.

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.