Atmospheric processes

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Postprocessing East African rainfall forecasts using a generative machine learning model

Copernicus Publications (2025)

Authors:

Bobby Antonio, Andrew McRae, Dave MacLeod, Fenwick Cooper, John Marsham, Laurence Aitchison, Tim Palmer, Peter Watson
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Statistical methods for estimating the forced component of historical SST and precipitation changes: A bias-variance tradeoff

(2025)

Authors:

Maren Höver, Robert Jnglin Wills, Nora Fahrenbach

Abstract:

Distinguishing the influences of externally forced responses and internal variability on the observed climate is critical for attributing historical climate change and for evaluating the forced responses simulated by climate models. Statistical methods such as optimal fingerprinting, low-frequency component analysis (LFCA), and dynamical adjustment have proven useful for this application. The skill of such statistical methods can be evaluated using climate model large ensembles, where the forced response is estimated by averaging over many realizations. Our study uses large ensemble simulations from five different climate models to evaluate the performance of three statistical methods for this application: (1) low-frequency component analysis, (2) signal-to-noise maximizing pattern optimal fingerprinting (SNMP-OF), which uses the patterns from an ensemble-based signal-to-noise maximizing pattern (SNMP) analysis for optimal fingerprinting, and (3) a novel method based on SNMP analysis called fingerprint maximizing patterns (FMP), which finds patterns within observed variability that have the maximum fingerprint of the model-based forced response. We investigate how the root mean square error (RMSE) of these three methods varies across the choices of hyperparameters and show that all methods have a similar maximum skill. However, the contribution to the RMSE from the mean bias in the forced response estimate varies across the methods, with SNMP-OF and FMP showing a larger mean bias than LFCA. This demonstrates that methods that largely rely on the model forced response to obtain the observed forced response may give biased estimates and underestimate the uncertainty in these estimates due to the bias-variance tradeoff. Additionally, we apply these methods to observed Sahel precipitation, which is extensively debated in terms of its forced component, and closely related North Atlantic sea surface temperatures (SSTs). We show that while the methods give a robust estimate of the forced response in North Atlantic SSTs from 1950 to 2022, their estimates of the forced response in Sahel precipitation over the same period differ in sign. The fact that these estimates of the Sahel precipitation response differ substantially, despite all methods performing similarly well for large ensembles, suggests substantial epistemic uncertainty in estimates of the forced precipitation response in this region.
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Machine learning for stochastic parametrisations

Environmental Data Science Cambridge University Press 3 (2025) e38

Authors:

Hannah Christensen, Greta Miller

Abstract:

Atmospheric models used for weather and climate prediction are traditionally formulated in a deterministic manner. In other words, given a particular state of the resolved scale variables, the most likely forcing from the subgrid scale processes is estimated and used to predict the evolution of the large-scale flow. However, the lack of scale separation in the atmosphere means that this approach is a large source of error in forecasts. Over recent years, an alternative paradigm has developed: the use of stochastic techniques to characterize uncertainty in small-scale processes. These techniques are now widely used across weather, subseasonal, seasonal, and climate timescales. In parallel, recent years have also seen significant progress in replacing parametrization schemes using machine learning (ML). This has the potential to both speed up and improve our numerical models. However, the focus to date has largely been on deterministic approaches. In this position paper, we bring together these two key developments and discuss the potential for data-driven approaches for stochastic parametrization. We highlight early studies in this area and draw attention to the novel challenges that remain.
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Source of rainfall above Mediterranean caves (Chauvet and Orgnac) and long-term trend of cave dripping oxygen isotopes based on 20 years monitoring records: Importance for speleothem-based climate reconstructions

Quaternary Science Reviews 349 (2025) 109145

Authors:

Jian Zhang, Dominique Genty, François Bourges, Simon LL Michel, Bénédicte Minster, Edouard Régnier, Ludovic Devaux, Stéphane Bujan, Zhen Su, Terhi K Laurila

Abstract:

Understanding the factors that shape climate and influence the isotopic composition of precipitation is crucial for paleoclimate reconstructions, especially in regions with Mediterranean climates where rainfall is influenced by both Atlantic and Mediterranean moisture sources. This study examines the relationship between moisture origins, climatic variables, and the stable isotopic composition of precipitation and cave drip water in the Orgnac and Chauvet caves, located in southern France, over a 20-year period. The research reveals notable seasonal variations in rainfall δ18O values, driven by temperature and Rayleigh distillation processes. As shown in our previous work in Villars Cave (SW-France), temperature changes alone cannot fully explain the observed isotopic variability. We observed that winter precipitation tends to have lower δ18O values due to longer transport distances from distant oceanic sources, while summer precipitation displays higher δ18O values due to shorter transport paths. Additionally, the study highlights the influence of sea surface wind speeds and evaporation rates on water vapor isotopes, further shaping the seasonal δ18O patterns. As rainwater infiltrates the soil and percolates into the karst system, the seasonal δ18O signal in drip water is often dampened due to mixing in the reservoirs above the caves, which typically reduces seasonality. The key findings include: (1) a multi-year increasing trend in drip water δ18O, likely associated with reduced local water excess and the effects of global warming, with significant implications for speleothem isotope records, and (2) moisture from the Mediterranean Sea contributes to 10% of the total precipitation source, despite the region's proximity to the sea, especially during intense storm events. This study provides new insights into the complex interactions between moisture sources, temperature, and isotopic signatures in Mediterranean climate regions, with implications for improving speleothem-based paleoclimate reconstructions.
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3D Cloud reconstruction through geospatially-aware Masked Autoencoders

Workshop paper at “Machine Learning and the Physical Sciences”, NeurIPS (2024)

Authors:

Stella Girtsou, Emiliano Diaz Salas-Porras, Lilli J Freischem, Joppe Massant, Kyriaki-Margarita Bintsi, Guiseppe Castiglione, William Jones, Michael Eisinger, Emmanuel Johnson, Anna Jungbluth

Abstract:

Clouds play a key role in Earth's radiation balance with complex effects that introduce large uncertainties into climate models. Real-time 3D cloud data is essential for improving climate predictions. This study leverages geostationary imagery from MSG/SEVIRI and radar reflectivity measurements of cloud profiles from CloudSat/CPR to reconstruct 3D cloud structures. We first apply self-supervised learning (SSL) methods-Masked Autoencoders (MAE) and geospatially-aware SatMAE on unlabelled MSG images, and then fine-tune our models on matched image-profile pairs. Our approach outperforms state-of-the-art methods like U-Nets, and our geospatial encoding further improves prediction results, demonstrating the potential of SSL for cloud reconstruction.
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