Short- to long-range climate forecasts with deep learning
Copernicus Publications (2026)
Abstract:
Uncertainty in projections of future regional climate change remains large, driven by structural differences among Earth System Models and the influence of internal climate variability. Existing uncertainty-reduction approaches, including emergent constraints and Bayesian variants, primarily focus on forced climate responses derived from simple aggregate metrics, thereby requiring strong assumptions and exploiting only low-dimensional climate information. Here we propose a data-driven deep-learning framework that directly forecasts spatially and monthly resolved decadal mean climatologies of surface temperature anomalies from the 2030s to the 2090s, using only recent monthly trajectories spanning 1980-2025. The training ensemble contains 265 historical+SSP2-4.5 simulations, distributed across 40 ESMs from 25 different families (i.e., modelling centers) over which the cross validation is performed. The architecture couples pluri-annual to multi-decadal temporal convolutions with a spatial U-Net encoder-decoder and is evaluated on CMIP6 simulations using a leave-one-model-family-out cross-validation (LOMFO-CV) design to ensure generalisation across separately developed ESMs. Predictive uncertainty is quantified via LOMFO-CV errors, yielding conservative and reliable ranges that incorporate irreducible internal variability and systematic model shifts.To further evaluate the predictive capacity beyond the CMIP6 distribution, we evaluated the network on historical+SSP2-4.5 simulations from a recent HadGEM3-GC5 model hierarchy developed within the European Eddy-Rich ESMs (EERIE) project, the European contribution to HighResMIP2 for CMIP7. In particular, the eddy-rich GC5-HH configuration explicitly simulates mesoscale ocean dynamics that are absent in CMIP6-type models, providing a rigorous test of generalisation to richer and more realistic physical representations. Despite these substantial differences, the network successfully reproduces warming trajectories and future climate patterns for all three model configurations (GC5-LL, GC5-MM, GC5-HH), with forecast errors largely contained within empirically calibrated uncertainty bounds from the LOMFO-CV, both globally and locally. These results, notably for GC5-HH and its more realistic physics, strengthens confidence in the applicability of the framework to real-world data.When applied to observations, the extracted end-of-century global-mean surface temperature and its uncertainty range are consistent with prior estimates from Bayesian frameworks. At local scales, the network reduces uncertainty by 40% (2030s) to 30% (2090s) on average, and by up to 75% in some regions for all future decades. Importantly, these uncertainty estimates account not only for uncertainty in the forced response (as emergent constraint methods do), but also for errors associated with predicting different realisations of internal variability, providing a physically meaningful reduction of local and global climate uncertainty.
Reconstruction of last millennium sea surface temperature on 1° grid using a random forest algorithm
Global and Planetary Change 258 (2026) 105279
Abstract:
Climate models and theoretical evidence show that the ocean drives climate from sub-decadal to centennial timescales through a variety of processes and their interactions. The range of direct climate observations, however, is too short to understand the exact role of the ocean in shaping observed and future climate variability on top of anthropogenic climate change. In the present study, we use a large set of paleoclimate records combined with a random forest algorithm to reconstruct a gridded dataset of sea surface temperatures since 850 C.E. to provide a better framework for the study of ocean surface variability. In line with modeling and paleodata studies, our reconstruction suggests that natural climate forcings have importantly influenced the last millennium climate variability. Our reconstruction also suggests that North Atlantic SST multidecadal variability influences Pacific SST on decadal timescales. However, the latter result is shown to be strongly dependent on background climate conditions. This new reconstruction offers a useful resource for testing the capabilities of climate models to reproduce the linkages between Atlantic and Pacific as well as the response to external forcings.
The Link between Gulf Stream Precipitation Extremes and European Blocking in General Circulation Models and the Role of Horizontal Resolution
Journal of Climate (2025)
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.
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.
Unravelling the role of increased model resolution on surface temperature fields using explainable AI
Copernicus Publications (2025)