Atmospheric processes

Minimizing climate model biases is essential to reduce uncertainties in future climate projections. Despite recent advances, improvements between generations of Earth System Models (ESMs) remain modest, largely due to continued reliance on subgrid-scale parametrizations. These are required because CMIP6 model resolutions are too coarse to explicitly simulate small-scale processes such as ocean mesoscale eddies and deep atmospheric convection, which strongly influence climate patterns. Recent gains in computational power have enabled higher-resolution ESMs that can resolve some of these processes, thereby reducing the need for parametrization. However, robustly detecting improved modeling with increased resolution remains challenging due to internal variability and model-to-model discrepancies. This study employs a convolutional neural network (CNN) classifier combined with explainable AI (XAI) to assess the role of resolution in simulating winter surface temperature fields in an ensemble of 17 control climate simulations with varying oceanic and atmospheric resolutions. The CNN distinguishes between ESMs of varying resolutions using temperature snapshots, while XAI identifies the regions driving these classifications, offering deeper insight into ESM behavior. The results show that ESMs with similar ocean grid resolutions are more often confused with each other by the CNN than those from different modeling centers, highlighting the central role of ocean resolution, particularly mesoscale eddies, in shaping climate simulations. Although limited to surface air temperature, the approach provides a more nuanced perspective on ESM differences and performance than traditional bias analyses. The framework can be extended to other variables and ESM features, offering a powerful tool for ESM intercomparison and evaluation.

Local temperatures increasingly exceed values without precedent in the historical record, but when these excursions become permanent is uncertain due to the underestimated role of internal climate variability. Here we recast the problem in probabilistic terms by asking, for a given global warming level, how likely is it that annual mean local temperature will remain above its 1850-1999 maximum through 2100, given the preceding climate history. We train a neural network on 408 CMIP6 simulations under SSP2-4.5, the emissions trajectory closest to current policy commitments, and obtain calibrated probabilities skilful under out-of-sample validation. Applied to observations, regions where temperatures remain above their historical maximum are confined to just 2.2% of global land area at 1.5°C, but expand rapidly thereafter, reaching nearly half the present-day global population and 35% of aboveground biomass by 2°C, and most of humanity and nearly 80% of aboveground biomass by 3°C. Current policy trajectories will therefore commit a rapidly growing share of humanity and Earth’s ecosystems to twenty-first century local temperatures sustained above their historical maxima.