A probabilistic generative deep learning framework reconstructs global historical climate fields from 1850 onward, revealing higher early 20th-century warming driven by stronger polar trends and localized modern hotspots compared to existing products.
& Boers, N.Diffusion models for probabilistic precipitation generation from atmospheric variables Apr
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A physics-constrained consistency model downscales Greenland SMB and surface temperature by a factor of 32 while preserving coarse-scale sums and outperforming interpolation on test metrics.
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Generative deep learning improves reconstruction of global historical climate records
A probabilistic generative deep learning framework reconstructs global historical climate fields from 1850 onward, revealing higher early 20th-century warming driven by stronger polar trends and localized modern hotspots compared to existing products.
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Physics-constrained generative machine learning-based high-resolution downscaling of Greenland's surface mass balance and surface temperature
A physics-constrained consistency model downscales Greenland SMB and surface temperature by a factor of 32 while preserving coarse-scale sums and outperforming interpolation on test metrics.