Towards accurate extreme event likelihoods from diffusion model climate emulators
ML climate model emulators are useful for scenario planning and adaptation, allowing for cost-efficient experimentation. Recently, the diffusion model Climate in a Bottle (cBottle) has been proposed for generation of atmospheric states compatible with boundary conditions of solar position and sea surface temperatures. Crucially, cBottle can be guided to generate extreme events such as Tropical Cyclones (TCs) over locations of interest. Diffusion models such as cBottle work by approximating the probability density of the training data. Here, we show use cases of the probability density estimates of atmospheric states obtained from this climate emulator. Most importantly, these estimates al...
| Authors | Peter Manshausen, Noah Brenowitz, Julius Berner et al. |
|---|---|
| Primary category | physics.ao-ph |
| Published | 5 May 2026 |
| Last updated at source | 12 October 2026 |
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