Statistics of extreme events in coarse-scale climate simulations via machine learning correction operators trained on nudged datasets

Understand

This work presents a systematic framework for improving the predictions of statistical quantities for turbulent systems, with a focus on correcting climate simulations obtained by coarse-scale models.

• While high resolution simulations or reanalysis data are available, they cannot be directly used as training datasets to machine learn a correction for the coarse-scale climate model outputs, since chaotic divergence, inherent in the climate dynamics, makes datasets from different resolutions incompatible.
• To overcome this fundamental limitation we employ coarse-resolution model simulations nudged towards high quality climate realizations, here in the form of ERA5 reanalysis data.
• The nudging term is sufficiently small to not pollute the coarse-scale dynamics over short time scales, but also sufficiently large to keep the coarse-scale simulations close to the ERA5 trajectory over larger time scales.