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

ABSTRACT: 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.
The result is a compatible pair of the ERA5 trajectory and the weakly nudged
coarse-resolution E3SM output that is used as input training data to machine
learn a correction operator. Once training is complete, we perform free-running
coarse-scale E3SM simulations without nudging and use those as input to the
machine-learned correction operator to obtain high-quality (corrected) outputs.
The model is applied to atmospheric climate data with the purpose of predicting
global and local statistics of various quantities of a time-period of a decade.
Using datasets that are not employed for training, we demonstrate that the
produced datasets from the ML-corrected coarse E3SM model have statistical
properties that closely resemble the observations. Furthermore, the corrected
coarse-scale E3SM output for the frequency of occurrence of extreme events,
such as tropical cyclones and atmospheric rivers are presented. We present
thorough comparisons and discuss limitations of the approach.
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.
The result is a compatible pair of the ERA5 trajectory and the weakly nudged
coarse-resolution E3SM output that is used as input training data to machine
learn a correction operator. Once training is complete, we perform free-running
coarse-scale E3SM simulations without nudging and use those as input to the
machine-learned correction operator to obtain high-quality (corrected) outputs.
The model is applied to atmospheric climate data with the purpose of predicting
global and local statistics of various quantities of a time-period of a decade.
Using datasets that are not employed for training, we demonstrate that the
produced datasets from the ML-corrected coarse E3SM model have statistical
properties that closely resemble the observations. Furthermore, the corrected
coarse-scale E3SM output for the frequency of occurrence of extreme events,
such as tropical cyclones and atmospheric rivers are presented. We present
thorough comparisons and discuss limitations of the approach.
Statistics of extreme events in coarse-scale climate simulations via machine learning correction operators trained on nudged datasets
http://arxiv.org/abs/2304.02117v1