Statistics of extreme events in climate models via coarse-scale simulations and machine learning correction operators based on nudged datasetsDownload PDF

01 Aug 2023AAAI 2023 Spring Symposium Series ACTD SubmissionReaders: Everyone
Keywords: machine learning, uncertainty quantification, climate, tropical cyclones
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 coarsescale models. Specifically, failure to incorporate all relevant scales in climate simulations leads to discrepancies in the energy spectrum as well as higher order statistics. While high resolution simulations or reanalysis data are available, at least for short periods, 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 (here we employ Energy Exascale Earth System Model, E3SM) 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 (used as output training data) and the weakly nudged coarse-resolution E3SM output that is used as input training data to machine learn a correction operator. We emphasize that the nudging step is used only for the training phase. Once training is complete, we perform free-running coarsescale 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 ERA5 datasets that are not employed for training, we demonstrate that the produced datasets from the MLcorrected coarse E3SM model have statistical properties that closely resemble the observations. In particular, the corrected coarse-scale E3SM output closely captures the non-Gaussian statistics of quantities such as temperature, wind speed and humidity, as well as the frequency of occurrence of extreme events, such as tropical cyclones and atmospheric rivers. We present thorough comparisons and discuss limitations of the approach.
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