English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This work presents a systematic framework for improving the predictions ofstatistical quantities for turbulent systems, with a focus on correctingclimate simulations obtained by coarse-scale models. While high resolutionsimulations or reanalysis data are available, they cannot be directly used astraining datasets to machine learn a correction for the coarse-scale climatemodel outputs, since chaotic divergence, inherent in the climate dynamics,makes datasets from different resolutions incompatible. To overcome thisfundamental limitation we employ coarse-resolution model simulations nudgedtowards high quality climate realizations, here in the form of ERA5 reanalysisdata. The nudging term is sufficiently small to not pollute the coarse-scaledynamics over short time scales, but also sufficiently large to keep thecoarse-scale simulations close to the ERA5 trajectory over larger time scales.The result is a compatible pair of the ERA5 trajectory and the weakly nudgedcoarse-resolution E3SM output that is used as input training data to machinelearn a correction operator. Once training is complete, we perform free-runningcoarse-scale E3SM simulations without nudging and use those as input to themachine-learned correction operator to obtain high-quality (corrected) outputs.The model is applied to atmospheric climate data with the purpose of predictingglobal and local statistics of various quantities of a time-period of a decade.Using datasets that are not employed for training, we demonstrate that theproduced datasets from the ML-corrected coarse E3SM model have statisticalproperties that closely resemble the observations. Furthermore, the correctedcoarse-scale E3SM output for the frequency of occurrence of extreme events,such as tropical cyclones and atmospheric rivers are presented. We presentthorough 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