Collaborative Research: Type 1 - LOIL02170097: Decadal Predictability of Extreme Events: Impact of a Model Error Representation and Numerical Resolution

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Type 1 - LOIL02170097: Decadal predictability of extreme events:Impact of a model error representation and numerical resolution(collaborative research)The investigators will implement a stochastic backscatter scheme into the CommunityAtmosphere Model and explore how to improve the internal variability and, in particular,the prediction of extreme events on decadal and regional scales. Only a small number ofpublications apply Extreme Value Theory to climate models and many questions remainopen. While some work has been conducted comparing model and reanalysis with constantgreenhouse forcing, the major body of published work in this area focuses on the occurrenceof extreme events in a changing climate and on the robustness of climate trends of extremeevents across di^erent low-resolution models.The emphasis of the investigators is very di^erent: they will look at the internal variabilityof models under a constant greenhouse forcing. The investigators focus both on the ability oflow-resolution climate models to realistically predict extreme events on decadal time-scalesand global spatial scales, and on the feasibility of replacing the missing variability due tolow-resolution with a stochastic model error scheme and if such a scheme can improve thedecadal prediction of extreme events. Model integrations with and without a stochasticbackscatter scheme would be conducted and extreme value statistics be used to determinethe impact of the scheme onto the occurrence of extreme events. For comparison, the samestatistic would be computed using the the ERA40, the ERA-Interim analysis and/or theNCEP/NCAR reanalysis as best proxy for multi-decadal observations.