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

University Corporation For Atmospheric Res, Boulder CO

Investigators

Abstract

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 Community Atmosphere 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 of publications apply Extreme Value Theory to climate models and many questions remain open. While some work has been conducted comparing model and reanalysis with constant greenhouse forcing, the major body of published work in this area focuses on the occurrence of extreme events in a changing climate and on the robustness of climate trends of extreme events across dierent low-resolution models. The emphasis of the investigators is very dierent: they will look at the internal variability of models under a constant greenhouse forcing. The investigators focus both on the ability of low-resolution climate models to realistically predict extreme events on decadal time-scales and global spatial scales, and on the feasibility of replacing the missing variability due to low-resolution with a stochastic model error scheme and if such a scheme can improve the decadal prediction of extreme events. Model integrations with and without a stochastic backscatter scheme would be conducted and extreme value statistics be used to determine the impact of the scheme onto the occurrence of extreme events. For comparison, the same statistic would be computed using the the ERA40, the ERA-Interim analysis and/or the NCEP/NCAR reanalysis as best proxy for multi-decadal observations.