Representing model uncertainty in weather and climate predictions by a stochastic kinetic energy backscatter scheme

Judith Berner
National Center for Atmospheric Research

Understanding model error in state-of-the-art climate and numerical
weather prediction models and representing its impact on flow-dependent
predictability and climate uncertainty remains a complex and mostly
unsolved problem. Here, a spectral stochastic kinetic energy
backscatter scheme is used to simulate upscale-propagating errors
caused by unresolved subgrid-scale processes. For this purpose,
stochastic streamfunction perturbations are generated by
autoregressive processes in spectral space and injected into regions
where numerical integration schemes and parameterizations in the model
lead to excessive systematic kinetic energy loss. It is demonstrated
how output from coarse-grained high-resolution models can be used to
inform the parameters of such a scheme. The spectral backscatter scheme
improves the prediction across scales by improving both, weather and climate forecasts.

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