Stochastic Parameterization: Towards a new view of Weather and Climate Models

TL;DR

Stochastic parameterizations enhance weather and climate models by improving forecast reliability and reducing biases, with recent advances promising significant progress through mathematically rigorous methods.

Contribution

The paper reviews recent developments showing stochastic parameterizations improve probabilistic forecasts and bias reduction in comprehensive weather and climate models.

Findings

Stochastic methods lead to more reliable probabilistic forecasts.

They reduce systematic biases in climate models.

Recent mathematical approaches can further improve model accuracy.

Abstract

The last decade has seen the success of stochastic parameterizations in short-term, medium-range and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to better represent model inadequacy and improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing longstanding climate biases and relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups which show that the stochastic representation of unresolved processes in the atmosphere, oceans, land surface and cryosphere of comprehensive weather and climate models (a) gives rise to more reliable probabilistic forecasts of weather and climate and (b) reduces systematic model bias. We make a case that the use of mathematically stringent methods for the derivation of stochastic dynamic equations will lead to substantial improvements in our ability to accurately simulate weather and climate at all scales. Recent work in mathematics, statistical mechanics and turbulence is reviewed, its relevance for the climate problem demonstrated, and future research directions outlined.