Stochastic Climate Theory and Modelling

TL;DR

This paper reviews the role of stochastic methods in climate modeling, emphasizing their importance in representing unresolved processes, reducing biases, and improving predictions in weather and climate models.

Contribution

It provides a comprehensive overview of stochastic climate theory, discusses their application in models, and highlights their potential to enhance prediction accuracy and reduce biases.

Findings

Stochastic effects are evident in laboratory experiments.

Stochastic parameterizations can address biases in climate models.

Stochastic methods improve the representation of unresolved processes.

Abstract

Stochastic methods are a crucial area in contemporary climate research and are increasingly being used in comprehensive weather and climate prediction models as well as reduced order climate models. Stochastic methods are used as subgrid-scale parameterizations as well as for model error representation, uncertainty quantification, data assimilation and ensemble prediction. The need to use stochastic approaches in weather and climate models arises because we still cannot resolve all necessary processes and scales in comprehensive numerical weather and climate prediction models. In many practical applications one is mainly interested in the largest and potentially predictable scales and not necessarily in the small and fast scales. For instance, reduced order models can simulate and predict large scale modes. Statistical mechanics and dynamical systems theory suggest that in reduced order models the impact of unresolved degrees of freedom can be represented by suitable combinations of deterministic and stochastic components and non-Markovian (memory) terms. Stochastic approaches in numerical weather and climate prediction models also lead to the reduction of model biases. Hence, there is a clear need for systematic stochastic approaches in weather and climate modelling. In this review we present evidence for stochastic effects in laboratory experiments. Then we provide an overview of stochastic climate theory from an applied mathematics perspectives. We also survey the current use of stochastic methods in comprehensive weather and climate prediction models and show that stochastic parameterizations have the potential to remedy many of the current biases in these comprehensive models.