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Stochastic climate theory and modeling

Citation

Franzke, CLE and O'Kane, TJ and Berner, J and Williams, PD and Lucarini, V, Stochastic climate theory and modeling, Wiley Interdisciplinary Reviews: Climate Change, 6, (1) pp. 63-78. ISSN 1757-7799 (2015) [Refereed Article]

Copyright Statement

Copyright 2014 John Wiley & Sons, Ltd.

DOI: doi:10.1002/wcc.318

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 (SSPs) 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 modeling. 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 perspective. 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.

Item Details

Item Type:Refereed Article
Keywords:climate theory, modelling, stochastic theory, climate prediction
Research Division:Mathematical Sciences
Research Group:Statistics
Research Field:Stochastic Analysis and Modelling
Objective Division:Environment
Objective Group:Climate and Climate Change
Objective Field:Climate Change Models
Author:O'Kane, TJ (Dr Terry O'Kane)
ID Code:118559
Year Published:2015
Web of Science® Times Cited:18
Deposited By:IMAS - Directorate
Deposited On:2017-07-13
Last Modified:2017-08-15
Downloads:0

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