DOI: 10.1002/wcc.318
Scopus记录号: 2-s2.0-84915794649
论文题名: Stochastic climate theory and modeling
作者: Franzke C ; L ; E ; , O'Kane T ; J ; , Berner J ; , Williams P ; D ; , Lucarini V
刊名: Wiley Interdisciplinary Reviews: Climate Change
ISSN: 17577780
出版年: 2015
卷: 6, 期: 1 起始页码: 63
结束页码: 78
语种: 英语
英文关键词: Climate models
; Climatology
; Degrees of freedom (mechanics)
; Dynamical systems
; Forecasting
; Meteorology
; Numerical models
; Statistical mechanics
; Stochastic systems
; Weather forecasting
; Applied mathematics
; Climate prediction model
; Ensemble prediction
; In-laboratory experiments
; Reduced order models
; Stochastic component
; Uncertainty quantifications
; Weather and climate models
; Stochastic models
英文摘要: 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. © 2014 John Wiley & Sons, Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/76294
Appears in Collections: 影响、适应和脆弱性 气候变化与战略
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作者单位: Meteorological Institute and Centre for Earth System Research and Sustainability (CEN), University of Hamburg, Hamburg, Germany; Centre for Australian Weather and Climate Research, CSIRO Marine and Atmospheric Research, Hobart, Australia; National Center for Atmospheric Research, Boulder, United States; Department of Meteorology, University of Reading, Reading, United Kingdom; Department of Mathematics and Statistics, University of Reading, Reading, United Kingdom
Recommended Citation:
Franzke C,L,E,et al. Stochastic climate theory and modeling[J]. Wiley Interdisciplinary Reviews: Climate Change,2015-01-01,6(1)