DOI: 10.1175/JCLI-D-11-00454.1
Scopus记录号: 2-s2.0-84859558963
论文题名: Quantifying uncertainty for climate change and long-range forecasting scenarios with model errors. Part I: Gaussian models
作者: Gershgorin B. ; Majda A.J.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2012
卷: 25, 期: 13 起始页码: 4523
结束页码: 4548
语种: 英语
Scopus关键词: Climate change scenarios
; Climate science
; Climate sensitivity
; Coarse Graining
; External perturbations
; Gaussian model
; Initial conditions
; Linear Gaussian model
; Long-range forecasting
; Long-range forecasts
; Model errors
; Non-Gaussian
; Passive tracers
; Periodic models
; Physical systems
; Relative entropy
; Rossby wave
; Seasonal cycle
; Seasonal fluctuations
; Spatially extended systems
; Statistical forecasting
; Stochastic forcing
; Systematic framework
; Three models
; Turbulent fields
; Climate change
; Errors
; Forecasting
; Information theory
; Ocean currents
; Stochastic models
; Climate models
; climate change
; climate modeling
; error analysis
; Gaussian method
; long range forecast
; numerical model
; Rossby wave
; statistical analysis
; stochasticity
; tracer
; uncertainty analysis
英文摘要: Information theory provides a concise systematic framework for measuring climate consistency and sensitivity for imperfect models. A suite of increasingly complex physically relevant linear Gaussian models with time periodic features mimicking the seasonal cycle is utilized to elucidate central issues that arise in contemporary climate science. These include the role of model error, the memory of initial conditions, and effects of coarse graining in producing short-, medium-, and long-range forecasts. In particular, this study demonstrates how relative entropy can be used to improve climate consistency of an overdamped imperfect model by inflating stochastic forcing. Moreover, the authors show that, in the considered models, by improving climate consistency, this simultaneously increases the predictive skill of an imperfect model in response to external perturbation, a property of crucial importance in the context of climate change. The three models range in complexity from a scalar time periodic model mimicking seasonal fluctuations in a mean jet to a spatially extended system of turbulent Rossby waves to, finally, the behavior of a turbulent tracer with a mean gradient with the background turbulent field velocity generated by the first two models. This last model mimics the global and regional behavior of turbulent passive tracers under various climate change scenarios. This detailed study provides important guidelines for extending these strategies to more complicated and non-Gaussian physical systems. © 2012 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/52334
Appears in Collections: 气候变化事实与影响
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作者单位: Courant Institute of Mathematical Sciences, New York University, New York, NY, United States
Recommended Citation:
Gershgorin B.,Majda A.J.. Quantifying uncertainty for climate change and long-range forecasting scenarios with model errors. Part I: Gaussian models[J]. Journal of Climate,2012-01-01,25(13)