DOI: 10.1175/JCLI-D-13-00026.1
Scopus记录号: 2-s2.0-84892452599
论文题名: Changes in seasonal predictability due to global warming
作者: Delsole T. ; Yan X. ; Dirmeyer P.A. ; Fennessy M. ; Altshuler E.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2014
卷: 27, 期: 1 起始页码: 300
结束页码: 311
语种: 英语
Scopus关键词: Climate prediction
; Climate variability
; Community climate system model
; Ensembles
; Physical interpretation
; Sea surface temperature (SST)
; Seasonal forecasting
; Teleconnection patterns
; Atmospheric pressure
; Atmospheric temperature
; Climate change
; Climate models
; Global warming
; Sea ice
; Climatology
; air temperature
; climate change
; climate prediction
; climate variation
; El Nino-Southern Oscillation
; ensemble forecasting
; global warming
; teleconnection
; Africa
; Atlantic Ocean
; Atlantic Ocean (North)
英文摘要: The change in predictability of monthly mean temperature in a future climate is quantified based on the Community Climate System Model, version 4. According to this model, the North Atlantic overtakes the El Niño-Southern Oscillation (ENSO) as the dominant area of seasonal predictability by 2095. This change arises partly because ENSO becomes less variable and partly because the ENSO teleconnection pattern expands into the Atlantic. Over land, the largest change in temperature predictability occurs in the tropics and is predominantly due to a decrease in ENSO variability. The southern peninsula of Africa and northeast South America are predicted to experience significant drying in a future climate, which decreases the effective heat capacity and memory, and hence increases variance independently of ENSO changes. Extratropical land areas experience enhanced precipitation in a future climate, which decreases temperature variance by the same mechanism. Finally, the model predicts that surface temperatures near the poles will become more predictable and less variable in a future climate, primarily because melting sea ice exposes the underlying sea surface temperature, which is more predictable owing to its longer time scale. Some of these results, especially the change in ENSO variance, are known to be model dependent. This paper also advances the use of information theory to quantify predictability, including 1) deriving a quantitative relation between predictability of the first and second kinds; 2) showing how differences in predictability can be decomposed in two dramatically different ways, facilitating physical interpretation; and 3) proposing a sample estimate of mutual information whose significance can be tested using standard techniques. © 2014 American Meteorological Society. 资助项目: NSF, National Science Foundation
; NSF, National Science Foundation
; NOAA, National Oceanic and Atmospheric Administration
; NASA, National Aeronautics and Space Administration
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/51251
Appears in Collections: 气候变化事实与影响
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作者单位: George Mason University, Fairfax, VA, United States; Center for Ocean-Land-Atmosphere Studies, Calverton, MD, United States
Recommended Citation:
Delsole T.,Yan X.,Dirmeyer P.A.,et al. Changes in seasonal predictability due to global warming[J]. Journal of Climate,2014-01-01,27(1)