DOI: 10.1007/s00382-012-1531-9
Scopus记录号: 2-s2.0-84881025233
论文题名: Statistical decadal predictions for sea surface temperatures: A benchmark for dynamical GCM predictions
作者: Ho C.K. ; Hawkins E. ; Shaffrey L. ; Underwood F.M.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2013
卷: 41, 期: 2017-03-04 起始页码: 917
结束页码: 935
语种: 英语
英文关键词: Decadal prediction
; Global climate model
; Sea surface temperatures
; Statistical
英文摘要: Accurate decadal climate predictions could be used to inform adaptation actions to a changing climate. The skill of such predictions from initialised dynamical global climate models (GCMs) may be assessed by comparing with predictions from statistical models which are based solely on historical observations. This paper presents two benchmark statistical models for predicting both the radiatively forced trend and internal variability of annual mean sea surface temperatures (SSTs) on a decadal timescale based on the gridded observation data set HadISST. For both statistical models, the trend related to radiative forcing is modelled using a linear regression of SST time series at each grid box on the time series of equivalent global mean atmospheric CO2 concentration. The residual internal variability is then modelled by (1) a first-order autoregressive model (AR1) and (2) a constructed analogue model (CA). From the verification of 46 retrospective forecasts with start years from 1960 to 2005, the correlation coefficient for anomaly forecasts using trend with AR1 is greater than 0.7 over parts of extra-tropical North Atlantic, the Indian Ocean and western Pacific. This is primarily related to the prediction of the forced trend. More importantly, both CA and AR1 give skillful predictions of the internal variability of SSTs in the subpolar gyre region over the far North Atlantic for lead time of 2-5 years, with correlation coefficients greater than 0.5. For the subpolar gyre and parts of the South Atlantic, CA is superior to AR1 for lead time of 6-9 years. These statistical forecasts are also compared with ensemble mean retrospective forecasts by DePreSys, an initialised GCM. DePreSys is found to outperform the statistical models over large parts of North Atlantic for lead times of 2-5 years and 6-9 years, however trend with AR1 is generally superior to DePreSys in the North Atlantic Current region, while trend with CA is superior to DePreSys in parts of South Atlantic for lead time of 6-9 years. These findings encourage further development of benchmark statistical decadal prediction models, and methods to combine different predictions. © 2012 The Author(s).
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/54837
Appears in Collections: 过去全球变化的重建
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作者单位: NCAS-Climate, Department of Meteorology, University of Reading, PO Box 243, Earley Gate, Reading, RG6 6BB, United Kingdom; Department of Mathematics and Statistics, University of Reading, PO Box 220, Whiteknights, Reading, RG6 6AX, United Kingdom
Recommended Citation:
Ho C.K.,Hawkins E.,Shaffrey L.,et al. Statistical decadal predictions for sea surface temperatures: A benchmark for dynamical GCM predictions[J]. Climate Dynamics,2013-01-01,41(2017-03-04)