DOI: 10.1175/JCLI-D-17-0142.1
Scopus记录号: 2-s2.0-85047079719
论文题名: Linear predictability: A sea surface height case study
作者: Sonnewald M. ; Wunsch C. ; Heimbach P.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2018
卷: 31, 期: 7 起始页码: 2599
结束页码: 2611
语种: 英语
英文关键词: Forecast verification/skill
; Ocean models
; Oceanic variability
; Sea level
; Statistical forecasting
Scopus关键词: Climate models
; Forecasting
; Sea level
; Akaike information criterion
; Autoregressive moving average
; Forecast verification/skill
; Ocean model
; Oceanic variabilities
; Prediction errors
; Sea surface height
; Statistical forecasting
; Surface waters
英文摘要: A benchmark of linear predictability of sea surface height (SSH) globally is presented, complementing more complicated studies of SSH predictability. Twenty years of the Estimating the Circulation and Climate of the Ocean (ECCOv4) state estimate (1992-2011) are used, fitting autoregressive moving average [ARMA()] models where the order of the coefficients is chosen by the Akaike information criteria (AIC). Up to 50% of the ocean SSH variability is dominated by the seasonal signal. The variance accounted for by the nonseasonal SSH is particularly distinct in the Southern and Pacific Oceans, containing > 95% of the total SSH variance, and the expected prediction error growth takes a few months to reach a threshold of 1 cm. Isolated regions take 12 months or more to cross an accuracy threshold of 1 cm. Including the trend significantly increases the time taken to reach the threshold, particularly in the South Pacific. Annual averaging has expected prediction error growth of a few years to reach a threshold of 1 cm. Including the trend mainly increases the time taken to reach the threshold, but the time series is short and noisy. © 2018 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/111589
Appears in Collections: 气候减缓与适应
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作者单位: Massachusetts Institute of Technology, Cambridge, MA, United States; Harvard University, Cambridge, MA, United States; The University of Texas at Austin, Austin, TX, United States
Recommended Citation:
Sonnewald M.,Wunsch C.,Heimbach P.. Linear predictability: A sea surface height case study[J]. Journal of Climate,2018-01-01,31(7)