DOI: 10.1007/s00382-016-3100-0
Scopus记录号: 2-s2.0-84964059471
论文题名: Sea surface temperature predictions using a multi-ocean analysis ensemble scheme
作者: Zhang Y. ; Zhu J. ; Li Z. ; Chen H. ; Zeng G.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2017
卷: 49, 期: 3 起始页码: 1049
结束页码: 1059
语种: 英语
英文关键词: Multi-ocean analysis ensemble
; Sea surface temperature
; Seasonal prediction
英文摘要: This study examined the global sea surface temperature (SST) predictions by a so-called multiple-ocean analysis ensemble (MAE) initialization method which was applied in the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2). Different from most operational climate prediction practices which are initialized by a specific ocean analysis system, the MAE method is based on multiple ocean analyses. In the paper, the MAE method was first justified by analyzing the ocean temperature variability in four ocean analyses which all are/were applied for operational climate predictions either at the European Centre for Medium-range Weather Forecasts or at NCEP. It was found that these systems exhibit substantial uncertainties in estimating the ocean states, especially at the deep layers. Further, a set of MAE hindcasts was conducted based on the four ocean analyses with CFSv2, starting from each April during 1982–2007. The MAE hindcasts were verified against a subset of hindcasts from the NCEP CFS Reanalysis and Reforecast (CFSRR) Project. Comparisons suggested that MAE shows better SST predictions than CFSRR over most regions where ocean dynamics plays a vital role in SST evolutions, such as the El Niño and Atlantic Niño regions. Furthermore, significant improvements were also found in summer precipitation predictions over the equatorial eastern Pacific and Atlantic oceans, for which the local SST prediction improvements should be responsible. The prediction improvements by MAE imply a problem for most current climate predictions which are based on a specific ocean analysis system. That is, their predictions would drift towards states biased by errors inherent in their ocean initialization system, and thus have large prediction errors. In contrast, MAE arguably has an advantage by sampling such structural uncertainties, and could efficiently cancel these errors out in their predictions. © 2016, Springer-Verlag Berlin Heidelberg. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/53124
Appears in Collections: 过去全球变化的重建
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作者单位: Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology (NUIST), Nanjing, China; Climate Prediction Center, NOAA/NWS/NCEP, 5830 University Research Court, College Park, MD, United States; Innovim, Greenbelt, MD, United States; Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, United States
Recommended Citation:
Zhang Y.,Zhu J.,Li Z.,et al. Sea surface temperature predictions using a multi-ocean analysis ensemble scheme[J]. Climate Dynamics,2017-01-01,49(3)