DOI: 10.1175/JCLI-D-17-0661.1
Scopus记录号: 2-s2.0-85049751208
论文题名: Skillful climate forecasts of the tropical indo-Pacific ocean using model-analogs
作者: Ding H. ; Newman M. ; Alexander M.A. ; Wittenberg A.T.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2018
卷: 31, 期: 14 起始页码: 5437
结束页码: 5459
语种: 英语
英文关键词: Atmosphere-ocean interaction
; Ensembles
; ENSO
; Hindcasts
; Interannual variability
; Seasonal forecasting
Scopus关键词: Atmospheric thermodynamics
; Climate change
; Oceanography
; Tropics
; Weather forecasting
; Atmosphere-ocean interactions
; Ensembles
; ENSO
; Hindcasts
; Interannual variability
; Seasonal forecasting
; Climate models
; air-sea interaction
; analog model
; annual variation
; ensemble forecasting
; hindcasting
; weather forecasting
英文摘要: Seasonal forecasts made by coupled atmosphere-ocean general circulation models (CGCMs) undergo strong climate drift and initialization shock, driving the model state away from its long-term attractor. Here we explore initializing directly on a model's own attractor, using an analog approach in which model states close to the observed initial state are drawn from a "library" obtained from prior uninitialized CGCM simulations. The subsequent evolution of those "model-analogs" yields a forecast ensemble, without additional model integration. This technique is applied to four of the eight CGCMs comprising the North American Multimodel Ensemble (NMME) by selecting from prior long control runs those model states whose monthly tropical Indo-Pacific SST and SSH anomalies best resemble the observations at initialization time. Hindcasts are then made for leads of 1-12 months during 1982-2015. Deterministic and probabilistic skill measures of these model-analog hindcast ensembles are comparable to those of the initialized NMME hindcast ensembles, for both the individual models and the multimodel ensemble. In the eastern equatorial Pacific, model-analog hindcast skill exceeds that of the NMME. Despite initializing with a relatively large ensemble spread, model-analogs also reproduce each CGCM's perfect-model skill, consistent with a coarse-grained view of tropical Indo-Pacific predictability. This study suggests that with little additional effort, sufficiently realistic and long CGCM simulations provide the basis for skillful seasonal forecasts of tropical Indo-Pacific SST anomalies, even without sophisticated data assimilation or additional ensemble forecast integrations. The model-analog method could provide a baseline for forecast skill when developing future models and forecast systems. © 2018 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/111469
Appears in Collections: 气候减缓与适应
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作者单位: Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, United States; NOAA/Earth System Research Laboratory, Boulder, CO, United States; NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ, United States
Recommended Citation:
Ding H.,Newman M.,Alexander M.A.,et al. Skillful climate forecasts of the tropical indo-Pacific ocean using model-analogs[J]. Journal of Climate,2018-01-01,31(14)