| DOI: | 10.1175/JCLI-D-15-0306.1
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| Scopus记录号: | 2-s2.0-84946221754
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| 论文题名: | A vector autoregressive ENSO prediction model |
| 作者: | Chapman D.; Cane M.A.; Henderson N.; Lee D.E.; Chen C.
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| 刊名: | Journal of Climate
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| ISSN: | 8948755
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| 出版年: | 2015
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| 卷: | 28, 期:21 | | 起始页码: | 8511
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| 结束页码: | 8520
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| 语种: | 英语
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| Scopus关键词: | Atmospheric pressure
; Atmospheric temperature
; Climatology
; Forecasting
; Inverse problems
; Oceanography
; Surface waters
; Value engineering
; Auto-regressive
; ENSO prediction models
; Inverse methods
; Sea surface temperature anomalies
; Southern oscillation
; State vector
; VAR models
; Vector autoregressive model
; Vectors
; climate modeling
; climate prediction
; El Nino-Southern Oscillation
; hindcasting
; sea surface temperature
; vector autoregression
; weather forecasting
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| 英文摘要: | The authors investigate a sea surface temperature anomaly (SSTA)-only vector autoregressive (VAR) model for prediction of El Niño-Southern Oscillation (ENSO). VAR generalizes the linear inverse method (LIM) framework to incorporate an extended state vector including many months of recent prior SSTA in addition to the present state. An SSTA-only VAR model implicitly captures subsurface forcing observable in the LIM residual as red noise. Optimal skill is achieved using a state vector of order 14-17 months in an exhaustive 120-yr cross-validated hindcast assessment. It is found that VAR outperforms LIM, increasing forecast skill by 3 months, in a 30-yr retrospective forecast experiment. © 2015 American Meteorological Society. |
| 资助项目: | ONR, Office of Naval Research
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| Citation statistics: |
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| 资源类型: | 期刊论文
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/50669
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| Appears in Collections: | 气候变化事实与影响
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作者单位: | Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, United States
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| Recommended Citation: | |
Chapman D.,Cane M.A.,Henderson N.,et al. A vector autoregressive ENSO prediction model[J]. Journal of Climate,2015-01-01,28(21)
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