Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1007/s00382-018-4323-z |
Remote and local influences in forecasting Pacific SST: a linear inverse model and a multimodel ensemble study | |
Dias, Daniela Faggiani1; Subramanian, Aneesh1; Zanna, Laure2; Miller, Arthur J.1 | |
2019-03-01 | |
发表期刊 | CLIMATE DYNAMICS
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ISSN | 0930-7575 |
EISSN | 1432-0894 |
出版年 | 2019 |
卷号 | 52页码:3183-3201 |
文章类型 | Article |
语种 | 英语 |
国家 | USA; England |
英文摘要 | A suite of statistical linear inverse models (LIMs) are used to understand the remote and local SST variability that influences SST predictions over the North Pacific region. Observed monthly SST anomalies in the Pacific are used to construct different regional LIMs for seasonal to decadal predictions. The seasonal forecast skills of the LIMs are compared to that from three operational forecast systems in the North American Multi-Model Ensemble (NMME), revealing that the LIM has better skill in the Northeastern Pacific than NMME models. The LIM is also found to have comparable forecast skill for SST in the Tropical Pacific with NMME models. This skill, however, is highly dependent on the initialization month, with forecasts initialized during the summer having better skill than those initialized during the winter. The data are also bandpass filtered into seasonal, interannual and decadal time scales to identify the relationships between time scales using the structure of the propagator matrix. Moreover, we investigate the influence of the tropics and extra-tropics in the predictability of the SST over the region. The Extratropical North Pacific seems to be a source of predictability for the tropics on seasonal to interannual time scales, while the tropics enhance the forecast skill for the decadal component. These results indicate the importance of temporal scale interactions in improving the predictions on decadal timescales. Hence, we show that LIMs are not only useful as benchmarks for estimates of statistical skill, but also to isolate contributions to the forecast skills from different timescales, spatial scales or even model components. |
英文关键词 | Linear inverse model Predictability Sea surface temperature Timescale interactions NMME |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000463842700038 |
WOS关键词 | SEA-SURFACE TEMPERATURE ; KUROSHIO EXTENSION ; MERIDIONAL MODES ; EASTERN-PACIFIC ; ENSO ; VARIABILITY ; PREDICTABILITY ; SKILL ; PREDICTION ; IMPACT |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/181447 |
专题 | 气候变化 |
作者单位 | 1.Scripps Inst Oceanog, 9500 Gilman Dr 0206, La Jolla, CA 92093 USA; 2.Univ Oxford, Oxford, England |
推荐引用方式 GB/T 7714 | Dias, Daniela Faggiani,Subramanian, Aneesh,Zanna, Laure,et al. Remote and local influences in forecasting Pacific SST: a linear inverse model and a multimodel ensemble study[J]. CLIMATE DYNAMICS,2019,52:3183-3201. |
APA | Dias, Daniela Faggiani,Subramanian, Aneesh,Zanna, Laure,&Miller, Arthur J..(2019).Remote and local influences in forecasting Pacific SST: a linear inverse model and a multimodel ensemble study.CLIMATE DYNAMICS,52,3183-3201. |
MLA | Dias, Daniela Faggiani,et al."Remote and local influences in forecasting Pacific SST: a linear inverse model and a multimodel ensemble study".CLIMATE DYNAMICS 52(2019):3183-3201. |
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