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DOI | 10.1007/s00382-017-3969-2 |
Dynamical attribution of oceanic prediction uncertainty in the North Atlantic: application to the design of optimal monitoring systems | |
Sevellec, Florian1; Dijkstra, Henk A.2; Drijfhout, Sybren S.1,3; Germe, Agathe4 | |
2018-08-01 | |
发表期刊 | CLIMATE DYNAMICS |
ISSN | 0930-7575 |
EISSN | 1432-0894 |
出版年 | 2018 |
卷号 | 51期号:4页码:1517-1535 |
文章类型 | Article |
语种 | 英语 |
国家 | England; Netherlands |
英文摘要 | In this study, the relation between two approaches to assess the ocean predictability on interannual to decadal time scales is investigated. The first pragmatic approach consists of sampling the initial condition uncertainty and assess the predictability through the divergence of this ensemble in time. The second approach is provided by a theoretical framework to determine error growth by estimating optimal linear growing modes. In this paper, it is shown that under the assumption of linearized dynamics and normal distributions of the uncertainty, the exact quantitative spread of ensemble can be determined from the theoretical framework. This spread is at least an order of magnitude less expensive to compute than the approximate solution given by the pragmatic approach. This result is applied to a state-of-the-art Ocean General Circulation Model to assess the predictability in the North Atlantic of four typical oceanic metrics: the strength of the Atlantic Meridional Overturning Circulation (AMOC), the intensity of its heat transport, the two-dimensional spatially-averaged Sea Surface Temperature (SST) over the North Atlantic, and the three-dimensional spatially-averaged temperature in the North Atlantic. For all tested metrics, except for SST, 75% of the total uncertainty on interannual time scales can be attributed to oceanic initial condition uncertainty rather than atmospheric stochastic forcing. The theoretical method also provide the sensitivity pattern to the initial condition uncertainty, allowing for targeted measurements to improve the skill of the prediction. It is suggested that a relatively small fleet of several autonomous underwater vehicles can reduce the uncertainty in AMOC strength prediction by 70% for 1-5 years lead times. |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000439440200015 |
WOS关键词 | MERIDIONAL OVERTURNING CIRCULATION ; GENERALIZED STABILITY THEORY ; STOCHASTIC CLIMATE MODELS ; SEA-SURFACE TEMPERATURE ; THERMOHALINE CIRCULATION ; DECADAL PREDICTABILITY ; TARGETED OBSERVATIONS ; FORECAST SKILL ; VARIABILITY ; PERTURBATIONS |
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/36033 |
专题 | 气候变化 |
作者单位 | 1.Univ Southampton, Ocean & Earth Sci, Waterfront Campus,European Way, Southampton SO14 3ZH, Hants, England; 2.Univ Utrecht, Inst Marine & Atmospher Res Utrecht, Dept Phys, Utrecht, Netherlands; 3.Koninklijk Nederlands Meteorol Inst, De Bilt, Netherlands; 4.Natl Oceanog Ctr, Southampton, Hants, England |
推荐引用方式 GB/T 7714 | Sevellec, Florian,Dijkstra, Henk A.,Drijfhout, Sybren S.,et al. Dynamical attribution of oceanic prediction uncertainty in the North Atlantic: application to the design of optimal monitoring systems[J]. CLIMATE DYNAMICS,2018,51(4):1517-1535. |
APA | Sevellec, Florian,Dijkstra, Henk A.,Drijfhout, Sybren S.,&Germe, Agathe.(2018).Dynamical attribution of oceanic prediction uncertainty in the North Atlantic: application to the design of optimal monitoring systems.CLIMATE DYNAMICS,51(4),1517-1535. |
MLA | Sevellec, Florian,et al."Dynamical attribution of oceanic prediction uncertainty in the North Atlantic: application to the design of optimal monitoring systems".CLIMATE DYNAMICS 51.4(2018):1517-1535. |
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