DOI: 10.1007/s00382-017-3969-2
Scopus记录号: 2-s2.0-85034215808
论文题名: Dynamical attribution of oceanic prediction uncertainty in the North Atlantic: application to the design of optimal monitoring systems
作者: Sévellec F. ; Dijkstra H.A. ; Drijfhout S.S. ; Germe A.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2018
卷: 51, 期: 4 起始页码: 1517
结束页码: 1535
语种: 英语
Scopus关键词: design
; heat transfer
; meridional circulation
; monitoring system
; oceanic general circulation model
; overturn
; prediction
; sea surface temperature
; Atlantic Ocean
; Atlantic Ocean (North)
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. © 2017, The Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/109174
Appears in Collections: 影响、适应和脆弱性 气候变化事实与影响
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作者单位: Ocean and Earth Science, University of Southampton, Waterfront campus, European Way, Southampton, SO14 3ZH, United Kingdom; Department of Physics, Institute for Marine and Atmospheric research Utrecht, Utrecht University, Utrecht, Netherlands; Koninklijk Nederlands Meteorologisch Instituut, De Bilt, Netherlands; National Oceanography Centre, Southampton, United Kingdom
Recommended Citation:
Sévellec F.,Dijkstra H.A.,Drijfhout S.S.,et al. Dynamical attribution of oceanic prediction uncertainty in the North Atlantic: application to the design of optimal monitoring systems[J]. Climate Dynamics,2018-01-01,51(4)