Why we need weather forecast analogues for marine ecosystems

被引:12
作者
Link, J. S. [1 ]
Thur, S. [2 ]
Matlock, G. [3 ]
Grasso, M. [4 ]
机构
[1] NOAA, Natl Marine Fisheries Serv, 166 Water St, Woods Hole, MA 02543 USA
[2] NOAA, Natl Ocean Serv, Natl Ctr Coastal Ocean Sci, 1305 East West Highway, Silver Spring, MD 20910 USA
[3] NOAA, Off Ocean & Atmospher Res, 1315 East West Highway, Silver Spring, MD 20910 USA
[4] NOAA, Performance Risk & Social Sci Off, Off Chief Financial Officer, 1401 Constitut Ave NW, Silver Spring, MD 20230 USA
关键词
biomass production; black swan; distribution; economic outlook; ecosystem modelling; forecasts; model coupling; oceanographic modelling; projection; system of systems; CLIMATE-CHANGE IMPACTS; SOCIAL VULNERABILITY; CHALLENGES; PREDICTION; MANAGEMENT; FISHERY; MODELS; BLOOMS; SUPPORT; SERVICE;
D O I
10.1093/icesjms/fsad143
中图分类号
S9 [水产、渔业];
学科分类号
0908 ;
摘要
Marine ecosystems face many consequential pressures. Yet, we lack an integrative and predictive capacity to understand how marine ecosystems will respond to the cumulative impacts of these pressures, including climate change. It is not enough to detect responses after the fact; it has become imperative to know in advance where major biological resources or hazards will occur, when they will peak, and how that will impact economic performance. Although forecasts exist for some components of marine ecosystems, these are disparate and suffer from a lack of coordination. There is a need for coordinated, cross-ecosystem scale, integrated, marine ecosystem predictions and synthesis products. The value proposition relative to the blue economy is quite high, positively influencing billions if not trillions of marine sector dollars.
引用
收藏
页码:2087 / 2098
页数:12
相关论文
共 111 条
[1]   Exploring the impact of artificial Intelligence: Prediction versus judgment [J].
Agrawal, Ajay ;
Gans, Joshua S. ;
Goldfarb, Avi .
INFORMATION ECONOMICS AND POLICY, 2019, 47 :1-6
[2]  
[Anonymous], 2022, ALASKA BEACON
[3]  
[Anonymous], 2019, PISA 2018 RESULTS VO, VII, DOI [DOI 10.1787/B5FD1B8F-EN, 10.1787/9789264251724-en, DOI 10.1787/9789264251724-EN]
[4]  
[Anonymous], 2023, Copernicus
[5]   Going Underwater? Flood Risk Belief Heterogeneity and Coastal Home Price Dynamics [J].
Bakkensen, Laura A. ;
Barrage, Lint .
REVIEW OF FINANCIAL STUDIES, 2022, 35 (08) :3666-3709
[6]   Uncertainty analysis in integrated environmental models for ecosystem service assessments: Frameworks, challenges and gaps [J].
Baustert, Paul ;
Othoniel, Benoit ;
Rugani, Benedetto ;
Leopold, Ulrich .
ECOSYSTEM SERVICES, 2018, 33 :110-123
[7]  
BEA (U.S. Bureau of Economic Analysis), 2023, 2324 BEA
[8]   The rise in climate change-induced federal fishery disasters in the United States [J].
Bellquist, Lyall ;
Saccomanno, Vienna ;
Semmens, Brice X. ;
Gleason, Mary ;
Wilson, Jono .
PEERJ, 2021, 9
[9]   Adaptation to climate change in coastal communities: findings from seven sites on four continents [J].
Berman, Matthew ;
Baztan, Juan ;
Kofinas, Gary ;
Vanderlinden, Jean-Paul ;
Chouinard, Omer ;
Huctin, Jean-Michel ;
Kane, Alioune ;
Maze, Camille ;
Nikulina, Inga ;
Thomson, Kaleekal .
CLIMATIC CHANGE, 2020, 159 (01) :1-16
[10]  
Bindoff N. L., 2019, P OC CRYOSPH CHANG C, P447, DOI [DOI 10.1017/9781009157964.007, 10.1017/9781009157964]