Opportunities for Earth Observation to Inform Risk Management for Ocean Tipping Points
Authors/Creators
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Wood, Richard1
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Baker, Jonathan1
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BEAUGRAND, GREGORY2
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Boutin, Jacqueline2
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Conversi, Alessandra
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Donner, Reik3, 4
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Frenger, Ivy5
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Goberville, Eric6
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Hayashida, Hakase7
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Koeve, Wolfgang
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Kvale, Karin5, 8
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Landolfi, Angela9
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Maslowski, Wieslaw10
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Oschlies, Andreas
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Romanou, Anastasia11, 12
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Somes, Christopher5
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Stocker, Thomas F.13
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Swingedouw, Didier14
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1.
Met Office
- 2. Centre National de la Recherche Scientifique
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3.
Hochschule Magdeburg-Stendal
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4.
Potsdam Institute for Climate Impact Research
- 5. GEOMAR Helmholtz Centre for Ocean Research Kiel
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6.
Sorbonne Université
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7.
Japan Agency for Marine-Earth Science and Technology
- 8. GNS Science
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9.
National Research Council
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10.
Naval Postgraduate School
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11.
Goddard Institute for Space Studies
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12.
Columbia University
- 13. University of Bern
- 14. CNRS Délégation Aquitaine-Limousin
Description
This is the accepted version. The published version is available on this DOI: 10.1007/s10712-024-09859-3
Abstract
As climate change continues, the likelihood of passing critical thresholds or tipping points increases. Hence, there is a need to advance the science for detecting such thresholds. In this paper, we assess the needs and opportunities for Earth Observation (EO, here understood to refer to satellite observations) to inform society in responding to the risks associated with ten potential large-scale ocean tipping elements: Atlantic Meridional Overturning Circulation; Atlantic Subpolar Gyre; Beaufort Gyre; Arctic halocline; Kuroshio Large Meander; deoxygenation; phytoplankton; zooplankton; higher level ecosystems (including fsheries); and marine biodiversity. We review current scientifc understanding and identify specifc EO and related modelling needs for each of these tipping elements. We draw out some generic points that apply across several of the elements. These common points include the importance of maintaining long-term, consistent time series; the need to combine EO data consistently with in situ data types (including subsurface), for example through data assimilation; and the need to reduce or work with current mismatches in resolution (in both directions) between climate models and EO datasets. Our analysis shows that developing EO, modelling and prediction systems together, with understanding of the strengths and limitations of each, provides many promising paths towards monitoring and early warning systems for tipping, and towards the development of the next generation of
climate models.
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