Potential predictability of the spring bloom in the Southern Ocean sea ice zone
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Every austral spring when Antarctic sea ice melts, favorable growing conditions lead to an intense phytoplankton bloom, which supports much of the local marine ecosystem. Recent studies have found that Antarctic sea ice is predictable several years in advance, suggesting that the spring bloom might exhibit similar predictability. Using a suite of perfect model predictability experiments, we find that November net primary production (NPP) is potentially predictable 7 to 10 years in advance in many Southern Ocean regions. Sea ice extent predictability peaks in late winter, followed by absorbed shortwave radiation and NPP with a 2 to 3 months lag. This seasonal progression of predictability supports our hypothesis that sea ice and light limitation control the inherent predictability of the spring bloom. Our results suggest skillful interannual predictions of NPP may be achievable, with implications for managing fisheries and the marine ecosystem, and guiding conservation policy in the Southern Ocean.
Buchovecky , B , MacGilchrist , G A , Bushuk , M , Haumann , F A , Frölicher , T L , Le Grix , N & Dunne , J 2023 , ' Potential predictability of the spring bloom in the Southern Ocean sea ice zone ' , Geophysical Research Letters , vol. 50 , no. 20 , e2023GL105139 . https://doi.org/10.1029/2023gl105139
Geophysical Research Letters
Copyright © 2023. The Authors. This is an open access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
DescriptionThis work was supported by the High Meadows Environmental Institute at Princeton University and the NSF's Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) Project under the NSF Award PLR-1425989. F.A.H. was supported by NASA Grant 80NSSC19K1115 and by the European Union (ERC, VERTEXSO, 101041743). G.A.M was supported under SOCCOM and UKRI Grant MR/W013835/1. T.L.F was supported by Swiss National Science Foundation (Grant P00P2_198897) and the Swiss National Supercomputing Centre. N.L was supported by the European Union's Horizon 2020 research and innovation program under Grant 820989 (project COMFORT) and no. 862923 (project AtlantECO) as well as the Bretscher Funds.
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