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Spring plankton dynamics in the Eastern Bering Sea, 1971-2050: Mechanisms of interannual variability diagnosed with a numerical model Public Deposited

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  • A new planktonic ecosystem model was constructed for the Eastern Bering Sea based on observations from the 2007–2010 BEST/BSIERP (Bering Ecosystem Study/Bering Sea Integrated Ecosystem Research Program) field program. When run with forcing from a data-assimilative ice-ocean hindcast of 1971–2012, the model performs well against observations of spring bloom time evolution (phytoplankton and microzooplankton biomass, growth and grazing rates, and ratios among new, regenerated, and export production). On the southern middle shelf (57°N, station M2), the model replicates the generally inverse relationship between ice-retreat timing and spring bloom timing known from observations, and the simpler direct relationship between the two that has been observed on the northern middle shelf (62°N, station M8). The relationship between simulated mean primary production and mean temperature in spring (15 February to 15 July) is generally positive, although this was found to be an indirect relationship which does not continue to apply across a future projection of temperature and ice cover in the 2040s. At M2, the leading direct controls on total spring primary production are found to be advective and turbulent nutrient supply, suggesting that mesoscale, wind-driven processes—advective transport and storminess—may be crucial to long-term trends in spring primary production in the southeastern Bering Sea, with temperature and ice cover playing only indirect roles. Sensitivity experiments suggest that direct dependence of planktonic growth and metabolic rates on temperature is less significant overall than the other drivers correlated with temperature described above.
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  • Banas, N. S., Zhang, J., Campbell, R. G., Sambrotto, R. N., Lomas, M. W., Sherr, E., ... & Lessard, E. J. (2016). Spring plankton dynamics in the Eastern Bering Sea, 1971–2050: Mechanisms of interannual variability diagnosed with a numerical model. Journal of Geophysical Research: Oceans, 121(2), 1476-1501. doi:10.1002/2015JC011449
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  • 121
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  • 2
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  • This work was supported by the National Science Foundation through grants ARC-1107187, ARC-1107303, and ARC-1107588, for BEST Synthesis, and PLR-1417365. This is BEST-BSIERP Bering Sea Project publication number 179. All observational data used here are archived at http://beringsea.eol.ucar.edu/, and model output is available upon request.
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