Investigating controls on sea ice algal production using E3SMv1.1-BGC
| Autor: | Susannah M. Burrows, Mathew Maltrud, Xiaoying Shi, Elizabeth Hunke, Wieslaw Maslowski, Shanlin Wang, Adrian K. Turner, Katherine Calvin, J. D. Wolfe, William H. Lipscomb, Nicole Jeffery |
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| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
geography Biogeochemical cycle geography.geographical_feature_category 010504 meteorology & atmospheric sciences 010604 marine biology & hydrobiology Biogeochemistry Snow Atmospheric sciences 01 natural sciences chemistry.chemical_compound Arctic Nitrate chemistry Sea ice Polar Polar climate Geology 0105 earth and related environmental sciences Earth-Surface Processes |
| Zdroj: | Annals of Glaciology. 61:51-72 |
| ISSN: | 1727-5644 0260-3055 |
| DOI: | 10.1017/aog.2020.7 |
| Popis: | We present the analysis of global sympagic primary production (PP) from 300 years of pre-industrial and historical simulations of the E3SMv1.1-BGC model. The model includes a novel, eight-element sea ice biogeochemical component, MPAS-Seaice zbgc, which is resolved in three spatial dimensions and uses a vertical transport scheme based on internal brine dynamics. Modeled ice algal chlorophyll-a concentrations and column-integrated values are broadly consistent with observations, though chl-a profile fractions indicate that upper ice communities of the Southern Ocean are underestimated. Simulations of polar integrated sea ice PP support the lower bound in published estimates for both polar regions with mean Arctic values of 7.5 and 15.5 TgC/a in the Southern Ocean. However, comparisons of the polar climate state with observations, using a maximal bound for ice algal growth rates, suggest that the Arctic lower bound is a significant underestimation driven by biases in ocean surface nitrate, and that correction of these biases supports as much as 60.7 TgC/a of net Arctic PP. Simulated Southern Ocean sympagic PP is predominantly light-limited, and regional patterns, particularly in the coastal high production band, are found to be negatively correlated with snow thickness. |
| Databáze: | OpenAIRE |
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