Assessing the Role of High-Frequency Winds and Sea Ice Loss on Arctic Phytoplankton Blooms in an Ice-Ocean-Biogeochemical Model

  • Author(s) / Creator(s)
  • The long-term trend of increasing phytoplankton net primary production (NPP) in the Arctic correlates with increasing light penetration due to sea ice loss. However, recent studies suggest that enhanced stormy wind mixing may also play a significant role enhancing NPP. Here, we isolate the role of sea ice and stormy winds (hereafter high-frequency winds) using an eddy-permitting ice-ocean-biogeochemical model configured for the North Atlantic and the Arctic. In the model, the presence of high-frequency winds stimulates nutrient upwelling by producing an earlier and longer autumn-winter mixing period with deeper mixing layer. The early onset of autumn mixing results in nutrients being brought-up to near-surface waters before the light becomes the dominant limiting factor, which leads to the autumn bloom. The enhanced mixing results in higher nutrient concentrations in spring and thus a large spring bloom. The model also shows significant iron limitation in the Labrador Sea, which is intensified by high-frequency winds. The effect of sea ice loss on NPP was found to be regionally dependent on the presence of high-frequency winds. This numerical study suggests high-frequency winds play significant role increasing NPP in the Arctic and sub-Arctic by alleviating phytoplankton nutrient limitation and that the isolated effect of sea ice loss on light plays a comparatively minor role.

  • Date created
    2019-01-01
  • Subjects / Keywords
  • Type of Item
    Article (Published)
  • DOI
    https://doi.org/10.7939/r3-se58-nn92
  • License
    ©2019. American Geophysical Union. All Rights Reserved.
  • Language
  • Citation for previous publication
    • Castro de la Guardia L*, Garcia-Quintana Y*, Claret M, Hu X, Galbraith ED, Myers PG. (2019). Assessing the Role of High-Frequency Winds and Sea Ice Loss on Arctic Phytoplankton Blooms in an Ice-Ocean-Biogeochemical Model. JGR Biogeosciences. 124: 1-23.