On the variability of the Bering Sea Cold Pool and implications for the biophysical environment

PLoS One. 2022 Apr 4;17(4):e0266180. doi: 10.1371/journal.pone.0266180. eCollection 2022.

Abstract

The Bering Sea experiences a seasonal sea ice cover, which is important to the biophysical environment found there. A pool of cold bottom water (<2°C) is formed on the shelf each winter as a result of cooling and vertical mixing due to brine rejection during the predominately local sea ice growth. The extent and distribution of this Cold Pool (CP) is largely controlled by the winter extent of sea ice in the Bering Sea, which can vary considerably and recently has been much lower than average. The cold bottom water of the CP is important for food security because it delineates the boundary between arctic and subarctic demersal fish species. A northward retreat of the CP will likely be associated with migration of subarctic species toward the Chukchi Sea. We use the fully-coupled Regional Arctic System Model (RASM) to examine variability of the extent and distribution of the CP and its relation to change in the sea ice cover in the Bering Sea during the period 1980-2018. RASM results confirm the direct correlation between the extent of sea ice and the CP and show a smaller CP as a consequence of realistically simulated recent declines of the sea ice cover in the Bering Sea. In fact, the area of the CP was found to be only 31% of the long-term mean in July of 2018. In addition, we also find that a low ice year is followed by a later diatom bloom, while a heavy ice year is followed by an early diatom bloom. Finally, the RASM probabilistic intra-annual forecast capability is reviewed, based on 31-member ensembles for 2019-2021, for its potential use for prediction of the winter sea ice cover and the subsequent summer CP area in the Bering Sea.

Publication types

  • Review
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Animals
  • Arctic Regions
  • Ice Cover*
  • Water*

Substances

  • Water

Grants and funding

This work was supported by the US National Science Foundation (GEO/PLR ARCSS IAA1417888 and IAA1603602), the US Department of Energy (DOE) Regional and Global Model Analysis (RGMA) (89243019SSC0036 and DESC0014117), and the Office of Naval Research (ONR) Arctic and Global Prediction (AGP) (N0001418WX00364). The Department of Defense (DOD) High Performance Computer Modernization Program (HPCMP) provided computer resources.