Preprints
https://doi.org/10.5194/os-2021-66
https://doi.org/10.5194/os-2021-66

  26 Jul 2021

26 Jul 2021

Review status: this preprint is currently under review for the journal OS.

A clustering approach to determine biophysical provinces and physical drivers of productivity dynamics in a complex coastal sea

Tereza Jarníková1, Elise M. Olson1, Susan E. Allen1, Debby Ianson2,1, and Karyn D. Suchy1 Tereza Jarníková et al.
  • 1Department of Earth, Ocean, and Atmospheric Sciences, University of British Columbia, Vancouver, BC, Canada
  • 2Institute of Ocean Sciences, Fisheries and Oceans Canada, Sidney, BC, Canada

Abstract. The balance between ocean mixing and stratification influences primary productivity through light limitation and nutrient supply in the euphotic ocean. Here, we apply a hierarchical clustering algorithm (Ward's method) to four factors relating to stratification and depth-integrated phytoplankton biomass extracted from a biophysical regional ocean model of the Salish Sea to assess spatial co-occurrence. Running the clustering algorithm on four years of model output, we identify distinct regions of the model domain that exhibit contrasting wind and freshwater input dynamics, as well as regions of varying watercolumn-averaged vertical eddy diffusivity and halocline depth regimes. The spatial regionalizations in physical variables are similar in all four analyzed years. We also find distinct interannually consistent biological zones. In the Northern Strait of Georgia and Juan de Fuca Strait, a deeper winter halocline and episodic summer mixing coincide with higher summer diatom abundance, while in the Fraser River stratified Central Strait of Georgia, shallower haloclines and stronger summer stratification coincide with summer flagellate abundance. Cluster based model results and evaluation suggest that the Juan de Fuca Strait supports more biomass than previously thought. Our approach elucidates probable physical mechanisms controlling phytoplankton abundance and composition. It also demonstrates a simple, powerful technique for finding structure in large datasets and determining boundaries of biophysical provinces.

Tereza Jarníková et al.

Status: open (until 18 Dec 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on os-2021-66', Jennifer Jackson, 05 Oct 2021 reply

Tereza Jarníková et al.

Data sets

SalishSeaCast ERDDAP The SalishSeaCast Model Team https://salishsea.eos.ubc.ca/erddap/index.html

Model code and software

SalishSeaCluster Tereza Jarníková https://github.com/tjarnikova/SalishSeaCluster

Tereza Jarníková et al.

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Short summary
Understanding drivers of phytoplankton biomass in dynamic coastal regions is key to predicting present and future ecosystem functioning. Using a clustering-based method, we objectively determined biophysical provinces in a complex estuarine sea. The Salish Sea contains three major distinct provinces where phytoplankton dynamics are controlled by diverse stratification regimes. Our method is simple to implement and broadly applicable for identifying structure in large model-derived datasets.