Presentation Abstract

How do ocean mixing regimes influence primary productivity and carbon dynamics? Primary productivity is a key quantity in the quality of habitat for higher trophic levels including larval salmon. Here, we analyze the physical oceanographic and primary productivity dynamics of the Salish Sea using the output of SalishSeaCast, a newly-developed biophysical model based on the NEMO framework (Olson et al, in preparation). The biophysical model estimates three classes of primary producers - diatoms, small flagellates and Mesodynium rubrum. Here, we consider daily depth-integrated biomass signals for all three organismal classes extracted from the model domain over the course of two years, as well as daily signals of halocline depth, river input, wind energy, and tidal mixing. These signals are then analyzed using a normalized hierarchical clustering approach. The analysis shows large biomass variance (~2 orders of magnitude) throughout the model domain, and clear spatial patterns in biomass correspond to regions dominated by different mixing and stratification regimes. The signal clusters demonstrate a clear boundary between the biomass patterns in the Northern and Southern Strait of Georgia, and offer a physical explanation for the difference. We then compare this output to carbonate chemistry data and the developing carbonate chemistry numerical model, to gain insight into biophysical drivers of carbonate chemistry distribution in different regions of the Strait. The study represents the first attempt at a large-scale statistical analysis of the newly-developed model, and demonstrates the unique utility of this approach in identifying discrete regions governed by various primary productivity regimes, and provides a framework for considering their effects on carbonate chemistry. In the future, such an analysis may be used to understand the impact of shifting stratification and mixing regimes on the interaction of primary productivity and carbonate chemistry under anthropogenic climate change.

Session Title

The Salish Sea Marine Survival Project: Phytoplankton and Zooplankton

Keywords

Carbonate chemistry, Data science, Numerical modelling, Physical oceanography

Conference Track

SSE11: Species and Food Webs

Conference Name

Salish Sea Ecosystem Conference (Seattle, WA : 2018)

Document Type

Event

SSEC Identifier

SSE11-497

Start Date

5-4-2018 3:45 PM

End Date

5-4-2018 4:00 PM

Type of Presentation

Oral

Contributing Repository

Digital content made available by University Archives, Heritage Resources, Western Libraries, Western Washington University.

Geographic Coverage

Salish Sea (B.C. and Wash.)

Rights

This resource is displayed for educational purposes only and may be subject to U.S. and international copyright laws. For more information about rights or obtaining copies of this resource, please contact University Archives, Heritage Resources, Western Libraries, Western Washington University, Bellingham, WA 98225-9103, USA (360-650-7534; heritage.resources@wwu.edu) and refer to the collection name and identifier. Any materials cited must be attributed to the Salish Sea Ecosystem Conference Records, University Archives, Heritage Resources, Western Libraries, Western Washington University.

Type

text

Language

English

Format

application/pdf

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Apr 5th, 3:45 PM Apr 5th, 4:00 PM

A data science approach to understanding physical drivers of coastal primary productivity and effects on carbonate chemistry

How do ocean mixing regimes influence primary productivity and carbon dynamics? Primary productivity is a key quantity in the quality of habitat for higher trophic levels including larval salmon. Here, we analyze the physical oceanographic and primary productivity dynamics of the Salish Sea using the output of SalishSeaCast, a newly-developed biophysical model based on the NEMO framework (Olson et al, in preparation). The biophysical model estimates three classes of primary producers - diatoms, small flagellates and Mesodynium rubrum. Here, we consider daily depth-integrated biomass signals for all three organismal classes extracted from the model domain over the course of two years, as well as daily signals of halocline depth, river input, wind energy, and tidal mixing. These signals are then analyzed using a normalized hierarchical clustering approach. The analysis shows large biomass variance (~2 orders of magnitude) throughout the model domain, and clear spatial patterns in biomass correspond to regions dominated by different mixing and stratification regimes. The signal clusters demonstrate a clear boundary between the biomass patterns in the Northern and Southern Strait of Georgia, and offer a physical explanation for the difference. We then compare this output to carbonate chemistry data and the developing carbonate chemistry numerical model, to gain insight into biophysical drivers of carbonate chemistry distribution in different regions of the Strait. The study represents the first attempt at a large-scale statistical analysis of the newly-developed model, and demonstrates the unique utility of this approach in identifying discrete regions governed by various primary productivity regimes, and provides a framework for considering their effects on carbonate chemistry. In the future, such an analysis may be used to understand the impact of shifting stratification and mixing regimes on the interaction of primary productivity and carbonate chemistry under anthropogenic climate change.