Journal article

Non-Redfieldian dynamics explain seasonal pCO2 drawdown in the Gulf of Bothnia


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Publication Details

Author list: Fransner F, Gustafsson E, Tedesco L, Vichi M, Hordoir R, Roquet F, Spilling K, Kuznetsov I, Eilola K, Mörth C, Humborg C, Nycander J.

Publisher: American Geophysical Union (AGU)

Publication year: 2018

Journal: Journal of Geophysical Research: Oceans

Journal name: Journal of Geophysical Research: Oceans

Volume number: 123

Start page: 166

End page: 188

Total number of pages: 23

ISSN: 2169-9291

eISSN: 2169-9291

URL: https://api.elsevier.com/content/abstract/scopus_id/85040164129


Abstract

High inputs of nutrients and organic matter make coastal seas places of intense air-sea CO2

exchange. Due to their complexity, the role of coastal seas in the global air-sea CO2 exchange is, however,

still uncertain. Here, we investigate the role of phytoplankton stoichiometric flexibility and extracellular DOC

production for the seasonal nutrient and CO2 partial pressure (pCO2) dynamics in the Gulf of Bothnia,

Northern Baltic Sea. A 3-D ocean biogeochemical-physical model with variable phytoplankton stoichiometry

is for the first time implemented in the area and validated against observations. By simulating non-

Redfieldian internal phytoplankton stoichiometry, and a relatively large production of extracellular dissolved

organic carbon (DOC), the model adequately reproduces observed seasonal cycles in macronutrients and

pCO2. The uptake of atmospheric CO2 is underestimated by 50% if instead using the Redfield ratio to

determine the carbon assimilation, as in other Baltic Sea models currently in use. The model further

suggests, based on the observed drawdown of pCO2, that observational estimates of organic carbon

production in the Gulf of Bothnia, derived with the 14C method, may be heavily underestimated. We

conclude that stoichiometric variability and uncoupling of carbon and nutrient assimilation have to be

considered in order to better understand the carbon cycle in coastal seas.


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Keywords

modeling


Last updated on 2019-18-04 at 10:09