A marine carbon monoxide (CO) model with a new parameterization of microbial oxidation

Young Shin Kwon, Hyoun-Woo Kang, Luca Polimene and Tae Siek Rhee

Ecological Modelling, 2020, vol. 432, issue C

Abstract: Traditionally, marine carbon monoxide (CO) models assume that the microbial oxidation of CO is only dependent on the concentration of CO in the water column. However, CO oxidation rates in the ocean have been reported to vary up to two orders of magnitude both spatially and temporally. Here, we developed a new model assuming that CO microbial oxidation is dependent on bacterial carbon biomass other than CO concentration. In addition to microbial oxidation, the model also describes CO photochemical production, vertical mixing, and air-sea gas exchange. The new CO model has been embedded in the European Regional Seas Ecosystem Model (ERSEM) and coupled with the General Ocean Turbulence Model (GOTM). The CO-GOTM-ERSEM model was implemented at the Bermuda Atlantic Time Series (BATS) station to simulate CO concentrations observed in March 1993 by Kettle (1994). The proposed second-order function describing CO microbial oxidation introduces a new parameter, the bacteria biomass specific oxidation rate, which was estimated to be 5.7 ± 0.2 (μg C m−3)−1 h−1. Statistical metrics indicates that the new CO model performs better than a previously published model with a first-order decay function to describe microbial oxidation, acknowledging the dependence of microbial oxidation on bacterial abundance is realistic. A long-term (1992 - 1994) simulation carried out with CO-GOTM-ERSEM reproduced the spatial and seasonal variability of CO reported in the literature. Our model provides a realistic description of the CO dynamics and is potentially usable in different environmental contexts worldwide.

Keywords: Marine carbon monoxide; Microbial oxidation; Marine ecosystem; Modelling; Second-order kinetics (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:432:y:2020:i:c:s0304380020302738

DOI: 10.1016/j.ecolmodel.2020.109203

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