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Modelling marine DOC degradation time scales

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Title
Modelling marine DOC degradation time scales
Author(s)
Polimene, Luca; Rivkin, Richard B.; Luo, Ya-Wei; Eun Young Kwon; Gehlen, Marion; Pena, M. Angelica; Wang, Nannan; Liang, Yantao; Kaartokallio, Hermanni; Jiao, Nianzhi
Publication Date
2018-07
Journal
NATIONAL SCIENCE REVIEW, v.5, no.4, pp.468 - 474
Publisher
OXFORD UNIV PRESS
Abstract
Marine dissolved organic carbon (DOC) is formed of a large number of highly diverse molecules. Depending on the environmental conditions, a fraction of these molecules may become progressively resistant to bacterial degradation and accumulate in the ocean for extended time scales. This long-lived DOC (the so-called recalcitrant DOC, RDOC) is thought to play an important role in the global carbon cycle by sequestering carbon into the ocean interior and potentially affecting the climate. Despite this, RDOC formation is underrepresented in climate models. Here we propose a model formulation describing DOC recalcitrance through two state variables: one representing the bulk DOC concentration and the other representing its degradability (κ) which varies depending on the balance between the production of ‘new’ DOC (assumed to be easily degradable) and bacterial DOC utilization assumed to leave behind more recalcitrant DOC. We propose this formulation as a means to include RDOC dynamics into climate model simulations. Assessing the capacity of the ocean to store atmospheric CO2 is one of the major challenges for oceanographers. Several physical and biological mechanisms have been proposed to ‘pump’ CO2 from the surface to the ocean interior, thus storing carbon for extended time frames [1,2]. Some of these mechanisms are driven by physical processes (i.e. the solubility pump) while others are the results of the interactions between biology (primary production, particle formation, prey–predators interactions) and physics (gravitational sinking, mixing, convection). The latter processes have collectively been termed the ‘Biological Carbon Pump’. The recently proposed Microbial Carbon Pump (MCP) provides an additional carbon sequestration mechanism primarily due to biological drivers [3]. Indeed, the main process underpinning the MCP is the bacterially mediated transformation of labile (i.e. rapidly degradable) dissolved organic carbon (DOC) into recalcitrant (i.e. slowly degradable) DOC (RDOC), which may accumulate into the ocean at time scales ranging from months to millennia, in this latter case sequestering atmospheric CO2 into stable long-lived organic molecules [4]. The production of RDOC is not directly affected by physical processes (mixing, sinking or thermohaline circulation) and its production is depth-independent—that is, it is active through the entire water column [2]. However, abiotic forcing such as vertical mixing and photo-degradation may also affect the RDOC fate and its spatial distribution, thus influencing the strength and the efficiency of the MCP. Being the latest recognized mechanism of ocean carbon sequestration, the MCP is also the least well investigated and represented in marine ecosystem models. Generally, DOC is modelled by using up to three state variables, with each of them characterized by a constant degradation time scale [5]. This approach is not consistent with the prevailing idea that the recalcitrance of DOC is an environmentally dependent property [3] emerging from the repeated transformation and selective use of the labile organic carbon substrates by bacteria [6]. Some models have explicitly described the bacterially mediated transformation of DOC into RDOC; however, these studies do not consider the long-lasting fractions of RDOC and are not able to simulate RDOC accumulation on time scales that are longer than seasonal [7]. One of the main challenges with modelling DOC accumulation beyond the seasonal time scale is representing the turnover time of the various pools of RDOC that is formed of a large number of highly diverse molecules with a continuum spectrum of degradation rates [4]. Explicitly modelling such a wide diversity would end up in an unmanageable number of state variables, increasing the computational costs of the model and yielding a large number of at best poorly constrained parameters. This is an important limiting factor, especially when a simulation is run within a global o
URI
https://pr.ibs.re.kr/handle/8788114/10597
DOI
10.1093/nsr/nwy066
ISSN
2053-714X
Appears in Collections:
Center for Climate Physics(기후물리 연구단) > 1. Journal Papers (저널논문)
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