The Role of Particle Size, Ballast, Temperature, and Oxygen in the Sinking Flux to the Deep Sea

Abstract

The “transfer efficiency” of organic particles from the surface to depth is a critical determinant of ocean carbon sequestration. Recently, direct observations and geochemical analyses have revealed a systematic geographical pattern of transfer efficiency, which is highest in high latitude regions and lowest in the subtropical gyres. We evaluate the possible causes of this pattern using a mechanistic model of sinking particle dynamics. The model represents the size distribution of particles, the effects of mineral ballast, seawater temperature (which influences both particle settling velocity and microbial metabolic rates), and O2. Parameters are optimized within reasonable ranges to best match the observational constraints. Our model shows that no single factor can explain the observed pattern of transfer efficiency, but the biological effect of temperature on remineralization rate and particle size effects together can reproduce most of the regional variability with both factors contributing to low transfer efficiency in the subtropical gyres and high transfer efficiency in high latitudes. Particle density from mineral ballast has a similar directional effect to temperature and size but plays a substantially smaller role in our optimum solution, due to the opposing patterns of silicate and calcium carbonate ballasting. Oxygen effects modestly improved model fit by depressing remineralization rates and thus increasing transfer efficiency in the Eastern Tropical Pacific. Our model implies that climate‐driven changes to upper ocean temperature and associated changes in surface plankton size distribution would reduce the carbon sequestration efficiency in a warmer ocean.

Sinking flux of particulate organic matter in the oceans: Sensitivity to particle characteristics

“…We show that different combinations of these parameters result in markedly different shapes in the flux profile. Similar to Cram et al 13 , we find the 'transfer efficiency' to be highly sensitive to the remineralization rate and the particle size spectrum. Cram et al 13 found their model was less sensitive to the dissolved oxygen concentration -a factor we did not include in ours.…”

“…We find this approach instructive in that it allows us to examine the contribution of specific size classes. Hence, we present a derivation of the model, even though the steady-state aggregate flux profile solution is similar to what was proposed in previous studies 10,13 .…”

Ventilation Pathways for the North Pacific Oxygen Deficient Zone

“…Uncertainty in future projections of low O 2 regions is reflected in the ©2019. These model biases reflect the sensitivity of low O 2 regions to numerous biological influences, including microbial community structure (Penn et al, 2016), organic matter stoichiometry (DeVries & Deutsch, 2014), and the depth scale of its remineralization (Cram et al, 2018;Van Mooy et al, 2001). This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.…”

“…Although observations reveal two distinct hypoxic zones separated by the equator, many ocean models produce an OMZ that spans the entire eastern tropics (Figure 1; see also Cabré et al, 2015). These model biases reflect the sensitivity of low O 2 regions to numerous biological influences, including microbial community structure (Penn et al, 2016), organic matter stoichiometry (DeVries & Deutsch, 2014), and the depth scale of its remineralization (Cram et al, 2018;Van Mooy et al, 2001). Physical processes that ventilate the OMZs, including strong zonal circulation (Aumont et al, 1999;Dietze & Loeptien, 2013;Duteil et al, 2014) and associated isopycnal stirring (Getzlaff & Dietze, 2013;Gnanadesikan et al, 2013), are also poorly represented in coarse global resolution models.…”

How Data Set Characteristics Influence Ocean Carbon Export Models

“…Intrinsic variability of export will be driven by variability in nutrient supply, phytoplankton growth, predator behavior, and aggregate formation that creates spatial heterogeneity and temporal intermittency in the POC flux (e.g., Abraham, 1998;Karl et al, 2003;Zehr et al, 2017). POC export is also influenced by particle size, density, morphology, lability, ecological interactions, and physical factors, all of which exhibit substantial spatial and temporal heterogeneity (Alldredge & Silver, 1988;Armstrong et al, 2001;Burd & Jackson, 2009;Cram et al, 2018;Mahadevan et al, 2012;Steinberg et al, 2000). The time lag between production of POC in the surface ocean and its subsequent export to depth (e.g., Karl et al, 2003;Estapa et al, 2015) introduces an additional source of variability to POC fluxes.…”