Journal
GLOBAL BIOGEOCHEMICAL CYCLES
Volume 29, Issue 1, Pages 33-45Publisher
AMER GEOPHYSICAL UNION
DOI: 10.1002/2014GB004965
Keywords
POC flux; carbon export; seasonal variability; biogeochemical model; empirical algorithm
Funding
- NERC [NE/J004383/1]
- NERC [noc010009] Funding Source: UKRI
- Natural Environment Research Council [NE/J004383/1, noc010009] Funding Source: researchfish
- Directorate For Geosciences [1260164] Funding Source: National Science Foundation
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The flux of organic carbon from the surface ocean to mesopelagic depths is a key component of the global carbon cycle and is ultimately derived from primary production (PP) by phytoplankton. Only a small fraction of organic carbon produced by PP is exported from the upper ocean, referred to as the export efficiency (herein e-ratio). Limited observations of the e-ratio are available, and there is thus considerable interest in using remotely sensed parameters such as sea surface temperature to extrapolate local estimates to global annual export flux. Currently, there are large discrepancies between export estimates derived in this way; one possible explanation is spatial or temporal sampling bias in the observations. Here we examine global patterns in the spatial and seasonal variability in e-ratio and the subsequent effect on export estimates using a high-resolution global biogeochemical model. The model used here represents export as separate slow- and fast-sinking detrital material whose remineralization is respectively temperature dependent and a function of ballasting minerals. We find that both temperature and the fraction of export carried by slow-sinking particles are factors in determining e-ratio, suggesting that current empirical algorithms for e-ratio that only consider temperature are overly simple. We quantify the temporal lag between PP and export, which is greatest in regions of strong variability in PP where seasonal decoupling can result in large e-ratio variability. Extrapolating global export estimates from instantaneous measurements of e-ratio is strongly affected by seasonal variability and can result in errors in estimated export of up to 60%.
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