POSTER Session 3

ESTIMATING PARTICULATE ORGANIC CARBON CONCENTRATION WITH BGC-ARGO FLOATS IN CONTRASTING OCEANIC ENVIRONMENTS

The capability to estimate the oceanic particulate organic carbon concentration (POC) from optical measurements is crucial for assessing the dynamics of this carbon reservoir and the capacity of the biological pump to sequester atmospheric carbon dioxide in the deep ocean. Optical approaches are routinely used to estimate oceanic POC from the spectral particulate backscattering coefficient (bbp), either directly (e.g., with backscattering sensors on underwater platforms like BGC-Argo floats) or indirectly (e.g., with satellite remote sensing). However, the performance of algorithms which relate bbp to POC is typically limited owing to the complexity of interactions between light and natural assemblages of marine particles, which depend on variations in particle concentration, composition, and size distribution. We describe an empirical multivariable approach to estimate POC from simultaneous measurements of bbp and chlorophyll-a concentration (Chla) to better account for the effects of variable particle composition on the relationship between POC and bbp. The multivariable regression models are formulated using a relatively large dataset of coincident measurements of POC, bbp, and Chla, including surface and subsurface data from the Atlantic, Pacific, Arctic, and Southern Oceans. We show that the multivariable algorithm provides reduced uncertainty of estimated POC across diverse marine environments when compared with a traditional univariate algorithm based on only bbp. We present results of recent efforts in algorithm development and validation, as well as applications to BGC-Argo floats in the Atlantic Ocean to examine spatial and temporal patterns in the vertical distribution of POC.

Daniel Koestner, University of Bergen, Norway, [email protected], https://orcid.org/0000-0002-2252-4847

Dariusz Stramski, Scripps Institution of Oceanography, USA, [email protected]

Rick A. Reynolds, Scripps Institution of Oceanography, USA, [email protected], https://orcid.org/0000-0002-1579-3600

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