Improved multivariable algorithms for estimating oceanic particulate organic carbon concentration from optical backscattering and chlorophyll-a measurements

• Published in: Frontiers in Marine Science
• Main Authors: Koestner, Daniel, Stramski, Dariusz, Reynolds, Rick A.
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Frontiers Media SA 2024
• Subjects: Ocean Engineering; Water Science and Technology; Aquatic Science; Global and Planetary Change; Oceanography; Arctic; Pacific; Pacific Arctic
• Online Access: http://dx.doi.org/10.3389/fmars.2023.1197953; https://www.frontiersin.org/articles/10.3389/fmars.2023.1197953/full

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 b bp , either directly (e.g., with backscattering sensors on underwater platforms like BGC-Argo floats) or indirectly (e.g., with satellite remote sensing). However, the reliability of algorithms which relate POC to b bp is typically limited due to the complexity of interactions between light and natural assemblages of marine particles, which depend on variations in particle concentration, composition, and size distribution. This study expands on our previous work by analysis of an extended field dataset created with judicious data inclusion criteria with the aim to provide POC algorithms for multiple light wavelengths of measured b bp , which can be useful for applications with in situ optical sensors as well as above-water active or passive measurement systems. We describe an improved empirical multivariable approach to estimate POC from simultaneous measurements of b bp and chlorophyll-a concentration (Chla) to better account for the effects of variable particle composition on the relationship between POC and b bp . The multivariable regression models are formulated using a relatively large dataset of coincident measurements of POC, b bp , 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 b bp . We also propose an improved formulation of univariate algorithm based on b bp alone. Finally, we examine performance of several algorithms to estimate POC using our dataset as well as a ...