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

• Main Authors: Koestner, Daniel, Stramski, Dariusz, Reynolds, Rick A
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: eScholarship, University of California 2024
• Subjects: Oceanography; Ecology; Geology; Arctic; Pacific; Pacific Arctic
• Online Access: https://escholarship.org/uc/item/7xb3w73b

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 reliability of algorithms which relate POC to bbp 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 bbp, 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 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 also propose an improved formulation of univariate algorithm based on bbp alone. Finally, we examine performance of several algorithms to estimate POC using our dataset as well as a dataset consisting ...