Comparison of in situ and satellite ocean color determinations of particulate organic carbon concentration in the global ocean

Thorium-234 (234Th) is a powerful tracer of particle dynamics and the biological pump in the surface ocean; however, variability in carbon:thorium ratios of sinking particles adds substantial uncertainty to estimates of organic carbon export. We coupled a mechanistic thorium sorption and desorption model to a one-dimensional particle sinking model that uses realistic particle settling velocity spectra. The model generates estimates of 238U-234Th disequilibrium, particulate organic carbon concentration, and the C:234Th ratio of sinking particles, which are then compared to in situ measurements from quasi-Lagrangian studies conducted on six cruises in the California Current Ecosystem. Broad patterns observed in in situ measurements, including decreasing C:234Th ratios with depth and a strong correlation between sinking C:234Th and the ratio of vertically-integrated particulate organic carbon (POC) to vertically-integrated total water column 234Th, were accurately recovered by models assuming either a power law distribution of sinking speeds or a double log normal distribution of sinking speeds. Simulations suggested that the observed decrease in C:234Th with depth may be driven by preferential remineralization of carbon by particle-attached microbes. However, an alternate model structure featuring complete consumption and/or disaggregation of particles by mesozooplankton (e.g. no preferential remineralization of carbon) was also able to simulate decreasing C:234Th with depth (although the decrease was weaker), driven by 234Th adsorption onto slowly sinking particles. Model results also suggest that during bloom decays C:234Th ratios of sinking particles should be higher than expected (based on contemporaneous water column POC), because high settling velocities minimize carbon remineralization during sinking.

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Ocean color algorithms to estimate the concentration of particulate organic carbon in surface waters of the global ocean in support of a long-term data record from multiple satellite missions

Remote Sensing of Environment ◽

10.1016/j.rse.2021.112776 ◽

2022 ◽

Vol 269 ◽

pp. 112776

Author(s):

Dariusz Stramski ◽

Ishan Joshi ◽

Rick A. Reynolds

Keyword(s):

Organic Carbon ◽

Particulate Organic Carbon ◽

Surface Waters ◽

Ocean Color ◽

Global Ocean ◽

Data Record ◽

Satellite Missions ◽

Term Data

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Estimation of the Particulate Organic Carbon to Chlorophyll-a Ratio Using MODIS-Aqua in the East/Japan Sea, South Korea

Remote Sensing ◽

10.3390/rs12050840 ◽

2020 ◽

Vol 12 (5) ◽

pp. 840 ◽

Cited By ~ 1

Author(s):

Dabin Lee ◽

SeungHyun Son ◽

HuiTae Joo ◽

Kwanwoo Kim ◽

Myung Joon Kim ◽

...

Keyword(s):

Organic Carbon ◽

Particulate Organic Carbon ◽

Chlorophyll A ◽

Primary Productivity ◽

Seasonal Variability ◽

Japan Sea ◽

Global Ocean ◽

Phytoplankton Communities

In recent years, the change of marine environment due to climate change and declining primary productivity have been big concerns in the East/Japan Sea, Korea. However, the main causes for the recent changes are still not revealed clearly. The particulate organic carbon (POC) to chlorophyll-a (chl-a) ratio (POC:chl-a) could be a useful indicator for ecological and physiological conditions of phytoplankton communities and thus help us to understand the recent reduction of primary productivity in the East/Japan Sea. To derive the POC in the East/Japan Sea from a satellite dataset, the new regional POC algorithm was empirically derived with in-situ measured POC concentrations. A strong positive linear relationship (R2 = 0.6579) was observed between the estimated and in-situ measured POC concentrations. Our new POC algorithm proved a better performance in the East/Japan Sea compared to the previous one for the global ocean. Based on the new algorithm, long-term POC:chl-a ratios were obtained in the entire East/Japan Sea from 2003 to 2018. The POC:chl-a showed a strong seasonal variability in the East/Japan Sea. The spring and fall blooms of phytoplankton mainly driven by the growth of large diatoms seem to be a major factor for the seasonal variability in the POC:chl-a. Our new regional POC algorithm modified for the East/Japan Sea could potentially contribute to long-term monitoring for the climate-associated ecosystem changes in the East/Japan Sea. Although the new regional POC algorithm shows a good correspondence with in-situ observed POC concentrations, the algorithm should be further improved with continuous field surveys.

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Erratum Dissolved organic carbon concentration controls benthic primary production: Results from in situ chambers in north-temperate lakes

Limnology and Oceanography ◽

10.1002/lno.10238 ◽

2015 ◽

Vol 61 (1) ◽

pp. 407-407

Author(s):

Sean C. Godwin ◽

Stuart E. Jones ◽

Brian C. Weidel ◽

Christopher T. Solomon

Keyword(s):

Organic Carbon ◽

Dissolved Organic Carbon ◽

Primary Production ◽

Carbon Concentration ◽

Temperate Lakes ◽

Benthic Primary Production ◽

Organic Carbon Concentration ◽

North Temperate Lakes

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ESTIMATION OF OCEANIC PARTICULATE ORGANIC CARBON WITH MACHINE LEARNING

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-2-2020-949-2020 ◽

2020 ◽

Vol V-2-2020 ◽

pp. 949-956

Author(s):

R. Sauzède ◽

J. E. Johnson ◽

H. Claustre ◽

G. Camps-Valls ◽

A. B. Ruescas

Keyword(s):

Machine Learning ◽

Optical Properties ◽

Organic Carbon ◽

Vertical Distribution ◽

Particulate Organic Carbon ◽

Ocean Color ◽

Global Scale ◽

Vertical Profiles ◽

The Vertical Distribution ◽

Satellite Ocean Color

Abstract. Understanding and quantifying ocean carbon sinks of the planet is of paramount relevance in the current scenario of global change. Particulate organic carbon (POC) is a key biogeochemical parameter that helps us characterize export processes of the ocean. Ocean color observations enable the estimation of bio-optical proxies of POC (i.e. particulate backscattering coefficient, bbp) in the surface layer of the ocean quasi-synoptically. In parallel, the Argo program distributes vertical profiles of the physical properties with a global coverage and a high spatio-temporal resolution. Merging satellite ocean color and Argo data using a neural networkbased method has already shown strong potential to infer the vertical distribution of bio-optical properties at global scale with high space-time resolution. This method is trained and validated using a database of concurrent vertical profiles of temperature, salinity, and bio-optical properties, i.e. bbp, collected by Biogeochemical-Argo (BGC-Argo) floats, matched up with satellite ocean color products. The present study aims at improving this method by 1) using a larger dataset from BGC-Argo network since 2016 for training, 2) using additional inputs such as altimetry data, which provide significant information on mesoscale processes impacting the vertical distribution of bbp, 3) improving the vertical resolution of estimation, and 4) examining the potential of alternative machine learning-based techniques. As a first attempt with the new data, we used some feature-specific preprocessing routines followed by a Multi-Output Random Forest algorithm on two regions with different ocean dynamics: North Atlantic and Subtropical Gyres. The statistics and the bbp profiles obtained from the validation floats show promising results and suggest this direction is worth investigating even further at global scale.

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