Impacts of Physical and Biological Processes on Spatial and Temporal Variability of Particulate Organic Carbon in the North Pacific Ocean during 2003-2017

Evaluation of particulate organic carbon from MODIS-Aqua in a marine-coastal water body

La Paz Bay (LPB) in Mexico is one of the largest marine-coastal bodies of water in the Gulf of California (GC) and is ecologically important for the feeding, reproduction, and refuge of marine species. Although particulate organic carbon (POC) is an important reservoir of oceanic carbon and an indicator of productivity in the euphotic zone, studies in this region are scarce. This study evaluates the performance of satellite-derived POC in LPB from January 2003 to December 2020. The metrics obtained for COP ( RMSE = 33.8 mg m - 3 ;P bias = 29.6 % yr P = 0.4 con p < 0.05 ), Chla-a ( RMSE = 0.23 mg m - 3 ;P bias = - 4.3 % yr P = 0.94 con p < 0.05 ), and SST ( RMSE = 2 . 3 ∘ C ;P bias = - 2.2 % yr P = 0.92 con p < 0.05 ) establish that although in some cases there was a slight over/underestimation, the satellite estimates consistently represent the variability and average values measured in situ. On the other hand, the spatio-temporal analysis of the POC allowed us to identify two seasons with their respective transition periods and five subregions in which the POC is characterized by having its maximum variability; two of these coincide with the locations of the eddies reported for the winter and summer seasons in the LPB, while the following three are located: one in the coastal zone and in the two areas in which the LPB interacts with the GC. The associations, variability nodes, and multiple linear regression analysis suggest that POC fluctuations in the LPB respond mainly to biological processes and, to some extent, to the seasonality of SST and wind. Finally, our results justify the use of the MODIS-Aqua satellite POC for studies in marine-coastal water bodies with similar characteristics to the LPB and suggest that this water body can be considered a reservoir for the marine region of northwestern Mexico.

Incorporating marine particulate carbon into machine learning for accurate estimation of coastal chlorophyll-a

Accurate predictions of coastal ocean chlorophyll-a (Chl-a) concentrations are necessary for dynamic water quality monitoring, with eutrophication as a critical factor. Prior studies that used the driven-data method have typically overlooked the relationship between Chl-a and marine particulate carbon. To address this gap, marine particulate carbon was incorporated into machine learning (ML) and deep learning (DL) models to estimate Chl-a concentrations in the Yang Jiang coastal ocean of China. Incorporating particulate organic carbon (POC) and particulate inorganic carbon (PIC) as predictors can lead to successful Chl-a estimation. The Gaussian process regression (GPR) model significantly outperforming the DL model in terms of stability and robustness. A lower POC/Chl-a ratio was observed in coastal areas, in contrast to the higher ratios detected in the southern regions of the study area. This study highlights the efficacy of the GPR model for estimating Chl-a and the importance of considering POC in modeling Chl-a concentrations.

Spatial and Temporal Variations of Particulate Organic Carbon Sinking Flux in Global Ocean from 2003 to 2018

The monitoring of particulate organic carbon (POC) flux at the bottom of the euphotic layer in global ocean using remote sensing satellite data plays an important role in clarifying and evaluating the ocean carbon cycle. Based on the in situ POC flux data, this paper evaluated various estimation models. The global ocean POC flux from 2003 to 2018 was calculated using the optimal model, and its temporal and spatial variation characteristics were analyzed. In general, the annual average of global ocean POC flux is about 8.5–14.3 Gt   C   yr − 1 for period of 2003–2018. In the spatial dimension, the POC flux in the mid-latitude ocean (30–60°) is higher than that in the low-latitude (0–30°). The POC flux in Continental Margins with water depth less than 2000 m accounted for 30% of global ocean, which should receive more attention in global carbon cycle research. In the time dimension, the global POC flux decreases year by year generally, but the POC flux abnormally decreases during El Niño and increases during La Niña. In addition, due to global warming, sea ice melting, and bipolar sea area expansion, POC flux in high-latitude oceans (60–90°) is increasing year by year.