Surface Particulate Organic Carbon Research Articles

A method for estimating particulate organic carbon at the sea surface based on geodetector and machine learning

Particulate organic carbon (POC) is an essential component of the carbon pump within marine organisms. Exploring estimation methods for POC holds substantial significance for understanding the marine carbon cycle. In this study, we investigated the spatial heterogeneity of 30 factors and POC concentrations using geodetector to account for nonlinearity, diversity, and complexity. Ultimately, 20 factors including sea surface temperature, sea surface salinity, and chlorophyll-a were selected as modeling variables. Six machine learning models—backpropagation neural network, convolutional neural network, attention-based neural network, random forest (RF), adaptive boosting, and extreme gradient boosting were used to compare their performance. The results indicate that among the six machine learning algorithms, RF exhibits the strongest performance, with a root mean square error of 0.11 [log(mg/m3)] and an average percentage deviation of 2.73%. Global annual average sea surface POC concentrations were estimated for 2007 and compared to NASA’s POC product. The outcomes indicate that the RF model-based estimation method displays enhanced accuracy in estimating POC concentrations within intricate coastal environments, while the backpropagation neural network performed better in estimating POC concentrations in open ocean areas. Leveraging the RF model, global sea surface POC concentrations were estimated for the years 2007 through 2016, enabling a spatiotemporal analysis. The analysis unveils heightened POC concentrations in coastal regions and lower levels in open ocean areas. Furthermore, POC concentrations were greater in high-latitude regions compared to mid and low latitude counterparts. In conclusion, the global sea surface POC product in this study exhibits heightened spatial resolution and improved data completeness in contrast to other products. It enhances the accuracy of conventional POC estimation methods, particularly within coastal regions.

Mid-Long-Term Prediction of Surface Seawater Organic Carbon in the Southern South China Sea Based on Multi-Applicability CNN-LSTM Prediction Model

The organic carbon pool is a crucial component of the ocean carbon cycle. The study of organic carbon distribution and interannual variability in the land-sea interface can contribute to understanding the global ocean carbon cycle and ecological effects in the context of the Anthropocene and help achieve the Sustainable Development Goals (SDGs). At present, there has been a certain amount of research on the source and flux of carbon in the ocean carbon cycle, but the prediction of marine carbon is still in its infancy. In this paper, a CNN-LSTM deep learning model that takes into account spatio-temporal features was used to make a 5-year mid-long-term rolling prediction of particulate organic carbon (POC) and yellow matter (CDOM) using MODIS Level 2 semimonthly synthetic data from the official website of NASA from January 2002 to June 2020. The model uses chlorophyll-a data to adjust the parameters. The results showed that the model could also be applied to the mid-long-term rolling prediction of POC and CDOM. The model was capable of accurately predicting POC and CDOM over periods of three and two years, respectively (R > 0.5). Meanwhile, the 5-year trends of the predicted and actual values were verified using the least squares method and the Mann-Kendall trend test. The results showed that the predicted and actual values of sea surface POC and CDOM in 2015–2020 showed an overall upward trend. The surface-level POC and CDOM in the ocean are considered to be related to primary production. The mid-long-term prediction of surface seawater organic carbon in the southern South China Sea helps humans explore the regional characteristics of organic carbon in the coral reef waters of the South China Sea and study the changing trend of surface seawater organic carbon.

Absorption-based algorithm for satellite estimating the particulate organic carbon concentration in the global surface ocean

Particulate organic carbon (POC) in the surface ocean contributes to understanding the global ocean carbon cycle system. The surface POC concentration can be effectively detected using satellites. In open oceans, the blue-to-green band ratio (BG) algorithm is often used to obtain global surface ocean POC concentrations. However, POC concentrations are underestimated in waters with complex optical environments. To generate a more accurate global POC mapping in the surface ocean, we developed a new ocean color algorithm using a mixed global-scale in situ POC dataset with the concentration ranging from 11.10 to 4389.28 mg/m3. The new algorithm (a-POC) was established to retrieve the POC concentration using the strong relationship between the absorption coefficient at 490 nm (a(490)) and POC, in which a(490) was from the Ocean Color Climate Change Initiative (OC-CCI) v5.0 suite. Afterward, the a-POC algorithm was applied to OC-CCI v5.0 data for special regions and the global ocean. The performances of the a-POC algorithm and the BG algorithm were compared by combining the match-ups of satellite data and in situ dataset. The results showed that the statistical parameters of the a-POC algorithm were similar to those of the BG algorithm in the Atlantic oligotrophic gyre regions, with a median absolute percentage deviation (MAPD) value of 22.04%. In the eastern coastal waters of the United States and the Chesapeake Bay, the POC concentration retrieved by the a-POC algorithm was highly consistent with the match-ups, and MAPD values were 33.06% and 26.11%. The a-POC algorithm was also applied to the Ocean and Land Color Instrument (OLCI) data pre-processed with different atmospheric correction algorithms to evaluate the universality. The result showed that the a-POC algorithm was robust and less sensitive to atmospheric correction than the BG algorithm.

Three-dimensional observations of particulate organic carbon in shallow eutrophic lakes from space

Particulate organic carbon (POC) storage play an essential role in determining carbon sinks and water quality in shallow eutrophic lakes. Although satellite data have been adopted to observe surface POC content, the previous remote sensing results cannot indicate water column-integrated POC storage in shallow eutrophic lakes with vertically nonuniform POC profiles. Based on 272 in-situ POC profiles collected from 19 shallow eutrophic lakes in China's Yangtze-Huai Plain (YHP), this study developed a novel process-oriented method to derive water column-integrated POC storage from OLCI/Sentinel-3A satellite data. The method included three critical steps: remotely estimating surface POC contents, identifying POC profile types (uniform, exponential decay, or power decay) through a binary decision tree, and parameterizing POC profiles using surface POC concentrations. The developed algorithms showed a bias of -25.79% for in-situ match-ups from 15 lakes (N = 88). The POC in the eutrophic lakes was mainly produced by phytoplankton (R2 = 0.87). As a result, the surface POC content presented a unimodal peak in summer. However, in addition to air temperature regulations (0.52 – 68.53%), the water level also showed apparent effects on increasing POC storage, with relative contributions of 29.29 – 95.51% for the six largest lakes. Therefore, multisource satellite data are needed to derive POC storage in global eutrophic lakes remotely. This study is the first attempt to three-dimensionally observe lake POC contents from space and is important for understanding the carbon cycle in shallow eutrophic lakes.

Study of the Spatiotemporal Variability of Oceanographic Parameters and Their Relationship to Holothuria Species Abundance in a Marine Protected Area of the Mediterranean Using Satellite Imagery

Marine protected areas (MPAs) are designated to protect marine ecosystems and, among other things, to monitor climate variability, which in turn affects aquatic species. The aim of this study is to examine the contribution of remotely sensed data as an indication of Holothuria abundance, by investigating the spatiotemporal variability of physicochemical parameters. The study area is in the National Marine Park of Alonissos Northern Sporades, which is included in the NATURA 2000 network. Firstly, the abundance of Holothuria species was measured by scuba diving. At the same time, depth profiles of five physicochemical parameters (temperature, salinity, pH, dissolved oxygen and Chl-a) were recorded by CTD (conductivity, temperature, depth), a primary instrument used to determine the essential physical and chemicals properties of seawater column profiles in the coastal zone. The physicochemical variables examined are the most common environmental parameters with the highest impact on growth, reproduction, productivity and survival rate of sea cucumber species, affecting the availability of food sources. Analysis of this data allows us to identify parameters which are essential for their existence. The analysis showed that only temperature and Chlorophyll-a (Chl-a) could be useful for identifying the abundance. These two parameters are readily available from satellite data. Additionally, particulate organic carbon (POC) is essential for Holothuria’s existence. Consequently, a time series of satellite data products from Terra/MODIS sensor were utilized from 2000 to 2020 for sea surface temperature (SST), Chl-a and POC. The monthly temporal trend shows that the abundance could be justified in areas where the Holothuria presence has been established. Monthly spatiotemporal analysis shows that SST, Chl-a and POC availability, could be an indication of the differences in abundance recorded.

Response of sea surface temperature, chlorophyll and particulate organic carbon to a tropical cyclonic storm over the Arabian Sea, Southwest India

The response of sea surface temperature (SST), chlorophyll (Chl) and particulate organic carbon (POC) to the tropical cyclonic storm Ockhi between 29th November and 6th December 2017 over the Arabian Sea, Southwest coast of India was studied. The unique observation is that the cyclone overturned northeastwards (clockwise), the wind speed in north-northwestwards (8–12 m s−1) which caused cold patches (< 28.5 °C) in the northwestern Arabian Sea compared to the southwest coast of India. Sea surface cooling (3–4 °C) was observed in concurrence with significant Chl response (0.5–7.5 mg m−3) along the Ockhi cyclone path. The post cyclonic low SST and high Chl from cyclone centre to farther areas suggested the extent of upwelled nutrient rich cold waters for plankton proliferation. POC levels showed a 3-fold increase, affected by significant biomass production due to subsurface phytoplankton and nutrient sources evidenced by deepening of mixed layer depth. The cyclone induced contrasting surface Chl (< 1 mg m−3) and POC (80–160 mg m−3) concentration substantiated the dominance of less labile refractory organic particulates in the offshore waters. The sub surface cooling and Chl bloom was noticed up to 50 m indicating the strong relation of vertical bio-physical structure during tropical cyclones. This study revealed that the tropical cyclones can act in such a way that it improves the positive magnitude of Chl and POC whereas reverses the SST behavior, on either side of the cyclone path within a short period in the oligotrophic waters of Arabian Sea.

Vertically stratified water source characteristics and associated driving mechanisms of particulate organic carbon in a large floodplain lake system

Particulate organic carbon (POC) is an important component of lake organic carbon (C) pools, of which different factors drive vertical distributions and sources. This study used the dual stable isotope (δ13C and δ15N) approach to investigate vertical POC sources and drivers in a large floodplain lake system. Findings showed that POC composition gradually changed from endogenous dominant to exogenous dominant sequentially from the surface layer to the bottom layer of Lake Poyang. Environmental factors associated with phytoplankton photosynthesis as well as nutrient levels primarily drove surface POC. Moreover, soil erosion, sediment deposition, and resuspension strongly affected POC distribution and composition in the middle and bottom layers of the lake. POC sources were also affected by factors associated with vertical mixing, such as wind speed and water depth. Litter from C3 plants significantly contributed to POC concentrations in the middle and bottom layers of the lake. Results from this study can benefit our overall understanding of the potential driving mechanisms of lake C cycling processes, aquatic ecosystem functions, and pollutant migration.

Satellite-based observation of particulate organic carbon in the northern Bay of Bengal

Particulate organic carbon (POC) plays a vital role in the ocean carbon cycle and is related to many important ocean biogeochemical processes. This study investigates the annual and seasonal variability of POC and its relationship with sea surface temperature (SST), chlorophyll-a (Chl-a), and wind vector in the northern Bay of Bengal (BoB) of Bangladesh and validates satellite data with in situ measurement data. Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite level-3 data of POC, Chl-a, and SST were used in this study. Wind vector data were obtained from WindSat Polarimetric Radiometer satellite. Annual average of 18-year (2002–2019) surface POC concentration in the northern BoB ranged from 126.83 mg m−3 to 180.58 mg m−3 (average of 153.84±11.17 mg m−3) with a declining rate of -1.12 mg m−3 year−1. The POC was highest (181.80±22.34 mg m−³) during the northeast-monsoon (December–February) and lowest (136.56±36.24 mg m−³) in the pre-monsoon (March-May). Statistically significant (F3,202 =18.09; p<0.05) difference of POC was found amongst the seasons. A very weak inverse relationship was observed between the SST and POC seasonally and annually, whereas POC and Chl-a showed a strong positive relation in the northern BoB. POC was observed to be very prominent with northeasterly wind in northeast and post-monsoon and low with southwesterly wind in southwest and pre-monsoon. POC variability was influenced mainly by Chl-a, followed by SST and wind speed for BoB. The POC:Chl-a ratio in the northern BoB were ranged from 240.91 to 294.68, which shows a strong influence of biological processes on POC composition. Additional mechanisms responsible for the seasonal POC variability are also addressed in this study. A significantly (p<0.001) moderate correlation (r=0.75) was found between the MODIS Aqua satellite data and in situ observation. Further detailed investigation is needed to observe the role of POC variability in biogeochemical processes of the northern BoB.

Spatial and temporal variations in particulate organic carbon in Indonesian waters over two decades

Particulate organic carbon (POC) is one of the essential variables in oceanic biogeochemical processes. Assessing the spatiotemporal variability of POC will help deepen our understanding of the carbon cycle. In this study we collected moderate-resolution imaging spectroradiometer (MODIS) Aqua satellite data for surface POC in Indonesian waters during the period 2002–21, together with in situ data from the south-western Sumatran waters (SSW), Sunda Strait, Sumba–Savu waters and the Halmahera Sea and surrounds. These data were analysed to gain an understanding of the spatial and temporal variations in POC. Generally, high POC concentrations in Indonesian waters were found at coastal and continental shelves connected to the estuaries of large rivers; we assume that the POC concentrations in these areas are affected by terrestrial input. Meanwhile, in Indonesian waters apart from these estuaries, marine-derived materials are the main components of POC. The seasonal pattern of POC in Indonesian waters generally shows highest levels during the south-east monsoon and lowest levels during the north-west monsoon. Trends in POC concentrations are positively correlated with chlorophyll-a and negatively correlated with sea surface temperature. This study suggests that the combination of natural physicochemical processes and anthropogenic perturbations has significant effects on spatial and temporal variations in POC in Indonesian waters.

Estimating particulate organic carbon flux in a highly dynamic estuary using satellite data and numerical modeling

Quantitative estimates of the flux, transportation, and burial of riverine particulate organic carbon (POC) in estuaries and shelf areas are crucial for understanding the carbon cycle in marginal seas. The complexity of the hydrodynamic environment and estuarine processes in the Changjiang River estuary (CRE) results in a POC flux with high spatiotemporal variability. In this study, we proposed a new method for time series monitoring of riverine POC flux by combining satellite ocean color and numerical modeling data. We used 500-m resolution geostationary satellite data from the Geostationary Ocean Color Imager (GOCI) to retrieve surface POC concentrations in the CRE and used the numerical Finite Volume Community Ocean Model (FVCOM) to simulate three-dimensional (3D) current and sediment distribution. Diurnal, seasonal, and annual variations in POC flux through the lower reaches (Datong Hydrological Station), upper estuary (Xuliujing Hydrological Station), and outlets of the CRE, as well as transportation and deposition outside the CRE, were examined from July 2011 to June 2018. The annual mean POC fluxes through the Datong, Xuliujing, and outlet sections were 1.16 ± 0.16 Tg C/yr, 1.29 ± 0.11 Tg C/yr, and 1.17 ± 0.11 Tg C/yr, respectively. After passing through the estuary, 35.2% of POC was deposited in the sandbar outside the outlets, 52.9% was transported to the south, and a small amount entered the eastern shelf. Thus, these results indicated that POC flux through the river and estuary sections differed from the effective riverine POC flux into the sea. The proposed method combining high spatiotemporal-resolution satellite data and numerical modeling not only makes full use of their advantages, but also reduces the uncertainty of their individual estimates. This approach also supports long-term monitoring of riverine fluxes to the sea and helps clarify the effects of terrestrial inputs on marginal seas under multiple stresses from human activities and climate change.

Modeling Spatiotemporal Patterns of Ecosystem Metabolism and Organic Carbon Dynamics Affecting Hypoxia on the Louisiana Continental Shelf.

The hypoxic zone on the Louisiana Continental Shelf (LCS) forms each summer due to nutrient enhanced primary production and seasonal stratification associated with freshwater discharges from the Mississippi/Atchafalaya River Basin (MARB). Recent field studies have identified highly productive shallow nearshore waters as an important component of shelf-wide carbon production contributing to hypoxia formation. In this study we present results from a three-dimensional hydrodynamic-biogeochemical model named CGEM (Coastal Generalized Ecosystem Model) applied to quantify the spatial and temporal patterns of hypoxia, carbon production, respiration, and transport between nearshore and middle shelf regions where hypoxia is most prevalent. We first demonstrate that our simulations successfully reproduced spatial and temporal patterns of carbon production, respiration, and bottom-water oxygen gradients compared to field observations. We then used interannual simulations to identify transport of particulate organic carbon (POC) from nearshore areas where riverine organic matter and phytoplankton carbon production are greatest. The spatial disconnect between carbon production and respiration in our simulations was driven by westward and offshore POC flux, a pattern that supported heterotrophic respiration on the middle shelf where hypoxia is frequently observed. These results validate the importance of offshore carbon flux to hypoxia formation, particularly on the west shelf where hypoxic conditions are more variable.

Impacts of Physical and Biological Processes on Spatial and Temporal Variability of Particulate Organic Carbon in the North Pacific Ocean during 2003\u20132017

The North Pacific Ocean is a significant carbon sink region, but little is known about the dynamics of particulate organic carbon (POC) and the influences of physical and biological processes in this region at the basin scale. Here, we analysed high-resolution surface POC data derived from MODIS-Aqua during 2003–2017, together with satellite-derived sea surface chlorophyll and temperature (SST). There are large spatial and temporal variations in surface POC in the North Pacific. Surface POC is much lower in the subtropical region (<50 mg m−3) than in the subarctic region (>100 mg m−3), primarily resulting from the south-to-north variability in biological production. Our analyses show significant seasonal and interannual variability in surface POC. In particular, there is one peak in winter-spring in the western subtropical region and two peaks in late spring and fall in the western subarctic region. Surface POC is positively correlated with chlorophyll (r = ~1) and negatively correlated with SST (r = ~−0.45, P < 0.001) south of 45°N, indicating the strong influence of physically driven biological activity on the temporal variability of POC in the subtropical region. There is a significantly positive but relatively lower correlation coefficient (0.6–0.8) between POC and chlorophyll and an overall non-significantly positive correlation between POC and SST north of 45°N, reflecting the reduction in the POC standing stock due to the fast sinking of large particles. The climate modes of the Pacific Decadal Oscillation, El Niño–Southern Oscillation and North Pacific Gyre Oscillation have large impacts on POC in various seasons in the subtropical region and weak influences in the subarctic region. Surface POC was anomalously high after 2013 (increased by ~15%) across the basin, which might be the result of complex interactions of physical and biological processes associated with an anomalous warming event (the Blob).

An absorption-specific approach to examining dynamics of particulate organic carbon from VIIRS observations in inland and coastal waters

An absorption-based approach was used to determine surface particulate organic carbon (POC) concentrations in both inland and coastal waters. The improved performance of this approach was based on the specification of local POC absorption characteristics based on dominant POC sources; phytoplankton or detritus based. This specification was made using a new POC-Index (PI), developed and tested across a range of POC (300–10,000 mg/m3) conditions in temporally and spatially heterogeneous inland and coastal waterbodies. The POC model was based on remote sensing reflectance (Rrs, sr−1) in four wavebands: Rrs(751), Rrs(488) and Rrs(R/G), where R is the red band [Rrs(672)] for detritus dominated waters and G is the green band [Rrs(555)] in the phytoplankton dominated waters. The model provided a high R2 (0.74) and relatively low rRMSE (42.0%, N = 136, p < 0.01). Validation with independent datasets from Chaohu Lake and the Yangtze River Estuary produced a larger positive bias (R2 = 0.59, rRMSE = 83%, δ = 634 mg/m3, S = 0.63, I = 1439 mg/m3); nevertheless, the bias was reduced when tuned with local data (R2 = 0.80, rRMSE = 45%, δ = 72 mg/m3, S = 0.81, I = 327 mg/m3). Additionally, HydroLight simulations presented an independent correlation between PI and CDOM conditions and reasonable POC estimates from the new approach developed in this study. The approach was tested using data from Visible Infrared Imaging Radiometer Suite (VIIRS) in a range of optically complex conditions to quantify carbon dynamics. We indicate the advantages and challenges of using this approach in ecosystems where multiple organic carbon sources are present.

Satellite estimation of particulate organic carbon flux from Changjiang River to the estuary

Rivers link land and ocean ecosystems, the two largest active carbon reservoirs in the world, and transport tremendous amounts of particulate organic carbon (POC) into marginal seas annually. Due to high spatiotemporal variations, estimations of riverine POC flux into the sea can be relatively inexact when based on field measurement only. In this study, remote sensing algorithms of riverine POC flux through the Xuliujing hydrological station in the Changjiang River Estuary (CRE) were developed using satellite data with hourly resolution from the Geostationary Ocean Color Imager (GOCI). Based on data collected during four seasonal cruises in the CRE from 2014 to 2016, POC concentration showed a significant linear relationship to total suspended matter (TSM) (N = 426, R2 = 0.97, p < 0.01). Thus, surface POC concentration was calculated by satellite-derived TSM. From the in-situ data, the vertical POC concentration profile at Xuliujing showed an exponentially increasing curve (p < 0.01), but a linear decrease (p < 0.01) for the water flow profile from the surface to the bottom. Combining the GOCI-derived surface POC concentration and vertical profiles of POC and water flow, we estimated monthly riverine POC fluxes to the CRE. Results showed that monthly POC flux at Xuliujing (0.071 ± 0.022 Tg C) was 22.64% higher, on average, than the commonly used value at the non-tidal Datong hydrological station. Moreover, under human activity pressures, the influences of POC input from the Changjiang River on POC concentration at Xuliujing have weakened in recent years. Monthly and diurnal POC variations in the CRE were mainly impacted by wind speed and tidal processes, respectively. Thus, satellite monitoring with high spatiotemporal resolution has great significance for accurately estimating riverine POC flux to estuary.

High variability of particulate organic carbon export along the North Atlantic GEOTRACES section GA01 as deduced from &amp;lt;sup&amp;gt;234&amp;lt;/sup&amp;gt;Th fluxes

Abstract. In this study we report particulate organic carbon (POC) export fluxes for different biogeochemical basins in the North Atlantic as part of the GEOTRACES GA01 expedition (GEOVIDE, May–June 2014). Surface POC export fluxes were deduced by combining export fluxes of total Thorium-234 (234Th) with the ratio of POC to 234Th of sinking particles at the depth of export. Particles were collected in two size classes (&gt;53 and 1–53 µm) using in situ pumps and the large size fraction was considered representative of sinking material. Surface POC export fluxes revealed latitudinal variations between provinces, ranging from 1.4 mmol m−2 d−1 in the Irminger basin, where the bloom was close to its maximum, to 12 mmol m−2 d−1 near the Iberian Margin, where the bloom had already declined. In addition to the state of progress of the bloom, variations of the POC export fluxes were also related to the phytoplankton size and community structure. In line with previous studies, the presence of coccolithophorids and diatoms appeared to enhance the POC export flux, while the dominance of picophytoplankton cells, such as cyanobacteria, resulted in lower fluxes. The ratio of POC export to primary production (PP) strongly varied regionally and was generally low (≤14 %), except at two stations located near the Iberian Margin (35 %) and within the Labrador basin (38 %), which were characterized by unusual low in situ PP. We thus conclude that during the GEOVIDE cruise, the North Atlantic was not as efficient in exporting carbon from the surface, as reported earlier by others. Finally, we also estimated the POC export at 100 m below the surface export depth to investigate the POC transfer efficiencies. This parameter was also highly variable amongst regions, with the highest transfer efficiency at sites where coccolithophorids dominated.

Spatial and temporal variations of particulate organic carbon in the Yellow-Bohai Sea over 2002\u20132016

The Yellow-Bohai Sea (YBS) is a typical marginal sea in the Northwest Pacific Ocean; however, little is known about the dynamics of particulate organic carbon (POC) and underlying mechanisms. Here, we analyze the spatial and temporal variations of surface POC derived from MODIS-Aqua during 2002–2016. Overall, POC is higher in the Bohai Sea (315–588 mg m−3) than in the Yellow Sea (181–492 mg m−3), and higher in the nearshore than in the offshore. Surface POC is highest in spring in the YBS, and lowest in winter (summer) in the Bohai Sea (the Yellow Sea). The spatial and seasonal patterns of POC are due to combined influences of primary productivity, water exchange, sediment resuspension and terrestrial inputs. Surface POC shows an overall decreasing trend prior to 2012 followed by an upward trend until 2015 in the YBS, which is almost opposite to chlorophyll; the decrease (increase) may result from strengthened (weakened) water exchange with the East China Sea through the Yellow Sea Warm Current. Declined terrestrial runoff is also partly responsible for the decrease prior to 2012. Our study suggests that water exchange and sediment resuspension are dominant factors regulating the spatial and temporal variability of POC in the YBS.

Evaluating Optical Proxies of Particulate Organic Carbon across the Surface Atlantic Ocean

Empirical relationships between particulate organic carbon (POC) and inherent optical properties (IOPs) are required for estimating POC from ocean-colour remote sensing and autonomous platforms. The main relationships studied are those between POC and particulate attenuation (cp) and backscattering (bbp) coefficients. The parameters of these relationships can however differ considerably due to differences in the methodologies applied for measuring IOPs and POC as well as variations in particle characteristics. Therefore it is important to assess existing relationships and explore new optical proxies of POC. In this study, we evaluated empirical relationships between surface POC and IOPs (cp, bbp and the particulate absorption coefficient, ap) using an extensive dataset collected during two Atlantic Meridional Transect (AMT 19 and 22) cruises spanning a wide range of oceanographic regimes. IOPs and POC were measured during the two cruises using consistent methodologies. To independently assess the accuracy of the POC-IOPs relationships, we predicted surface POC for AMT-22 using relationships developed based on independent data from AMT-19. We found typical biases in predicting POC ranging between 2-3%, 4-9%, and 6-13% for cp, bbp and ap, respectively, and typical random uncertainties of 20-30%. We conclude that 1) accurate POC-cp and POC-bbp relationships were obtained due to the consistent methodologies used to estimate POC and IOPs and 2) ap could be considered as an alternative optical proxy for POC in open-ocean waters, only if all physiological variability in the POC:chl ratio could be modeled and used to correct ap.

Developing a semi-analytical algorithm to estimate particulate organic carbon (POC) levels in inland eutrophic turbid water based on MERIS images: A case study of Lake Taihu

Particulate organic carbon (POC) plays an important role in the carbon cycle in water due to its biological pump process. In the open ocean, algorithms can accurately estimate the surface POC concentration. However, no suitable POC-estimation algorithm based on MERIS bands is available for inland turbid eutrophic water. A total of 228 field samples were collected from Lake Taihu in different seasons between 2013 and 2015. At each site, the optical parameters and water quality were analyzed. Using in situ data, it was found that POC-estimation algorithms developed for the open ocean and coastal waters using remote sensing reflectance were not suitable for inland turbid eutrophic water. The organic suspended matter (OSM) concentration was found to be the best indicator of the POC concentration, and POC has an exponential relationship with the OSM concentration. Through an analysis of the POC concentration and optical parameters, it was found that the absorption peak of total suspended matter (TSM) at 665nm was the optimum parameter to estimate POC. As a result, MERIS band 7, MERIS band 10 and MERIS band 12 were used to derive the absorption coefficient of TSM at 665nm, and then, a semi-analytical algorithm was used to estimate the POC concentration for inland turbid eutrophic water. An accuracy assessment showed that the developed semi-analytical algorithm could be successfully applied with a MAPE of 31.82% and RMSE of 2.68mg/L. The developed algorithm was successfully applied to a MERIS image, and two full-resolution MERIS images, acquired on August 13, 2010, and December 7, 2010, were used to map the POC spatial distribution in Lake Taihu in summer and winter.

Seasonal carbon cycling in a Greenlandic fjord: an integrated pelagic and benthic study

Climate change is expected to have a pronounced effect on biogeochemical cycling in Arctic fjords, but current insight on the biogeochemical functioning of these systems is limited. Here, we present seasonal data on primary production, export of particulate organic carbon (POC), and the coupling to benthic biogeochemistry in Kobbefjord (SW Greenland). Primary production and associated POC export from the photic zone showed marked seasonality, with annual integrated values of 7.2 and 19.9 mol C m-2 yr-1, respectively. This discrepancy, the isotopic signature, and C:N ratio of the sedimentating material suggested substantial import of marine POC from outside the fjord. At least 52% of the POC export reached the sediment, but the seasonality in pelagic productivity was not reflected in the sediment biogeochemistry, showing only moderate variation. Benthic mineralization and burial of organic carbon amounted to 3.2 and 5.3 mol C m-2 yr-1, respectively. Sulfate reduction was the most prominent mineralization pathway, accounting for 69% of the benthic mineralization, while denitrification accounted for 2%. Overall, the carbon mineralization and burial in Kobbefjord were significantly higher than previously observed in other more northerly Arctic fjords. Data compilation from Arctic fjords suggests proportional increases in surface production, POC export, benthic mineralization and burial of organic material with increasing duration of the ice-free period. Thus, the projected decline in ice coverage in higher Arctic Greenlandic fjords will, as a first approximation, entail proportional increases in productivity, mineralization, and burial of organic carbon in the fjords, which will thus become similar to present-day southerly systems.

Ocean colour estimates of particulate organic carbon reservoirs in the global ocean – revisited

We have examined 16 years (1998–2013) of particulate organic carbon (POC) concentrations derived from remotely sensed ocean colour. POC concentrations vary spatially from more than 300 mg m−3 in the northern North Atlantic in summer to about 20 mg m−3 in the oligotrophic South Pacific (16-year global average = 67.7 mg m−3). The seasonal variability is weak at lower latitudes and stronger at higher latitudes. The annual mean surface POC concentrations show statistically significant regional trends (p < 0.05, 95% confidence level), and are decreasing in the North Atlantic and North Pacific and increasing in the South Pacific and Southern Oceans. The global trend is not significant. The 16-year global average water column POC biomass integrated over the euphotic depth, the mixed layer depth, or based on a combination of these two depths is estimated to be about 3.97, 3.92, and 5.03 g m−2, respectively. Water column integrated biomass shows different spatial and seasonal patterns than the surface POC concentrations, and is increasing in many ocean regions. Globally averaged POC biomass is also increasing. At the same time ocean colour data indicate a decrease in the global oceanic productivity (PP). This means that there is a negative trend in the ratio of PP to POC biomass almost everywhere in the ocean. Such a decrease could indicate that the biological pump in the ocean is weakening, but longer time series of the ocean colour data are needed to confirm this observation.