Global Ocean Carbon Cycle Research Articles

Satellite retrieval of oceanic particulate organic carbon: Towards an accurate and seamless dataset for the global ocean

Particulate organic carbon (POC) plays crucial roles in the global ocean carbon cycle and the oceanic biological pump. Satellite remote sensing has been demonstrated to be an effective technique for the retrieval of surface oceanic POC concentration. However, the complex spatiotemporal variations of the relationships between POC and oceanic optical properties across different waters posed challenges for accurate retrieval of POC concentration from satellite observations. Additionally, interference factors, such as cloud cover and sun glint, resulted in severe data missing problems and impeding daily coverage of the global ocean. With an attempt to generate accurate, seamless and readily available POC products for the global ocean, this study aimed to develop accurate satellite POC retrieval models for the Moderate Resolution Imaging Spectroradiometer (MODIS) data from both Terra and Aqua satellites, and to explore the possibility of using the empirical orthogonal function interpolation technique (DINEOF) to reconstruct satellite-retrieved POC data to generate gap-free global oceanic POC products. Results showed that the eXtreme Gradient Boosting (XGBoost) method could accurately retrieve POC with R2 approximately 0.80 and RMSE about 0.20 in log10 scale, obviously outperforming the operational blue-to-green band ratio algorithm and the hybrid polynomial algorithm based on two multi-band indices; and the DINEOF method, which could reconstruct approximately 88 % missing pixels for the global ocean, contributed to better revealing the global oceanic POC variations at a daily scale than the satellite-retrieved POC products. Based on the developed models, a suit of long time-series accurate and seamless POC products of the global surface ocean were generated, which is readily available for other applications and should be helpful to investigate the spatiotemporal variations of POC concentrations over global ocean and its roles in the global carbon cycle. The generated seamless products are openly accessible via the DOIs listed in the data availability section.

Combining deep learning with physical parameters in POC and PIC inversion from spaceborne lidar CALIOP

POC and PIC are indispensable components in the global ocean carbon cycle, their transport and space distribution being driven by the biological carbon pump and the carbonate pump. However, passive ocean color remote sensing, usually employed for POC and PIC research, experiences serious shortcomings in polar winter conditions due to its reliance on sunlight, leading to scarce data coverage in the polar regions. In contrast, CALIOP has shown considerable promise in high-latitude ocean observing. Past approaches to estimate POC from CALIOP data relied on bbp measurements obtained through the application of algorithms that presume an empirical linear correlation between bbp and the backscatter coefficient measured at 180°. This method does not account for any spatiotemporal variability in the conversion coefficient. Furthermore, the potential of CALIOP to provide estimates of PIC has not been explored yet. Here, we developed an innovative Two-Branch-Two-Step (TBTS) model to estimate POC and PIC from CALIOP data, which effectively expands the spatial coverage of the MODIS products. This method exploits the strength of deep learning while encapsulating the generalizability of physical parameters. This method consists of two branches: (1) a deep learning branch based on lidar attenuated backscatter waveform and (2) a branch focusing on physical parameters, including the total column-integrated depolarization ratio and the subsurface cross-polarized component of column-integrated backscatter. The model’s generalizability and accuracy are confirmed through the evaluation of a test dataset and validation using in-situ measurements. Our model outperforms several other prevalent machine learning models. We also dissected the importance of different parts of the input data using SHAP tools, thereby providing insights into the black-box nature of deep learning models. Using CALIOP products, we put forth the inaugural estimation of interannually resolved PIC and POC standing stocks in polar regions. The implementation of CALIOP in polar regions bridges the gap inherent in passive ocean color measurements. Lidar-derived total global PIC standing stock is estimated to be 8% higher than that from MODIS, while the POC standing stock is boosted by 17.2%. The carbon standing stock in polar regions exhibits significant inter-annual variability and apparent seasonal periodicity. Hence, results from this research effort clearly reveal that the exploitation of the CALIOP-derived POC and PIC measurements, in combination with the application of new approaches and algorithms to future space lidar data, will undeniably enhance our comprehension of the polar ocean carbon cycle. However, it is important to acknowledge that the CALIOP products may inherit biases from the MODIS data used for training. Hence, where MODIS data is available, it’s still the “better” product to use, but where there isn’t MODIS data, the CALIOP product is extremely useful, especially in the polar regions.

Novel Insights into Ocean Trace Element Cycling from Biogeochemical Models

Ocean biogeochemical models have become critical tools for interpreting trace element and isotope (TEI) distributions observed during the GEOTRACES program and understanding their driving processes. Models stimulate new research questions that cannot be addressed with observations alone, for instance, concerning processes that occur over vast spatial scales and linkages between TEIs and other elemental cycles. A spectrum of modeling approaches has been applied to date, including (1) fully prognostic models that couple TEIs to broader biogeochemical frameworks, (2) simpler element-specific mechanistic models that allow for assimilation of observations, and (3) machine learning models that have no mechanistic underpinning but allow for skillful extrapolation of sparse data. Here, we evaluate the strengths and weaknesses of these approaches and review three sets of novel insights they have facilitated. First, models have advanced our understanding of global-scale micronutrient distributions, and their deviations from macronutrients, in terms of a “ventilation-regeneration-scavenging” balance. Second, models have yielded global-scale estimates of TEI inputs to and losses from the ocean, revealing, for instance, a rapid iron (Fe) cycle with an oceanic residence time on the order of decades. Third, models have identified novel links among various TEI cycling processes and the global ocean carbon cycle, such as tracing the supply of hydrothermally sourced Fe to iron-starved microbial communities in the Southern Ocean. We foresee additional important roles for modeling work in the next stages of trace element research, including synthesizing understanding from the GEOTRACES program in the form of TEI state estimates, and projecting the responses of TEI cycles to global climate change.

Drivers of Air‐Sea CO2 Flux in the Subantarctic Zone Revealed by Time Series Observations

AbstractThe subantarctic zone is an important region in the Southern Ocean with respect to its influence on air‐sea CO2 exchange and the global ocean carbon cycle. However, understanding of the magnitude and drivers of the flux are still being refined. Using observations from the Southern Ocean Time Series (SOTS) station (∼47°S, 142°E) and auxiliary data, we developed a multiple linear regression model to compute the sea surface partial pressure of CO2 (pCO2) over the past two decades. The mean amplitude of the pCO2 seasonal cycle between 2004 and 2021 was 44 μatm (range 30–54 μatm). Summer minima ranged from 310 to 370 μatm and winter maxima were near equilibrium with the atmosphere. The non‐thermal (i.e., biological processes and mixing) contribution to the seasonal variability in pCO2 was several times larger than the thermal contribution. The SOTS region acted as a net carbon sink at annual time scales, with mean magnitude of 6.0 mmol m−2 d−1. The positive phase of the Southern Annular Mode (SAM) increased ocean carbon uptake primarily through an increase in wind speed at zero time lag. Increased surface pCO2 was correlated with a positive SAM with a lag of 4 months, mainly due to reduced biological uptake and increased mixing. During the autotrophic season, pCO2 was predominantly impacted by primary productivity, whereas water mass movement, inferred by temperature and salinity anomalies, had a larger impact on the heterotrophic season. In general, mesoscale processes such as eddies and frontal movement impact the local biogeochemical features more than the SAM.

Spatiotemporal reconstruction of global ocean surface pCO2 based on optimized random forest

The partial pressure of ocean surface CO2 (pCO2) plays an important role in quantifying the carbon budget and assessing ocean acidification. For such a vast and complex ocean system as the global ocean, most current research practices tend to study the ocean into regions. In order to reveal the overall characteristics of the global ocean and avoid mutual influence between zones, a holistic research method was used to detect the correlation of twelve predictive factors, including chlorophyll concentration (Chlor_a), diffuse attenuation coefficient at 490 nm (Kd_490), density ocean mixed layer thickness (Mlotst), eastward velocity (East), northward velocity (North), salinity (Sal), temperature (Temp), dissolved iron (Fe), dissolved silicate (Si), nitrate (NO3), potential of hydrogen (pH), phosphate (PO4), at the global ocean scale. Based on measured data from the Global Surface pCO2 (LDEO) database, combined with National Aeronautics and Space Administration (NASA) Ocean Color satellite data and Copernicus Ocean reanalysis data, an improved optimized random forest (ORF) method is proposed for the overall reconstruction of global ocean surface pCO2, and compared with various machine learning methods. The results indicate that the ORF method is the most accurate in overall modeling at the global ocean scale (mean absolute error of 6.27μatm, root mean square error of 15.34μatm, R2 of 0.92). Based on independent observations from the LDEO dataset and time series observation stations, the ORF model was further validated, and the global ocean surface pCO2 distribution map of 0.25° × 0.25° for 2010 to 2019 was reconstructed, which is of significance for the global ocean carbon cycle and carbon assessment.

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.

A new approach for determination of orthophosphate based on mixed valent molybdenum oxide/poly 1,2-diaminoanthraquinone in seawater

Orthophosphate is an essential macronutrient in natural water that controls primary production and strongly influences the global ocean carbon cycle. Electrochemical determination of orthophosphate is highly recommended because electrochemistry provides the simplest means of determination. Here the determination of orthophosphate based on the formation of a phosphomolybdate complex is reported. Mixed-valent molybdenum oxide (MoxOy) was prepared by cyclic voltammetry on poly-1,2-diaminoanthraquinone (1,2-DAAQ), which was performed by cyclic voltammetry on the surface of a glassy carbon electrode under pre-optimized conditions for the thickness of the modified electrode layers. The proposed modified electrode was used for square-wave voltammetry of orthophosphate ions under pre-optimized square-wave parameters (i.e., frequency and amplitude) in strongly acidic medium (pH < 1). The linear range was 0.05–4 µM with a limit of quantification (LOD) of 0.0093 µM with no effect on two peaks due to cross interference from silicate. Furthermore, MoxOy/PDAAQ shows good reproducibility with a relative standard deviation (RSD) of 2.17% for the peak at 0.035 V and 3.56% for the peak at 0.2 V. Real seawater samples were also analyzed for PO43− analysis by UV spectrophotometry and the results were compared with the measurement results of our proposed electrode, with good recoveries obtained.

The Changjiang River plume shifts from carbon source to sink when net community production exceeds a threshold in early autumn

Estuaries are crucial components of the global ocean carbon cycle due to their high productivity. However, our understanding of the carbon source-sink dynamics at the air-sea interface of estuaries is incomplete, largely due to the rapidly changing environmental conditions. To address this, we conducted a study in early autumn 2016 using high-resolution biogeochemical data collected through buoy observations in the Changjiang River plume (CRP). Using a mass balance approach, we examined the factors driving changes in the sea surface partial pressure of carbon dioxide (pCO2) and quantified the net community production (NCP) in the mixed layer. We also explored the relationship between NCP and the carbon source-sink dynamics at the air-sea interface. Our results revealed that biological activities (64.0 %) and seawater mixing (19.7 %, including lateral transport and vertical mixing) were the dominant factors controlling changes in sea surface pCO2 during the study period. Moreover, NCP in the mixed layer was affected by factors such as light availability and the presence of respired organic carbon associated with vertical mixing of seawater. Notably, we observed a strong correlation between NCP and the difference in pCO2 between air and sea (δpCO2), with a threshold NCP value of 308.4 mmol m−2 d−1 identified as an indicator of the transition from a CO2 source to a sink in the CRP. Hence, we suggest that the NCP in a specific ocean box has a threshold, beyond which the air-sea interface in estuaries will change from a carbon source to a carbon sink, and vice versa.

Factors Controlling the Sea Surface Partial Pressure of Carbon Dioxide in Upwelling Regions: A Case Study of the Southern East China Sea Before and After Typhoon Maria

AbstractUpwelling regions, which account for 10%–20% of the global ocean's primary production but occupy only 3% of its area, play an important role in the global ocean carbon cycle. However, controlling factors of the partial pressure of carbon dioxide (pCO2) in upwelling regions before and after typhoons remain unclear. Sea surface pCO2 was measured in two upwelling regions of the southern East China Sea (sECS) before (6–9 July 2018) and after (13–17 July 2018) Typhoon “Maria.” Surface pCO2 values were 325.1 ± 18.3 and 322.9 ± 17.4 μatm on the shelf break and middle shelf, respectively, before the typhoon. After the typhoon, the surface pCO2 values dropped to 315.5 ± 15.9 and 305.7 ± 7.2 μatm on the shelf break and middle shelf, respectively. The analysis shows that factors controlling pCO2 variations are the temperature effect (38% to 40%), net biological activities (−33% to −36%), and mixing (−24%), irrespective of whether the measurements were taken before or after the typhoon. During our study period, the upwelling regions in sECS acted as strong sinks of atmospheric CO2 (−4.2 ± 1.2 to −14.1 ± 1.2 mmol m−2 d−1). We suggested that the temperature change, net biological activities, and mixing during the upwelling process characterized the biogeochemical responses over the sECS.

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.

A review of zooplankton and deep carbon fixation contributions to carbon cycling in the dark ocean

Models of the marine carbon cycle assume that virtually all heterotrophic production in the open ocean is derived from near-surface carbon fixation (primary production) by phytoplankton. However, current carbon budget estimates show that respiration throughout the ocean far exceeds surface primary production. This disconnect can be grouped into two categories: Inaccurate estimates of water column respiration and carbon transport from metazoans; and missing primary production sources and. heterotrophic processing in the dark ocean. In this review, we examine the contribution to the ocean carbon cycle of chemoautotrophic production, as well as secondary production and respiration from meso-zooplankton and micro-nekton below 400 m depth. About one-third of epipelagic biomass in the ocean migrates diurnally, distributing dissolved organic carbon (DOC) and total nitrogen (TN), along with about 30–80% of the particulate organic carbon (POC) flux, from the upper ocean. Although mostly this occurs in the upper 400 m, migration depths can extend to 3000 m. In addition, up to 80% of the biomass of secondary consumers in the open ocean live part of their life cycle at depths up to 2000 m, contributing significantly to deep-sea respiration and particle flux, particularly over fall/winter in temperate-subarctic oceans, submarine canyons, and deep seas such as the Mediterranean. This active flux provides fresh organic input to the deep ocean at a time of year when surface primary productivity, and thus organic carbon (OC) flux to the deep ocean, is low. The complex spatial, temporal and depth scales of horizontal and vertical migration make modelling of the global oceanic carbon cycle extremely complex, requiring consideration of biomass movements throughout the entire water column over diurnal, lunar and seasonal cycles over broad geographic regions. An additional 10 to 50% of surface primary production occurs within mid-depth oxygen minimum zones (OMZs), fuelled by ammonia excreted from vertically migrating zooplankton concentrated near OMZ boundaries. Crustal sources such as gas and methane seeps, hydrothermal vents and submarine volcanoes support active deep-sea food webs, as well as contributing to upper ocean productivity. Crustal sources are conservatively estimated to provide >30%, and probably up to 50%, of oceanic OC flux to the dark ocean. These estimates are still poorly constrained but can no longer be ignored in global oceanic carbon cycles.

Microbial Community Structure and Ecological Networks during Simulation of Diatom Sinking.

Microbial-mediated utilization of particulate organic matter (POM) during its downward transport from the surface to the deep ocean constitutes a critical component of the global ocean carbon cycle. However, it remains unclear as to how high hydrostatic pressure (HHP) and low temperature (LT) with the sinking particles affects community structure and network interactions of the particle-attached microorganisms (PAM) and those free-living microorganisms (FLM) in the surrounding water. In this study, we investigated microbial succession and network interactions in experiments simulating POM sinking in the ocean. Diatom-derived 13C- and 12C-labeled POM were used to incubate surface water microbial communities from the East China Sea (ECS) under pressure (temperature) of 0.1 (25 °C), 20 (4 °C), and 40 (4 °C) MPa (megapascal). Our results show that the diversity and species richness of the PAM and FLM communities decreased significantly with HHP and LT. Microbial community analysis indicated an increase in the relative abundance of Bacteroidetes at high pressure (40 MPa), mostly at the expense of Gammaproteobacteria, Alphaproteobacteria, and Gracilibacteria at atmospheric pressure. Hydrostatic pressure and temperature affected lifestyle preferences between particle-attached (PA) and free-living (FL) microbes. Ecological network analysis showed that HHP and LT enhanced microbial network interactions and resulted in higher vulnerability to networks of the PAM communities and more resilience of those of the FLM communities. Most interestingly, the PAM communities occupied most of the module hubs of the networks, whereas the FLM communities mainly served as connectors of the modules, suggesting their different ecological roles of the two groups of microbes. These results provided novel insights into how HHP and LT affected microbial community dynamics, ecological networks during POM sinking, and the implications for carbon cycling in the ocean.

Carbon Fluxes in the Coastal Ocean: Synthesis, Boundary Processes, and Future Trends

This review examines the current understanding of the global coastal ocean carbon cycle and provides a new quantitative synthesis of air-sea CO2 exchange. This reanalysis yields an estimate for the globally integrated coastal ocean CO2 flux of −0.25 ± 0.05 Pg C year−1, with polar and subpolar regions accounting for most of the CO2 removal (&gt;90%). A framework that classifies river-dominated ocean margin (RiOMar) and ocean-dominated margin (OceMar) systems is used to conceptualizecoastal carbon cycle processes. The carbon dynamics in three contrasting case study regions, the Baltic Sea, the Mid-Atlantic Bight, and the South China Sea, are compared in terms of the spatio-temporal variability of surface pCO2. Ocean carbon models that range from box models to three-dimensional coupled circulation-biogeochemical models are reviewed in terms of the ability to simulate key processes and project future changes in different continental shelf regions. Common unresolved challenges remain for implementation of these models across RiOMar and OceMar systems. The long-term trends in coastal ocean carbon fluxes for different coastal systems under anthropogenic stress that are emerging in observations and numerical simulations are highlighted. Knowledge gaps in projecting future perturbations associated with before and after net-zero CO2 emissions in the context of concurrent changes in the land-ocean-atmosphere coupled system pose a key challenge. ▪ A new synthesis yields an estimate for a globally integrated coastal ocean carbon sink of −0.25 Pg C year−1, with greater than 90% of atmospheric CO2 removal occurring in polar and subpolar regions. ▪ The sustained coastal and open ocean carbon sink is vital in mitigating climate change and meeting the target set by the Paris Agreement. ▪ Uncertainties in the future coastal ocean carbon cycle are associated with concurrent trends and changes in the land-ocean-atmosphere coupled system. ▪ The major gaps and challenges identified for current coastal ocean carbon research have important implications for climate and sustainability policies.

Reconstructing the Preindustrial Coastal Carbon Cycle Through a Global Ocean Circulation Model: Was the Global Continental Shelf Already Both Autotrophic and a CO2 Sink?

AbstractThe contribution of continental shelves to the marine carbon cycle is still poorly understood. Their preindustrial state is, for one, essentially unknown, which strongly limits the quantitative assessment of their anthropogenic perturbation. To date, approaches developed to investigate and quantify carbon fluxes on continental shelves have strongly simplified their physical and biogeochemical features. In this study, we enhance the global ocean biogeochemistry model HAMburg Ocean Carbon Cycle by explicitly representing riverine loads of carbon and nutrients, as well as improving the representation of organic matter dynamics in the coastal ocean. Our simulations, at a resolution of ∼0.4°, reveal a globally averaged shelf water residence time (RT) of 12–17 months, which is much shorter than the global RTs previously assumed in benchmark studies (&gt;4 years). This shorter global RT, induced primarily through outer shelf regions with large oceanic inflows, promotes an efficient offshore transport of terrestrial and marine organic carbon (0.44 PgCyr−1) and a dissolved inorganic carbon sink from the organic cycling of carbon on the global shelf (net ecosystem productivity [NEP] equal to +0.20 PgCyr−1). In turn, this autotrophic state of continental shelves contributes to a weak global preindustrial sink of atmospheric CO2 (0.04 PgCyr−1), dominated by extensive regions with large oceanic inflows and positive NEPs, such as the Patagonian shelf, the East China Sea and the outer North Sea. The contemporary global shelf CO2 uptake of 0.15 PgCyr−1 furthermoresuggests that the anthropogenic CO2 uptake (0.11 PgCyr−1) on the global continental shelf is less efficient with respect to the open ocean.

Niche partitioning of low‐light adapted Prochlorococcus subecotypes across oceanographic gradients of the North Pacific Subtropical Front

AbstractThe cyanobacterium Prochlorococcus is the most abundant photosynthetic cell on Earth and contributes to global ocean carbon cycling and food webs. Prochlorococcus is known for its extensive diversity that falls into two groups of ecotypes, the low‐light (LL) and high‐light (HL) adapted ecotypes. Previous work has shown niche partitioning of the very abundant HL adapted ecotypes and subecotypes across oceanographic gradients including temperature, nutrients, and day length. However, niche partitioning of subecotypes within the LL adapted ecotypes has not been studied as well because they are less abundant and less accessible than surface, HL populations. Through high‐throughput, cyanobacterial‐specific, sequencing of high‐resolution depth profiles from three stations across the North Pacific Subtropical Front, we discovered extensive diversity and strong latitudinal partitioning of subecotype populations within LL ecotypes, in contrast to fairly consistent patterns at the broader level of LL Prochlorococcus ecotypes. These results indicate that both shallow and deep‐water processes shape microbial community structure in this region despite strong connectivity in microbial communities across the North Pacific Subtropical Front, supported by a few HL and LL sequences found ubiquitously across stations. This study expands understanding of the diversity and complexity of Prochlorococcus community structure over oceanographic gradients.

Toward high-performance SPAD arrays for space-based atmosphere and ocean profiling LiDARs

Space-based light detection and ranging (LiDAR) sensors have provided valuable insight into the global, vertical distribution of aerosol and cloud layers in Earth’s atmosphere, and, more recently, of the distribution of phytoplankton in the ocean. However, the photodetectors in these sensors lack the performance necessary to capture the vertical structure of cloud tops and ocean phytoplankton to a fidelity sufficient for advancing our understanding of the global water cycle and ocean carbon cycle, respectively. Recent advancements in high-performance single photon avalanche diode (SPAD) arrays promise to enable these measurements, while also offering a sensitivity that will allow significant reductions in laser power and telescope size, with associated sensor-level size, weight, and power (SWaP) savings. To harness the unique benefits of SPADs for these measurements, we propose to develop a large-format array of photon counting SPADs with <10 ns dead time, along with readout integrated circuitry that sums and bins (histograms) photon counts in real time to the desired temporal resolution for the target application. The feasibility of this approach has been investigated with a small-scale 8 × 8 SPAD array proof of concept hardware demonstration developed at Politecnico di Milano, with promising initial results. Progress is reported on designs that will allow scaling the array and readout integrated circuit electronics to the requisite of 128 × 128 size in a chip-scale, low power, photodetector ideal for LiDAR remote sensing of the atmosphere and ocean from SWaP-constrained platforms.

Marine carbon and sulfur cycling across the Ediacaran-Cambrian boundary in Tarim Block and its implications for paleoenvironmental changes

The Ediacaran-Cambrian transition was a critical period of Earth history because of the coincidence among climate change, bioevolution, and tectonic activity. The nature of carbon and sulfur-cycling processes spanning the Ediacaran-Cambrian boundary remains debatable. This study considers high-resolution stable isotope chemostratigraphy of the Sugetbrak and Yutixi sections of the Keping area of the Tarim Basin, northwestern China. The sections display a pronounced negative δ13Ccarb shift (the Basal Cambrian Carbon Isotope Excursion) that spans the Ediacaran-Cambrian boundary. Deposits overlying the negative excursion are characterized by heavier δ13Ccarb values ranging from −7.8‰ to +4.1‰. The biostratigraphically calibrated δ13Ccarb profiles likely reflect widespread perturbation of the global oceanic carbon cycle and associated mass extinction and subsequent biological evolution. The δ34Spy profiles of the studied sections display a wide distribution of values, ranging from −16.4‰ to 23.8‰. The variable δ34Spy signatures of these sections are partially controlled by microbial sulfate reduction rates, which are sensitive to local sulfate concentration, availability of organic matter and the marine redox state. The strong coupling between enhanced organic carbon and pyrite burial, the appearance of Cambrian-type animals and a shift to a more oxidizing marine environment might suggest a temporal causality of higher oxygen levels of the atmosphere-ocean realm with bio-events during the Ediacaran–Cambrian transition. The development of ecosystems across the Ediacaran–Cambrian transition may have significantly impacted the oceanic carbon and sulfur biogeochemical processes in Earth's history and, in turn, affecting themselves.

Evaluation of a New Carbon Dioxide System for Autonomous Surface Vehicles

AbstractCurrent carbon measurement strategies leave spatiotemporal gaps that hinder the scientific understanding of the oceanic carbon biogeochemical cycle. Data products and models are subject to bias because they rely on data that inadequately capture mesoscale spatiotemporal (kilometers and days to weeks) changes. High-resolution measurement strategies need to be implemented to adequately evaluate the global ocean carbon cycle. To augment the spatial and temporal coverage of ocean–atmosphere carbon measurements, an Autonomous Surface Vehicle CO2 (ASVCO2) system was developed. From 2011 to 2018, ASVCO2 systems were deployed on seven Wave Glider and Saildrone missions along the U.S. Pacific and Australia’s Tasmanian coastlines and in the tropical Pacific Ocean to evaluate the viability of the sensors and their applicability to carbon cycle research. Here we illustrate that the ASVCO2 systems are capable of long-term oceanic deployment and robust collection of air and seawater pCO2 within ±2 μatm based on comparisons with established shipboard underway systems, with previously described Moored Autonomous pCO2 (MAPCO2) systems, and with companion ASVCO2 systems deployed side by side.

Carbon Export Buffering and CO2 Drawdown by Flexible Phytoplankton C:N:P Under Glacial Conditions

AbstractModern observations indicate that variations in marine phytoplankton stoichiometry correlate with the boundaries of major surface waters. For example, phytoplankton in the oligotrophic subtropical gyres typically have much higher C:N:P ratios (i.e., higher C:P and higher N:P ratios) than those in eutrophic upwelling regions and polar regions. Such a spatial pattern points to nutrient availability as a key environmental driver of stochiometric flexibility. Environmental dependence of phytoplankton C:N:P opens unexplored possibilities for modifying the strength of the biological pump under different climate conditions. Here we present a power law formulation of C:N:P flexibility that is driven by nutrients, temperature, and light. We embed the formulation in a global ocean carbon cycle model with multiple phytoplankton types and explore biogeochemical implications under glacial conditions. We find three key controls on export C:N:P ratio: phytoplankton physiology and community structure as well as the balance in regional production at the global level. Glacial inputs of iron and sea ice expansion are important modifiers of these three controls. We also find that global export C:N:P increases substantially under glacial conditions, and this strongly buffers global carbon export against decrease and draws down approximately 20 μatm of atmospheric CO2. These results point to the importance of including phytoplankton C:N:P flexibility in a mix of mechanisms that drive atmospheric CO2 over glacial‐interglacial time scale. Finally, our simulations indicate decoupling of nutrients, which may provide a resolution to the longstanding disagreement regarding nutrient utilization in the glacial Southern Ocean derived from different nutrient proxies.

Microstructure and composition of marine aggregates as co-determinants for vertical particulate organic carbon transfer in the global ocean

Abstract. Marine aggregates are the vector for biogenically bound carbon and nutrients from the euphotic zone to the interior of the oceans. To improve the representation of this biological carbon pump in the global biogeochemical HAMburg Ocean Carbon Cycle (HAMOCC) model, we implemented a novel Microstructure, Multiscale, Mechanistic, Marine Aggregates in the Global Ocean (M4AGO) sinking scheme. M4AGO explicitly represents the size, microstructure, heterogeneous composition, density and porosity of aggregates and ties ballasting mineral and particulate organic carbon (POC) fluxes together. Additionally, we incorporated temperature-dependent remineralization of POC. We compare M4AGO with the standard HAMOCC version, where POC fluxes follow a Martin curve approach with (i) linearly increasing sinking velocity with depth and (ii) temperature-independent remineralization. Minerals descend separately with a constant speed. In contrast to the standard HAMOCC, M4AGO reproduces the latitudinal pattern of POC transfer efficiency, as recently constrained by Weber et al. (2016). High latitudes show transfer efficiencies of ≈0.25±0.04, and the subtropical gyres show lower values of about 0.10±0.03. In addition to temperature as a driving factor for remineralization, diatom frustule size co-determines POC fluxes in silicifier-dominated ocean regions, while calcium carbonate enhances the aggregate excess density and thus sinking velocity in subtropical gyres. Prescribing rising carbon dioxide (CO2) concentrations in stand-alone runs (without climate feedback), M4AGO alters the regional ocean atmosphere CO2 fluxes compared to the standard model. M4AGO exhibits higher CO2 uptake in the Southern Ocean compared to the standard run, while in subtropical gyres, less CO2 is taken up. Overall, the global oceanic CO2 uptake remains the same. With the explicit representation of measurable aggregate properties, M4AGO can serve as a test bed for evaluating the impact of aggregate-associated processes on global biogeochemical cycles and, in particular, on the biological carbon pump.