Surface Ocean CO2 Research Articles

A mapped dataset of surface ocean acidification indicators in large marine ecosystems of the United States

Mapped monthly data products of surface ocean acidification indicators from 1998 to 2022 on a 0.25° by 0.25° spatial grid have been developed for eleven U.S. large marine ecosystems (LMEs). The data products were constructed using observations from the Surface Ocean CO2 Atlas, co-located surface ocean properties, and two types of machine learning algorithms: Gaussian mixture models to organize LMEs into clusters of similar environmental variability and random forest regressions (RFRs) that were trained and applied within each cluster to spatiotemporally interpolate the observational data. The data products, called RFR-LMEs, have been averaged into regional timeseries to summarize the status of ocean acidification in U.S. coastal waters, showing a domain-wide carbon dioxide partial pressure increase of 1.4 ± 0.4 μatm yr−1 and pH decrease of 0.0014 ± 0.0004 yr−1. RFR-LMEs have been evaluated via comparisons to discrete shipboard data, fixed timeseries, and other mapped surface ocean carbon chemistry data products. Regionally averaged timeseries of RFR-LME indicators are provided online through the NOAA National Marine Ecosystem Status web portal.

Accuracy of Ocean CO2 Uptake Estimates at a Risk by a Reduction in the Data Collection

AbstractObservation‐based quantification of ocean carbon dioxide (CO2) uptake relies on synthesis data sets such as the Surface Ocean CO2 ATlas (SOCAT). However, the data collection effort has dramatically declined and the number of annual data sets in SOCATv2023 decreased by ∼35% from 2017 to 2021. This decline has led to a 65% increase (from 0.15 to 0.25 Pg C yr−1) in the standard deviation of seven SOCAT‐based air‐sea CO2 flux estimates. Reducing the availability of the annual data to that in the year 2000 creates substantial bias (50%) in the long‐term flux trend. The annual mean CO2 flux is insensitive to the seasonal skew of the SOCAT data and to the addition of the lower accuracy data set available in SOCAT. Our study highlights the need for sustained data collection and synthesis, to inform the Global Carbon Budget assessment, the UN‐led climate negotiations, and measurement, reporting, and verification of ocean‐based CO2 removal projects.

Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling

Abstract. The Southern Ocean plays an important role in the exchange of carbon between the atmosphere and oceans and is a critical region for the ocean uptake of anthropogenic CO2. However, estimates of the Southern Ocean air–sea CO2 flux are highly uncertain due to limited data coverage. Increased sampling in winter and across meridional gradients in the Southern Ocean may improve machine learning (ML) reconstructions of global surface ocean pCO2. Here, we use a large ensemble test bed (LET) of Earth system models and the “pCO2-Residual” reconstruction method to assess improvements in pCO2 reconstruction fidelity that could be achieved with additional autonomous sampling in the Southern Ocean added to existing Surface Ocean CO2 Atlas (SOCAT) observations. The LET allows for a robust evaluation of the skill of pCO2 reconstructions in space and time through comparison to “model truth”. With only SOCAT sampling, Southern Ocean and global pCO2 are overestimated, and thus the ocean carbon sink is underestimated. Incorporating uncrewed surface vehicle (USV) sampling increases the spatial and seasonal coverage of observations within the Southern Ocean, leading to a decrease in the overestimation of pCO2. A modest number of additional observations in Southern Hemisphere winter and across meridional gradients in the Southern Ocean leads to an improvement in reconstruction bias and root-mean-squared error (RMSE) of as much as 86 % and 16 %, respectively, as compared to SOCAT sampling alone. Lastly, the large decadal variability of air–sea CO2 fluxes shown by SOCAT-only sampling may be partially attributable to undersampling of the Southern Ocean.

Global Analysis of Surface Ocean CO2 Fugacity and Air‐Sea Fluxes With Low Latency

AbstractThe Surface Ocean CO2 Atlas (SOCAT) of CO2 fugacity (fCO2) observations is a key resource supporting annual assessments of CO2 uptake by the ocean and its side effects on the marine ecosystem. SOCAT data are usually released with a lag of up to 1.5 years which hampers timely quantification of recent variations of carbon fluxes between the Earth System components, not only with the ocean. This study uses a statistical ensemble approach to analyze fCO2 with a latency of one month only based on the previous SOCAT release and a series of predictors. Results indicate a modest degradation in a retrospective prediction test for 2021–2022. The generated fCO2 and fluxes for January–August 2023 show a progressive reduction in the Equatorial Pacific source following the La Niña retreat. A breaking‐record decrease in the northeastern Atlantic CO2 sink has been diagnosed on account of the marine heatwave event in June 2023.

Synthesis Product for Ocean Time Series (SPOTS) – a ship-based biogeochemical pilot

Abstract. The presented pilot for the Synthesis Product for Ocean Time Series (SPOTS) includes data from 12 fixed ship-based time-series programs. The related stations represent unique open-ocean and coastal marine environments within the Atlantic Ocean, Pacific Ocean, Mediterranean Sea, Nordic Seas, and Caribbean Sea. The focus of the pilot has been placed on biogeochemical essential ocean variables: dissolved oxygen, dissolved inorganic nutrients, inorganic carbon (pH, total alkalinity, dissolved inorganic carbon, and partial pressure of CO2), particulate matter, and dissolved organic carbon. The time series used include a variety of temporal resolutions (monthly, seasonal, or irregular), time ranges (10–36 years), and bottom depths (80–6000 m), with the oldest samples dating back to 1983 and the most recent one corresponding to 2021. Besides having been harmonized into the same format (semantics, ancillary data, units), the data were subjected to a qualitative assessment in which the applied methods were evaluated and categorized. The most recently applied methods of the time-series programs usually follow the recommendations outlined by the Bermuda Time Series Workshop report (Lorenzoni and Benway, 2013), which is used as the main reference for “method recommendations by prevalent initiatives in the field”. However, measurements of dissolved oxygen and pH, in particular, still show room for improvement. Additional data quality descriptors include precision and accuracy estimates, indicators for data variability, and offsets compared to a reference and widely recognized data product for the global ocean: the GLobal Ocean Data Analysis Project (GLODAP). Generally, these descriptors indicate a high level of continuity in measurement quality within time-series programs and a good consistency with the GLODAP data product, even though robust comparisons to the latter are limited. The data are available as (i) a merged comma-separated file that is compliant with the World Ocean Circulation Experiment (WOCE) exchange format and (ii) a format dependent on user queries via the Environmental Research Division's Data Access Program (ERDDAP) server of the Global Ocean Observing System (GOOS). The pilot increases the data utility, findability, accessibility, interoperability, and reusability following the FAIR philosophy, enhancing the readiness of biogeochemical time series. It facilitates a variety of applications that benefit from the collective value of biogeochemical time-series observations and forms the basis for a sustained time-series living data product, SPOTS, complementing relevant products for the global interior ocean carbon data (GLobal Ocean Data Analysis Project), global surface ocean carbon data (Surface Ocean CO2 Atlas; SOCAT), and global interior and surface methane and nitrous oxide data (MarinE MethanE and NiTrous Oxide product). Aside from the actual data compilation, the pilot project produced suggestions for reporting metadata, implementing quality control measures, and making estimations about uncertainty. These recommendations aim to encourage the community to adopt more consistent and uniform practices for analysis and reporting and to update these practices regularly. The detailed recommendations, links to the original time-series programs, the original data, their documentation, and related efforts are available on the SPOTS website. This site also provides access to the data product (DOI: https://doi.org/10.26008/1912/bco-dmo.896862.2, Lange et al., 2024) and ancillary data.

CO2 sink and source zones delimited by marine fronts in the Drake Passage

Net sea-air CO2 fluxes (FCO2) in the Drake Passage (DP) were studied at a climatological scale (1999–2019) using observations from the Surface Ocean CO2 Atlas (SOCAT) database. Based on the monthly climatological position of the main circumpolar fronts of the DP (the Subantarctic Front (SAF), the Polar Front (PF) and the Southern Antarctic Circumpolar Current Front (SACCF)) and the thermal and nonthermal contributions to FCO2, we present a regional subdivision into different regimes that provide new insights into the processes controlling these fluxes. Our results indicate that the region in the north of SAF (R1) behaves as an annual CO2 sink (-1.3 ± 1.0 mmol m−2 d−1); this sink weakens between SAF-PF (R2) and PF-SACCF (R3) and the region south of SACCF (R4) acts as an annual CO2 source (2.2 ± 3.3 mmol m−2 d−1). The annual mean CO2 uptake in DP is 1.3 ± 15.5 Tg C yr-1. Analysis of thermal (TE) and nonthermal (nonTE) effects on seasonal sea surface CO2 partial pressure (pCO2sw) variability indicates that DP is mainly dominated by nonTE. Results emphasize that carbon fluxes are driven by mesoscale and submesoscale processes north of the PF and by the upwelling of Upper Circumpolar Deep Waters in the Antarctic boundary of the DP, while seasonal patterns are mostly modulated by local factors such as nutrient availability, biological activity and ice cover.

Technical note: Assessment of float pH data quality control methods – a case study in the subpolar northwest Atlantic Ocean

Abstract. Since a pH sensor has become available that is principally suitable for use on demanding autonomous measurement platforms, the marine CO2 system can be observed independently and continuously by Biogeochemical Argo floats. This opens the potential to detect variability and long-term changes in interior ocean inorganic carbon storage and quantify the ocean sink for atmospheric CO2. In combination with a second parameter of the marine CO2 system, pH can be a useful tool to derive the surface ocean CO2 partial pressure (pCO2). The large spatiotemporal variability in the marine CO2 system requires sustained observations to decipher trends and study the impacts of short-term events (e.g., eddies, storms, phytoplankton blooms) but also puts a high emphasis on the quality control of float-based pH measurements. In consequence, a consistent and rigorous quality control procedure is being established to correct sensor offsets or drifts as the interpretation of changes depends on accurate data. By applying current standardized routines of the Argo data management to pH measurements from a pH / O2 float pilot array in the subpolar North Atlantic Ocean, we assess the uncertainties and lack of objective criteria associated with the standardized routines, notably the choice of the reference method for the pH correction (CANYON-B, LIR-pH, ESPER-NN, and ESPER-LIR) and the reference depth for this adjustment. For the studied float array, significant differences ranging between ca. 0.003 pH units and ca. 0.04 pH units are observed between the four reference methods which have been proposed to correct float pH data. Through comparison against discrete and underway pH data from other platforms, an assessment of the adjusted float pH data quality is presented. The results point out noticeable discrepancies near the surface of > 0.004 pH units. In the context of converting surface ocean pH measurements into pCO2 data for the purpose of deriving air–sea CO2 fluxes, we conclude that an accuracy requirement of 0.01 pH units (equivalent to a pCO2 accuracy of 10 µatm as a minimum requirement for potential future inclusion in the Surface Ocean CO2 Atlas, SOCAT, database) is not systematically achieved in the upper ocean. While the limited dataset and regional focus of our study do not allow for firm conclusions, the evidence presented still calls for the inclusion of an additional independent pH reference in the surface ocean in the quality control routines. We therefore propose a way forward to enhance the float pH quality control procedure. In our analysis, the current philosophy of pH data correction against climatological reference data at one single depth in the deep ocean appears insufficient to assure adequate data quality in the surface ocean. Ideally, an additional reference point should be taken at or near the surface where the resulting pCO2 data are of the highest importance to monitor the air–sea exchange of CO2 and would have the potential to very significantly augment the impact of the current observation network.

A carbonate system time series in the Eastern Mediterranean Sea. Two years of high-frequency in-situ observations and remote sensing

The rate of ocean uptake of anthropogenic CO2 has declined over the past decade, so a critical question for science and policy is whether the ocean will continue to act as a sink. Large areas of the ocean remain without observations for carbonate system variables, and oceanic CO2 observations have declined since 2017. The Mediterranean Sea is one such an area, especially its eastern part, where there is a paucity of carbonate system data, with large areas not sampled or only sampled by ship-based discrete measurements as opposed to high frequency, sensor-equipped time-series fixed stations. The aim of this study was to analyze a multi-year time-series of high-frequency (hourly) partial pressure CO2 (pCO2) and pH measurements in the Eastern Mediterranean, along with low-frequency (monthly) measurements of total dissolved inorganic carbon and total alkalinity. The pCO2 time-series was the first obtained in the Eastern Mediterranean. The study was conducted at a fixed platform of the POSEIDON system (Heraklion Coastal Buoy) located near Crete Island. Temperature was the dominant factor controlling the temporal variability of pCO2 and pH, while the remaining non-thermal variability appeared to be related to evaporation, water mixing, and biological remineralization-production. The air-sea CO2 fluxes indicated a transition from a winter-spring sink period to a summer-autumn source period. The annual air-sea CO2 flux was too low (-0.16 ± 0.02 mol m-2 yr-1) and variable to conclusively characterize the area as a net source or sink of CO2, highlighting the need for additional high frequency observation sites. Algorithms were developed using temperature, chlorophyll and salinity data to estimate pCO2 and total alkalinity, in an effort to provide tools for estimates in poorly observed areas/periods from remotely sensed products. The applicability of the algorithms was tested using Surface Ocean CO2 Atlas (SOCAT) data from the Eastern Mediterranean Sea (1999 to 2020) which showed that the algorithm pCO2 estimates were generally within ±20 μatm of the pCO2 values reported by SOCAT. Finally, the integration and analysis of the data provided directions on how to optimize the observing strategy, by readapting sensor location and using estimation algorithms with remote sensing data.

A novel sea surface pCO2-product for the global coastal ocean resolving trends over 1982–2020

Abstract. In recent years, advancements in machine learning based interpolation methods have enabled the production of high-resolution maps of sea surface partial pressure of CO2 (pCO2) derived from observations extracted from databases such as the Surface Ocean CO2 Atlas (SOCAT). These pCO2-products now allow quantifying the oceanic air–sea CO2 exchange based on observations. However, most of them do not yet explicitly include the coastal ocean. Instead, they simply extend the open ocean values onto the nearshore shallow waters, or their spatial resolution is simply so coarse that they do not accurately capture the highly heterogeneous spatiotemporal pCO2 dynamics of coastal zones. Until today, only one global pCO2-product has been specifically designed for the coastal ocean (Laruelle et al., 2017). This product, however, has shortcomings because it only provides a climatology covering a relatively short period (1998–2015), thus hindering its application to the evaluation of the interannual variability, decadal changes and the long-term trends of the coastal air–sea CO2 exchange, a temporal evolution that is still poorly understood and highly debated. Here we aim at closing this knowledge gap and update the coastal product of Laruelle et al. (2017) to investigate the longest global monthly time series available for the coastal ocean from 1982 to 2020. The method remains based on a two-step Self-Organizing Maps and Feed-Forward Network method adapted for coastal regions, but we include additional environmental predictors and use a larger pool of training and validation data with ∼18 million direct observations extracted from the latest release of the SOCAT database. Our study reveals that the coastal ocean has been acting as an atmospheric CO2 sink of −0.40 Pg C yr−1 (−0.18 Pg C yr−1 with a narrower coastal domain) on average since 1982, and the intensity of this sink has increased at a rate of 0.06 Pg C yr−1 decade−1 (0.02 Pg C yr−1 decade−1 with a narrower coastal domain) over time. Our results also show that the temporal changes in the air–sea pCO2 gradient plays a significant role in the long-term evolution of the coastal CO2 sink, along with wind speed and sea-ice coverage changes that can also play an important role in some regions, particularly at high latitudes. This new reconstructed coastal pCO2-product (https://doi.org/10.25921/4sde-p068; Roobaert et al., 2023) allows us to establish regional carbon budgets requiring high-resolution coastal flux estimates and provides new constraints for closing the global carbon cycle.

CMEMS-LSCE: a global, 0.25°, monthly reconstruction of the surface ocean carbonate system

Abstract. Observation-based data reconstructions of global surface ocean carbonate system variables play an essential role in monitoring the recent status of ocean carbon uptake and ocean acidification, as well as their impacts on marine organisms and ecosystems. So far, ongoing efforts are directed towards exploring new approaches to describe the complete marine carbonate system and to better recover its fine-scale features. In this respect, our research activities within the Copernicus Marine Environment Monitoring Service (CMEMS) aim to develop a sustainable production chain of observation-derived global ocean carbonate system datasets at high space–time resolutions. As the start of the long-term objective, this study introduces a new global 0.25∘ monthly reconstruction, namely CMEMS-LSCE (Laboratoire des Sciences du Climat et de l'Environnement) for the period 1985–2021. The CMEMS-LSCE reconstruction derives datasets of six carbonate system variables, including surface ocean partial pressure of CO2 (pCO2), total alkalinity (AT), total dissolved inorganic carbon (CT), surface ocean pH, and saturation states with respect to aragonite (Ωar) and calcite (Ωca). Reconstructing pCO2 relies on an ensemble of neural network models mapping gridded observation-based data provided by the Surface Ocean CO2 ATlas (SOCAT). Surface ocean AT is estimated with a multiple-linear-regression approach, and the remaining carbonate variables are resolved by CO2 system speciation given the reconstructed pCO2 and AT; 1σ uncertainty associated with these estimates is also provided. Here, σ stands for either the ensemble standard deviation of pCO2 estimates or the total uncertainty for each of the five other variables propagated through the processing chain with input data uncertainty. We demonstrate that the 0.25∘ resolution pCO2 product outperforms a coarser spatial resolution (1∘) thanks to higher data coverage nearshore and a better description of horizontal and temporal variations in pCO2 across diverse ocean basins, particularly in the coastal–open-ocean continuum. Product qualification with observation-based data confirms reliable reconstructions with root-mean-square deviation from observations of less than 8 %, 4 %, and 1 % relative to the global mean of pCO2, AT (CT), and pH. The global average 1σ uncertainty is below 5 % and 8 % for pCO2 and Ωar (Ωca), 2 % for AT and CT, and 0.4 % for pH relative to their global mean values. Both model–observation misfit and model uncertainty indicate that coastal data reproduction still needs further improvement, wherein high temporal and horizontal gradients of carbonate variables and representative uncertainty from data sampling would be taken into account as a priority. This study also presents a potential use case of the CMEMS-LSCE carbonate data product in tracking the recent state of ocean acidification. The data associated with this study are available at https://doi.org/10.14768/a2f0891b-763a-49e9-af1b-78ed78b16982 (Chau et al., 2023).

Estimating marine carbon uptake in the northeast Pacific using a neural network approach

Abstract. The global ocean takes up nearly a quarter of anthropogenic CO2 emissions annually, but the variability in this uptake at regional scales remains poorly understood. Here we use a neural network approach to interpolate sparse observations, creating a monthly gridded seawater partial pressure of CO2 (pCO2) data product from January 1998 to December 2019, at 1/12∘ × 1/12∘ spatial resolution, in the northeast Pacific open ocean, a net sink region. The data product (ANN-NEP; NCEI Accession 0277836) was created from pCO2 observations within the 2021 version of the Surface Ocean CO2 Atlas (SOCAT) and a range of predictor variables acting as proxies for processes affecting pCO2 to create nonlinear relationships to interpolate observations at a spatial resolution 4 times greater than leading global products and with better overall performance. In moving to a higher resolution, we show that the internal division of training data is the most important parameter for reducing overfitting. Using our pCO2 product, wind speed, and atmospheric CO2, we evaluate air–sea CO2 flux variability. On sub-decadal to decadal timescales, we find that the upwelling strength of the subpolar Alaskan Gyre, driven by large-scale atmospheric forcing, acts as the primary control on air–sea CO2 flux variability (r2=0.93, p<0.01). In the northern part of our study region, divergence from atmospheric CO2 is enhanced by increased local wind stress curl, enhancing upwelling and entrainment of naturally CO2-rich subsurface waters, leading to decade-long intervals of strong winter outgassing. During recent Pacific marine heat waves from 2013 on, we find enhanced atmospheric CO2 uptake (by as much as 45 %) due to limited wintertime entrainment. Our product estimates long-term surface ocean pCO2 increase at a rate below the atmospheric trend (1.4 ± 0.1 µatm yr−1) with the slowest increase in the center of the subpolar gyre where there is strong interaction with subsurface waters. This mismatch suggests the northeast Pacific Ocean sink for atmospheric CO2 may be increasing.

Disentangling the impact of Atlantic Niño on sea-air CO2 flux

Atlantic Niño is a major tropical interannual climate variability mode of the sea surface temperature (SST) that occurs during boreal summer and shares many similarities with the tropical Pacific El Niño. Although the tropical Atlantic is an important source of CO2 to the atmosphere, the impact of Atlantic Niño on the sea-air CO2 exchange is not well understood. Here we show that the Atlantic Niño enhances (weakens) CO2 outgassing in the central (western) tropical Atlantic. In the western basin, freshwater-induced changes in surface salinity, which considerably modulate the surface ocean CO2 partial pressure (pCO2), are the primary driver for the observed CO2 flux variations. In contrast, pCO2 anomalies in the central basin are dominated by the SST-driven solubility change. This multi-variable mechanism for pCO2 anomaly differs remarkably from the Pacific where the response is predominantly controlled by upwelling-induced dissolved inorganic carbon anomalies. The contrasting behavior is characterized by the high CO2 buffering capacity in the Atlantic, where the subsurface water mass contains higher alkalinity than in the Pacific.

High interannual surface pCO2 variability in the southern Canadian Arctic Archipelago's Kitikmeot Sea

Abstract. Warming of the Arctic due to climate change means the Arctic Ocean is now free from ice for longer, as sea ice melts earlier and refreezes later. Yet, it remains unclear how this extended ice-free period will impact carbon dioxide (CO2) fluxes due to scarcity of surface ocean CO2 measurements. Baseline measurements are urgently needed to understand spatial and temporal air–sea CO2 flux variability in the changing Arctic Ocean. There is also uncertainty as to whether the previous basin-wide surveys are representative of the many smaller bays and inlets that make up the Canadian Arctic Archipelago (CAA). By using a research vessel that is based in the remote Inuit community of Ikaluqtuutiak (Cambridge Bay, Nunavut), we have been able to reliably survey pCO2 shortly after ice melt and access previously unsampled bays and inlets in the nearby region. Here we present 4 years of consecutive summertime pCO2 measurements collected in the Kitikmeot Sea in the southern CAA. Overall, we found that this region is a sink for atmospheric CO2 in August (average of all calculated fluxes over the four cruises was −4.64 mmol m−2 d−1), but the magnitude of this sink varies substantially between years and locations (average calculated fluxes of +3.58, −2.96, −16.79 and −0.57 mmol m−2 d−1 during the 2016, 2017, 2018 and 2019 cruises, respectively). Surface ocean pCO2 varied by up to 156 µatm between years, highlighting the importance of repeat observations in this region, as this high interannual variability would not have been captured by sparse and infrequent measurements. We find that the surface ocean pCO2 value at the time of ice melt is extremely important in constraining the magnitude of the air–sea CO2 flux throughout the ice-free season. However, further constraining the air–sea CO2 flux in the Kitikmeot Sea will require a better understanding of how pCO2 changes outside of the summer season. Surface ocean pCO2 measurements made in small bays and inlets of the Kitikmeot Sea were ∼ 20–40 µatm lower than in the main channels. Surface ocean pCO2 measurements made close in time to ice breakup (i.e. within 2 weeks) were ∼ 50 µatm lower than measurements made > 4 weeks after breakup. As previous basin-wide surveys of the CAA have focused on the deep shipping channels and rarely measure close to the ice breakup date, we hypothesize that there may be an observational bias in previous studies, leading to an underestimate of the CO2 sink in the CAA. These high-resolution measurements constitute an important new baseline for gaining a better understanding of the role this region plays in the uptake of atmospheric CO2.

A status assessment of selected data synthesis products for ocean biogeochemistry

Ocean data synthesis products for specific biogeochemical essential ocean variables have the potential to facilitate today’s biogeochemical ocean data usage and comply with the Findable Accessible Interoperable and Reusable (FAIR) data principles. The products constitute key outputs from the Global Ocean Observation System, laying the observational foundation for information and services regarding climate and environmental status of the ocean. Using the Framework of Ocean Observing (FOO) readiness level concept, we present an evaluation framework for biogeochemical data synthesis products, which enables a systematic assessment of each product’s maturity. A new criteria catalog provides the foundation for assigning scores to the nine FOO readiness levels. As an example, we apply the assessment to four existing biogeochemical essential ocean variables data products. In descending readiness level order these are: The Surface Ocean CO2 Atlas (SOCAT); the Global Ocean Data Analysis Project (GLODAP); the MarinE MethanE and NiTrous Oxide (MEMENTO) data product and the Global Ocean Oxygen Database and ATlas (GO2DAT). Recognizing that the importance of adequate and comprehensive data from the essential ocean variables will grow, we recommend using this assessment framework to guide the biogeochemical data synthesis activities in their development. Moreover, we envision an overarching cross-platform FAIR biogeochemical data management system that sustainably supports the products individually and creates an integrated biogeochemical essential ocean variables data synthesis product; in short a system that provides truly comparable and FAIR data of the entire biogeochemical essential ocean variables spectrum.

Surface ocean CO2 concentration and air-sea flux estimate by machine learning with modelled variable trends

The global ocean is a major sink of anthropogenic carbon dioxide (CO2) emitted into the atmosphere. Machine learning has been actively used in the past decades to estimate the oceanic sink, but it is still a challenge to obtain an accurate estimate due to scarcely available CO2 measurements. One of the methods to deal with data scarcity was normalizing multiple years’ CO2 values to a reference year to increase the spatial coverage. The practice assumed a constant CO2 trend for the normalization. Here, we used three machine learning models to extract variable ocean CO2 trends on a decadal scale and proposed a method to use the extracted ocean CO2 trends to correct the decadal atmospheric CO2 trends for data normalization. The method minimizes assumptions of using the extracted ocean CO2 trends directly. Comparisons of our CO2 flux estimate with machine learning products included in Global Carbon Budget 2021 indicates that using the variable trends improved the bias resulted from using a constant trend and that the trends are a critical factor for machine learning methods. Our dataset includes monthly distributions of surface ocean CO2 concentration and air-sea flux in 1980-2020 with a spatial resolution of 1×1 degree.

The sensitivity ofpCO2reconstructions to sampling scales across a Southern Ocean sub-domain: a semi-idealized ocean sampling simulation approach

Abstract. The Southern Ocean is a complex system yet is sparsely sampled in both space and time. These factors raise questions about the confidence in present sampling strategies and associated machine learning (ML) reconstructions. Previous studies have not yielded a clear understanding of the origin of uncertainties and biases for the reconstructions of the partial pressure of carbon dioxide (pCO2) at the surface ocean (pCO2ocean). We examine these questions through a series of semi-idealized observing system simulation experiments (OSSEs) using a high-resolution (± 10 km) coupled physical and biogeochemical model (NEMO-PISCES, Nucleus for European Modelling of the Ocean, Pelagic Interactions Scheme for Carbon and Ecosystem Studies). Here we choose 1 year of the model sub-domain of 10∘ of latitude (40–50∘ S) by 20∘ of longitude (10∘ W–10∘ E). This domain is crossed by the sub-Antarctic front and thus includes both the sub-Antarctic zone and the polar frontal zone in the south-east Atlantic Ocean, which are the two most sampled sub-regions of the Southern Ocean. We show that while this sub-domain is small relative to the Southern Ocean scales, it is representative of the scales of variability we aim to examine. The OSSEs simulated the observational scales of pCO2ocean in ways that are comparable to existing ocean CO2 observing platforms (ships, Wave Gliders, carbon floats, Saildrones) in terms of their temporal sampling scales and not necessarily their spatial ones. The pCO2 reconstructions were carried out using a two-member ensemble approach that consisted of two machine learning (ML) methods, (1) the feed-forward neural network and (2) the gradient boosting machines. The baseline data were from the ship-based simulations mimicking ship-based observations from the Surface Ocean CO2 Atlas (SOCAT). For each of the sampling-scale scenarios, we applied the two-member ensemble method to reconstruct the full sub-domain pCO2ocean. The reconstruction skill was then assessed through a statistical comparison of reconstructed pCO2ocean and the model domain mean. The analysis shows that uncertainties and biases for pCO2ocean reconstructions are very sensitive to both the spatial and the temporal scales of pCO2 sampling in the model domain. The four key findings from our investigation are as follows: (1) improving ML-based pCO2 reconstructions in the Southern Ocean requires simultaneous high-resolution observations (<3 d) of the seasonal cycle of the meridional gradients of pCO2ocean; (2) Saildrones stand out as the optimal platforms to simultaneously address these requirements; (3) Wave Gliders with hourly/daily resolution in pseudo-mooring mode improve on carbon floats (10 d period), which suggests that sampling aliases from the 10 d sampling period might have a greater negative impact on their uncertainties, biases, and reconstruction means; and (4) the present seasonal sampling biases (towards summer) in SOCAT data in the Southern Ocean may be behind a significant winter bias in the reconstructed seasonal cycle of pCO2ocean.

A monthly surface <i>p</i>CO<sub>2</sub> product for the California Current Large Marine Ecosystem

Abstract. A common strategy for calculating the direction and rate of carbon dioxide gas (CO2) exchange between the ocean and atmosphere relies on knowledge of the partial pressure of CO2 in surface seawater (pCO2(sw)), a quantity that is frequently observed by autonomous sensors on ships and moored buoys, albeit with significant spatial and temporal gaps. Here we present a monthly gridded data product of pCO2(sw) at 0.25∘ latitude by 0.25∘ longitude resolution in the northeastern Pacific Ocean, centered on the California Current System (CCS) and spanning all months from January 1998 to December 2020. The data product (RFR-CCS; Sharp et al., 2022; https://doi.org/10.5281/zenodo.5523389) was created using observations from the most recent (2021) version of the Surface Ocean CO2 Atlas (Bakker et al., 2016). These observations were fit against a variety of collocated and contemporaneous satellite- and model-derived surface variables using a random forest regression (RFR) model. We validate RFR-CCS in multiple ways, including direct comparisons with observations from sensors on moored buoys, and find that the data product effectively captures seasonal pCO2(sw) cycles at nearshore sites. This result is notable because global gridded pCO2(sw) products do not capture local variability effectively in this region, suggesting that RFR-CCS is a better option than regional extractions from global products to represent pCO2(sw) in the CCS over the last 2 decades. Lessons learned from the construction of RFR-CCS provide insight into how global pCO2(sw) products could effectively characterize seasonal variability in nearshore coastal environments. We briefly review the physical and biological processes – acting across a variety of spatial and temporal scales – that are responsible for the latitudinal and nearshore-to-offshore pCO2(sw) gradients seen in the RFR-CCS reconstruction of pCO2(sw). RFR-CCS will be valuable for the validation of high-resolution models, the attribution of spatiotemporal carbonate system variability to physical and biological drivers, and the quantification of multiyear trends and interannual variability of ocean acidification.

A seamless ensemble-based reconstruction of surface ocean <i>p</i>CO<sub>2</sub> and air–sea CO<sub>2</sub> fluxes over the global coastal and open oceans

Abstract. We have estimated global air–sea CO2 fluxes (fgCO2) from the open ocean to coastal seas. Fluxes and associated uncertainty are computed from an ensemble-based reconstruction of CO2 sea surface partial pressure (pCO2) maps trained with gridded data from the Surface Ocean CO2 Atlas v2020 database. The ensemble mean (which is the best estimate provided by the approach) fits independent data well, and a broad agreement between the spatial distribution of model–data differences and the ensemble standard deviation (which is our model uncertainty estimate) is seen. Ensemble-based uncertainty estimates are denoted by ±1σ. The space–time-varying uncertainty fields identify oceanic regions where improvements in data reconstruction and extensions of the observational network are needed. Poor reconstructions of pCO2 are primarily found over the coasts and/or in regions with sparse observations, while fgCO2 estimates with the largest uncertainty are observed over the open Southern Ocean (44∘ S southward), the subpolar regions, the Indian Ocean gyre, and upwelling systems. Our estimate of the global net sink for the period 1985–2019 is 1.643±0.125 PgC yr−1 including 0.150±0.010 PgC yr−1 for the coastal net sink. Among the ocean basins, the Subtropical Pacific (18–49∘ N) and the Subpolar Atlantic (49–76∘ N) appear to be the strongest CO2 sinks for the open ocean and the coastal ocean, respectively. Based on mean flux density per unit area, the most intense CO2 drawdown is, however, observed over the Arctic (76∘ N poleward) followed by the Subpolar Atlantic and Subtropical Pacific for both open-ocean and coastal sectors. Reconstruction results also show significant changes in the global annual integral of all open- and coastal-ocean CO2 fluxes with a growth rate of +0.062±0.006 PgC yr−2 and a temporal standard deviation of 0.526±0.022 PgC yr−1 over the 35-year period. The link between the large interannual to multi-year variations of the global net sink and the El Niño–Southern Oscillation climate variability is reconfirmed.

The Distribution of pCO2W and Air-Sea CO2 Fluxes Using FFNN at the Continental Shelf Areas of the Arctic Ocean

A feed-forward neural network (FFNN) was used to estimate the monthly climatology of partial pressure of CO2 (pCO2W) at a spatial resolution of 1° latitude by 1° longitude in the continental shelf of the European Arctic Sector (EAS) of the Arctic Ocean (the Greenland, Norwegian, and Barents seas). The predictors of the network were sea surface temperature (SST), sea surface salinity (SSS), the upper ocean mixed-layer depth (MLD), and chlorophyll-a concentration (Chl-a), and as a target, we used 2 853 pCO2W data points from the Surface Ocean CO2 Atlas. We built an FFNN based on three major datasets that differed in the Chl-a concentration data used to choose the best model to reproduce the spatial distribution and temporal variability of pCO2W. Using all physical–biological components improved estimates of the pCO2W and decreased the biases, even though Chl-a values in many grid cells were interpolated values. General features of pCO2W distribution were reproduced with very good accuracy, but the network underestimated pCO2W in the winter and overestimated pCO2W values in the summer. The results show that the model that contains interpolating Chl-a concentration, SST, SSS, and MLD as a target to predict the spatiotemporal distribution of pCO2W in the sea surface gives the best results and best-fitting network to the observational data. The calculation of monthly drivers of the estimated pCO2W change within continental shelf areas of the EAS confirms the major impact of not only the biological effects to the pCO2W distribution and Air-Sea CO2 flux in the EAS, but also the strong impact of the upper ocean mixing. A strong seasonal correlation between predictor and pCO2W seen earlier in the North Atlantic is clearly a yearly correlation in the EAS. The five-year monthly mean CO2 flux distribution shows that all continental shelf areas of the Arctic Ocean were net CO2 sinks. Strong monthly CO2 influx to the Arctic Ocean through the Greenland and Barents Seas (>12 gC m−2 day−1) occurred in the fall and winter, when the pCO2W level at the sea surface was high (>360 µatm) and the strongest wind speed (>12 ms−1) was present.

SeaFlux: harmonization of air–sea CO<sub>2</sub> fluxes from surface <i>p</i>CO<sub>2</sub> data products using a standardized approach

Abstract. Air–sea flux of carbon dioxide (CO2) is a critical component of the global carbon cycle and the climate system with the ocean removing about a quarter of the CO2 emitted into the atmosphere by human activities over the last decade. A common approach to estimate this net flux of CO2 across the air–sea interface is the use of surface ocean CO2 observations and the computation of the flux through a bulk parameterization approach. Yet, the details for how this is done in order to arrive at a global ocean CO2 uptake estimate vary greatly, enhancing the spread of estimates. Here we introduce the ensemble data product, SeaFlux (Gregor and Fay, 2021, https://doi.org/10.5281/zenodo.5482547​​​​​​​, https://github.com/luke-gregor/pySeaFlux, last access: 9 September 2021​​​​​​​); this resource enables users to harmonize an ensemble of products that interpolate surface ocean CO2 observations to near-global coverage with a common methodology to fill in missing areas in the products. Further, the dataset provides the inputs to calculate fluxes in a consistent manner. Utilizing six global observation-based mapping products (CMEMS-FFNN, CSIR-ML6, JENA-MLS, JMA-MLR, MPI-SOMFFN, NIES-FNN), the SeaFlux ensemble approach adjusts for methodological inconsistencies in flux calculations. We address differences in spatial coverage of the surface ocean CO2 between the mapping products, which ultimately yields an increase in CO2 uptake of up to 17 % for some products. Fluxes are calculated using three wind products (CCMPv2, ERA5, and JRA55). Application of a scaled gas exchange coefficient has a greater impact on the resulting flux than solely the choice of wind product. With these adjustments, we present an ensemble of global surface ocean pCO2 and air–sea carbon flux estimates. This work aims to support the community effort to perform model–data intercomparisons which will help to identify missing fluxes as we strive to close the global carbon budget.