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A feed-forward neural network is used to create a monthly climatology of the sea surface fugacity of CO2 (fCO2) on a 1° × 1° spatial resolution. Using 127 880 data points from 1990 to 2011 in the track-gridded database of the Surface Ocean CO2 Atlas version 2.0 (Bakker et al.), the model yields a global mean fCO2 increase rate of 1.50 μatm yr−1. The rate was used to normalize multiple years’ fCO2 observations to the reference year of 2000. A total of 73 265 data points from the normalized data were used to model the global fCO2 climatology. The model simulates monthly fCO2 distributions that agree well with observations and yields an anthropogenic CO2 update of −1.9 to −2.3 PgC yr−1. The range reflects the uncertainty related to using different wind products for the flux calculation. This estimate is in good agreement with the recently derived best estimate by Wanninkhof et al. The model product benefits from a finer spatial resolution compared to the product of Lamont–Doherty Earth Observatory (Takahashi et al.), which is currently the most frequently used product. It therefore has the potential to improve estimates of the global ocean CO2 uptake. The method’s benefits include but are not limited to the following: (i) a fixed structure is not required to model fCO2 as a nonlinear function of biogeochemical variables, (ii) only one neural network configuration is sufficient to model global fCO2 in all seasons, and (iii) the model can be extended to produce global fCO2 maps at a higher resolution in time and space as long as the required data for input variables are available.

This study uses a neural network technique to produce maps of the partial pressure of oceanic carbon dioxide (pCO2sea ) in the North Pacific on a 0.25° latitude × 0.25° longitude grid from 2002 to 2008. The pCO2sea distribution was computed using a self-organizing map (SOM) originally utilized to map the pCO2sea in the North Atlantic. Four proxy parameters - sea surface temperature (SST), mixed layer depth, chlorophyll a concentration, and sea surface salinity (SSS) - are used during the training phase to enable the network to resolve the nonlinear relationships between the pCO2sea distribution and biogeochemistry of the basin. The observed pCO2sea data were obtained from an extensive dataset generated by the volunteer observation ship program operated by the National Institute for Environmental Studies (NIES). The reconstructed pCO2sea values agreed well with the pCO2sea measurements, with the root-mean-square error ranging from 17.6 μatm (for the NIES dataset used in the SOM) to 20.2 μatm (for independent dataset). We confirmed that the pCO2sea estimates could be improved by including SSS as one of the training parameters and by taking into account secular increases of pCO2sea that have tracked increases in atmospheric CO2 . Estimated pCO2sea values accurately reproduced pCO2sea data at several time series locations in the North Pacific. The distributions of pCO2sea revealed by 7 yr averaged monthly pCO2sea maps were similar to Lamont-Doherty Earth Observatory pCO2sea climatology, allowing, however, for a more detailed analysis of biogeochemical conditions. The distributions of pCO2sea anomalies over the North Pacific during the winter clearly showed regional contrasts between El Niño and La Niña years related to changes of SST and vertical mixing.

Air-sea CO2 fluxes over the Pacific Ocean are known to be characterized by coherent large-scale structures that reflect not only ocean subduction and upwelling patterns, but also the combined effects of wind-driven gas exchange and biology. On the largest scales, a large net CO2 influx into the extratropics is associated with a robust seasonal cycle, and a large net CO2 efflux from the tropics is associated with substantial interannual variability. In this work, we have synthesized estimates of the net air-sea CO2 flux from a variety of products, drawing upon a variety of approaches in three sub-basins of the Pacific Ocean, i.e., the North Pacific extratropics (18-66° N), the tropical Pacific (18° S-18° N), and the South Pacific extratropics (44.5-18° S). These approaches include those based on the measurements of CO2 partial pressure in surface seawater (pCO2 sw), inversions of ocean-interior CO2 data, forward ocean biogeochemistry models embedded in the ocean general circulation models (OBGCMs), a model with assimilation of pCO2 sw data, and inversions of atmospheric CO2 measurements. Long-term means, interannual variations and mean seasonal variations of the regionally integrated fluxes were compared in each of the sub-basins over the last two decades, spanning the period from 1990 through 2009. A simple average of the long-term mean fluxes obtained with surface water pCO2 diagnostics and those obtained with ocean-interior CO2 inversions are -0.47 ± 0.13 Pg C yr-1 in the North Pacific extratropics, +0.44 ± 0.14 Pg C yr-1 in the tropical Pacific, and -0.37 ± 0.08 Pg C yr-1 in the South Pacific extratropics, where positive fluxes are into the atmosphere. This suggests that approximately half of the CO2 taken up over the North and South Pacific extratropics is released back to the atmosphere from the tropical Pacific. These estimates of the regional fluxes are also supported by the estimates from OBGCMs after adding the riverine CO2 flux, i.e., -0.49 ± 0.02 Pg C yr-1 in the North Pacific extratropics, +0.41 ± 0.05 Pg C yr-1 in the tropical Pacific, and -0.39 ± 0.11 Pg C yr-1 in the South Pacific extratropics. The estimates from the atmospheric CO2 inversions show large variations amongst different inversion systems, but their median fluxes are consistent with the estimates from climatological pCO2 sw data and pCO2 sw diagnostics. In the South Pacific extratropics, where CO2 variations in the surface and ocean interior are severely undersampled, the difference in the air-sea CO2 flux estimates between the diagnostic models and ocean-interior CO2 inversions is larger (0.18 Pg C yr-1 ). The range of estimates from forward OBGCMs is also large (-0.19 to -0.72 Pg C yr-1 ). Regarding interannual variability of air-sea CO2 fluxes, positive and negative anomalies are evident in the tropical Pacific during the cold and warm events of the El Niño-Southern Oscillation in the estimates from pCO2 sw diagnostic models and from OBGCMs. They are consistent in phase with the Southern Oscillation Index, but the peak-to-peak amplitudes tend to be higher in OBGCMs (0.40 ± 0.09 Pg C yr-1 ) than in the diagnostic models (0.27 ± 0.07 Pg C yr-1 ).

Some dominant spatial and temporal structures of the North Pacific air-sea CO2 fluxes in response to the Pacific Decadal Oscillation (PDO) are identified in three data products from three independent sources: an assimilated CO2 flux product and two forward model solutions. The interannual variability of CO2 flux is found to be an order of magnitude weaker compared to the seasonal cycle of CO2 flux in the North Pacific. A statistical approach is employed to quantify the signal-to-noise ratio in the reconstructed dataset to delineate the representativity errors. The dominant variability with a signal-to-noise ratio above one is identified and its correlations with PDO are examined. A tentative four-pole pattern in the North Pacific air-sea CO2 flux variability linked to PDO emerges in which two positively correlated poles are oriented in the northwest and southeast directions and contrarily, the negatively correlated poles are oriented in the northeast and southwest directions. This pattern is identified in three products, providing CO2 and pCO2 . Its relations to the interannual El Niño-Southern Oscillation (ENSO) and lower-frequency PDO are separately identified. A combined EOF analysis between air-sea CO2 flux and key variables representing ocean-atmosphere interactions is carried out to elicit robust oscillations in the North Pacific CO2 flux in response to the PDO. The proposed spatial and temporal structures of the North Pacific CO2 fluxes are insightful since they separate the secular trends of the surface ocean carbon from the interannual variability. The regional characterization of the North Pacific in terms of PDO and CO2 flux variability is also instructive for determining the homogeneous oceanic domains for the Regional Carbon Cycle and Assessment Processes (RECCAP).

The Global Ocean Data Analysis Project (GLODAP) is a synthesis effort providing regular compilations of surface to bottom ocean biogeochemical data, with an emphasis on seawater inorganic carbon chemistry and related variables determined through chemical analysis of water samples. This update of GLODAPv2, v2.2019, adds data from 116 cruises to the previous version, extending its coverage in time from 2013 to 2017, while also adding some data from prior years. GLODAPv2.2019 includes measurements from more than 1.1 million water samples from the global oceans collected on 840 cruises. The data for the 12 GLODAP core variables (salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, CFC-11, CFC-12, CFC-113, and CCl4) have undergone extensive quality control, especially systematic evaluation of bias. The data are available in two formats: (i) as submitted by the data originator but updated to WOCE exchange format and (ii) as a merged data product with adjustments applied to minimize bias. These adjustments were derived by comparing the data from the 116 new cruises with the data from the 724 quality-controlled cruises of the GLODAPv2 data product. They correct for errors related to measurement, calibration, and data handling practices, taking into account any known or likely time trends or variations. The compiled and adjusted data product is believed to be consistent to better than 0.005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate, 4 µmol kg−1 in dissolved inorganic carbon, 4 µmol kg−1 in total alkalinity, 0.01–0.02 in pH, and 5 % in the halogenated transient tracers. The compilation also includes data for several other variables, such as isotopic tracers. These were not subjected to bias comparison or adjustments. The original data, their documentation and DOI codes are available in the Ocean Carbon Data System of NOAA NCEI (https://www.nodc.noaa.gov/ocads/oceans/GLODAPv2_2019/, last access: 17 September 2019). This site also provides access to the merged data product, which is provided as a single global file and as four regional ones – the Arctic, Atlantic, Indian, and Pacific oceans – under https://doi.org/10.25921/xnme-wr20 (Olsen et al., 2019). The product files also include significant ancillary and approximated data. These were obtained by interpolation of, or calculation from, measured data. This paper documents the GLODAPv2.2019 methods and provides a broad overview of the secondary quality control procedures and results.

The oceans are a major sink for atmospheric carbon dioxide (CO2). Historically, observations have been too sparse to allow accurate tracking of changes in rates of CO2 uptake over ocean basins, so little is known about how these vary. Here, we show observations indicating substantial variability in the CO2 uptake by the North Atlantic on time scales of a few years. Further, we use measurements from a coordinated network of instrumented commercial ships to define the annual flux into the North Atlantic, for the year 2005, to a precision of about 10%. This approach offers the prospect of accurately monitoring the changing ocean CO2 sink for those ocean basins that are well covered by shipping routes.

A successful integrated ocean acidification (OA) observing network must include 1) scientists and technicians from a range of disciplines (from physics to chemistry to biology to technology development) and across the globe; 2) government, private, and intergovernmental support; 3) regional cohorts working together on regionally specific issues; 4) publicly accessible data from the open ocean to coastal to estuarine systems; 5) close integration with other networks focusing on related measurements or issues including the social and economic consequences of OA; and 6) observation-based informational products useful for decision making such as management of fisheries and aquaculture. The Global Ocean Acidification Observing Network (GOA-ON), a key player in this vision, seeks to expand and enhance geographic extent and availability of coastal and open ocean observing data to ultimately inform adaptive measures and policy action, especially in support of the United Nations 2030 Agenda for Sustainable Development. GOA-ON works to empower and support regional collaborative networks such as the Latin American Ocean Acidification Network, supports new scientists entering the field with training, mentorship, and equipment, refines approaches for tracking biological impacts, and stimulates development of lower-cost methodology and technologies allowing for wider participation of scientists. GOA-ON seeks to collaborate with and complement work done by other observing networks such as those focused on carbon flux into the ocean, tracking of carbon and oxygen in the ocean, observing biological diversity, and determining short- and long-term variability in these and other ocean parameters through space and time.

A feed-forward neural network is used to create a monthly climatology of the sea surface fugacity of CO sub(2) (fCO sub(2)) on a 1 degree x 1 degree spatial resolution. Using 127 880 data points from 1990 to 2011 in the track-gridded database of the Surface Ocean CO sub(2) Atlas version 2.0 (Bakker et al.), the model yields a global mean fCO sub(2) increase rate of 1.50 atm yr super(-1). The rate was used to normalize multiple years fCO sub(2) observations to the reference year of 2000. A total of 73 265 data points from the normalized data were used to model the global fCO sub(2) climatology. The model simulates monthly fCO sub(2) distributions that agree well with observations and yields an anthropogenic CO sub(2) update of 1.9 to 2.3 PgC yr super(-1). The range reflects the uncertainty related to using different wind products for the flux calculation. This estimate is in good agreement with the recently derived best estimate by Wanninkhof et al. The model product benefits from a finer spatial resolution compared to the product of LamontDoherty Earth Observatory (Takahashi et al.), which is currently the most frequently used product. It therefore has the potential to improve estimates of the global ocean CO sub(2) uptake. The methods benefits include but are not limited to the following: (i) a fixed structure is not required to model fCO sub(2) as a nonlinear function of biogeochemical variables, (ii) only one neural network configuration is sufficient to model global fCO sub(2) in all seasons, and (iii) the model can be extended to produce global fCO sub(2) maps at a higher resolution in time and space as long as the required data for input variables are available.

A feed-forward neural network is used to create a monthly climatology of the sea surface fugacity of CO2 (fCO^sub 2^) on a 1° × 1° spatial resolution. Using 127880 data points from 1990 to 2011 in the track-gridded database of the Surface Ocean CO2 Atlas version 2.0 (Bakker et al.), the model yields a global mean fCO^sub 2^ increase rate of 1.50µatm yr^sup -1^. The rate was used to normalize multiple years' fCO^sub 2^ observations to the reference year of 2000. A total of 73 265 data points from the normalized data were used to model the global fCO^sub 2^ climatology. The model simulates monthly fCO^sub 2^ distributions that agree well with observations and yields an anthropogenic CO2 update of -1.9 to -2.3 PgC yr^sup -1^. The range reflects the uncertainty related to using different wind products for the flux calculation. This estimate is in good agreement with the recently derived best estimate by Wanninkhof et al. The model product benefits from a finer spatial resolution compared to the product of Lamont-Doherty Earth Observatory (Takahashi et al.), which is currently the most frequently used product. It therefore has the potential to improve estimates of the global ocean CO2 uptake. The method's benefits include but are not limited to the following: (i) a fixed structure is not required to model fCO^sub 2^ as a nonlinear function of biogeochemical variables, (ii) only one neural network configuration is sufficient to model global fCO^sub 2^ in all seasons, and (iii) the model can be extended to produce global fCO^sub 2^ maps at a higher resolution in time and space as long as the required data for input variables are available.

The Surface Ocean CO2 Atlas (SOCAT), an activity of the international marine carbon research community, provides access to synthesis and gridded fCO2 (fugacity of carbon dioxide) products for the surface oceans. Version 2 of SOCAT is an update of the previous release (version 1) with more data (increased from 6.3 million to 10.1 million surface water fCO2 values) and extended data coverage (from 1968-2007 to 1968-2011). The quality control criteria, while identical in both versions, have been applied more strictly in version 2 than in version 1. The SOCAT website (http://www.socat.info/) has links to quality control comments, metadata, individual data set files, and synthesis and gridded data products. Interactive online tools allow visitors to explore the richness of the data. Applications of SOCAT include process studies, quantification of the ocean carbon sink and its spatial, seasonal, year-to-year and longerterm variation, as well as initialisation or validation of ocean carbon models and coupled climate-carbon models. Data coverage Repository-References: Individual data set files and synthesis product: doi:10.1594/PANGAEA.811776 Gridded products: doi:10.3334/CDIAC/OTG.SOCAT_V2_GRID Available at: http://www.socat.info/ Coverage: 79° S to 90° N; 180° W to 180° E Location Name: Global Oceans and Coastal Seas Date/Time Start: 16 November 1968 Date/Time End: 26 December 2011