In version 3, new and updated data sets were obtained from the Carbon Dioxide Information Analysis Centre (CDIAC), PANGAEA^® and public websites. In addition, many data sets were directly submitted to SOCAT. As well as 887 new data sets, version 3 also contains 1258 updated version of data sets previously submitted to versions 1 and 2, with revised metadata or data. Some of these were updates of data sets previously suspended from SOCAT (e.g. Table 10 in Bakker et al., 2014). As in previous versions, all new and updated data sets were put in a uniform format (Pfeil et al., 2013). Similar to version 2, an expocode was assigned to all data sets, including moorings and drifters (Bakker et al., 2014). In general, an expocode consists of 12 characters, describing the country, the vessel or platform, and the data set start day (Swift, 2008). The expocode 320620090306, for example, indicates a data set collected on the US (32) ship R/V Nathaniel B. Palmer (06) with the first day of the cruise on 6 March 2009. There are a few exceptions to this. If two American mooring data sets (which always start with 3164) have the same start date, they will end with “-1” and “-2”, corresponding to an expocode of 14 characters.

In version 3, the SOCAT data managers used the new SOCAT Upload Dashboard for upload of data and metadata (Table 1). All data sets previously included in versions 1 and 2 were also uploaded, automatically screened for obvious outliers and added to version 3 via the SOCAT Upload Dashboard (Table 1). This new capability is an important step in the ongoing SOCAT automation effort and integrates data and metadata upload, (re)calculation of fCO2, automated data checks, data visualisation and data submission in a single application which is tightly coupled to the SOCAT QC Editor. Once fully operational in version 4, the Upload Dashboard will allow data providers to upload, verify and submit their data for SOCAT quality control.

Not all data sets had time stamps which included seconds. In such cases, multiple occurrences of a time stamp were often present. Artificial seconds were added to data sets with 50 or more duplicate time stamps. For these data sets, evenly distributed artificial seconds were added for each equal time stamp. However, if there were less than 50 duplicate times in a data set, a WOCE flag of 4 was generated for the fCO2rec values (or “recommended” fCO2 values; see Sect. 4.2) with duplicate time stamps during the automated data checks (Sect. 4.3). Adding artificial seconds is time-consuming and there was insufficient time available for adding artificial seconds to all duplicate times in all data sets.

Data providers reported CO2 values as xCO2, pCO2 and/or fCO2, at the equilibration temperature (Tequ) and/or the sea surface temperature (SST or intake temperature). In order to ensure a coherent SOCAT synthesis product, surface water fCO2 values at sea surface temperature were recalculated from the reported CO2 values using a strict calculation protocol with the following procedure (quoting Pfeil et al., 2013):

when possible, (re)calculate fCO2;

In total, 14 algorithms were used for (re)calculating these “recommended” fCO2 (fCO2rec) values from the xCO2, pCO2 and/or fCO2 values reported by the data providers (Table 6). The particular algorithm used for a given data set is included in the data products (Sect. 5). Equations recommended by Dickson et al. (2007) were applied for the conversion of the dry CO2 mole fraction to pCO2, for the calculation of the water vapour pressure and for the correction of pCO2 to fCO2 (Pfeil et al., 2013). The temperature correction suggested by Takahashi et al. (1993) was used to correct for temperature change between the seawater intake and the equilibrator. Atmospheric pressure from reanalysis and climatological values of salinity were used in the calculation if in situ values had not been reported (Table 6). The 2014 version of the atmospheric pressure data product was used (NCEP, 2014), which is an update of the 2012 data product used in the previous SOCAT version (NCEP, 2012). Sea surface salinity was from the World Ocean Atlas (WOA) 2005 (Antonov et al., 2006). Full details on the external pressure and salinity products are in the footnotes of Table 9. Note that the use of external atmospheric pressure data would rule out data set quality control flags of A and B during subsequent quality control, while use of external salinity values would not affect the data set quality control flag (Sect. 4.4).

An important change relative to earlier versions is that the (re)calculation in version 3 took place using Ferret scripts on the new SOCAT Upload Dashboard after data upload (Sect. 4.1), rather than in Matlab before the bulk upload of the data (Table 1). The implementation of the Ferret scripts enables full integration of SOCAT data submission, (re)calculation of fCO2 and quality control on a single software platform. This streamlines and simplifies the SOCAT data flow. The Matlab code used for the (re)calculation in versions 1 and 2 was transferred to Ferret scripts on the Upload Dashboard for version 3. The new Ferret scripts were checked by comparing fCO2rec values in version 2 calculated using Matlab and new values calculated using Ferret. Almost all new values were within 0.01 µatm of the value calculated in Matlab, if not identical to it. Significant changes (smaller than 5 µatm) for less than 200 data points were attributed to changes in atmospheric pressure from reanalysis (Table 1).

A newly developed, automated data checker performed checks on parameters directly influencing the position, time or calculation of fCO2rec values (Tables 1 and 7). A WOCE flag of 4 (meaning a bad data point) was assigned to all fCO2rec values with an incorrect position or time stamp or otherwise identified as inaccurate. These automated checks were carried out on all data in version 3 after (re)calculation of fCO2rec and before submission to the quality control system.

Unintentionally, WOCE flags of 4 were also assigned for values which were out of range in parameters which do not directly affect fCO2rec values, such as wind speed and ship direction (Table 7). This resulted in a WOCE flag of 4 being given to some good-quality fCO2rec values in newly added and updated data sets in version 3. The criteria for the automated checks will be reconsidered for version 4.

Automated data checks were also performed for data sets previously included in versions 1 and 2 (and not updated in version 3). For these data sets all WOCE flags of 4 assigned by the automated data checker, other than for duplicate time stamps, were removed to preserve the data sets as reported for version 2.

Secondary quality control is a key part of the creation of a high-quality data synthesis product. During secondary quality control, scientists, also known as quality controllers, assess the quality of each new and updated data set by following a checklist of specific criteria, while also examining the documentation of the data, known as metadata, for completeness. The quality controllers assign a data set quality control flag to each data set, based on their findings (Table 2).

The SOCAT quality control system has been upgraded (Table 1), as part of the ongoing SOCAT automation. In particular, the ease of use, search options and visualisation tools have been improved. Other modifications are that the quality control criteria used for setting the data set quality control flag now must be specified (by a tick box system) and that a comment needs to be entered when assigning a WOCE flag (Table 1). Text relating to the tick boxes and the comments accompanying WOCE flags are incorporated into the quality control comments.

The definitions of the data set quality control flags in version 3 have been revised relative to versions 1 and 2 (Tables 1 and 2) (Wanninkhof et al., 2013b; Olsen et al., 2015). These revised QC criteria were applied to all new and updated data sets in version 3, but not retrospectively to data sets included in earlier versions, unless data providers had updated these. Version 3 has data set quality control flags of A to E and WOCE flags of 2, 3 and 4 for individual fCO2rec values. For a data set to obtain a data set quality control flag, it needs to meet all the criteria of that specific data set flag (Table 2).

All data set flags now have an accuracy requirement for the fCO2rec values. Previously, flags of C and D did not have an accuracy requirement (Pfeil et al., 2013; Bakker et al., 2014). In version 3, requirements are an accuracy of better than 2 µatm for flags of A and B, and of better than 5 µatm for flags of C and D and of better than 10 µatm for a flag of E (Table 2). The accuracy requirement takes precedent over the criteria that follow (Wanninkhof et al., 2013b; Olsen et al., 2015), implying that, if the accuracy requirement is not met, a data set is given a data set flag with a lower accuracy requirement, appropriate to the accuracy of the data set.

Seven approved methods or SOP (standard operating procedure) criteria need to be met for a data set quality control flag of A and B (after Pfeil et al., 2013):

The data are based on xCO2 analysis, not fCO2 calculated from the other carbon parameters pH, total alkalinity and dissolved inorganic carbon.

The requirement regarding the accuracy of the equilibrator pressure has been relaxed to an accuracy of 2.0 hPa in version 3, replacing the earlier requirement of 0.5 hPa, as an accuracy of 2.0 hPa in pressure is sufficient for achieving an accuracy of 2.0 µatm in fCO2 (Wanninkhof et al., 2013b; Olsen et al., 2015). The six other criteria are the same in all SOCAT versions.

In version 3, a high-quality cross-over has become a prerequisite for a data set flag of A, replacing the earlier requirement of “an acceptable comparison (or cross-over) with other data” (Wanninkhof et al., 2013b; Olsen et al., 2015). As in previous versions, a cross-over is defined by an equivalent distance of less than 80 km between two data sets (Pfeil et al., 2013). This criterion combines distance and time as ([Δx2+ (Δt×30)2]0.5)≤80 with distance x in kilometres and time t in days. One day (or 24 h) of separation in time is equivalent (heuristically) to 30 km of separation in space. According to this definition, the maximum time separation (at a spatial distance of 0 km) is 64 h for a cross-over to occur. The new definition of a high-quality cross-over between two data sets requires that differences in sea surface temperature and fCO2rec between the data sets do not exceed 0.3 ∘C and 5 µatm, respectively. These criteria reflect the test for a high-quality cross-over between two data sets with a flag of A or B, i.e. each with an accuracy for fCO2rec of better than 2 µatm or a joint accuracy of better than 4 µatm with 1 µatm added to account for differences in time and space. A temperature difference of 0.3 ∘C roughly corresponds to an fCO2 difference of 5 µatm. “Inconclusive” cross-overs, where differences in temperature or fCO2rec exceed these values, do not qualify for a data set flag of A in version 3.

It is worth noting that meaningful high-quality cross-overs are rarely found in coastal waters, near sea ice and in regions of freshwater influence (ROFIs), as a result of high spatial variation in sea surface temperature and fCO2rec, not for lack of measurement quality. Even if a small number of sea surface temperature and fCO2rec values are within 0.3 ∘C and 5 µatm, this tends to be a coincidence rather than a meaningful correspondence between data sets. This can be illustrated for the US research ships Nathaniel B. Palmer and the Lawrence M. Gould, which have frequent high-quality cross-overs in the open Southern Ocean but few high-quality cross-overs near Palmer station, where they both make port calls.

In version 3, a data set with a flag of C “did or did not follow approved methods or SOP criteria” (Wanninkhof et al., 2013b; Olsen et al., 2015). This is an amendment from the earlier requirement that the data set “did not follow approved methods or SOP criteria” (Pfeil et al., 2013; Bakker et al., 2014). The new flag of E enables inclusion of fCO2 values from calibrated alternative sensors and platforms (Wanninkhof et al., 2013b; Olsen et al., 2015). A flag of E requires complete metadata and a demonstrable accuracy for fCO2rec of better than 10 µatm by in situ calibration of the sensor. The WOCE flags for individual fCO2rec values are defined as 2 (good), 3 (questionable) and 4 (bad) in versions 1, 2 and 3 (Pfeil et al., 2013). New is the requirement to add a comment when assigning WOCE flags of 3 and 4 (Table 1).

As in version 2, five additional guidelines were considered for open-ocean fCO2rec values, away from sea ice. The guidelines were used for assigning data set quality control flags and WOCE flags (after Pfeil et al., 2013, and Bakker et al., 2014):

warming between the seawater intake and the equilibrator should be less than 3 ∘C;

In essence, the data products and data platforms are the same as for earlier SOCAT versions with some modifications (Table 8). Improvements include a major upgrade of the search and visualisation capabilities of the Data Set Viewer (previously known as the Cruise Data Viewer) and uniform contents for the files downloadable from the Data Set Viewer (Tables 1 and 9). Access to the data products is via the SOCAT website (http://www.socat.info/) and the web addresses for the individual data platforms (Table 8).

Quality-controlled recommended surface ocean fCO2 measurements in a uniform format are available in individual data set files, in regional and global synthesis files and in gridded form (Table 8). These three data products can be accessed via the user-friendly, interactive online Data Set Viewer and Gridded Data Viewer, by downloading data files, or in Ocean Data View (Schlitzer, 2015). Similar to earlier versions, data sets with a quality control flag of A to D and recommended fCO2 values with a WOCE flag of 2 (good) are included in the synthesis files and gridded products. Data sets with a flag of E are available in a separate synthesis file. Data set flags of A to E and a WOCE flag of 2 for fCO2 values is the default setting for the Data Set Viewer. Quality control comments can be accessed via the Data Set Viewer (Table 8). While the SOCAT data products include seawater temperature and salinity, as these are required for (re)calculation of fCO2, these two parameters have not been quality-controlled to the high standards required by the physical oceanographic community (SOCAT, 2014a).

As in earlier versions, each individual data set has a digital object identifier (DOI), which provides a direct link to the metadata, including the name and affiliation of the data provider. This DOI for the data set is available for each recommended surface ocean fCO2 value in the synthesis files. This enables users to easily identify the data provider and to gain access to the original data set and to detailed information on the data set, including any relevant peer-reviewed journal articles that we are aware of. The Data Set Viewer now enables to search the data collection by data provider. Data providers are also prominently displayed in the Table of Datasets (access via the Data Set Viewer) (Table 8). A more detailed description of the data products follows.

Individual data set files are available for all data sets with flags of A, B, C, D and E. Each individual data set has a DOI. The files contain all original CO2 measurements and recommended fCO2 values with a WOCE flag of 2, 3 and 4 (Table 8), as set by the data originator, by the automated range checker or during the secondary quality control. The files also contain other parameters, such as atmospheric pressure from reanalysis, climatological salinity and the atmospheric CO2 mole fraction. Metadata reported by the data provider accompany the files and links to the original data sets are provided. The files are available in text format at PANGAEA^® (https://doi.org/10.1594/PANGAEA.849770).

The global and regional synthesis files contain recommended fCO2 values with a WOCE flag of 2 (good) for data sets with flags of A, B, C and D (Table 8). A separate synthesis file is available for data sets with a flag of E. Each line of the global and regional synthesis files contains the DOI for the corresponding individual data set, as archived at PANGAEA^®, thus enabling retrieval of metadata, name of the data provider and the original CO2 values reported by the data provider (Table 9) (Sect. 5.2). Global and regional files are available as compressed zip text files via CDIAC (http://cdiac.ornl.gov/ftp/oceans/SOCATv3/). Matlab code is available for reading these text files. Regional files for the SOCAT regions (Table 4) only contain data for a specific region with no overlap, so that many data sets on moving ships are split between several regional files. The global synthesis product for data sets with flags of A to D is also available in Ocean Data View format (https://odv.awi.de/en/data/ocean/socat_fCO2_data) (Schlitzer, 2015).

The interactive Data Set Viewer (http://ferret.pmel.noaa.gov/SOCAT_Data_Viewer/) has powerful search capabilities and an attractive graphical interface following the upgrade for version 3 (Tables 1 and 8). The SOCAT Data Viewer now hosts the Data Set Viewer and the Gridded Data Viewer on a single software platform. The move of the Data Set Viewer onto this platform in version 3 streamlines access to the SOCAT synthesis and gridded products via a Live Access Server (LAS). The move and upgrade of the Data Set Viewer accompany that of the closely associated SOCAT quality control system (Sects. 2 and 4.4).

The Data Set Viewer enables subsetting of the global SOCAT data collection. The default setting is for data sets with flags of A to E and “valid” fCO2 values with a WOCE flag of 2 for years 1957 to 2014, corresponding to 3640 data sets for version 3. Recommended fCO2 values with flags of 3 and 4 can also be selected. In the Data Set Viewer, the user can select data sets by, for example, year, month, region, platform/vessel, “valid” values, data provider, data set flag, WOCE flag and SOCAT version. It is also possible to define limits for the values shown. Maps of surface ocean fCO2 demonstrate the data distribution, as well as temporal and spatial variation in surface ocean fCO2 for the selected data sets (Figs. 1, 3 and 4). High-quality figures can be rapidly created for scientific presentations to fellow scientists, funding agencies and policy makers. Scatter plots or property–property plots, available via the Correlation Viewer, can be used to depict any two variables of a data set or data sets, enabling further investigation. Examples are figures of fCO2 or sea surface temperature as a function of time, salinity or latitude.

The data shown on the Data Set Viewer have been subsampled for system efficiency, such that only part of the data are shown. Visual display of these data sets on maps in the Data Set Viewer is subject to further improvement, as the interpolation of sparse data ignores topographic features. As a result cruise tracks occasionally appear to cross land. This issue does not affect the data sets themselves. The Table of Datasets (previously known as the Table of Cruises) can be accessed from the Data Set Viewer. It provides access to the original CO2 measurements; fCO2 values with a WOCE flag of 2, 3 and 4; metadata; comments entered during quality control; and thumbnail plots (Table 8) (Sect. 4.4). Thumbnail plots consist of a series of scatter plots for key parameters in an individual data set and are useful for obtaining a quick overview of a data set. Both the Data Set Viewer and the Table of Datasets allow download of data sets in NetCDF and text format (Tables 8 and 9). All downloadable files now contain the same parameters (Table 9).

The performance speed of the Data Set Viewer may be slower if the full SOCAT data collection is accessed. Subsetting the data collection by decade or region considerably improves the system speed of the Data Set Viewer. Updates of web browsers occasionally result in less than perfect web access to the Data Set Viewer. In such cases, another web browser may provide better access. The web manager (socat.support@noaa.gov) may have useful advice.

The protocol for the creation of gridded fCO2 products in version 3 follows that for versions 1 and 2, as described by Sabine et al. (2013). The gridded products have a 1∘ latitude by 1∘ longitude resolution with a higher resolution of 0.25∘ latitude by 0.25∘ longitude for coastal seas. Recommended surface ocean fCO2 values from 1970 to 2014 with a WOCE flag of 2 from data sets with flags of A to D have been used for the gridded products. The gridded products have no interpolation – i.e. there is no gap-filling and grid cells without fCO2 values are empty. No correction is made for the long-term increase in surface ocean fCO2.

Gridded fCO2 values are reported as unweighted means and as data-set-weighted means (Sabine et al., 2013). In an unweighted mean, all fCO2 values in a grid cell have equal weight for calculating the mean. In a data-set-weighted mean, averages of the fCO2 values are calculated per data set for each grid cell, before calculating averages of these data set means. In version 3, a small error was corrected in the procedure for creating the gridded data products. This resulted in a small reduction in the number of grid cells with data in the data-set-weighted product for versions 1 and 2. This problem was corrected in gridded files in version 3 with the revised gridded data set made public on 2 November 2015.

Gridded products are available per decade, per year and monthly per year (Table 10). A monthly climatological fCO2 product has not been made available for version 3, out of concern, that such a product without a correction for the long-term increase in fCO2 could be misinterpreted. Gridded fCO2 values may have temporal bias, for example, if only summertime fCO2 values are available for a grid cell in the annual gridded product. Several auxiliary variables are reported per grid cell, for example the number of data sets and observations and the standard deviation in the unweighted and weighted fCO2 mean values (Table 10).

Gridded products are available in NetCDF format at CDIAC (http://cdiac.ornl.gov/ftp/oceans/SOCATv3/SOCATv3_Gridded_Dat/) (Table 8). Matlab code is available for reading the files. The Gridded Data Viewer (http://www.socat.info/; select “Gridded Data Viewer”) provides easy access to the gridded data products, as well as comparison to gridded products from earlier versions. Figures 5 and 6 have been made with the gridded data product.

The SOCAT automation system was formally launched on 7 September 2015 (SOCAT and SOCOM, 2015). Data providers can now directly upload, check and submit their data on the SOCAT quality control system for future SOCAT versions. The SOCAT automation was first discussed at the 2011 Data2Flux Workshop in Paris (SOCAT, 2011). The automation system was designed at the 2012 Automation Planning Meeting (SOCAT, 2012a) and approved shortly afterwards by global and regional group leads (SOCAT, 2012b) (Table 5). The automation system has been implemented in the background, with all the work for the biannual SOCAT releases of versions 2 and 3 taking place in the foreground (Bakker et al., 2014, this study). This considerable achievement has been made possible by the hard work and planning of the NOAA-PMEL and University of Washington Live Access Server team and other members of the SOCAT automation team (Table 3). The new automation system allows data providers to upload their data, to check their data with the automated data checker and to visualise their data. Finally, if the data provider deems the data of good quality, he or she can submit them to the SOCAT quality control system. As part of the data submission to SOCAT, the data provider is encouraged to make the original data set public, for example at CDIAC (SOCAT and SOCOM, 2015), either immediately or upon the release of the SOCAT version of which the data set is part. The automation system will enable annual SOCAT releases. The timetable for future SOCAT versions envisages that data upload will end in late January of each year and quality control in late March for a release in summer later that year. With the new system it is now possible to upload and submit data to SOCAT, while quality control of previously submitted data sets is in progress. Thus, both data upload and quality control can now be carried out in parallel. Data upload and quality control for the next SOCAT version will start as soon as they have finished for the preceding version. Thus, the automation system will enable rolling, continuous data upload and quality control, as well as annual SOCAT releases. The system for automated data upload is under continuous improvement. Metadata templates and upload will be integrated into the SOCAT data upload system. Other planned improvements include searchable information for funding agency and entry of preliminary data set flags by the data provider. A number of additional features are being considered for future SOCAT versions, some of which may be implemented as early as version 4. These are discussed below.

Data providers can now submit measurements of atmospheric CO2 mole fraction, made in parallel to surface water fCO2. A separate WOCE flag will be created for measurements of the atmospheric CO2 mole fraction in future SOCAT versions. Once quality control has been carried out on the atmospheric CO2 measurements, such values will be included in the SOCAT data products.

In future, atmospheric fCO2 will be calculated from atmospheric xCO2 values, both from the measurements and from GLOBALVIEW-CO2 (2014) values. New graphics will enable comparison of surface ocean fCO2 values to atmospheric fCO2 values. The graphs will become an important quality control tool. Future data products will contain atmospheric fCO2 values calculated from atmospheric measurements and from GLOBALVIEW-CO2, in addition to the atmospheric mole fractions from GLOBALVIEW-CO2 already part of the SOCAT data products (Table 9).

In 2014, SOCAT scientists decided to allow inclusion of additional surface water parameters accompanying surface water fCO2 values in SOCAT data output files (SOCAT, 2014a). Such additional parameters might include dissolved inorganic carbon, total alkalinity, pH, nutrients, methane (CH4) and nitrous oxide (N2O) concentrations. SOCAT scientists will not carry out quality control on these additional parameters, but would welcome collaboration with other communities taking responsibility for this. These additional parameters will be made public in parallel to the official SOCAT releases. The extra parameters will be posted in separate data files to emphasise that they have not been quality-controlled. A SOCAT and MEMENTO (MarinE MethanE and NiTrous Oxide; Bange et al., 2009) working group is considering the way forward for surface water CH4 and N2O measurements (SOCAT and SOCOM, 2015).

SOCAT provides a record of the history of surface ocean CO2 research (Fig. 3). Initial, exploratory surface water CO2 measurements in the late 1950s, 1960s and 1970s were followed by more frequent CO2 data collection on research ships in the 1980s and large (inter)national research programs, such as the World Ocean Circulation Experiment (WOCE), the Joint Global Ocean Flux Study (JGOFS) and the Tropical Atmosphere Ocean (TAO) network in the 1990s. The operation of CO2 instruments on ships part of the Carbon Voluntary Observing Ships (Carbon VOS) programme, also referred to as the Ships Of Opportunity Programme (SOOP), strongly increased the number of available fCO2 values from the 1990s onwards. Data availability in the SOCAT collection has increased 4-fold from 0.2 to 0.4 million fCO2 values per year for the years 1995 to 2000 to 1.0 to 1.2 million values per year for 2005 to 2012. Nevertheless, large gaps are notable in the data collection since the year 2000, e.g. in the Indian Ocean, the South Pacific Ocean, the Mediterranean Sea, the East China Sea, the Malay Archipelago and the Sea of Okhotsk. Elsewhere, in the Arctic Ocean, measurements are being reported for the first time.

The seasonal distribution of surface ocean fCO2 values in the relatively data-rich decade from 2000 to 2009 is shown in Fig. 4. This figure highlights the lack of winter data in the high-latitude oceans, as well as the opposing seasonal cycle of surface ocean fCO2 in the subtropical and temperate oceans (Takahashi et al., 2002). The distribution of surface ocean fCO2 values per decade clearly shows the long-term increase in surface ocean fCO2 (Fig. 5), while suggesting that surface ocean fCO2 has increased slower than the atmospheric CO2 concentration since the 1990s. Figure 6 visualises the data availability as the number of months in each 1∘ latitude by 1∘ longitude grid cell with fCO2 values since 1970, both as unique months and as total months.

SOCAT and its data products are cited or named in influential international reports, in more than 100 peer-reviewed scientific publications, PhD and master's theses, book chapters and numerous other publications, as listed on the SOCAT website (http://www.socat.info/publications.html). Figure 7 shows the rapid increase in such publications, since the initiation of SOCAT in 2007 (IOCCP, 2007) and the first SOCAT release in 2011 (Pfeil et al., 2013; Sabine et al., 2013). The SOCAT data collection forms the basis of several data products (http://www.socat.info/products.html) and diverse scientific applications. These include a dozen mapping products of surface ocean pCO2 and air–sea CO2 fluxes for the global oceans (see overview in Rödenbeck et al., 2015). The SOCAT gridded product and one data product based on SOCAT (Landschützer et al., 2015) are integrated with the ESMValTool (Eyring et al., 2016) for routine evaluation of Earth system models. For the same purpose, the SOCAT gridded product is currently being integrated into the Obs4MIPs (Observations for Model Intercomparison Projects) data repository (Ferraro et al., 2015). Citation of SOCAT in high-impact reports, scientific applications of SOCAT and scientific findings based on SOCAT are discussed below.

The importance of the SOCAT synthesis is highlighted by its citation in three categories of high-impact reports, notably reports on ocean observing systems, assessments of climate change and global carbon budgeting, including carbon observing strategies, and ocean acidification studies.

Reports on ocean observing systems include publications from OceanObs'09 (Borges et al., 2010; Monteiro et al., 2010), the Framework for Ocean Observing (FOO, 2012), the Tropical Pacific Observing System 2020 (Mathis et al., 2014) and the 2nd International Indian Ocean Expedition (Hood et al., 2015).

SOCAT is used for a variety of scientific applications (Fig. 7b), some of which imply a wider relevance for SOCAT data products than envisaged during the creation of SOCAT (IOCCP, 2007). Scientific applications of SOCAT include

figures of surface ocean CO2 observations;

Examples of scientific applications of SOCAT are given below. There is no strict separation between the different types of applications identified here, with several studies belonging to more than one type of application. Many of the studies use surface ocean pCO2 values, derived from the fCO2 values reported in SOCAT data products.

Figures of surface ocean CO2 observations. Newly created figures based on the SOCAT data collection and existing figures from SOCAT publications have been used in scientific publications. Such figures generally highlight the availability or lack of surface ocean CO2 data in specific regions or seasons or over time (Chierici et al., 2012; Regnier et al., 2013; Wanninkhof et al., 2013a; Ciais et al., 2014; Majkut et al., 2014a; Brévière et al., 2015; Hofmann et al., 2015).

Use of SOCAT tools and protocols. A variety of tools and protocols has been developed in SOCAT. One of these is the definition of a continental margin mask which defines coastal waters as waters within 400 km from land (Pfeil et al., 2013). Evans and Mathis (2013) and Evans et al. (2015) use this continental margin mask. Other studies have adopted SOCAT protocols for calculation of fCO2 (Ulfsbo et al., 2014) and quality control (Sutton et al., 2014b).

Use of surface ocean fCO2 in diverse environmental studies. Regional fCO2 values from SOCAT are used in diverse environmental studies with topics ranging from ocean acidification to genomics, gas transfer velocity and evaluation of independent measurements (Blomquist et al., 2014; Larsen et al., 2014; Holding et al., 2015; Marrec et al., 2015; Bonou et al., 2016; Reum et al., 2016). Reum et al. (2016) assess the co-variance between pCO2, pH and other environmental parameters with the aim to improve the design of future ocean acidification incubation experiments. Larsen et al. (2014) establish a significant correlation between gene expression for the relative turnover (synthesis or consumption) of CO2 and surface ocean fCO2. SOCAT fCO2 values are also used for evaluation of surface ocean fCO2 estimates from eddy correlation (Blomquist et al., 2014) or from other carbonate parameters (Bonou et al., 2016) and for evaluation of regression parameterisations (Marrec et al., 2015; Xu et al., 2016).

Model-to-data comparison, model evaluation and data assimilation. SOCAT data products are used for model-to-data comparison, model evaluation and data assimilation in coupled and ocean-only biogeochemical models. Model-to-data comparisons of surface water fCO2 have been carried out for seasonal (Tjiputra et al., 2012; Arruda et al., 2015) to multi-year timescales (Tjiputra et al., 2014; McKinley et al., 2016). In several studies, model data are subsampled to surface ocean pCO2 observations from SOCAT (Séférian et al., 2014; Tjiputra et al., 2014; Turi et al., 2014). Cooley et al. (2015) evaluate surface ocean pCO2 values from an integrated assessment model with pCO2 observations from SOCAT and other sources. SOCAT data products are supporting model evaluation in context of the Coupled Model Intercomparison Project (CMIP) and beyond (Eyring et al., 2016). The SOCAT data collection is used for assimilation of surface ocean pCO2 values in global ocean biogeochemical models (While et al., 2012; Simon and Bertino, 2013, as cited in Gehlen et al., 2015). Ocean biogeochemical models have many applications, such as quantification and attribution of trends in the ocean carbon sink (Le Quéré et al., 2014, 2015a, b; Séférian et al., 2014) and forecasting population dynamics of sea scallops, which are the basis of important commercial fisheries (Cooley et al., 2015).

Detection of ocean acidification trends. A number of studies estimate trends in surface ocean pH or the carbonate concentration by combining SOCAT fCO2 values with another carbonate parameter (Lauvset and Gruber, 2014; Freeman and Lovenduski, 2015; Lauvset et al., 2015).

Regional process studies of surface ocean fCO2. Several authors investigate regional processes driving temporal or spatial variation in surface ocean fCO2 and CO2 air–sea fluxes. Examples are for the Subantarctic Indian Ocean (Lourantou and Metzl, 2011) and the eastern equatorial Pacific Ocean (Walker Brown et al., 2015).

Quantification of coastal ocean carbon sinks and sources. SOCAT data products are used for quantification of CO2 sources and sinks in coastal seas. Such studies are regional or global in extent (Chen et al., 2013; Signorini et al., 2013; Laruelle et al., 2014, 2015).

Quantification of the ocean carbon sink and its variation. An important application of the SOCAT data collection is quantification of the ocean carbon sink on seasonal to multi-year timescales with a mapping or gap-filling method. Such studies may be regional or global in extent. Studies tend to be either for the coastal seas (Signorini et al., 2013) or for the open ocean (Rödenbeck et al., 2015). The studies interpolate sparse pCO2 data from a SOCAT or LDEO synthesis product in time and space by a gap-filling method. Approaches include statistical interpolation (Rödenbeck et al., 2013; Goddijn-Murphy et al., 2015; S. D. Jones et al., 2015), multiple linear regression (Schuster et al., 2013; Signorini et al., 2013; Iida et al., 2015), neural network approaches (Landschützer et al., 2013, 2014; Nakaoka et al., 2013; Sasse et al., 2013; Zeng et al., 2014) and model-based regression and tuning (Valsala and Maksyutov, 2010; Majkut et al., 2014b). Mapping methods may be specific to individual regions (“biomes”) (Signorini et al., 2013; Landschützer et al., 2014) or may apply to the full (global) domain (e.g. Rödenbeck et al., 2013; S. D. Jones et al., 2015). Most of these approaches use additional parameters with good data coverage during the gap-filling process, for example satellite-derived sea surface temperature and chlorophyll a, as well as sea surface salinity and mixed layer depth from reanalysis. Many mapping methods use a time-dependent variable, such as time itself or the steadily increasing atmospheric CO2 mole fraction, in order to be able to reproduce a long-term increase in surface ocean pCO2.

The Surface Ocean pCO2 Mapping Intercomparison (http://www.bgc-jena.mpg.de/SOCOM/) compares the surface ocean pCO2 distribution and air–sea CO2 fluxes in 14 data-based mapping products, 10 of them using SOCAT (Rödenbeck et al., 2015). The methods vary in their characteristics, making them suitable for different space and timescales. The SOCOM initiative aims to quantify uncertainties and to identify common features in the gap-filling methods. The first SOCOM results highlight considerable differences between mapping products, especially in data-sparse regions (Rödenbeck et al., 2015).

The high-profile Global Carbon Budget uses ocean biogeochemical models for estimating trends in the global ocean carbon sink (Le Quéré et al., 2014, 2015a, b). Recent budgets also consider observation-based estimates of the ocean carbon sink using the LDEO and SOCAT synthesis products (Park et al., 2010; Landschützer et al., 2014, 2015; Rödenbeck et al., 2014). The 2015 Global Carbon Budget assesses the uncertainty in the ocean carbon sink by comparing model results to observation-based estimates (Le Quéré et al., 2015b).

Quantification of the land carbon sink. Quantification of the ocean carbon sink is critical to resolving the Global Carbon Budget and underpins the estimate of the land carbon sink (Le Quéré et al., 2014, 2015a, b). In addition, quantification of ocean–atmosphere CO2 fluxes in space and time provides priors for atmospheric inversion, thus improving estimates of the land carbon sink (Rödenbeck et al., 2014; Van der Laan et al., 2014; S. D. Jones et al., 2015).

This section provides an overview of scientific findings obtained using the SOCAT data collection.

Model-to-data comparison. Schuster et al. (2013) carry out a comparison of CO2 air–sea fluxes for the Atlantic Ocean from data-based methods, ocean biogeochemical models, ocean inversion, and atmospheric inversions. The seasonal cycle and year-to-year variation in the fluxes differ between the various methods for most Atlantic regions.

Two studies subsample model pCO2 data to surface ocean pCO2 observations derived from SOCAT. The authors conclude that ocean biogeochemical models on average underestimate the spatial and temporal variation in regional and global surface ocean pCO2 by 10 to 40 % (Séférian et al., 2014; Turi et al., 2014). This corroborates the SOCOM finding that ocean biogeochemical models underestimate the year-to-year and decadal variation in the global air–sea CO2 flux (Rödenbeck et al., 2015). However, at least one model-to-data comparison study concludes that the Community Earth System Model captures the annual to 30-year variability in the ocean carbon cycle at regional to global scales (McKinley et al., 2016). Landschützer et al. (2015) demonstrate how ocean carbon observations are delivering new insights into large and globally significant decadal changes in the ocean carbon sink. The variability in the ocean carbon sink in regions like the Southern Ocean is not apparent in modelled estimates of ocean carbon uptake or from atmospheric inverse calculations (e.g. Lenton et al., 2013).

Detection of ocean acidification trends. SOCAT-based research indicates a decrease in global surface ocean pH at a rate of -0.0018 ± 0.0004 yr-1 for 1991 to 2011 with significant decreases in 70 % of all ocean regions (Lauvset et al., 2015).

Data-based carbon budgeting. Using SOCAT and other data sources, Regnier et al. (2013) estimate that anthropogenic activities may have increased open-ocean outgassing of land-derived carbon by 0.1 Pg C yr-1. The global CO2 sink in continental shelf seas has been estimated as 0.4 Pg C yr-1 (Chen et al., 2013) and 0.19 ± 0.05 Pg C yr-1 (Laruelle et al., 2014).

Several mapping studies highlight large year-to-year variation in air–sea CO2 fluxes in the tropical Pacific Ocean (Landschützer et al., 2014; Rödenbeck et al., 2014, 2015). This variation is closely related to the El Niño–Southern Oscillation (ENSO) (Feely et al., 1999, 2002; Inoue et al., 2001; Rödenbeck et al., 2014). The variation in the equatorial Pacific Ocean roughly corresponds to 40 % of the interannual variation in the global ocean carbon sink (Rödenbeck et al., 2014), which has been estimated as 0.31 Pg C yr-1 (Rödenbeck et al., 2015).

The SOCOM comparison of mapping methods identifies an increase in global ocean carbon sink by 1 Pg C decade-1 since 2000 (Rödenbeck et al., 2015). About half of this increase in the global ocean carbon sink originates south of 35∘ S in the Southern Ocean (Landschützer et al., 2014, 2015).