Recalculated (depth and temperature consistent) surface ocean CO₂ atlas (SOCAT) version 2026
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Product Information SOCAT dataset version v2026 Recalculated SOCAT dataset version V0-1 Changelog at end of repository FluxEngine version v4.2.0 Contacts Daniel J. Fordd.ford@exeter.ac.uk Jamie D. Shutlerj.d.shutler@exeter.ac.uk Introduction The Surface Ocean CO₂ Atlas (SOCAT) version 2026 dataset (Bakker et al., 2016; https://doi.org/10.25921/8dba-fr90) is a quality-controlled dataset containing 44.0 million surface ocean gaseous CO₂ measurements collated from thousands of individual submissions. These gaseous CO₂ measurements are typically collected at many different depths (of the order of several metres below the surface) using many different systems, and the sampling depth varies dependent upon the sampling platform and/or setup. Different platforms (e.g. ships of opportunity, research vessels) and systems will collect water samples at different depths, and the sampling depth can even vary dependent upon sea state. Therefore, the collated SOCAT dataset contains high quality data, but these data are all valid for different and inconsistent depths. Therefore, the SOCAT provided individual gaseous CO₂ measurements and gridded data are sub-optimal for calculating global or regional atmosphere-ocean gas exchange (and the resultant net CO₂ sinks) and sub-optimal for verifying gas fluxes from (or assimilation into) numerical models. Accurate calculations of CO₂ flux between the atmosphere and oceans require CO₂ concentrations at the top and bottom of the mass boundary layer, the ~100 μm deep layer that forms the interface between the ocean and the atmosphere (Woolf et al., 2016). Ignoring vertical temperature gradients across this very small layer can result in significant biases in the concentration differences and the resulting gas fluxes (e.g. ~5 to 29% underestimate in global net CO₂ sink values; (Dong et al., 2022, 2024; Ford et al., 2024; Watson et al., 2020; Woolf et al., 2016)). It is currently impossible to measure the CO₂ concentrations either side of this very thin layer, but it is possible to calculate the concentrations either side of this layer using the SOCAT data, satellite observations and knowledge of the carbonate system. Therefore to enable the SOCAT data to be optimal for an accurate atmosphere-ocean gas flux calculation and optimal for assessing model data, a recalculation methodology was developed to enable the calculation of the fugacity of CO₂ (fCO₂) for the bottom of the mass boundary layer (termed here as the reference SST). The theoretical basis and justification for this is described in detail within Woolf et al., (2016) and the updated recalculation methodology is described in Ford et al. (2026), developed from Goddijn-Murphy et al. (2015). The recalculation exploits paired in situ temperature and fCO₂ measurements in the SOCAT dataset and uses an Earth observation dataset to provide a depth-consistent (reference) temperature field to which all fugacity data are recalculated. The outputs provide paired fCO₂ (and partial pressure of CO₂) and temperature data that correspond to a consistent reference temperature. This accounts for the inconsistency in depth in the SOCAT data set, which can be used to accurately calculate concentration differences and atmosphere-ocean CO₂ gas fluxes. This data submission contains a recalculation of the fugacity of CO₂ in seawater (fCO₂ (sw)) from the SOCAT version 2026 dataset to a consistent reference (subskin) temperature field. The recalculation was performed using a tool that is distributed within the FluxEngine open source software toolkit (https://github.com/oceanflux-ghg/FluxEngine) (Holding et al., 2019; Shutler et al., 2016). The recalculated SOCAT dataset was produced with a climate quality, time and depth consistent (0.2 m @ 10:30) temperature dataset, the European Space Agency (ESA) Climate Change Initiative sea surface temperature (CCI-SST) product V3 (Embury et al., 2024; Good & Embury, 2024) Following the recommendations in Dong et al (2022), the CCI-SST was bias corrected with respect to SST drifters (~0.2m) to remove a cool bias (0.04K) identified in the CCI-SST validation report (Embury, 2023) and a climate data record intercomparison (Atkinson et al., 2023). This bias correction was applied as a globally fixed value. The daily CCI-SST data (0.05 º; ~5km at the equator) were linearly interpolated to the SOCAT observations, providing both an SST and SST uncertainty values representative for each individual SOCAT observation. The SOCAT fCO2 (sw) was then recalculated to the CCI-SST temperature using the updated temperature sensitivities described in Humphreys (2024) using PyCO2SYS v1.8.3.3 (Humphreys et al., 2022, 2024). The uncertainty on the recalculated fCO2 (sw) was calculated by propagating the in situ SST, in situ air pressure (based on the SOCAT quality flags; A, B, C, D and E) and reference SST uncertainties through the recalculation processing. Finally the fCO2 (sw) was converted to equivalent pCO2 (sw) and uncertainty. The methodology and analysis are described fully in Ford et al. (2026) where we refer the reader. Data records We have applied the recalculation process to: 1) The main SOCAT dataset (covering data flags A, B, C and D) - file name structure begins with: Fordetal_SOCATv2026_ESACCIv3... 2) The SOCAT flag E dataset (covering data flag E) - file name structure begins with: Fordetal_SOCATv2026_FlagE_ESACCIv3.... The resulting recalculated data are provided as a tab-separated value file (individual cruise points) and as netCDF-5 file (gridded monthly means). These are the same file formats as provided by SOCAT and analogous to the SOCAT single data point and gridded data. Each row in the tab-separated value file corresponds to a row in the original SOCAT version 2026 dataset. The original SOCAT version 2026 data are included in full within the tab-separated files, with seven additional columns containing the recalculated data summarised in Table 1. Table 1: Column information for the 7 added columns within the recalculated SOCAT tab-separated files. Column Name Description T_reference The temperature (in degrees C) taken from the consistent temperature field for the corresponding time and location. T_reference_uncertainty The uncertainty (in degrees C) taken from the consistent temperature field for the corresponding time and location. This represents a 1 standard deviation equivalent uncertainty. fCO2_recalculated The fugacity of CO₂ (in μatm) recalculated to the consistent surface temperature indicated by T_reference. fCO2_recalculated_unc The uncertainty in the recalculated fugacity of CO2 (in µatm) due to the in situ temperature, pressure and the satellite reference temperature uncertainties. This represents a 1 standard deviation equivalent uncertainty. pCO2_rec The partial pressure of CO₂ (in μatm) corresponding to the in situ (measured) temperature. pCO2_recalculated The partial pressure of CO₂ (in μatm) recalculated to the consistent surface temperature indicated by T_reference. pCO2_recalculated_unc The uncertainty in the recalculated partial pressure of CO2 (in µatm) due to the in situ temperature, pressure and the satellite reference temperature uncertainties. This represents a 1 standard deviation equivalent uncertainty. The netCDF gridded version of the recalculated dataset contains monthly mean data, binned into a 1º by 1º grid and uses the same units, missing value indicators and time and space resolution as the original SOCAT gridded product to maximise compatibility. The gridding is performed using the SOCAT gridding methodology (Sabine et al., 2013). As a consistency check to confirm the gridding method and precision, values within the gridded dataset are cross-checked against the original SOCAT gridded dataset. The unweighted SOCAT fCO2 (sw) showed a mean absolute difference of 0.04 μatm and for the cruise weighted fCO2 (sw) a difference of 0.22 μatm (N = 398396). Within the unweighted data, 1043 monthly 1 degree regions (~0.3 %) have a difference greater than ±1 μatm, which occur in locations where SOCAT fCO2 (sw) observations could not be matched to the satellite SST reference data and therefore were not included in the gridding. The cruise-weighted data has 11,026 of the monthly 1 degree regions (~2 %) with a difference greater than ±1 μatm, which occur in the same regions as the unweighted data. The original SOCAT data are included within these netCDF data, along with additional variables containing the equivalent results for the recalculated SOCAT data. Statistical sample mean, minimum, maximum, standard deviation and count data for each grid cell are included, with unweighted and cruise-weighted versions (following the convention used by SOCAT). In addition the full satellite reference SST fields at monthly 1 degree resolution are included within the netCDF file (‘sst_reference_full’), so that the reference SST data can be used in any further processing (e.g. used within fCO2 (sw) interpolation approaches). Full meta data are included within the file. Technical Validation In this section we provide statistical analysis of the recalculated SOCATv2026 following the analysis in Ford et al. (2026). Please find this section within the documentation PDF within the repository files. Quick-start Guide Individual cruise data (.tsv files) The individual cruise data files include the original SOCAT data as well as recalculated fCO2 (sw) (‘recalculated_fCO2 [uatm]’ column) with their paired temperatures (‘T_reference [deg.C]’ column). The original SOCAT data columns for fCO2 (sw) (‘fCO2rec [uatm]’) and SST (‘SST [deg.C]’) can be replaced with the recalculated columns as a quick start. Gridded cruise data (monthly 1 degree; .nc files) The gridded cruise data files are the original SOCAT netcdf files (unweighted and cruise weighted), that have been appended with the recalculated values. If users currently use the unweighted SOCAT fCO2 (sw) (‘fco2_ave_unwtd’) with its paired temperature (‘sst_ave_unwtd’). These variables can be replaced with the recalculated fCO2 (sw) (‘fco2_recalculated_ave_unwtd’) and the paired temperature (‘sst_reference_unweighted’). If users currently use the cruise-weighted SOCAT fCO2 (sw) (‘fco2_ave_weighted’) with its paired temperature (‘sst_ave_weighted’). These variables can be replaced with the recalculated fCO2 (sw) (‘fco2_recalculated_ave_weighted’) and the paired temperature (‘sst_reference_weighted’). Additional information 1. Due to the temporal range of the ESA CCI-SST the recalculated values are only available from 1980 onwards. 2. This submission contains four files contained within individual zip files (i.e 4 zip files) and a documentation file (pdf file): Fordetal_SOCATv2026_ESACCIv3_biascorrected_Humpherys_daily_v0-1.nc Fordetal_SOCATv2026_ESACCIv3_biascorrected_Humpherys_daily_unc_withheader_v0-1.tsv Fordetal_SOCATv2026_FlagE_ESACCIv3_biascorrected_Humpherys_daily_v0-1.nc Fordetal_SOCATv2026_FlagE_ESACCIv3_biascorrected_Humpherys_daily_unc_withheader_v0-1.tsv Recalculated_SOCATv2026_documentation_technical_summary_v0-1.pdf How to cite these data Please cite the DOI of this dataset, the data description manuscript (Ford et al., 2026), the theory (Woolf et al., 2016), the FluxEngine toolbox which was used to perform the reanalysis (Holding et al., 2019; Shutler et al., 2016) and the original SOCAT dataset (Bakker et al., 2016) and/or gridded equivalent (Sabine et al., 2013). Within the acknowledgments section of any work using these recalculated SOCAT data please include the SOCAT fair use acknowledgement: https://socat.info/index.php/citing-socat/ Previous versions Recalculated (or previously called reanalysed) SOCAT dataset are available going back to SOCATv2019, where each version has been recalculated using the best method at the time of creation. v2019: https://doi.org/10.1594/PANGAEA.905316 v2020: https://doi.org/10.18160/vmt4-4563 v2021: https://doi.org/10.1594/PANGAEA.939233 v2022: https://doi.org/10.5281/zenodo.8228585 v2023: https://doi.org/10.5281/zenodo.8229316 v2024: https://doi.org/10.5281/zenodo.12775306 v2025: https://doi.org/10.5281/zenodo.15656802 Changelog Version Changes on previous v0-1 Preliminary release with basic checks applied to the dataset. FluxEngine version used v4.2.0 Acknowledgements The Surface Ocean CO2 Atlas (SOCAT) is an international effort, endorsed by the SCOR Infrastructural Project International Ocean Carbon Coordination Project (IOCCP) and the Surface Ocean Lower Atmosphere Study (SOLAS), to deliver a uniformly quality-controlled surface ocean CO2 database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT. These data were produced with funding from the European Space Agency Ocean Carbon for Climate project (OC4C; 3-18399/24/I-NB). References Atkinson, C., Rayner, N., Kennedy, J., Sikorski, T., Bonino, G., Quilestino-Olario, R., et al. (2023, November 30). ESA CCI Phase 3 Sea Surface Temperature (SST): Climate Assessment Report D5.1 v1.1. Retrieved July 18, 2024, from https://climate.esa.int/documents/2370/SST_CCI_D5.1_CAR_v1.1-signed.pdf Bakker, D. C. E., Pfeil, B., Landa, C. S., Metzl, N., O’Brien, K. M., Olsen, A., et al. (2016). A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT). Earth System Science Data, 8(2), 383–413. https://doi.org/10.5194/essd-8-383-2016 Dong, Y., Bakker, D. C. E., Bell, T. G., Huang, B., Landschützer, P., Liss, P. S., & Yang, M. (2022). Update on the Temperature Corrections of Global Air‐Sea CO2 Flux Estimates. Global Biogeochemical Cycles, 36(9). https://doi.org/10.1029/2022GB007360 Dong, Y., Bakker, D. C. E., Bell, T. G., Yang, M., Landschützer, P., Hauck, J., et al. (2024). Direct observational evidence of strong CO2 uptake in the Southern Ocean. Science Advances, 10(30), eadn5781. https://doi.org/10.1126/sciadv.adn5781 Embury, O. (2023). SST CCI Product Validation and Intercomparison Report. Retrieved from https://climate.esa.int/documents/2369/SST_CCI_D4.1_PVIR_v2.1-signed.pdf Embury, O., Merchant, C. J., Good, S. A., Rayner, N. A., Høyer, J. L., Atkinson, C., et al. (2024). Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data, 11(1), 326. https://doi.org/10.1038/s41597-024-03147-w Ford, D. J., Shutler, J. D., Blanco-Sacristán, J., Corrigan, S., Bell, T. G., Yang, M., et al. (2024). Enhanced ocean CO2 uptake due to near-surface temperature gradients. Nature Geoscience. https://doi.org/10.1038/s41561-024-01570-7 Ford, D. J., Shutler, J. D., Holding, T., Sims, R. P., Ashton, I., Woolf, D. K., et al. (2026). Recalculating the Surface Ocean CO2 Atlas (SOCAT) to a sea surface temperature climate data record. Scientific Data. https://doi.org/10.1038/s41597-026-07532-5 Goddijn-Murphy, L. M., Woolf, D. K., Land, P. E., Shutler, J. D., & Donlon, C. (2015). The OceanFlux Greenhouse Gases methodology for deriving a sea surface climatology of CO2 fugacity in support of air-sea gas flux studies. Ocean Science, 11(4), 519–541. https://doi.org/10.5194/os-11-519-2015 Good, S. A., & Embury, O. (2024). ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Level 4 Analysis product, version 3.0 [Application/xml]. NERC EDS Centre for Environmental Data Analysis [dataset]. https://doi.org/10.5285/4A9654136A7148E39B7FEB56F8BB02D2 Holding, T., Ashton, I. G., Shutler, J. D., Land, P. E., Nightingale, P. D., Rees, A. P., et al. (2019). The FluxEngine air–sea gas flux toolbox: simplified interface and extensions for in situ analyses and multiple sparingly soluble gases. Ocean Science, 15(6), 1707–1728. https://doi.org/10.5194/os-15-1707-2019 Humphreys, M. P. (2024). Temperature effect on seawater fCO2 revisited: theoretical basis, uncertainty analysis and implications for parameterising carbonic acid equilibrium constants. Ocean Science, 20(5), 1325–1350. https://doi.org/10.5194/os-20-1325-2024 Humphreys, M. P., Lewis, E. R., Sharp, J. D., & Pierrot, D. (2022). PyCO2SYS v1.8: marine carbonate system calculations in Python. Geoscientific Model Development, 15(1), 15–43. https://doi.org/10.5194/gmd-15-15-2022 Humphreys, M. P., Schiller, A. J., Sandborn, D., Gregor, L., Pierrot, D., van Heuven, S. M. A. C., et al. (2024, September 13). PyCO2SYS: marine carbonate system calculations in Python (Version v1.8.3.3). Zenodo. https://doi.org/10.5281/ZENODO.3744275 Sabine, C. L., Hankin, S., Koyuk, H., Bakker, D. C. E., Pfeil, B., Olsen, A., et al. (2013). Surface Ocean CO2 Atlas (SOCAT) gridded data products. Earth System Science Data, 5(1), 145–153. https://doi.org/10.5194/essd-5-145-2013 Shutler, J. D., Land, P. E., Piolle, J. F., Woolf, D. K., Goddijn-Murphy, L., Paul, F., et al. (2016). FluxEngine: A flexible processing system for calculating atmosphere-ocean carbon dioxide gas fluxes and climatologies. Journal of Atmospheric and Oceanic Technology, 33(4), 741–756. https://doi.org/10.1175/JTECH-D-14-00204.1 Watson, A. J., Schuster, U., Shutler, J. D., Holding, T., Ashton, I. G. C., Landschützer, P., et al. (2020). Revised estimates of ocean-atmosphere CO2 flux are consistent with ocean carbon inventory. Nature Communications, 11(1), 1–6. https://doi.org/10.1038/s41467-020-18203-3 Woolf, D. K., Land, P. E., Shutler, J. D., Goddijn-Murphy, L. M., & Donlon, C. J. (2016). On the calculation of air-sea fluxes of CO2 in the presence of temperature and salinity gradients. Journal of Geophysical Research: Oceans, 121(2), 1229–1248. https://doi.org/10.1002/2015JC011427



