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Reanalysed (depth and temperature consistent) surface ocean CO₂ atlas (SOCAT) version 2023
<p><strong>Note: The authors recommend the use of the ESA CCI-SST version of this dataset.</strong></p> <p>The Surface Ocean CO₂ Atlas (SOCAT) version 2023 dataset (Bakker et al., 2016; <a href="https://doi.org/10.25921/r7xa-bt92">https://doi.org/10.25921/r7xa-bt92</a>) is a quality-controlled dataset containing 35.6 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.</p> <p>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; 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.</p> <p>Therefore to enable the SOCAT data to be optimal for an accurate atmosphere-ocean gas flux calculation, a reanalysis methodology was developed to enable the calculation of the fugacity of CO₂ (fCO₂) for the bottom of the mass boundary layer (termed sub-skin value). The theoretical basis and justification for this is described in detail within Woolf et al., (2016) and the re-analysis methodology is described in detail in Goddijn-Murphy et al. (2015). The re-analysis calculation exploits paired in situ temperature and fCO₂ measurements in the SOCAT dataset, and uses an Earth observation dataset to provide a depth-consistent (sub-skin) temperature field to which all fugacity data are reanalysed. The outputs provide paired fCO₂ (and partial pressure of CO₂) and temperature data that correspond to a consistent sub-skin layer temperature. These can then be used to accurately calculate concentration differences and atmosphere-ocean CO₂ gas fluxes.</p> <p>This data submission contains a reanalysis of the fugacity of CO₂ (fCO₂) from the SOCAT version 2023 dataset to a consistent sub-skin temperature field. The reanalysis 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). All data processing and driver scripts are available from the FluxEngine ancillary tools repository https://github.com/oceanflux-ghg/FluxEngineAncillaryTools. The reanalysis dataset was produced for two climate quality and depth consistent temperature datasets: (1) The ESA SST-CCI sea surface temperature product (Merchant et al., 2019) and (2) The NOAA Optimum Interpolation Sea Surface Temperature (OISST) dataset (Banzon et al., 2016; Huang et al., 2021; Reynolds et al., 2007).</p> <p>For both datasets, the original daily data were first resampled to provide monthly mean values on a 1º by 1º degree grid. These data were then used as the temperature input for the reanalysis. The resulting reanalysed data are provided as a tab-separated value file (individual data 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 2023 dataset.</p> <p>The original SOCAT version 2023 data are included in full, with four additional columns containing the reanalysed data:</p> <p>* T_reynolds - The temperature (in degrees C) taken from the consistent temperature field for the corresponding time and location.</p> <p>* fCO2_reanalysed - The fugacity of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.</p> <p>* pCO2_SST - The partial pressure of CO₂ (in μatm) corresponding to the in situ (measured) temperature.</p> <p>* pCO2_reanalysed - The partial pressure of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.</p> <p>The netCDF gridded version of the reanalysed 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). The implementation of the gridding has been verified by performing the gridding on the original (non-reanalysed) SOCAT data and all results were identical to 8 decimal places. The result of gridding the original SOCAT data are included within these netCDF data, along with additional variables containing the equivalent results for the reanalysed 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). Full meta data are included within the file.</p> <p><strong>Comments</strong></p> <p>1. Due to the temporal range of the OISST and CCI-SST datasets the reanalysed values are only available from 1981 onwards. Pre-1981 rows contain "NaN" (not-a-number) in the reanalysis columns.</p> <p>2. This submission contains four files contained within a single zip file: SOCATv2023with_header.tsv, SOCATv2023.nc, SOCATv2023with_header_ESACCI.tsv and SOCATv2023_ESACCI.nc. The first two files correspond to the OISST version, and the second two the ESA SST-CCI version. The .tsv files are the ungridded data, and the .nc files are the gridded data for the corresponding temperature datasets.</p> <p>3. Please contact Daniel J. Ford (d.ford@exeter.ac.uk) if there are any questions on the dataset.</p> <p><strong>How to cite these data</strong></p> <p>Please cite the DOI of this dataset, the theory (Woolf et al., 2016), the reanalysis methodology (Goddijn-Murphy et al., 2015), 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).</p> <p><strong>Previous versions</strong></p> <p>v2019: <a href="https://doi.org/10.1594/PANGAEA.905316">https://doi.org/10.1594/PANGAEA.905316</a></p> <p>v2020: <a href="https://doi.org/10.18160/vmt4-4563">https://doi.org/10.18160/vmt4-4563</a></p> <p>v2021: <a href="https://doi.org/10.1594/PANGAEA.939233">https://doi.org/10.1594/PANGAEA.939233</a></p> <p>v2022: <a href="https://doi.org/10.5281/zenodo.8228585">https://doi.org/10.5281/zenodo.8228585</a></p> <p><strong>Acknowledgements</strong></p> <p>The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, 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.</p> <p>These data were produced with funding from the Ocean ICU project (<a href="https://ocean-icu.eu/">https://ocean-icu.eu/</a>) and the Convex Seascape Survey (<a href="https://convexseascapesurvey.com/">https://convexseascapesurvey.com/</a>). The UK part of the Horizon Europe OceanICU project is funded by UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10063673].</p>
Reanalysed (depth and temperature consistent) surface ocean CO₂ atlas (SOCAT) version 2022
<p><strong>Note: The authors recommend the use of the ESA CCI-SST version of this dataset.</strong></p> <p>The Surface Ocean CO₂ Atlas (SOCAT) version 2022 dataset (Bakker et al., 2016; <a href="https://doi.org/10.25921/1h9f-nb73">https://doi.org/10.25921/1h9f-nb73</a>) is a quality-controlled dataset containing 33.7 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.</p> <p>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; 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.</p> <p>Therefore to enable the SOCAT data to be optimal for an accurate atmosphere-ocean gas flux calculation, a reanalysis methodology was developed to enable the calculation of the fugacity of CO₂ (fCO₂) for the bottom of the mass boundary layer (termed sub-skin value). The theoretical basis and justification for this is described in detail within Woolf et al., (2016) and the re-analysis methodology is described in detail in Goddijn-Murphy et al. (2015). The re-analysis calculation exploits paired in situ temperature and fCO₂ measurements in the SOCAT dataset, and uses an Earth observation dataset to provide a depth-consistent (sub-skin) temperature field to which all fugacity data are reanalysed. The outputs provide paired fCO₂ (and partial pressure of CO₂) and temperature data that correspond to a consistent sub-skin layer temperature. These can then be used to accurately calculate concentration differences and atmosphere-ocean CO₂ gas fluxes.</p> <p>This data submission contains a reanalysis of the fugacity of CO₂ (fCO₂) from the SOCAT version 2022 dataset to a consistent sub-skin temperature field. The reanalysis 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). All data processing and driver scripts are available from the FluxEngine ancillary tools repository https://github.com/oceanflux-ghg/FluxEngineAncillaryTools. The reanalysis dataset was produced for two climate quality and depth consistent temperature datasets: (1) The ESA SST-CCI sea surface temperature product (Merchant et al., 2019) and (2) The NOAA Optimum Interpolation Sea Surface Temperature (OISST) dataset (Banzon et al., 2016; Huang et al., 2021; Reynolds et al., 2007).</p> <p>For both datasets, the original daily data were first resampled to provide monthly mean values on a 1º by 1º degree grid. These data were then used as the temperature input for the reanalysis. The resulting reanalysed data are provided as a tab-separated value file (individual data 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 2022 dataset.</p> <p>The original SOCAT version 2022 data are included in full, with four additional columns containing the reanalysed data:</p> <p>* T_reynolds - The temperature (in degrees C) taken from the consistent temperature field for the corresponding time and location.</p> <p>* fCO2_reanalysed - The fugacity of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.</p> <p>* pCO2_SST - The partial pressure of CO₂ (in μatm) corresponding to the in situ (measured) temperature.</p> <p>* pCO2_reanalysed - The partial pressure of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.</p> <p>The netCDF gridded version of the reanalysed 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). The implementation of the gridding has been verified by performing the gridding on the original (non-reanalysed) SOCAT data and all results were identical to 8 decimal places. The result of gridding the original SOCAT data are included within these netCDF data, along with additional variables containing the equivalent results for the reanalysed 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). Full meta data are included within the file.</p> <p><strong>Comments</strong></p> <p>1. Due to the temporal range of the OISST and CCI-SST datasets the reanalysed values are only available from 1981 onwards. Pre-1981 rows contain "NaN" (not-a-number) in the reanalysis columns.</p> <p>2. This submission contains four files contained within a single zip file: SOCATv2022with_header.tsv, SOCATv2022.nc, SOCATv2022with_header_ESACCI.tsv and SOCATv2022_ESACCI.nc. The first two files correspond to the OISST version, and the second two the ESA SST-CCI version. The .tsv files are the ungridded data, and the .nc files are the gridded data for the corresponding temperature datasets.</p> <p>3. Please contact Daniel J. Ford (d.ford@exeter.ac.uk) if there are any questions on the dataset.</p> <p><strong>How to cite these data</strong></p> <p>Please cite the DOI of this dataset, the theory (Woolf et al., 2016), the reanalysis methodology (Goddijn-Murphy et al., 2015), 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).</p> <p>Previous versions:</p> <p>v2019: <a href="https://doi.org/10.1594/PANGAEA.905316">https://doi.org/10.1594/PANGAEA.905316</a></p> <p>v2020: <a href="https://doi.org/10.18160/vmt4-4563">https://doi.org/10.18160/vmt4-4563</a></p> <p>v2021: <a href="https://doi.org/10.1594/PANGAEA.939233">https://doi.org/10.1594/PANGAEA.939233</a></p> <p><strong>Acknowledgements</strong></p> <p>The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, 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.</p> <p>These data were produced with funding from the Ocean ICU project (<a href="https://ocean-icu.eu/">https://ocean-icu.eu/</a>) and the Convex Seascape Survey (<a href="https://convexseascapesurvey.com/">https://convexseascapesurvey.com/</a>). The UK part of the Horizon Europe OceanICU project is funded by UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10063673].</p>
SOCAT+USV sampling masks for ML reconstruction of surface ocean pCO2 using the Large Ensemble Testbed
<p>Here we provide sampling masks used in the study "Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling" (Heimdal et al., 2023, https://doi.org/10.5194/bg-2023-160). In this paper, we reconstruct surface ocean pCO2 using the Large Ensemble Testbed (Gloege et al., 2021, https://doi.org/10.1029/2020GB006788) and the pCO2-Residual method (Bennington et al., 2022, https://doi.org/10.1029/2021MS002960). We provide 11 different sampling masks that correspond to the experiments presented in Heimdal et al. (2023), which include different sampling patterns of USV Saildrones in the Southern Ocean (SOCAT+USV sampling).</p>
Reanalysed (depth and temperature consistent) surface ocean CO₂ atlas (SOCAT) version 2024
<p><strong>Changelog:</strong></p> <p>v2: Updates the recalculation process to using daily ESA CCI-SSTv3 and updates the temperature sensivity to those in Humphreys (2024). OISST version of the recalculated SOCAT data are no longer produced.</p> <p>v1.1: Corrects an error in the ESA CCI-SSTv3 tsv file having all NaN's for the reanalysis temperature and fCO2(sw). No other files were affected.</p> <p>v1: Initial Release</p> <p> </p> <p><strong>Description</strong></p> <p>The Surface Ocean CO₂ Atlas (SOCAT) version 2024 dataset (Bakker et al., 2016; <a href="https://doi.org/10.25921/9wpn-th28">https://doi.org/10.25921/648f-fv35</a>) is a quality-controlled dataset containing 38.6 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.</p> <p>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.</p> <p>Therefore to enable the SOCAT data to be optimal for an accurate atmosphere-ocean gas flux calculation, a recalculation methodology was developed to enable the calculation of the fugacity of CO₂ (fCO₂) for the bottom of the mass boundary layer (termed sub-skin value). The theoretical basis and justification for this is described in detail within Woolf et al., (2016) and the recalculation methodology is described in detail in 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 (sub-skin) temperature field to which all fugacity data are reanalysed. The outputs provide paired fCO₂ (and partial pressure of CO₂) and temperature data that correspond to a consistent sub-skin layer temperature. These can then be used to accurately calculate concentration differences and atmosphere-ocean CO₂ gas fluxes.</p> <p>This data submission contains a recalculation of the fugacity of CO₂ (fCO₂<sub> (sw)</sub>) from the SOCAT version 2024 dataset to a consistent sub-skin 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 and depth consistent (0.2 m) temperature dataset, the European Space Agency (ESA) Climate Change Initiative sea surface temperature (CCI-SST) product V3 (Embury et al., 2024; Good & Embury, 2024)</p> <p>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 fCO<sub>2 (sw)</sub> was then recalculated to the CCI-SST temperature using the updated temperature sensitivities described in Humphreys (2024).</p> <p>We have applied the recalculation process to both the main SOCAT dataset (data flags A,B,C,D).</p> <p><strong> </strong></p> <p><strong>Data records</strong></p> <p>The resulting reanalysed 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 2024 dataset.</p> <p>The original SOCAT version 2024 data are included in full, with five additional columns containing the reanalysed data:</p> <p>* T_subskin - The temperature (in degrees C) taken from the consistent temperature field for the corresponding time and location.</p> <p>* T_subskin_uncertainty - The uncertainty (in degrees C) taken from the consistent temperature field for the corresponding time and location.</p> <p>* fCO2_reanalysed - The fugacity of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_subskin.</p> <p>* pCO2_SST - The partial pressure of CO₂ (in μatm) corresponding to the in situ (measured) temperature.</p> <p>* pCO2_reanalysed - The partial pressure of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_subskin.</p> <p>The netCDF gridded version of the reanalysed 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 0.02 μatm and for the cruise weighted fCO2 (sw) a difference of 0.21 μatm (N = 370920). Within the unweighted data, ~1200 monthly 1 degree regions (~0.3 %) have a difference greater than ±1 μatm, which occur in locations where SOCAT fCO<sub>2 (sw)</sub> observations could not be matched to the satellite reference data and therefore were not included in the gridding. The cruise-weighted data has ~10,000 of the monthly 1 degree regions (~2 %) with a difference greater than ±1 μatm, which occur in the same regions as the unweighted data.</p> <p>The original SOCAT data are included within these netCDF data, along with additional variables containing the equivalent results for the reanalysed 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 SST fields at monthly 1 degree resolution are included within the netCDF file (‘sst_subskin_full’), so that the SST data can be used in any further processing (e.g. used with fCO<sub>2 (sw)</sub><sup> </sup>interpolation approaches). Full meta data are included within the file.</p> <p><strong> </strong></p> <p><strong>Quick-start Guide</strong></p> <p><strong>Individual cruise data (.tsv files)</strong></p> <p>The individual cruise data files include the original SOCAT data as well as recalculated fCO<sub>2 (sw)</sub> (‘fCO2_reanalysed [uatm]’ column) with their paired temperatures (‘T_subskin [C]’ column). The original SOCAT data columns for fCO<sub>2 (sw)</sub> (‘fCO2_rec [uatm]’) and SST (‘SST [deg C]’) can be replaced with the recalculated columns as a quick start.</p> <p> </p> <p><strong>Gridded cruise data (monthly 1 degree; .nc files)</strong></p> <p>The gridded cruise data files are the original SOCAT netcdf files (unweighted and cruise weighted), that have been appended with the recalculated values.</p> <p>If users use the unweighted SOCAT fCO<sub>2</sub><sub> </sub><sub>(sw)</sub> (‘fco2_ave_unwtd’) with its paired temperature (‘sst_ave_unwtd’). These variables can be replaced with the recalculated fCO<sub>2 (sw)</sub> (‘fco2_reanalysed_ave_unwtd’) and the paired temperature (‘sst_subskin_unweighted’).</p> <p>If users use the cruise-weighted SOCAT fCO<sub>2</sub><sub> </sub><sub>(sw)</sub> (‘fco2_ave_weighted’) with its paired temperature (‘sst_ave_weighted’). These variables can be replaced with the recalculated fCO<sub>2 (sw)</sub> (‘fco2_reanalysed_ave_weighted’) and the paired temperature (‘sst_subskin_weighted’).</p> <p><strong> </strong></p> <p><strong>Additional information</strong></p> <p>1. Due to the temporal range of the ESA CCI-SST the recalculated values are only available from 1980 onwards.</p> <p>2. This submission contains two files contained within a single zip file: Fordetal_SOCATv2024_ESACCIv3_biascorrected_Humpherys_daily_v2.nc</p> <p>Fordetal_SOCATv2024_ESACCIv3_biascorrected_Humpherys_daily_unc_withheader_v2.tsv</p> <p>3. Please contact Daniel J. Ford (d.ford@exeter.ac.uk) if there are any questions on the dataset.</p> <p> </p> <p><strong>How to cite these data </strong></p> <p>Please cite the DOI of this dataset, the theory (Woolf et al., 2016), the reanalysis methodology (Goddijn-Murphy et al., 2015), 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).</p> <p><strong> </strong></p> <p><strong>Previous versions</strong></p> <p>v2019: <a href="https://doi.org/10.1594/PANGAEA.905316">https://doi.org/10.1594/PANGAEA.905316</a></p> <p>v2020: <a href="https://doi.org/10.18160/vmt4-4563">https://doi.org/10.18160/vmt4-4563</a></p> <p>v2021: <a href="https://doi.org/10.1594/PANGAEA.939233">https://doi.org/10.1594/PANGAEA.939233</a></p> <p>v2022: <a href="https://doi.org/10.5281/zenodo.8228585">https://doi.org/10.5281/zenodo.8228585</a></p> <p>v2023: <a href="https://doi.org/10.5281/zenodo.8229316">https://doi.org/10.5281/zenodo.8229316</a></p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, 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.</p> <p>These data were produced with funding from the Ocean ICU project (<a href="https://ocean-icu.eu/">https://ocean-icu.eu/</a>), the Convex Seascape Survey (<a href="https://convexseascapesurvey.com/">https://convexseascapesurvey.com/</a>) and the European Space Agency Ocean Carbon 4 Climate project (OC4C; 3-18399/24/I-NB). This work was funded by the European Union under grant agreement no. 101083922 (OceanICU) and UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10054454, 10063673, 10064020, 10059241, 10079684, 10059012, 10048179]. The views, opinions and practices used to produce this dataset/software are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</p> <p> </p> <p><strong>References</strong></p> <p>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</p> <p>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 fCO<sub>2</sub> data in version 3 of the Surface Ocean CO<sub>2</sub> Atlas (SOCAT). <em>Earth System Science Data</em>, <em>8</em>(2), 383–413. https://doi.org/10.5194/essd-8-383-2016</p> <p>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 CO<sub>2</sub> Flux Estimates. <em>Global Biogeochemical Cycles</em>, <em>36</em>(9). https://doi.org/10.1029/2022GB007360</p> <p>Dong, Y., Bakker, D. C. E., Bell, T. G., Yang, M., Landschützer, P., Hauck, J., et al. (2024). Direct observational evidence of strong CO<sub>2</sub> uptake in the Southern Ocean. <em>Science Advances</em>, <em>10</em>(30), eadn5781. https://doi.org/10.1126/sciadv.adn5781</p> <p>Embury, O. (2023). SST CCI Product Validation and Intercomparison Report. https://climate.esa.int/documents/2369/SST_CCI_D4.1_PVIR_v2.1-signed.pdf</p> <p>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. <em>Scientific Data</em>, <em>11</em>(1), 326. https://doi.org/10.1038/s41597-024-03147-w</p> <p>Ford, D. J., Shutler, J. D., Blanco-Sacristán, J., Corrigan, S., Bell, T. G., Yang, M., et al. (2024). Enhanced ocean CO<sub>2</sub> uptake due to near-surface temperature gradients. <em>Nature Geoscience</em>. https://doi.org/10.1038/s41561-024-01570-7</p> <p>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 CO<sub>2</sub> fugacity in support of air-sea gas flux studies. <em>Ocean Science</em>, <em>11</em>(4), 519–541. https://doi.org/10.5194/os-11-519-2015</p> <p>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. https://doi.org/10.5285/4A9654136A7148E39B7FEB56F8BB02D2</p> <p>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. <em>Ocean Science</em>, <em>15</em>(6), 1707–1728. https://doi.org/10.5194/os-15-1707-2019</p> <p>Humphreys, M. P. (2024). Temperature effect on seawater <em>f</em> CO<sub>2</sub> revisited: theoretical basis, uncertainty analysis and implications for parameterising carbonic acid equilibrium constants. <em>Ocean Science</em>, <em>20</em>(5), 1325–1350. https://doi.org/10.5194/os-20-1325-2024</p> <p>Sabine, C. L., Hankin, S., Koyuk, H., Bakker, D. C. E., Pfeil, B., Olsen, A., et al. (2013). Surface Ocean CO<sub>2</sub> Atlas (SOCAT) gridded data products. <em>Earth System Science Data</em>, <em>5</em>(1), 145–153. https://doi.org/10.5194/essd-5-145-2013</p> <p>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. <em>Journal of Atmospheric and Oceanic Technology</em>, <em>33</em>(4), 741–756. https://doi.org/10.1175/JTECH-D-14-00204.1</p> <p>Watson, A. J., Schuster, U., Shutler, J. D., Holding, T., Ashton, I. G. C., Landschützer, P., et al. (2020). Revised estimates of ocean-atmosphere CO<sub>2</sub> flux are consistent with ocean carbon inventory. <em>Nature Communications</em>, <em>11</em>(1), 1–6. https://doi.org/10.1038/s41467-020-18203-3</p> <p>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 CO<sub>2</sub> in the presence of temperature and salinity gradients. <em>Journal of Geophysical Research: Oceans</em>, <em>121</em>(2), 1229–1248. https://doi.org/10.1002/2015JC011427</p> <p> </p>
Float+SOCAT sampling masks for ML reconstruction of surface ocean pCO2 using the Large Ensemble Testbed
<p>Here we provide sampling masks used in the study "The importance of adding unbiased Argo observations to the ocean carbon observing system" (Heimdal & McKinley, 2024, Scientific Reports). In this paper, we reconstruct surface ocean pCO2 using the Large Ensemble Testbed (Gloege et al., 2021, https://doi.org/10.1029/2020GB006788) and the pCO2-Residual method (Bennington et al., 2022, https://doi.org/10.1029/2021MS002960). We provide 2 different sampling masks used in the experiments presented in Heimdal & McKinley (2024). These masks represent two different float sampling schemes (+SOCAT) including 500 floats, corresponding to historical Argo float observations (https://fleetmonitoring.euro-argo.eu/dashboardpatterns) and potential optimized float sampling (following Chamberlain et al., 2023, <a href="https://doi.org/10.1175/JTECH-D-22-0093.1" target="_blank" rel="noopener">https://doi.org/10.1175/JTECH-D-22-0093.1</a>). </p>
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