DOI: 10.5281/zenodo.13284017
title: Reanalysed (depth and temperature consistent) surface ocean CO₂ atlas (SOCAT) version 2024 (v1.1)

description: Note: The authors recommend the use of the ESA CCI-SSTv3 version of this dataset. The OISSTv2.1 version is produced as a legacy product, and do not recommend using it.

Changelog:

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.

v1: Initial Release

 

Description

The Surface Ocean CO₂ Atlas (SOCAT) version 2024 dataset (Bakker et al., 2016; https://doi.org/10.25921/9wpn-th28) 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.

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.

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.

This data submission contains a reanalysis of the fugacity of CO₂ (fCO₂) from the SOCAT version 2024 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 European Space Agency (ESA) Climate Change Initiative sea surface temperature (CCI-SST) product V3 (Embury et al., 2024) and (2) The NOAA Optimum Interpolation Sea Surface Temperature (OISSTv2.1) dataset (Banzon et al., 2016; Huang et al., 2021; Reynolds et al., 2007).

For both datasets, the original daily data were first resampled to provide monthly mean values on a 1º by 1º degree grid. The analysis in Dong et al. (2022), identified that the OISSTv2.1 presented a cool bias with respect to surface drifter buoy temperatures that are considered representative of the subskin temperature (~20cm). Embury et al. (2024) shows that the CCI-SSTv3 also has a cool bias with respect to the surface drifters. For the CCI-SST, we correct this global and stable in time bias of ~0.05K before using these monthly 1º as input for the reanalysis process. The OI-SST has a time varying bias with respect to the global drifters, which in the 1980s and 1990s is ~0.09K (Dong et al., 2022), and reduces to ~0.00K in 2023. Huang et al. (2021) highlight modifications to the OI-SSTv2.1 methodology that are only applied from January 2016. However, the CCI-SST team have identified that the OISST data record appears to be drifting from the CCI-SST data from 2015 onwards (Atkinson et al., 2023), which could be a time calibration issue or a methodological issue with some of the satellites being used (e.g. a cloud masking problem). We therefore do not correct the OI-SSTv2.1 data to surface drifters due to this time varying bias, but still produce the reanalysed SOCAT OISST version for legacy purposes. We highly recommend that the CCI-SST data are used instead of OISST.

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 2024 dataset.

The original SOCAT version 2024 data are included in full, with four additional columns containing the reanalysed data:

* T_reynolds - The temperature (in degrees C) taken from the consistent temperature field for the corresponding time and location.

* fCO2_reanalysed - The fugacity of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.

* pCO2_SST - The partial pressure of CO₂ (in μatm) corresponding to the in situ (measured) temperature.

* pCO2_reanalysed - The partial pressure of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.

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). In addition the full satellite SST fields are included within each netCDF file (‘sst_reynolds_full’), so that the SST data for the reanalysis can be used in any further processing. Full meta data are included within the file.

Comments:

1. Due to the temporal range of the OISST the reanalysed values are only available from 1981 onwards. Pre-1981 rows contain "NaN" (not-a-number) in the reanalysis columns. This also applies to the ESA CCI version but from 1980. Pre-1980 rows contain “NaN” in the reanalysis columns.

2. This submission contains four files contained within a single zip file: SOCATv2024with_header.tsv, SOCATv2024.nc, SOCATv2024with_header_ESACCIv3_biascorrected.tsv and SOCATv2024_ESACCIv3_biascorrected.nc. The first two files correspond to the OISST version, and the second two the ESA CCI-SST version. The .tsv files are the ungridded data, and the .nc files are the gridded data for the corresponding temperature datasets.

3. Please contact Daniel J. Ford (d.ford@exeter.ac.uk) if there are any questions on the dataset.

How to cite these data:

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).

Previous versions:

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

Acknowledgements

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.

These data were produced with funding from the Ocean ICU project (https://ocean-icu.eu/), the Convex Seascape Survey (https://convexseascapesurvey.com/) and the European Space Agency Ocean Carbon 4 Climate project. 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.