Published August 9, 2023 | Version 2023-0
Dataset Open

Reanalysed (depth and temperature consistent) surface ocean CO₂ atlas (SOCAT) version 2023

Authors/Creators

  • 1. Faculty of Environment, Science and Economy, University of Exeter, Penryn, Cornwall, TR10 9EZ, UK
  • 2. Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany

Description

Note: The authors recommend the use of the ESA CCI-SST version of this dataset.

The Surface Ocean CO₂ Atlas (SOCAT) version 2023 dataset (Bakker et al., 2016; https://doi.org/10.25921/r7xa-bt92) 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.

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 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).

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.

The original SOCAT version 2023 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). Full meta data are included within the file.

Comments

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.

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.

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

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/) and the Convex Seascape Survey (https://convexseascapesurvey.com/). 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].

Notes

These data were produced with funding from the Ocean ICU project (https://ocean-icu.eu/) and the Convex Seascape Survey (https://convexseascapesurvey.com/). 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].

Files

SOCATv2023_reanalysed.zip

Files (3.9 GB)

Name Size
md5:da7f91d5ad9ffd02bd2eb5a6ae341c7d
3.9 GB Preview Download

Additional details

Funding

UK Research and Innovation
OceanICU: Ocean-ICU Improving Carbon Understanding 10063673

References

  • 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
  • Banzon, V., Smith, T. M., Chin, T. M., Liu, C., & Hankins, W. (2016). A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies. Earth System Science Data, 8(1), 165–176.
  • 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.
  • 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.
  • Huang, B., Liu, C., Banzon, V., Freeman, E., Graham, G., Hankins, B., et al. (2021). Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1. Journal of Climate, 34(8), 2923–2939. https://doi.org/10.1175/JCLI-D-20-0166.1
  • Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., et al. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific Data, 6(1), 223. https://doi.org/10.1038/s41597-019-0236-x
  • Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., & Schlax, M. G. (2007). Daily High-Resolution-Blended Analyses for Sea Surface Temperature. Journal of Climate, 20(22), 5473–5496. https://doi.org/10.1175/2007JCLI1824.1
  • 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