UExP-FNN-U full surface ocean carbonate system

Skip to main Communities My dashboard Log in Sign up Published April 16, 2026 | Version v2026-pre1 Dataset Open UExP-FNN-U full surface ocean carbonate system Authors/Creators Ford, Daniel J. 1 Kulk, Gemma 2 Watson, Andrew J. 1 Sathyendranath, Shubha 2 Shutler, Jamie D. 1 Show affiliations 1. University of Exeter 2. Plymouth Marine Laboratory Description Product Information Product name UExP-FNN-U SOCOM-style name UExP-FNN-U Product version v2026-pre1 Changelog at end of repository Coverage January 1980 – December 2025 Note: 2025 is a 'forecast' year and not constrained by new in situ observations Global ocean (including under ice regions) at ~0.2 m depth Resolution Monthly 1° x 1° Contact Daniel J. Ford [email protected] Jamie D. Shutler [email protected] Traceable code and inputs Information on the input datasets and code can be found after the changelog Disclaimer: v2026-pre1 incorporates all the changes expected for the v2026-1 release but has not recieved any new training data from SOCAT. Therefore, 2025 is produced in a 'forecast' mode. SOCATv2026 is expected to be released in early June 2026, and a v2026-1 will be produced after this with the new fCO 2 (sw) training data. Product Description The UExP-FNN-U approach is described in detail within Ford et al. (2024a) and therefore we provide a summary of the algorithm for interpolating the fugacity of CO 2 in seawater (fCO 2 (sw) ). The UExP-FNN-U is a two step neural network interpolation technique, the self-organising map feed forward neural network (SOM-FNN) (Landschützer et al., 2014, 2016). The first step is a self-organising map (SOM) which was used to divide the global oceans into regions, or provinces, of similar oceanic conditions. The inputs to this step were monthly climatology of sea surface temperature (SST) from the European Space Agency Climate Change Initiative (ESA-CCI) SST, merged sea surface salinity (SSS) from the CCI and the CMEMS reanalysis (GLORYS12V1; merged using a hierarchy approach described in Gregor et al., 2024), CMEMS GLORYS12V1 mixed layer dapth (MLD), and spatailly and temporally complete OC-CCI chlorophyll-a (chl-a) (Ford et al. 2026a; Sathyendranath et al. 2019) and the Takahashi et al. (2009) fCO 2 (sw) climatology. The SOM produces 16 provinces, and two manual provinces are implemented to cover the Arctic Ocean and Mediterranean + Red Sea using Longhurst biogeochemical provinces (Longhurst, 1998). The second step uses a feed forward neural network (FNN) ensemble (10 members) for each province to estimate the relationships between the target variable (i.e in situ fCO 2 (sw) from the recalculated SOCAT dataset; Bakker et al., 2016; Ford et al., 2025; Ford et al. 2026c) and oceanic properties that likely control their variability. For the UExP-FNN-U v2026-pre1 these were SST, SSS, MLD, chl-a and xCO 2 (atm) and anomalies of each. In v2026-pre1, we implement a probability based SOM approach, which provides a probability that each monthly 1 degree pixel is assigned to each of the 18 provinces. To achieve the probabilities, the SOM training occurs as in Ford et al. (2024a) and then the inputs are perturbed within the limits of their uncertainties and the resulting SOM provinces retrieved for 400 ensembles. The probability that each monhtly 1 degree regions fall within a province are calculated. If a province probability is found to be less than 5%, this is set to 0 % to reduce computation. Locations where the sum of the probability is greater than 1 or less than 1 are scaled linearly to equal 1. In the FNN training stage the provinces are defined for the initial SOM (i.e the hard provinces that the SOM predicts with the data unperturbed). In the mapping stage the fCO 2 (sw) and the uncertainties are predicted for each location and province, and a weighted sum of the resulting fCO 2 (sw) and uncertainties is taken. This approach was shown to reduce large biases at the edge of provinces and reduced the appearance of provinces in the geographical outputs within a model testbed approach within the Surface Ocean CO 2 mapping intercomparison (SOCOMv2 Experiment 2). Additionally, the global ocean CO 2 sink for v2026-pre1 using the standard SOM approach as in Ford et al. (2024a) compared to this updated probability approach showed only minor differences (~2%). Expansion to Total Alkalinity using a consistent SOM-FNN The UExP-FNN-U approach was expanded to estimate Total Alkalinity (TA) on the same monthly 1 degree grid. The first step, the SOM, was trained on a monthly climatology of CCI-SST, CCI+CMEMS SSS and an annual TA climatology (DIVA interpolated in situ TA). Gregor and Gruber (2021) use the gridded GLODAPv2.2016 surface TA field for this step, but these have not been updated in recent years. Therefore, we use a merged in situ TA dataset produced from observations in GLODAP, SNAP-O-CO2 and Sharkweb datasets to produce a surface TA annual climatology using DIVA interpolation. Data are currently too sparse to produce a monthly climatology of TA (highlighted in Gregor and Gruber; 2021). The SOM produces 16 provinces for the second FNN step, and there were no manual province modifications for TA. For the FNN step, as described in Gregor and Gruber (2021) the available TA observations are much lower than that for fCO 2 (sw) . Gridding the TA observations onto a monthly 1 degree grid before input into the UExP-FNN-U would greatly reduce the available constraints. Consistent to Gregor and Gruber (2021), the individual bottle observations were provided to the neural network (as the target), and the coincident temperature, salinity, and the WOA phosphate and silicate (WOA nutrients extracted from the monthly 1 degree climatology and linear interpolated to the spatial location). This parameter combination was consistent to Gregor and Gruber (2021), and testing indicated from the quality assessment this was the optimal parameter choice. For the mapping to a monthly 1 degree global grid, the CCI-SST, CCI+CMEMS SSS and WOA phosphate and silicate (for the WOA nutrients monthly climatologies) were used. The selection of CCI-SST and CMEMS SSS ensures that the TA fields are produced to the same SST and SSS as the fCO 2 (sw) , and therefore consistency in the carbonate system. In 2026-pre1 we also use the probability based SOM approach for the TA mapping as descirbed in the fCO 2 (sw) section. Calculation of full surface ocean carbonate system The remaining components of the carbonate system (i.e DIC, pH etc) were calculated from fCO 2 (sw) and TA using pyCO2SYS (v1.8.3.3) (Humphreys et al., 2022, 2024). The calculation also requires SST, SSS, phosphate and silicate, where the same temperature, salinity and nutrient datasets were used (as used in the neural network stages) to consistently calculate the carbonate system. pH was calculated on the total scale. The dissociation constants of Lueker et al. (2000), bisulfate dissociation constants of Dickson (1990) and total boron-salinity relationship of Uppström (1974) were used as recommended in Orr et al. (2015) and Raimondi et al. (2019) (and are the default sets used in pyCO2sys). The surface ocean carbonate system was therefore considered representative of ~0.2 m water depth. Calculation of air-sea CO 2 fluxes The air-sea CO 2 fluxes (F) were calculated, such that vertical temperature gradients can be accounted for (Dong et al., 2022, 2024; Ford, Shutler, et al., 2024; Shutler et al., 2020; Watson et al., 2020; Woolf et al., 2016, 2019) as described in detail by Woolf et al. (2016), using FluxEngine v4.1.0 (Holding et al., 2019; Shutler et al., 2016). The CO 2 flux takes the form: F = K 600 (Sc / 600) -0.5 (α subskin fCO 2(sw, subskin) – α skin fCO 2(atm) ) (1-ice) where K 600 is the gas transfer coefficient estimated using the Nightingale et al. (2000) parameterisation and wind speeds from the CCMP (v3.1) (Mears et al., 2022; Remote Sensing Systems et al., 2022). Sc is the Schmidt number estimated using the calculation in Wanninkhof et al. (2014) and the ocean’s skin temperature. α is the solubility of CO 2 at the respective subskin or skin temperature and salinities which was estimated as in Weiss (1974). fCO 2 (atm) and fCO 2 (sw,subskin) are the fugacity of CO 2 in the atmosphere and the seawater subskin layer respectively. The CCI-SST and CCI+CMEMS SSS are considered representative of the subskin temperature and salinities and used in the calculation of α subskin . For the atmospheric side, the ocean’s skin temperature was estimated from the CCI-SST with a cool skin deviation calculated with NOAA-COARE3.6 (Edson et al., 2013; Fairall et al., 1996) using CCMP wind speed, CCI-SST and ERA5 fields as inputs. Skin salinity was calculated assuming a +0.1 psu change from the CCI+CMEMS SSS (i.e a salty skin) as in Watson et al. (2020) and Woolf et al. (2019). fCO 2 (atm) was calculated using NOAA-GML atmospheric dry mixing ratio of CO 2 (xCO 2 (atm) ; Lan et al., 2023), the skin temperature and ERA5 atmospheric pressure. Sea ice concentrations from the OSISAF dataset (OSI SAF, 2022) were used for the ice component. Variables All variables are provided in a single netCDF file, that has been zipped to reduce file sizes with a filename: Fordetal_UExP-FNN-U_surface-carbonate-system_vXXXX-X.nc. XXXX-X refers to the version number. Variable Units Description alpha mol m -3 uatm -1 Solubility of CO 2 in seawater alpha_skin mol m -3 uatm -1 Skin solubilty of CO 2 in seawater area m 2 Total surface area of each 1 degree region dic μmol kg -1 Dissolved Inorganic Carbon fco2 μatm Fugacity of CO 2 in seawater flux g C m -2 d -1 Air-sea CO 2 flux (+ve indicates outgassing) ice unitless Sea ice concentration kw cm hr -1 Gas transfer velocity mask_sfc unitless Proportion of ocean in each 1 degree region pH unitless pH on the total scale (i.e [H + ] + [HSO 4 - ]) pH_free unitless pH on the free scale (i.e [H + ]) saturation_aragonite unitless Aragonite Saturation State skin_salinity psu Skin Salinity skin_temperature ºC Skin Temperature subskin_salinity psu Subskin Salinity subskin_temperature ºC Subskin Temperature ta μmol kg -1 Total Alkalinity wind_speed m s -1 Wind speed wind_speed_second_moment m 2 s -2 Second moment of wind speed Each variable contains an uncertainty estimate that follows the BIPM (2008) principles, comprising of multiple components and then a total uncertainty. We refer the user to the netCDF file for available uncertainties, but in most cases the total uncertainty is the required component. Acknowledgements and Funding This dataset has been funded by funding from the European Space Agency under the projects ‘Satellite-based observations of Carbon in the Ocean: Pools, Fluxes and Exchanges’ (SCOPE; 4000142532/23/I-DT) and ‘Ocean Carbon for Climate’ (OC4C; 3-18399/24/I-NB). This dataset was also funded by OceanICU which was funded by the European Union under grant agreement no. 101083922 and UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10054454, 1006367, 10064020, 10059241, 10079684, 10059012, 10048179]. Views and opinions expressed 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. 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. Changelog Version Changes since previous version v2026-pre1 Updates temporal period to 1980 to 2025. Note 2025 is estimated in a forecast mode, and therefore should be used with caution. Adds chl-a as a predictor to the fCO 2 (sw) FNN. This dataset is the spatially and temporally complete fields described in Ford et al. (2026a), and avaiable at Ford et al. (2026b; https://doi.org/10.5281/zenodo.19555449) Updates SOM-FNN methodology to use a probability based SOM approach, which gives each monthly 1 degree region a probability of being assigned to a province. This information is used in the mapping phase, which reduces the effect of provinces appearing in the gridded fields. Updated NOAA-COARE 3.5 to NOAA-COARE 3.6 Adds additional ancillary fields to the output file including 'area', 'mask_sfc', 'wind_speed', 'wind_speed_second_moment', 'kw' and 'pH_free'. Updated ocean proportion mask to one generated from the ESA CCI-Land water bodies mask (Lamarche et al. 2017) v2025-1 Modification of naming from 'OC4C-SCOPE UExP-FNN-U' to 'UExP-FNN-U'. Data is identical to v2025-0 v2025-0 Updated SOCAT data to recalculated SOCATv2025 (Ford et al., 2025) Updated SNAP-O-CO2 to v2 (Metzl et al., 2025) Updated CCI-SSS to v5.5 (Boutin et al., 2025) v2024-5 Added aragonite saturation state and uncertainties to published file. Added subskin and skin temperature and salinities to published file. Verification of pH and DIC against independent in situ observations from GLODAPv2.2023 (Lauvset et al., 2024) and from SNAP-O-CO2-1 (Metzl et al., 2024). Prior versions Included initial testing of the adding SOM-FNN Total Alkalinity approach to the UExP-FNN-U fCO 2 (sw) and air sea CO 2 fluxes (as submitted to the Global Carbon Budget 2024; (Ford, et al., 2024a). Iterative refinements of Total Alkalinity approach. Addition of further in situ TA observations to constraint FNN particularly in the Baltic Sea (Sharkweb) and the ingestion of the SNAP-O-CO2-v1 (Metzl et al., 2024) Extension to full surface carbonate system with pyCO2sys (Humphreys et al., 2022, 2024) Added hybrid tiered salinity dataset approach as described in Gregor et al. (2024). This includes the CCI-SSS v4.41 (Boutin et al., 2024), falling back to the CMEMS GLORYS12V1 reanalysis when not available (Jean-Michel et al., 2021) Implementation of comprehensive uncertainties on the carbonate system components based on the BIPM (2008) principles, and following the Ford et al. (2024) approaches. Traceable input datasets Variable Dataset Version Reference Wind speed Cross-Calibrated Multi-Platform v3.1 Dataset: Remote Sensing Systems (2022; https://doi.org/10.56236/rss-uv6h30) Reference: Mears et al. (2022) Sea surface temperature ESA CCI-SST v3.0 Dataset: Good and Embury (2024; https://dx.doi.org/10.5285/4a9654136a7148e39b7feb56f8bb02d2 ) References: Embury et al. (2024) Sea Surface Salinity ESA CCI-SSS v5.5 Dataset: Boutin et al. (2025; https://catalogue.ceda.ac.uk/uuid/3339dec1fbd94599802aba7f1c665679) References: Boutin et al. (2021) Sea Surface Salinity CMEMS GLORYS12V1 No version provided Dataset: https://doi.org/10.48670/moi-00021 Reference: Jean-Michel et al. (2021) Mixed Layer Depth CMEMS GLORYS12V1 No version provided Dataset: https://doi.org/10.48670/moi-00021 Reference: Jean-Michel et al. (2021) Chlorophyll-a Monthly gap filled Ocean Colour Climate Change Initiative (OC-CCI) chlorophyll-a using BGC-Argo as an observational constraint v1-0 Dataset: Ford et al. (2026b; https://zenodo.org/records/19555449) References: Ford et al. (2026a) Atmospheric dry mixing ratio of CO2 NOAA-GML Marine Boundary Layer No version provided (Ingested 16th September 2025) Dataset: Lan et al. (2023; https://doi.org/10.15138/DVNP-F961) fCO 2 (sw) climatology Takahashi et al. (2009) fCO2 (sw) climatology No version provided (Legacy file provided by Andrew Watson) Dataset: https://www.ldeo.columbia.edu/res/pi/CO2/carbondioxide/pages/air_sea_flux_2010.html Land cover ESA CCI-Land Water bodies mask v4.0 Dataset: Avaiable via FTP - https://maps.elie.ucl.ac.be/CCI/viewer/download.php Reference: Lamarche et al. (2017) Recalculated SOCATv2025 fCO 2 (sw) observations Recalculated SOCATv2025 v0-2 Dataset: Ford et al. (2025; https://doi.org/10.5281/ZENODO.15656802) References: Bakker et al. (2016) Total Alkalinity, pH, Dissolved Inorganic Carbon GLODAP v2.2023 Dataset: Lauvset et al. (2023; https://doi.org/10.25921/zyrq-ht66) Reference: Lauvset et al. (2024) Total Alkalinity, pH, Dissolved Inorganic Carbon SNAP-O-CO2 v2 Dataset: Metlz et al. (2024; https://doi.org/10.17882/102337) Reference: Metlz et al. (2025) Total Alkalinity, pH, Dissolved Inorganic Carbon Sharkweb No version provided Dataset: Data subset from: https://shark.smhi.se Nitrate, Phosphate, Silicate World Ocean Atlas v2023 Dataset: Reagan et al. (2023; https://doi.org/10.25921/VA26-HV25) Wind speed ERA5 Dataset: Hersbach et al. (2023; https://doi.org/10.24381/cds.adbb2d47) Reference: Hersbach et al. (2020) Air pressure, boundary layer height, longwave downward radiation, shortwave downward radiation ERA5 Dataset: Hersbach et al. (2019; https://doi.org/10.24381/cds.f17050d7) Reference: Hersbach et al. (2020) Sea ice concentration OSISAF v3.0 Dataset: OSISAF (2022; https://doi.org/10.15770/EUM_SAF_OSI_0013) Traceable software Software Version Reference Github UExP-FNN-U base code (including Python environment install) v2026-pre1 Ford et al. (2024) Ford et al. (2026d) https://github.com/JamieLab/OceanICU FluxEngine v4.1.0 Shutler et al. (2016) Holding et al. (2019) https://github.com/oceanflux-ghg/FluxEngine PyCO2SYS v1.8.3.3 Humphreys et al. (2022) Humphreys et al. (2024) https://github.com/mvdh7/PyCO2SYS NOAA-COARE v3.6 https://github.com/NOAA-PSL/COARE-algorithm/tree/master 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 fCO 2 data in version 3 of the Surface Ocean CO 2 Atlas (SOCAT). Earth System Science Data , 8 (2), 383–413. https://doi.org/10.5194/essd-8-383-2016 Bariteau Ludovic, Blomquist Byron, Fairall Christopher, Thompson Elizabeth, Jim, E., & Pincus Robert. (2021, July 16). Python implementation of the COARE 3.5 Bulk Air-Sea Flux algorithm (Version v1.1). Zenodo. https://doi.org/10.5281/ZENODO.5110991 BIPM. (2008). Evaluation of measurement data—Guide to the expression of uncertainty in measurement. Boutin, J., Reul, N., Koehler, J., Martin, A., Catany, R., Guimbard, S., et al. (2021). Satellite-based sea surface salinity designed for ocean and climate studies. Journal of Geophysical Research: Oceans, 126, e2021JC017676. https://doi.org/10.1029/2021JC017676 Boutin, J.; Vergely, J.-L.; Reul, N.; Catany, R.; Jouanno, J.; Martin, A.; Rouffi, F.; Bertino, L.; Bonjean, F.; Corato, G.; Gévaudan, M.; Guimbard, S.; Khvorostyanov, D.; Kolodziejczyk, N.; Matthews, M.; Olivier, L.; Raj, R.; Rémy, E.; Reverdin, G.; Supply, A.; Thouvenin-Masson, C.; Vialard, J.; Sabia, R.; Mecklenburg, S. (2025): ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product on a 0.25 degree global grid, v5.5, for 2010 to 2023. NERC EDS Centre for Environmental Data Analysis, https://catalogue.ceda.ac.uk/uuid/3339dec1fbd94599802aba7f1c665679 Dickson, A. G. (1990). Standard potential of the Standard potential of the reaction -AgCl(s)+1/2H-2(g)=Ag(s)+HCl(aq) and the standard acidity constant of the ion HSO4- in synthetic sea-water from 273.15-K to 318.15-K. The Journal of Chemical Thermodynamics , 22 (2), 113–127. https://doi.org/10.1016/0021-9614(90)90074-Z 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 2 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 CO 2 uptake in the Southern Ocean. Science Advances , 10 (30), eadn5781. https://doi.org/10.1126/sciadv.adn5781 Edson, J. B., Jampana, V., Weller, R. A., Bigorre, S. P., Plueddemann, A. J., Fairall, C. W., et al. (2013). On the Exchange of Momentum over the Open Ocean. Journal of Physical Oceanography , 43 (8), 1589–1610. https://doi.org/10.1175/JPO-D-12-0173.1 Fairall, C. W., Bradley, E. F., Godfrey, J. S., Wick, G. A., Edson, J. B., & Young, G. S. (1996). Cool-skin and warm-layer effects on sea surface temperature. Journal of Geophysical Research: Oceans , 101 (C1), 1295–1308. https://doi.org/10.1029/95JC03190 Ford, D. J., Blannin, J., Watts, J., Watson, A. J., Landschützer, P., Jersild, A., & Shutler, J. D. (2024a). A Comprehensive Analysis of Air‐Sea CO 2 Flux Uncertainties Constructed From Surface Ocean Data Products. Global Biogeochemical Cycles , 38 (11), e2024GB008188. https://doi.org/10.1029/2024GB008188 Ford, D. J., Shutler, J. D., Blanco-Sacristán, J., Corrigan, S., Bell, T. G., Yang, M., et al. (2024b). Enhanced ocean CO 2 uptake due to near-surface temperature gradients. Nature Geoscience . https://doi.org/10.1038/s41561-024-01570-7 Ford, D. J., Shutler, J. D., Ashton, I., Sims, R. P., & Holding, T. (2025). Recalculated (depth and temperature consistent) surface ocean CO₂ atlas (SOCAT) version 2025 (Version v0-2) [Data set]. Zenodo. https://doi.org/10.5281/ZENODO.15656802 Ford, D. J., Kulk, G., Sathyendranath, S., and Shutler, J. D. (2026a) Decadal and spatially complete global surface chlorophyll-a data record from satellite and BGC-Argo observations, Earth Syst. Sci. Data, 18, 569–584, https://doi.org/10.5194/essd-18-569-2026. Ford, D. J., Kulk, G., Sathyendranath, S., & Shutler, J. D. (2026b). Monthly gap filled Ocean Colour Climate Change Initiative (OC-CCI) chlorophyll-a using BGC-Argo as an observational constraint (v1-0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.19555449 Daniel J. Ford, Jamie D. Shutler, Thomas Holding et al. (2026c) Recalculating the Surface Ocean CO2 Atlas (SOCAT) to a sea surface temperature climate data record, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-8260066/v1] Daniel Ford. (2026d). JamieLab/OceanICU: v2026-pre1 (v2026-pre1). Zenodo. https://doi.org/10.5281/zenodo.19609492 Good, S.A.; Embury, O. (2024): ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Level 4 Analysis product, version 3.0. NERC EDS Centre for Environmental Data Analysis, 09 April 2024 . doi:10.5285/4a9654136a7148e39b7feb56f8bb02d2. https://dx.doi.org/10.5285/4a9654136a7148e39b7feb56f8bb02d2 Gregor, L., & Gruber, N. (2021). OceanSODA-ETHZ: a global gridded data set of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification. Earth System Science Data , 13 (2), 777–808. https://doi.org/10.5194/essd-13-777-2021 Gregor, L., Shutler, J., & Gruber, N. (2024). High‐Resolution Variability of the Ocean Carbon Sink. Global Biogeochemical Cycles , 38 (8). https://doi.org/10.1029/2024gb008127 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. 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Global Biogeochemical Cycles , 33 (12), 1548–1563. https://doi.org/10.1029/2018GB006041 Files Fordetal_UExP-FNN-U_surface-carbonate-system_v2026-pre1.zip Files (4.5 GB) Name Size Fordetal_UExP-FNN-U_surface-carbonate-system_v2026-pre1.zip md5:e4eb5f6f786b9679d19525cd82a257b3 4.5 GB Preview Download Additional details Funding European Space Agency Ocean Carbon for Climate 3-18399/24/I-NB European Space Agency Satellite-based observations of Carbon in the Ocean: Pools, Fluxes and Exchanges 4000142532/23/I-DT UK Research and Innovation OceanICU 10048179 896 Views 147 Downloads Show more details All versions This version Views Total views 896 191 Downloads Total downloads 147 14 Data volume Total data volume 752.1 GB 62.3 GB More info on how stats are collected.... 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