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opencc-by-4.0Dec 2020View details →

Air-Sea Ammonia Fluxes Calculated from High-Resolution Summertime Observations Across the Atlantic Southern Ocean

This data set includes ocean ammonium concentrations, atmospheric ammonia gas concentrations, and calculated air-sea ammonia fluxes from the Atlantic sector of the Southern Ocean during summer. Associated with the folloiwng paper:   https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020GL091963

opencc-by-4.0Dec 2023View details →

Global Carbon Budget 2023, surface ocean fugactiy of CO2 (fCO2) and air-sea CO2 flux of individual global ocean biogechemical models and surface ocean fCO2-based data-products

Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (fCO2-products). There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs.  Note: These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. The best estimates of the annual global ocean carbon sink, based on the native grids of fCO2-products and GOBMs and with the adjustments described in the Global Carbon Budget 2023 (https://doi.org/10.5194/essd-15-5301-2023), are available in the Global Carbon Budget 2023 spreadsheet.The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 13 of the Global Carbon Budget 2023 paper (https://doi.org/10.5194/essd-15-5301-2023), the river flux adjustment needs to be added to the CO2 flux estimated from the data-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2023 paper, section 2.5.1). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because some adjustments were applied only for global fluxes.What is in the files?(1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly): fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: north, tropics, south fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude area: Area per pixel, dimensions: latitude, longitude area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south(2) The files for the GOBMs contain the following fields, for simulation A ('contemporary simulation', including effects of rising CO2, climate change and variability) and simulation B ('control simulation', constant CO2, no climate change and variability). Temporal resolution: monthlyfgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude area: Area per pixel, dimensions: latitude, longitude (3) One file 'GCB-2023_OceanModel_RegionalBreakdown_1959-2022.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Temporal resolution: annual.Fair data use statement: The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use. Citation: Please cite the Global Carbon Budget 2023 (Friedlingstein et al., 2023, ESSD, https://doi.org/10.5194/essd-15-5301-2023) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2023 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720). Acknowledgement: Please add the following text in the acknowledgement of your paper: "We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output." Co-authorship: An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication.Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make additional 3D output from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudgetdata.org/closed-access-requests.html

opencc-by-4.0Nov 2024View details →

Global Carbon Budget 2024, surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux of individual global ocean biogeochemical models and surface ocean fCO2-based data-products

v2 update: <ul> <li>update to data in UoEX-UEPFFNU fCO2-product</li> <li>fix of lat-lon issue in Jena-MLS fCO2-product</li> <li>minor fixes to metadata in fCO2-products</li> </ul> The v2 data is used for the final published version of the Global Carbon Budget 2024. ----------------- Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (fCO2-products). There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs.   Note: These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. The best estimates of the annual global ocean carbon sink, based on the native grids of fCO2-products and GOBMs and with the adjustments described in the Global Carbon Budget 2024 (https://essd.copernicus.org/preprints/essd-2024-519), are available in the Global Carbon Budget 2024 spreadsheet. The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 14 of the Global Carbon Budget 2024 paper (https://essd.copernicus.org/preprints/essd-2024-519), the river flux adjustment needs to be added to the CO2 flux estimated from the fCO2-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2024 paper). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because some adjustments were applied only for global fluxes. What is in the files? (1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly): fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: global, north, tropics, south fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude area: Area per pixel, dimensions: latitude, longitude area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south (2) The files for the GOBMs contain the following fields, for simulation A ('contemporary simulation', including effects of rising CO2, climate change and variability) and simulation B ('control simulation', constant CO2, no climate change and variability). Temporal resolution: monthly fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude area: Area per pixel, dimensions: latitude, longitude (3) One file 'GCB-2024_OceanModel_RegionalBreakdown_1959-2023.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Regions: North, tropics, south. Temporal resolution: annual. Fair data use statement: The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use. Citation: Please cite the Global Carbon Budget 2024 (Friedlingstein et al., 2024, ESSD, https://essd.copernicus.org/preprints/essd-2024-519) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2024 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720). Acknowledgement: Please add the following text in the acknowledgement of your paper: "We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output." Co-authorship: An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication. Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make additional 3D output from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudgetdata.org/closed-access-requests.html

openmit-licenseSep 2024View details →

Dataset for "Regional Uncertainty Analysis in the Air-Sea CO2 Flux"

This repository contains processed and output data used in the "Regional Uncertainty Analysis in the Air-Sea CO2 Flux" project.  <ul> <li>fractional-uncertanties-1x1-1993-2022.nc : fractional uncertanies calculated with FluxError</li> </ul> The following is the processed data used to calculate fractional uncertanties. Individual Datasets Sea Surface Temperature (SST) <ul> <li>oisst-1x1-1993-2022.nc : NOAA SST</li> <li>cobe2-1x1-1993-2022.nc : COBE2 SST                                                                                                                           </li> <li>esa-1x1-1993-2022.nc  : ESA SST</li> <li>ostia-1x1-1993-2022.nc  : OSTIA SST</li> </ul> 10m Wind Speed <ul> <li>ccmp-1x1-1993-2022.nc : CCMP 10m wind speed</li> <li>jra3q-wind-1x1-1993-2022.nc : JRA wind speed</li> <li>era5-wind-1x1-1993-2022.nc  : ERA5 wind speed</li> </ul> Sea Surface Salinity (SSS) <ul> <li>en4-1x1-1993-2022.nc : EN4 salinity </li> <li> glorys-1x1-1993-2022.nc : GLORYS salinity                                                                                                                                 </li> <li>oras5-1x1-1993-2022.nc : ORAS5 salinity                                                                              </li> </ul> Atmospheric xCO2 <ul> <li>noaa-mbl_197901-202301_1x1.nc : atmospheric xCO2</li> </ul> Ocean pCO2 <ul> <li>pco2-1x1-1993-2022.nc : Global Carbon Budget ocean model and data product output, converted to pCO2</li> </ul> Sea Level Pressure  <ul> <li>era5-slp-1x1-1993-2022.nc : ERA5 sea level pressure</li> </ul> 1 Degree Ocean Mask <ul> <li>ocean-mask_invariant_1x1.nc : Ocean mask </li> </ul> Merged datasets: these datasets are larger and contain the ensemble of datasets above merged into single files <ul> <li>salinity-1x1-1993-2022.nc : merged salinity datasets</li> <li>sst-1x1-1993-2022.nc : merged SST datasets</li> <li>wind-1x1-1993-2022.nc : merged wind speed datasets</li> </ul>

opencc-by-4.0Apr 2022View details →

Estimating historical air-sea CO2 fluxes: Incorporating physical knowledge within a data-only approach

Reconstructed surface ocean pCO2 and air-sea CO2 fluxes for 1990-2019 using the pCO2-Residual Approach (JAMES 2021MS002960, in review) Surface ocean pCO2 (spo2) and resulting estimates of the air-sea CO2 flux (fCO2) are included in the netcdf file at monthly temporal resolution and for 1x1 grid cell spatial resolution. SeaFlux (https://zenodo.org/record/5482547#.YlT72y-B0_U) variables are used to calculate the fluxes from surface ocean pCO2.

opencc-by-4.0Dec 2022View details →

Datasets and analysis scripts for air-sea flux study using CESM-MOM6

This repository provides the fully-coupled CESM-MOM6 simulation datasets for the ocean surface and the analysis scripts for studying air-sea flux variability. This study aimed to quantify the effects of the stochastic ocean density corrections on the ocean-intrinsic component of air-sea fluctuations, which is the strongest at mesoscales, i.e., 10-1000 Km.

Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the East China Sea (2003-2019)

Based on in situ seawater pCO2 data collected on 51 cruises/legs over the past two decades, a satellite retrieval algorithm for seawater pCO2 was developed by combining the semi-mechanistic algorithm and machine learning method (MeSAA-ML). MeSAA-ML introduces semi-analytical parameters, including the temperature-dependent seawater pCO2 (pCO2,therm ) and upwelling index (UISST), to characterise the combined effect of atmospheric CO2 forcing, thermodynamic effects, and multiple mixing processes on seawater pCO2. Additionally, considering the biological effects and various sub-regional features, multiple ocean colour parameters were also used as inputs in XGBoost, the best-selected machine learning algorithm. Independent cruise-based data were used to validate the satellite-derived pCO2, which achieved excellent performance in this complicated marginal sea, with low root mean square error (RMSE=19.6 μatm) and mean absolute percentage deviation (APD=4.12%). Air-sea CO2 fluxes are calculated based on retrieved seawater pCO2.

Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the South China Sea (2003-2019)

The South China Sea (SCS) is one of the largest marginal seas worldwide. It includes a river-dominated, highly productive marginal sea on the north shelf and a wide, oligotrophic ocean-dominated basin with various dynamic sub-regions. Based on an in situ seawater partial pressure of CO2 (pCO2) datasets of 44 cruises/legs collected for the last two decades in the SCS, we proposed a seawater pCO2 retrieval algorithm by combining the semi-mechanistic and machine learning (ML) methods (MeSAA-ML). The parameter selection strategy was based on the mechanistic analysis of pCO2 variation, separating impacts of thermodynamics, biological activities, water mixing, and the atmospheric CO2 forcing. We set a few semi-analytical parameters: pCO2_therm, which was a proxy for the combined effect of thermodynamics and the atmospheric CO2 forcing on seawater pCO2; an upwelling index (UISST) and mixing layer depth (MLD) to characterize the multiple mixing processes; chlorophyll-a concentration (Chl-a) with remote sensing reflectance at 443 and 555 nm (Rrs(443) and Rrs(555)), which were the inputs to proxy the biological effect and other characteristics for distinguishing shelf, basin, and sub-regions. As the seawater pCO2 and atmospheric pCO2 ( pCO2air) have similar data values and characteristics in the vast SCS oligotrophic basin, it will cause instability of the model if one is input and the other is output; thus the difference between them (ΔpCO2sea-air) was set as the output, and the seawater pCO2 was obtained finally by summing pCO2air and ΔpCO2sea-air. We compared several ML models, and the XGBoost model was confirmed as the best model. Completely independent cruise-based and observed datasets from Southeastern Asia Time-series Study (SEATS) were used to validate the satellite products, with low root mean square error (RMSE = 11.69 μatm) and mean absolute percentage deviation (APD = 1.59%). The increasing trend of satellite-derived pCO2 (2.44 ± 0.24 μatm/yr) at the location of SEATS was found to be consistent with observed data. We presented that the SCS as a whole is a source of atmospheric CO2, releasing an average of 11.00 ± 2.45 Tg C/yr from a total area of 3.32 × 106 km2, and the northern shelf is a sink (1.69 ± 0.53 Tg C/yr). The area-integrated CO2 efflux over the entire SCS may decrease with a rate of 0.34 Tg C/yr during 2003–2019. This high-accuracy dataset with 1 km resolution provides a refined understanding of the air-sea CO2 exchange dynamics in the SCS during 2003–2019.

Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the East China Sea (2003-2019)

opencc-by-4.0Dec 2023View details →

Data used in "The Complex Role of Storms in Modulating Air-Sea CO2 Fluxes in the sub-Antarctic Southern Ocean"

The data included in this repository were used to generate the figures for the paper "The Complex Role of Storms in Modulating Air-Sea CO2 Fluxes in the sub-Antarctic Southern Ocean" in Geophysical Research Letter. Abstract: "The intra-seasonal CO2 flux (FCO2) variability across the Southern Ocean is poorly understood due to sparse observations at the required temporal and spatial scales. Twinned Waveglider-Seaglider experiments were used to investigate how storms influence FCO2 through both the gas transfer velocity (kw) and the air-sea gradient in partial pressure of CO2 (ΔpCO2) in the sub-Antarctic zone. Winter-spring storms caused ΔpCO2 to weaken (by 15-55 μatm) due to mixing/entrainment and weaker stratification. This response in ΔpCO2 was in phase with kw resulting in a counteractive weakening in FCO2 (by 6.6 - 26.5% per storm), despite the wind-driven increase in kw. Stronger stratification during summer explained the weaker sensitivity of ΔpCO2 to storms, instead its thermal drivers dominated the ΔpCO2 variability. These results highlight the importance of observing synoptic-scale variability in ΔpCO2, the absence of which may propagate significant biases to the mean annual FCO2 estimates from large-scale observing programmes and reconstructions." The data collected from the Wave Glider, such as the concentration of CO2 in the atmosphere (xCO2air) and in the ocean (xCO2sea), surface temperature and salinity were used to calculate the different parameters of the bulk CO2 flux formula (FCO2 = kw x ko x ΔpCO2). Note that the meteorological weather station of one of the Wave Gliders was faulty and the wind speed, wind direction and wind stress data was replaced by hourly ERA5 data provided by ECMWF available at <a href="https://doi.org/10.24381/cds.bd0915c6">https://doi.org/10.24381/cds.bd0915c6</a>.  The temperature, pressure and salinity data collected by the Seaglider were used to calculate the Mixed Layer Depth and the Brunt Vaisala Frequency of the first 300m of the ocean.

opencc-by-4.0Oct 2024View details →

Data supporting "A comprehensive analysis of air-sea CO2 flux uncertainties constructed from surface ocean data products"

Changelog v2: Fixes an identified issue in FluxEngine v4.0.7 that affects the calculation of fCO2atm. Fluxes have been recalculated using FluxEngine v4.0.9.1, and the analysis regenerated. The intergrated air-sea CO2 flux (or ocean sink) has reduced by ~0.2-0.3Pg C yr-1 but uncertainties are unchanged.  v1: Initial dataset released along with the supporting manuscript   Data included in this repository supports the manuscript "A comprehensive analysis of air-sea CO2 flux uncertainties constructed from surface ocean data products". Two files are present: <ol> <li>A Python config file used to run the software developed for the analysis (Ford et al., 2024)</li> <li>A ZIP file containing the input, neural network, and output files for the analysis.</li> </ol> Within the ZIP file, multiple folders are present: <ol> <li>Decorrelation contains .csv files that contain the annual estimates of the decorrelation lengths for the parameters requiring these (SST, sea ice, wind, fCO2 and fCO2 network).</li> <li>Flux contains the individual FluxEngine output files that provide all the flux calculations, and auxillary data to the flux calculations.</li> <li>Fluxengine_input contains the input files to FluxEngine, which specifies the fCO2 (sw), xCO2 (atm) and the temperature, salinities for the skin and subskin layers.</li> <li>Inputs contains all the monthly 1 degree input data used. Many of the data used are not native monthly 1 deg, and so these are generated from the higher resolution data. These are all combined into the neural_network_input.nc file, so a single file can be distributed with all the inputs used.</li> <li>Networks contains the TensorFlow neural network (FNN) files, where each province has 10 folders (one for each ensemble).</li> <li>Plots contains output plots for debugging and final plots of uncertainties</li> <li>Scalars contains the scalars used to normalise the data before input into the neural network. These are saved as Python pickle files, as they are needed if the neural network is used on other data.</li> <li>Unc_lut contains the look up tables to generate the parameter uncertainty as described in the manuscript. These are Python pickle files.</li> <li>Validation contains a csv file with the independent test RMSD, along with Python Pickle files of the validation data.</li> </ol> In the main folder, three files are present: <ol> <li>Annual_flux.csv contains the annual air-sea CO2 flux (or ocean sink estimate) estimated from the fCO2 (sw) fields. This also contains the annual integrated uncertainties for each component in the uncertainty flow chart in the manuscript.</li> <li>Output.nc contrains the gridded global fields of the fCO2 (sw), the air-sea CO2 flux, and the uncertainties for all the individual components. Metadata within the file should provide all the information required.</li> <li>Training.tsv contains the training/validation data alongside the input parameters for neural network training</li> </ol>   Please contact Daniel J. Ford (<a href="mailto:d.ford@exeter.ac.uk">d.ford@exeter.ac.uk</a>) if you have any questions. Acknowledgements This work was funded by the Convex Seascape Survey (https://convexseascapesurvey.com/) and the European Union under grant agreement no. 101083922 (OceanICU; https://ocean-icu.eu/) 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. 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.   References Ford, D. J., Blannin, J., Watts, J., Watson, A. J., Landschutzer, P., Jersild, A., & Shutler, J. D. (2024, June 30). OceanICU Neural Network Framework with per pixel uncertainty propagation (v1.1) (Version v1.1). Zenodo. https://doi.org/10.5281/ZENODO.12597803

opencc-by-4.0Aug 2024View details →

Assets (code, scripts and datasets) for the manuscript "Correction of the Air-Sea Heat Fluxes in Ocean General Circulation Models Using Neural Networks"

This dataset contains all relevant software and data related to the manuscript "Correction of the Air-Sea Heat Fluxes in Ocean General Circulation Models Using Neural Networks", submitted to AGU journals.

opencc-by-4.0Sep 2024View details →

TSSCXG-17: Global Gridded Dataset of Surface Ocean pCO2 and Air-Sea CO2 Flux (1993-2020)

This dataset presents a global gridded reconstruction of the partial pressure of CO2 (pCO2) in the surface ocean and the corresponding air-sea CO2 flux, covering the period from 1993 to 2020. Developed to enhance understanding of climate change and the global carbon cycle, this dataset addresses gaps in oceanic carbon flux data through innovative machine learning techniques. The reconstruction process integrates in situ observations, satellite data, and reanalysis products, employing a three-step algorithm involving dimensionality reduction, clustering, and regression.

opencc-by-4.0Oct 2024View details →

West Antarctic Peninsula (WAP) MITgcm-REcoMv2 outputs for 1991 (T, S, DIC, TA, DIN, Chl, air-sea CO2 flux)

This dataset contains the netCDF files for the year 1991 of dissolved inorganic nitrogen (DIN), dissolved inorganic carbo (DIC), total alkalinity (TA), diatom chlorophyll concentration (DiaPhyto), small phytoplankton chlorophyll (SmPhyto), air-sea CO2 fluxes (CO2flx), ocean temperature (Temp) and salinity (Salt) for the MITgcm-REcoMv2 ocean circulation and biogeochemistry model implemented for the West Antarctic Peninsula. Latitude and longitude for all variables are contained in the grid.glob.nc file. Files names start with the variable name, followed by the month and year. They are saved either as monthly means, as 10 day means, or 5 day means. The units for each variable are as follows: DIN: mmol/m3 DIC: mmol/m3 TA: mmol/m3 DiaPhyto: mg/m3 SmPhyto: mg/m3 CO2flx: mmol/s Temp: ˚C Results from this simulation are published in: SCHULTZ, C., DONEY, S.C., ZHANG, W.G., REGAN, H.C., HOLLAND, P., MEREDITH, M., STAMMERJOHN, S. Modeling of the Influence of Sea Ice Cycle and Langmuir Circulation on the Upper Ocean Mixed Layer Depth and Freshwater distribution at the West Antarctic Peninsula. Journal of Geophysics Research Oceans, 125, 8, 2020. doi:10.1029/2020JC016109 SCHULTZ, C., DONEY, S.C., HAUCK, J., KAVANAUGH, M.T., SCHOFIELD, O. Modeling Phytoplankton Blooms and Inorganic Carbon Responses to Sea-Ice Variability in the West Antarctic Peninsula. Journal of Geophysical Research Biogeosciences, 126, 4, 2021. doi: 10.1029/2020JG006227

Air-Sea fluxes of CO2 in the Indian Ocean between 1985 and 2018: A synthesis based on Observation-based surface CO2, hindcast and atmospheric inversion models.

This data set contains 14 hindcast models (CCSM-WHOI.nc, CESC_ETHZ.nc, CNRM-ESM2-1.nc, EC_Earth3.nc, FESOM_REcoM_LR.nc, MOM6_Princeton.nc, MPIOM_HAMOCC.nc; MRI-ESM2-1.nc, NorESM-OC1.2.nc, ORCA1-LIM3-PISCES.nc, ORCA025-EOMAR.nc, Plankotom12, INCOIS-BIO-ROMS.nc, ROMS-NYUAD.nc), nine empirical models (CMEMS-LSCE-FFNN.nc, CSIRML6.nc, Jena-MLS.nc, JMAMLR.nc, Spco2_LDEO_HPD.nc, SOMFNN.nc, NIES-MLR3.nc, UOEX-WAT20.nc, OceanSODAETHZ.nc) and CO2 flux climatology data (CO2_Climatology.nc). This data set also has two atmospheric inversion models output and those are - MACTM (MACTM.nc) and CAMSv20r1 (CMSv20r1.zip format and inside the zip folder files are .nc format).