Find research datasets worth reusing

Simulations from the JULES dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison

Outputs from the JULES dynamic global vegetation model are provided for two global simulations. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 1.875 x 1.25 degree spatial resolution. Carbon turnover fluxes for live vegetation for each individual turnover-causing process in the model were outputted individually. For a full description of the simulations, please refer to the accompanying paper.

Simulations from the LPJ-GUESS dynamic global vegetation model v3.0 for the Vegetation Carbon Turnover Intercomparison

Outputs from the LPJ-GUESS dynamic global vegetation model v3.0 are provided for two global simulations. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 0.5 x 0.5 degree spatial resolution. Carbon turnover fluxes for live vegetation for mortality, leaf phenological turnover and fine root phenological turnover were outputted individually. For a full description of the simulations, please refer to the accompanying paper.

opencc-by-4.0Mar 2020View details →

Carbon emissions and economic assessment of farm operations under different tillage practices in organic rainfed almond orchards under semiarid Mediterranean conditions

This dataset corresponds to yield, price and fuel consumption from organic rainfed almond orchards in SE Spain  under different diversification and tillage practices. The objective is to carry out an integrated environmental (focused on the CO2 emissions) and economic assessment of farm operations under different diversification and tillage practices through a cradle-to-farm gate life cycle assessment (LCA) based on these data. These data correspond to the open-access article " Carbon emissions and economic assessment of farm operations under different tillage practices in organic rainfed almond orchards under semiarid Mediterranean conditions" published in Scientia Horticulturae. (https://doi.org/10.1016/j.scienta.2019.108978), funded by the European Commission Horizon 2020 project Diverfarming [grant agreement 728003].

opencc-by-4.0May 2020View details →

Example Perturbed Parameter Ensemble (Black Carbon)

This dataset contains the parameter design and example ECHAM-HAM output from the AeroCom Black Carbon (BC) multi-model Perturbed Parameter Ensemble (PPE) experiment described here: https://wiki.met.no/aerocom/phase3-experiments#multi-model_ppe_bc_experiment

opencc-by-4.0Feb 2020View details →

mom6 cobalt model result for oceanic carbon response to El Niños

GEOS_Chem atmospheric transport model, monthly, 2.5 degree resolution in tropical Pacific Ocean (120-300E, 30N-30S) 1. GEOS_Chem_tropical_pacific_monthly_clim_1992_2017.nc 2. GEOS_Chem_tropical_pacific_monthly_iav_1992_2017.nc 1 and 2 are GEOS_Chem atmospheric transport model results MOM6 COBALT model results Note1: region range is 120-300E, 30N-30S with a resolution of half degree, monthly result from 1982.1.1 to 2018.1.1 Note2: if the file name has a label "_detrend_deseason", this file is detrended and deseasonized using full value in 1982-2018 with CDO "cdo -ymonsub -detrend $fin -ymonmean -detrend $fin $fout" Note3: ocean/sea surface is the first layer which is 1 meter deep Note4: MLD_001_temp/salt/dic/alk/kd is the vertical mean value in the mixing layer depth (criterial of 0.01 kg/m3) MOM6_COBALT_tropical_pacific_monthly_dic_deltap_1982_2018_detrend_deseason.nc     (detrended and deseasonalized delta pCO2 (ocean pCO2 minus air pCO2) in the ocean surface) MOM6_COBALT_tropical_pacific_monthly_dic_deltap_1982_2018.nc     (delta pCO2 (ocean pCO2 minus air pCO2) in the ocean surface) MOM6_COBALT_tropical_pacific_monthly_dic_stf_1982_2018_detrend_deseason.nc     (detrended and deseasonalized air-sea CO2 flux, positive to ocean) MOM6_COBALT_tropical_pacific_monthly_dic_stf_1982_2018.nc     (air-sea CO2 flux, positive to ocean) MOM6_COBALT_tropical_pacific_monthly_MLD_001_1982_2018.nc     (Mixing layer depth with a density criterial of 0.01 kg/m3) MOM6_COBALT_tropical_pacific_monthly_MLD_001_alk_1982_2018_detrend_deseason.nc     (first compute vertical mean alkilinity in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized) MOM6_COBALT_tropical_pacific_monthly_MLD_001_alk_zgradient_1982_2018_detrend_deseason.nc     (first compute vertical mean alkilinity gradient (dalk/dz) in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized) MOM6_COBALT_tropical_pacific_monthly_MLD_001_dic_1982_2018_detrend_deseason.nc     (first compute vertical mean dissolved inorganic carbon (DIC) in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized) MOM6_COBALT_tropical_pacific_monthly_MLD_001_dic_zgradient_1982_2018_detrend_deseason.nc     (first compute vertical mean DIC (ddic/dz) in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized) MOM6_COBALT_tropical_pacific_monthly_MLD_001_Kd_interface_1982_2018_detrend_deseason.nc     (first compute vertical mean Diapycnal diffusivity at interfaces layers (kd_interface) in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized) MOM6_COBALT_tropical_pacific_monthly_MLD_001_salt_1982_2018_detrend_deseason.nc     (first compute vertical mean salinity in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized) MOM6_COBALT_tropical_pacific_monthly_MLD_001_temp_1982_2018_detrend_deseason.nc     (first compute vertical mean temperature in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized) MOM6_COBALT_tropical_pacific_monthly_MLD_001_u_1982_2018_detrend_deseason.nc     (first compute vertical mean velocity u in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized) MOM6_COBALT_tropical_pacific_monthly_MLD_001_v_1982_2018_detrend_deseason.nc     (first compute vertical mean velocity v in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized) MOM6_COBALT_tropical_pacific_monthly_MLD_003_1982_2018.nc     (Mixing layer depth with a density criterial of 0.03 kg/m3) MOM6_COBALT_tropical_pacific_monthly_pco2surf_1982_2018_detrend_deseason.nc     (detrended and deseasonalized sea surface pCO2 (ocean pCO2)) MOM6_COBALT_tropical_pacific_monthly_pco2surf_1982_2018.nc     (sea surface pCO2 (ocean pCO2)) MOM6_COBALT_tropical_pacific_monthly_sfc_chl_1982_2018.nc     (sea surface chlorophyll) MOM6_COBALT_tropical_pacific_monthly_sfc_dic_1982_2018.nc     (sea surface dissolved inorganic carbon (DIC)) MOM6_COBALT_tropical_pacific_monthly_sfc_no3_1982_2018.nc     (sea surface nitrate, NO3) MOM6_COBALT_tropical_pacific_monthly_sfc_po4_1982_2018.nc     (sea surface phosphate, PO4) MOM6_COBALT_tropical_pacific_monthly_SSS_1982_2018.nc     (sea surface salinity) MOM6_COBALT_tropical_pacific_monthly_SST_1982_2018.nc     (sea surface temperature) MOM6_COBALT_tropical_pacific_monthly_taux_1982_2018_detrend_deseason.nc     (detrended and deseasonalized wind stress in zonal direction) MOM6_COBALT_tropical_pacific_monthly_taux_1982_2018.nc     (wind stress in zonal direction) MOM6_COBALT_tropical_pacific_monthly_tauy_1982_2018_detrend_deseason.nc     (detrended and deseasonalized wind stress in meridional direction) MOM6_COBALT_tropical_pacific_monthly_tauy_1982_2018.nc     (wind stress in meridional direction) MOM6_COBALT_tropical_pacific_monthly_tc_depth_1982_2018.nc     (thermocline depth defined as depth where the temperature equals 20oC) JRA_rain_tropical_pacific_monthly_prrn_1982_2018_detrend_deseason.nc (detrended and deseasonalized JRA rainfall)   Budget terms based MOM6 COBALT model results Note1: region range is 120-300E, 30N-30S with a resolution of half degree, monthly result from 1982.1.1 to 2018.1.1 Note2: this is the vertical mean result in the mixing layer depth (criterial of 0.01 kg/m3) after detrend and deseasonalize MOM6_COBALT_tropical_pacific_monthly_MLD_001_pco2w_budget_1982_2018_detrend_deseason.nc     (budget terms used for ocean pCO2 budget analysis, vertical mean in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized) pco2_hadv_hdif: horizontal transport term, H_circ; dpco2_hadvx: zonal advection term; dpco2_hadvy: meridional advection term; dpco2_hdif: horizontal diffusivity term; dpco2_vadv_vdif: vertical transport term; dpco2_dic_vdif_vdif: vertical transport term induced by DIC; dpco2_alk_vdif: vertical transport term induced by Alk; dpco2_bio: biological term dpco2_rain: surface freshwater term dpco2_sst: thermal term dpco2_dt: pco2 response term dpco2_flux: CO2 flux response term

opencc-by-4.0Aug 2020View details →

VDMBC_vertical_distribution_soil_microbial_biomass_carbon

Soil microbial biomass carbon (SMBC) is important in regulating soil organic carbon (SOC) dynamics along soil profiles by mediating the decomposition and formation of SOC. The dataset (VDMBC) is about the vertical distributions of SOC, SMBC, and soil microbial quotient (SMQ = SMBC/SOC) and their relations to environmental factors across five continents. Data were collected from literature, with a total of 289 soil profiles and 1040 observations in different soil layers compiled. The associated environment data collectd include climate, ecosystem types, and edaphic factors. We developed this dataset by searching the the Web of Sciene and the China National Knowledge Infrastructure from the year of 1970 to 2019. All the data in this dataset met two creteria: 1) there were at least three mineral soil layers along a soil profile, and 2) SMBC was measured using the fumigation extraction method. The data in tables and texts were obtained from literature directly, and the data in figures were extracted by using the GetData Graph digitizer software version 2.25. When climate and soil properties were not available from publications, we obtainted the data from the World Weather Information Service (https://worldweather.wmo.int/en/home.html) and SoilGrids at a spatial resolution of 250 meters (version 0.5.3, https://soilgrids.org). The units of all the variables were converted to the standard international units or commonly used ones and the values were converted correspondingly. For example, the value of soil organic matter (SOM) was converted to SOC using the equation (SOC = SOM × 0.58). Soil depth was calculated as the arithmetic mean value of the upper and lower boundaries for a given soil layer. This dataset can be used in predicting global SOC change along soil profiles using the multi-layer soil carbon models. It can also be used to analyse how soil microbial biomass changes with plant roots as well as the composition, structure, and functions of soil microbial communities along soil profiles at large spatial scales. This dataset offers opportunities to improve our prediction of SOC dynamics under global changes and to advance our understanding of the environmental controls.

opencc-by-4.0Sep 2020View details →

Nano-sized calcium carbonate particles in cement mortars (DS18)

This dataset will provide the selection of the optimal mix-design of cement mortars, optimizing the characteristics of nanoCaCO3 particles (additional percentages, morphology, particle size distribution, crystal phase) according to their use in cement-based composites. These commercial nanoparticles have characteristics comparable with those of the synthesized particles used up to now in the RECODE project.

opencc-by-4.0Mar 2020View details →

RECODE_DS19.Toxicological profile of calcium carbonate nanoparticles for industrial applications

The documentation will include: for the in vitro and in vivo studies all the data acquired after the exposure of cells or zebrafish to the nano-sized CaCO3 particles.

Data from: Dwarf shrubs impact tundra soils: drier, colder, and less organic carbon

In the tundra, woody plants are dispersing towards higher latitudes and altitudes due to increasingly favourable climatic conditions. The coverage and height of woody plants are increasing, which may influence the soils of the tundra ecosystem. Here, we use structural equation modelling to analyse 171 study plots and to examine if the coverage and height of woody plants affect the growing-season topsoil moisture and temperature (< 10 cm) as well as soil organic carbon stocks (< 80 cm). In our study setting, we consider the hierarchy of the ecosystem by controlling for other factors, such as topography, wintertime snow depth and the overall plant coverage that potentially influence woody plants and soil properties in this dwarf-shrub dominated landscape in northern Fennoscandia. We found strong links from topography to both vegetation and soil. Further, we found that woody plants influence multiple soil properties: the dominance of woody plants inversely correlated with soil moisture, soil temperature, and soil organic carbon stocks (standardised regression coefficients = -0.39; -0.22; -0.34, respectively), even when controlling for other landscape features. Our results indicate that the dominance of dwarf shrubs may lead to soils that are drier, colder, and contain less organic carbon. Thus, there are multiple mechanisms through which woody plants may influence tundra soils. Kemppinen, Niittynen, Virkkala, Happonen, Riihimäki, Aalto & Luoto (2021). Dwarf shrubs impact tundra soils: drier, colder, and less organic carbon. Ecosystems. These are the data from Kemppinen et al. (2021).

Case study result data set for Energy Economics article "Demystifying market clearing and price setting effects in low-carbon energy systems"

The data set contains country-specific power generation and consumption time series data for the European energy system, including both traditional and new market participants due to cross-sectoral integration. Country codes: ALPHA-3 Unit: Megawatt (electric) (interval average values, i.e. MWh/h) Generation technology types <ul> <li>batteryStorage (Li-Ion)</li> <li>conventionalHydro (aggregated for different equivalent hydropower systems)</li> <li>natural_gas_CC_COND (Combined Cycle Gas Turbine)</li> <li>natural_gas_CC_EXCOND (Combined Cycle Gas Turbine as extraction condensing CHP plant for district heating)</li> <li>natural_gas_GT_COND (Open-Cycle Gas Turbine)</li> <li>natural_gas_GT_EXCOND (Open-Cycle Gas Turbine as extraction condensing CHP plant for industry)</li> <li>offshoreWind (aggregated for different LCOE and IEC wind turbine classes)</li> <li>offshoreWindExplicit (offshore wind generation considered for offshore grid investments in the North Seas area, aggregated for different LCOE classes)</li> <li>onshoreWind (solar PV, aggregated for different LCOE classes)</li> <li>other (geothermal, waste)</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> <li>solar (solar PV, aggregated for different LCOE classes)</li> <li>uran_ST_COND (steam turbine condensing power plant)</li> </ul> Consumption technology types <ul> <li>BEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>PHEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>airConditioning</li> <li>batteryStorage (Li-Ion)</li> <li>conventionalLoad</li> <li>heatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridHeatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridTruck (Hybrid Overhead-Line truck)</li> <li>largeScaleDirectResistiveHeating (Centralised CHP systems)</li> <li>natural_gas_CC_EXCOND_electrodeHeater</li> <li>natural_gas_CC_EXCOND_heatpumpHeater</li> <li>natural_gas_GT_EXCOND_electrodeHeater</li> <li>natural_gas_GT_EXCOND_heatpumpHeater</li> <li>powerToGas</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> </ul>

Mechanical data of rotary shear experiments and temperature measurements for the manuscript: "Fast and localized temperature measurements during simulated earthquakes in carbonate rocks"

Mechanical data of rotary shear experiments and temperature measurements Each experiment is presented in a file with the experiment name (mechanical data of rotary shear experiment) and a file with the experiment name and _Temp (temperature measurement with the optical fiber). Mechanical data are presented in a tab-delimited file with calibrated measurements of: <ul> <li>Time (milliseconds)</li> <li>Normal stress: Normal (MPa) </li> <li>Fault displacement: Slip (mm)</li> <li>Fault velocity: Velocity (mm/s)</li> <li>Shear stress: Shearstress (MPa)</li> <li>Axial shortening: Shortening (mm).</li> </ul>  In a separate file, temperature data are presented as tab-delimited file with calibrated measurements of: <ul> <li>Time (milliseconds)</li> <li>Temperature from optical fiber in the channel at 1.5 µm : Temperature_1,5 (°C) </li> </ul>

Carbonyl Sulfide (OCS/COS) and Carbon Disulfide (CS2): global modelled marine surface concentrations and emissions, 2000-2019

This dataset contains a global ocean emission inventory of the sulfur-containing trace gases carbonyl sulfide (OCS/COS) and carbon disulfide (CS2). It covers the period 2000-2019, and includes a monthly average and an average diel cycle for each month for sea surface concentrations and emissions to the atmosphere. The spatial resolution is  2.8° x 2.8° at the equator (T42 grid), the depth extends from the surface to the mixed layer depth. Carbonyl sulfide (OCS) is the most abundant, long-lived sulphur gas in the atmosphere and a major supplier of sulfur to the stratospheric sulfate aerosol layer. The short-lived gas carbon disulfide (CS2) is oxidized to OCS and constitutes a major indirect source to the atmospheric budget of OCS. We encourage the use of the data provided here as input for atmospheric modelling studies to further assess the atmospheric OCS budget and the role of OCS in climate.

opencc-by-4.0Dec 2020View details →

Soil organic carbon stocks and trends (1984-2019) predicted at 30m spatial resolution for topsoil in natural areas of South Africa

Link to scientific publication: <a href="https://doi.org/10.1016/j.scitotenv.2021.145384">https://doi.org/10.1016/j.scitotenv.2021.145384</a> Soil organic carbon (SOC) stocks (kg C m-2) are predicted over natural areas (excluding water, urban, and cultivated) of South Africa using a machine learning workflow driven by optical satellite data and other ancillary climatic, morphometric and biological covariates. The temporal scope covers 1984-2019. The spatial scope covers 0-30cm topsoil in South Africa natural land area (84% of the country). See methodology in linked publication for details. Data are provided here at 30m spatial resolution in GeoTIFF files. There is a dataset for the long-term average SOC and trend in SOC. Each dataset is split into four files (suffix *_1, *_2 etc.) covering separate regions of South Africa for ease of download. The raster files are: <ul> <li>"SOC_mean_30m..." - average of annual SOC predictions between 1984 and 2019. Values are expressed in kg C m-2</li> <li>"SOC_trend_30m..." - long-term trend in SOC derived from the Sens slope (M) across annual SOC values between 1984 and 2019. Pixel values (Y) are expressed as a percentage change over the 35 years relative to the long-term mean (X). Y = M / X * 100 * 35 years</li> </ul> NB: All files are scaled by *100 and converted to floating data point to save space. To back-convert to original values, simply divide the raster values by 100.

opencc-by-4.0Oct 2020View details →

Supplementary files for "Influence of the Artificial Nanostructure on the LiF Formation at the Solid−Electrolyte Interphase of Carbon-Based Anodes"

Databases containing DFT optimized structures used for the paper: ''Influence of the Artificial Nanostructure on the LiF Formation at the Solid−Electrolyte Interphase of Carbon-Based Anodes". For further details of the computational setup we refer to this paper. Each database contains structures for one carbon substrate. The structures can be retrieved using the Atomic Simulation Environment (ASE).

Damage Monitoring of Structural Resins Loaded with Carbon Fillers: Experimental and Theoretical Study

Dynamic Mechanical Analysis, Electro-Mechanical Measurement, Simulation model, Dynamic Light Scattering,  FTIR spectroscopy Thermogravimetric analysis,  Differential Scanning Calorimetry, Electro-Temperature Measurement,  Thermal Image Camera ,  Water sorption measurement, Transmission Electron Microscopy, Stress Strain

Carbon dioxide, methane, and chemical data from Batang Ai reservoir

The dataset contains biogeochemical in situ field measurements taken in Batang Ai reservoir (located on the Borneo Island, Malaysia). Samples were taken over four sampling campaigns from 2016 to 2018. Data was used to analyse carbon dioxide and methane flux patterns and to calculate the carbon footprint of the reservoir in the paper: “The carbon footprint of a Malaysian tropical reservoir: measured versus modeled estimates highlight the underestimated key role of downstream processes” (<a href="https://doi.org/10.5194/bg-17-1-2020">https://doi.org/10.5194/bg-17-1-2020</a>). Data were also used to calculate budgets of CO2 and CH4 in the epilimnion of Batang Ai reservoir in the paper: “Changing sources and processes sustaining surface CO2 and CH4 fluxes along a tropical river to reservoir system” (<a href="https://doi.org/10.5194/bg-2020-258">https://doi.org/10.5194/bg-2020-258</a>).

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-4.0Oct 2023View details →

Land, carbon and biodiversity data for supply chain impact calculations

Monitoring, halting and reversing land conversion is fundamental to meeting international biodiversity and climate targets, and agriculture is the major driver of land conversion. We present an open access set of global data for calculating land use change impacts of agricultural supply chains. These data, originally prepared for the LandGriffon service, include indicators of deforestation, conversion of natural ecosystems, greenhouse gas emissions, and loss of intact or high integrity ecosystems following international standards and guidelines for reporting and target setting in the agriculture, forestry, and land use sector. In order to assign impacts to agricultural production, we prepare data using a spatial adaptation of the statistical Land Use Change (sLUC) accounting approach distributing impact to human activities across the local area using a 50km radius. The results are high resolution global maps of impact per hectare of land occupation. These can then be combined with land footprint data, cropland extent, or productivity maps to calculate land use change related impacts for specific crop volumes sourced from specific regions. Carbon and deforestation results are validated against FAO statistics at the national level.

opencc-by-4.0Nov 2023View details →

Carbon outwelling and uptake along a tidal glacier-lagoon-ocean continuum

Data_Jokulsarlon2022: Excel file containing raw data collected at Jökulsárlón Glacial Lagoon in September 2022. The data set includes parameters measured in the surface water of our spatial survey and timeseries. This includes temperature and salinity, oxygen concentration, nutrients (total dissolved nitrogen, phosphate, silica), dissolved organic carbon, photosynthetic pigments (chlorophyll a and fucoxanthin) and carbonate species (total alkalinity and dissolved inorganic carbon), as well as atmospheric data (temperature and wind speed).