Find research datasets worth reusing

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-4.0Jul 2024View details →

Monthly TM5-4DVar CO2 fluxes based on GOSAT and in situ measurements for the South American Temperate region from 2009 to 2018

The data set contains monthly CO2 land-atmosphere exchange fluxes (Net Biome Productivity, NBP) for the South American Temperate (SAT) region, as defined by TRANSCOM, from 2009 to 2018. The fluxes are calculated using the atmospheric inversion TM5-4DVar (Basu et al., 2013), as described in Metz et al. (2023), assimilating in situ and/or Greenhouse Gases Observing Satellite (GOSAT) measurements. If the data is used for publications, please contact sanam.vardag@uni-heidelberg.de to discuss potential co-authorship and technical details. The following data sets are included: TM5-4DVar_ACOS_SAT: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating GOSAT/ACOSv9 XCO2 data and in situ CO2 concentration measurements together. TM5-4DVar_RT_SAT: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating GOSAT/RemoTeCv2.4.0 XCO2 data and in situ CO2 concentration measurements together. TM5-4DVar_GOSAT_MeanAcosRt_SAT: Mean of the monthly NBP fluxes of TM5-4DVar_ACOS_SAT and TM5-4DVar_RT_SAT. TM5-4DVar_IS_SAT: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating only in situ CO2 concentration measurements. TM5-4DVar_prior_SAT: Monthly NBP fluxes for the whole South American Temperate region used as prior in the atmospheric inversion TM5-4DVar. TM5-4DVar_GOSAT_MeanAcosRt_arideast: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the arid regions in the eastern SAT region. TM5-4DVar_GOSAT_MeanAcosRt_aridwest: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the arid regions in the western SAT region. TM5-4DVar_GOSAT_MeanAcosRt_humid: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the humid regions in the SAT region. All data sets have the following variables: MonthDate: date (YYYY-MM-DD) of the middle of the individual month Month: MM Year: YYYY NBP_flux_monthly_TgC_per_subregion: NBP flux as total monthly flux over the whole individual region (SAT, SAT humid, SAT arid east, west) in TgC/month. NBP fluxes are calculated as Net Ecosystem Exchange fluxes + fire emissions. For more details about the atmospheric inversion and the used measurement data, please see Metz et al., 2023.   Basu, S., Guerlet, S., Butz, A., Houweling, S., Hasekamp, O., Aben, I., et al. (2013). Global CO 2 fluxes estimated from GOSAT retrievals of total column CO 2. Atmospheric Chemistry and Physics, 13(17), 8695–8717, 2013.  Metz, E.-M., Vardag, S.N.,  Basu, S., Jung, M., Ahrens, B., El-Madany, T., Sitch, S., Arora, V.  K., Briggs, P. R. , Friedlingstein, P., Goll, D.S., Jain, A.K.,  Kato, E., Lombardozzi, D., Nabel,J .E. M. S., Poulter, B., Séférian, R., Tian, H., Wiltshire, A., Yuan, W., Yue, X., Zaehle, S.,  Deutscher, N.M.,  Griffith, D.W.T., Butz, A. Soil respiration–driven CO2 pulses dominate Australia’s flux variability. Science, 379, 1332-1335, https://doi.org/10.1126/science.add7833, 2023.

opencc-by-4.0Feb 2019View details →

Dataset for "Gross primary productivity of four European ecosystems constrained by joint CO2 and COS flux measurements"

Data of measurements and model output of the publication "Gross primary productivity of four European ecosystems constrained by joint CO2 and COS flux measurements". Data consists of micrometeorological data, COS and CO2 flux measurements for 4 sites including filters for the fluxes. The sites include: a managed temperate mountain grassland in Austria (18.06.-21.08.2015), a Mediterranean savanna ecosystem in Spain(29.04.-24.05.2016)), a Temperate beach forest in Denmark(07.06.-03.07.2016) and an agricultural soy bean field in Italy(03.07.-01.08.2017). Version 2: param2950** are now correct (were filled with the same values for all field sites)   For additional information please contact: <a href="mailto:Georg.Wohlfahrt@uibk.ac.at">Georg.Wohlfahrt@uibk.ac.at</a>

opencc-by-4.0Aug 2024View details →

Dataset for the preprint: "Intercomparison of biogenic CO2 flux models in four urban parks in the city of Zurich"

Dataset supporting the submission of the manuscript titled "Intercomparison of biogenic CO2 flux models in four urban parks in the city of Zurich" to the to the international journal "Biogeosciences". Meteorological data Hourly aggregated meteorological dataset for the urban area of Zurich, originating from two urban stations: Kaserne (8°32'/47°23'), which is a station of the Swiss national air pollution monitoring network NABEL, and Hardau II (8°30'/47°23'), which is a station established for the ICOS-Cities project.  Zurich Kaserne is located in a large courtyard. Wind and global radiation are measured on top of a four-storey building. Wind is measured at 35 m and global radiation at 27 m above ground. Hardau II station is established on the top of a high-rise building (110 m a.g.l.). Meteorological observation gaps were filled using Copernicus ERA5-Land data. Data format: comma separated values (csv) Time step: 60 min (aggregated) Time stamp: yyyy-MM-dd HH:mm, UTC, end of aggregated period Period: 01/2022–09/2023 Monthly mean atmospheric CO2 concentration data derived from the ICOS-Cities Hardau II station (07/2022–09/2023) and the Beromunster station (11/2012–02/2022). Observation gaps were filled using Copernicus ERA5-Land data. Further information on the dataset can be found in the submitted manuscript.  Data format: comma separated values (csv) Time step: Monthly (mean) Time stamp: yyyy-MM-dd  Period: 11/2012–09/2023 <table> <tbody> <tr> <td> Variable </td> <td> Acronym </td> <td> Units </td> <td> Height above ground </td> <td> Location </td> <td> Geographic location </td> </tr> <tr> <td> Global radiation </td> <td> G </td> <td> W m-2 </td> <td> 27 m </td> <td> Kaserne </td> <td> 8°32'/47°23' </td> </tr> <tr> <td> Air temperature </td> <td> Tair </td> <td> Degrees Celsius </td> <td> 2 m </td> <td> Kaserne </td> <td> 8°32'/47°23' </td> </tr> <tr> <td> Relative humidity </td> <td> RH </td> <td> % </td> <td> 2 m </td> <td> Kaserne </td> <td> 8°32'/47°23' </td> </tr> <tr> <td> Air pressure </td> <td> P </td> <td> hPa </td> <td> 2 m </td> <td> Kaserne </td> <td> 8°32'/47°23' </td> </tr> <tr> <td> Wind speed </td> <td> u </td> <td> m s-1 </td> <td> 35 m </td> <td> Kaserne </td> <td> 8°32'/47°23' </td> </tr> <tr> <td> Precipitation </td> <td> R </td> <td> mm </td> <td> 2 m </td> <td> Kaserne </td> <td> 8°32'/47°23' </td> </tr> <tr> <td> Downward longwave radiation </td> <td> LW </td> <td> W m-2 </td> <td> 110 m </td> <td> Hardau II, ERA-5 </td> <td> 8°30'/47°23' </td> </tr> <tr> <td> Soil temperature </td> <td> Tsoil </td> <td> Degrees Celsius </td> <td> -0.15 m </td> <td> Parks </td> <td> - </td> </tr> <tr> <td> Soil water content </td> <td> SWC </td> <td> m3 m-3 </td> <td> -0.15 m </td> <td> Parks </td> <td> - </td> </tr> <tr> <td> Atmospheric CO2 concentration </td> <td> CO2 </td> <td> ppmv </td> <td> 2 m </td> <td> Hardau II, Beromunster, ERA-5 </td> <td> 8°30'/47°23', 8°10'/47°11 </td> </tr> </tbody> </table>   In-situ ecophysiological data In-situ ecophysiology measurements performed on park trees and lawns in the city of Zurich during the ICOS-Cities project. LAI (leaf area index) was measured in dense Platanus sp. tree stands, found only in Bullingerhof and Hardaupark, during sunny conditions using a ceptometer (SS1 SunScan, Delta-T Devices). Sap flow was measured at six trees (Platanus sp., Tilia sp.), at Bullingerhof, Hardaupark and Fritschiwiese, with heat pulse sap flow sensors (3 x 3 cm probes, Implexx Sense), providing continuous measurements at 10-min sampling intervals. Daily aggregated sap flux densities (cm3 cm−2 d−1) were calculated from the 10-min data using the sensor inner thermistors, averaged for the six sampled trees. Soil and grass respiration were measured using a portable CO2 soil efflux system equipped with a 20 cm diameter survey chamber (LI-8200-01S, LI-COR Biosciences) and a CO2/H2O analyser (LI-870, LI-COR Biosciences). The observations originate from a total of 10 soil collars (Bullingerhof, Hardaupark, Fritschiwiese, Heiligfeld) separated to undisturbed grass collars (Reco, μmol CO2 m-2 s-1) and collars where the aboveground grass was clipped (Rsoil, μmol CO2 m-2 s-1). Further information on the dataset can be found in the submitted manuscript. Data format: comma separated values (csv) Time stamp: yyyy-MM-dd Period: 04/2022–09/2023   Land cover map Land cover map of part of Zurich urban area. Datasets used to derive this map: <ul> <li>·        Land Use Cadastre of the Canton of Zurich (https://www.geolion.zh.ch/geodatensatz/show?gdsid=443)</li> <li>·        Urban Atlas (https://doi.org/10.2909/fb4dffa1-6ceb-4cc0-8372-1ed354c285e6)</li> <li>·        Vegetation Height Model (VHM) from the Swiss federal forest inventory (https://opendata.swiss/de/dataset/vegetationshohenmodell-lfi)</li> <li>·        Forest Mixture from the Swiss Federal Forest Inventory (https://opendata.swiss/de/dataset/waldmischungsgrad-lfi)</li> </ul> Further information on the dataset can be found in the submitted manuscript.   Data specifications: CRS: EPSG:32632 - WGS 84 / UTM zone 32N - Projected Spatial Extent: 461972.1, 5246490.4 : 463972.1, 5248490.4 Temporal Extent: 2023 Units: meters Width: 2000 Height: 2000 Bands: 1 Pixel Size: 1,-1 Data type: Float32 - Thirty two bit floating point GDAL Driver Description: GTiff GDAL Driver Metadata: GeoTIFF   Legend: 30           Grass 40           Crops 50           Paved 60           Buildings 70           Deciduous trees 80           Water   CO2 fluxes Hourly mean CO2 fluxes estimated by the models diFUME, JSBACH, SUEWS and VPRM for the trees and lawns of the Zurich urban parks: Bullingerhof, Hardaupark, Fritschiwiese and Heiligfeld. GPP stands for gross primary productivity, Reco for ecosystem respiration and NEE for net ecosystem exchange. All fluxes are in units: μmol CO2 m-2 s-1. The parameter sets used by each model are presented in the Tables below. Further information on the dataset can be found in the submitted manuscript. Parameters used by diFUME model <table> <tbody> <tr> <td> Parameter </td> <td> Value </td> <td> Units </td> <td> Description </td> </tr> <tr> <td> A_max </td> <td> 15 </td> <td> μmol CO2 m-2 s-1 </td> <td> maximum leaf gross photosynthetic rate </td> </tr> <tr> <td> a </td> <td> 0.045 </td> <td> mol CO2 mol-1 PAR </td> <td> quantum yield for CO2 assimilation </td> </tr> <tr> <td> a_1 </td> <td> 25 </td> <td> N/A </td> <td> empirical coefficient in Leuning (1995) model </td> </tr> <tr> <td> b_ </td> <td> 0.65 </td> <td> N/A </td> <td> empirical coefficient in β-factor formula </td> </tr> <tr> <td> b_1 </td> <td> 5 </td> <td> N/A </td> <td> empirical coefficient </td> </tr> <tr> <td> D_o </td> <td> 0.3 </td> <td> kPa </td> <td> empirically determined coefficient for the VPD scalar inside Leuning (1995) model </td> </tr> <tr> <td> D_sc </td> <td> 1 </td> <td> N/A </td> <td> daylight scalar for dark respiration inhibition during day (1: no inhibition) </td> </tr> <tr> <td> E_0 </td> <td> 487.75 </td> <td> K </td> <td> temperature sensitivity parameter for soil respiration </td> </tr> <tr> <td> g_o </td> <td> 0.01 </td> <td> mol CO2 m-2 s-1 </td> <td> residual stomatal conductance for CO2 (g_s when Anet = 0, PAR = 0). </td> </tr> <tr> <td> Q_10 </td> <td> 1.85 </td> <td> N/A </td> <td> temperature sensitivity of leaf respiration </td> </tr> <tr> <td> R_(l,ref) </td> <td> 1.53 </td> <td> μmol CO2 m-2 s-1 </td> <td> reference leaf respiration at Tair = 25 °C </td> </tr> <tr> <td> R_(S,ref) </td> <td> 2.49 </td> <td> μmol CO2 m-2 s-1 </td> <td> reference soil respiration at Tsoil = 10 °C </td> </tr> <tr> <td> T_opt </td> <td> 23 </td> <td> °C </td> <td> optimum air temperature for gross photosynthesis </td> </tr> <tr> <td> T_0 </td> <td> -46 </td> <td> °C </td> <td> low-temperature limit for soil respiration </td> </tr> <tr> <td> T_(ref,S) </td> <td> 10 </td> <td> °C </td> <td> reference soil temperature for soil respiration </td> </tr> <tr> <td> T_(ref,l) </td> <td> 25 </td> <td> °C </td> <td> reference air temperature for leaf respiration </td> </tr> <tr> <td> W </td> <td> 10 </td> <td> °C </td> <td> width of the bell-shape curve at f(T_air )  = 0.5 </td> </tr> <tr> <td> θ_ref </td> <td> 0.4 </td> <td> m3 m-3 </td> <td> saturated soil volumetric water content  </td> </tr> <tr> <td> θ_g </td> <td> 0.1 </td> <td> m3 m-3 </td> <td> minimum soil volumetric water content, limit to stomatal conductance </td> </tr> <tr> <td> θ_0 </td> <td> 0.04 </td> <td> m3 m-3 </td> <td> minimum soil volumetric water content, limit to soil respiration </td> </tr> </tbody> </table>   Parameters used by JSBACH model <table> <tbody> <tr> <td> Parameter </td> <td> Trees </td> <td> Lawn </td> <td> Units </td> <td> Description </td> </tr> <tr> <td> J_max </td> <td> 104.5 </td> <td> 148.6 </td> <td> μmol(CO2) m-2(leaf) s-1 </td> <td> Maximum electron transport rate at 25 °C </td> </tr> <tr> <td> T_alt </td> <td> 4.0–4.5 </td> <td>   </td> <td> °C </td> <td> Alternation temperature </td> </tr> <tr> <td> θ_cap </td> <td> 0.32– 0.39 </td> <td> 0.32– 0.34 </td> <td> m m-1 </td> <td> Volumetric soil field capacity </td> </tr> <tr> <td> θ_pwp </td> <td> 0.13– 0.21 </td> <td> 0.135–0.165 </td> <td> m m-1 </td> <td> Volumetric wilting point </td> </tr> <tr> <td> V_max </td> <td> 55.0 </td> <td> 78.2 </td> <td> μmol(CO2) m-2(leaf) s-1 </td> <td> Maximum carboxylation rate at 25 °C </td> </tr> <tr> <td> z_root </td> <td> 0.5 </td> <td> 0.12 </td> <td> m </td> <td> Root depth </td> </tr> <tr> <td> CC </td> <td> 1.25 </td> <td> 1.25 </td> <td> N/A </td> <td> Relative cost to produce one carbon </td> </tr> <tr> <td> f_faeces </td> <td> 0.3 </td> <td> 0.3 </td> <td> N/A </td> <td> Fraction of carbon from herbivore faeces that goes into the green litter pool </td> </tr> <tr> <td> f_leaf </td> <td> 0.4 </td> <td> 0.4 </td> <td> N/A </td> <td> A fixed fraction of canopy maintenance respiration that makes up the dark respiration </td> </tr> <tr> <td> k </td> <td> 0.1 </td> <td> 0.09 </td> <td> N/A </td> <td> LAI growth rate during growth phase </td> </tr> <tr> <td> LAI_max </td> <td> 3.6–4.1 </td> <td> 3.0 </td> <td> m2 m-2 </td> <td> Maximum leaf area index </td> </tr> <tr> <td> p </td> <td> veg: 0.004 rest: 0.1 </td> <td> growth: 0.1 dry: 0.015 </td> <td> N/A </td> <td> LAI shedding rate (trees: vegetative and rest phase; grass: growth and dry season) </td> </tr> <tr> <td> r_d </td> <td> 0.605 </td> <td> 0.8602 </td> <td> μmol(CO2) m-2(leaf) s-1 </td> <td> Dark respiration at 25 °C, fraction of Vmax </td> </tr> </tbody> </table>   Parameters used by SUEWS model <table> <tbody> <tr> <td> Parameter </td> <td> Trees </td> <td> Lawn </td> <td> Units </td> <td> Description </td> </tr> <tr> <td> f_i </td> <td> 0.21 </td> <td> 0.18 </td> <td> N/A </td> <td> Fraction of each vegetation type i </td> </tr> <tr> <td> F_(pho,max,i) </td> <td> 8.3 </td> <td> 8.92 </td> <td> μmol m-2 s-1 </td> <td> Maximum potential photosynthesis </td> </tr> <tr> <td> LAI_(max,i) </td> <td> 4.8 </td> <td> 3 </td> <td> m2 m-2 </td> <td> Full leaf-on summertime value </td> </tr> <tr> <td> LAI_(min,i) </td> <td> 0.66 </td> <td> 1.6 </td> <td> m2 m-2 </td> <td> Leaf-off wintertime value </td> </tr> <tr> <td> T_L </td> <td> -10 </td> <td> -10 </td> <td> °C </td> <td> Lower air temperature limit </td> </tr> <tr> <td> T_H </td> <td> 55 </td> <td> 55 </td> <td> °C </td> <td> Upper air temperature limit </td> </tr> <tr> <td> G_5 </td> <td> 30 </td> <td> 30 </td> <td> °C </td> <td> Parameter related to temperature dependence </td> </tr> <tr> <td> G_3 </td> <td> 0.66 </td> <td> 0.538 </td> <td> N/A </td> <td> Parameter related to VPD dependence </td> </tr> <tr> <td> G_4 </td> <td> 0.89 </td> <td> 0.87 </td> <td> N/A </td> <td> Parameter related to VPD dependence </td> </tr> <tr> <td> G_6 </td> <td> 0.36 </td> <td> 0.55 </td> <td> mm-1 </td> <td> Parameter related to soil moisture dependence </td> </tr> <tr> <td> G_2 </td> <td> 477 </td> <td> 263.5 </td> <td> W m-2 </td> <td> Parameter related to  dependence </td> </tr> <tr> <td> Δθ_WP </td> <td> 132.5 </td> <td> 143 </td> <td> mm </td> <td> Wilting point deficit </td> </tr> <tr> <td> K_(↓max) </td> <td> 1200 </td> <td> 1200 </td> <td> W m-2 </td> <td> Maximum incoming shortwave radiation </td> </tr> <tr> <td> a_i </td> <td> 0.78 </td> <td> 1.7 </td> <td> N/A </td> <td> Empirical soil and vegetation respiration coefficient </td> </tr> <tr> <td> b_i </td> <td> 0.08 </td> <td> 0.06 </td> <td> N/A </td> <td> Empirical soil and vegetation respiration coefficient </td> </tr> <tr> <td> ω_(1,GDD,i) </td> <td> 0.04 </td> <td> 0.04 </td> <td> N/A </td> <td> Parameter for LAI calculation </td> </tr> <tr> <td> ω_(2,GDD,i) </td> <td> 0.0005 </td> <td> 0.0005 </td> <td> N/A </td> <td> Parameter for LAI calculation </td> </tr> <tr> <td> ω_(1,SDD,i) </td> <td> -1.5 </td> <td> -1.5 </td> <td> N/A </td> <td> Parameter for LAI calculation </td> </tr> <tr> <td> ω_(1,SDD,i) </td> <td> 0.0025 </td> <td> 0.0025 </td> <td> N/A </td> <td> Parameter for LAI calculation </td> </tr> <tr> <td> GDD </td> <td> 300 </td> <td> 300 </td> <td> days </td> <td> The growing degree days (GDD) needed for full capacity of the leaf area index </td> </tr> <tr> <td> SDD </td> <td> -300 </td> <td> -300 </td> <td> days </td> <td> The senescence degree days (SDD) needed to initiate leaf off </td> </tr> <tr> <td> T_(base,GDD) </td> <td> 5 </td> <td> 5 </td> <td> °C </td> <td> Base Temperature for initiating growing degree days (GDD) for leaf growth </td> </tr> <tr> <td> T_(base,SDD) </td> <td> 10 </td> <td> 10 </td> <td> °C </td> <td> Base temperature for initiating senescence degree days (SDD) for leaf off </td> </tr> </tbody> </table>   Parameters used by VPRM model <table> <tbody> <tr> <td> Parameter </td> <td> Trees </td> <td> Lawn </td> <td> Units </td> <td> Description </td> </tr> <tr> <td> λ </td> <td> <div> -0.16 </div> </td> <td> <div> -0.13 </div> </td> <td> <div> μmol CO2 m-2 s-1 </div> </td> <td> <div> light use efficiency </div> </td> </tr> <tr> <td> PAR_0 </td> <td> <div> 356.99 </div> </td> <td> <div> 545.61 </div> </td> <td> <div> μmol m-2 s-1 </div> </td> <td> <div> half-saturation value </div> </td> </tr> <tr> <td> α </td> <td> <div> 0.22 </div> </td> <td> <div> 0.40 </div> </td> <td> <div> μmol CO2 m-2 s-1 /0C </div> </td> <td> <div> empirical coefficient </div> </td> </tr> <tr> <td> β </td> <td> <div> 1.09 </div> </td> <td> <div> 0.42 </div> </td> <td> <div> μmol CO2 m-2 s-1 </div> </td> <td> <div> empirical coefficient </div> </td> </tr> <tr> <td> T_max </td> <td> <div> 40 </div> </td> <td> <div> 40 </div> </td> <td> <div> °C </div> </td> <td> <div> maximum temperature for photosynthesis </div> </td> </tr> <tr> <td> T_min </td> <td> <div> 0 </div> </td> <td> <div> 2 </div> </td> <td> <div> °C </div> </td> <td> <div> minimum temperature for photosynthesis </div> </td> </tr> <tr> <td> T_opt </td> <td> <div> 20 </div> </td> <td> <div> 18 </div> </td> <td> <div> °C </div> </td> <td> <div> optimal temperature for photosynthesis </div> </td> </tr> <tr> <td> T_low </td> <td> <div> 0 </div> </td> <td> <div> 0 </div> </td> <td> <div> °C </div> </td> <td> <div> to account for the persistence of soil respiration in winter </div> </td> </tr> </tbody> </table>   Data format: comma separated values (csv) Time step: 60 min (aggregated) Time stamp: yyyy-MM-dd HH:mm, UTC, end of aggregated period Period: 01/2022–09/2023

opencc-by-4.0Aug 2024View details →

Data from: Lower-cost eddy covariance for CO2 and H2O fluxes over grassland and agroforestry

This datasets contains eddy covariance fluxes calculated with EddyPro, average (co)spectra from EddyPro and meteorological data from the measurement campaigns conducted in Mariensee, Lower Saxony (Germany) in 2020 and 2021. The results were presented in the paper "Lower-cost eddy covariance for CO2 and H2O fluxes over grassland and agroforestry"; <a href="https://doi.org/10.5194/amt-2024-30">https://doi.org/10.5194/amt-2024-30</a>.  For more information on the data see the accompanying paper. Note that the eddy covariance data are not filtered, quality checked or gap-filled in these files!

opencc-by-4.0Jul 2021View details →

CO2 and CH4 gas fluxes in disturbed and intact northern peatlands

The data were collected in seven Estonian peatlands (5 disturbed and 2 intact) during three to four (2017–2020) years with closed chamber technique. Table 1 (see CO2_CH4_fluxes_README.docx) shows the variables presented in this dataset.

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.

Data and code: Disentangling the impact of Atlantic Niño on sea-air CO2 flux

This repository share (1) post-processed data of Norwegian Earth system model version 2 (LM) and (2)  codes for calculating carbon system from NorESM variables (MATLAB format).

opencc-by-4.0Apr 2023View details →

diFUME Modeled CO2 fluxes V03

Description: Weekly mean CO2 fluxes (FC, μmol CO2 m-2 s-1) after the inversion method (posteriors) in weekly cycles developed in diFUME project. The variance (σ2) of the posterior distributions is also provided. The inversion method is different from the posteriors of V02.   Each flux component is provided separately: Building heating emissions: Eb Commercia/Industrial emissions: Ec Vehicle emissions: Ev Human respiration: Rh Biogenic flux: Bm (sum of soil respiration, plant respiration and plant photosynthesis) Total FC = Eb + Ec + Ev + Rh + Bm   Filenames: **_postyyyyMMdd: Posterior model estimations for the week that starts at the day defined by yyyy: year, MM: month, dd: day **_post_varyyyyMMdd: Posterior model variances (σ2) for the week that starts at the day defined by yyyy: year, MM: month, dd: day   Data specifications: CRS: EPSG:32632 - WGS 84 / UTM zone 32N - Projected Spatial Extent: 392120.0,5266860.0 : 395160.0,5269840.0 Temporal Extent: 2018 Units: meters Width: 152 Height: 149 Bands: 1 Pixel Size: 20,-20 Data type: Float32 - Thirty two bit floating point GDAL Driver Description: GTiff GDAL Driver Metadata: GeoTIFF

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-4.0Sep 2023View details →

CO2 Flux partioning in a Eddy Covariance tower of marsh ecosystem (Fuente Duque) in Doñana Biological Reserve

This dataset consists of the estimated data of the CO2 flux partition: assimilation or gross primary production (GPP) and heterotrophic or ecosystem respiration (Reco), as well as the standard deviation of each estimate. These data have been estimated from the data provided by the ICTS of the Doñana Biological Reserve of the eddy covariance tower located in Fuente Duque, in the Hinojos marsh in the period between October 2020 and December 2022.

edi44/100

CO2, CH4, and H2O flux data and associated environmental variables for the BBC collapse scar for 2004

This data set contains flux measurements for the transect from the center of the BBC collapse scar (0m) into the surrounding fire scar (30m) of the Survey Line Fire (burned in June-July 2001). We measured CO2, H2O and CH4 fluxes every one to two weeks throughout the growing season of 2004. We measured fluxes at permanent plots established from the center of the bog into the surrounding burn at 0, 6, 12, 18, 24, and 30 m on the east and west side of the transect. Flux measurements on either side of the transect were treated as replicates. CO2 and H2O fluxes were measured using a Li-840 infrared gas analyzer (Licor Inc., Lincoln, NB, USA). The IRGA was calibrated before each trip to the field using a span of 400 ppm and a zero of N2 gas. We logged data every 0.5 seconds for 2 min. To account for measurement variability, we conducted two measurements in succession at each location, after flushing the chamber for accumulated CO2 and H2O. For the flux measurements, we built plexiglass chambers with pipe insulation bases with dimensions of 61 x 61 x 30.5 cm, 61 x 61 x 61 cm, or 61 x 61 x 122 cm. The shorter chambers were used in the collapse portion of the transect. Chambers included fans for air circulation, inlet and outlet ports for CO2 measurements, or just outlet ports for CH4 measurements (Carroll and Crill 1997). We placed chambers directly on the soil surface and used pipe insulation and plastic sheeting to make a solid seal during the measurement. To estimate the volume for each chamber measurement, we measured the distance to the soil surface from a 6 cm grid suspended 30 cm above each plot, the surface area was then used to calculate the chamber volume for each measurement. Dark measurements were used to determine CO2 derived from soil and root respiration (ecosystem respiration) using a two-layer cloth shroud with a reflective surface to exclude solar radiation. To estimate net ecosystem exchange (NEE) of CO2, we conducted chamber measurements of plant and soi

openOpenNov 2005View details →

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-4.0Nov 2021View details →

CO2 flux measurements in Lake Geneva

Dataset contain CO2 flux, CO2 in water and CO2 in atmosphere

opencc-by-4.0Jul 2022View details →

Dataset for "Intercomparison of methods to estimate gross primary production based on CO2 and COS flux measurements"

The final dataset used in manuscript "Intercomparison of methods to estimate gross primary production based on CO2 and COS flux measurements" by Kohonen et al. (2022). The dataset contains carbonyl sulfide (COS) and carbon dioxide (CO2) eddy covariance flux data and in-situ meteorological data measured at Hyytiälä forest in Juupajoki, Southern Finland, as well as GPP estimates derived from COS and CO2 flux measurements as described in the manuscript from January 2013 to December 2017. Raw data are available upon request from the author.

opencc-by-4.0Aug 2022View details →

Chamber flux data (CH4 , CO2) from Finland (1991 -1993) originally used in Nykänen et al. GBC, 12(1), 53 - 69, 1988,

Chamber flux (CH4 , CO2), temperature, water table and vegetaion data from Särkkä (62° 48'; 30° 58') and Lakkasuo (61° 47'; 24°16') from Finland (1991 -1993). These are natural and drained site data few decades after drainage nearby each other.  This data was originally used in following articles:  <table> <tbody> <tr> <td>Chamber measuremets CH4, CO2   </td> </tr> <tr> <td>Martikainen P.J., Nykänen H., Alm J. and Silvola J. 1995. Changes in fluxes of carbon dioxide, methane and nitrous oxide due to forest drainage of mire sites of different trophy. Plant and Soil 168/169: 571-577.</td> </tr> <tr> <td>Martikainen P.J., Nykänen H., Crill P. and Silvola J. 1993. Effect of a lowered water table on nitrous oxide fluxes from northern peatlands. Nature 366 (4): 51-53.</td> </tr> <tr> <td>Martikainen P.J., Nykänen H., Crill P. and Silvola J. 1993. The effect of changing water table on methane fluxes at two Finnish mire sites. Suo Mires and Peat 43: 237-240.</td> </tr> <tr> <td>Nykänen H., Alm J., Lang K., Silvola J. and Martikainen P.J. 1995. Emissions of CH4, N2O and CO2 from a virgin fen and a fen drained for grassland in Finland. Journal of Biogeography 22: 351-357.</td> </tr> <tr> <td>Descriptions: Nykänen H., Alm J., Silvola J. and Tolonen K., Martikainen P.J. 1998. Methane fluxes on boreal peatlands of different fertility and the effect of long-term experimental lowering of the water table on flux rates. Global Biogeochemical Cycles 12: 53-69.</td> </tr> </tbody> </table>

opencc-by-4.0Oct 2022View details →

Dataset of measurements of the soil CO2 flux and soil brightness temperature at Le Biancane (geothermal field of Larderello-Travale, Tuscany, Italy) in the May-June 2021 period.

Dataset of measurements of the soil CO2 flux and soil brightness temperature at Le Biancane (geothermal field of Larderello-Travale, Tuscany, Italy) in the period May-June 2021. The dataset is structured as follows: Column A is the progressive number of the point (#); Column B is the Longitude of the point, datum WGS 1984; Column C is the Latitude of the point, datum WGS 1984; Column D is the Universal Transverse Mercator (UTM) Longitude coordinate, datum WGS 1984, zone 32N; Column E is the Universal Transverse Mercator (UTM) Latitude coordinate, datum WGS 1984, zone 32N; Column F is the soil brightness temperature, in °C; Column G is the soil CO2 flux in grams of CO2 per square meter, per day (g m-2 day-1)