Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
86
datasets available to search
ShareScore release 0.9.0
Dataset results
86 results for “atmospheric CO2”
Data Product for "Toward a Cenozoic history of atmospheric CO2"
<p>These data were vetted and revised from published paleo-CO2 data (original estimates archived at https://zenodo.org/uploads/8052599) by an international group of proxy experts supported through an NSF-funded Research Coordination Network. It brings together paleo-CO2 reconstruction data from terrestrial and marine archives, and the compilation includes estimates derived from multiple proxies including Phytoplankton, Boron, Stomatal Frequencies, Leaf Gas Exchange, Liverworts, Land Plant d13C, Paleosols, and Nahcolite. Data are visualized in an interactive product plot made available on the Paleo-CO2 project web page at (https://www.paleo-co2.org) and are also archived in the NCDC database (<a href="https://www.ncei.noaa.gov/pub/data/paleo/climate_forcing/trace_gases/Paleo-pCO2/product_files/">https://www.ncei.noaa.gov/pub/data/paleo/climate_forcing/trace_gases/Paleo-pCO2/product_files/</a>). </p>
Accessible Oceans: Auditory Display. Net flux of CO2 between Ocean and Atmosphere
<p>The seven tracks make up an auditory display of the net flux of carbon dioxide between the ocean and the atmosphere. The seven tracks in the auditory display are comprised of data sonifications and contextual audio supports (dialogue, auditory icons, and music). You may <a href="https://samply.app/p/RhkRBKbRTueE2F86b5QS">listen online here</a>.</p> <p>The data comes from the National Science Foundation (NSF) Ocean Observatories Initiative (OOI) and the display is based on the OOI Nugget developed by Dr. Leslie Smith. (<a href="https://datalab.marine.rutgers.edu/ooi-nuggets/co2-flux/">https://datalab.marine.rutgers.edu/ooi-nuggets/co2-flux/</a>)</p> <p>The “Accessible Oceans” AISL Pilots and Feasibility study aims to inclusively design auditory displays that support the perception and understanding of ocean data in informal learning environments (ILEs). More can be found on the project website: <a href="https://accessibleoceans.whoi.edu/">https://accessibleoceans.whoi.edu/</a></p>
CESM2 Atmospheric CO2 without agricultural management
This dataset was created to understand the impacts of agriculture on CO2 concentrations. Two 1-degree simulations were branched from the CMIP6 "CESM2-esm-hist" simulation in 1970. The first of these turned off the explicit representation of agriculture so that all crop areas are represented as "generic" C3 crops, where crop phenology is simulated as C3 grasses and do not include irrigation or fertilization (referred to as "generic crop"). The second uses the explicit representation of agriculture but removes industrial N fertilization (referred to as "no fertilization"). To ensure that changes in CO2 fluxes were minimally impacted by model drift, each simulation equilibrated carbon fluxes in 1970 by cycling over a single year of forcing for ten years. The CESM2 simulated these alternative representations of agriculture in a CO2 emissions-forced historical scenario following the "esm-hist" experimental protocol.
Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon (Open)
<p>This work was carried out in the scope and with the support of the project: Climate Services Through Knowledge Co-Production: A Euro-South American Initiative for Strengthening Societal Adaptation Response to Extreme Events (CLIMAX).</p> <p>The project consortium includes the following institutions: Centre National de la Recherche Scientifique CNRS/Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos (UMI-IFAECI) (Argentina-France); General Coordination of Earth Sciences /National Institute for Space Research (INPE) (Brazil); Institut de Recherche pour le Développement (IRD)/ Unité Mixte de Recherche (UMR 245) (France); Le Laboratoire des Sciences du Climat et de l'Environnement (LSCE) (France); Potsdam Institute for Climate Impact Research (PIK) (Germany); Technical University of Munich (TUM) (Germany) and Wageningen University and Research (WUR) Netherlands). The project is sponsored by the Collaborative Research Action (CRA) on “Climate Predictability and Inter-Regional Linkages” of the Belmont Forum, launched in 2015.</p> <p>Climate variability patterns linking the South American Monsoon region, including Amazonia, with southeastern South America influence climate extremes and impact several societal sectors. More than 200 million people live in the study region, which is also one of the largest agricultural production regions of the world and home to the world’s second largest hydroelectric power plant.</p> <p>The objectives of CLIMAX include better understanding the combined role of remote and local drivers on South American climate variability from sub-seasonal to decadal timescales, and its impact on the occurrence and intensity of extreme events. Special focus is given to an improved understanding of the effects of land use changes from the Amazon to the subtropics and their impact on climate.</p> <ol> <li> <p><strong>EXPERIMENT DESIGN</strong></p> </li> </ol> <p>We used four models that are classified as Dynamic Global Vegetation Models (DGVMs) (Prentice et al., 2007; Rezende et al., 2015): Integrated Model of Land Surface Processes (INLAND) (Tourigny, 2014); Lund-Potsdam-Jena managed Land model version 4 (LPJmL4) (Schaphoff et al., 2018), Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) (Smith et al. 2001, Hickler et al., 2012), and Organising Carbon and Hydrology In Dynamic Ecosystems model (ORCHIDEE) (Krinner et al., 2005).</p> <p>We used three forcings with climate data (GLDAS, GSWP3, and WATCH+WFDEI), Land Use Change (LUC) data and validation data (FLUXCOM (Remote sensor+meteorological data+artificial neural network approach), FLUXCOM (eddy covariance), MODIS (Light Use Efficiency), GLEAM, and TerraClimate (Rezende et al., 2022).</p> <p>We conducted two sets of simulation experiments with different values of CO2: 1) increasing CO2 from the pre-industrial period to 2010 named <strong>historical CO</strong><strong>2</strong> (<strong>hist CO</strong><strong>2</strong>); 2) constant concentration of 278 ppm of (pre-industrial) atmospheric CO2 named <strong>constant CO</strong><strong>2</strong><strong> (const CO</strong><strong>2</strong><strong>)</strong>. We ran both CO2 experiments under <strong>Land Use Change</strong> (<strong>LUC</strong>) and <strong>Potential Natural Vegetation </strong>(<strong>PNV</strong>) conditions. All combinations of CO2 and land use change resulted in four sets of simulation experiments per climate input: 1. <strong>LUC historical CO</strong><strong>2</strong>; 2. <strong>LUC constant CO</strong><strong>2</strong>; 3. <strong>PNV historical CO</strong><strong>2</strong>; 4. <strong>PNV constant CO</strong><strong>2 </strong> (Rezende et al., 2022).</p> <p><strong>2. DATA DESCRIPTION</strong></p> <p>The complete description of the data, including the climate forcing, LUC, the validation datasets, methodology, simulations, discussion and conclusion is in Rezende et al. (2022). This archive contains only the data description from the simulations (outputs) by the DGVMs.</p> <p><strong>2.1 SOFTWARE </strong></p> <p><br> The data were manipulated, worked, standardized, converted using the software: <strong>Climate Data Operators (cdo) version 1.7.0</strong>, <strong>Grid Analysis and Display System (Grads) (</strong><a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a><strong>) version 2.0.2, and RStudio Desktop version</strong> 1.3.1093 <strong>(R Core Team, 2020)</strong>, through command lines and several scripts developed for this purpose. The figures were generated with <strong>Grads,</strong> and <strong>RStudio</strong>, and some images were enhanced with <strong>Gimp version 2.8.22</strong>. All the software used is freeware.</p> <p><strong> 2.2 PRIMARY DATA FROM SIMULATIONS</strong></p> <p>Data originating from the simulations are in monthly resolution, covering South America, with all the forcings. Despite data spanning over 1948-2010 or 1950-2010 our study focuses on the period 1981-2010.</p> <p><strong>Variables</strong>: Gross Primary Productivity (GPP) (kg m-2 month-1), evaporation (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).</p> <p>The naming of the files is according to the following rules:</p> <p><strong>DGVM_forcing_vegetation cover_CO2 concentration_attribute</strong></p> <p><strong>DGVMs</strong>;</p> <p> InLand (INLAND)</p> <p> LPJ-G (LPJ-GUESS)</p> <p> LPJmL (LPJmL4)</p> <p> ORCHI (ORCHIDEE)</p> <p>forcings: </p> <p> gld - GLDAS</p> <p> gsw – GSWP3</p> <p> wat – WATCH+WFDEI</p> <p> </p> <p>vegetation cover:</p> <p> LU – Land Use Change</p> <p> PNV – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration:</p> <p> CO2 – historical CO2</p> <p> noCO2 – constant CO2 = 278 ppm</p> <p> </p> <p>variables:</p> <p> E – evaporation</p> <p> Et – transpiration</p> <p> gpp – Gross Primary Productivity</p> <p> npp - Net Primary Productivity (not used in the experiment)</p> <p> </p> <p><strong>Example</strong>:</p> <p> inLand_gld_LU_noCO2_E.nc</p> <p> </p> <p><strong> 2.3 SUPPLEMENTARY DATA </strong></p> <p>These interception loss data (mm month-1) were requested by a reviewer to complement the analysis and are available only for the LUC CO2 scenario and for the study region: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).</p> <p>Files are named according to the following rules:</p> <p>variable_season_forcing_DGVM_vegetation cover CO2 concentration_region</p> <p>variable:</p> <p> inter – loss by interception</p> <p> </p> <p>season:</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>forcings: </p> <p> gl - GLDAS</p> <p> gs – GSWP3</p> <p> wa – WATCH+WFDEI</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or – ORCHIDEE</p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>region</p> <p> SA – southern Amazon</p> <p> </p> <p>Example:</p> <p> inter_D_gl_in_LC_SA.nc</p> <p><strong>2.4 PROCESSED DATA</strong></p> <p>Processed data cover all scenarios and input data sets and are restricted to the study area: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).They are in seasonal resolution with averages for January-February-March-April (JFMA) (rainy season) and averages for June-July-August-September (JJAS). Each of the files contains the Gross Primary Productivity variables (GPP) (kg m-2 month-1), evaporation (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).</p> <p> </p> <p>Files are named according to the following rules:</p> <p> </p> <p><strong>season_DGVM_forcing_vegetation cover CO2 concentration_region</strong></p> <p>season</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or - ORCHIDEE</p> <p> </p> <p>forcings: </p> <p> Gl - GLDAS</p> <p> Gs – GSWP3</p> <p> Wa – WATCH+WFDEI </p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>Example:</p> <p>D_in_Gs_LN.nc</p> <p><strong>2.5 DERIVED DATA</strong></p> <p> </p> <p>The variable Water Use Efficiency (WUE) (kg m-2 mm-1 month-1) results from rate: GPP / Tr (transpiration) (Eq. 1). </p> <p> </p> <table> <tbody> <tr> <td> <p> WUE = GPP / Tr</p> </td> <td> <p>(Eq. 1)</p> </td> </tr> </tbody> </table> <p> </p> <p>These data refer to the study region: southern Amazon and apply to only one scenario: Land Use Change and historic CO2. Files are named naming of according to the following rules:</p> <p> </p> <p>Variable:</p> <p> WUE – Water Use Efficiency</p> <p> </p> <p>season</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or - ORCHIDEE</p> <p> </p> <p>forcings: </p> <p> Gl - GLDAS</p> <p> Gs – GSWP3</p> <p> Wa – WATCH+WFDEI </p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>region</p> <p> SA – southern Amazon</p> <p> </p> <p>Example:</p> <p>wue_D_lm_Gl_LC_SA.nc</p> <p> </p> <p><strong>How to cite this work</strong>:</p> <p>Rezende, Luiz F. C., Aline Castro, Celso Von Randow, Romina Ruscica, Boris Sakschewski, Phillip Papastefanou, Nicolas Viovy, Kirsten Thonicke, Anna Sörensson, Anja Rammig, Iracema F. A. Cavalcanti. Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022.</p> <p><strong>References</strong></p> <p><a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a>. Center for Ocean-Land-Atmosphere Studies, Institute of Global Environment and Society,<a href="https://en.wikipedia.org/wiki/George_Mason_University"> George Mason University</a>. Archived from<a href="http://www.iges.org/grads/gadoc/"> the original</a> on 7 April 2015. Retrieved 14 March 2015.</p> <p>Hickler T. et al., 2012. Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species based dynamic vegetation model. Glob Ecol Biogeograp 21:50–63, <a href="https://doi.org/10.1111/j.1466-8238.2010.00613.x">https://doi.org/10.1111/j.1466-8238.2010.00613.x</a></p> <p>Krinner, G.et al., 2005. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochemical Cycles, 19, GB1015, doi:10.1029/2003GB002199.</p> <p>R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. </p> <p>Prentice IC (2007) Dynamic global vegetation modeling: quantifying terrestrial ecosystem responses to large-scale environmental change. In: Canadell J, Pataki D, Pitelka L (eds) Terrestrial ecosystems in a changing world. Springer, Berlin Heidelberg.</p> <p>Rezende, Luiz F. C. et al., 2015. Evolution and challenges of dynamic global vegetation models for some aspects of plant physiology and elevated atmospheric CO2. Int J Biometeorol., 2015, doi: 10.1007/s00484-015-1087-6.</p> <p>Rezende, Luiz F. C. et al., 2022. Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research - Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022.</p> <p>Schaphoff, S. et al., 2018. LPJmL4 – a dynamic global vegetation model with managed land –Part 1: Model description. Geosci. Model Dev., 11, 1343–1375, 2018, <a href="https://doi.org/10.5194/gmd-11-1343-2018">https://doi.org/10.5194/gmd-11-1343-2018</a>.</p> <p>Smith B. et al (2001) Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Glob Ecol Biogeograp 10:621–637</p> <p>Tourigny, E. (2014). Multi-scale fire modeling in the neotropics: coupling a land surface model to a high resolution fire spread model, considering land cover heterogeneity. Phd dissertation, Meteorology. INPE. Retrieved from http://urlib.net/sid.inpe.br/mtc-m21b/2014/05.30.00.36</p> <p><br> </p> <p> </p>
Analysis of regional CO2 contributions at the high Alpine observatory Jungfraujoch by means of atmospheric transport simulations and δ13C
<p>The data set complementary to manuscript "Analysis of regional CO<sub>2</sub> contributions at the high Alpine observatory Jungfraujoch by means of atmospheric transport simulations and δ<sup>13</sup>C" in <em>Atmospheric Chemistry and Physics</em> (<a href="https://acp.copernicus.org">https://acp.copernicus.org</a>).</p>
Data associated with: Emergence of the physiological effects of elevated CO2 on land-atmosphere exchange of carbon and water
<p>Elevated atmospheric CO<sub>2</sub> (eCO<sub>2</sub>) influences the carbon assimilation rate and stomatal conductance of plants and thereby can affect the global cycles of carbon and water. Yet, the detection of these physiological effects of eCO<sub>2</sub> in observational data remains challenging, because natural variations and confounding factors (e.g., warming) can overshadow the eCO<sub>2</sub> effects in observational data of real-world ecosystems. In this study, we aim at developing a method to detect the emergence of the physiological CO<sub>2</sub> effects on various variables related to carbon and water fluxes. We mimic the observational setting in ecosystems using a comprehensive process-based land surface model QUINCY to simulate the leaf-level effects of increasing atmospheric CO<sub>2</sub> concentrations and their century-long propagation through the terrestrial carbon and water cycles across different climate regimes and biomes. We then develop a statistical method based on the signal-to-noise ratio to detect the emergence of the eCO<sub>2</sub> effects. The signal in gross primary production (GPP) emerges at relatively low CO<sub>2</sub> increase (Δ[CO<span>2</span>] ~ 20 ppm) where the leaf area index is relatively high. Compared to GPP, the eCO<sub>2</sub> effect causing reduced transpiration water flux (normalized to leaf area) emerges only at relatively high CO<sub>2</sub> increase (Δ[CO<sub>2</sub>] >> 40 ppm), due to the high sensitivity to climate variability and thus lower signal-to-noise ratio. In general, the response to eCO<sub>2</sub> is detectable earlier for variables of the carbon cycle than the water cycle, when plant productivity is not limited by climatic constraints, and stronger in forest-dominated rather than in grass-dominated ecosystems. Our results provide a step toward when and where we expect to detect physiological CO<sub>2</sub> effects in in-situ flux measurements, how to detect them and encourage future efforts to improve the understanding and quantification of these effects in observations of terrestrial carbon and water dynamics.</p>
Atmospheric CO2 simulations over Indian sites using STILT driven by WRF meteorology.
<p>This data contains atmospheric CO2 simulations at a temporal resolution of 3 hours over 15 Indian sites using a Lagrangian transport model, STILT driven by WRF meteorology during May 2017.</p> <p>Note: You are encouraged to contact the creator before using this data in any presentation or publication. </p> <p>Reference:</p> <p>Jithin Sukumaran, Dhanyalekshmi Pillai, Vishnu Thilakan, Saradambal Lekshmi, Gokul Udayakumar, Thara Anna Mathew, Aparnna Ravi, Manoj M G. How critical is the accuracy of the atmospheric transport modelling to improve the urban CO2 emission in India? - A Lagrangian-based approach. (2024), JGR Atmospheres. [Under review]</p> <p> </p>
Atmospheric CO2 simulations over Indian sites using STILT driven by ECMWF meteorology.
<p>This data contains atmospheric CO2 simulations at a temporal resolution of 3 hours over 15 Indian sites using a Lagrangian transport model, STILT driven by ECMWF meteorology during May 2017.</p> <p>Note: You are encouraged to contact the creator before using this data in any presentation or publication. </p> <p>Reference:</p> <p>Jithin Sukumaran, Dhanyalekshmi Pillai, Vishnu Thilakan, Saradambal Lekshmi, Gokul Udayakumar, Thara Anna Mathew, Aparnna Ravi, Manoj M G. How critical is the accuracy of the atmospheric transport modelling to improve the urban CO2 emission in India? - A Lagrangian-based approach. (2024), JGR Atmospheres. [Under review]</p> <p> </p>
Input data for running forward simulations of CO2 atmospheric concentrations over Europe for the year 2019.
<p>This dataset provides input data (fluxes, background concentrations, and observations) for running forward simulations of CO2 atmospheric concentrations over Europe for the year 2019 using chemical transport models (CTMs). While some components of the dataset are available in other repositories, this compilation serves to 1) streamline the data collection process for other users and 2) bypass the need to perform data aggregation.</p> <p>Here is a description of each dataset:</p> <p><strong>cams73_latest_co2_conc_surface_inst_2019*.nc</strong></p> <p>CO2 mole fractions from the CAMS global inversion-optimised product v20r2 (Chevallier et al., 2010).</p> <p>The data are provided at a resolution of 3.75° in longitude and 1.9° in latitude, with a 3-hourly temporal resolution. </p> <p><strong>monitor_CO2_CIF_2019.nc</strong></p> <div> <div> <div> <div> <div> <div> <p>Observed CO2 atmospheric mixing ratios in Europe, compiled in version V8 of the ICOS GlobalView Obspack (ICOS RI et al., 2023), include continuous measurements from 58 stations across Europe, incorporating both ICOS and non-ICOS facilities.</p> <p>The original dataset has been aggregated and adapted to match the format of the monitor files used in the Community Inversion Framework (CIF; Berchet et al., 2021).</p> </div> </div> </div> </div> </div> </div> <p><strong>EDGARv4.3_BP2021_CO2_EU2_2019.nc</strong></p> <p>Anthropogenic CO2 fluxes (European, hourly) obtained from EDGAR-v4.2 and BP.</p> <p>The anthropogenic CO2 emissions are based on the spatial distribution from the EDGAR-v4.2 inventory, national and annual budgets from British Petroleum (BP) statistics, and hourly temporal profiles derived using the COFFEE approach (Steinbach et al., 2011, available on the ICOS Carbon Portal). This data is provided at a 0.1° × 0.1° horizontal resolution and hourly temporal resolution.</p> <p><strong>FG2.TRENDY11.ORC3.S3.3H_NBP_resp_2019.nc</strong></p> <p>NBP CO2 fluxes (global, 3-hourly) obtained from ORCHIDEE simulations. </p> <p>The ORCHIDEE-TRENDY simulation is conducted as part of the TRENDY model intercomparison project (e.g., Sitch et al., 2015; Friedlingstein et al., 2022). This simulation uses inputs provided by the project, including the CRUERA atmospheric climate forcing (global, 6-hourly, 0.5-degree resolution), LUH2 land-use change dataset, global atmospheric CO2 concentration data, and nitrogen fertilizer input datasets. All TRENDY simulations adhere to a standardized protocol: a model spin-up phase using recycled forcing data from 1901-1920, with other inputs from 1700, continues until the model's carbon pools reach equilibrium (340 years of spin-up for ORCHIDEE). This is followed by a transient simulation from 1700-1900, varying CO2 and land-use data while recycling climate forcing, and a historical simulation from 1901-2020 with all data inputs varied.</p> <p><strong>FR2.ORC3v7267.CRUERA3.NBP_3H.2019.nc</strong></p> <p>NBP CO2 fluxes (Europe, 3-hourly) obtained from ORCHIDEE simulations. </p> <p>The ORCHIDEE-VERIFY simulation is performed as part of the VERIFY project over the European region. This simulation is driven by the CRUERA dataset, which is derived from the ERA5-Land dataset (originally global, 1-hourly, at 0.1-degree resolution), transformed to the VERIFY region of interest (35°N to 73°N, 25°W to 45°E, 3-hourly, at 0.125-degree resolution), and re-aligned with the CRU observation dataset (for air temperature, shortwave radiation, humidity, and precipitation). The Hilda+ dataset is used for land use, and the EMEP model outputs are used for nitrogen inputs. The VERIFY simulation follows the general protocol used in the TRENDY project.</p> <p><strong>FR2.ORC3v7267.CRUERA3.hetero_resp_3H.2019.nc</strong></p> <p>Heterotrophic respiration CO2 fluxes (Europe, 3-hourly) obtained from ORCHIDEE simulations as described in the previous section.</p> <p><strong>Becker_coastal_fluxes_RF_v2021_2_2019.nc</strong></p> <p>Ocean CO2 fluxes (Europe, daily). </p> <p>The ocean fluxes come from a hybrid product combining the University of Bergen coastal ocean flux estimate and the Rödenbeck global ocean estimate (Rödenbeck et al., 2014). This data is provided at a 0.125° × 0.125° horizontal resolution and at a daily temporal resolution.</p> <p> </p> <p><em><strong>References</strong></em> </p> <p> </p> <p>Berchet, A., Sollum, E., Pison, I., Thompson, R. L., Thanwerdas, J., Fortems-Cheiney, A., Peet, J. C. A. v., Potier, E., Chevallier, F., Broquet, G., and Berchet, A.: The Community Inversion Framework: codes and documentation, https://doi.org/10.5281/zenodo.6304912, 2022</p> <p>Chevallier, F., Ciais, P., Conway, T. J., Aalto, T., Anderson, B. E., Bousquet, P., Brunke, E. G., Ciattaglia, L., Esaki, Y., Fröhlich, M., Gomez, A., Gomez-Pelaez, A. J., Haszpra, L., Krummel, P. B., Langenfelds, R. L., Leuenberger, M., Machida, T., Maignan, F., Matsueda, H., Morguí, J. A., Mukai, H., Nakazawa, T., Peylin, P., Ramonet, M., Rivier, L., Sawa, Y., Schmidt, M., Steele, L. P., Vay, S. A., Vermeulen, A. T., Wofsy, S., and Worthy, D.: CO2 surface fluxes at grid point scale estimated from a global 21 year reanalysis of atmospheric measurements, Journal of Geophysical Research: Atmospheres, 115, https://doi.org/10.1029/2010JD013887, 2010</p> <p>Friedlingstein, P., O’Sullivan, M., Jones, M. W., Andrew, R. M., Gregor, L., Hauck, J., Le Quéré, C., Luijkx, I. T., Olsen, A., Peters, G. P.,Peters, W., Pongratz, J., Schwingshackl, C., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Alkama, R., Arneth, A., Arora,V. K., Bates, N. R., Becker, M., Bellouin, N., Bittig, H. C., Bopp, L., Chevallier, F., Chini, L. P., Cronin, M., Evans, W., Falk, S., Feely, R. A., Gasser, T., Gehlen, M., Gkritzalis, T., Gloege, L., Grassi, G., Gruber, N., Gürses, O., Harris, I., Hefner, M., Houghton, R. A.,Hurtt, G. C., Iida, Y., Ilyina, T., Jain, A. K., Jersild, A., Kadono, K., Kato, E., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken,J. I., Landschützer, P., Lefèvre, N., Lindsay, K., Liu, J., Liu, Z., Marland, G., Mayot, N., McGrath, M. J., Metzl, N., Monacci, N. M.,Munro, D. R., Nakaoka, S.-I., Niwa, Y., O’Brien, K., Ono, T., Palmer, P. I., Pan, N., Pierrot, D., Pocock, K., Poulter, B., Resplandy, L.,Robertson, E., Rödenbeck, C., Rodriguez, C., Rosan, T. M., Schwinger, J., Séférian, R., Shutler, J. D., Skjelvan, I., Steinhoff, T., Sun, Q., Sutton, A. J., Sweeney, C., Takao, S., Tanhua, T., Tans, P. P., Tian, X., Tian, H., Tilbrook, B., Tsujino, H., Tubiello, F., van der Werf,G. R., Walker, A. P., Wanninkhof, R., Whitehead, C., Willstrand Wranne, A., Wright, R., Yuan, W., Yue, C., Yue, X., Zaehle, S., Zeng, J., and Zheng, B.: Global Carbon Budget 2022, Earth System Science Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022,https://essd.copernicus.org/articles/14/4811/2022/, publisher: Copernicus GmbH, 2022</p> <p>ICOS RI, Bergamaschi, P., Colomb, A., De Mazière, M., Emmenegger, L., Kubistin, D., Lehner, I., Lehtinen, K., Lund Myhre, C., Marek, M., Platt, S. M., Plaß-Dülmer, C., Schmidt, M., Apadula, F., Arnold, S., Blanc, P.-E., Brunner, D., Chen, H., Chmura, L., Conil, S., Couret, C., Cristofanelli, P., Delmotte, M., Forster, G., Frumau, A., Gheusi, F., Hammer, S., Haszpra, L., Heliasz, M., Henne, S., Hoheisel, A., Kneuer, T., Laurila, T., Leskinen, A., Leuenberger, M., Levin, I., Lindauer, M., Lopez, M., Lunder, C., Mammarella, I., Manca, G., Manning, A., Marklund, P., Martin, D., Meinhardt, F., Müller-Williams, J., Necki, J., O’Doherty, S., Ottosson-Löfvenius, M., Philippon, C., Piacentino, S., Pitt, J., Ramonet, M., Rivas-Soriano, P., Scheeren, B., Schumacher, M., Sha, M. K., Spain, G., Steinbacher, M., Sørensen, L. L., Vermeulen, A., Vítková, G., Xueref-Remy, I., di Sarra, A., Conen, F., Kazan, V., Roulet, Y.-A., Biermann, T., Heltai, D., Hensen, A., Hermansen, O., Komínková, K., Laurent, O., Levula, J., Pichon, J.-M., Smith, P., Stanley, K., Trisolino, P., ICOS Carbon Portal, ICOS Atmosphere Thematic Centre, ICOS Flask And Calibration Laboratory, and ICOS Central Radiocarbon Laboratory: European Obspack compilation of atmospheric carbon dioxide data from ICOS and non-ICOS European stations for the period 1972-2023;<br>obspack_co2_466_GLOBALVIEWplus_v8.0_2023-04-26, https://doi.org/10.18160/CEC4-CAGK, 2023</p> <p>Rödenbeck, C., Bakker, D. C. E., Metzl, N., Olsen, A., Sabine, C., Cassar, N., Reum, F., Keeling, R. F., and Heimann, M.: Interannual sea–air CO2 flux variability from an observation-driven ocean mixed-layer scheme, Biogeosciences, 11, 4599–4613, https://doi.org/10.5194/bg-11-4599-2014, 2014</p> <p>Sitch, S., Friedlingstein, P., Gruber, N., Jones, S. D., Murray-Tortarolo, G., Ahlström, A., Doney, S. C., Graven, H., Heinze, C., Huntingford,C., Levis, S., Levy, P. E., Lomas, M., Poulter, B., Viovy, N., Zaehle, S., Zeng, N., Arneth, A., Bonan, G., Bopp, L., Canadell, J. G.,Chevallier, F., Ciais, P., Ellis, R., Gloor, M., Peylin, P., Piao, S. L., Le Quéré, C., Smith, B., Zhu, Z., and Myneni, R.: Recent trends and drivers of regional sources and sinks of carbon dioxide, Biogeosciences, 12, 653–679, https://doi.org/10.5194/bg-12-653-2015, https://bg.copernicus.org/articles/12/653/2015/, publisher: Copernicus GmbH, 2015.</p> <p>Steinbach, J., Gerbig, C., Rödenbeck, C., Karstens, U., Minejima, C., and Mukai, H.: The CO2 release and Oxygen uptake from Fossil Fuel Emission Estimate (COFFEE) dataset: effects from varying oxidative ratios, Atmospheric Chemistry and Physics, 11, 6855–6870,1160 https://doi.org/10.5194/acp-11-6855-2011, 2011</p> <p> </p> <p> </p> <p> </p>
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.
<p>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).</p>
Data associated with: Emergence of the physiological effects of elevated CO2 on land-atmosphere exchange of carbon and water
Open the record for dataset details and reuse information.
Representing Model Uncertainty for Global Atmospheric CO2 Flux Inversions Using ECMWF-IFS-46R1
<p>Data used in the work "Representing Model Uncertainty for Global Atmospheric CO2 Flux Inversions Using ECMWF-IFS-46R1" - McNorton et al. (2020)</p> <p>All data generated using version 46R1 of the Integrated Forecast System based at the European Centre for Medium-Range Weather Forecasts, with work funded as part of the European Commission CO2 Human Emissions Project.</p> <p>Data includes global total standard errors for the total column CO2 mixing ratios at 3 hourly intervals for 2015 and both total column and surface transport errors at hourly intervals for January and July 2015, derived from a 50 member ensemble. It is suggested that the data are used by the inverse modelling community to account for transport model errors.</p> <p>Please view the README.txt file for a full description.</p> <p> </p> <p>###########################<br> ## EXPERIMENTAL SETUP ##<br> ###########################</p> <p># FLUXES #</p> <p>CHE-EDGAR-2015 EMISSIONS<br> CHE-TIER-2-FIRE/OCEAN<br> ONLINE CHTESSEL BIOGENIC FLUXES (FOR TRANSPORT ERROR THESE USE THE CONTROL MEMBER FLUXES)</p> <p># MODEL #</p> <p>IFS-CYCLE 46R1<br> RESOLUTION TCO399 (~25km)<br> 137 VERTICAL LEVELS<br> ALL DATA PROVIDED HERE ARE EITHER COLUMN INTEGRATED MIXING RATIO (XCO2) OR SURFACE (LEVEL 137)<br> ALL DATA PROVIDED HERE ARE STANDARD DEVIATION ACROSS 50 ENSEMBLE MEMBERS<br> </p>
Elevated atmospheric concentrations of CO2 increase endogenous immune function in a specialist herbivore
<p>1. Animals rely on a balance of endogenous and exogenous sources of immunity to mitigate parasite attack. Understanding how environmental context affects that balance is increasingly urgent under rapid environmental change. In herbivores, immunity is determined, in part, by phytochemistry which is plastic in response to environmental conditions. Monarch butterflies, <i>Danaus plexippus,</i> consistently experience infection by a virulent parasite, <i>Ophryocystis elektroscirrha</i>, and some medicinal milkweed (<i>Asclepias</i>) species, with high concentrations of toxic steroids (cardenolides), provide a potent source of exogenous immunity. 2. We investigated plant-mediated influences of elevated CO<sub>2</sub> (eCO<sub>2</sub>) on endogenous immune responses of monarch larvae to infection by <i>O. elektroscirrha</i>. Recently, transcriptomics have revealed that infection by <i>O. elektroscirrha </i>does not alter monarch immune gene regulation in larvae, corroborating that monarchs rely more on exogenous than endogenous immunity. However, monarchs feeding on medicinal milkweed grown under eCO<sub>2</sub> lose tolerance to the parasite, associated with changes in phytochemistry. Whether changes in milkweed phytochemistry induced by eCO<sub>2</sub> alter the balance between exogenous and endogenous sources of immunity remains unknown. 3. We fed monarchs two species of milkweed; <i>A. curassavica</i> (medicinal) and <i>A. incarnata </i>(non-medicinal) grown under ambient CO<sub>2</sub> (aCO2) or eCO<sub>2</sub>. We then measured endogenous immune responses (phenoloxidase activity, hemocyte concentration, and melanization strength), along with foliar chemistry, to assess mechanisms of monarch immunity under future atmospheric conditions. 4. The melanization response of late-instar larvae was reduced on medicinal milkweed in comparison to non-medicinal milkweed. Moreover, the endogenous immune responses of early-instar larvae to infection by <i>O. elektroscirrha</i> were generally lower in larvae reared on foliage from aCO<sub>2</sub> plants and higher in larvae reared on foliage from eCO<sub>2</sub> plants. When grown under eCO<sub>2</sub>, milkweed plants exhibited lower cardenolide concentrations, lower phytochemical diversity, and lower nutritional quality (higher C:N ratios). Together, these results suggest that the loss of exogenous immunity from foliage under eCO<sub>2</sub> results in increased endogenous immune function. 5. Animal populations face multiple threats induced by anthropogenic environmental change. Our results suggest that shifts in the balance between exogenous and endogenous sources of immunity to parasite attack may represent an underappreciated consequence of environmental change. </p>
Simulated atmospheric CO2 concentration at Point Barrow, Alaska
<p>This dataset is provided in order to enable the reproduction of findings from a series of experiments with the CASA-TOMCAT model setup (Chipperfield, 2006). </p> <p>The Carnegie Ames Stanford Approach (CASA) is a land-surface model that was used to produce fluxes of net ecosystem exchange (NEE) and fires. Our simulations held various input parameters constant in CASA (described below). We then forced the TOMCAT atmospheric chemistry model with these data to produce an estimate of atmospheric CO2 at the Barrow Observatory in Alaska. Further information on the details of the model setup is described in the 'Model_setup.txt' file.</p> <p>Enclosed in this directory are the simulated CO2 at Barrow observatory, Alaska (71.3N, 156.6E) for a number of experiments which are described below. Half of the text files have variable meteorology and are described as 'atmos_vary' in the title, the remaining half have constant (periodical) meteorology, in which atmospheric transport is retained at 1992 values (and described as 'atmos_const').</p> <p>Within each file is the time and date of each measurement of atmospheric CO2 in ppm. In order to obtain the required simulated atmospheric value, add the background, ocean, and fossil fuel (FF) tracer values to the relevant NEE and fire value from the simulation of interest.</p> <p>The simulations are as follows (with their abbreviation given in parentheses): </p> <p>Constant, periodical temperature scalar (temp)<br> Constant, periodical temperature and moisture scalars (temppre)<br> Constant, periodical fraction of photosynthetically active radiation (fpar)<br> Constant, periodical solar radiation (solrad)<br> All the above variables held constant, periodical (all)<br> Control run in which everything varies (ctrl)</p>
Flowfield Properties for Calculating Electrical Conductivity in a CO2 Atmosphere
<p>This dataset provides flowfield quantities necessary to calculate the electrical conductivity in the flowfield around an entry vehicle in a CO2 atmosphere. The CFD data was generated using the Data Parallel Line Relaxation Code. The freestream conditions cover velocities from 5 - 12 km/s and altitudes from 20 - 80 km at Mars (85 - 115 km at Venus). The vehicle geometry is a 10 m diameter 70 deg spherecone.</p>
Elevated atmospheric CO2 changes defence allocation in wheat but herbivore resistance persists
<p>Predicting how plants allocate to different anti-herbivore defences in response to elevated carbon dioxide (CO<sub>2</sub>) concentrations is important for understanding future patterns of crop susceptibility to herbivory. Theories of defence allocation, especially in the context of environmental change, largely overlook the role of silicon (Si), despite it being the major anti-herbivore defence in the Poaceae. We demonstrated that elevated levels of atmospheric CO<sub>2</sub> (e[CO<sub>2</sub>]) promoted plant growth by 33% and caused wheat (<i>Triticum aestivum</i>) to switch from Si (–19%) to phenolic (+44%) defences. Despite the lower levels of Si under e[CO<sub>2</sub>], resistance to the global pest <i>Helicoverpa armigera</i> persisted; relative growth rates (RGR) were reduced by at least 33% on Si supplied plants, irrespective of CO<sub>2</sub> levels. RGR was negatively correlated with leaf Si concentrations. Mandible wear was c. 30% higher when feeding on Si supplemented plants compared to those feeding on plants with no Si supply. We conclude that higher carbon availability under e[CO<sub>2</sub>] reduces silicification and causes wheat to increase concentrations of phenolics. However, Si supply, at all levels, suppressed the growth of <i>H. armigera</i> under both CO<sub>2</sub> regimes, suggesting that shifts in defence allocation under future climate change may not compromise herbivore resistance in wheat.</p>
High-spatiotemporal resolution mapping of spatiotemporally continuous atmospheric CO2 concentrations over the global continent
<p>This dataset contains global continental-scale carbon dioxide inversion results for four periods in 2015 with a spatial resolution of 0.01°.</p>
Evaluating the Arabian Sea as a regional source of atmospheric CO2: seasonal variability and drivers
<p>The netCDF file included here corresponds to datasets used in the Biogeosciences paper entitled "Evaluating the Arabian Sea as a regional source of atmospheric CO2: seasonal variability and drivers" by Alain de Verneil, Zouhair Lachkar, Shafer Smith, and Marina Levy</p> <p>The data included here comprises of model output used in the paper to generate figures in the main manuscript. Many of the figures also contain data from publicly available sources, which is detailed in the "Data availability" section at the end of the paper.</p> <p>The data are in standard netCDF file format, readily readable using netCDF tools (i.e. netCDF4 package in Python, ncread function in Matlab, etc.).</p> <p>Variables names, dimensions, and units are described in the metadata within the netCDF file.</p> <p>Questions regarding this dataset and how it can be used to reproduce the results in the article can be forwarded to Alain de Verneil through email at ajd11@nyu.edu</p>
Model simulation results for "Enhanced seasonal amplitude of atmospheric CO2 by the changing Southern Ocean carbon sink"
<p>This dataset contains the seasonal variations of monthly mean atmospheric CO<sub>2</sub> concentration derived from GEOS-Chem model simulations during 2000-2016. Monthly terrestrial CO2 fluxes derived from CLM4.5-CN, used as an input dataset for the GEOS-Chem simulations, are also included.</p> <p>There are six sets of GEOS-Chem simulation results; "ctrl", "BIOfix", "OCNfix", and "FFfix" are the main experiments to evaluate the effects of changes in terrestrial CO<sub>2</sub> fluxes, air-sea CO<sub>2</sub> fluxes, and fossil fuel CO<sub>2</sub> emissions on the seasonal amplitude of atmospheric CO<sub>2</sub> over the globe; "ALLfix" and "OCNfix_SO" are additional experiments for identifying the effects of changes in the other factors (i.e., atmospheric transport and biomass burning) and regional changes in air-sea fluxes in the Southern Ocean. </p> <p>Detailed explanations for each simulation are described in the main text.</p> <p>*We recommend contacting us first if you want to utilize the dataset for study (yjm921@gmail.com).</p>
Data from: Estimates of late Early Cretaceous atmospheric CO2 from Mongolia based on stomatal and isotopic analysis of Pseudotorellia
<p>Our dataset includes 115 leaf cuticles related to two species of <em>Pseudotorellia</em> Florin from three stratigraphically similar samples at the Tevshiin Govi lignite mine in central Mongolia (~119.7–100.5 Ma, Aptian–Albian, Cretaceous). We apply a well-vetted paleo-CO<sub>2</sub> proxy based on leaf gas-exchange principles (the Franks model) to those leaves for paleo-CO<sub>2</sub> reconstruction, which requires leaf stomata and carbon isotope analysis. All cuticle measurements are summarized in this dataset. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.