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26 results for “ghg”
Monthly aerosol emissions and GHG concentration projections from 2020-2025: modified SSP2-4.5 to account for COVID-19 impacts on sector activity
<p>This repository holds the netcdf files for emissions and concentrations projected by the scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown, projected out for 5 years after 2020. The details of these activity estimates are available from <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a>.</p> <p>The methodology behind these calculations is based on <a href="https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/">https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/tree/endof2020</a>, a slight modification of the approach used in <a href="https://zenodo.org/record/3947917#.XxR_qyhKhPZ">https://zenodo.org/record/3947917#.XxR_qyhKhPZ</a> to have a different timeframe. </p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p>
GHG Dataset for the frontiers publication "Soil Nitrous Oxide Emission and Methane Exchange from Diversified Cropping Systems in Pannonian Region"
<p>GHG Dataset used in the Frontiers Publication "Soil Nitrous Oxide Emission and Methane Exchange from Diversified Cropping Systems in Pannonian Region". Additionally including CO2 besides N2O and CH4. Includes 3 cropping seasons.</p> <p>The data is also available online on the GHG flux visualisation and calculation tool "gasflxvis": https://sae-interactive-data.ethz.ch/gasflxvis/</p> <p>Further details on the calulation are provided both on gasflxvis and the Frontiers publication. Calculation procedure according the following PLOS ONE publication: http://dx.doi.org/10.1371/journal.pone.0200876</p>
SERENA EJPSOIL PL GHG NEP
<p>General description of SERENA</p> <p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <p>Files description</p> <p>Data was prepared as a result of SERENA EJP SOIL. The attached files are a part of the analysis of Assessment of Soil Threats and Ecosystem Services from each MS with the harmonized procedures. SERENA deliverable 3.3 (https://doi.org/10.5281/zenodo.13991087). </p> <p><span>The present dataset corresponds to a map of Net Ecosystem Productivity (NEP) for Poland (agricultural areass) as wheat by the Eurocrop 2028 spatial product (d’Andrimont et al. 2021). </span>The map is the result of applying the NEP cookbook developed in SERENA/EJP-Soil to the area of interest (AOI) input data. NEP is expressed for a single 8-day period in early summer as it is the date with the highest value. The NEP value is a 2014 average for the particular 8-day period. <span>NEP was used as an indicator of greenhouse gasses and climate regulation supported by soils. </span></p>
GHG-depths: Greenhouse gas depth-profile data in 522 lakes worldwide
Lakes, ponds, and reservoirs (hereafter: “lakes”) are significant sources of the greenhouse gases carbon dioxide (CO2) and methane (CH4). Emissions of CO2 and CH4 from lakes are regulated in part by in-lake processes, including the production and storage of gases in the lower parts of the water column (bottom waters). However, while substantial efforts have been made to improve estimates of greenhouse gas emissions from lakes, limited data on gas concentrations along depth profiles have prevented the incorporation of bottom-water processes in global emission estimates. Here, we present GHG-depths: the largest existing dataset of depth-profile CO2 and CH4 measurements worldwide, including 522 lakes across 38 countries and all seven continents. These data include contributions from 45 research teams and 56 published studies, totaling 2558 discrete sampling events. As global change continues to alter biogeochemical cycling in lakes, these data can help improve mechanistic models to better predict greenhouse gas production and emission from lakes worldwide.
SERENA EJPSOIL NL GHG NEP
<p><span>The internal EJP SOIL project </span><span>SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</span></p> <p><span>The dataset corresponds to a map of greenhouse gas emissions, measured in net ecosystem productivity. The map is the result of applying the GHG cookbook developed in SERENA/EJP-Soil. It is based on GPP products derived from the MODIS algorithm in 2014, combined with soil moisture and temperature data. The final map includes 45 bands with the NEP values (in g C/m2) of each 8-day period between 2-1-2014 and 31-12-2014. The map has a 500 m spatial resolution.</span></p>
Dataset for the paper: "Di Felice, L.J.; Ripa, M.; Giampietro, M. Deep Decarbonisation from a Biophysical Perspective: GHG Emissions of a Renewable Electricity Transformation in the EU."
<p>Dataset used for the development of scenarios in the publication "Di Felice, L.J.; Ripa, M.; Giampietro, M. Deep Decarbonisation from a Biophysical Perspective: GHG Emissions of a Renewable Electricity Transformation in the EU. Sustainability 2018, 10, 3685." and used for a case study in "Di Felice L., Dunlop T., Giampietro M., Kovacic Z., Renner A., Ripa M., Velasco-Fernández R. – Report on the Quality Check of the Robustness of the Narrative behind Energy Directives. MAGIC (H2020–GA 689669) Project Deliverable 5.4, 30 November 2018". (link: https://magic-nexus.eu/documents/d54-report-narratives-behind-energy-directives).</p> <p>Sources of other secondary data (from papers, reports) specified in the dataset (under tab "input codes")</p>
SERENA EJPSOIL IE MEATH GHG NEP
<p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <p>Data for NEP analysis was provided through the Microsoft Teams shared folder. However, MODIS data was obtained using the MODISTools R package in RStudio (R 4.3.1). Nine MODIS tiles to cover the extent of Ireland were merged and exported to QGIS for analysis. </p> <p>An 8-day NEP analysis was conducted for the entirety of the Republic of Ireland under wheat crop from 02-09 of January. However, wheat production in Ireland is largely centred around several key regions in the east/central-east due to the relative dominance of grassland and alternative agricultural regimes elsewhere. This resulted in quite a sparse visualisation at national scale. Hence, a refined analysis for the same period in County Meath</p>
Uncertainty of EXIOBASE GHG emission acounts 2015
<p>This repository contains GHG emission accounts (also referred to as GHG extensions) and their uncertainties for the year 2015 according to the country and sector resolution of the Multi-Regional Input-Output (MRIO) database EXIOBASE. </p> <p>The data is the outcome of our study published in the Journal of Earth System Science Data (ESSD): <a href="https://essd.copernicus.org/articles/16/2669/2024/essd-16-2669-2024.html">https://essd.copernicus.org/articles/16/2669/2024/essd-16-2669-2024.html</a></p> <p>The GHG emission accounts contain production-based emissions of the three major GHGs (CO2, CH4, N2O) from 11 different categories for 163 industry sectors each in 49 countries and regions covering the entire world. They are aligned with the EXIOBASE version 3.8.2 available on <a href="../records/5589597">Zenodo</a>. </p> <p>All data files starting with <strong>F_ </strong>are stored in the <a href="https://arrow.apache.org/docs/index.html">feather format</a> which allows sharing of data between different platforms (Python, R, C, etc.). The <strong>F_*.feather </strong>files all contain numeric matrices with 33 rows (3 GHGs x 11 categories) and 7987 columns (49 regions x 163 sectors). The columns are in the same order as the EXIOBASE v3 tables thus they can be directly used together with the EXIOBASE v3.8.2 data to calculate GHG footprints.</p> <p><strong>Content of the data files: </strong></p> <ul> <li><a href="../api/records/10041196/draft/files/samples.zip/content">samples.zip</a> contains the 1000 Monte-Carlo samples (1000 F-matrices).</li> <li><a href="../api/records/10041196/draft/files/F_mean.feather/content">F_mean.feather</a>: Mean over all samples.</li> <li><a href="../api/records/10041196/draft/files/F_cv.feather/content">F_cv.feather</a>: Coefficient of Variation (CV) over all samples. CV is defined as the standard deviation divided by the mean.</li> <li><a href="../api/records/10041196/draft/files/F_median.feather/content">F_median.feather</a>: Median over all samples.</li> <li><a href="../api/records/10041196/draft/files/correlation_table.feather/content">correlation_table.feather</a>: A (large) table listing the Pearson correlation coefficients between each different data item of the F-samples. The columns i and j represent the sector-ID</li> <li><a href="../api/records/10041196/draft/files/index_rows.csv/content">index_rows.csv</a> and <a href="../api/records/10041196/draft/files/index_cols.csv/content">index_cols.csv</a>: The indices of the <strong>F_*.feather </strong>matrices (row-names and column-names, respectively). <a href="../api/records/10041196/draft/files/index_cols.csv/content">index_cols.csv</a> can also be merged with the correlation table to find out which sectors are behind the IDs.</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Source data to create the figures of the study "Rising greenhouse gas emissions embodied in the global bioeconomy supply chain" using REX3 with new GHG extension including LULUCF
<p>This repository contains the source data to create the figures of the study <a href="https://doi.org/10.1038/s43247-025-02144-0">Rising greenhouse gas emissions embodied in the global bioeconomy supply chain</a> published in <em>Communications Earth & Environment</em>. The results were calculated with the REX3 database in Version 3.2 of this repository and the GHG extension and matlab codes in Version 3.4 of this repository.</p> <p>Figure 1, and 3–5 were created in Rstudio with the attached Rcode <em>Bioeconomy_GHG_sankeys.R</em></p> <p>Figure 2 was created in tableau with an <a href="https://public.tableau.com/app/profile/livia.cabernard/vizzes">interactive data visualizer</a> that allows to zoom into the global bioeconomy supply chain.</p>
Peatland maps and wetland GHG emission factors
<p>The data set includes maps of degraded (~46 Mha globally) and intact peatland (~375 Mha globally) for the year 2015. The spatial resolution is 0.5 degree. The data set also includes IPCC wetland GHG emission factors for degraded and rewetted peatlands.</p> <p>This dataset has been published originally as supplementary data set in </p> <p>Humpenöder, F., Karstens, K., Lotze-Campen, H., Leifeld, J., Menichetti, L., Barthelmes, A., and Popp, A. (2020). Peatland protection and restoration are key for climate change mitigation. Environ. Res. Lett. <em>15</em>, 104093. DOI <a href="https://10.1088/1748-9326/abae2a">10.1088/1748-9326/abae2a</a>.</p> <p> </p>
FAOSTAT GHG Emissions from Organic Soils
<p>The FAOSTAT domain “Cultivation of Organic soils” contains estimates of nitrous oxide (N<sub>2</sub>O) emissions associated with the drainage of organic soils <em>–</em> using <em>histosols </em>as proxy <em>–</em> for agriculture. Data are computed geospatially, using the Tier 1 default factors of the Intergovernmental Panel on Climate Change (IPCC, 2006). Estimates are available by country, by FAOSTAT regional aggregation and special group, including the Annex I and Non-Annex I Parties to the United Nations Framework Convention on Climate Change (UNFCCC), and with global coverage for the period 1990–2019, with estimates for 2030 and 2050.</p> <p>The FAOSTAT domain “Cultivation of Organic soils” disseminates N<sub>2</sub>O emissions, implied emission factors and underlying activity data, i.e. area (in ha) of organic soils drained for agriculture. Drainage and associated emissions are assessed separately for IPCC land use categories cropland and grassland, corresponding to FAO land use categories ‘’cropland’’ and ‘’permanent meadows and pastures.’’ GHG estimates are available in N<sub>2</sub>O and in CO<sub>2</sub> equivalent (CO<sub>2</sub>eq). Conversion to CO<sub>2</sub>eq is made via Global Warming Potentials (GWP) coefficients. Results are disseminated separately for three different options currently in use in reporting, namely GWPs from: <em>a)</em> IPCC Second Assessment Report (SAR)(IPCC, 1996); <em>b)</em> IPCC Fourth Assessment Report (AR4) (IPCC, 2007); and <em>c)</em> IPCC Fifth Assessment Report (AR5)(IPCC, 2014). The original FAOSTAT domains can be found here:</p> <p>Cultivation of Organic Soils: <a href="http://www.fao.org/faostat/en/#data/GV">http://www.fao.org/faostat/en/#data/GV</a></p> <p>Drained Organic Soils on cropland: <a href="http://www.fao.org/faostat/en/#data/GC">http://www.fao.org/faostat/en/#data/GC</a> and</p> <p>Drained Organic Soils on grassland: <a href="http://www.fao.org/faostat/en/#data/GC">http://www.fao.org/faostat/en/#data/G</a>G</p> <p> </p> <p>The FAOSTAT emissions estimates may not coincide with GHG data reported by member countries to relevant international reporting processes. The aim of this domain is to provide a global reference database for assessing regional and global trends and in support of national data quality/data assurance processes.</p>
Cryosphere Inland Water Greenhouse Gases Database (CIWD-GHG)
<p>This is a database called Cryosphere Inland Water Greenhouse Gases Database (CIWD-GHG), which provides spatial-temporally resolved greenhouse gases (CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O) data from cryosphere inland waters (lakes, ponds, reservoirs, rivers, and streams). The dataset was created through a synthesis procedure.</p> <p>To compile the dataset, we conducted searches in various sources including peer-reviewed papers, dissertations, theses, and public data repositories. The searches were conducted using platforms such as Web of Science, Google Scholar, ProQuest Dissertations & Theses Global, China National Knowledge Infrastructure, Arctic Data Center, Zenodo, Environmental Data Initiative, and PANGAEA. The search string is: (methane OR CH4 OR carbon dioxide OR CO2 OR nitrous oxide OR N2O OR greenhouse*) AND (river OR stream OR lake OR pond OR reservoir) AND (Arctic* OR Tibet* OR Greenland OR Antarctic OR glacier* OR permafrost). We ensured completeness by conducting multiple searches before April 2023.</p> <p>We applied a consistent criterion for screening and selecting the searched results. Specifically, we included data on greenhouse gas concentrations or fluxes measured in inland water systems such as streams, rivers, ponds, lakes, and reservoirs that are associated with permafrost or glaciers. The study focused on the cryosphere extent, excluding sites outside this extent. Wetland ecosystems and inland waters in cold regions without glaciers or permafrost were also excluded. Additionally, floodplain lakes connected with river channels were excluded, except for those barely connected with high-closure river channels, which were included as lake sites. Gas concentration data in permanently ice-covered water bodies were not included. We also collected auxiliary data on waterbody physical and chemical characteristics, climate, and land cover to the extent possible.</p> <p>The dataset was divided into two separate files: CryoLake.xlsx and CryoRiver.xlsx. CryoLake.xlsx contained data on lakes, ponds, and reservoirs, while CryoRiver.xlsx contained data on rivers and streams. Each file consisted of four sub-tables: source table (data sources), sites table (sites information), concentration table (concentration data), and flux table (flux data). All sub-tables were linked using unique source IDs, and the sites, concentration, and flux tables were further linked using unique site IDs. The temporal resolution varied, with daily data being the shortest resolution. Sub-daily measurements were averaged to daily data, and data reported in monthly, seasonal, and annual scales were also included. Considering the difficulty in obtaining cryosphere-related GHG data, we included all available data. The spatial resolution primarily focused on the plot scale, although aggregated sites were also included. Detailed spatiotemporal information of the measured data was recorded in the data table for further analysis. Due to regional heterogeneity, we did not have a uniform standard for dividing seasons. Instead, seasons were assigned based on the site descriptions provided in each study.</p> <p>The R script file is the multilevel bootstrap method used for Inland water greenhouse gas emissions upscaling. The ziped file contains bootstrap results for further calculating zonal and monthly GHG emissions.</p>
GHG data from inverse models and UNFCCC national inventories v0.1
<p><strong>GHG (CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O) data from inverse models and UNFCCC national inventories</strong></p> <p>This dataset contains 5 datasets, including GHG data from inverse models and UNFCCC national inventories in the top emitter countries:</p> <p>- <strong>CO2_inversion_1990-2019</strong>: annual CO<sub>2</sub> flux from from 6 inversion models in three sectors:</p> <ul> <li>'land flux (all land)' -> land flux from all land </li> <li>'land flux (managed land)' -> land flux from managed land</li> <li>'land flux (managed land + lateral adjustment)' -> land flux from managed land by adjusting the lateral flux</li> </ul> <p>- <strong>CH4_inversion_2000-2017</strong>: CH<sub>4</sub> flux from from 10 in-situ inversion (2000-2017) and 11 satellite inversion (2010-2017) models from four sectors:</p> <ul> <li>'anthropogenic (method x)' -> anthropogenic emissions from managed land. x could be 1, 2, 3.1 and 3.2, representing different methods to calculate the emissions in this sector:</li> <li>'fossil' -> emissions from the fossil sector</li> <li>'agriculture & waste' -> emissions from the agriculture and waste sector combined</li> <li>'biomass burning' -> emissions from biomass burning</li> </ul> <p>- <strong>N2O_inversion_1997-2016</strong>: anthropogenic N<sub>2</sub>O emissions from from 3 models.</p> <p>- <strong>Inventory_1990-2019</strong>: inventory data collecting from UNFCCC national inventories. The classification of sectors is corresponding with the inversion data files for each gas specie.</p> <p>- <strong>Inventory_1990-2019_IPCC</strong>: inventory data collecting from UNFCCC national inventories in IPCC category.</p> <ul> </ul>
Code from: Structural design to mitigate concrete GHG emissions
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Model output for PI, LGM, IS_albedo, IS_topo, LSC, ORB, GHG experiments
<p>Monthly mean outputs for LGM, PI, IS_topo, IS_albedo, GHG and ORB experiments.</p>
Calculation of the GHG emissions of a European research project on electrified vehicles
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LULUCF data based on National GHG inventories (NGHGI DB)
<p>The National GHG Inventory database (NGHGI DB) presents a detailed, updated and comprehensive collection of LULUCF CO2 data, based on countries' submissions to the United Nations Framework Convention on Climate Change (UNFCCC). This file contains the main data included the paper "Carbon fluxes from land 2000-2020: bringing clarity on countries’ reporting" by Grassi et al. (2022), and aims supporting the analysis of country LULUCF data by the scientific and policy communities.</p> <p>For Annex I countries, data are sourced from annual GHG inventories. </p> <p>For non-Annex I countries, we compiled the most recent and complete information from different sources, including National Communications, Biennial Update Reports, submissions to the REDD+ (Reducing Emissions from Deforestation and Forest Degradation) framework and Nationally Determined Contributions. The data are disaggregated into fluxes from forest land, deforestation, organic soils and other sources (including non-forest land uses). </p> <p>The CO2 flux database is complemented by information on managed and unmanaged forest area as available in NGHGIs, in few cases using data from the Forest Resources Assessment report 2020. To ensure completeness of time series, we filled the gaps without altering the levels and trends of the country reported data. Expert judgement was applied in a few cases when data inconsistencies existed. </p> <p>For further methodological details, see:</p> <p>Grassi G, Conchedda G, Federici S, Abad Viñas R, Korosuo A, Melo J, Rossi S, Sandker M, Somogyi Z, Vizzarri M, Tubiello F. Carbon fluxes from land 2000-2020: bringing clarity on countries’ reporting. ESSD</p>
Greenhouse gas (GHG) during the Atmospheric Research Expedition to Abu Dhabi (AREAD)
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Greenhouse gases (GHG) profiling over Cyprus with Aircore
<p>AirCore is a unique system for sampling air and rendering vertical profiles of greenhouse gases (GHGs) concentrations from the surface up to the stratosphere. Carried out by a weather balloon filled with helium, it can reach altitudes up to 35 km. On the ascending phase the tube empties out (via expansion along with decreasing atmospheric pressure) and samples the ambient air on the descending phase (via compression). Once at ground level the sample is analysed to measure CO2 CH4 and CO concentrations using a Cavity Ring Down Spectrometer (CRDS).</p> <p>In collaboration with LSCE and the French Aircore (AC) program, AC activities have been initiated in June 2020 in Cyprus within the framework of the EMME-CARE (Eastern Mediterranean and Middle East – Climate and Atmosphere Research). The EMME region has been identified as a global climate change "hot spot". Climate and atmosphere research is now taking place with the establishment of a regional Centre of Excellence.</p> <p> </p> <p>This project has received funding rom the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 856612 and the Cyprus Government.</p> <p> </p>
Inventory and GHG emissions for Milk Production Systems from systematic review
<p>Inventory table created from a systematic review on LCA of milk production systems in Europe and the emissions from animals and soils and background emissions obtained from the proof of concept.</p>
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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)
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DANDI Archive for NWB datasets
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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.