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4 results for “greenhouse gas inventory”

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zenodo48/100

IPCC Climate Zones (from the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories)

Description These data (re)create spatial data for the 2019 IPCC Climate Zones, shown in Figure 3A.5.1 of <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/pdf/4_Volume4/19R_V4_Ch03_Land%20Representation.pdf">Chapter 3: Consistent Representation of Lands</a> in <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/vol4.html">Volume 4: Agriculture, Forestry and Other Land Use</a> of the <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/index.html">2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories</a>. I recreated these data because I could not readily identify the data in a spatial format online, a problem which has previously been noted by ESDAC, who produced a <a href="https://esdac.jrc.ec.europa.eu/content/support-renewable-energy-directive#tabs-0-description=1">spatial version of Figure 3A.5.1 from the original 2006 guidelines</a>. Resolution: 0.5 arc degree CRS: lon/lat WGS 84 If you use these data please ensure you also cite the IPCC - Calvo Buendia, E et al. (2019). 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. IPCC, Switzerland.   Methods The data were derived using the classification scheme shown in Figure 3A.5.2 based on the gridded Climate Research Unit (CRU) Time Series (TS) monthly climate data (<a href="https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.3711">Harris et al., 2014</a>) for the period from 1985 to 2015 following the methods described in Annex 3A.5 Default climate and soil classifications of the above Chapter. All data were processed in R version 4.2.1, with the packages <a href="https://cran.r-project.org/web/packages/elevatr/index.html">elevatr</a> (v0.4.2), <a href="https://cran.r-project.org/web/packages/lubridate/index.html">lubridate</a> (v1.8.0), <a href="https://cran.r-project.org/web/packages/magrittr/index.html">magrittr</a> (v2.0.3), and <a href="https://cran.r-project.org/web/packages/terra/index.html">terra</a> (v1.6-7) attached. The full session info is included as a .txt file. As these methods are not exhaustively described in the Annex, the following assumptions were made: <ul> <li><a href="http://http://dx.doi.org/10.5285/c311c7948e8a47b299f8f9c7ae6cb9af">CRU TS3.25</a> was used as the most recently published data (published on 2017-09-22) that could have been incorporated into the Refinement. Other possibilities include CRU TS3.24 (which are the first data to include 2015), or CRU TS4.00 or CRU TS4.01 (both of which were published in parallel to 3.24 and 3.25). These data were all investigated, and CRU TS3.25 produced results that were the most visually similar to the published Figure 3A.5.1 (though non-identical).</li> <li>As the methods did not mention a preferred elevation data source, the <a href="https://cran.r-project.org/web/packages/elevatr/index.html">elevatr</a> R package was used to obtain data at zoom level 2 (approx resolution of 0.15 arc degree), that was then resampled to match the 0.5-degree resolution of the CRU data. These data originally come from the <a href="https://www.ngdc.noaa.gov/mgg/global/global.html">ETOPO1 global relief model</a>.</li> </ul>   Known discrepancies <ul> <li>The distribution of Tropical Wet and Tropical Moist in South America does not exactly match the original data.</li> <li>There are small discrepancies in Tropical Montane classifications (likely arising from the use of a different elevation layer). These are most noticeable in, but not restricted to, Africa.</li> <li>The classification of Boreal Dry, Polar Dry, and Polar Moist in northern Russia and (to a lesser extent) in northern Canada does not exactly match the original data.</li> <li>There are a small number of Cool Temperate Dry pixels in the UK, and Warm Temperate Dry pixels around Brittany which do not occur in the original data.</li> </ul>   Disclaimer I am not affiliated with the IPCC in any way, I just needed spatial data of the Climate Zones, and could not readily identify any online. This is a problem which has previously been noted by ESDAC, who produced a <a href="https://esdac.jrc.ec.europa.eu/content/support-renewable-energy-directive#tabs-0-description=1">spatial version of Figure 3A.5.1 from the original 2006 guidelines</a>.   File description <ul> <li>README.html - ~this description file.</li> <li>IPCC_Climate_Zones_ts_3.25.tif - the output Climate Zones map at 0.5-arc degree resolution based on the CRU TS3.25 data.</li> <li>IPCC_Climate_Zones_colour_map.clr - a colour map file to render the output map with the same colours as in the IPCC 2019 Refinement figure.</li> <li>IPCC_Climate_Zones_ts_3.25.png - an image file of the output Climate Zones map.</li> <li>ipcc_climate_zones_2019.R - the script used to produce these data.</li> <li>session_info.txt - the R session info.</li> </ul>

opencc-by-4.0Nov 2022View details →

zenodo44/100

Gridded EPA U.S. Anthropogenic Methane Greenhouse Gas Inventory (gridded GHGI)

<h2>About</h2>The gridded EPA U.S. anthropogenic methane greenhouse gas inventory (gridded methane GHGI) includes spatially and temporally resolved (gridded) maps of annual anthropogenic methane emissions (0.1°×0.1°) for the contiguous United States (CONUS). Total gridded methane emissions for each emission source sector are consistent with national annual U.S. anthropogenic methane emissions reported in the U.S. EPA <a href="https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks">Inventory of U.S. Greenhouse Gas Emissions and Sinks</a> (U.S. GHGI). More information is available on the <a href="https://www.epa.gov/ghgemissions/gridded-methane-emissions">U.S. EPA website</a>. This repository accompanies the peer-reviewed manuscript <a href="https://pubs.acs.org/doi/10.1021/acs.est.3c05138">Maasakkers, et al., 2023</a>. Data in this repository are an update to the gridded GHGI version 1, previously described in <a href="https://pubs.acs.org/doi/10.1021/acs.est.6b02878">Maasakkers, et al., 2016</a> and available on the <a href="https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions">U.S. EPA website</a>. <h4>This repository contains two data products:</h4><ol><li>Gridded GHGI v2 (main product; 2 file types). Gridded annual U.S. anthropogenic methane emissions for 2012-2018 for 26 source categories (gridded GHGI). This dataset is developed to be consistent with the national U.S. GHGI published in 2020 (U.S. EPA, Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 - 2020. U.S. Environmental Protection Agency, 2020, EPA 430-R-22-003, <a href="https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks-1990-2018">https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks-1990-2018</a>). This dataset includes 2 file types:  a. Annual methane emission fluxes for 26 inventory source categories. Files contain one year of emissions per source category and include a time dimension variable to make the data suitable (COARDS-compliant) for atmospheric models.    (Dimensions: latitude x longitude x time; units: molecules CH4 cm-2 s-1):      - Gridded_GHGI_Methane_v2_YYYY.nc b. Monthly emission scaling factors for inventory source categories with strong interannual variability (see 'Data Details' below). To use these factors to calculate absolute monthly methane emission fluxes, multiply the scaling factors for each relevant source category by the corresponding emission fluxes in the annual flux files.  (Dimensions: latitude x longitude x month; units: dimensionless):       - Gridded_GHGI_Methane_v2_Monthly_Scale_Factors_YYYY.nc  </li><li>Gridded GHGI v2 Express Extension (1 file type). The v2 Express Extension includes gridded annual U.S. anthropogenic methane emissions for 2012-2020 for 27 source categories (one additional source category compared to the main v2 dataset above). This dataset is developed to be consistent with total methane emissions from the U.S. GHGI published in 2022 (EPA (2022) Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020. U.S. Environmental Protection Agency, EPA 430-R-22-003. <a href="https://www.epa.gov/ghgemissions/draft-inventory-us-greenhouse-gas-emissionsand-sinks-1990-2020">https://www.epa.gov/ghgemissions/draft-inventory-us-greenhouse-gas-emissionsand-sinks-1990-2020</a>).  **Note**: This dataset is not a full update to the main gridded GHGI v2 product. To quickly incorporate more recent national methane emission estimates into gridded products, national methane emissions from a more recent U.S. GHGI were spatially allocated (i.e., gridded) using the annual source-specific spatial emission patterns developed for the 2012-2018 main v2 product. Emissions for years 2019 and 2020 were allocated using 2018 spatial patterns. This dataset includes 1 file type: a. Annual emission files    (Dimensions: latitude x longitude x time; units: molecules CH4 cm-2 s-1):      - Express_Extension_Gridded_GHGI_Methane_v2_YYYY.nc</li></ol>--------------------------------------------------

opencc-by-4.0Jun 2023View details →

zenodo40/100

Data and R-scripts for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland

Introduction A new method for estimating carbon dioxide emissions from rained peatland forest soils was developed for the Greenhouse Gas Inventory of Finland (GHG inventory). The method is based on a set of models (Ojanen et al. 2014, Tuomi et al., 2009) that dynamically compile all relevant carbon inputs and outputs into a time series of soil CO2 emission. A complete description of the method is described in Alm et al. (2023). Here we present the input data and R-scripts (R Core Team, 2020) for computing the time series from year 1990 to 2022 of CO2 emission from soil in forest land on drained organic soil, like it was reported by the Finnish GHG inventory (Statistics Finland, 2023). Time series data The source of forest and area data is the Finnish National Forest Inventory (NFI) as a part of Luke Statutory Services. The NFI standing forest data in the data files includes annual country-wide estimates of mean basal area and standing biomass of Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H. Karst) and all the broadleaved forest trees combined. The data concerns forest land on drained organic soil only (class FRA 1 according to the FAO forest land definition). The NFI data for each year has been averaged by different drained peatland forest site types (FTYPE) and by inventory regions of southern and northern Finland. The areas and proportions of FTYPEs of all drained peatland “forests remaining forests” (i.e., forests that have not undergone another change in land use in the past 20 years) in southern and northern Finland (Alm et al., 2023), derived from NFI12 (2014–2018). Annual litter input from harvest residues was estimated using statistics of harvested stem volumes by species, collected and published by Luke (Luke statistics). The stem volumes were converted to whole trees and further to litter fractions and further to The share of residues remaining in forest is estimated by subtracting the amount of the logging residues collected for energy use, the data obtained from Luke statistics/energy. The biomass of live trees, annual litterfall from live trees aboveground and root litter belowground are derived from the National Forest Inventory of Finland (inventory rounds NFI8 to NFI13). The R-code also includes calculation of annual litter production from the harvesting residues. The regression-based transfer models, implemented in the R-code, also need meteorological time series inputs: The soil organic matter decomposition model (Ojanen et al. 2014) uses May-October mean temperature. Decomposition model yasso07 (Tuomi et al., 2009), applied for estimating the CO2 release by decomposition of harvesting residues and above ground litter from natural mortality, is constrained by annual temperature, annual temperature amplitude and annual precipitation. Starting from the original country-wide grid produced by the Finnish Meteorological Institute (FMI) the weather time series were spatially averaged so that the FMI weather grid values were collected from those locations where peatlands representing each FTYPE in southern and northern Finland were observed by the NFI, respectively. The pre-prepared input data are given in files, see Table 1 for descriptions.     Table 1. Description of input data files. <table> <tbody> <tr> <td> File </td> <td> Description of data </td> </tr> <tr> <td> basal.areas.csv </td> <td> Time series of years 1990-2022 for annual average basal area (m2 ha-1) by year, by peatland forest site type (peat_type) and by tree species or group (tree_type).   Values of peat_type correspond to FTYPE: 1  Herb-rich type 2  Vaccinium myrtillus type 4  Vaccinium vitis-idaea type 6  Dwarf shrub type 7  Cladina type   Values of tree species or group correspond to: 1  Scots pine 2  Norway spruce 3  Broadleaved species </td> </tr> <tr> <td> biomass.csv </td> <td> Time series of years 1990-2022 for annual biomass (biomass, t ha-1 of dry mass) by year, by biomass component, by tree species and by peatland forest site type (tkg).   Values of peat_type correspond to FTYPE: 1  Herb-rich type 2  Vaccinium myrtillus type 4  Vaccinium vitis-idaea type 6  Dwarf shrub type 7  Cladina type   </td> </tr> <tr> <td> dead_litter.csv </td> <td> Time series of years 1990-2022 of annual aboveground litter from dead wood: Harvesting residues and natural mortality combined (C, t ha-1 of dry mass; lognat_litter).   Values of region correspond to GHG inventory region: south  South Finland north  North Finland </td> </tr> <tr> <td> ghgi_litter.csv </td> <td> Time series of years 1990-2022 for litter AWEN-fractions (A=acid soluble, W=water soluble, E=ethanol soluble, N=non-soluble; C, t ha-1) by different litter types: Above-ground coarse woody litter (coarse_woody_litter), fine woody litter (fine_woody_litter), non-woody litter (non_woody_litter) by litter source and deposition type by region. “org” denotes organic soil.   Values of region correspond to GHG inventory region: south  South Finland north  North Finland   Values of ground correspond to litter deposition environment: above  Above-ground litter below  Below-ground litter </td> </tr> <tr> <td> lognat_decomp.csv </td> <td> Time series of years 1990-2022 for C, t ha-1 of dry mass, decomposed from logging residues and natural mortality by region.   Values of variable “region” correspond to GHG inventory region: south  South Finland north  North Finland </td> </tr> <tr> <td> logyasso_weather_data.csv </td> <td> Time series of years 1990-2022 for regional (region) precipitation sum (mm, sum_P), average annual temperature (°C, mean_T) and amplitude of the annual temperature (°C , ampli_T).   Values of region correspond to GHG inventory region: south  South Finland north  North Finland   </td> </tr> <tr> <td> total_area.csv </td> <td> Areas (ha) of drained peatland forests remaining forest land by region and peat_type.   Values of variable “region” correspond to GHG inventory region: south  South Finland north  North Finland   Values of peat_type correspond to FTYPE: 1  Herb-rich type 2  Vaccinium myrtillus type 4  Vaccinium vitis-idaea type 6  Dwarf shrub type 7  Cladina type   </td> </tr> <tr> <td> weather_data.csv </td> <td> Time series of years 1990-2022 for 30-year rolling mean temperature for the May-October period (roll_T) used by the soil decomposition models. The values are calculated for each FTYPE (peat_type) using their spatial distributions (see details in Alm et al., 2023).   Values of variable “region” correspond to GHG inventory region: south  South Finland north  North Finland   Values of peat_type correspond to FTYPE: 1  Herb-rich type 2  Vaccinium myrtillus type 4  Vaccinium vitis-idaea type 6  Dwarf shrub type 7  Cladina type   </td> </tr> </tbody> </table>   The R-scripts The scripts are an excerpt from the Finnish greenhouse gas inventory code set, applying the necessary pre-processed input data and producing the soil CO2 emissions for each FTYPE separately. The necessary R-packages (R Core Team, 2020) are managed in the script LIBRARIES.R. Guidance for running the R-scripts is given in the README.txt. References Alm, J., Wall, A., Myllykangas, J-P., Ojanen, P., Heikkinen, J., Henttonen, H. M., Laiho, R., Minkkinen, K., Tuomainen, T. and Mikola, J. A new method for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland. Biogeosciences https://doi.org/10.5194/bg-20-1-2023, 2023. LUKE Statistics <ul> <li>https://www.luke.fi/en/statistics/total-roundwood-removals-and-drain, last access 8.12.2022.</li> </ul> <ul> <li>https://www.luke.fi/en/statistics/commercial-fellings/commercial-fellings-72023. last access 8.12.2022.</li> </ul> Statistics Finland 2023. URL: https://unfccc.int/documents/627718 (last access 13.9.2023). Ojanen, P., Lehtonen, A., Heikkinen, J., Penttilä, T., and Minkkinen, K.: Soil CO2 balance and its uncertainty in forestry drained peatlands in Finland, Forest Ecol. Manage., 325, 60–73, 2014. R Core Team: R: A language and environment for statistical computing. R Foundation forStatistical Computing, Vienna, Austria, URL https://www.R-project.org, 2020. Tuomi, M., Thum, T., Järvinen, H., Fronzek, S., Berg, B., Harmon, M., Trofymow, J.A., Sevanto, S. and Liski, J.: Leaf litter decomposition - Estimates of global variability based on Yasso07 model, Ecol. Modell. 220 (23):3362-3371, 2009.

opencc-by-4.0Sep 2023View details →

nasa28/100

NACP Regional: National Greenhouse Gas Inventories and Aggregated Gridded Model Data

This data set provides two products that were derived from the recently published North American Carbon Program (NACP) Regional Synthesis 1-degree terrestrial biosphere model (TBM) and inverse model (IM) outputs (Gridded 1-deg Observation Data and Biosphere and Inverse Model Outputs, Wei et al., 2013). The first product is the aggregation of the standardized gridded 1-degree TBM and IM outputs to the Greenhouse Gas (GHG) inventory zones as defined for North America (United States, Canada, and Mexico). Depending on the data availability, the monthly/yearly Net Ecosystem Exchange (NEE), Net Primary Production (NPP), Total Vegetation Carbon (VegC), Heterotrophic Respiration (Rh), and Fire Emissions (FE) outputs from the 22 TBM and 7 IM models were aggregated from the 1-degree resolution gridded format to the inventory zones and then, further divided into Forest Lands, Crop Lands, and Other Lands sectors within each inventory zone based on the 1-km resolution GLC2000 land cover map (GLC2000, 2003).The second product is the North American national GHG inventories on the scale of inventory zones which contain estimated land-atmosphere exchange of CO2 (NEE) in forest lands, crop lands, and other lands sectors. NEE estimates were synthesized from inventory-based data on productivity, ecosystem carbon stock change, and harvested product stock change, and additional information from national-level GHG inventories of the United States, Canada, and Mexico including EPA (2011) and Environment Canada (2011).An additional summary file of annual mean NEE (2000-2006)is provided for both land sectors and reporting zones in North America and was created by combining the aggregated model output and the national GHG database and is provided. The aggregated monthly and yearly model output data and the national GHG inventories data are available in comma separated value (*.csv) format files. Also provided are detailed inventory zone spatial data as an ESRI Shapefile. Included are zone names, boundaries, and zone and land cover type area attributes. For mapping convenience, the inventory zones shapefile was merged with 1-km forest, crop, and other lands masks to create a 1-km resolution reference data file that was converted to GeoTIFF format. The GeoTIFF defines to which inventory zone and land cover type each 1-km grid cell belongs.This document provides detailed information about the content, format, and processing procedures of these two data products. Detailed descriptions of the TBMs and IMs can be found in a separate companion document: NACP Regional Synthesis - Description of Observations and Models.

restrictednotspecifiedApr 2025View details →

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