Find research datasets worth reusing

opencc-by-4.0Mar 2022View details →

PM2.5 emissions from Siberian forest fires 2004-2021

The dataset contains Supplementary Materials for the article ''Catastrophic PM2.5 emissions from Siberian forest fires: impacting factors analysis'' in the Environmental Pollution journal. There are files with PM2.5 emissions from forest fires in Russia 2004-2021 and SARIMAX modelling data for impacting factors analysis.   Supplementary Figures: - Figure 1. Total wildfires PM2.5 emissions from Russian forests (yellow colour) with the average value for 2004-2021 (grey line) and emissions trend (orange dotted line);  - Figure 2. PM2.5 emissions from wildfires in different fire protection zones during 2004-2021: ground zone (green colour), aviation zone (indigo colour) and control zone (beige colour). A) total PM2.5 emissions, Mt; B) average monthly PM2.5 emissions, kg/ha; C) average annual PM2.5 emissions, kg/ha.  - Figure 3. The location of the seven federal subjects with the highest PM2.5 emissions in Russia (schematic map); - Figure 4. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Amur Region; - Figure 5. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in the Buryatia Republic; - Figure 6. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Irkutsk Region;  - Figure 7. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Khabarovsk Territory;  - Figure 8. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Transbaikal Territory.    We share Copernicus Atmosphere Monytoring Service PM2.5 emissions maps (GeoTIFF, EPSG:4326, 0.1 degrees). Coverage: 27.9493818283081055,42.9493612670349520 : 190.0498617200859712,78.0494651794433594.   To determine emissions from the territory of Russia, we provide shapefiles with state (EPSG:4326. Coverage: -180.0000000000000000,41.1888656599999976 : 180.00000000000000000,81.8562469499999992) and Federal subjects borders (ESRI:102025. Coverage: -4073239.7565327030606568,1966601.6932600045111030 : 3971631.5190406017936766,6412842.0674155252054334).  Also, there are initial dataset for analysis (Initital data_SARIMAX archive) and SARIMAX model settings (doc.).

opencc-by-4.0Sep 2019View details →

Model outputs: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP)

This dataset contains the fire model outputs of emissions for 34 species (elements, compounds, and classes of compounds) as described in the following: Li, F., Val Martin, M., Hantson, S., Andreae, M. O., Arneth, A., Lasslop, G., Yue, C., Bachelet, D., Forrest, M., Kaiser, J. W., Kluzek, E., Liu, X., Melton, J. R., Ward, D. S., Darmenov, A., Hickler, T., Ichoku, C., Magi, B. I., Sitch, S., van der Werf, G. R., Wiedinmyer, C., and Rabin, S.: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP), Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-37, accepted pending technical corrections, 2019. See Readme for more information.

opencc-by-4.0Feb 2023View details →

Measurements of savanna landscap fire emission factors for CO2, CO, CH4 and N2O using a UAV-based sampling methodology

This dataset contains direct measurements of biomass burning emission factors for CO2, CO, CH4 and N2O. It includes over 4500 EF bag measurements sampled using an unmanned aerial system (UAS), and measured fuel parameters and fire severity proxies during 129 individual fires. The measurements cover a variety of savanna ecosystems in Brazil, Australia, Botswana, Zambia, South-Africa and Mozambique under different seasonal conditions, sampled over the course of six fire seasons between 2017 and 2022. The table in the included word file explains the individual columns in the excell file.

opencc-by-4.0Feb 2023View details →

Global Fire Emissions Database (GFED5) Burned Area

The monthly GFED5 burned area data produced in this study are available in netCDF files. For years between 2001 and 2020, five layers of burned area (Norm: normal type, Crop: cropland burning, Defo: deforestation burning, Peat: peatland burning, Total: the sum of all burning) are provided at 0.25°×0.25° resolution. The ‘Norm’ layer contains burned areas in each grid cell (in km2) separated by 17 major land cover types. For the pre-MODIS era (1997-2000), only the ‘Total’ burned area layer with reduced spatial resolution (1°×1°) is provided. We also provide two global maps of burnable area (with water and snow/ice cover excluded) in each grid cell (0.25°×0.25° for the MODIS era and 1°×1° for the pre-MODIS era). Please refer to readme.html or readme.pdf for more detail about the dataset.

opencc-by-4.0Sep 2020View details →

Retrofitting coal-fired power plants with biomass co-firing and CCS for net zero carbon emission: A plant-by-plant assessment based on GIS-LCA framework

Dataset for "Retrofitting coal-fired power plants with biomass co-firing and CCS for net zero carbon emission: A plant-by-plant assessment based on GIS-LCA framework"

opencc-by-4.0Nov 2024View details →

REFIT.AC v22 fire emissions for Forkel et al. "Burning of woody debris dominates fire emissions in the Amazon and Cerrado"

Emissions for Amazon and Cerrado biome areas generated by the Remote sensing based Emission model by FIre types - Amazon and Cerrado (REFIT.AC)  for the publication Forkel et al. "Burning of woody debris dominates fire emissions in the Amazon and Cerrado". REFIT.AC is a bookkeeping-based fire model using ESA CCI biomass, LULCC data and MODIS based fire occurrence information (burned area and active fires).  Emission information is provided in 0.1 degree spatial resolution for dry matter (DM), CO2, CO, CH4, NOx and PM2.5, in units of g/m2/month.

opencc-by-4.0Mar 2022View details →

Burnt forest area and CO2 emissions from fires in Russian forests by fire protection zones in 2010-2020

The dataset is Supplementary Materials for the article ''Reassessment of carbon emissions from fires and a new estimate of net carbon uptake in Russian forests in 2010-2020'' in the Carbon Balance and Management journal. It contains files with burnt forest area and carbon dioxide emissions from fires data in Russia 2010-2020. Article Supplementary materials are stored in the file Supplementary Tables and contains: - Table 1. Burnt forest area from NIR and MODIS (MCD64A1) in 2010-2020; - Table 2. Burnt forest area in the ground, aviation and the control (no fire protection) zones in 2010-2020 using MCD64A1; - Table 3. Carbon emissions from forest fires from National Inventory Report (NIR) and Copernicus Atmosphere Monitoring Service (CAMS) in 2010-2020 Also, there is Supplementary Figure 1 with the Federal Districts of Russia schematic map. There are 22 files in GeoTIFF format for every year:  1. Burnt forest area obtained using MODIS product MCD64A1 (250 m pixel, ESRI:102025). Coverage: -4064059.5401764437556267,1967242.6686790268868208 : 3658440.4598235562443733, 6012242.6686790268868208 2. CO2 emissions using Copernicus Atmosphere Monitoring System (CAMS) (0.1 degrees, VGS 84).  Coverage: 27.9493818283081055,42.9493612670349520 : 190.0498617200859712,78.0494651794433594 In addition, we share Shapefiles: 1. Russian borders (necessary to cut Russia from CO2 GeoTIFFs), EPSG:4326. Coverage: -180.0000000000000000,41.1888656599999976 : 180.00000000000000000,81.8562469499999992; 2. Forest Fire Protection zoning in 2019: ground zone, aviation zone, the so-called control zone (no fire protection), EPSG:4326. Coverage: 27.4019779002987676,41.3483353426717599 : 173.8255532772949721,72.6575707670955353.

opencc-by-sa-4.0Apr 2018View details →

Review of Emissions from Smouldering Peat Fires

The file contains two table compilations of up-to-date inter-study of peat fire gas and particle emission factors (EFs) found in the scientific literature, both from laboratory and field studies. According to the geographical origins of the peat used in fire emission studies, we classified the samples into two categories: boreal and temperate peat (we merge these two climate zones into one category owing to the limited sampling location information reported in the literature), and tropical peat. By doing this,  the best estimate peat fire EFs were calculated and compared between the two peat categories for the first time. It is hoped that the complied peat fire EFs can be used to improve the estimation of the total peat fire emission.  This data was analysed in our journal paper paper: Y. Hu, N. Fernandez-Anez, T. E. L. Smith, G. Rein, Review of Emissions from Smouldering Peat Fires and Their Contribution to Regional Haze Episodes, International Journal of Wildland Fire, 2018 (in press), DOI:10.1071/WF17084.

dryad36/100

Dynamic inferential NOx emission prediction model with delay estimation for SCR de-NOx process in coal-fired power plants

The selective catalytic reduction (SCR) de-NOx process in coal-fired power plants not only displays nonlinearity, large inertia, and time variation but also a lag in NOx analysis; hence, it is difficult to obtain an accurate model that can be used to control NH3 injection during changes in the operating state. In this work, a novel dynamic inferential model with delay estimation was proposed for NOx emission prediction. First, k-nearest neighbour mutual information (knnMI) was used to estimate the time-delay of the descriptor variables, followed by reconstruction of the phase space of the model data. Second, multi-scale wavelet kernel partial least square (mwKPLS) was used to improve the prediction ability, and this was followed by verification using benchmark dataset experiments. Finally, the delay-time difference (DTD) method and feedback correction strategy were proposed to deal with the time variation of the SCR de-NOx process. Through the analysis of the experimental field data in the steady state, the variable state and the NOx analyser blowback process, the results proved that this dynamic model has high prediction accuracy during state changes and can realize advance prediction of the NOx emission.

opencc-zeroJan 2020View details →

opencc-by-4.0Sep 2024View details →

GFAS4HTAP vegetation fire emissions 2003-2023

<h1>Overview</h1> This dataset contains emission flux from wildfires for various species and combustion rate. The data based on daily dry matter burnt estimates (DM) from CAMS GFASv1.2, downloaded at https://ads.atmosphere.copernicus.eu/datasets/cams-global-fire-emissions-gfas. Subsequently, an updated spurius signal mask is applied and emissions are calculated with a new land cover map derived from ESA CCI and PEATMAP for 2018, and emission factors from NEIVAv1.1 (<a href="https://gmd.copernicus.org/articles/17/7679/2024/">https://gmd.copernicus.org/articles/17/7679/2024/</a>) and further literature. Each archive contains a folder with the daily emissions for one species in the *_daily.nc file. Other NetCDF files with approximative fields at monthly, annual and 21-year resolution and plots have been added for illustration. The archives of the injection height parameters MAMI and APT contain the Mean Altitude of Maximal Injection and Altitude of Plume Top, respectively. They have been downloade from CAMS GFAS and converted to the standard date format of this reository. A detailed description is available in Remy et al. (2017) at <a href="https://acp.copernicus.org/articles/17/2921/2017/">https://acp.copernicus.org/articles/17/2921/2017/</a>. The data is available in netCDF4 format, where the data for each species is contained in individual files. The dataset contains daily data from 01/01/2003 to 31/12/2023 on a regular lat-lon grid with 0.1deg resolution. The date of the time coordinate identifies the validity period. For example for daily data, "2003-01-01 00:00:00" denotes emissions during 00:00:00-23:59:59 UTC of the first of January 2003. All emission data is in [kg m**-2 s**-1]. The CO2 data is the instantaneous emission of CO2 from wildfires. On a longer timescale CO2 will increase due to oxidation of, primarily, CO and CH4. C, PM2.5 and TPC, and only these, constitute a double-counting with other included species. Due to the data volume restriction of Zenodo, "toxic" emissions are provided in this sister repository: <a href="https://doi.org/10.5281/zenodo.15721938">10.5281/zenodo.15721938</a> A paper explaining the methods and data used in the creation of the dataset is being worked on. Until it has been published, please cite <a href="https://gmd.copernicus.org/articles/18/3265/2025/">https://gmd.copernicus.org/articles/18/3265/2025/</a> when using GFAS4HTAP. <h1>Calculating emissions locally</h1> The G4H archive contains software and static data (emission factor table and land cover mask), with which users can calculate emission consistently with GFAS4HTAP from any dry matter burnt field: Install and activate the conda environment env_g4h.yml, adapt the configuration section in the main() routine of the emissions.py file and run the script. <h1>Q&As</h1> <h3>Q1: Are emissions in beta and v2 for their common periods/species the same?</h3> No, all emissions have changed: All are shifted by one day (fixing a "feature" of the CAMS ADS netCDF conversion) and the emmission factor for CO in savannah has been updated. Use of the beta version is discouraged. If bandwidth is an issue consider calculating the emissions locally. Additionally, the metadata in the NetCDF files has been completed. <h3>Q2: Should VOCs not explicitly treated in the used model chemistry be ignored or lumped with other species to preserve the total mass? Is NMOC_g the total VOC mass?</h3> The NEVIA database includes measurements of a lot of gaseous emissions of larger organic molecules, which have not been represented in emissions estimates or chemical mechanisms in the past.  These are reported in the database as NMOC_g.  Thus, NMOC_g is the mass of gaseous non methane organic carbon that is NOT included in the mass of other individual or lumped species.  It is what is left over in the unspeciated bin after individual species have been accounted for.  It can be very large, around half of the organic mass.  In other words, total gaseous non-methane organic carbon = sum of all individual VOC species provided + sum of lumped VOC species provided + NMOC_g So, what do you do with NMOC_g or other explicit species that are not in your mechanism when preparing emissions inputs?  … It depends on the mechanism that you are putting it into.  A reasonable default approach may be to represent as much of the mass of explicit species provided in GFAS4HTAP as makes sense for the proxy/lumping scheme in your mechanism.  There is little understanding of how to represent NMOC_g and assigning its mass to other species in the mechanism may well create too much hydrocarbon reactivity.  So, a reasonable default approach may be to ignore NMOC_g.  Different modelers are going to make different choices, and it will be useful for each model to provide their emissions inputs (total VOC and if possible speciated VOC) along with the outputs for comparison. <h3>Q3: The daily file has emission rate in kg/m2/s – Is this a flat rate for the day (GMT)?</h3> Yes, this is correct. <h3>Q4: Where is the vegetation map?</h3> It is part of the package for calculating emissions locally, i.e. in the file G4H.tgz. <h3>Q5: Why did the Zenodo link change?</h3> Zenodo provides one link/DOI for all version of a repository, which ends with "1". Additionally, each version has its own link/DOI, counting up in the last digit. <h3>Q6: How do I get total particulate matter (TPM)?</h3> The GFAS4HTAP emissions are based on NEIVA EFs and GFED5 speciation. Most PM measurements are now operationally defined, e.g., based on inlet cutoffs, so TPM is rarely reported. However, if TPM or PM10 are needed, it is recommended take the provided PM2.5 emissions and multiply them by 1.2 (inflate by 20%). <h3>Q7: Which enhancement factor should be used for aerosol/PM emissions?</h3> It is recommended to tune PM/aerosol emissions to each model setup with (at least) one universal scaling/enhancement parameter. As reference, atmospheric observations targeted by the model can be used. If no such reference is available, the total atmospheric load from an aerosol observation-constrained (re)analysis, e.g. from CAMS, might be use4d as reference. The underlying reason for the need to tune is that the fast aerosol chemistry in the smoke plumes near fires are represented to different degrees in different models and model configurations. <h1>List of included species</h1> <table><colgroup><col><col></colgroup> <tbody> <tr> <td>species</td> <td>long_name</td> </tr> <tr> <td>C</td> <td>carbon combustion (C in CO2, CO, CH4, TPC)</td> </tr> <tr> <td>CO2</td> <td>carbon dioxide</td> </tr> <tr> <td>CO</td> <td>carbon monoxide</td> </tr> <tr> <td>CH4</td> <td>methane</td> </tr> <tr> <td>NMOC_g</td> <td>gaseous non-methane organic compounds not included otherwise</td> </tr> <tr> <td>H2</td> <td>hydrogen</td> </tr> <tr> <td>NOx</td> <td>nitrogen oxides(NOx as NO)</td> </tr> <tr> <td>N2O</td> <td>nitrous oxide</td> </tr> <tr> <td>PM2p5</td> <td>PM 2.5 (particulate matter <2.5u)</td> </tr> <tr> <td>TPC</td> <td>total particulate carbon (OC+BC)</td> </tr> <tr> <td>OC</td> <td>organic carbon (carbon in organic matter)</td> </tr> <tr> <td>BC</td> <td>black carbon</td> </tr> <tr> <td>SO2</td> <td>sulfur dioxide</td> </tr> <tr> <td>C2H6</td> <td>ethane</td> </tr> <tr> <td>CH3OH</td> <td>methanol</td> </tr> <tr> <td>C2H5OH</td> <td>ethanol</td> </tr> <tr> <td>C3H8</td> <td>propane</td> </tr> <tr> <td>C2H2</td> <td>acetylene</td> </tr> <tr> <td>C2H4</td> <td>ethylene</td> </tr> <tr> <td>C3H6</td> <td>propylene</td> </tr> <tr> <td>C5H8</td> <td>isoprene</td> </tr> <tr> <td>C10H16</td> <td>terpenes</td> </tr> <tr> <td>C7H8</td> <td>toluene</td> </tr> <tr> <td>C6H6</td> <td>benzene</td> </tr> <tr> <td>C8H10</td> <td>xylene</td> </tr> <tr> <td>Higher_Alkenes</td> <td>C4H8 + c5H10 + C6H12 + C8H16 (1 butene + i butene + tr-2-butene + cis-2-butene + 1 pentene + 2 pentene + hexene + octene)</td> </tr> <tr> <td>Higher_Alkanes</td> <td>C4H10 + C5H12 + C6H14 + C7H16 (n-butane + i-butane + n-pentane + i-pentane(me-butane) + n-hexane + i-hexane + Heptane)</td> </tr> <tr> <td>CH2O</td> <td>formaldehyde</td> </tr> <tr> <td>C2H4O</td> <td>acetaldehyde</td> </tr> <tr> <td>C3H6O</td> <td>acetone</td> </tr> <tr> <td>NH3</td> <td>ammonia</td> </tr> <tr> <td>C2H6S</td> <td>dimethyl sulfide (DMS)</td> </tr> <tr> <td>HCN</td> <td>hydrogen cyanide</td> </tr> <tr> <td>HCOOH</td> <td>formic acid</td> </tr> <tr> <td>CH3COOH</td> <td>acetic acid</td> </tr> <tr> <td>MEK</td> <td>methyl Ethyl Ketone / 2-butanone</td> </tr> <tr> <td>CH3COCHO</td> <td>methylglyoxal</td> </tr> <tr> <td>HOCH2CHO</td> <td>hydroxyacetaldehyde</td> </tr> <tr> <td>PCDD2378</td> <td>2,3,7,8-TeCDD</td> </tr> <tr> <td>PCDD12378</td> <td>1,2,3,7,8-PeCDD</td> </tr> <tr> <td>PCDD123478</td> <td>1,2,3,4,7,8-HxCDD</td> </tr> <tr> <td>PCDD123678</td> <td>1,2,3,6,7,8-HxCDD</td> </tr> <tr> <td>PCDD123789</td> <td>1,2,3,7,8,9-HxCDD</td> </tr> <tr> <td>PCDD1234678</td> <td>1,2,3,4,6,7,8-HpCDD</td> </tr> <tr> <td>OCDD</td> <td>OctaCDD</td> </tr> <tr> <td>PCDF2378</td> <td>2,3,7,8-TeCDF</td> </tr> <tr> <td>PCDF12378</td> <td>1,2,3,7,8-PeCDF</td> </tr> <tr> <td>PCDF23478</td> <td>2,3,4,7,8-PeCDF</td> </tr> <tr> <td>PCDF123478</td> <td>1,2,3,4,7,8-HxCDF</td> </tr> <tr> <td>PCDF123678</td> <td>1,2,3,6,7,8-HxCDF</td> </tr> <tr> <td>PCDF123789</td> <td>1,2,3,7,8,9-HxCDF</td> </tr> <tr> <td>PCDF234678</td> <td>2,3,4,6,7,8-HxCDF</td> </tr> <tr> <td>PCDF1234678</td> <td>1,2,3,4,6,7,8-HpCDF</td> </tr> <tr> <td>PCDF1234789</td> <td>1,2,3,4,7,8,9-HpCDF</td> </tr> <tr> <td>OCDF</td> <td>OctaCDF</td> </tr> <tr> <td>NAP</td> <td>Naphthalene</td> </tr> <tr> <td>ACY</td> <td>Acenaphthylene</td> </tr> <tr> <td>ACE</td> <td>Acenaphthene</td> </tr> <tr> <td>FLO</td> <td>Fluorene</td> </tr> <tr> <td>PHE</td> <td>Phenanthrene</td> </tr> <tr> <td>ANT</td> <td>Anthracene</td> </tr> <tr> <td>FLA</td> <td>Fluoranthene</td> </tr> <tr> <td>PYR</td> <td>Pyrene</td> </tr> <tr> <td>BaA</td> <td>Benz(a)anthracene</td> </tr> <tr> <td>CHR</td> <td>Chrysene</td> </tr> <tr> <td>BbF</td> <td>Benzo(b)fluoranthene</td> </tr> <tr> <td>BkF</td> <td>Benzo(k)fluoranthene</td> </tr> <tr> <td>BaP</td> <td>Benzo(a)pyrene</td> </tr> <tr> <td>IcdP</td> <td>Indeno(1,2,3-cd)pyrene</td> </tr> <tr> <td>DahA</td> <td>Dibenz(a,h)anthracene</td> </tr> <tr> <td>BghiP</td> <td>Benzo(g,h,i)perylene</td> </tr> <tr> <td>Hg</td> <td>Mercury as Hg0+HgP</td> </tr> </tbody> </table> <h1>List of other parameters</h1> <table><colgroup><col><col></colgroup> <tbody> <tr> <td>short name</td> <td>description</td> </tr> <tr> <td>MAMI</td> <td>Mean Altitude of Maximal Injection</td> </tr> <tr> <td>APT</td> <td>Altitude of Plume Top</td> </tr> <tr> <td>G4H</td> <td>software for calculating enissions locally, including emission factor table and land cover mask</td> </tr> </tbody> </table>

opencc-by-4.0Apr 2022View details →

VIIRS-based Fire Emission Inventory (data)

The VIIRS-based Fire Emission Inventory provides daily open biomass burning emission fluxes for 46 species of aerosols and gases at ~500 m resolution (globally). The data starts on early 2012 because it uses the VIIRS I-band active fire product.

opencc-by-4.0Apr 2022View details →

VIIRS-based Fire Emission Inventory (data)

opencc-by-4.0Oct 2022View details →

Field data synthesis accompanying "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"

Synthesis of fuel load and fuel consumption field measurements accompanying the publication:"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"Dave van Wees1, Guido R. van der Werf1, James T. Randerson2, Brendan M. Rogers3, Yang Chen2, Sander Veraverbeke1, Louis Giglio4, and Douglas C. Morton51Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands 2Department of Earth System Science, University of California, Irvine, CA 92697, USA 3Woodwell Climate Research Center, Falmouth, MA 02540, USA 4Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA 5Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USADOI: https://doi.org/10.5194/gmd-15-8411-2022 Units are g C / m2

opencc-by-sa-4.0Sep 2017View details →

Supporting Information for 'in situ fire emission factors for Malaysian tropical peatlands with the first investigation of the influence of physicochemical controls on peat fire emission factor variability'

Two files containing: 1. Raw mole fraction retrievals from our OP-FTIR spectra (as described in the paper) 2. Calculated emission ratios used for the calculation of emission factors (as published in the paper)

opencc-by-4.0Dec 2017View details →

Variability of Global Fire Emissions - Data and Model Code

Netcdf files including all relevant data for the manuscript entitled "Trends and variability of global fire emissions due to historical anthropogenic activities", submitted to Global Biogeochemical Cycles in 2017.   FINALv2_presentday_2002-2009.nc: Monthly fire area burned and carbon emissions data from FINAL.2 for the years 2002 through 2009 FINALv2C_*_1700-2009.ts.nc: Historical time series of monthly area burned and carbon emissions for natural, secondary, crop and pasture land cover for years 1700 to 2009 vegn_fire.F90: The main module of FINAL.2 in the GFDL LM3

openmit-licenseMay 2024View details →

Dataset related to the manuscript Wagner and Schepanski (submitted to JAMES, 2024): "Quantifying fire-driven dust emissions using a global aerosol model"

This dataset belongs to the manuscript of Wagner and Schepanski (2024) entitled "Quantifying fire-driven dust emissions using a global aerosol model" submitted to the "Journal of Advances in Modeling Earth Systems (JAMES)". It contains the for the 10 year simulation period 2004-2013 the monthly, seasonal, or yearly averaged fields of the variables (variable name in brackets) that were used to prepare the plots and statements made in the manuscript. These are in detail: <ol> <li>GFAS input data of FRP (frp)</li> <li>simulated AOD (tau_2d_550nm) and dust AOD (tau_comp_du_550nm)</li> <li>simulated wind-driven (emi_du_dust) and fire-driven (emi_du_fdust) dust emission fluxes</li> <li>simulated atmospheric dust concentration (du_all) including the soluble/insoluble coarse (du_ci, du_cs) and accumulation (du_ai, du_as) mode together with vertical atmospheric pressure levels (pfull)</li> </ol> The simulated results are provided for both simulations, the control run without the additional fire-dust emissions and the actual firedust simulation with the new fire-dust emission parameterization.

opencc-by-4.0Oct 2022View details →

Model data for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"

500 m fire carbon emissions and burned area as part of the publication: "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)" Dave van Wees1, Guido R. van der Werf1, James T. Randerson2, Brendan M. Rogers3, Yang Chen2, Sander Veraverbeke1, Louis Giglio4, and Douglas C. Morton5 1Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands 2Department of Earth System Science, University of California, Irvine, CA 92697, USA 3Woodwell Climate Research Center, Falmouth, MA 02540, USA 4Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA 5Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA DOI: https://doi.org/10.5194/gmd-15-8411-2022   UPDATE OF DATASET TO 2023: This dataset has now been extended to 2023. Since the first release of this dataset, multiple updates to the model input data have been made: - Update from MODIS C6 to MODIS C6.1 for all MODIS input data, including MCD12Q1 land cover types, MCD14ML active fires, MCD15A2H fPAR, MOD44B VCF, MOD44W land-water mask, and MCD64A1 burned area. - Update of Hansen forest loss data from v1.9 to v1.11. - Update of GLEAM evaporative stress data from v3.6b to v3.7b. - Extension of ERA5-land data to 2023. - Addition of land cover type layers to the 500-m resolution data files.   Files contain 500-m (per MODIS tile) and 0.25 degree aggregated (global grid) carbon emissions and burned area from biomass burning for 2002-2022, as part of the paper "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-15-8411-2022). 500-m resolution files include land cover type grids. 0.25 degree global grid files also include biome partitioning and accompanying biome fractional cover grids. Zip archives with filenames "500m_YYYY.zip" contain annual files named "Model500m_2002-2023yr_h##v##_YYYY.nc", which are the 500-meter resolution model results per MODIS tile using the MODIS sinusoidal projection. Carbon emission data layers are: - Total biomass burning carbon emissions from aboveground; g C m-2 month-1 (/MOD_Grid/emissions/C_AG_TOT) - Total biomass burning carbon emissions from belowground; g C m-2 month-1 (/MOD_Grid/emissions/C_BG_TOT) - Fire-related forest loss carbon emissions from aboveground; g C m-2 month-1 (/MOD_Grid/emissions/C_AG_FL) - Fire-related forest loss carbon emissions from belowground; g C m-2 month-1 (/MOD_Grid/emissions/C_BG_FL) Total emissions are calculated as: C_AG_TOT + C_BG_TOT. Total fire-related forest loss emissions are calculated as: C_AG_FL + C_BG_FL. Burned area data layers are: - Total burned area; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_TOT) - Burned area from fire-related forest loss; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_FL) The Zip archive with filename "025d_2002_2023.zip" contains annual files named "Model500m_2002-2023yr_025d_YYYY.nc", which are the 500-m model results aggregated to a 0.25 degree global lat-lon grid. These files contain the same variables as the 500-m files, but aggregated to 0.25 degree resolution (MOD_CMG025). Furthermore, these files include biome partitioning of emissions and burned area (MOD_CMG025BIOME) and provide accompanying biome fractional cover grids for all 20 biomes (variable 'biomes'). Biomes are listed in detail in Table S1 of the van Wees et al. (2022) paper. The biomes 'water', 'snow/ice' and 'barren' were excluded from Table S1 because of their negligible share, but are included in the files provided here for completeness.