The Global Fire Assimilation System (GFAS) assimilates fire radiative power (FRP) observations from satellite-based sensors to produce daily estimates of emissions from wildfires and biomass burning. FRP is a measure of the energy released by the fire and is therefore a measure of how much vegetation is burned.

Emissions of atmospheric pollutants from biomass burning and vegetation fires are key drivers of the evolution of atmospheric composition, with a high degree of spatial and temporal variability, and an accurate representation of them in models is essential. The CAMS Global Fire Assimilation System (GFAS) utilises satellite observations of fire radiative power (FRP) to provide near-real-time information on the location, relative intensity and estimated emissions from biomass burning and vegetation fires. Emissions are estimated by (i) conversion of FRP observations to the dry matter (DM) consumed by the fire, and (ii) application of emission factors to DM for different biomes, based on field and laboratory studies in the scientific literature, to estimate the emissions. Emissions estimates for 40 pyrogenic species are available from GFAS, including aerosols, reactive gases and greenhouse gases, on a regular grid with a spatial resolution of 0.1 degrees longitude by 0.1 degrees latitude.

This dataset supports the State of Wildfires 2023-24 report under review at Earth System Science Data Discussions (Jones et al., under review, https://doi.org/10.5194/essd-2024-218). The dataset provides annual data and final-year anomalies in burned area (BA), fire carbon (C) emissions, and fire properties (e.g. distributional statistics for fire count, size, rate of growth). Annual data relate to the global fire season defined as March-February (e.g., March 2023-February 2024), aligning with an annuall lull in the global fire calendar (see Jones et al., 2024). The complete methodology is described by Jones et al. (2024).

Citation

Work utilising our regional summaries should cite both Jones et al. (2024, under review, ESSD) AND the primary reference for the variable(s) of interest as follows:

• Giglio et al. (2018) for MODIS MCD64A1 BA.
• van der Werf et al. (2017) for GFED4.1s fire C emissions.
• Kaiser er al. (2012) for GFAS fire C emissions.
• van der Werf et al. (2017) AND Kaiser er al. (2012) for the average of GFED4.1s and GFAS fire C emissions.
• Andela et al. (2019) for the Global Fire Atlas.

Input Data

Burned Area (BA)

• BA data from NASA’s MODIS BA product (MCD64A1) are extended from Giglio et al. (2018) and are available at Giglio et al. (2021, https://lpdaac.usgs.gov/products/mcd64a1v061/).
  • Period: 2001-February 2024
  • Resolution: 500m

Fire Carbon (C) Emissions

• GFED4.1s fire C emissions data are extended from van der Werf and are available at https://globalfiredata.org/.
  • Period: 2003-February 2024
  • Resolution: 0.25 degree, daily

• GFAS fire C emissions data are extended from Kaiser et al. (2012) and are available at https://confluence.ecmwf.int/display/CKB/CAMS+global+biomass+burning+emissions+based+on+fire+radiative+power+%28GFAS%29%3A+data+documentation.
  • Period: 2003-February 2024
  • Resolution: 0.1 degree, daily

Global Fire Atlas (Individual Fire Atlas and Properties)

• Global Fire Atlas are extended from Andela et al. (2019) and are available at Andela and Jones (2024, https://doi.org/10.5281/zenodo.11400062, last access: 31 May 2024).
  
  • Period: 2002-February 2024
  • Driven by 500m MODIS BA data (collection 6.1)

Regional Analysis

We performed "cookie-cutting" (spatial and temporal masking) of the above input data sets to features in each of the following regional layers (e.g. per country in the "Countries" layer).

The statistics derived from cookie-cutting are listed below. Full details in Jones et al. (2024).

Regional Statistics and Anomalies

• Burned Area (BA)
  • Calculated regional totals for each fire season.
  • Relative and standardized anomalies from historical data (since 2001).
  • Ranking amongst all recorded fire seasons.
  • Onset, peak, and cessation based on monthly deviations from climatological means.

• Carbon Emissions
  • Calculated regional totals for each fire season.
  • Relative and standardized anomalies from historical data (since 2003).
  • Ranking amongst all recorded fire seasons.
  • Onset, peak, and cessation based on monthly deviations from climatological means.
  • Statistics available for GFAS, GFED, and their mean.

• Individual Fire Properties
  • Based on ignition point vectors from the Global Fire Atlas.
  • Calculated regional count.
  • Calculated regional maxima and 95th percentiles for each fire season.
  • Relative and standardized anomalies from historical data (since 2002).
  • Ranked anomalies among all recorded fire seasons.

Anthropogenic carbon emissions induce rising temperatures and climate change. Fire carbon emissions contribute to the accumulation of atmospheric CO2 and affect climate change. It is crucial to accurately monitor the dynamics of global fire emissions for fire management and climate change mitigation. This study merged six commonly used satellite-based fire emission products (the GFED 4.1s, GFAS 1.2, QFED 2.5, FINN 2.5, FEER 1.0, and Xu et al. (2021)) using the three-corner hat method (TCH) (Premoli and Tavella, 1993) and produced a new global fire emission dataset, FiTCH. The FiTCH dataset provides global annual fire carbon emissions from 2001 to 2021 at the spatial resolution of 0.1 degree. The unit of carbon emissions is gigagram carbon (Gg C) per pixel. There are 21 bands in the stacked raster data, where each band indicates a year since 2001.

Note: The GFED 4.1s data were from the Global Fire Emissions Database (GFED) (van der Werf et al., 2017). The GFAS 1.2 data were from the Global Fire Assimilation System (GFAS) (Kaiser et al., 2012). The QFED 2.5 data were from the Quick Fire Emissions Dataset (QFED) (Koster et al., 2015). The FINN 2.5 data were from the Fire INventory from National Center for Atmospheric Research (NCAR) (FINN) (Wiedinmyer et al., 2023). The FEER 1.0 data were from the Fire Energetics and Emissions Research (FEER) (Ichoku and Ellison, 2014). The Xu et al. (2021) global fire emission data came from "Changes in global terrestrial live biomass over the 21st century" published in Science Advance.

References Ichoku, C., Ellison, L., 2014. Global top-down smoke-aerosol emissions estimation using satellite fire radiative power measurements. Atmospheric Chemistry and Physics 14, 6643–6667. https://doi.org/10.5194/acp-14-6643-2014
Kaiser, J.W., Heil, A., Andreae, M.O., Benedetti, A., Chubarova, N., Jones, L., Morcrette, J.-J., Razinger, M., Schultz, M.G., Suttie, M., van der Werf, G.R., 2012. Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power. Biogeosciences 9, 527–554. https://doi.org/10.5194/bg-9-527-2012 Koster, R.D., Darmenov, A.S., da Silva, A.M., 2015. The Quick Fire Emissions Dataset (QFED): Documentation of Versions 2.1, 2.2 and 2.4 (No. NASA/TM-2015-104606 /Vol. 38).
Premoli, A., Tavella, P., 1993. A revisited three-cornered hat method for estimating frequency standard instability. IEEE Transactions on Instrumentation and Measurement 42, 7–13. https://doi.org/10.1109/19.206671 van der Werf, G.R., Randerson, J.T., Giglio, L., Leeuwen, T.T. van, Chen, Y., Rogers, B.M., Mu, M., Marle, M.J.E. van, Morton, D.C., Collatz, G.J., Yokelson, R.J., Kasibhatla, P.S., 2017. Global fire emissions estimates during 1997–2016. Earth System Science Data 9, 697–720. https://doi.org/10.5194/essd-9-697-2017
Wiedinmyer, C., Kimura, Y., McDonald-Buller, E.C., Emmons, L.K., Buchholz, R.R., Tang, W., Seto, K., Joseph, M.B., Barsanti, K.C., Carlton, A.G., Yokelson, R., 2023. The Fire Inventory from NCAR version 2.5: an updated global fire emissions model for climate and chemistry applications. EGUsphere 1–45. https://doi.org/10.5194/egusphere-2023-124 Xu, L., Saatchi, S.S., Yang, Y., Yu, Y., Pongratz, J., Bloom, A.A., Bowman, K., Worden, J., Liu, J., Yin, Y., Domke, G., McRoberts, R.E., Woodall, C., Nabuurs, G.-J., de-Miguel, S., Keller, M., Harris, N., Maxwell, S., Schimel, D., 2021. Changes in global terrestrial live biomass over the 21st century. Science Advances 7, eabe9829. https://doi.org/10.1126/sciadv.abe9829

The current repository contains inputs, products and codes associated with the study by Rosu et al., "Large-scale impacts of the 2023 Canadian wildfires on the Northern Hemisphere atmosphere". This study investigates the short-term large-scale weather impacts of the 2023 Canadian wildfire emissions through the use the Earth System Model (ESM) EC-Earth3 [1]. It is noted that for this work, the EC-Earth3-AerChem configuration 2 was used. For the needs of this work, two sets of simulations took place, one while considering the fire emissions (NX) and one without (NC). Regarding the emissions used, the GFAS wildfire emission dataset was applied (refer to Kaiser et al. [3] and to [4]), while for the rest of the emissions, i.e. natural and anthropogenic, refer to van Noije et al. [2] and to [5]. The current repository contains the output derived from the aforementioned simulations, specifically black carbon AOD, cloud cover, organic AOD, total AOD, net downward radiation flux, secondary organic AOD, surface atmospheric pressure, atmospheric temperature, zonal wind, and meridional wind. Moreover, this repository also contains the MODIS AOD data [6] used in the study and the Python code used for post-processing the EC-Earth3 output. Finally, the AERONET V3 [7] and the MERRA-2 dataset [8] were also used in this work.

References

[1] Döscher et al. (2022) The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6. Geosci Model Dev 15:2973–3020. https://doi.org/10.5194/gmd-15-2973-2022

[2] van Noije et al. (2021) EC-Earth3-AerChem: a global climate model with interactive aerosols and atmospheric chemistry participating in CMIP6. Geosci Model Dev 14:5637–5668. https://doi.org/10.5194/gmd-14-5637-2021

[3] Kaiser et al. (2012) Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power. Biogeosciences 9:527–554. https://doi.org/10.5194/bg-9-527-2012

[4] Copernicus Atmosphere Monitoring Service. CAMS Global Fire Assimilation System (GFAS) [Dataset]. ECMWF. https://ads.atmosphere.copernicus.eu/datasets/cams-global-fire-emissions-gfas

[5] EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP ssp370. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.4885

[6] NASA Earth Observations, MODIS Aerosol Optical Depth (MODAL2_M_AER_OD) [Dataset]. NASA GSFC. https://neo.gsfc.nasa.gov/view.php?datasetId=MODAL2_M_AER_OD

[7] NASA Goddard Space Flight Center, Aerosol Robotic Network (AERONET) Version 3 [Dataset]. NASA. https://aeronet.gsfc.nasa.gov/new_web/draw_map_display_inv_v3.html

[8] Global Modeling and Assimilation Office (GMAO) (2015), MERRA-2 tavg1_2d_slv_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC). https://doi.org/10.5067/VJAFPLI1CSIV

Overview

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 (https://doi.org/10.5281/zenodo.12675193) 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 https://acp.copernicus.org/articles/17/2921/2017/.

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: 10.5281/zenodo.15721938

A paper explaining the methods and data used in the creation of the dataset is being worked on.

Calculating emissions locally

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.

Q&As

Q1: Are emissions in beta and v2 for their common periods/species the same?

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.

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?

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.

Q3: The daily file has emission rate in kg/m2/s – Is this a flat rate for the day (GMT)?

Yes, this is correct.

Q4: Where is the vegetation map?

It is part of the package for calculating emissions locally, i.e. in the file G4H.tgz.

Q5: Why did the Zenodo link change?

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.

List of included species

Fire emissions of carbon monoxide (CO) in the Amazon basin over the 2003 to 2018 time period, optimized with CO column data from the Thermal InfraRed (TIR) product of the Measurements of Pollution In The Troposphere (MOPITT) satellite in the TM5-4DVAR inverse system. These emissions were produced in the reference inversions described in Chapter 4 of Stijn Naus' PhD thesis (available on https://edepot.wur.nl/536720), as well as in Naus et al. (2022). The inversions start from prior emissions of the Global Fire Assimilation System (GFAS), also available online (https://atmosphere.copernicus.eu/global-fire-emissions).

The optimized emissions are given for the two zoom domains over which satellite data have been assimilated. Advised is to only use emissions from the 1 by 1 degree inner zoom domain. Emissions only cover the April to December inversion window.

The SeasFire Cube is a scientific datacube for seasonal fire forecasting around the globe. Apart from seasonal fire forecasting, which is the aim of the SeasFire project, the datacube can be used for several other tasks. For example, it can be used to model teleconnections and memory effects in the earth system. Additionally, it can be used to model emissions from wildfires and the evolution of wildfire regimes.

It has been created in the context of the SeasFire project, which deals with "Earth System Deep Learning for Seasonal Fire Forecasting" and is funded by the European Space Agency (ESA) in the context of ESA Future EO-1 Science for Society Call.

It contains 21 years of data (2001-2021) in an 8-days time resolution and 0.25 degrees grid resolution. It has a diverse range of seasonal fire drivers. It expands from atmospheric and climatological ones to vegetation variables, socioeconomic and the target variables related to wildfires such as burned areas, fire radiative power, and wildfire-related CO2 emissions.

Datacube properties

Feature

Value

Spatial Coverage

Global

Temporal Coverage

2001 to 2021

Spatial Resolution

0.25 deg x 0.25 deg

Temporal Resolution

8 days

Number of Variables

54

Tutorial Link

https://github.com/SeasFire/seasfire-datacube

    Full name
    DataArray name
    Unit
    Contact *




    Dataset: ERA5 Meteo Reanalysis Data





    Mean sea level pressure
    mslp
    Pa
    NOA


    Total precipitation
    tp
    m
    MPI


    Relative humidity
    rel_hum
    %
    MPI


    Vapor Pressure Deficit
    vpd
    hPa
    MPI


    Sea Surface Temperature
    sst
    K
    MPI


    Skin temperature
    skt
    K
    MPI


    Wind speed at 10 meters
    ws10
    m*s-2
    MPI


    Temperature at 2 meters - Mean
    t2m_mean
    K
    MPI


    Temperature at 2 meters - Min
    t2m_min
    K
    MPI


    Temperature at 2 meters - Max
    t2m_max
    K
    MPI


    Surface net solar radiation
    ssr
    MJ m-2
    MPI


    Surface solar radiation downwards
    ssrd
    MJ m-2
    MPI


    Volumetric soil water level 1
    swvl1
    m3/m3
    MPI







          Volumetric soil water level 2




    swvl2
    m3/m3
    MPI


    Volumetric soil water level 3
    swvl3
    m3/m3
    MPI


    Volumetric soil water level 4
    swvl4
    m3/m3
    MPI


    Land-Sea mask
    lsm
    0-1
    NOA


    Dataset: Copernicus

CEMS

    Drought Code Maximum
    drought_code_max
    unitless
    NOA


    Drought Code Average
    drought_code_mean
    unitless
    NOA


    Fire Weather Index Maximum
    fwi_max
    unitless
    NOA


    Fire Weather Index Average
    fwi_mean
    unitless
    NOA


    Dataset: CAMS: Global Fire Assimilation System (GFAS)





    Carbon dioxide emissions from wildfires
    cams_co2fire
    kg/m²
    NOA


    Fire radiative power
    cams_frpfire
    W/m²
    NOA


    Dataset: FireCCI - European Space Agency’s Climate Change Initiative





    Burned Areas from Fire Climate Change Initiative (FCCI)
    fcci_ba
    ha
    NOA


    Valid mask of FCCI burned areas
    fcci_ba_valid_mask
    0-1
    NOA



    Fraction of burnable area
    fcci_fraction_of_burnable_area
    %
    NOA


    Number of patches
    fcci_number_of_patches
    N
    NOA


    Fraction of observed area
    fcci_fraction_of_observed_area
    %
    NOA


    Dataset: Nasa MODIS MOD11C1, MOD13C1, MCD15A2





    Land Surface temperature at day
    lst_day
    K
    MPI


    Leaf Area Index
    lai
    m²/m²
    MPI


    Normalized Difference Vegetation Index
    ndvi
    unitless
    MPI


    Dataset: Nasa SEDAC Gridded Population of the World (GPW), v4





    Population density
    pop_dens
    persons per square kilometers
    NOA


    Dataset: Global Fire Emissions Database (GFED)





    Burned Areas from GFED (large fires only)
    gfed_ba
    hectares (ha)
    MPI


    Valid mask of GFED burned areas
    gfed_ba_valid_mask
    0-1
    NOA


    GFED basis regions
    gfed_region
    N
    NOA


    Dataset: Global Wildfire Information System (GWIS)





    Burned Areas from GWIS
    gwis_ba
    ha
    NOA


    Valid mask of GWIS burned areas
    gwis_ba_valid_mask
    0-1
    NOA


    Dataset: NOAA Climate Indices





    Arctic Oscillation Index
    oci_ao
    unitless
    NOA


    Western Pacific Index
    oci_wp
    unitless
    NOA


    Pacific North American Index
    oci_pna
    unitless
    NOA


    North Atlantic Oscillation
    oci_nao
    unitless
    NOA


    Southern Oscillation Index
    oci_soi
    unitless
    NOA


    Global Mean Land/Ocean Temperature
    oci_gmsst
    unitless
    NOA


    Pacific Decadal Oscillation
    oci_pdo
    unitless
    NOA


    Eastern Asia/Western Russia
    oci_ea
    unitless
    NOA


    East Pacific/North Pacific Oscillation
    oci_epo
    unitless
    NOA


    Nino 3.4 Anomaly
    oci_nino_34_anom
    unitless
    NOA


    Bivariate ENSO Timeseries
    oci_censo
    unitless
    NOA


    Dataset: ESA CCI





    Land Cover Class 0 - No data
    lccs_class_0
    %
    NOA


    Land Cover Class 1 - Agriculture
    lccs_class_1
    %
    NOA


    Land Cover Class 2 - Forest
    lccs_class_2
    %
    NOA


    Land Cover Class 3 - Grassland
    lccs_class_3
    %
    NOA


    Land Cover Class 4 - Wetlands
    lccs_class_4
    %
    NOA


    Land Cover Class 5 - Settlement
    lccs_class_5
    %
    NOA


    Land Cover Class 6 - Shrubland
    lccs_class_6
    %
    NOA


    Land Cover Class 7 - Sparse vegetation, bare areas, permanent snow and ice
    lccs_class_7
    %
    NOA


    Land Cover Class 8 - Water Bodies
    lccs_class_8
    %
    NOA


    Dataset: Biomes





    Dataset: Calculated





    Grid Area in square meters
    area
    m²
    NOA

*The datacube specifications (temporal, spatial resolution, chunk size) have been set up by the Max Planck Institut (MPI) team. For the variables that the contact is MPI, Lazaro Alonso (lalonso bgc-jena.mpg.de) has led the efforts to collect and process them. For the variables that the contact is NOA, Ilektra Karasante (ile.karasante noa.gr) has led the efforts to collect and process them.

In May 2016, the Horse River wildfire led to the evacuation of ∼ 88 000 people from Fort McMurray and surrounding areas and consumed ∼ 590 000 ha of land in Northern Alberta and Saskatchewan. Within the plume, satellite instruments measured elevated values of CO, NH3, and NO2. CO was measured by two Infrared Atmospheric Sounding Interferometers (IASI-A and IASI-B), NH3 by IASI-A, IASI-B, and the Cross-track Infrared Sounder (CrIS), and NO2 by the Ozone Monitoring Instrument (OMI). Daily emission rates were calculated from the satellite measurements using fire hotspot information from the Moderate Resolution Imaging Spectroradiometer (MODIS) and wind information from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis, combined with assumptions on lifetimes and the altitude range of the plume. Sensitivity tests were performed and it was found that uncertainties of emission estimates are more sensitive to the plume shape for CO and to the lifetime for NH3 and NOx. The satellite-derived emission rates were ∼ 50–300 kt d−1 for CO, ∼ 1–7 kt d−1 for NH3, and ∼ 0.5–2 kt d−1 for NOx (expressed as NO) during the most active fire periods. The daily satellite-derived emission estimates were found to correlate fairly well (R∼0.4–0.7) with daily output from the ECMWF Global Fire Assimilation System (GFAS) and the Environment and Climate Change Canada (ECCC) FireWork models, with agreement within a factor of 2 for most comparisons. Emission ratios of NH3∕CO, NOx∕CO, and NOx∕NH3 were calculated and compared against enhancement ratios of surface concentrations measured at permanent surface air monitoring stations and by the Alberta Environment and Parks Mobile Air Monitoring Laboratory (MAML). For NH3∕CO, the satellite emission ratios of ∼ 0.02 are within a factor of 2 of the model emission ratios and surface enhancement ratios. For NOx∕CO satellite-measured emission ratios of ∼0.01 are lower than the modelled emission ratios of 0.033 for GFAS and 0.014 for FireWork, but are larger than the surface enhancement ratios of ∼0.003, which may have been affected by the short lifetime of NOx. Total emissions from the Horse River fire for May 2016 were calculated and compared against total annual anthropogenic emissions for the province of Alberta in 2016 from the ECCC Air Pollutant Emissions Inventory (APEI). Satellite-measured emissions of CO are ∼1500 kt for the Horse River fire and exceed the total annual Alberta anthropogenic CO emissions of 992.6 kt for 2016. The satellite-measured emissions during the Horse River fire of ∼30 kt of NH3 and ∼7 kt of NOx (expressed as NO) are approximately 20 % and 1 % of the magnitude of total annual Alberta anthropogenic emissions, respectively.

This collection contains time series of cumulative fire intensity along the backward trajectories starting at the ATTO (Amazon Tall Tower Observatory) site. The time series are based on HYSPLIT back trajectory calculations and fire data of the Global Fire Assimilation System (GFAS). The data in file "H1000_Cer_Rain.dat" are used in paper "Influx of African biomass burning aerosol during the Amazonian dry season through layered transatlantic transport of black carbon-rich smoke" (Holanda et al., 2019, ACP, "https://doi.org/10.5194/acp-2019-775"). See there for further information. When using the data, please also refer to that paper.

These data comprise methane (CH4) column concentrations calculated from a GEOS-Chem model run, performed in a nested configuration at high spatial resolution (0.25deg x 0.3125deg latitude-longitude) centred on Uganda. The data included in the netCDF4 files cover a 6.0deg x 8.0deg box centred approximately on the Methane Observations and Yearly Assessments (MOYA) project EM27/SUN measurement site in Jinja.

For the a priori methane emissions inside the nested domain the EDGAR v4.3.2 database is used for anthropogenic emissions, the WetCHARTS dataset for emissions from wetlands, and the GFAS database for daily biomass burning emissions. The boundary conditions for the nested domain come from a global GEOS-Chem model run at lower spatial resolution (2.0deg x 2.5deg latitude-longitude). An ensemble Kalman Filter system is used to perform the inversion. Two netCDF4 files are included: one where we just use the a priori emissions to determine the CH4 fluxes in the model domain, and one where TROPOMI CH4 (satellite observation) data is used to constrain the emissions.

EDGAR - Emissions Database for Global Atmospheric Research (linked in the Details/Docs section) WetCHARTs - Wetland Methane Emissions and Uncertainty (linked in the Details/Docs section) GFAS - Global Fire Assimilation System (linked in the Details/Docs section) TROPOMI - TROPOspheric Monitoring Instrument

These predictors were collected from SCORE (Schistosomiasis Consortium for the Operational Research and Evaluation) [21], ERA5 (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5) [45], GFAS (Global Fire Assimilation System) [46], CGLS (Copernicus Global Land Service) [47], GLAD (Global Land Analysis Discovery) [48,49], and SDAC (Socioeconomic Data and Applications Center) [50]. (XLSX)