Climate change impacts on global wildfire ignitions by lightning and the safe management of landscape fuels
Lead Research Organisation:
University of East Anglia
Department Name: Environmental Sciences
Abstract
2020 will be remembered not only for Covid-19, but also for its devastating wildfires. The year began with the Australian bushfires, which caused 34 deaths and over US$70 billion of damages. The wildfires that are currently burning across the Western US look set to be the costliest in US history, with over 30 people already killed directly. The wildfires of 2020 join a growing list of extreme wildfires seen in Mediterranean Europe, North America, Australia, Siberia and the Arctic in the past decade.
Wildfires are strongly tied to climatic droughts, which enhance the flammability of vegetation. As drought frequency is projected to rise in future, there are serious concerns that the fires seen in recent years are a glimpse into a more fire-prone future. Lightning strikes are the dominant cause of wildfire in many regions and, for example, ignited many of the recent Australian and US fire complexes.
It is critical that we understand the drivers of wildfire, build capacity to predict their future likelihood, and take steps to mitigate their impacts. Our current understanding of fires at the global scale is built around satellite observations. However, these observations are insufficient to disentangle the diverse drivers of fire; they see only patterns. Satellite observations provide a mixed signal of many different types of fire, including wildfires but also a range of fires under human control (e.g. agricultural fires and deforestation fires), meaning that observations of wildfire are 'contaminated' with other fire types. Critically, this obscures trends in wildfire activity and compromises our understanding of climate impacts on wildfire activity.
The proposed project will create the first global capacity to isolate lightning-ignited wildfires from satellite observations. It will use new observations of lightning strikes from ground- and satellite-based lightning sensors to 'decontaminate' satellite observations and introduce a global dataset of lightning-ignited wildfire activity. The new dataset will be used to make key advances in the understanding of wildfires and their relationship with climate. The project will assess how wildfire activity has changed in recent decades, and it will specifically determine the climatic conditions under which lightning fires occur. This new understanding of fire drivers will be built into the UK Earth System model and used to predict the impact of climate change on wildfire activity in the future century.
The new capacity to observe wildfires will also enable a major advance in the estimation of deforestation fire emissions, specifically in Amazonia which accounts around 40% of global emissions due to land use change. Deforestation fire emissions contribute to an increase in atmospheric concentrations of CO2 and contribute to climate change. However, emissions from Amazonian deforestation fires are known to be overestimated because emissions from lightning-ignited wildfires are undesirably included in the estimates. The new record of lightning-ignited wildfires developed in this study will be used to correct the emissions estimates and discount wildfires that occur as part of a natural disturbance-recovery cycle in the region.
Finally, this project will evaluate our future capacity to manage the threats of wildfires in a changing climate using conventional approaches to forest fuel management. It is common in some regions (e.g. Australia and the western US) to manage forest fuel stocks by burning off the most flammable fuels on the forest floor. However, this practice can only be applied safely during cool, moist, wind-free weather that occur in an annual 'window' of opportunity. It is feared that this window will narrow in future due to climate change. In this project, climate model outputs will be used to predict change in the window to 2100, informing forest management agencies of their future challenges and resource needs.
Wildfires are strongly tied to climatic droughts, which enhance the flammability of vegetation. As drought frequency is projected to rise in future, there are serious concerns that the fires seen in recent years are a glimpse into a more fire-prone future. Lightning strikes are the dominant cause of wildfire in many regions and, for example, ignited many of the recent Australian and US fire complexes.
It is critical that we understand the drivers of wildfire, build capacity to predict their future likelihood, and take steps to mitigate their impacts. Our current understanding of fires at the global scale is built around satellite observations. However, these observations are insufficient to disentangle the diverse drivers of fire; they see only patterns. Satellite observations provide a mixed signal of many different types of fire, including wildfires but also a range of fires under human control (e.g. agricultural fires and deforestation fires), meaning that observations of wildfire are 'contaminated' with other fire types. Critically, this obscures trends in wildfire activity and compromises our understanding of climate impacts on wildfire activity.
The proposed project will create the first global capacity to isolate lightning-ignited wildfires from satellite observations. It will use new observations of lightning strikes from ground- and satellite-based lightning sensors to 'decontaminate' satellite observations and introduce a global dataset of lightning-ignited wildfire activity. The new dataset will be used to make key advances in the understanding of wildfires and their relationship with climate. The project will assess how wildfire activity has changed in recent decades, and it will specifically determine the climatic conditions under which lightning fires occur. This new understanding of fire drivers will be built into the UK Earth System model and used to predict the impact of climate change on wildfire activity in the future century.
The new capacity to observe wildfires will also enable a major advance in the estimation of deforestation fire emissions, specifically in Amazonia which accounts around 40% of global emissions due to land use change. Deforestation fire emissions contribute to an increase in atmospheric concentrations of CO2 and contribute to climate change. However, emissions from Amazonian deforestation fires are known to be overestimated because emissions from lightning-ignited wildfires are undesirably included in the estimates. The new record of lightning-ignited wildfires developed in this study will be used to correct the emissions estimates and discount wildfires that occur as part of a natural disturbance-recovery cycle in the region.
Finally, this project will evaluate our future capacity to manage the threats of wildfires in a changing climate using conventional approaches to forest fuel management. It is common in some regions (e.g. Australia and the western US) to manage forest fuel stocks by burning off the most flammable fuels on the forest floor. However, this practice can only be applied safely during cool, moist, wind-free weather that occur in an annual 'window' of opportunity. It is feared that this window will narrow in future due to climate change. In this project, climate model outputs will be used to predict change in the window to 2100, informing forest management agencies of their future challenges and resource needs.
Organisations
- University of East Anglia (Lead Research Organisation)
- National Institute for Space Research Brazil (Collaboration)
- Meteorological Office UK (Collaboration)
- University of Melbourne (Collaboration)
- Swansea University (Collaboration)
- University of Zurich (Collaboration)
- Cardiff University (Collaboration)
- Global Carbon Project (Collaboration)
- UK CENTRE FOR ECOLOGY & HYDROLOGY (Collaboration)
- Free University of Amsterdam (Collaboration)
- University of California, Merced (Collaboration)
- European Centre for Medium Range Weather Forecasting ECMWF (Collaboration)
People |
ORCID iD |
| Matthew Jones (Principal Investigator / Fellow) |
Publications
Bowring S
(2022)
Pyrogenic carbon decomposition critical to resolving fire's role in the Earth system
in Nature Geoscience
Ciais P
(2022)
Definitions and methods to estimate regional land carbon fluxes for the second phase of the REgional Carbon Cycle Assessment and Processes Project (RECCAP-2)
in Geoscientific Model Development
Coppola A
(2022)
The black carbon cycle and its role in the Earth system
in Nature Reviews Earth & Environment
Coppola, Alysha I.
(2022)
The black carbon cycle and its role in the Earth system
De Oliveira G
(2023)
Increasing wildfires threaten progress on halting deforestation in Brazilian Amazonia.
in Nature ecology & evolution
Friedlingstein P
(2022)
Global Carbon Budget 2022
in Earth System Science Data
Friedlingstein P
(2023)
Global Carbon Budget 2023
Friedlingstein P
(2022)
Global Carbon Budget 2022
Friedlingstein P
(2023)
Global Carbon Budget 2023
Friedlingstein P
(2022)
Global Carbon Budget 2021
in Earth System Science Data
| Description | - Global rise in forest fire emissions https://www.science.org/doi/10.1126/science.adl5889 - Lightning fires threatening high-latitude forests https://www.nature.com/articles/s41561-023-01322-z - International project tracking annual wildfire seasons and their impacts, and explaining reasons https://essd.copernicus.org/articles/16/3601/2024/ - Initiation of a new international project "State of Wildfire Project" (stateofwildfires.com - site under construction) |
| Exploitation Route | My group and collaborators are increasingly targetting our work (including submitted proposals) on the study of extreme fires and their impacts on society and the environment. |
| Sectors | Agriculture Food and Drink Construction Energy Environment Healthcare Leisure Activities including Sports Recreation and Tourism Government Democracy and Justice Security and Diplomacy |
| Description | BeZero Carbon Ltd using data from my paper https://www.science.org/doi/10.1126/science.adl5889 in their risk ratings for carbon projects. |
| First Year Of Impact | 2024 |
| Sector | Agriculture, Food and Drink,Digital/Communication/Information Technologies (including Software),Environment |
| Impact Types | Societal Economic |
| Description | BEIS consultation: Climate services for a Net Zero resilient world (CS-N0W) |
| Geographic Reach | National |
| Policy Influence Type | Contribution to a national consultation/review |
| Impact | Content of the consultation report us being used to inform BEIS policy on climate change. |
| URL | https://www.gov.uk/government/publications/climate-services-for-a-net-zero-resilient-world/cs-n0w-ov... |
| Description | Citation in European Union Report Deforestation and forest degradation in the Amazon |
| Geographic Reach | Multiple continents/international |
| Policy Influence Type | Citation in other policy documents |
| Impact | https://wedocs.unep.org/handle/20.500.11822/46404 |
| URL | https://op.europa.eu/en/publication-detail/-/publication/ce27d242-d701-11ef-be2a-01aa75ed71a1/langua... |
| Description | Citation in FAO Report Mediterranean Forest Initiative road map |
| Geographic Reach | Multiple continents/international |
| Policy Influence Type | Citation in other policy documents |
| Impact | https://openknowledge.fao.org/items/996ded1a-5276-4b30-bc39-e88aa48c7ef7 |
| URL | https://openknowledge.fao.org/items/996ded1a-5276-4b30-bc39-e88aa48c7ef7 |
| Description | Citation in UNEP Report: Emissions Gap Report 2024: No more hot air please! |
| Geographic Reach | Multiple continents/international |
| Policy Influence Type | Citation in other policy documents |
| Impact | https://wedocs.unep.org/handle/20.500.11822/46404 |
| URL | https://wedocs.unep.org/handle/20.500.11822/46404 |
| Description | Citation in WMO Greenhouse Gas Bulletin |
| Geographic Reach | Multiple continents/international |
| Policy Influence Type | Citation in other policy documents |
| Impact | https://library.wmo.int/records/item/69057-no-20-28-october-2024?offset=120 |
| URL | https://library.wmo.int/records/item/69057-no-20-28-october-2024?offset=120 |
| Description | NERC-NSF Directed Research Programme Workshop: Directed Research Scoping Workshop |
| Geographic Reach | Multiple continents/international |
| Policy Influence Type | Contribution to a national consultation/review |
| Impact | Recommendations to NERC & NSF re wildfire as a topic of directed research funding. |
| Description | Organisation for Economic Co-operation and Development (OECD) report "Taming wildfires in the context of climate change" |
| Geographic Reach | Multiple continents/international |
| Policy Influence Type | Contribution to new or improved professional practice |
| Impact | The report contributes to formation of public awareness of wildfire causes and influences public policy on climate change/net zero, land management, firefighting services, and disaster management. |
| URL | https://www.oecd.org/environment/taming-wildfires-in-the-context-of-climate-change-dd00c367-en.htm |
| Description | UK Climate Change Committee / Game and Wildlife Trust Workshop: "Wildfire impact, risk and mitigation." |
| Geographic Reach | Multiple continents/international |
| Policy Influence Type | Contribution to a national consultation/review |
| Impact | Whitepaper / report summarising policy changes for use by gov departments and the Climate Change Committee. |
| URL | https://www.gwct.org.uk/media/1381625/GWCT-Wildfire-workshop-report-and-proceedings-web.pdf |
| Description | Whitepaper: "Why Nature? Why Now?" World Resources Institute / Food and Land Use Coalition |
| Geographic Reach | National |
| Policy Influence Type | Contribution to a national consultation/review |
| Impact | WRI / FOLU use the reports to influence the environmental policies of government, large enterprises and SME and also produce public products stemming from the "Why Nature? Why Now?" report. |
| URL | https://www.foodandlandusecoalition.org/why-nature/ |
| Description | ARIES: ADVANCED RESEARCH AND INNOVATION IN ENVIRONMENTAL SCIENCES |
| Amount | ÂŁ7,227,514 (GBP) |
| Funding ID | NE/S007334/1 |
| Organisation | Natural Environment Research Council |
| Sector | Public |
| Country | United Kingdom |
| Start | 09/2019 |
| End | 09/2027 |
| Description | Climate change impacts on wildfire risk in seasonally dry forests |
| Amount | ÂŁ8,695,685 (GBP) |
| Funding ID | 2879227 |
| Organisation | Natural Environment Research Council |
| Sector | Public |
| Country | United Kingdom |
| Start | 08/2023 |
| End | 03/2027 |
| Description | Critical Decade for Climate Change Leverhulme Doctoral Scholars |
| Amount | ÂŁ1,350,000 (GBP) |
| Funding ID | DS-2020-028 |
| Organisation | The Leverhulme Trust |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 09/2022 |
| End | 04/2026 |
| Description | ESRC-NERC SeNSS-ARIES Joint Studentship |
| Amount | ÂŁ14,512,266 (GBP) |
| Funding ID | ES/P00072X/1 |
| Organisation | Economic and Social Research Council |
| Sector | Public |
| Country | United Kingdom |
| Start | 09/2022 |
| End | 04/2026 |
| Title | Data for the consolidated European synthesis of CO2 emissions and removals for EU27 and UK: 1990-2020 |
| Description | The annual carbon dioxide fluxes used to create all graphs in the main text of McGrath et al, "European synthesis of CO2 emissions and removals for EU27 and UK: 1990-2020", submitted to Earth System Science Data. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | Used at European Commission level |
| URL | https://zenodo.org/record/7365863 |
| Title | Data for the consolidated European synthesis of CO2 emissions and removals for EU27 and UK: 1990-2020 |
| Description | The annual carbon dioxide fluxes used to create all graphs in the main text of McGrath et al, "European synthesis of CO2 emissions and removals for EU27 and UK: 1990-2020", submitted to Earth System Science Data. A second version was uploaded after responding to reviewer comments. The main difference is primarily that some data was discarded as the plot is no longer included; the units in the fossil fuel timeseries have been changed to Tg C instead of Tg CO2; and the uncertainties have been modified on the NGHGI data for FL, CL, and GL, resulting in a large change for GL (353.8% instead of 752%). |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | Used at European Comission-level |
| URL | https://zenodo.org/record/8148461 |
| Title | FLAME: a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept - Input Data |
| Description | This repository contains driving data used by training and evaluation of FLAME in the "FLAME: a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept" paper. All NetCDF files are on regular, 0.5-degree grids on a monthly timestep over Brazil. Not all variables were used in the final analysis NetCDF File Variable Used/not Used Source/Reference burned_area.nc Burned area As training data MCD64A1/ Giglio et al. (2018) burned_area_nat_veg.nc Burned area in natural vegetation As training data MCD64A1/ Giglio et al. (2018) and Mapbiomas, 2022 burned_area_non_nat_veg.nc Burned area in non natural vegetation As training data MCD64A1/ Giglio et al. (2018) and Mapbiomas, 2022 tas_max.nc Maximum Temperature Used ISIMIP3a FRIELER et al. (2023) precip.nc Precipitation Used vpd.nc Vapor pressure deficit Not Used rhumid.nc Relative Humidity Not Used consec_dry_days.nc Consecutive number of dry days Not Used soilM.nc Soil Moisture Not Used JULES-ES lightn.nc Lightning Not Used ISIMIP3a FRIELER et al. (2023) popDen.nc Population density Not Used road_density.nc Road density Used GRIP global (MEIJER et al., 2018) cveg.nc Vegetation carbon Not Used JULES-ES csoil.nc Carbon in dead vegetation Used JULES-ES forest.nc Forest Used MAPBIOMAS, 2022 grassland.nc Grassland Not Used savanna.nc Savanna Not Used cropland.nc Cropland Not Used pasture.nc Pasture Used np.nc Number of patches Not Used Calculated from MAPBIOMAS,2022 ed.nc Edge density Used |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| Impact | Usage by other researchers to look at trends/variability etc in wildfire |
| URL | https://zenodo.org/doi/10.5281/zenodo.11491124 |
| Title | FLAME: a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept - Input Data |
| Description | This repository contains driving data used by training and evaluation of FLAME in the "FLAME: a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept" paper. All NetCDF files are on regular, 0.5-degree grids on a monthly timestep over Brazil. Not all variables were used in the final analysis NetCDF File Variable Used/not Used Source/Reference burned_area.nc Burned area As training data MCD64A1/ Giglio et al. (2018) burned_area_nat_veg.nc Burned area in natural vegetation As training data MCD64A1/ Giglio et al. (2018) and Mapbiomas, 2022 burned_area_non_nat_veg.nc Burned area in non natural vegetation As training data MCD64A1/ Giglio et al. (2018) and Mapbiomas, 2022 tas_max.nc Maximum Temperature Used ISIMIP3a FRIELER et al. (2023) precip.nc Precipitation Used vpd.nc Vapor pressure deficit Not Used rhumid.nc Relative Humidity Not Used consec_dry_days.nc Consecutive number of dry days Not Used soilM.nc Soil Moisture Not Used JULES-ES lightn.nc Lightning Not Used ISIMIP3a FRIELER et al. (2023) popDen.nc Population density Not Used road_density.nc Road density Used GRIP global (MEIJER et al., 2018) cveg.nc Vegetation carbon Not Used JULES-ES csoil.nc Carbon in dead vegetation Used JULES-ES forest.nc Forest Used MAPBIOMAS, 2022 grassland.nc Grassland Not Used savanna.nc Savanna Not Used cropland.nc Cropland Not Used pasture.nc Pasture Used np.nc Number of patches Not Used Calculated from MAPBIOMAS,2022 ed.nc Edge density Used |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| Impact | Usage by other researchers to look at trends/variability etc in wildfire |
| URL | https://zenodo.org/doi/10.5281/zenodo.11491125 |
| Title | Gridded fossil CO2 emissions and related O2 combustion consistent with national inventories |
| Description | Data Access Notice Please note that, at present, the data for a sample of years are provided in this data record due to Zenodo's 50GB data limit. Data for all years 1959-2022 can be accessed via the following link: http://opendap.uea.ac.uk:8080/opendap/hyrax/greenocean/GridFED/GridFEDv2023.1/contents.html Product Description See Jones et al. (2021) for a detailed description of this dataset and the core methods used to produce it. Key details are provided below. GCP-GridFED (version 2023.1) is a gridded fossil emissions dataset that is consistent with the national CO2 emissions reported by the Global Carbon Project (GCP; https://www.globalcarbonproject.org/) in the annual editions of its Global Carbon Budget (Friedlingstein et al., 2023). GCP-GridFEDv2023.1 provides monthly fossil CO2 emissions for the period 1959-2022 at a spatial resolution of 0.1° × 0.1°. The gridded emissions estimates are provided separately for fossil CO2 emitted by the oxidation of oil, coal and natural gas, international bunkers, and the calcination of limestone during cement production. The dataset also includes the cement carbonation sink of CO2. Note that positive values in GridFED signify a surface-to-atmosphere CO2 flux (emissions). Negative values signify an atmosphere-to-surface flux and apply only to the cement carbonation sink. GCP-GridFED also includes gridded uncertainties in CO2 emission, incorporating differences in uncertainty across emissions sectors and countries, and gridded estimates of corresponding O2 uptake based on oxidative ratios for oil, coal and natural gas (see Jones et al., 2021). Core Methodology in Brief GCP-GridFEDv2023.1 was produced by scaling monthly gridded emissions for the year 2010, from the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2; Janssens-Maenhout et al., 2019), to the national annual emissions estimates compiled as part of the 2022 global carbon budget (GCP-NAE) for the years 1959-2022 (Friedlingstein et al., 2023). GCP-GridFEDv2023.1 uses a preliminary release of GCP-NAE covering the years 1959-2022 (timestamp 1st August 2022; an update from Andrew and Peters [2022]). The GCP-NAE estimates for year 2022 are based on data available at the timestamp and the estimates are thus expected to differ somewhat from those that will be presented by Friedlingstein et al. (2023), which will adopt updates to GCP-NAE since the timestamp. For full details of the core methodology, see Jones et al. (2021). Changes to the Seasonality of Emissions in GCP-GridFEDv2022.2 onwards The seasonality of emissions (monthly distribution of annual emissions) for the following countries/sources is now based on the seasonality observed in the Carbon Monitor dataset (Liu et al., 2020; Dou et al., 2022): Austria, Belgium, Brazil, Bulgaria, China, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, India, Ireland, Italy, Japan, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Russia, Slovakia, Slovenia, Spain, Sweden, United Kingdom, United States. State or province-level data is used for Brazil, China, Russia, and the United States. This also applies for the Bunker Aviation and Bunker Shipping sectors. Seasonality is determined in the following ways for those countries/sources: The seasonality of emissions in 2019-2021 is taken from Carbon Monitor. The seasonality of emissions in all years prior to 2019 is assigned as the average of the seasonality from Carbon Monitor in all years excluding 2020 (due to the impact of COVID-19 on the seasonality of emissions in 2020). For all countries not listed above and all years 1959-2021, GCP-GridFED adopts the seasonality from EDGAR v4.3.2 (year 2010; Janssens-Maenhout et al., 2019) and applies a small correction based on heating/cooling degree days to account for inter-annual climate variability which effects emissions in some sectors (see Jones et al., 2021). Other New Features of GCP-GridFEDv2023.1 There have been no changes to the functionality of the GridFED code in this update (v2023.1) versus the previous update (v2022.2). |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| Impact | Dataset used to prepare model simulations for the Global Carbon Budget and for many other science projects, e.g. involving atmospheric inversions and regional emissions estimation.. |
| URL | https://zenodo.org/doi/10.5281/zenodo.4277266 |
| Title | Gridded fossil CO2 emissions and related O2 combustion consistent with national inventories |
| Description | Data Access Notice Please note that, at present, the data for a sample of years are provided in this data record due to Zenodo's 50GB data limit. Data for all years 1959-2021 can be accessed via the following link: http://opendap.uea.ac.uk:8080/opendap/hyrax/greenocean/GridFED/GridFEDv2022.2/contents.html Product Description See Jones et al. (2021) for a detailed description of this dataset and the core methods used to produce it. Key details are provided below. GCP-GridFED (version 2022.2) is a gridded fossil emissions dataset that is consistent with the national CO2 emissions reported by the Global Carbon Project (GCP; https://www.globalcarbonproject.org/) in the annual editions of its Global Carbon Budget (Friedlingstein et al., 2022). GCP-GridFEDv2022.2 provides monthly fossil CO2 emissions for the period 1959-2021 at a spatial resolution of 0.1° × 0.1°. The gridded emissions estimates are provided separately for fossil CO2 emitted by the oxidation of oil, coal and natural gas, international bunkers, and the calcination of limestone during cement production. The dataset also includes the cement carbonation sink of CO2. Note that positive values in GridFED signify a surface-to-atmosphere CO2 flux (emissions). Negative values signify an atmosphere-to-surface flux and apply only to the cement carbonation sink. GCP-GridFED also includes gridded uncertainties in CO2 emission, incorporating differences in uncertainty across emissions sectors and countries, and gridded estimates of corresponding O2 uptake based on oxidative ratios for oil, coal and natural gas (see Jones et al., 2021). Core Methodology in Brief GCP-GridFEDv2022.2 was produced by scaling monthly gridded emissions for the year 2010, from the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2; Janssens-Maenhout et al., 2019), to the national annual emissions estimates compiled as part of the 2022 global carbon budget (GCP-NAE) for the years 1959-2021 (Friedlingstein et al., in preparation [Earth System Science Data]), an update from the 2021 edition of the Global Carbon Budget (Friedlingstein et al., 2022). GCP-GridFEDv2022.2 uses a preliminary release of GCP-NAE covering the years 1959-2021 (timestamp 18th July 2021; and update from Andrew and Peters [2021]). The GCP-NAE estimates for year 2021 are based on data available at the timestamp and the estimates are thus expected to differ somewhat from those that will be presented by Friedlingstein et al. (in preparation [Earth System Science Data]), which will adopt updates to GCP-NAE since the timestamp. For full details of the core methodology, see Jones et al. (2021). Changes to the Seasonality of Emissions in GCP-GridFEDv2022.2 The seasonality of emissions (monthly distribution of annual emissions) for the following countries/sources is now based on the seasonality observed in the Carbon Monitor dataset (Liu et al., 2020; Dou et al., 2022): Austria, Belgium, Brazil, Bulgaria, China, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, India, Ireland, Italy, Japan, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Russia, Slovakia, Slovenia, Spain, Sweden, United Kingdom, United States. State or province-level data is used for Brazil, China, Russia, and the United States. This also applies for the Bunker Aviation and Bunker Shipping sectors. Seasonality is determined in the following ways for those countries/sources: The seasonality of emissions in 2019-2021 is taken from Carbon Monitor. The seasonality of emissions in all years prior to 2019 is assigned as the average of the seasonality from Carbon Monitor in all years excluding 2020 (due to the impact of COVID-19 on the seasonality of emissions in 2020). For all countries not listed above and all years 1959-2021, GCP-GridFED adopts the seasonality from EDGAR v4.3.2 (year 2010; Janssens-Maenhout et al., 2019) and applies a small correction based on heating/cooling degree days to account for inter-annual climate variability which effects emissions in some sectors (see Jones et al., 2021). Other New Features of GCP-GridFEDv2022.2 Emissions of CO2 from international shipping and bunker sources are now provided separately. The sink of CO2 arising from the carbonation of cement is also included. Values are negative as this is an atmosphere-to-surface flux. A total surface flux of fossil CO2 is now provided. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | Used in several international Earth System Modelling projects |
| URL | https://zenodo.org/record/7016360 |
| Title | National contributions to climate change due to historical emissions of carbon dioxide, methane and nitrous oxide |
| Description | A complete description of the dataset is given by Jones et al. (2023). Key information is provided below. Background A dataset describing the global warming response to national emissions CO2, CH4 and N2O from fossil and land use sources during 1851-2021. National CO2 emissions data are collated from the Global Carbon Project (Andrew and Peters, 2022; Friedlingstein et al., 2022). National CH4 and N2O emissions data are collated from PRIMAP-hist (HISTTP) (Gütschow et al., 2022). We construct a time series of cumulative CO2-equivalent emissions for each country, gas, and emissions source (fossil or land use). Emissions of CH4 and N2O emissions are related to cumulative CO2-equivalent emissions using the Global Warming Potential (GWP*) approach, with best-estimates of the coefficients taken from the IPCC AR6 (Forster et al., 2021). Warming in response to cumulative CO2-equivalent emissions is estimated using the transient climate response to cumulative carbon emissions (TCRE) approach, with best-estimate value of TCRE taken from the IPCC AR6 (Forster et al., 2021, Canadell et al., 2021). 'Warming' is specifically the change in global mean surface temperature (GMST). The data files provide emissions, cumulative emissions and the GMST response by country, gas (CO2, CH4, N2O or 3-GHG total) and source (fossil emissions, land use emissions or the total). Data records: overview The data records include three comma separated values (.csv) files as described below. All files are in 'long' format with one value provided in the Data column for each combination of the categorical variables Year, Country Name, Country ISO3 code, Gas, and Component columns. Component specifies fossil emissions, LULUCF emissions or total emissions of the gas. Gas specifies CO2, CH4, N2O or the three-gas total (labelled 3-GHG). Country ISO3 codes are specifically the unique ISO 3166-1 alpha-3 codes of each country. Data records: specifics EMISSIONS_ANNUAL_1830-2021.csv: Data includes annual emissions of CO2 (Pg CO2 year-1), CH4 (Tg CH4 year-1) and N2O (Tg N2O year-1) during 1830-2021. The Data column provides values for every combination of the categorical variables. EMISSIONS_CUMULATIVE_CO2e100_1851-2021.csv: Data includes the cumulative CO2 equivalent emissions in units Pg CO2-e100 during 1851-2021. The Data column provides values for every combination of the categorical variables. GMST_response_1851-2021.csv: Data includes the change in global mean surface temperature (GMST) due to emissions of the three gases during 1851-2021 in units °C. The Data column provides values for every combination of the categorical variables. Accompanying Code Code is available at: https://github.com/jonesmattw/National_Warming_Contributions . The code requires Input.zip to run (see README at the GitHub link). Further info: Country Groupings We also provide estimates of the contributions of various country groupings as defined by the UNFCCC: Annex I countries (number of countries, n = 42) Annex II countries (n = 23) economies in transition (EITs; n = 15) the least developed countries (LDCs; n = 47) the like-minded developing countries (LMDC; n = 24). And other country groupings: the organisation for economic co-operation and development (OECD; n = 38) the European Union (EU27 post-Brexit) the Brazil, South Africa, India and China (BASIC) group. See COUNTRY_GROUPINGS.xlsx for the lists of countries in each group. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| Impact | Usage cases at government department level in the US, UK, Germany |
| URL | https://zenodo.org/record/7076346 |
| Title | National contributions to climate change due to historical emissions of carbon dioxide, methane and nitrous oxide |
| Description | A complete description of the dataset is given by Jones et al. (2023). Key information is provided below. Background A dataset describing the global warming response to national emissions CO2, CH4 and N2O from fossil and land use sources during 1851-2021. National CO2 emissions data are collated from the Global Carbon Project (Andrew and Peters, 2022; Friedlingstein et al., 2022). National CH4 and N2O emissions data are collated from PRIMAP-hist (HISTTP) (Gütschow et al., 2022). We construct a time series of cumulative CO2-equivalent emissions for each country, gas, and emissions source (fossil or land use). Emissions of CH4 and N2O emissions are related to cumulative CO2-equivalent emissions using the Global Warming Potential (GWP*) approach, with best-estimates of the coefficients taken from the IPCC AR6 (Forster et al., 2021). Warming in response to cumulative CO2-equivalent emissions is estimated using the transient climate response to cumulative carbon emissions (TCRE) approach, with best-estimate value of TCRE taken from the IPCC AR6 (Forster et al., 2021, Canadell et al., 2021). 'Warming' is specifically the change in global mean surface temperature (GMST). The data files provide emissions, cumulative emissions and the GMST response by country, gas (CO2, CH4, N2O or 3-GHG total) and source (fossil emissions, land use emissions or the total). Data records: overview The data records include three comma separated values (.csv) files as described below. All files are in 'long' format with one value provided in the Data column for each combination of the categorical variables Year, Country Name, Country ISO3 code, Gas, and Component columns. Component specifies fossil emissions, LULUCF emissions or total emissions of the gas. Gas specifies CO2, CH4, N2O or the three-gas total (labelled 3-GHG). Country ISO3 codes are specifically the unique ISO 3166-1 alpha-3 codes of each country. Data records: specifics EMISSIONS_ANNUAL_1830-2021.csv: Data includes annual emissions of CO2 (Pg CO2 year-1), CH4 (Tg CH4 year-1) and N2O (Tg N2O year-1) during 1830-2021. The Data column provides values for every combination of the categorical variables. EMISSIONS_CUMULATIVE_CO2e100_1851-2021.csv: Data includes the cumulative CO2 equivalent emissions in units Pg CO2-e100 during 1851-2021. The Data column provides values for every combination of the categorical variables. GMST_response_1851-2021.csv: Data includes the change in global mean surface temperature (GMST) due to emissions of the three gases during 1851-2021 in units °C. The Data column provides values for every combination of the categorical variables. Accompanying Code Code is available at: https://github.com/jonesmattw/National_Warming_Contributions . The code requires Input.zip to run (see README at the GitHub link). Further info: Country Groupings We also provide estimates of the contributions of various country groupings as defined by the UNFCCC: Annex I countries (number of countries, n = 42) Annex II countries (n = 23) economies in transition (EITs; n = 15) the least developed countries (LDCs; n = 47) the like-minded developing countries (LMDC; n = 24). And other country groupings: the organisation for economic co-operation and development (OECD; n = 38) the European Union (EU27 post-Brexit) the Brazil, South Africa, India and China (BASIC) group. See COUNTRY_GROUPINGS.xlsx for the lists of countries in each group. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| Impact | Usage cases at government department level in the US, UK, Germany |
| URL | https://zenodo.org/record/7636699 |
| Title | National contributions to climate change due to historical emissions of carbon dioxide, methane and nitrous oxide |
| Description | A complete description of the dataset is given by Jones et al. (2023). Key information is provided below. Background A dataset describing the global warming response to national emissions CO2, CH4 and N2O from fossil and land use sources during 1851-2021. National CO2 emissions data are collated from the Global Carbon Project (Andrew and Peters, 2023; Friedlingstein et al., 2023). National CH4 and N2O emissions data are collated from PRIMAP-hist (HISTTP) (Gütschow et al., 2023). We construct a time series of cumulative CO2-equivalent emissions for each country, gas, and emissions source (fossil or land use). Emissions of CH4 and N2O emissions are related to cumulative CO2-equivalent emissions using the Global Warming Potential (GWP*) approach, with best-estimates of the coefficients taken from the IPCC AR6 (Forster et al., 2021). Warming in response to cumulative CO2-equivalent emissions is estimated using the transient climate response to cumulative carbon emissions (TCRE) approach, with best-estimate value of TCRE taken from the IPCC AR6 (Forster et al., 2021, Canadell et al., 2021). 'Warming' is specifically the change in global mean surface temperature (GMST). The data files provide emissions, cumulative emissions and the GMST response by country, gas (CO2, CH4, N2O or 3-GHG total) and source (fossil emissions, land use emissions or the total). Data records: overview The data records include three comma separated values (.csv) files as described below. All files are in 'long' format with one value provided in the Data column for each combination of the categorical variables Year, Country Name, Country ISO3 code, Gas, and Component columns. Component specifies fossil emissions, LULUCF emissions or total emissions of the gas. Gas specifies CO2, CH4, N2O or the three-gas total (labelled 3-GHG). Country ISO3 codes are specifically the unique ISO 3166-1 alpha-3 codes of each country. Data records: specifics Data are provided relative to 2 reference years (denoted ref_year below): 1850 and 1991. 1850 is a mutual first year of data spanning all input datasets. 1991 is relevant because the United Nations Framework Convention on Climate Change was operationalised in 1992. EMISSIONS_ANNUAL_{ref_year-20}-2022.csv: Data includes annual emissions of CO2 (Pg CO2 year-1), CH4 (Tg CH4 year-1) and N2O (Tg N2O year-1) during the period ref_year-20 to 2022. The Data column provides values for every combination of the categorical variables. Data are provided from ref_year-20 because these data are required to calculate GWP* for CH4. EMISSIONS_CUMULATIVE_CO2e100_{ref_year+1}-2022.csv: Data includes the cumulative CO2 equivalent emissions in units Pg CO2-e100 during the period ref_year+1 to 2022 (i.e. since the reference year). The Data column provides values for every combination of the categorical variables. GMST_response_{ref_year+1}-2022.csv: Data includes the change in global mean surface temperature (GMST) due to emissions of the three gases in units °C during the period ref_year+1 to 2022 (i.e. since the reference year). The Data column provides values for every combination of the categorical variables. Accompanying Code Code is available at: https://github.com/jonesmattw/National_Warming_Contributions . The code requires Input.zip to run (see README at the GitHub link). Further info: Country Groupings We also provide estimates of the contributions of various country groupings as defined by the UNFCCC: Annex I countries (number of countries, n = 42) Annex II countries (n = 23) economies in transition (EITs; n = 15) the least developed countries (LDCs; n = 47) the like-minded developing countries (LMDC; n = 24). And other country groupings: the organisation for economic co-operation and development (OECD; n = 38) the European Union (EU27 post-Brexit) the Brazil, South Africa, India and China (BASIC) group. See COUNTRY_GROUPINGS.xlsx for the lists of countries in each group. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| Impact | Usage cases at government department level in the US, UK, Germany |
| URL | https://zenodo.org/doi/10.5281/zenodo.10839859 |
| Title | National contributions to climate change due to historical emissions of carbon dioxide, methane and nitrous oxide |
| Description | Background A dataset describing the global warming response to national emissions CO2, CH4 and N2O from fossil and land use sources during 1871-2021. National CO2 emissions data are collated from the Global Carbon Project (Andrew and Peters, 2022; Friedlingstein et al., 2022). National CH4 and N2O emissions data are collated from PRIMAP-hist (HISTTP) (Gütschow et al., 2022). We construct a time series of cumulative CO2-equivalent emissions for each country, gas, and emissions source (fossil or land use). Emissions of CH4 and N2O emissions are related to cumulative CO2-equivalent emissions using the Global Warming Potential (GWP*) approach, with best-estimates of the coefficients taken from the IPCC AR6 (Forster et al., 2021). Warming in response to cumulative CO2-equivalent emissions is estimated using the transient climate response to cumulative carbon emissions (TCRE) approach, with best-estimate value of TCRE taken from the IPCC AR6 (Forster et al., 2021, Canadell et al., 2021). 'Warming' is specifically the change in global mean surface temperature (GMST). The data files provide emissions, cumulative emissions and the GMST response by country, gas (CO2, CH4, N2O or 3-GHG total) and source (fossil emissions of land use emissions). A complete description of the dataset is under consideration for publication. A link will be provided here upon publication. Data records: overview The data records include three comma separated values (.csv) files as described below. All files are in 'long' format with one value provided in the Data column for each combination of the categorical variables Year, Country Name, Country ISO3 code, Gas, and Component columns. Component specifies fossil emissions, LULUCF emissions or total emissions of the gas. Gas specifies CO2, CH4, N2O or the three-gas total (labelled 3-GHG). Country ISO3 codes are specifically the unique ISO 3166-1 alpha-3 codes of each country. Data records: specifics EMISSIONS_ANNUAL_1850-2021.csv: Data includes annual emissions of CO2 (Pg CO2 year-1), CH4 (Tg CH4 year-1) and N2O (Tg N2O year-1) during 1850-2021. The Data column provides values for every combination of the categorical variables. EMISSIONS_CUMULATIVE_CO2e100_1871-2021.csv: Data includes the cumulative CO2 equivalent emissions in units Pg CO2-e100 during 1871-2021. The Data column provides values for every combination of the categorical variables. GMST_response_1871-2021.csv: Data includes the change in global mean surface temperature (GMST) due to emissions of the three gases during 1871-2021 in units °C. The Data column provides values for every combination of the categorical variables. Accompanying Code Code is available at: https://github.com/jonesmattw/National_Warming_Contributions . The code requires Input.zip to run (see README at the GitHub link). Further info: Country Groupings We also provide estimates of the contributions of various country groupings as defined by the UNFCCC: Annex I countries (number of countries, n = 42) Annex II countries (n = 23) economies in transition (EITs; n = 15) the least developed countries (LDCs; n = 47) the like-minded developing countries (LMDC; n = 24) as defined by the UNFCCC. And other country groupings: the organisation for economic co-operation and development (OECD; n = 38) the European Union (EU27 post-Brexit) the Brazil, South Africa, India and China (BASIC) group. See COUNTRY_GROUPINGS.xlsx for the lists of countries in each group. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| Impact | Used by US State Department and the Government of Vanuatu to prepare for UNFCCC COP 2023, which focussed on loss and damage. |
| URL | https://zenodo.org/record/7076347 |
| Title | State of Wildfires 2023-24 - ConFire data |
| Description | This contains driving and output data used by ConFire in the State of Wildfire's 2023/24 report. All NetCDF files are on regular, 0.5-degree grids on a monthly timestep over the three regions used and defined in the report. Driving Data The "Driving_data" directory contains data used to run the ConFire model and produce analyses. This directory is divided into three focal regions, with NW_Amazon corresponding to the report's "Western Amazonia". Each region contains the following files: raw_burnt_area.nc: The original 0.25-degree burnt area dataset before being regridded for use in ConFire. nrt: Near Real Time (NRT) driving data used for driver identification. isimip3a: ISIMIP3a data used for attribution. isimip3b: ISIMIP3b GCM bias-corrected data used for future projections. NRT Within the nrt directory, data is organized by periods, with the numbers corresponding to the year range. The report utilizes the period_2013_2023 directory, which contains the NetCDF files in the table below. Filename ending with the following show: 12Annual - 12 month running mean 12monthMax - 12 month running maximum Deficity - current month over 12 month running mean Quarter - 3 month running mean Not all were used in the final analysis. For full data info, see Table 3 of the report https://doi.org/10.5194/essd-2024-218: NetCDF File Variable Used/Not Used Source Notes burnt_area.nc Burnt Area As training data cropland.nc Cropland Used HYDE Klein Goldewijk et al., 2011 d2m.nc 2m Dewpoint Temperature Used ERA5-Land Muñoz-Sabater et al. 2021 DeadFuelFoilage-cvh_C.nc Dead Foliage Fuel Load Not Used Fuel Model McNorton et al. 2024a DeadFuelFoilage-cvl_C.nc Dead Foliage Fuel Load Not Used Fuel Model McNorton et al. 2024a DeadFuelFoilage.nc Dead Foliage Fuel Load Not Used Fuel Model McNorton et al. 2024a DeadFuelWood-cvh_C.nc Dead Wood Fuel Load Not Used Fuel Model McNorton et al. 2024a DeadFuelWood-cvl_C.nc Dead Wood Fuel Load Not Used Fuel Model McNorton et al. 2024a DeadFuelWood.nc Dead Wood Fuel Load Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Foilage-12Annual.nc Dead Foliage Fuel Moisture Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Foilage-12monthMax.nc Dead Foliage Fuel Moisture Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Foilage-Deficity.nc Dead Foliage Fuel Moisture Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Foilage.nc Dead Foliage Fuel Moisture Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Foilage-Quater.nc Dead Foliage Fuel Moisture Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Wood-12Annual.nc Dead Wood Fuel Moisture Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Wood-12monthMax.nc Dead Wood Fuel Moisture Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Wood-Deficity.nc Dead Wood Fuel Moisture Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Wood.nc Dead Wood Fuel Moisture Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Live-12Annual.nc Live Fuel Moisture Content Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Live-12monthMax.nc Live Fuel Moisture Content Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Live-Deficity.nc Live Fuel Moisture Content Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Live.nc Live Fuel Moisture Content Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Live-Quater.nc Live Fuel Moisture Content Not Used Fuel Model McNorton et al. 2024a grazing_land.nc Grazing Land Not Used lightn.nc Lightning Used LIS/OTD Cecil et al., 2014 LiveFuelFoilage-cvh_C.nc Live Leaf Fuel Load Not Used Fuel Model McNorton et al. 2024a LiveFuelFoilage-cvl_C.nc Live Leaf Fuel Load Not Used Fuel Model McNorton et al. 2024a LiveFuelFoilage.nc Live Leaf Fuel Load Not Used Fuel Model McNorton et al. 2024a LiveFuelWood-cvh_C.nc Live Wood Fuel Load Not Used Fuel Model McNorton et al. 2024a LiveFuelWood-cvl_C.nc Live Wood Fuel Load Not Used Fuel Model McNorton et al. 2024a LiveFuelWood.nc Live Wood Fuel Load Not Used Fuel Model McNorton et al. 2024a pasture.nc Pasture Used HYDE Klein Goldewijk et al., 2011 population_density.nc Population Density Used rangeland.nc Rangeland Not Used rural_population.nc Rural Population Used HYDE Klein Goldewijk et al., 2011 snowCover.nc Snow Cover Used ERA5-Land Muñoz-Sabater et al. 2021 t2m.nc 2m Temperature Used ERA5-Land Muñoz-Sabater et al. 2021 total_irrigated.nc Irrigated Area Not Used tp-12Annual.nc Precipitation Not Used ERA5-Land Muñoz-Sabater et al. 2021 tp-12monthMax.nc Precipitation Not Used ERA5-Land Muñoz-Sabater et al. 2021 tp-Deficity.nc Precipitation Not Used ERA5-Land Muñoz-Sabater et al. 2021 tp.nc Precipitation Used ERA5-Land Muñoz-Sabater et al. 2021 tp-Quater.nc Precipitation Not Used ERA5-Land Muñoz-Sabater et al. 2021 urban_population.nc Urban Population Used HYDE Klein Goldewijk et al., 2011 VOD-12Annual.nc Mean & Max VOD Used Satellite (SMOS) Wigneron et al 2021 VOD-12monthMax.nc Mean & Max VOD Used Satellite (SMOS) Wigneron et al 2021 VOD-Deficity.nc Vegetation Optical Depth (VOD) Not Used Satellite (SMOS) Wigneron et al 2021 VOD.nc Vegetation Optical Depth (VOD) Used Satellite (SMOS) Wigneron et al 2021 VOD-Quater.nc Vegetation Optical Depth (VOD) Not Used Satellite (SMOS) Wigneron et al 2021 ISIMIP3a The isimip3a directory follows the structure: <>/<>/period_yyyy_yyyy/. <>: Can be either: obsclim: Reanalysis targeting observed climate. counterclim: Detrended obsclim approximating climate without climate change. <>: Currently contains only GSWP3-W5E5, with more sources to follow in subsequent years. yyyy_yyyy: Corresponds to the year range. For attribution experiments in the report, the following directories are used: Factual: obsclim/GSWP3-W5E5/period_2002_2019/ Counterfactual: counterclim/GSWP3-W5E5/period_2002_2019/ Early Industrial: counterclim/GSWP3-W5E5/period_1901_1920/ Additional details on setting the temporal range for the report can be found here. ISIMIP3b The isimip3b directory structure is similar to ISIMIP3a: <>/<>/period_yyyy_yyyy/. <> includes: historical: Historical GCM output. ssp126 ssp370 ssp585 <>: Refers to the General Circulation Model used. yyyy_yyyy: Corresponds to the year range. Both ISIMIP3a and ISIMIP3b contain the same NetCDF files, as follows: netcdf file variable used/not used source Notes consec_dry_mean.nc Max. consecutive dry days used ISIMIP3a/3b Based on precipitation crop_jules-es.nc Cropland used ISIMIP3a/3b Interpolated from annual to monthly debiased_nonetree_cover_jules-es.nc Total vegetation cover not used JULES-ES-ISIMIP VCF using ibicus Non-tree vegetated cover simulated by JULES and bias-corrected debiased_tree_cover_jules-es.nc Tree Cover not used JULES-ES-ISIMIP VCF using ibicus Annual mean tree cover bias-corrected to VCF dry_days.nc No. dry days used ISIMIP3a/3b Fractional number of days with rainfall < 0.1mm/m filled_debiased_nonetree_cover_jules-es.nc Total vegetation cover used JULES-ES-ISIMIP VCF using ibicus Filled and bias-corrected non-tree vegetated cover filled_debiased_tree_cover_jules-es.nc Tree Cover used JULES-ES-ISIMIP VCF using ibicus Filled and bias-corrected tree cover filled_debiased_vegCover_jules-es.nc Total vegetation cover used JULES-ES-ISIMIP VCF using ibicus Filled and bias-corrected vegetation cover lightning.nc Lightning used ISIMIP3a Climatology nonetree_cover_jules-es.nc Total vegetation cover not used JULES-ES-ISIMIP Non-tree vegetated cover simulated by JULES pasture_jules-es.nc Pasture used ISIMIP3a/3b Interpolated from annual to monthly pr_mean.nc Precipitation used ISIMIP3a/3b Monthly mean precipitation tas_max.nc Maximum monthly temperature used ISIMIP3a/3b Maximum of maximum daily temperature within the month tas_mean.nc Mean monthly temperature used ISIMIP3a/3b Daily mean temperature tree_cover_jules-es.nc Tree Cover not used JULES-ES-ISIMIP Annual mean tree cover bias-corrected to VCF urban_jules-es.nc Urban fraction used JULES-ES Urban area fraction vpd_max.nc Maximum monthly VPD used ISIMIP3a/3b Maximum of daily VPD values vpd_mean.nc Mean monthly VPD used ISIMIP3a/3b Mean of daily VPD values nontree_cover_VCF-obs.nc Total vegetation cover not used VCF Non-tree vegetated cover observed nontree_raw_VCF-obs.nc Total vegetation cover not used VCF Raw non-tree vegetated cover observed nonveg_cover_VCF-obs.nc Non-vegetated cover not used VCF Observed non-vegetated cover nonveg_raw_VCF-obs.nc Non-vegetated cover not used VCF Raw observed non-vegetated cover tree_cover_VCF-obs.nc Tree Cover not used VCF Observed tree cover tree_raw_VCF-obs.nc Tree Cover not used VCF Raw observed tree cover ISIMIP3a/3b is detailed in Frieler et al. (2024) and raw data can be obtained from https://data.ISIMIP.org While not used as driving data, VCF is used to proceed bias corrected driving data. VCF is taken from MODIS Vegetation Continuous Fields collection 6.1 remote sensed data for <60?N DiMiceli et al. (2022) and collection 6 for <60?N DiMiceli et al. (2015). JULES-ES (Mathison et al. 2023) was driven using the corresponding ISIMIP datasets. Outputs Outputs contain the ConFire outputs when driven with the provided datasets. The directories are named according to the regions, and for each region, there are four sets of outputs: isimip-evaluation1 isimip-final.tar nrt-evaluation1 nrt-final Each of these directories contains the following files necessary for rerunning the model without redoing the optimization. While you are unlikely to need to look at these files, they are useful for setting up your own model experiments (see the ConFire configuration settings): scalers-_*.csv trace-_*.nc variables_info-_*.txt Additionally, there are two other directories: figs: Contains automatically generated figures and some of their outputs. sample: Contains model outputs. Within the sample directory, there is a subdirectory indicating the model run name, which contains a series of experiments. These experiments differ for each run (see below), and each experiment contains some or all of the following directories: Evaluate: Contains the burnt area from the full model including stochastic parameters. Often used for evaluation (see report supplement for more information). Control: Contains burnt area driven purely by driving datasets with stochasticity. Used as the control in most of the analysis. Standard_X: A series of directories with burnt areas from individual controls. This describes the burnt area in the presence of that control in otherwise ideal burning conditions. The numbers are: 0: Fuel load for all runs 1: Fuel moisture for all runs 2: Fire weather for NRT and ignitions for ISIMIP3a 3: Ignitions for ISIMIP3a and suppression for ISIMIP3a 4: Suppression for NRT 5: Snow for NRT Within each of these directories is a series of ensemble members sample-predX.nc. Within the same optimization (i.e., the same model run, so across all experiments), samples are paired, meaning the sample-predX.nc corresponds to the sample in another experiment. Experiments isimip-evaluation1 & nrt-evaluation1 The only experiment for evaluation is called baseline, which has an 'Evaluate' and 'Control' run and is used to evaluate the model. Automatically generated evaluation figures can be found in the figs/ directory. nrt-final This also contains only one run, baseline, but includes runs for each of the controls. isimip-final This has more runs: factual: Uses the ISIMIP3a obsclim driving dataset (see driving dataset above). counterfactual: Uses the ISIMIP3a counterclim dataset. early_industrial: Uses the early period ISIMIP3a counterclim dataset. historical/<>/, ssp126/<>/, ssp370/<>/, ssp585/<>/: Uses the ISIMIP3b datasets outlined above, where <> is one of each of the five GCMs used in ISIMIP3b. Additional Analysis The analysis in the report also utilizes 95th and 90th percentile burnt area totals. These aren't as neatly organized as the NetCDF files yet, but we're getting there. They can be found in: figs/ _13-frac_points_0.5-<>-control_TS/<>-control_TS/pc-%%/ points-<>.csv |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| Impact | Usage by other researchers to look at trends/variability etc in wildfire |
| URL | https://zenodo.org/doi/10.5281/zenodo.11420742 |
| Title | State of Wildfires 2023-24 - ConFire data |
| Description | This contains driving and output data used by ConFire in the State of Wildfire's 2023/24 report. All NetCDF files are on regular, 0.5-degree grids on a monthly timestep over the three regions used and defined in the report. Driving Data The "Driving_data" directory contains data used to run the ConFire model and produce analyses. This directory is divided into three focal regions, with NW_Amazon corresponding to the report's "Western Amazonia". Each region contains the following files: raw_burnt_area.nc: The original 0.25-degree burnt area dataset before being regridded for use in ConFire. nrt: Near Real Time (NRT) driving data used for driver identification. isimip3a: ISIMIP3a data used for attribution. isimip3b: ISIMIP3b GCM bias-corrected data used for future projections. NRT Within the nrt directory, data is organized by periods, with the numbers corresponding to the year range. The report utilizes the period_2013_2023 directory, which contains the NetCDF files in the table below. Filename ending with the following show: 12Annual - 12 month running mean 12monthMax - 12 month running maximum Deficity - current month over 12 month running mean Quarter - 3 month running mean Not all were used in the final analysis. For full data info, see Table 3 of the report https://doi.org/10.5194/essd-2024-218: NetCDF File Variable Used/Not Used Source Notes burnt_area.nc Burnt Area As training data cropland.nc Cropland Used HYDE Klein Goldewijk et al., 2011 d2m.nc 2m Dewpoint Temperature Used ERA5-Land Muñoz-Sabater et al. 2021 DeadFuelFoilage-cvh_C.nc Dead Foliage Fuel Load Not Used Fuel Model McNorton et al. 2024a DeadFuelFoilage-cvl_C.nc Dead Foliage Fuel Load Not Used Fuel Model McNorton et al. 2024a DeadFuelFoilage.nc Dead Foliage Fuel Load Not Used Fuel Model McNorton et al. 2024a DeadFuelWood-cvh_C.nc Dead Wood Fuel Load Not Used Fuel Model McNorton et al. 2024a DeadFuelWood-cvl_C.nc Dead Wood Fuel Load Not Used Fuel Model McNorton et al. 2024a DeadFuelWood.nc Dead Wood Fuel Load Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Foilage-12Annual.nc Dead Foliage Fuel Moisture Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Foilage-12monthMax.nc Dead Foliage Fuel Moisture Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Foilage-Deficity.nc Dead Foliage Fuel Moisture Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Foilage.nc Dead Foliage Fuel Moisture Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Foilage-Quater.nc Dead Foliage Fuel Moisture Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Wood-12Annual.nc Dead Wood Fuel Moisture Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Wood-12monthMax.nc Dead Wood Fuel Moisture Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Wood-Deficity.nc Dead Wood Fuel Moisture Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Wood.nc Dead Wood Fuel Moisture Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Live-12Annual.nc Live Fuel Moisture Content Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Live-12monthMax.nc Live Fuel Moisture Content Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Live-Deficity.nc Live Fuel Moisture Content Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Live.nc Live Fuel Moisture Content Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Live-Quater.nc Live Fuel Moisture Content Not Used Fuel Model McNorton et al. 2024a grazing_land.nc Grazing Land Not Used lightn.nc Lightning Used LIS/OTD Cecil et al., 2014 LiveFuelFoilage-cvh_C.nc Live Leaf Fuel Load Not Used Fuel Model McNorton et al. 2024a LiveFuelFoilage-cvl_C.nc Live Leaf Fuel Load Not Used Fuel Model McNorton et al. 2024a LiveFuelFoilage.nc Live Leaf Fuel Load Not Used Fuel Model McNorton et al. 2024a LiveFuelWood-cvh_C.nc Live Wood Fuel Load Not Used Fuel Model McNorton et al. 2024a LiveFuelWood-cvl_C.nc Live Wood Fuel Load Not Used Fuel Model McNorton et al. 2024a LiveFuelWood.nc Live Wood Fuel Load Not Used Fuel Model McNorton et al. 2024a pasture.nc Pasture Used HYDE Klein Goldewijk et al., 2011 population_density.nc Population Density Used rangeland.nc Rangeland Not Used rural_population.nc Rural Population Used HYDE Klein Goldewijk et al., 2011 snowCover.nc Snow Cover Used ERA5-Land Muñoz-Sabater et al. 2021 t2m.nc 2m Temperature Used ERA5-Land Muñoz-Sabater et al. 2021 total_irrigated.nc Irrigated Area Not Used tp-12Annual.nc Precipitation Not Used ERA5-Land Muñoz-Sabater et al. 2021 tp-12monthMax.nc Precipitation Not Used ERA5-Land Muñoz-Sabater et al. 2021 tp-Deficity.nc Precipitation Not Used ERA5-Land Muñoz-Sabater et al. 2021 tp.nc Precipitation Used ERA5-Land Muñoz-Sabater et al. 2021 tp-Quater.nc Precipitation Not Used ERA5-Land Muñoz-Sabater et al. 2021 urban_population.nc Urban Population Used HYDE Klein Goldewijk et al., 2011 VOD-12Annual.nc Mean & Max VOD Used Satellite (SMOS) Wigneron et al 2021 VOD-12monthMax.nc Mean & Max VOD Used Satellite (SMOS) Wigneron et al 2021 VOD-Deficity.nc Vegetation Optical Depth (VOD) Not Used Satellite (SMOS) Wigneron et al 2021 VOD.nc Vegetation Optical Depth (VOD) Used Satellite (SMOS) Wigneron et al 2021 VOD-Quater.nc Vegetation Optical Depth (VOD) Not Used Satellite (SMOS) Wigneron et al 2021 ISIMIP3a The isimip3a directory follows the structure: <>/<>/period_yyyy_yyyy/. <>: Can be either: obsclim: Reanalysis targeting observed climate. counterclim: Detrended obsclim approximating climate without climate change. <>: Currently contains only GSWP3-W5E5, with more sources to follow in subsequent years. yyyy_yyyy: Corresponds to the year range. For attribution experiments in the report, the following directories are used: Factual: obsclim/GSWP3-W5E5/period_2002_2019/ Counterfactual: counterclim/GSWP3-W5E5/period_2002_2019/ Early Industrial: counterclim/GSWP3-W5E5/period_1901_1920/ Additional details on setting the temporal range for the report can be found here. ISIMIP3b The isimip3b directory structure is similar to ISIMIP3a: <>/<>/period_yyyy_yyyy/. <> includes: historical: Historical GCM output. ssp126 ssp370 ssp585 <>: Refers to the General Circulation Model used. yyyy_yyyy: Corresponds to the year range. Both ISIMIP3a and ISIMIP3b contain the same NetCDF files, as follows: netcdf file variable used/not used source Notes consec_dry_mean.nc Max. consecutive dry days used ISIMIP3a/3b Based on precipitation crop_jules-es.nc Cropland used ISIMIP3a/3b Interpolated from annual to monthly debiased_nonetree_cover_jules-es.nc Total vegetation cover not used JULES-ES-ISIMIP VCF using ibicus Non-tree vegetated cover simulated by JULES and bias-corrected debiased_tree_cover_jules-es.nc Tree Cover not used JULES-ES-ISIMIP VCF using ibicus Annual mean tree cover bias-corrected to VCF dry_days.nc No. dry days used ISIMIP3a/3b Fractional number of days with rainfall < 0.1mm/m filled_debiased_nonetree_cover_jules-es.nc Total vegetation cover used JULES-ES-ISIMIP VCF using ibicus Filled and bias-corrected non-tree vegetated cover filled_debiased_tree_cover_jules-es.nc Tree Cover used JULES-ES-ISIMIP VCF using ibicus Filled and bias-corrected tree cover filled_debiased_vegCover_jules-es.nc Total vegetation cover used JULES-ES-ISIMIP VCF using ibicus Filled and bias-corrected vegetation cover lightning.nc Lightning used ISIMIP3a Climatology nonetree_cover_jules-es.nc Total vegetation cover not used JULES-ES-ISIMIP Non-tree vegetated cover simulated by JULES pasture_jules-es.nc Pasture used ISIMIP3a/3b Interpolated from annual to monthly pr_mean.nc Precipitation used ISIMIP3a/3b Monthly mean precipitation tas_max.nc Maximum monthly temperature used ISIMIP3a/3b Maximum of maximum daily temperature within the month tas_mean.nc Mean monthly temperature used ISIMIP3a/3b Daily mean temperature tree_cover_jules-es.nc Tree Cover not used JULES-ES-ISIMIP Annual mean tree cover bias-corrected to VCF urban_jules-es.nc Urban fraction used JULES-ES Urban area fraction vpd_max.nc Maximum monthly VPD used ISIMIP3a/3b Maximum of daily VPD values vpd_mean.nc Mean monthly VPD used ISIMIP3a/3b Mean of daily VPD values nontree_cover_VCF-obs.nc Total vegetation cover not used VCF Non-tree vegetated cover observed nontree_raw_VCF-obs.nc Total vegetation cover not used VCF Raw non-tree vegetated cover observed nonveg_cover_VCF-obs.nc Non-vegetated cover not used VCF Observed non-vegetated cover nonveg_raw_VCF-obs.nc Non-vegetated cover not used VCF Raw observed non-vegetated cover tree_cover_VCF-obs.nc Tree Cover not used VCF Observed tree cover tree_raw_VCF-obs.nc Tree Cover not used VCF Raw observed tree cover ISIMIP3a/3b is detailed in Frieler et al. (2024) and raw data can be obtained from https://data.ISIMIP.org While not used as driving data, VCF is used to proceed bias corrected driving data. VCF is taken from MODIS Vegetation Continuous Fields collection 6.1 remote sensed data for <60?N DiMiceli et al. (2022) and collection 6 for <60?N DiMiceli et al. (2015). JULES-ES (Mathison et al. 2023) was driven using the corresponding ISIMIP datasets. Outputs Outputs contain the ConFire outputs when driven with the provided datasets. The directories are named according to the regions, and for each region, there are four sets of outputs: isimip-evaluation1 isimip-final.tar nrt-evaluation1 nrt-final Each of these directories contains the following files necessary for rerunning the model without redoing the optimization. While you are unlikely to need to look at these files, they are useful for setting up your own model experiments (see the ConFire configuration settings): scalers-_*.csv trace-_*.nc variables_info-_*.txt Additionally, there are two other directories: figs: Contains automatically generated figures and some of their outputs. sample: Contains model outputs. Within the sample directory, there is a subdirectory indicating the model run name, which contains a series of experiments. These experiments differ for each run (see below), and each experiment contains some or all of the following directories: Evaluate: Contains the burnt area from the full model including stochastic parameters. Often used for evaluation (see report supplement for more information). Control: Contains burnt area driven purely by driving datasets with stochasticity. Used as the control in most of the analysis. Standard_X: A series of directories with burnt areas from individual controls. This describes the burnt area in the presence of that control in otherwise ideal burning conditions. The numbers are: 0: Fuel load for all runs 1: Fuel moisture for all runs 2: Fire weather for NRT and ignitions for ISIMIP3a 3: Ignitions for ISIMIP3a and suppression for ISIMIP3a 4: Suppression for NRT 5: Snow for NRT Within each of these directories is a series of ensemble members sample-predX.nc. Within the same optimization (i.e., the same model run, so across all experiments), samples are paired, meaning the sample-predX.nc corresponds to the sample in another experiment. Experiments isimip-evaluation1 & nrt-evaluation1 The only experiment for evaluation is called baseline, which has an 'Evaluate' and 'Control' run and is used to evaluate the model. Automatically generated evaluation figures can be found in the figs/ directory. nrt-final This also contains only one run, baseline, but includes runs for each of the controls. isimip-final This has more runs: factual: Uses the ISIMIP3a obsclim driving dataset (see driving dataset above). counterfactual: Uses the ISIMIP3a counterclim dataset. early_industrial: Uses the early period ISIMIP3a counterclim dataset. historical/<>/, ssp126/<>/, ssp370/<>/, ssp585/<>/: Uses the ISIMIP3b datasets outlined above, where <> is one of each of the five GCMs used in ISIMIP3b. Additional Analysis The analysis in the report also utilizes 95th and 90th percentile burnt area totals. These aren't as neatly organized as the NetCDF files yet, but we're getting there. They can be found in: figs/ _13-frac_points_0.5-<>-control_TS/<>-control_TS/pc-%%/ points-<>.csv |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| Impact | Usage by other researchers to look at trends/variability etc in wildfire |
| URL | https://zenodo.org/doi/10.5281/zenodo.11420743 |
| Title | State of Wildfires 2023-24: Regional Summaries of Burned Area, Fire Emissions, and Individual Fire Characteristics for National, Administrative and Biogeographical Regions |
| Description | 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). Layer Short Form Source Biomes NA Olson et al. (2001) Continents NA ArcGIS Hub (2024) Continental Biomes NA See above Countries NA EU Eurostat (2020) UC Davis Global Administrative Areas (GADM) Level 1 GADM-L1 UC Davis (2022) Intergovernmental Panel on Climate Change Sixth Assessment Report (AR6) Working Group I (WGI) Reference Regions IPCC AR6 WGI Regions IPCC (2021); SantanderMetGroup (2021) Global C Project Regional C Cycle Assessment and Processes (RECCAP2) Reference Regions RECCAP2 Regions Ciais et al. (2022) Global Fire Emissions Database (GFED) Basis Regions GFED4.1s Regions van der Werf et al. (2006) 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. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| Impact | Usage by other researchers to look at trends/variability etc in wildfire |
| URL | https://zenodo.org/doi/10.5281/zenodo.11400540 |
| Title | State of Wildfires 2023-24: Regional Summaries of Burned Area, Fire Emissions, and Individual Fire Characteristics for National, Administrative and Biogeographical Regions |
| Description | 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). Layer Short Form Source Biomes NA Olson et al. (2001) Continents NA ArcGIS Hub (2024) Continental Biomes NA See above Countries NA EU Eurostat (2020) UC Davis Global Administrative Areas (GADM) Level 1 GADM-L1 UC Davis (2022) Intergovernmental Panel on Climate Change Sixth Assessment Report (AR6) Working Group I (WGI) Reference Regions IPCC AR6 WGI Regions IPCC (2021); SantanderMetGroup (2021) Global C Project Regional C Cycle Assessment and Processes (RECCAP2) Reference Regions RECCAP2 Regions Ciais et al. (2022) Global Fire Emissions Database (GFED) Basis Regions GFED4.1s Regions van der Werf et al. (2006) 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. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| Impact | Usage by other researchers to look at trends/variability etc in wildfire |
| URL | https://zenodo.org/doi/10.5281/zenodo.11400539 |
| Title | State of Wildfires 2023-24: Regional Summaries of Burned Area, Fire Emissions, and Individual Fire Characteristics for National, Administrative and Biogeographical Regions |
| Description | This dataset supports the State of Wildfires 2023-24 report under review at Earth System Science Data (Jones et al., 2024; https://doi.org/10.5194/essd-16-3601-2024). 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: 2002-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). Layer Short Form Source Biomes NA Olson et al. (2001) Continents NA ArcGIS Hub (2024) Continental Biomes NA See above Countries NA EU Eurostat (2020) UC Davis Global Administrative Areas (GADM) Level 1 GADM-L1 UC Davis (2022) Intergovernmental Panel on Climate Change Sixth Assessment Report (AR6) Working Group I (WGI) Reference Regions IPCC AR6 WGI Regions IPCC (2021); SantanderMetGroup (2021) Global C Project Regional C Cycle Assessment and Processes (RECCAP2) Reference Regions RECCAP2 Regions Ciais et al. (2022) Global Fire Emissions Database (GFED) Basis Regions GFED4.1s Regions van der Werf et al. (2006) Regional Statistics and Anomalies Burned Area (BA) Calculated regional totals for each fire season. Relative and standardized anomalies from historical data (since 2002). 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. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| Impact | Usage by other researchers to look at trends/variability etc in wildfire |
| URL | https://zenodo.org/doi/10.5281/zenodo.13269327 |
| Title | Update of: The Global Fire Atlas of individual fire size, duration, speed and direction |
| Description | This is an updated and extended record of the Global Fire Atlas introduced by Andela et al. (2019). Input data (burned area and land cover products) are updated to the MODIS Collection 6.1 (the previous version was based on collection 6.0). The timeseries is extended to cover the period January 2002 to February 2024. Methodological Notes: The method employed to create the dataset precisely follows the approach described by Andela et al. (2019). The input burned area product is MCD64A1 Collection 6.1. It is described by Giglio et al. (2018) and available at: https://lpdaac.usgs.gov/products/mcd64a1v061/. The input land cover product is MCD12Q1 Collection 6.1. It is described by Sulla-Menashe et al. (2019) and available at: https://lpdaac.usgs.gov/products/mcd12q1v061/. Usage Notes: Table 1: Overview of the Global Fire Atlas data layers. The shapefiles of ignition locations (point) and fire perimeters (polygon) contain attribute tables with summary information for each individual fire, while the underlying 500 m gridded layers reflect the day-to-day behavior of the individual fires. In addition, we provide aggregated monthly summary layers at a 0.25° resolution for regional and global analyses. File name Content SHP_ignitions.zip Shapefiles of ignition locations with attribute tables (see Table 2) SHP_perimeters.zip Shapefiles of final fire perimeters with attribute tables (see Table 2) GeoTIFF_direction.zip 500 m resolution daily gridded data on direction of spread (8 classes) GeoTIFF_day_of_burn.zip 500 m resolution daily gridded data on day of burn (day of year; 1-366) GeoTIFF_speed.zip 500 m resolution daily gridded data on speed (km/day) GeoTIFF_fire_line.zip 500 m resolution daily gridded data on the fire line (day of year; 1-366) GeoTIFF_monthly_summaries.zip Aggregated 0.25° resolution monthly summary layers. These files include the sum of ignitions, average size (km2), average duration (days), average daily fire line (km), average daily fire expansion (km2/day), average speed (km/day), and dominant direction of spread (8 classes). Table 2: Overview of the Global Fire Atlas shapefile attribute tables. The shapefiles of ignition locations (point) and fire perimeters (polygon) contain attribute tables with summary information for each individual fire. Attribute Explanation / units lat, lon Coordinates of ignition location (°) size Fire size (km2) perimeter Fire perimeter (km) start_date, start_DOY Start date (yyyy-mm-dd), start day of year (1-366) end_date, end_DOY End date (yyyy-mm-dd), end day of year (1-366) duration Duration (days) fire_line Average length of daily fire line (km) spread Average daily fire growth (km2/day) speed Average speed (km/day) direction, direc_frac Dominant direction of spread (N, NE, E, SE, S, SW, W, NW) and associated fraction MODIS_tile MODIS tile id landcover, landc_frac MCD12Q1 dominant land cover class and fraction (UMD classification), provided for 2002-2023 GFED_regio GFED region (van der Werf et al., 2017; available at https://www.globalfiredata.org/) File Naming Convention: GFA_v{time-stamp}_{data-type}_{fire_season}.{file_type} {time-stamp} = Date that code was run. {data-type} = "ignitions" or "perimeters" for vector files; "day_of_burn", "direction", "fire_line", or "speed" for raster files. {fire_season} = the locally-defined fire season in which the fire was ignited (see more below). {file_type} = ".shp" for vector files; ".tif" for raster files. Fire Season Convention: Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. Hence the file GFA_v20240409_perimeters_2003.shp can include fires from the 2003 fire season that ignited in the calendar years 2002 or 2004. This is particularly relevant in the Southern extratropics and the northern hemisphere subtropics, where the fire seasons often span the new year. The local definition of the fire season is based on climatological peak in burned area as described by Andela et al. (2019). Projections: Vector data are provided on the WGS84 projection. Raster data are provided on the MODIS sinusoidal projection used in NASA tiled products. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| Impact | Usage by other researchers to look at trends/variability etc in wildfire |
| URL | https://zenodo.org/doi/10.5281/zenodo.11400061 |
| Title | Update of: The Global Fire Atlas of individual fire size, duration, speed and direction |
| Description | This is an updated and extended record of the Global Fire Atlas introduced by Andela et al. (2019). Input data (burned area and land cover products) are updated to the MODIS Collection 6.1 (the previous version was based on collection 6.0). The timeseries is extended to cover the period January 2002 to February 2024. Methodological Notes: The method employed to create the dataset precisely follows the approach described by Andela et al. (2019). The input burned area product is MCD64A1 Collection 6.1. It is described by Giglio et al. (2018) and available at: https://lpdaac.usgs.gov/products/mcd64a1v061/. The input land cover product is MCD12Q1 Collection 6.1. It is described by Sulla-Menashe et al. (2019) and available at: https://lpdaac.usgs.gov/products/mcd12q1v061/. Usage Notes: Table 1: Overview of the Global Fire Atlas data layers. The shapefiles of ignition locations (point) and fire perimeters (polygon) contain attribute tables with summary information for each individual fire, while the underlying 500 m gridded layers reflect the day-to-day behavior of the individual fires. In addition, we provide aggregated monthly summary layers at a 0.25° resolution for regional and global analyses. File name Content SHP_ignitions.zip Shapefiles of ignition locations with attribute tables (see Table 2) SHP_perimeters.zip Shapefiles of final fire perimeters with attribute tables (see Table 2) GeoTIFF_direction.zip 500 m resolution daily gridded data on direction of spread (8 classes) GeoTIFF_day_of_burn.zip 500 m resolution daily gridded data on day of burn (day of year; 1-366) GeoTIFF_speed.zip 500 m resolution daily gridded data on speed (km/day) GeoTIFF_fire_line.zip 500 m resolution daily gridded data on the fire line (day of year; 1-366) GeoTIFF_monthly_summaries.zip Aggregated 0.25° resolution monthly summary layers. These files include the sum of ignitions, average size (km2), average duration (days), average daily fire line (km), average daily fire expansion (km2/day), average speed (km/day), and dominant direction of spread (8 classes). Table 2: Overview of the Global Fire Atlas shapefile attribute tables. The shapefiles of ignition locations (point) and fire perimeters (polygon) contain attribute tables with summary information for each individual fire. Attribute Explanation / units lat, lon Coordinates of ignition location (°) size Fire size (km2) perimeter Fire perimeter (km) start_date, start_DOY Start date (yyyy-mm-dd), start day of year (1-366) end_date, end_DOY End date (yyyy-mm-dd), end day of year (1-366) duration Duration (days) fire_line Average length of daily fire line (km) spread Average daily fire growth (km2/day) speed Average speed (km/day) direction, direc_frac Dominant direction of spread (N, NE, E, SE, S, SW, W, NW) and associated fraction MODIS_tile MODIS tile id landcover, landc_frac MCD12Q1 dominant land cover class and fraction (UMD classification), provided for 2002-2023 GFED_regio GFED region (van der Werf et al., 2017; available at https://www.globalfiredata.org/) File Naming Convention: GFA_v{time-stamp}_{data-type}_{fire_season}.{file_type} {time-stamp} = Date that code was run. {data-type} = "ignitions" or "perimeters" for vector files; "day_of_burn", "direction", "fire_line", or "speed" for raster files. {fire_season} = the locally-defined fire season in which the fire was ignited (see more below). {file_type} = ".shp" for vector files; ".tif" for raster files. Fire Season Convention: Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. Hence the file GFA_v20240409_perimeters_2003.shp can include fires from the 2003 fire season that ignited in the calendar years 2002 or 2004. This is particularly relevant in the Southern extratropics and the northern hemisphere subtropics, where the fire seasons often span the new year. The local definition of the fire season is based on climatological peak in burned area as described by Andela et al. (2019). Projections: Vector data are provided on the WGS84 projection. Raster data are provided on the MODIS sinusoidal projection used in NASA tiled products. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| Impact | Usage by other researchers to look at trends/variability etc in wildfire |
| URL | https://zenodo.org/doi/10.5281/zenodo.11400062 |
| Description | Collaboration with Alysha Coppola at UTH Zurich |
| Organisation | University of Zurich |
| Country | Switzerland |
| Sector | Academic/University |
| PI Contribution | - Co-author on a global review of the black carbon cycle. - Lead author on a global-scale analysis of dissolved black carbon export. - Co-author on an analysis of dissolved black carbon export from Amazonia. - Co-author on an analysis dissolved black carbon export from North America (in preparation) |
| Collaborator Contribution | - Lead author on a global review of the black carbon cycle. - Lead author on an analysis dissolved black carbon export from Amazonia. - Co-author on a global-scale analysis of dissolved black carbon export. - Lead author on an analysis dissolved black carbon export from North America (in preparation) |
| Impact | - https://www.nature.com/articles/s41467-019-11543-9 - https://www.nature.com/articles/s41467-020-16576-z - https://www.nature.com/articles/s43017-022-00316-6 |
| Start Year | 2018 |
| Description | Global Carbon Project |
| Organisation | Global Carbon Project |
| Country | Australia |
| Sector | Learned Society |
| PI Contribution | - Member of the global carbon project - Contribute to annual publications of the global carbon budget - Contribute to media effort associated with publications of the global carbon budget - Attend UNFCCC COP events to unveil the global carbon budget - Co-author of 4 global carbon budget papers - Co-author of 2 papers connected to the global carbon budget - Lead author of two papers connected to the global carbon budget |
| Collaborator Contribution | - We annually publish the global carbon budget - We engage with the media upon publication of the global carbon budget - Global carbon budget is unveiled annually at UNFCCC COP events |
| Impact | - https://essd.copernicus.org/articles/11/1783/2019/ - https://essd.copernicus.org/articles/12/3269/2020/ - https://essd.copernicus.org/articles/14/1917/2022/ - https://essd.copernicus.org/articles/14/4811/2022/ - https://www.nature.com/articles/s41558-020-%200797-x - https://www.nature.com/articles/s41558-021-01001-0 - https://www.nature.com/articles/s41597-020-00779-6 |
| Start Year | 2019 |
| Description | Partnership with Chantelle Burton, UK Met Office |
| Organisation | Meteorological Office UK |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | We collaborate on an upcoming annual scientific report, the "State of Wildfires", as part of a network including myself (UEA), Douglas Kelley (CEH), Francesca Di Giuseppe (ECMWF) and Chantelle Burton (UK Met Office). |
| Collaborator Contribution | We collaborate on an upcoming annual scientific report, the "State of Wildfires", as part of a network including myself (UEA), Douglas Kelley (CEH), Francesca Di Giuseppe (ECMWF) and Chantelle Burton (UK Met Office). |
| Impact | We collaborate on an upcoming annual scientific report, the "State of Wildfires", as part of a network including myself (UEA), Douglas Kelley (CEH), Francesca Di Giuseppe (ECMWF) and Chantelle Burton (UK Met Office). |
| Start Year | 2023 |
| Description | Partnership with Douglas Kelley at UK Centre for Ecology and Hydrology |
| Organisation | UK Centre for Ecology & Hydrology |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | We collaborate on an upcoming annual scientific report, the "State of Wildfires", as part of a network including myself (UEA), Douglas Kelley (CEH), Francesca Di Giuseppe (ECMWF) and Chantelle Burton (UK Met Office). |
| Collaborator Contribution | We collaborate on an upcoming annual scientific report, the "State of Wildfires", as part of a network including myself (UEA), Douglas Kelley (CEH), Francesca Di Giuseppe (ECMWF) and Chantelle Burton (UK Met Office). |
| Impact | We collaborate on an upcoming annual scientific report, the "State of Wildfires", as part of a network including myself (UEA), Douglas Kelley (CEH), Francesca Di Giuseppe (ECMWF) and Chantelle Burton (UK Met Office). |
| Start Year | 2023 |
| Description | Partnership with Francesca Di Giuseppe, ECMWF |
| Organisation | European Centre for Medium Range Weather Forecasting ECMWF |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | We collaborate on an upcoming annual scientific report, the "State of Wildfires", as part of a network including myself (UEA), Douglas Kelley (CEH), Francesca Di Giuseppe (ECMWF) and Chantelle Burton (UK Met Office). |
| Collaborator Contribution | We collaborate on an upcoming annual scientific report, the "State of Wildfires", as part of a network including myself (UEA), Douglas Kelley (CEH), Francesca Di Giuseppe (ECMWF) and Chantelle Burton (UK Met Office). |
| Impact | We collaborate on an upcoming annual scientific report, the "State of Wildfires", as part of a network including myself (UEA), Douglas Kelley (CEH), Francesca Di Giuseppe (ECMWF) and Chantelle Burton (UK Met Office). |
| Start Year | 2023 |
| Description | Partnership with Hamish Clarke, Trent Penman, and Luke Kelly, University of Melbourne |
| Organisation | University of Melbourne |
| Country | Australia |
| Sector | Academic/University |
| PI Contribution | Co-authorship |
| Collaborator Contribution | Co-authorship |
| Impact | Papers in preparation covering impact of wildfires on society, the economy, and biodiversity and management of wildlands |
| Start Year | 2024 |
| Description | Partnership with John Abatzoglou and Crystal Kolden, UC Merced |
| Organisation | University of California, Merced |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | - Authored one paper. - Leading a proposal for NERC Pushing the Frontiers. |
| Collaborator Contribution | - Co-authorship of one paper. Partners provided data, wrote sections of text, provided feedback and comments. - John is contributing as Co-I on a proposal I am preparing for NERC. |
| Impact | https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020RG000726 |
| Start Year | 2020 |
| Description | Partnership with Luiz Aragão and Liana Anderson, INPE |
| Organisation | National Institute for Space Research Brazil |
| Country | Brazil |
| Sector | Public |
| PI Contribution | - Leading 3 papers (in preparation) |
| Collaborator Contribution | - Co-authors on 3 papers (in preparation) - Postdoc will visit UEA for one year and engage in joint research under my supervision (subject to sign-off on funding from FAPESP) |
| Impact | - 3 papers (in preparation) - 1 postdoc exchange (subject to sign-off on funding from FAPESP) |
| Start Year | 2022 |
| Description | Partnership with Niels Andela |
| Organisation | Cardiff University |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | - Lead author on 3 papers (2 in preparation) - Co-developer of model code (Global Fire Atlas) |
| Collaborator Contribution | - Co-author on 3 papers (2 in preparation) - Access to model code (Global Fire Atlas) |
| Impact | - https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020RG000726 |
| Start Year | 2022 |
| Description | Partnership with Sander Veraverbeke and Guido van der Werf, VU Amsterdam |
| Organisation | Free University of Amsterdam |
| Country | Netherlands |
| Sector | Academic/University |
| PI Contribution | - Joint purchase ($50,000 contribution) of global lightning dataset - Lead author 3 papers (2 published, 1 under revision) - Co-author 1 paper (under revision) |
| Collaborator Contribution | - Joint purchase ($50,000 contribution) of global lightning dataset - Co-author 3 papers - Lead author 1 paper (under revision) |
| Impact | - https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020RG000726 - https://www.nature.com/articles/s41561-019-0403-x |
| Start Year | 2021 |
| Description | Partnership with Stefan Doerr and Cristina Santín, Swansea University |
| Organisation | Swansea University |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | - Lead author of 3 papers |
| Collaborator Contribution | - Co-author of 1 paper |
| Impact | - https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020RG000726 - https://www.nature.com/articles/s41561-019-0403-x - https://www.nature.com/articles/s41467-020-16576-z - https://agupubs.onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2020GB006647 |
| Start Year | 2019 |
| Title | ConFire: State of Wildfires 2023/24 |
| Description | Project Overview: This is the first release of our Bayesian-based fire models, designed for fire prediction and analysis using Bayesian inference and simple fire models. The release here is the base code and information used in the "State of Wildfire's report 2023/24". https://doi.org/10.5194/essd-2024-218 Key Features: ConFire fire model now implemented with zero-inflated logistic link distribution Configuration files for near real-time, attribution and future projections for Greece, Canada, and NW Amazon. Utilizes various environmental and climatic data for isimip and Copernicus data store Robust statistical analysis now uses PyMC at version 5 and ArviZ. Installation and Usage: For detailed installation and usage instructions, please refer to the README, also in this repository archive. Acknowledgments: Special thanks to all contributors and the developers of the dependencies used in this project. Particularly Maria Lucia Ferreira Barbosa, Douglas Kelley, Chantelle Burton Full Changelog: https://github.com/douglask3/Bayesian_fire_models/compare/v0.1...SoW23_v0.1 |
| Type Of Technology | Software |
| Year Produced | 2024 |
| Open Source License? | Yes |
| URL | https://zenodo.org/doi/10.5281/zenodo.11421746 |
| Title | ConFire: State of Wildfires 2023/24 |
| Description | Project Overview: This is the first release of our Bayesian-based fire models, designed for fire prediction and analysis using Bayesian inference and simple fire models. The release here is the base code and information used in the "State of Wildfire's report 2023/24". https://doi.org/10.5194/essd-2024-218 Key Features: ConFire fire model now implemented with zero-inflated logistic link distribution Configuration files for near real-time, attribution and future projections for Greece, Canada, and NW Amazon. Utilizes various environmental and climatic data for isimip and Copernicus data store Robust statistical analysis now uses PyMC at version 5 and ArviZ. Installation and Usage: For detailed installation and usage instructions, please refer to the README, also in this repository archive. Acknowledgments: Special thanks to all contributors and the developers of the dependencies used in this project. Particularly Maria Lucia Ferreira Barbosa, Douglas Kelley, Chantelle Burton Full Changelog: https://github.com/douglask3/Bayesian_fire_models/compare/v0.1...SoW23_v0.1 |
| Type Of Technology | Software |
| Year Produced | 2024 |
| Open Source License? | Yes |
| URL | https://zenodo.org/doi/10.5281/zenodo.11460232 |
| Title | State of Wildfires report 2023/24 Analysis code |
| Description | Project Overview: Code for data creation, analysis and plotting for the State of Wildfires report 2023/24. For script information, see README in the repository and in this archive |
| Type Of Technology | Software |
| Year Produced | 2024 |
| Open Source License? | Yes |
| URL | https://zenodo.org/doi/10.5281/zenodo.11460380 |
| Title | State of Wildfires report 2023/24 Analysis code |
| Description | Project Overview: Code for data creation, analysis and plotting for the State of Wildfires report 2023/24. For script information, see README in the repository and in this archive |
| Type Of Technology | Software |
| Year Produced | 2024 |
| Open Source License? | Yes |
| URL | https://zenodo.org/doi/10.5281/zenodo.11460379 |
| Title | malu-barbosa/FLAME: FLAME 1.0 : Fogo local analisado pela Máxima Entropia |
| Description | This is the first release of our fire model, designed for fire prediction and analysis. FLAME (Fogo local analisado pela Máxima Entropia or Fire Landscape using the Maximum Entropy) is a Bayesian inference implementation of a maximum entropy fire model specifically tailored to simulating fires in heterogeneous territories like Brazil. The release here is the base code and information used in the "FLAME 1.0 : a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept" paper. Acknowledgments: Special thanks to all contributors and the developers of the dependencies used in this project. Particularly Maria Lucia Ferreira Barbosa, Douglas Kelley, Chantelle Burton. Full Changelog: https://github.com/malu-barbosa/FLAME |
| Type Of Technology | Software |
| Year Produced | 2024 |
| Open Source License? | Yes |
| URL | https://zenodo.org/doi/10.5281/zenodo.13367375 |
| Title | malu-barbosa/FLAME: FLAME 1.0 : Fogo local analisado pela Máxima Entropia |
| Description | This is the first release of our fire model, designed for fire prediction and analysis. FLAME (Fogo local analisado pela Máxima Entropia or Fire Landscape using the Maximum Entropy) is a Bayesian inference implementation of a maximum entropy fire model specifically tailored to simulating fires in heterogeneous territories like Brazil. The release here is the base code and information used in the "FLAME 1.0 : a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept" paper. Acknowledgments: Special thanks to all contributors and the developers of the dependencies used in this project. Particularly Maria Lucia Ferreira Barbosa, Douglas Kelley, Chantelle Burton. Full Changelog: https://github.com/malu-barbosa/FLAME |
| Type Of Technology | Software |
| Year Produced | 2024 |
| Open Source License? | Yes |
| URL | https://zenodo.org/doi/10.5281/zenodo.13367374 |
| Description | 82. Le Monde: Gigantic wildfires in Canada, the Amazon and Greece amplified by global warming |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | Le Monde: Gigantic wildfires in Canada, the Amazon and Greece amplified by global warming |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://www.lemonde.fr/en/environment/article/2024/08/15/gigantic-fires-in-canada-the-amazon-and-gre... |
| Description | BBC News Bulletin - Consulted by the programme's researchers for data and statistics describing the extremity of the 2023 wildfire season in the northern hemisphere |
| Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | Consulted by the programme's researchers for data and statistics describing the extremity of the 2023 wildfire season in the northern hemisphere. |
| Year(s) Of Engagement Activity | 2023 |
| Description | BBC: LA Wildfires - Climate trends make explosive fires more likely, scientists say |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | LA Wildfires - Climate trends make explosive fires more likely, scientists say |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://www.bbc.co.uk/news/live/cg7z9zjv90jt?post=asset%3A56c6d5dc-74e7-4699-bc49-d8fbdb40ecd6#post |
| Description | BBC: Why wildfires are becoming faster and more furious |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | Why wildfires are becoming faster and more furious |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://www.bbc.co.uk/news/articles/c70qj7kyppjo#:~:text=Climate%20change%20is%20creating%20hotter,r... |
| Description | CBC Radio Canada |
| Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | https://www.cbc.ca/listen/live-radio/1-10/clip/16087614 |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://www.cbc.ca/listen/live-radio/1-10/clip/16087614 |
| Description | Expert Reaction used: 'Meteorological mayhem' set to rock Britain 'climate breakdown' hits |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | Issued rapid reaction to Science Media Centre, published by The Express. |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://www.express.co.uk/news/uk/1654339/uk-weather-forecast-latest-climate-change-temperatures-upd... |
| Description | Expert Reaction used: How drones, robots and AI are used to tackle wildfires |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | Expert reaction provided to Science Media Centre and used by 6 outlets |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://research-portal.uea.ac.uk/en/clippings/how-drones-robots-and-ai-are-used-to-tackle-wildfires... |
| Description | Global News Canada (Live TV) |
| Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | https://globalnews.ca/video/10698079/whats-causing-the-deadly-greek-wildfires |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://globalnews.ca/video/10698079/whats-causing-the-deadly-greek-wildfires |
| Description | Greece wildfires: how climate change is involved, and what we can do about it |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | Article in The Conversation: "Greece wildfires: how climate change is involved, and what we can do about it" |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://theconversation.com/greece-wildfires-how-climate-change-is-involved-and-what-we-can-do-about... |
| Description | Interview: BBC weather world, broadcast on BBC World News / BBC News / Breakfast / Regional News |
| Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | Interviewed by the BBC during the UK, European and North American wildfires summer 2022. Segment broadcast on all BBC TV channels multiple times over 2-3 days. |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://www.bbc.co.uk/iplayer/episode/m001bmm7/weather-world-august-2022 |
| Description | Invited Talk: Fate of Pyrogenic Carbon in the Earth System, École Normale Supérieure (ENS), Paris, January 2023 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Gave talk in Paris, hybrid. Scientific and wider audience of those following research on pyrogenic carbon. |
| Year(s) Of Engagement Activity | 2022 |
| Description | Invited Talk: Re-imagining Global Pyromes, Brazilian Institute for Space Research (INPE) |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Gave talk at INPE during research visit. Hybrid event - wider scientific / third sector audience. |
| Year(s) Of Engagement Activity | 2023 |
| Description | Invited Talk: Re-imagining Global Pyromes, US National Academies workshop on GHG emissions from wildland fires |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Policymakers/politicians |
| Results and Impact | Invited to talk by the US National Academies workshop on GHG emissions from wildland fires. See report by Carbon Brief in link. |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://www.carbonbrief.org/qa-how-scientists-tackle-the-challenges-of-estimating-wildfire-co2-emiss... |
| Description | New York Times: The World's Carbon Sinks Are on Fire |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | The World's Carbon Sinks Are on Fire |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://www.nytimes.com/2024/10/17/climate/carbon-fires-forests-global-warming.html |
| Description | Press Conference Science Media Centre London |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | State of Wildfires Report 2023-24 launch |
| Year(s) Of Engagement Activity | 2024 |
| Description | Press release: Wildfire risk has grown nearly everywhere, but we can still influence where and how fires strike |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | PR Covered by 49 outlets worldwide including BBC online |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://research-portal.uea.ac.uk/en/persons/matthew-jones/clippings/ |
| Description | Rolling Stone: The Case for Kamala Harris in a Burning World |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | Rolling Stone: The Case for Kamala Harris in a Burning World |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://www.rollingstone.com/politics/political-commentary/case-kamala-harris-climate-election-2024-... |
| Description | Taming wildfires in the context of climate change |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Policymakers/politicians |
| Results and Impact | Contributed to the OECD Environment Directorate report "Taming wildfires in the context of climate change". Provided data visuals and reviewed report. First two figures in summary for policymakers are mine. |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://www.oecd-ilibrary.org/docserver/dd00c367-en.pdf?expires=1710428136&id=id&accname=oid033621&c... |
| Description | The Conversation: Climate change: wildfire risk has grown nearly everywhere - but we can still influence where and how fires strike |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | Article to convey take-homes our review article https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020RG000726 |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://theconversation.com/climate-change-wildfire-risk-has-grown-nearly-everywhere-but-we-can-stil... |
| Description | The Economist: Wildfires are getting more frequent and more devastating |
| Form Of Engagement Activity | A magazine, newsletter or online publication |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | Wildfires are getting more frequent and more devastating |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://www.economist.com/science-and-technology/2024/08/22/wildfires-are-getting-more-frequent-and-... |
| Description | UK Health Security Agency Workshop: Atmospheric Dispersion of Wildfire Smoke |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Policymakers/politicians |
| Results and Impact | https://admlc.com/ |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://admlc.com/ |
| Description | Workshop: UK Committee on Climate Change - Wildfire Impact, Risk and Mitigation Workshop |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Policymakers/politicians |
| Results and Impact | UK CCC event at the Royal Geographical Society, financed by the Game & Wildlife Conservation Trust. Among experts invited to contribute to policy workshop on wildfire risk reduction in the UK. |
| Year(s) Of Engagement Activity | 2023 |