State of wildfires 2023–24

Climate change is increasing the frequency and intensity of wildfires globally, with significant impacts on society and the environment. However, our understanding of the global distribution of extreme fires remains skewed, primarily influenced by media coverage and regional research concentration. This inaugural State of Wildfires report systematically analyses fire activity worldwide, identifying extreme events from the March 2023–February 2024 fire season. We assess the causes, predictability, and attribution of these events to climate change and land use, and forecast future risks under different climate scenarios. During the 2023–24 fire season, 3.9 million km2 burned globally, slightly below the average of previous seasons, but fire carbon (C) emissions were 16 % above average, totaling 2.4 Pg C. This was driven by record emissions in Canadian boreal forests (over 9 times the average) and dampened by reduced activity in African savannahs. Notable events included record-breaking wildfire extent and emissions in Canada, the largest recorded wildfire in the European Union (Greece), drought-driven fires in western Amazonia and northern parts of South America, and deadly fires in Hawai’i (100 deaths) and Chile (131 deaths). Over 232,000 people were evacuated in Canada alone, highlighting the severity of human impact. Our analyses revealed that multiple drivers were needed to cause areas of extreme fire activity. In Canada and Greece a combination of high fire weather and an abundance of dry fuels increased the probability of fires by 4.5-fold and 1.9–4.1-fold, respectively, whereas fuel load and direct human suppression often modulated areas with anomalous burned area. The fire season in Canada was predictable three months in advance based on the fire weather index, whereas events in Greece and Amazonia had shorter predictability horizons. Formal attribution analyses indicated that the probability of extreme events has increased significantly due to anthropogenic climate change, with a 2.9–3.6-fold increase in likelihood of high fire weather in Canada and a 20.0–28.5-fold increase in Amazonia. By the end of the century, events of similar magnitude are projected to occur 2.22–9.58 times more frequently in Canada under high emission scenarios. Without mitigation, regions like Western Amazonia could see up to a 2.9-fold increase in extreme fire events. For the 2024–25 fire season, seasonal forecasts highlight moderate positive anomalies in fire weather for parts of western Canada and South America, but no clear signal for extreme anomalies is present in the forecast. This report represents our first annual effort to catalogue extreme wildfire events, explain their occurrence, and predict future risks. By consolidating state-of-the-art wildfire science and delivering key insights relevant to policymakers, disaster management services, firefighting agencies, and land managers, we aim to enhance society’s resilience to wildfires and promote advances in preparedness, mitigation, and adaptation

Global cloud-to-ground lightning data to inform wildfire ignition patterns

Lightning is recognised as a crucial wildfire ignition source worldwide, especially in remote regions including boreal and temperate forests where large carbon stocks are held. The societal consequences of these wildfires, as well as their contribution to climate change, can be immense. The occurrence of lightning is projected to increase in these areas under climate change, however robust assessments of lightning contribution to wildfire risk have been restricted to selected regions due to the narrow spatial extent of cloud-to-ground lightning records. Consequently, evaluations of lightning-fire relationships using existing global lightning observational datasets have been limited to considering the total amount of lightning. Only cloud-to-ground lightning can ignite a wildfire, therefore when considering impacts on wildfire risk it is essential to distinguish between lightning types. Using Vaisala’s unique Global Lightning Dataset (GLD360), which discriminates between cloud lightning and cloud-to-ground lightning strikes, we present our preliminary analyses of the spatial patterns and seasonality of cloud-to-ground lightning. Here, we show the regional variation in the lightning frequency and the cloud-to-ground fraction, as well as the strength (current) and polarity of cloud-to-ground lightning strikes. By considering cloud-to-ground lightning strikes only, we characterise the spatial and seasonal variation in lightning events with the potential to ignite wildfires. Combining global observations of lightning strikes with observations of individual fires and coincident meteorology will advance our mechanistic understanding of wildfire ignition potential in a range of weather conditions, improve the process representation of the ignition process in global models, and refine projections of changing wildfire risks under climate change

Unravelling Variability: Discrepancies in Amazonian Biomass Burning Emissions Under Different Emission Factor Scenarios

Biomass burning (BB) plays a key role in the biosphere–atmosphere interaction. It is a major source of trace gases and aerosols that alters the atmosphere and the water cycle. Additionally, these emissions are often related to other detrimental impacts including biodiversity loss in fire-sensitive biomes, increase of respiratory diseases, and massive economic losses. BB emissions are used as inputs in models that estimate air quality and the effect of fires on Earth’s climate. Hence, an accurate estimation of BB emissions is paramount. While BB emissions spread over most of the global vegetated areas, the integration of orbital remote sensing and modelling is the most effective approach to estimate them from regional to global scales. BB emission estimation follows the relationship between burned biomass and the emission factor (EF - mass emitted of a given species, for example carbon dioxide, per mass of dry matter burned). The burned biomass can be estimated using two approaches: (i) based on the relationship among burned area, above-ground biomass, and combustion completeness; or (ii) based on fire radiative power (FRP), a quantitative measurement that is directly related to the rate of burned biomass and is estimated to each active fire detected by several orbital sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. EF values, which are Land Use and Land Cover (LULC) based, are required to estimate BB emissions independently on the approach adopted to estimate the burned biomass. Although novel approaches to improve the accuracy of BB emissions have been developed, the impact of EF values on the final estimated emissions remains uncertain. We have evaluated the impact of the EFs on the final estimate of fine particulate matter (PM2.5) emitted from BB in the Brazilian Amazon during a nineteen years’ time series (2002-2020) by running the PREP-CHEM-SRC emissions preprocessor tool under four EF scenarios: the tool original EF values based on the work of Andreae and Merlet (2001), the average EF values recently updated by Andreae (2019), and the minimum and maximum EF values also proposed by this author. The minimum (maximum) EF values were defined as the average EF value for each LULC class minus (plus) one standard deviation. The PM2.5 emissions were estimated at the spatial resolution of 0.1º using the FRP approach implemented on PREP-CHEM-SRC (3BEM_FRP model) having MODIS active fires as input, since this approach requires fewer inputs and the impact of the EFs over the emissions would be more evident. Our results showed that the annual average PM2.5 emission in the Amazon varied by 163% between the four EF scenarios (from1,426 Gg and 3,747 Gg), while the scenario based on the average values was the closest to the one based on PREP-CHEM-SRC original EF values (2,582 Gg and 2,213 Gg, respectively – an increase of 17%). These results contribute to the better understanding of how this single parameter impacts on the estimation of BB emissions