Christine Wiedinmyer

Wildfire emission inventories exhibit large variability that complicates assessments of smoke impacts. Here we compare fuel consumed (in mass per burned area units) from multiple burn area‐based and energy‐based approaches for fires in the western US during 2020. Average fuel consumed can vary by up to factors of 2–16 between approaches across burn severity classes and fuel types. Fuel consumed estimates typically increase with burn severity, except for the energy‐based approaches for forest land cover, where it decreases for high burn severity. Also, in contrast to other approaches, energy‐based estimates decrease for tree cover greater than 40% regardless of burn severity class. This implies that corrections to the energy‐based approach are likely needed across burn severity categories to account for canopy and smoke shading. The methodological recommendations provided would likely result in greater consistency between wildfire emission estimates and highlight the need to better constrain fuel loading and consumption.

Biomass burning emissions in Southeast Asia (SE Asia) remain highly uncertain due to limited ground data and persistent model-observation discrepancies. A major source of uncertainty arises from differences in how emission inventories estimate burned area. The Fire Inventory from the National Center for Atmospheric Research version 2.5 (FINNv2.5) relies on low Earth orbit (LEO) Terra/Aqua MODIS (1km) and SNPP VIIRS (375m) active-fire detections, assuming full-pixel burns. Because these sensors pass over the region only twice daily, they miss late-afternoon and evening agricultural fires that often dominate burning activity in this region. Himawari-9, a geostationary (GEO) satellite, provides hourly observations and captures diurnal burning patterns but at a coarser 2 km resolution. To leverage this temporal coverage, we compared burned areas from Himawari-9 and SNPP VIIRS during their overlapping daytime overpass (~12-2 pm local time) and derived a corrected Himawari-9 pixel area of 0.14 km², equivalent to a VIIRS pixel. This correction enables a combined GEO-LEO approach that retains VIIRS-level spatial detail. We integrated Himawari-9 detections with corrected pixel sizes into FINNv2.5, removed tropical duplicate-pixel routine which can inflate burned-area estimates, particularly given MODIS’s coarse 1-km pixel size, and developed additional configurations using NOAA-20 and NOAA-21 VIIRS. Across February-March 2024, the configuration using Himawari-9 and all VIIRS sensors performed best when evaluated against independent burned-area datasets (GISTDA LANDSAT8/9 for Thailand, Copernicus Sentinel-3, and NASA VNP64A1). Relative to default FINNv2.5, this configuration detected substantially more small distinct fires: +73-319% in croplands, +59-113% in grass/savanna, and +59-93% in woody shrubs across Vietnam, Laos, Myanmar, Thailand, and Cambodia. Against March 2024 GISTDA burned areas in Thailand, correlations improved across crop types, with the largest gains in rice (r = 0.29 to 0.41) and sugarcane (r = 0.19 to 0.31). These results demonstrate how combining high-temporal GEO fire detections with fine-spatial VIIRS observations can enhance fire detection and strengthen emission inventories in SE Asia and beyond.

Structure fires in the wildland–urban interface (WUI) are becoming more frequent and destructive, yet their emissions of air pollutants remain poorly quantified and are not included in national inventories. Here we present a conterminous-scale inventory of WUI-related structure fire emissions in the United States from 2000 to 2020. A small number of highly destructive events dominate structure fire emissions—the 20 most destructive fires account for 68% of total carbon monoxide emissions. Structure fire emissions are more spatially concentrated than vegetation fire emissions, and in several states emissions of specific hazardous air pollutants such as hydrochloric acid exceed those from all anthropogenic sources combined. We show that structure burning in wildfires is strongly influenced by fire-conducive weather, and destructive structure fires are more likely to occur in forested and urbanized landscapes. These results reveal structure fires as a major source of toxic air pollution, with important implications for air quality, public health, and fire management.

Accurate estimates of biomass burning (BB) emissions are of great importance worldwide due to the impacts of these emissions on human health, ecosystems, air quality, and climate. Atmospheric modeling efforts to represent these impacts require BB emissions as a key input. This paper is presented by the Biomass Burning Uncertainty: Reactions, Emissions and Dynamics (BBURNED) activity of the International Global Atmospheric Chemistry project and largely based on a workshop held in November 2023. The paper reviews 9 of the BB emissions datasets widely used by the atmospheric chemistry community, all of which rely heavily on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations of fires scheduled to be discontinued at the end of 2025. In this time of transition away from MODIS to new fire observations, such as those from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite instruments, we summarize the contemporary status of BB emissions estimation and provide recommendations on future developments. Development of global BB emissions datasets depends on vegetation datasets, emission factors, and assumptions of fire persistence and phase, all of which are highly uncertain with high degrees of variability and complexity and are continually evolving areas of research. As a result, BB emissions datasets can have differences on the order of factor 2–3, and no single dataset stands out as the best for all regions, species, and times. We summarize the methodologies and differences between BB emissions datasets. The workshop identified 5 key recommendations for future research directions for estimating BB emissions and quantifying the associated uncertainties: development and uptake of satellite burned area products from VIIRS and other instruments; mapping of fine scale heterogeneity in fuel type and condition; identification of spurious signal detections and information gaps in satellite fire radiative power products; regional modeling studies and comparison against existing datasets; and representation of the diurnal cycle and plume rise in BB emissions.

Landscape fires in subtropical southern Africa (2-20°S) are a prominent regional source of nitrogen oxides (NO_x) and ammonia (NH₃), affecting climate and air quality as precursors of tropospheric ozone and aerosols. Here we evaluate GEOS-Chem model skill at reproducing satellite observations of vertical column densities of NO₂ from TROPOMI and NH₃ from IASI driven with three distinct and widely used biomass burning inventories (FINNv2.5, GFEDv4s, GFASv1.2). We identify that GFASv1.2 use of fire radiative power and a NO_x emission factor that is almost half that used by the other two inventories is most consistent with TROPOMI and that FINNv2.5 use of active fires and landscape-specific fuel loads and biomass consumed is most consistent with IASI. We use a simple mass-balance inversion to calculate top-down NO_x emissions of 1.9±0.6 Tg NO for June-October and NH₃ emissions of 1.2±0.4 Tg for July-October. All inventories collocate NO_x and NH₃ emissions, whereas most of the pronounced emissions of NO_x and NH₃ are separate and have distinct seasonality in the top-down estimate. We infer with GEOS-Chem more efficient ozone production (13 Tg ozone per Tg NO) with the top-down informed NO_x emissions than the inventory emissions, as GFASv1.2 NO_x is almost 20% less than top-down NO_x and the 2.3- to 2.5-times greater FINNv2.5 and GFEDv4s NO_x reduces sensitivity of ozone formation to NO_x. Both NO_x and NH₃ top-down emissions are unaffected by use of plume injection heights, limited to GFASv1.2 in GEOS-Chem, and NH₃ is insensitive to acidic sulfate and nitrate aerosol emissions absent in all inventories. The top-down emissions estimates and comparison to satellite observations suggest a hybrid bottom-up approach could be adopted to discern byproducts of smouldering and flaming fires.

Open biomass burning has major impacts globally and regionally on atmospheric composition. Fire emissions include particulate matter, tropospheric ozone precursors, and greenhouse gases, as well as persistent organic pollutants, mercury, and other metals. Fire frequency, intensity, duration, and location are changing as the climate warms, and modelling these fires and their impacts is becoming more and more critical to inform climate adaptation and mitigation, as well as land management. Indeed, the air pollution from fires can reverse the progress made by emission controls on industry and transportation. At the same time, nearly all aspects of fire modelling – such as emissions, plume injection height, long-range transport, and plume chemistry – are highly uncertain. This paper outlines a multi-model, multi-pollutant, multi-regional study to improve the understanding of the uncertainties and variability in fire atmospheric science, models, and fires' impacts, in addition to providing quantitative estimates of the air pollution and radiative impacts of biomass burning. Coordinated under the auspices of the Task Force on Hemispheric Transport of Air Pollution, the international atmospheric modelling and fire science communities are working towards the common goal of improving global fire modelling and using this multi-model experiment to provide estimates of fire pollution for impact studies. This paper outlines the research needs, opportunities, and options for the fire-focused multi-model experiments and provides guidance for these modelling experiments, outputs, and analyses that are to be pursued over the next 3 to 5 years. The paper proposes a plan for delivering specific products at key points over this period to meet important milestones relevant to science and policy audiences.

Aerosol particles are an important part of the Earth climate system, and their concentrations are spatially and temporally heterogeneous, as well as being variable in size and composition. Particles can interact with incoming solar radiation and outgoing longwave radiation, change cloud properties, affect photochemistry, impact surface air quality, change the albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. High particulate matter concentrations at the surface represent an important public health hazard. There are substantial data sets describing aerosol particles in the literature or in public health databases, but they have not been compiled for easy use by the climate and air quality modeling community. Here, we present a new compilation of PM_2.5 and PM₁₀ surface observations, including measurements of aerosol composition, focusing on the spatial variability across different observational stations. Climate modelers are constantly looking for multiple independent lines of evidence to verify their models, and in situ surface concentration measurements, taken at the level of human settlement, present a valuable source of information about aerosols and their human impacts complementarily to the column averages or integrals often retrieved from satellites. We demonstrate a method for comparing the data sets to outputs from global climate models that are the basis for projections of future climate and large-scale aerosol transport patterns that influence local air quality. Annual trends and seasonal cycles are discussed briefly and are included in the compilation. Overall, most of the planet or even the land fraction does not have sufficient observations of surface concentrations – and, especially, particle composition – to characterize and understand the current distribution of particles. Climate models without ammonium nitrate aerosols omit ∼ 10 % of the globally averaged surface concentration of aerosol particles in both PM_2.5 and PM₁₀ size fractions, with up to 50 % of the surface concentrations not being included in some regions. In these regions, climate model aerosol forcing projections are likely to be incorrect as they do not include important trends in short-lived climate forcers.

The Copernicus Atmosphere Monitoring Service CAMS is using ECMWF's Integrated Forecasting System IFS-COMPO with fire emissions from its Global Fire Assimilations System GFAS to monitor and forecast the effect of smoke from vegetation fires, resp. biomass burning, on atmospheric composition. The simulated atmospheric composition fields are routinely validated against observations including from satellites, aircraft and ground stations.The emissions calculation by the operational GFAS version 1.2 have recently been updated for use in the upcoming HTAP3 multi-model, multi-pollutant study of fire impacts (Whaley et al. 2024), creating the dataset GFAS4HTAP. It is based on the dry matter burnt estimates of GFASv1.2, and uses an updated spurious signal mask, ESA CCI land cover data for 2018, a global peat map (Xu et al. 2018) and emission factors from NEIVA (Shahid et al. 2024) to calculate emission fluxes for various smoke constituents for 2003-2024. An additional GFAS-based dataset has been created by calibration against GFED5beta.Global comparisons of dry matter, resp. biomass, combustion rates of the three GFAS-based inventories with GFED4s, GFED5beta, and the two variants of FINN2.5 reveal that these inventories can be roughly classified into one group of "traditional" inventories with lower fire activity, resp. emissions, and another of "more recent" inventories with higher fire activity. The pyrogenic carbon monoxide emission estimates from an inversion of satellite observations of atmospheric composition (Zheng et al. 2019) lie between these two groups in terms of global annual values. However, at a global level, they are more consistent with the "more recent" inventories during the late boreal summer peak of the global fire activity and with the "traditional" inventories during periods of lower fire activity.In order to gain more insight from independent validation, we here present simulations with IFS-COMPO for 2019 based on the three GFAS-based inventories and compare these with atmospheric observations of carbon monoxide, nitrogen dioxide and aerosol optical depth. We find that the best agreement of simulation and observations is achieved by different inventories for different regions, seasons and smoke constituents. However, the emissions of the GFAS4HTAP dataset appears to lead to the overall most balanced atmospheric composition simulation. This supports the group of "traditional" inventories mentioned above.