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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. 

Abstract. Wildfire smoke is one of the most significant concerns ofhuman and environmental health, associated with its substantial impacts onair quality, weather, and climate. However, biomass burning emissions andsmoke remain among the largest sources of uncertainties in air qualityforecasts. In this study, we evaluate the smoke emissions and plumeforecasts from 12 state-of-the-art air quality forecasting systemsduring the Williams Flats fire in Washington State, US, August 2019, whichwas intensively observed during the Fire Influence on Regional to GlobalEnvironments and Air Quality (FIREX-AQ) field campaign. Model forecasts withlead times within 1 d are intercompared under the same framework basedon observations from multiple platforms to reveal their performanceregarding fire emissions, aerosol optical depth (AOD), surface PM2.5,plume injection, and surface PM2.5 to AOD ratio. The comparison ofsmoke organic carbon (OC) emissions suggests a large range of daily totalsamong the models, with a factor of 20 to 50. Limited representations of thediurnal patterns and day-to-day variations of emissions highlight the needto incorporate new methodologies to predict the temporal evolution andreduce uncertainty of smoke emission estimates. The evaluation of smoke AOD(sAOD) forecasts suggests overall underpredictions in both the magnitude andsmoke plume area for nearly all models, although the high-resolution modelshave a better representation of the fine-scale structures of smoke plumes.The models driven by fire radiativepower (FRP)-based fire emissions or assimilating satellite AODdata generally outperform the others. Additionally, limitations of thepersistence assumption used when predicting smoke emissions are revealed bysubstantial underpredictions of sAOD on 8 August 2019, mainly over thetransported smoke plumes, owing to the underestimated emissions on7 August. In contrast, the surface smoke PM2.5 (sPM2.5) forecastsshow both positive and negative overall biases for these models, with mostmembers presenting more considerable diurnal variations of sPM2.5.Overpredictions of sPM2.5 are found for the models driven by FRP-basedemissions during nighttime, suggesting the necessity to improve verticalemission allocation within and above the planetary boundary layer (PBL).Smoke injection heights are further evaluated using the NASA LangleyResearch Center's Differential Absorption High Spectral Resolution Lidar(DIAL-HSRL) data collected during the flight observations. As the firebecame stronger over 3–8 August, the plume height became deeper, with aday-to-day range of about 2–9 km a.g.l. However, narrower ranges arefound for all models, with a tendency of overpredicting the plume heights forthe shallower injection transects and underpredicting for the days showingdeeper injections. The misrepresented plume injection heights lead toinaccurate vertical plume allocations along the transects corresponding totransported smoke that is 1 d old. Discrepancies in model performance forsurface PM2.5 and AOD are further suggested by the evaluation of theirratio, which cannot be compensated for by solely adjusting the smoke emissionsbut are more attributable to model representations of plume injections,besides other possible factors including the evolution of PBL depths andaerosol optical property assumptions. By consolidating multiple forecastsystems, these results provide strategic insight on pathways to improvesmoke forecasts. 

Noetzli, J., Christiansen, H.H, Guglielmin, M., Hrbáček, F., Hu, G., Isaksen, K., Magnin, F., Pogliotti, P., Smith, S. L., Zhao, L. and Streletskiy, D. A. 2024. Permafrost temperature and active layer thickness. In: State of the Climate in 2023. Bulletin of the American Meteorological Society, 105 (8), S43–S44, https://doi.org/10.1175/BAMS-D-24-0116.1