Fangjun Li

Wildfires are increasing in frequency and intensity, strongly influencing both regional air quality and the Earth's radiation budget via their aerosol emissions. Relying on an unparalleled observational data set from the 2019 NOAA/NASA FIREX‐AQ campaign, this study quantifies the direct radiative effects of two wildfires using the HRRR‐Chem. Model predictions agree well with observed aerosol absorption, composition, and mass concentrations in fresh smoke (PM _2.5 > 1,500 μg m ⁻³ ). Our results show pronounced regional direct radiative effects, including daily mean surface cooling exceeding −150 W m ⁻² and atmospheric warming up to +40 W m ⁻² in dense plumes. However, our analysis indicates that surface cooling is underestimated in the densest fresh smoke plumes due to aerosol scattering underpredictions. This scattering underestimation is linked to the grid‐scale dilution of dense fresh‐smoke gradients at the 3 km scale, as well as challenges in capturing complex fresh‐smoke particle size distributions using prescribed emission sizes within modal aerosol schemes, emphasizing the need for fire‐specific emission parameters in future biomass burning modeling studies. Furthermore, sensitivity analyses reveal that the volatility of primary organic aerosol is the dominant source of uncertainty in the direct radiative effect (up to 80%), while the optical properties of brown carbon and aerosol mixing state contribute 10%–20% and 5%–15% uncertainty, respectively.

Drought is increasingly reshaping terrestrial ecosystems under climate change, yet how aridity modulates the climatic sensitivities of satellite‐derived green‐up rate (GR) and senescence rate (SR) in natural ecosystems remains poorly understood at the global scale. Here, we combine two decades of satellite observations and climate data sets to identify the primary climatic drivers (temperature, precipitation, solar radiation, soil moisture, and vapor pressure deficit) of maximum GR and SR across 764 global ecoregions and to quantify their sensitivities to interannual variability in climate‐forcing accumulation rates of these drivers. We reveal contrasting temperature‐response patterns along the aridity gradient in arid versus humid ecoregions. In arid ecoregions, decreasing aridity increases the GR sensitivity to temperature accumulation rates during the green‐up phase, whereas in humid ecoregions this sensitivity weakens and can even become negative as conditions become wetter. Similar, though asymmetric, those sensitivity reversal patterns are observed for SR. These findings demonstrate that aridity fundamentally reshapes vegetation‐climate interactions, with important implications for the stability of ecosystem carbon uptake under increasing hydroclimatic variability. By identifying critical transitions in climate sensitivities across aridity gradients, this study provides new insights for improving predictions of ecosystem responses to intensifying drought and for assessing risks to carbon sequestration under future climate change.

Wildfires emit smoke particles and trace gases including greenhouse gases into the atmosphere, impacting the environment and leading to detrimental impacts on human health and economy. The estimation of spatially and temporally resolved methane emissions from biomass burning (BB) provides critical information in developing measurement‐informed methane inventories. The use of satellite active fire products (fire radiative power) is an effective pathway to investigate wildfire emissions around the world. In this study, the Global BB Emissions Product‐eXtended algorithm is employed to estimate long‐term temporal variation and geographic distribution of methane emissions from BB using satellite observations from the Moderate Resolution Imaging Spectroradiometer and the Visible Infrared Imaging Radiometer Suite. Globally, on average about 19 Megatonnes of BB methane are released to the atmosphere every year, nearly half of it originating from Africa, where BB represents a significant proportion of the total methane emissions. Our findings show that methane emissions from wildfires are substantial and can exceed other source sectors during extreme wildfire events, negating gains from years of emission reductions from anthropogenic sources. The contribution of fires to the methane budget is significant for regions with intense fire activities. Effective wildfire prevention and management could be beneficial to rapidly reduce methane emissions from BB.

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.

In January 2025, wildfires caused catastrophic impacts to the infrastructure, communities, and environment of Los Angeles. This study documents the remotely sensed fire spread, intensity, and smoke emissions from the wildfires by leveraging fire and emission observations from multiple satellites. We found that during the first 24 hr after ignition, two main fires moved through urban settlements, under hurricane‐like Santa Ana winds, at rates of up to 3.7 km hr ⁻¹ while burning 75%–80% of the final fire areas, including all affected residential areas. We also report the first satellite‐derived intensities of wildfires burning through residential areas. With a mean intensity of 72–297 W m ⁻² , these fires at night were significantly more intense than the contemporaneous vegetation fires in the Palisades Fire. Smoke emissions exhibited strong hourly variations that were closely coupled with temporal fire intensity patterns, and the emission coefficients (Ce) of carbon monoxide (CO), total particulate matter, and nitrogen oxides (NO _x ) were 146, 10.2–21.1, and 0.89 g MJ ⁻¹ , respectively. In comparison between vegetation fires and residential‐area fires, CO Ce discrepancies suggest that residential fires likely released less CO emissions per unit of radiative energy, whereas temporal variations in NO _x Ce discrepancies varied temporally indicate complex factors influencing Ce in wildfires occurring at the wildland‐Urban Interface. Our findings reveal the extreme characteristics of the Los Angeles wildfires and illustrate how integrated remote sensing imagery can provide critical wildfire information to support safe evacuation, firefighting operations, and air quality forecasting.

The operation of geostationary (GEO) instruments such as the Tropospheric Emissions: Monitoring of Pollution (TEMPO) provides unprecedented hourly nitrogen dioxide (NO ₂ ) observations compared to the once‐daily data from a low‐Earth orbit (LEO) platform like the TROPOspheric Monitoring Instrument (TROPOMI). This study investigates the performance and challenges of using TEMPO versus TROPOMI measurements to constrain anthropogenic nitrogen oxides (NO _x ) emissions. The accuracy of TEMPO and TROPOMI NO ₂ tropospheric columns are assessed using Pandora observations, finding a low bias of 9%–12.3% in TEMPO, and TROPOMI data during August 2023, while TEMPO midday and late afternoon observations are less of low bias. Top‐down NO _x emissions derived by midday TEMPO and TROPOMI data are generally consistent over urban areas, being 5%–20% lower than bottom‐up emissions provided by the 2021 GReenhouse gas And Air Pollutants Emissions System (GRA ² PES), and align with 2023 GRA ² PES emissions, demonstrating the reliability of using satellite data for timely updates of bottom‐up inventories. However, assimilating additional morning/late afternoon TEMPO data leads to the poorest top‐down NO _x emissions, likely resulting from larger negative measurement biases. NO _x emission inversions effectively mitigate NO _x overprediction, though the top‐down NO _x emissions might be over‐corrected in urban cores. NO _x emissions optimization also improves ozone forecasts by reducing the model's positive biases, especially when assimilating midday TEMPO data. Our study suggests that TEMPO midday observations provide better constraints on the magnitude and spatiotemporal variation of anthropogenic NO _x emissions than TROPOMI, while morning TEMPO v3 data should be used cautiously due to potential negative impact on NO _x emissions inversion.

Greenhouse gas (GHG) emissions from biomass burning, including cropland fires, are a critical component affecting climate change. However, the long-term variations in GHGs from cropland biomass-burning emissions (CBBEs) remain unclear across the world’s countries. Here, we used satellite observations of CBBE of carbon dioxide (CO ₂ ) and methane (CH ₄ ), both GHGs, to examine the emission trends from 2003 to 2023 and analyze the causes across 139 agriculture-producing countries. The global CO ₂ and CH ₄ emissions from CBBE are decreasing at rates of −8.21 Mt CO ₂ and −1.55 × 10 ⁻² Mt CH ₄ per year, respectively. However, these emissions are increasing significantly (P < 0.05) in eight countries, mainly in South and East Asia. The CBBE was strongly correlated to the human development levels (indicated by the Human Development Index, HDI) of the countries, resulting in overall higher unit area CBBE in low-medium HDI countries and lower unit area CBBE in high-very high HDI countries. In 13 countries, variations in CBBE are associated with changes in the crop residue from productivity. However, there is also a strong variation in CBBE associated with the regulation of agricultural fires, where appropriate agricultural residue management policies have effectively reversed the increasing CBBE trends in some countries.

Increasing impacts of wildfires on Western US air quality highlights the need for forecasts of smoke emissions based on dynamic modeled wildfires. This work utilizes knowledge of weather, fuels, topography, and firefighting, combined with machine learning and other statistical methods, to generate 1‐ and 2‐day forecasts of fire radiative energy (FRE). The models are trained on data covering 2019 and 2021 and evaluated on data for 2020. For the 1‐day (2‐day) forecasts, the random forest model shows the most skill, explaining 48% (25%) of the variance in observed daily FRE when trained on all available predictors compared to the 2% (R² = 0.39–0.48, 2‐day R² = 0.13–0.34). FRE is used to compute emissions, so these results demonstrate potential for improved fire emissions forecasts for air quality models.

The western United States experienced a record‐breaking wildfire season in 2020. This study quantifies the contribution of wildfire emissions to the exceedances of health‐based National Ambient Air Quality Standard (NAAQS) for fine particles (PM_2.5) by comparing two CMAQ simulations, with and without wildfire emissions. During August to October 2020, western wildfires contributed 23% of surface PM_2.5 in the contiguous US (CONUS), with a larger contribution in Pacific Coast (43%) and Mountain Region (42%). Consequently, wildfires were the primary contributor to the 3,720 observed exceedances. The wildfire influence peaked on September 14th, 2020, when 273 exceedances were recorded and wildfire emissions contributed 41%, 81%, and 72% to surface PM_2.5 concentrations in the CONUS, Pacific Coast, and Mountain Region, respectively. Our finding highlights the predominating influence of wildfires on air quality, and potentially human health, that is expected to grow with increasing fire activities, while anthropogenic emissions decrease.