Secondary organic aerosol (SOA) constitutes a major component of fine particulate matter (PM2.5) that models must account for to assess how human activities influence air quality, climate, and public health. We characterize the current state of SOA modeling by analyzing eight SOA schemes implemented in five widely used air quality models: CAMx, CMAQ, GEOS-Chem, WRF-Chem and CHIMERE. We performed offline calculations to compare non-aged SOA yields, the effects of SOA aging processes, and the influence of NOxx) conditions on SOA yields is also highly variable among schemes. While some schemes include SOA aging, their treatments differ substantially, with some schemes showing large increases in SOA mass, while others exhibit minimal changes. Box model simulations confirmed the substantial discrepancies in predicted SOA concentrations and their responses to precursor emission changes. The substantial differences among current SOA schemes highlight a lack of consensus within the air quality modelling community. Evaluating model simulation results using ambient measurements is unlikely to resolve these discrepancies because uncertainties in SOA formation and precursor emissions are deeply intertwined. The limitations of current SOA schemes should be recognized and acknowledged because model choice can greatly influence predicted SOA concentrations and their evolution, ultimately impacting air quality forecasts, assessments, and regulatory decisions.
Lake-land thermal contrasts significantly modulate regional air quality, yet the coupling mechanisms by which inland lakes regulate the diurnal evolution of PM2.52.5µg m−32.52.5, as the cooler and more humid lake air thermodynamically favors the ammonium nitrate formation. Furthermore, convergence zones where lake breezes meet background winds create localized stagnation traps that intensify shoreline pollution. At night, while the lake surface maintains higher PM2.52.5µg m−3
Atmospheric new particle formation (NPF) is a vital source of aerosol and cloud condensation nuclei, regulated by complex meteorological and chemical factors. Utilizing ground aerosol particle size distributions and vertical observations, this study employs a generalized additive model (GAM) and SHapley Additive exPlanations (SHAP) to quantitatively assess the marginal contributions of factors driving NPF under the NPF scenario and three non-NPF scenarios. We found that NPF depends on the synchronization of enhanced source strength and weakened sink intensity, a process controlled by planetary boundary layer evolution. Under the NPF scenario, the breakup of the inversion layer and entrainment of cleaner air aloft promote vertical mixing. This rapidly reduces the condensation sink (CS) while transporting ozone (O3) to the surface, allowing precursor formation to coincide with a clean background, thus creating a favorable nucleation window. In contrast, nucleation is inhibited in non-NPF scenarios through distinct mechanisms: insufficient oxidation capacity (Non-O3O3O3SO2, with high temperature and sufficient precursors contributing positively to its predicted value. However, predicted Nuc values are dominated by background particles under the Low-O3TSO2O3O3
Using 22 years (2002–2023) of TIMED/SABER satellite observations, we investigate the long-term coupling between mesospheric hydroxyl (OH) airglow and gravity wave potential energy (Ep). Continuous wavelet transform analysis extracts gravity wave signatures from temperature perturbations, and multiple linear regression decomposes the observed variability into contributions from solar activity, geomagnetic activity, the Quasi-Biennial Oscillation (QBO), and El Niño–Southern Oscillation (ENSO). Three major findings emerge. First, OH emissions and gravity wave Epp<0.05) correlation coefficients of 0.3–0.7 that peak during winter at mid-latitudes. Second, long-term trends reveal contrasting latitudinal patterns: OH trends are negative at mid-latitudes in both hemispheres (−1
Satellite observations play a crucial role in quantifying ammonia sources by capturing large-scale variations of atmospheric NH3NH3. Using NH3NH3NH3NH3NH3, with a correlation coefficient of 0.77 and an RMSE of 9.67×1015 molec cm−2, while reproducing daytime variations observed by FTIR. For major agricultural areas, the NH3NH3
Landfills are a major anthropogenic source of methane (CH4), contributing up to 20 % of global CH44434, a mean absolute error (MAE) of 4.91 million m34, and Pearson correlation coefficients of 0.89, when compared with field measurements. Sensitivity analysis and OLS regression showed that simulated CH4p<0.001), while precipitation exhibited inverted-U response, peaking at intermediate levels (9–10 mm d−1, p<0.01). Quantification of the contributions of the meteorological variables, revealed that temperature accounted for 5.96 ± 3.06 %, and precipitation for 7.38 ± 0.58 % of the total modeled CH444
Methane is a powerful greenhouse gas contributing significantly to global warming. South Asia is a major methane emission region, yet source-diagnostic isotopic signatures remain poorly constrained, limiting top-down source attribution. To address this gap, we conducted extensive sampling and isotopic analyses of major methane sources in South Asia. Our results reveal substantial deviations of South Asian methane source fingerprints from global means. Methane from C3 biomass burning is more depleted in δ13C
On 15 January 2022, the Hunga volcano eruption released unprecedented amounts of water vapor into the atmosphere alongside a modest amount of SO2. In this work we analyse results from multiple Earth system models as part of the Hunga Tonga-Hunga Ha'apai Volcano Impact Model Observation Comparison Project. Our results show a good model agreement over the climatic outcomes of the eruption, overall indicating a significant negative radiative forcing from the Hunga eruption. The multi-model mean of global instantaneous radiative forcing averaged over 2022–2023 is estimated at −0.19 ± 0.06 W m−2−0.16 ± 0.06 W m−2−0.14 ± 0.10 W m−2−0.09 ± 0.10 W m−2SO2Tg-SO2SO2
Long-range transport of trace elements (TEs) by aerosols plays a critical role in modulating marine biogeochemistry; however, their source contributions and spatial variability across land-sea gradients remain poorly constrained. Here, we conducted a refined source apportionment of TEs (e.g., Fe, Mn, Cr, V, Ni, Cu, Zn, As, Pb and Cd) in PM2.5ng m−3), where the pure dust contributions declined sharply in spring marine areas (Fe: 25.4 %, 145.2 ng m−3). However, part of the dust likely underwent aging during transport and was incorporated into the aged marine aerosol factor, which contributed 33.6 % of Fe, indicating that dust-related influence remained important offshore and that spring marine aerosols experienced substantial mixing among transported dust, marine processing and anthropogenic emissions. In contrast, coal combustion became the dominant source in summer marine aerosols (Fe: 43.2 %, 82.8 ng m−3), exceeding its contribution in Qingdao (Fe: 14.1 %, 45.5 ng m−3). Residual oil combustion was identified as the primary source of marine Ni and V (V: 65.7 % in spring and 79.8 % in summer) and also made substantial contributions to Fe, Mn, and Cr, particularly in summer marine aerosols (e.g., Fe: 26.1 %, 50.0 ng m−3). Overall, the refined source apportionment demonstrates that anthropogenic emissions, especiallyThese results advance our understanding of land-sea interactions in atmospheric TE cycling and provide new constraints for regional air quality and climate models.
High-resolution 3D fields of nitrogen dioxide (NO2) are critical for air quality management and satellite retrievals, yet traditional chemistry-transport models (CTMs) face challenges in fine-scale modeling. Machine learning (ML) alternatives often struggle with generalization and transferability, inheriting biases from CTMs or being limited by sparse surface measurements. We present the Deep Atmospheric Chemistry NO22), a deep learning model that generates daily 2 km × 2 km 3D NO22R2=0.90) and improves agreement with independent EEA stations over the CAMS reanalysis (R2−1.15 to −0.38 µg m−3). The model resolves spatial details and exhibits physically plausible behavior. This hybrid training approach fuses the physical consistency of a process-based model with the real-world surface measurements, overcoming the limitations of using either constraint alone. Applying DACNO222
Aerosol-cloud-precipitation interactions are assessed over the non-polar ocean using more than 11 years of combined Aqua-MODIS, CALIPSO-CALIOP, and CloudSat products. The analysis first shows the benefit of incorporating vertically resolved aerosol extinction coefficient (σext) in aerosol-cloud interactions (ACI) assessments, demonstrating that: σext
Hydrofluorocarbons (HFCs) are potent greenhouse gases widely used in refrigeration, air-conditioning, and heat pump systems. Accurate monitoring of HFC emissions is essential to evaluate compliance with climate regulations and inform mitigation strategies. This study presents trends of HFC emissions across north-western Europe between 2013 and 2024, derived from atmospheric inverse modelling combining atmospheric measurements at eleven monitoring stations with two transport models (NAME and FLEXPART) and three Bayesian inversion systems (InTEM, ELRIS, RHIME). Although global emissions continue to rise for most HFCs, in north-western Europe our results show an overall steady decline in total HFC emissions from 40 ± 3  Tg CO2-eq yr−1± 2  Tg CO2-eq yr−1
Northern high latitude wetlands are significant sources of methane, with emissions driven by seasonal soil freezing and thawing. To better understand the seasonality of northern high latitude methane emissions, we defined the melt period occurring in spring time using the remote sensing Soil Moisture and Ocean Salinity Freeze/Thaw data from 2011–2021. To estimate methane emissions in the northern high latitudes, we used the atmospheric inverse model CarbonTracker Europe-CH4. The melt period was defined for three permafrost zones and for a seasonally frozen non-permafrost region using two approaches: region-based, which considered climatological conditions of permafrost regions, and grid-based, which defines the melt period at a finer 1°×1°The length and timing of the melt period varied significantly depending on the approach. The melt period generally occurred between March and June and was influenced by air temperature, with a negative correlation between the length and the mean temperature of the melt period. The longest melt period was in the non-permafrost zone and the shortest varied between the two methods. The melt period emissions were on average 1.83 Tg with the region-based approach and 0.45 Tg with the grid-based approach, the non-permafrost zone having the largest share of the emissions. They were largely dependent on the season’s length. Year-to-year variation was modest, within 15 % (region-based) and 23 % (grid-based) of average emissions, and there was also no trend during the study period. Our dual-method approach allows for robust comparison with both large-scale regional studies and localized site-level research.
East Asian dust storms impact the health and livelihoods of millions but the atmospheric processes responsible are far from fully understood because suitable observations are lacking. Here we analyse dust source activation (DSA) frequency data for East Asia (80–130° E, 27–52° N, January 2016 through December 2023, Chen et al., 2025, https://doi.org/10.1088/1748-9326/addee6) to understand atmospheric controls on dust activation. We show that East Asia's two primary dust source regions (Chen et al., 2025) display distinct diurnal and seasonal variations in DSA frequency. A southern region, sandwiched between the Mongolian Plateau and the Tibetan Plateau, chiefly consisting of the Taklimakan Desert and the Alashan Plateau, is active year-round, with 40 %–60 % of events predominantly occurring during late morning (09:00–12:00 local solar time, LST) under clear-sky conditions. We show that breakdown of the Low-level Jet (LLJ) is a major control on dust activation across this region (not only the Taklimakan Desert), driven by morning heating of the land surface, deepening the convective boundary layer and momentum transfer to the land surface. Here, convective activities also contribute to cloud-associated dust source activations during summer afternoon (i.e., haboobs). A northern region, centred on the Mongolian Plateau-Gobi Desert is dust-active from morning to afternoon (08:00–19:00 LST), primarily under cloudy conditions, driven by the passage of low-pressure systems. A third (less active) dust source region, the Tibetan Plateau, is typically active during winter afternoons presumably in response to strong mountain-valley winds. Meso- and local-scale winds are more extensive drivers of dust activation across East Asia than previously documented, adding uncertainty to model predictions of future dust emissions in East Asia under a warming climate.
Large-scale stratiform clouds are widespread and dominate the Earth's radiation budget. Their radiative and microphysical properties are inseparable, depending on ambient aerosol conditions and on properties of any convective outflow. In the Community Atmospheric Model, version 6 (CAM6), large-scale clouds were originally treated two decades ago with a two-moment bulk microphysics approach. Since then, the technological and empirical basis of global models has improved, for example by representing cloud microphysics to encompass extra processes of ice and droplet initiation, and by including dependencies on aerosol conditions of size, composition, and loading.To advance the microphysical realism of the large-scale cloud scheme of the global model CAM6, most of the known mechanisms of secondary ice production (SIP) and an empirical formulation for heterogeneous ice nucleation have been represented in the stratiform scheme of the Global model CAM6. We included a hybrid bin/bulk microphysics scheme that treats aerosol activation, growth processes of accretion, aggregation, and riming, and three SIP mechanisms in the stratiform cloud scheme. We simulated an observed case of a mesoscale convective system during the Mid-latitude Continental Convective Clouds Experiment (MC3E) in Oklahoma, USA, using the Single-Column Atmosphere Model (SCAM6). The results from the simulations are validated against the aircraft, satellite, and ground measurements.Results show that the modified stratiform scheme can predict the cloud properties of the observed stratiform clouds realistically. Together with our improved convective scheme in CAM6, this paves the way for more realism in the treatment of aerosol-cloud interactions in global climate change by conventional General Circulation Models.
Advances in computational power and model development have enabled the generation of global high-resolution models. These new models can resolve a large proportion of gravity waves (GWs) explicitly, reducing reliance on subgrid parametrizations. GWs are vital components of the middle and upper atmosphere, they transport energy and momentum both horizontally and vertically, driving the atmospheric circulation. Evaluating the realism of these resolved waves is a crucial step in advancing future model development.Here we provide the first global multi-model GW observational comparison that accounts for the observational filter. We assess the representation of stratospheric GWs in three high-resolution (3–5 kmTime-mean wave amplitudes are systematically lower in the models than observations, consistent with previous studies. GW occurrence rates are higher in all models than the observations, dominated by low amplitude waves in the models. During the first 10 dThese results benchmark the current state of high-resolution modelling and demonstrate that whilst there are strengths in models' ability to capture the morphology of GWs (particularly orographically generated waves), there is room for improvement in modelling amplitudes, occurrence rates and zonal-mean flux magnitudes globally, with the largest discrepancies in the tropical convective regions.
Warm conveyor belts (WCBs) in extratropical cyclones transport moisture and hydrometeors into the upper troposphere and lower stratosphere (UTLS), influencing the radiative balance. Earlier research has shown that the horizontal grid spacing of numerical weather prediction (NWP) models has an impact on the modelled WCB properties, such as ascent rates and diabatic heating. This first part of a two-part study examines the impact of model grid spacing on the transport of moisture from a Lagrangian perspective. We analyze two ICON model simulations of one North Atlantic WCB case study: a convection-parameterizing run at ∼ 13 km and a convection-permitting run at ∼ 3.5 km approximate grid spacing. We hypothesize that key differences in the modelled transport of moisture arise from higher vertical velocities in the high-resolution simulation. The convection-permitting simulation produces more rapid ascent and a drier WCB outflow with lower specific and relative humidity. We attribute this to higher ice number concentrations, which deplete supersaturation more efficiently. This high-resolution simulation also exhibits more pronounced frozen-phase microphysics, stronger frozen precipitation, notably different hydrometeor mass mixing ratios, number concentrations, and radii than the lower resolution simulation – indicating that horizontal resolution substantially influences modelled WCB cloud composition. These results demonstrate that weather and climate models using convection-parameterizing resolutions may systematically misrepresent WCB cloud properties and UTLS humidity, with potential consequences for accurately simulating Earth's radiative budget and upper-level flow.
We report initial results for G6-1.5K-SAI, a climate model experiment proposed by the Geoengineering Model Intercomparison Project (GeoMIP). G6-1.5K-SAI simulates a stratospheric aerosol injection (SAI) to limit global warming to ∼ 1.5 °C above preindustrial in each model and features several design updates relative to previous GeoMIP experiment G6sulfur, such as hemispherically symmetric subtropical injection (30° N and 30° S) instead of equatorial injection. Due to differences in climate sensitivity, models disagree on the amount of warming to be offset, and therefore on the total injection required. While they agree strongly on the rate of cooling per unit rate of injection (∼ 0.1 °C (Tg SO2 yr−1)−1, a similar value to G6sulfur models with interactive SO2), similarities in aerosol representation and disagreements in aerosol optical depth (AOD) per rate of unit injection and in rate of cooling per unit AOD mean this agreement may not imply accuracy. In all participating models, SAI cools the land surface more than the ocean and offsets mid- and high-latitude precipitation increases under global warming, but models disagree on the magnitude of residual Arctic amplification and changes to tropical precipitation. Relative to G6sulfur, G6-1.5K-SAI cools the Arctic more strongly, and also decreases precipitation less, especially in the tropics and over land. All in all, while the new G6-1.5K-SAI experiment constitutes an update over the older G6sulfur, due to the differences in scenario across these two experiments, any differences in SAI impacts must be evaluated carefully.
In recent years, the need for high-quality long-term mesospheric ozone records has become increasingly evident, as they are essential for understanding chemical, dynamical, and radiative processes in the middle and upper atmosphere and their coupling with the lower layers. Here, we present a new merged dataset of ozone profiles in the middle atmosphere (METEOR-O3), created from several limb-viewing satellite instruments: HALOE, GOMOS, MIPAS, ACE-FTS, MLS, and SOFIE. The merged dataset covers the period from 1991 to 2023 and provides deseasonalized ozone anomalies in 10° latitude bins between 80° S and 80° N, from approximately 22 to 100 km. The deseasonalized ozone anomalies are used for global and seasonal trend analysis. The results show positive upper stratospheric ozone trends in both hemispheres, with magnitudes of 1 %–2 % per decade between 35 and 45 km, indicating continued ozone recovery consistent with previous assessments. In contrast, mesospheric ozone (above ∼ 60 km) exhibits negative trends of −1 % to −3 % per decade, with the strongest decreases of about −8 % to −12 % per decade between 80 and 90 km. Seasonal analyses confirm positive trends in the upper stratosphere across all seasons and persistent negative trends in the upper mesosphere, strongest at high latitudes above 80 km. The METEOR-O3 dataset provides the first global, long-term merged record suitable for detailed studies of mesospheric/lower thermospheric ozone variability and trend evaluation, providing valuable information for model validation and assessments of upper atmospheric changes.
Stratospheric Aerosol Injection (SAI) has been proposed to counteract global warming. Countering SAI may prove attractive to actors who oppose deployment and methods have been suggested but not tested for efficacy. Using a global climate model with double moment aerosol microphysics, we investigate the viability of “Stratospheric Aerosol Scrubbing” (SAS) scenarios where coarse calcite aerosol is deliberately injected to enhance aerosol growth, reduce particle radiative efficiency, and enhance sedimentation thereby reducing SAI impacts. We simulate two equatorial SAI and SAS scenarios: pulse interventions lasting 2 months, and sustained interventions lasting 20 years. We find that SAS reduces the global Stratospheric Aerosol Optical Depth by 30 %–40 % when the calcite mass is equal to the sulphur dioxide (SO2) mass in the pulse intervention and half of the SO2−3.3 to −2.3 Wm−2