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The region surrounding the Tibetan Plateau (TP) is widely considered a primary global dust source, with mineral dust comprising a significant proportion of aerosols over the TP. Current research on TP dust has mainly focused on transport from the surrounding deserts, with little focus on dust emissions from the TP's interior. The erodibility factor used by the WRF‐Chem (ERODDEF) is 0 for the TP, so the model cannot simulate the dust emissions inside the plateau. Thus, we constructed a high‐resolution erodibility data set (ERODSDS) based on a reliable dust source distribution and intensity map. Based on the modified EROD map, the WRF‐Chem model was used to simulate dust emissions and direct radiative forcing on the TP in 2018. With the modified EROD map, WRF‐Chem can well simulate the temporal variation and spatial pattern of mineral dust on the plateau, which greatly improves the model's dust emissions simulation accuracy on the TP. Plain Language Summary Due to certain limitations of the ERODDEF, the previous WRF‐Chem model was unable to accurately simulate dust emissions from the TP's interior. Thus, we constructed a more realistic ERODSDS and improved simulations of dust emissions and dust direct radiative forcing within the plateau. The west‐central part of the plateau is also an important source of dust emissions, and the amount of dust emitted within the plateau should not be neglected. Key Points The WRF‐Chem model seriously underestimates local dust emissions on the Tibetan Plateau High‐precision erodibility data on the Tibetan Plateau is constructed to accurately describe the surface dust emission potential The temporal and spatial distribution of the local dust emission on the Tibetan Plateau was estimated

This article is based on the study of the seasonal and interannual variability of carbon monoxide (CO) and ozone (O 3 ) at different altitudes of the troposphere over Hyderabad, India, during 2006-2010 using Measurement of OZone and water vapour by Airbus In-Service Aircraft (MOZAIC) and observation from Tropospheric Emission Spectrometer (TES) aboard NASA's Aura satellite. The MOZAIC observations show maximum seasonal variability in both CO and O 3 during winter and pre-monsoon season, with CO in the range (100-200)±13 ppbv and O 3 in the range (50-70)±9 ppbv. The time-series of MOZAIC data shows a significant increase of 4.2±1.3 % in the surface CO and 6.7±1.3 % in the surface O 3 during 2006-2010 in Hyderabad. From MOZAIC observations, we identify CO and O 3 profiles that are anomalous with respect to the monthly mean and compare those with Weather Research Forecast model coupled with Chemistry (WRF-Chem) and Model for OZone and Related Tracers, version 4 profiles for the same day. The anomalous profiles of WRF-Chem are simulated using three convection schemes. The goodness of comparison depends on the convection scheme and the altitude region of the troposphere.

Brown carbon (BrC), a light‐absorbing organic aerosol, remains poorly constrained in climate models due to unclear sources and formation pathways. In this study, we developed a BrC parameterization scheme by applying multivariate regression to observational data in China, relating BrC concentrations to organic carbon, meteorological conditions, and chemical variables. This scheme was implemented into the WRF‐Chem model to simulate BrC distributions and radiative effects during a haze episode from 1 January–12, 2019. The simulation revealed BrC concentrations ranging from 2.20 to 69.38 μg/m3 (mean: 11.63 μg/m3), with elevated values in the Beijing‐Tianjin‐Hebei region and Central China. BrC absorption notably decreased surface shortwave radiation by 7.07 W/m2 and increased atmospheric shortwave radiation by 0.73 W/m2, inducing non‐negligible land surface cooling, positive pressure anomalies, and higher near‐surface humidity. These findings underscore the notable radiative influence of BrC in haze‐prone regions of China, with implications for local climate and atmospheric dynamics.

Airborne particles of mineral dust play a key role in Earth's climate system and affect human activities around the globe. The numerical weather modeling community has undertaken considerable efforts to accurately forecast these dust emissions. Here, for the first time in the literature, we thoroughly describe and document the Air Force Weather Agency (AFWA) dust emission scheme for the Georgia Institute of Technology–Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) aerosol model within the Weather Research and Forecasting model with chemistry (WRF-Chem) and compare it to the other dust emission schemes available in WRF-Chem. The AFWA dust emission scheme addresses some shortcomings experienced by the earlier GOCART-WRF scheme. Improved model physics are designed to better handle emission of fine dust particles by representing saltation bombardment. WRF-Chem model performance with the AFWA scheme is evaluated against observations of dust emission in southwest Asia and compared to emissions predicted by the other schemes built into the WRF-Chem GOCART model. Results highlight the relative strengths of the available schemes, indicate the reasons for disagreement, and demonstrate the need for improved soil source data.

Climate policies will affect future surface ozone pollution in China. Here, we simulate changes in summertime ozone across China by 2030 under four emission scenarios reflecting different levels of climate action. We also contrast results obtained with two different chemical mechanisms employed in the chemical transport model (WRF‐Chem). With emission reductions in ozone precursors introduced by climate policies, both mechanisms show promising ozone mitigation for most parts of China. However, they disagree starkly in China's three main city clusters, where one mechanism projects worsening ozone pollution by 2030 despite the emission reductions. We analyze possible drivers of this important discrepancy, in particular the role of varying ozone chemical regimes affecting its sensitivity to emission changes. We recommend an intercomparison project to examine this critical modeling uncertainty among other models/mechanisms, which would be invaluable for informing local and regional emission control strategies that are based on single‐model results. Plain Language Summary Surface ozone pollution is harmful to both human health and ecosystems. Reducing ozone formation through effective emission control strategies has therefore been identified as a pressing need. Chemical transport models (CTMs) are important tools that can help scientists and policymakers assess how effectively the emission reductions may alleviate ozone pollution. However, we show that the predicted effectiveness of emission control strategies for ozone mitigation in areas within the three city clusters of China are strongly dependent on the choice of chemical mechanism commonly employed in CTMs. For example, given emission reductions driven by ambitious climate action, we find that projected ozone pollution in these regions could be improved or worsened by the year 2030 depending on the model mechanism used. Our work underlines the importance of considering and understanding this disagreement when it comes to projecting even near‐term emission‐control strategies. Furthermore, we highlight the potential benefits of conducting a multi‐model/mechanism intercomparison project to better understand how and why different models/mechanisms disagree on the simulated ozone response to emission changes, as to produce more robust mitigation scenario assessments. Key Points Future emission pathways driven by climate actions are projected to alleviate surface ozone pollution in most parts of China by 2030 However, for China's three main city clusters, model projections disagree strongly for two widely used chemical mechanisms This modeling uncertainty may arise from the inconsistency of categorizing ozone chemical regimes by different chemical mechanisms

Wet and dry depositions of aerosols in WRF‐Chem are revisited and updated based on recent observational findings. Traditionally, in‐cloud scavenging was thought to play a more dominant role in aerosol wet removal than below‐cloud scavenging. However, recent field measurements indicated a considerable contribution of below‐cloud scavenging of 50%–60% to total wet deposition. In contrast, the simulated contribution of in‐cloud scavenging in the previous version of WRF‐Chem was too large, exhibiting 88%–95%, likely due to the binary representation of cloud fraction. To reduce the model bias, this study adopts a continuous‐type cloud fraction and implements a semi‐empirical below‐cloud scavenging parameterization. Simulation results with the new scheme show that the contribution of below‐cloud (in‐cloud) scavenging is increased to 63%–66% (decreased to 34%–37%), well capturing the observational estimates. The magnitude of total wet deposition is increased by 18.2% for SO4, 7.16% for NO3, and 14.8% for NH4, showing better agreements with observations particularly for SO4 and NH4 deposition. The increased wet removal with the new scheme reduces and so better reproduces surface PM2.5 and PM10 concentrations, which is also partly attributed to the increased contribution of below‐cloud scavenging. It is found that dry deposition velocity in the previous version was too high for coarse mode particles when friction velocity is large, which underestimates surface PM10 concentration. The updated dry deposition scheme that is constrained by observations effectively improves PM10 performance by reducing the dry deposition velocity for coarse mode particles. Plain Language Summary Aerosols in the atmosphere are ultimately removed by wet and dry deposition processes. There are two wet scavenging processes: in‐cloud and below‐cloud scavenging. It has been believed that aerosols are scavenged more by in‐cloud processes, but recent field measurements revealed that the contribution of below‐cloud scavenging accounts for 50%–60% of total wet scavenging. A numerical air quality model, WRF‐Chem, was found to underrepresent the contribution of below‐cloud scavenging (∼5%–10%), however. Therefore, we update in‐cloud scavenging processes and implement a below‐cloud scavenging parameterization. The new method shows the below‐cloud scavenging contribution of ∼60%, increases wet deposition fluxes, and hence decreases surface aerosol concentrations. The wet deposition and aerosol concentrations simulated using the new method show better agreements with observations than those using the old one. It is also found that the dry deposition for large particles is overestimated in the previous version of WRF‐Chem, leading to low PM10 concentrations. We implemented a recent dry deposition parameterization constrained by observations, and the results show that PM10 concentration is greatly increased and so agrees better with observed PM10. Key Points Too large (small) contribution of in‐cloud (below‐cloud) scavenging was found in the previous WRF‐Chem due to binary‐type cloud fraction New scheme updating cloud fraction and below‐cloud scavenging better captures observed wet deposition fluxes and surface PM2.5 Too large dry deposition velocities for coarse particles in the previous WRF‐Chem are updated and surface PM10 is better reproduced

Sulfate aerosol greatly contributes to wintertime haze pollution in emission‐intensive regions like the North China Plain (NCP) in China. Fast sulfate increase and accumulation are usually recorded during winter haze; however, the multiphase oxidation of sulfur dioxide (SO2) and the physical processes affecting near‐surface sulfate are not fully understood. By combining in situ observations and numerical simulations, we found that high sulfur oxidation ratios (>0.6) under heavily polluted conditions are associated with low clouds and fog over NCP, induced by the moist southerly airflow. Thick low clouds and high SO2 levels in NCP provide a reaction environment for sulfate production. The sulfate production rate in cloud water can reach 0.5–1.3 μg m−3 h−1. The results demonstrate that the vertical mixing of sulfate generated within the cloud water to the surface plays a significant role in rapid sulfate production, highlighting the importance of understanding cloud‐water processes in haze pollution. Plain Language Summary Sulfate has been recognized as an important chemical component of atmospheric aerosols, especially during winter haze events. Rapid increases in sulfate concentrations are frequently observed during heavy pollution in the North China Plain (NCP) of China. However, the processes involved in the multiphase oxidation of sulfur dioxide (SO2) and the physical processes influencing sulfate variations near the surface remain unclear. In particular, the contribution of traditional in‐cloud sulfate production to the surface sulfate has been considered to be negligible in the NCP. Here, we revisited the role of in‐cloud sulfate production in the NCP by using ground‐based observations, radiosonde measurements, and model simulation. Our results indicate that the rapid conversion of SO2 to sulfates during heavy pollution is associated with the presence of low clouds and fog. We find that high sulfate production rates in cloud water lead to the rapid accumulation of sulfate, which is vertically mixed to the surface, resulting in a rapid increase in surface sulfate concentrations. This work sheds a new perspective on understanding the role of sulfate production in cloud water and its impact on air pollution. Key Points High sulfur oxidation ratios under heavy pollution conditions in the North China Plain are associated with low clouds and fog formation Sulfate production rates within cloud water can reach 0.5–1.3 μg m−3 h−1, with NO2 and O3 oxidation pathways dominating Vertical mixing of sulfate produced in cloud water to the surface causes a rapid increase in near‐surface sulfate concentration

Dust storms are one of the most frequent meteorological disasters in China, endangering agricultural production, transportation, air quality, and the safety of people’s lives and property. Against the backdrop of climate change, Mongolia’s contribution to China’s dust cannot be ignored in recent years. In this study, we used the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), along with dynamic dust sources and the HYSPLIT model, to analyze the contributions of different dust sources to dust concentrations in northern China in March and April 2023. The results show that the frequency of dust storms in 2023 was the highest observed in the past decade. Mongolia and the Taklimakan Desert were identified as two main dust sources contributing to northern China. Specifically, Mongolia contributed more than 42% of dust, while the Taklimakan Desert accounted for 26%. A cold high-pressure center, a cold front, and a Mongolian cyclone resulted in the transport of dust aerosols from Mongolia and the Taklimakan Desert to northern China, where they affected most parts of the region. Moreover, two machine learning methods [the XGBoost algorithm and the Synthetic Minority Oversampling Technique (SMOTE)] were used to forecast the dust storms in March 2023, based on ground observations and WRF-Chem simulations over East Asia. XGBoost-SMOTE performed well in predicting hourly PM 10 concentrations in China in March 2023, with a mean absolute error of 33.8 µg m −3 and RMSE of 54.2 µg m −3 .

The uncharacteristically extreme outbreak of particulate matter took place over the Balkan region from 27 to 30 March 2020. Observations at air quality stations in Croatia recorded hourly PM10 concentrations up to 412 μgm−3. The meteorological analysis shows that the increase in PM10 concentrations was primarily due to the advection of dust from the deserts east of the Caspian Sea. The anticyclone north of Croatia and the cyclone over Anatolia formed a strong pressure gradient driving transport from the east. Both back trajectories and satellite products pointed to the dry Aral Sea as the main source of dust. A dust plume influenced the PM10 increase observed in Croatia, starting at the easternmost air quality stations. The modeling study shows that the vertical extent of the dust plume was up to ∼2 km. However, the chemical and morphological (scanning electron microscope analysis) composition of PM10 at the sites in the northeastern Adriatic Sea showed mainly the presence of Saharan dust. Prior to the advection of the Asian dust, the transport of Saharan dust, driven by Sharav cyclone, was observed in the PM10 values at several stations in the Adriatic Sea and on the Croatian mainland on 26 March 2020. Modeling results showed that the Saharan dust transport occurred at altitudes below ∼8 km. The mixing of the Asian and Saharan dust plumes over the Balkans was favored by the subsidence due to anticyclonic high‐pressure conditions and is the most likely explanation for the observed PM chemical and morphological results. Plain Language Summary The event of extreme air pollution in Croatia occurred at the end of March 2020. Exceptionally high aerosol concentrations were observed at several air quality stations. The outbreak was studied using a numerical chemical transport model. Chemical analysis was also performed for the aerosol sample and the sample was viewed with an electronic microscope. The analysis revealed that the composition of the aerosol was due to desert dust transported by air masses from the deserts east of the Caspian Sea and the Sahara Desert. Key Points The extraordinary dust episode over the Balkan region was simulated with the WRF‐Chem model The dried Aral Sea was identified as the main source, but the dust plume from the Sahara was also present Chemical and morphological analysis of the PM10 sample shows that the dust originated from the Sahara Desert

Accurate simulation of atmospheric photochemistry is essential for air quality and climate studies but computationally expensive in three‐dimensional atmospheric models. Artificial intelligence (AI) algorithms show promise for accelerating photochemical simulations, but integrating them reliably into numerical models as replacements for complex mechanisms has been challenging, with success mostly limited to simplified schemes (e.g., 12 species). We present a novel AI PhotoChemistry (AIPC) scheme using the Feature‐Mapping Subspace Self‐Attention (FMSSA) algorithm, enabling fast, accurate, and stable online simulation of the full SAPRC‐99 mechanism (79 species, 229 reactions) within WRF‐Chem. Feature‐mapping subspace self‐attention reduces computational cost by 91% versus standard attention architectures via global feature mapping and subspace attention decomposition while maintaining high fidelity to nonlinear chemistry. Offline evaluations show FMSSA's superior accuracy (mean NRMSE = 3.09% for 69 species) over Multi‐Layer Perceptron and Residual Neural Network baselines, especially for ozone. Ablation experiments confirm the critical role of attention and LayerNorm modules for accuracy and generalizability. Monthly‐scale online simulations conducted in August show stable FMSSA‐AIPC performance, accurately reproducing species spatiotemporal distributions with 77% faster computation than the numerical solver. However, simulations conducted in February show performance degradation for all AIPC schemes, with FMSSA‐AIPC exhibiting unique synchronous errors, highlighting generalization challenges across significantly distinct atmospheric regimes. This work advances integrating sophisticated chemical processes in weather and climate models, with future efforts targeting expanded training data sets, architectural refinements and broader spatiotemporal testing. Plain Language Summary Atmospheric photochemistry critically influences air quality and climate change by modulating atmospheric composition, but simulating these processes within three‐dimensional atmospheric models is computationally expensive, particular for sophisticated mechanisms, hindering high‐resolution studies and integration into Earth system models. While artificial intelligence (AI) algorithms demonstrate potential for accelerating photochemical simulations, the highly nonlinear reaction networks of sophisticated mechanisms restrict the reliable integration of AI PhotoChemistry (AIPC) schemes into numerical models, with successful implementations predominantly limited to oversimplified mechanisms (e.g., 12 species). Here, we developed a novel AIPC scheme using the feature‐mapping subspace self‐attention (FMSSA) algorithm, which enables fast, accurate, and stable monthly‐scale online continuous simulations of the entire SAPRC‐99 mechanism (79 species, 229 reactions) within WRF‐Chem. FMSSA reduces computational time by 77% compared to traditional solvers and outperforms multi‐layer perceptron and residual neural network baselines, particularly for ozone. However, FMSSA exhibits unique synchronization errors during online continuous simulations when atmospheric conditions significantly differ from those in the training phase. This work advances the integration of complex photochemical mechanisms into weather and climate models, but future efforts are needed to extend FMSSA to more mechanisms and improve its stability across broader spatiotemporal conditions. Key Points Feature‐mapping subspace self‐attention (FMSSA) surpasses multi‐layer perceptron and residual neural network in modeling photochemistry The FMSSA‐based scheme enables accurate and stable simulations of full SAPRC‐99 photochemical mechanism within WRF‐Chem The FMSSA‐based scheme reduces computation time by 77% versus the SAPRC‐99 numerical scheme