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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

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

Comprehensive studies comparing impacts of building and street levels interventions on air temperature at metropolitan scales are still lacking despite increased urban heat‐related mortality and morbidity. We therefore model the impact of 9 interventions on air temperatures at 2 m during 2 hot days from the summer 2018 in the Greater London Authority area using the WRF BEP‐BEM climate model. We find that on average cool roofs most effectively reduce temperatures (∼−1.2°C), outperforming green roofs (∼0°C), solar panels (∼−0.5°C) and street level vegetation (∼−0.3°C). Application of air conditioning across London (United Kingdom) increases air temperatures by ∼+0.15°C. A practicable deployment of solar panels could cover its related energetic consumption. Current practicable deployments of green roofs and solar panels are ineffective at large scale reduction of temperatures. We provide a detailed decomposition of the surface energy balance to explain changes in air temperature and guide future decision‐making. Plain Language Summary Multiple common city scale passive and active interventions exist to reduce urban population's exposure to extreme heat during hot spells. Nonetheless, a proper comparison of the effect that each of these interventions may have on the temperatures experienced within large cities is missing. Additionally, the radiative and thermal mechanisms that lead to outdoor temperature changes are often not detailed and could lead to detrimental effects for local populations, such as indirect increase of water vapor or reflection of solar radiation. Our study, focusing over London, compares several common interventions through a modeling experiment and finds that cool roofs largely outperform other interventions during the two hottest days of the summer 2018. We also find that green roofs are ineffective on average and that solar panels and tree vegetation would only marginally change temperature exposures. Large scale deployment of air conditioning would lead to increased temperature in the core of London. Solar panels could potentially provide sufficient energy for running air conditioning all over London, creating comfortable indoor environments, and green roofs could reduce temperatures during the day. We argue that such inter‐comparisons should guide future decision making. Key Points City scale deployment of cool roofs leads to the greatest reduction in 2 m air temperature Green roofs do not decrease daily average temperature but have a daytime cooling effect Solar photovoltaic panels can reduce temperatures in London by capturing sensible heat flux and generate electrical power

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

The WRF‐Hydro hydrological model has been used in many applications in the past with some level of history matching in the majority of these studies. In this study, we use the iterative Ensemble Smoother (iES), a powerful parameter estimation methodology implemented in the open‐source PEST++ software. The iES provides an ensemble solution with an uncertainty bound instead of a single best estimate which has been the common approach in the previous WRF‐Hydro studies. We discuss the importance of accounting for observation noise which results in a wider spread in the model solution. We investigate the impact of constructing objective functions by differentially weighting the observations to tune the model response toward model outputs appropriate for a specific application. Results confirm the necessity of differentially weighting the observations before calculation of the objective function as the optimization algorithm struggles with calculating parameter updates with uniform weighting. We also show that we achieve better model performance in terms of verification metrics with higher emphasis on the high flow events, when the objective function is tuned toward an application where the extreme events are of importance. We then investigate the impact of estimating more parameters, in particular we estimate a larger number of snow parameters. Results show a large improvement in the model performance. In summary, our study demonstrates the efficacy of employing iES alongside differential weighting of observations, highlighting its potential to enhance hydrological model parameter estimation. Plain Language Summary The WRF‐Hydro model is commonly used in hydrology research and applications. Previous studies focused on finding the best single set of model parameters, and they only looked at a few sensitive parameters. In this study, we use a more advanced method called the iterative Ensemble Smoother (iES) to adjust the parameters. Instead of just one solution, this method gives a range of possibilities. We also set the objective function to improve the model's accuracy and performance for specific purposes. For instance, we can find parameters that work well for extreme events like floods, or others for estimating droughts. Unlike previous studies that only looked at a few parameters, our approach works with a large number of parameters, making it suitable for complex models. Key Points iterative Ensemble Smoother (iES) could be used successfully with WRF‐hydro to estimate parameters Constructing the objective function by differentially weighting observations enhances the parameter estimation, refining the procedure to specifically cater to a chosen application

Observations show increasing trends in wintertime fog episodes and aerosol loading over India. However, whether aerosols affect the fog trends and, if so, through what mechanisms remains unclear. Using long‐term observations, we find that aerosols substantially intensify the nighttime fog and lead to a 36% increase in fog duration in India. Aerosol‐coupled simulations show that high aerosols activate more fog droplets during the formative stages and increase the longwave cooling. Cooling affects supersaturation and enhances the condensational growth of the fog droplets. This aerosol‐longwave cooling‐condensational growth relationship enhances fog water content during nighttime. The subsequent morning, intense fog and aerosol‐radiative effects reduce incoming radiation and promote stagnation under high aerosol loading, delaying fog dissipation. Our study illustrates the aerosol‐cloud interactions induced fog intensification and aerosol's potential role in manifesting the observed fog trends over Indo‐Gangetic Plain, North India. Plain Language Summary The Indo‐Gangetic Plain faces long periods of fog during the winter, causing widespread socio‐economic disruptions. Recent studies show an increase in the number (frequency) and duration of such events. Incidentally, these fog events generally occur in highly polluted environments. However, the impact of particulate pollution (aerosols) on the longevity of the fog layer has been hard to understand due to the complexity of the problem and insufficient information on fog and aerosols from observations. Here, we used multiple (space‐borne and in situ) observations and found that aerosols strongly modulate fog duration. Using numerical simulations, we confirm that aerosols quickly intensify fog at night. The following day, the effects of aerosols and intense fog reduce solar radiation, leading to cooler and moister conditions near the surface. These conditions delay the natural evaporation of fog during the daytime. Hence, our results signify the crucial role of aerosols in modulating fog duration and their potential to modify fog trends over the Indo‐Gangetic Plain. Key Points Aerosols can constrain fog duration trends over Indo‐Gangetic Plain Aerosol‐cloud interaction effects cause the intensification of nighttime fog Aerosol‐radiative effects promote calm, cool, and moist conditions during daytime and prolong fog dissipation

In recent decades, the Tibetan Plateau (TP) has experienced a notable rise in fine particulate matter (PM2.5) levels, impacting its climate and ecology. However, accurately simulating PM2.5 concentrations on the TP remains challenging. This study investigates the uncertainties in PM2.5 underestimation, including meteorological conditions, dust emissions, regional transport, and emission inventories. Results show that discrepancies in various anthropogenic emission inventories significantly hinder accurate PM2.5 concentration reproduction on the TP, overwhelming meteorological predictions, dust emissions, and regional transport contributions. With meteorological simulations showing similar discrepancies to those in other regions, limited improvement from increased dust, and a relatively high proportion of regional transport, it becomes evident that local emissions are underestimated. Modeling with adjusted anthropogenic emissions reveals a seasonal underestimation of 82.6%–92.6% of local anthropogenic emissions on the TP. This research emphasizes the need for an accurate anthropogenic emission inventory in understanding climate change on the TP. Plain Language Summary The Tibetan Plateau (TP), often called the “Third Pole,” plays an important role in local weather and climate. Because of its high‐altitude environment, it is particularly vulnerable to the effects of global climate change. Although it is a remote area, the TP has been affected by rising levels of aerosol pollution, which has had a major impact on its climate and ecosystems in recent years. This study examines the challenges of predicting PM2.5 levels on the TP, focusing on factors such as weather conditions, dust, regional transport, and emission data. While the WRF‐CMAQ model performs well in many areas, it tends to underestimate PM2.5 levels on the TP. Our findings show that the main cause of this underestimation is inaccurate data on anthropogenic emissions. After excluding other factors, we found that emissions data does not align with the actual PM2.5 levels on the TP. When we adjusted the emission data, we discovered that the model was underestimating emissions by 82.6%–92.6% each month. This highlights the urgent need for more accurate emission data to enhance air pollution models and improve our understanding of air quality in the region. Key Points CMAQ with default inputs underestimates PM2.5 concentrations by 72.9% on the Tibetan Plateau (TP) Discrepancies in anthropogenic emission inventories lead to inaccurate PM2.5 predictions Modeling with adjusted emissions reveals a seasonal underestimation in local anthropogenic emissions on the TP by 82.6%–92.6%

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.

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.

We recently developed a dynamically coupled hydrological‐ocean modeling system that provides seamless coverage across the land‐ocean continuum during hurricane‐induced compound flooding. This study introduced a local inertial equation and a diagonal flow algorithm to the overland routing of the coupled system’s hydrology model (WRF‐Hydro). Using Hurricane Florence (2018) as a test case, the performance of the coupled model was significantly improved, evidenced by its enhanced capability of capturing backwater and increased water level simulation accuracy and stability. With four model experiments, we present a framework to detangle, define, and quantify compound and nonlinear effects. The results revealed that the flood peaks in the lower Cape Fear River Basin and the coastal waters were contributed by inland flooding and storm surge, respectively. These two processes had comparable contributions to the flooding in the Cape Fear River Estuary. The compound effect was identified when the flood levels resulting from the combination of land and ocean processes surpassed those caused by an individual process alone. The compound effect during Hurricane Florence exhibited limited impact on flood peaks, primarily due to the time lag between the peaks of the storm surge and the inland flooding. In the period between the two peaks, the compound effect was salient and significantly impacted the magnitude and variation of the flood level. The nonlinear effect, defined as the difference between the compound flood level and the superposition of storm surge and inland flooding water levels, reduced flood levels in the river channels while increasing flood levels on the floodplain. Plain Language Summary This study addresses the phenomenon of hurricane‐induced compound flooding, which arises when inland waters and storm surges coincide at the land‐ocean boundary. We’ve devised a hydrological‐ocean model that effectively covers such events. This model, enhanced with new algorithms, was tested using Hurricane Florence (2018) data, showing marked improvements in predicting water levels and tide effects. Our research delineates and quantifies the complex interplay between different flooding sources during such events. Key findings include the determination that both inland flooding and storm surges contributed equally to the flooding in the Cape Fear River Estuary. However, the overlapping impact of these processes, termed the “compound effect,” was limited in its influence on peak flood levels, mainly due to the time gap between storm surge and inland flooding peaks. Another crucial discovery was the “nonlinear effect,” which accounts for discrepancies in predicted flood levels. This effect tended to decrease flood levels in river channels but increased them on floodplains. Key Points A local inertial equation and a diagonal flow algorithm were introduced to a newly developed dynamically coupled hydrological‐ocean model Strong compound effect between hydrological and ocean processes occurred between their peaks The nonlinear effect reduced the flood peaks in the river channels while amplifying them on the floodplains