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A new long-term emission inventory called the Inversed Emission Inventory for Chinese Air Quality (CAQIEI) was developed in this study by assimilating surface observations from the China National Environmental Monitoring Centre (CNEMC) using an ensemble Kalman filter (EnKF) and the Nested Air Quality Prediction Modeling System. This inventory contains the constrained monthly emissions of NOx, SO2, CO, primary PM2.5, primary PM10, and non-methane volatile organic compounds (NMVOCs) in China from 2013 to 2020, with a horizontal resolution of 15 km × 15 km. This paper documents detailed descriptions of the assimilation system and the evaluation results for the emission inventory. The results suggest that CAQIEI can effectively reduce the biases in the a priori emission inventory, with the normalized mean biases ranging from −9.1 % to 9.5 % in the a posteriori simulation, which are significantly reduced from the biases in the a priori simulations (−45.6 % to 93.8 %). The calculated root-mean-square errors (RMSEs) (0.3 mg m−3 for CO and 9.4–21.1 µg m3 for other species, on the monthly scale) and correlation coefficients (0.76–0.94) were also improved from the a priori simulations, demonstrating good performance of the data assimilation system. Based on CAQIEI, we estimated China's total emissions (including both natural and anthropogenic emissions) of the six species in 2015 to be as follows: 25.2 Tg of NOx, 17.8 Tg of SO2, 465.4 Tg of CO, 15.0 Tg of PM2.5, 40.1 Tg of PM10, and 46.0 Tg of NMVOCs. From 2015 to 2020, the total emissions decreased by 54.1 % for SO2, 44.4 % for PM2.5, 33.6 % for PM10, 35.7 % for CO, and 15.1 % for NOx but increased by 21.0 % for NMVOCs. It is also estimated that the emission reductions were larger during 2018–2020 (from −26.6 % to −4.5 %) than during 2015–2017 (from −23.8 % to 27.6 %) for most of the species. In particular, the total Chinese NOx and NMVOC emissions were shown to increase during 2015–2017, especially over the Fenwei Plain area (FW), where the emissions of particulate matter (PM) also increased. The situation changed during 2018–2020, when the upward trends were contained and reversed to downward trends for the total emissions of both NOx and NMVOCs and the PM emissions over FW. This suggests that the emission control policies may be improved in the 2018–2020 action plan. We also compared CAQIEI with other air pollutant emission inventories in China, which verified our inversion results in terms of the total emissions of NOx, SO2, and NMVOCs and more importantly identified the potential uncertainties in current emission inventories. Firstly, CAQIEI suggested higher CO emissions in China, with CO emissions estimated by CAQIEI (426.8 Tg) being more than twice the amounts in previous inventories (120.7–237.7 Tg). Significantly higher emissions were also suggested over western and northeastern China for the other air pollutants. Secondly, CAQIEI suggested higher NMVOC emissions than previous emission inventories by about 30.4 %–81.4 % over the North China Plain (NCP) but suggested lower NMVOC emissions by about 27.6 %–0.0 % over southeastern China (SE). Thirdly, CAQIEI suggested lower emission reduction rates during 2015–2018 than previous emission inventories for most species, except for CO. In particular, China's NMVOC emissions were shown to have increased by 26.6 % from 2015 to 2018, especially over NCP (by 38.0 %), northeastern China (by 38.3 %), and central China (60.0 %). These results provide us with new insights into the complex variations in air pollutant emissions in China during two recent clean-air actions, which has the potential to improve our understanding of air pollutant emissions in China and their impacts on air quality. All of the datasets are available at https://doi.org/10.57760/sciencedb.13151 (Kong et al., 2023a).

The unprecedented lockdown of human activities during the COVID-19 pandemic has significantly influenced social life in China. However, understanding the impact of this unique event on the emissions of different species is still insufficient, prohibiting the proper assessment of the environmental impacts of COVID-19 restrictions. Here we developed a multi-air-pollutant inversion system to simultaneously estimate the emissions of NOx, SO2, CO, PM2.5 and PM10 in China during COVID-19 restrictions with high temporal (daily) and horizontal (15 km) resolutions. Subsequently, contributions of emission changes versus meteorological variations during the COVID-19 lockdown were separated and quantified. The results demonstrated that the inversion system effectively reproduced the actual emission variations in multi-air pollutants in China during different periods of COVID-19 lockdown, which indicate that the lockdown is largely a nationwide road traffic control measure with NOx emissions decreasing substantially by ∼40 %. However, emissions of other air pollutants were found to only decrease by ∼10 % because power generation and heavy industrial processes were not halted during lockdown, and residential activities may actually have increased due to the stay-at-home orders. Consequently, although obvious reductions of PM2.5 concentrations occurred over the North China Plain (NCP) during the lockdown period, the emission change only accounted for 8.6 % of PM2.5 reductions and even led to substantial increases in O3. The meteorological variation instead dominated the changes in PM2.5 concentrations over the NCP, which contributed 90 % of the PM2.5 reductions over most parts of the NCP region. Meanwhile, our results suggest that the local stagnant meteorological conditions, together with inefficient reductions of PM2.5 emissions, were the main drivers of the unexpected PM2.5 pollution in Beijing during the lockdown period. These results highlighted that traffic control as a separate pollution control measure has limited effects on the coordinated control of O3 and PM2.5 concentrations under current complex air pollution conditions in China. More comprehensive and balanced regulations for multiple precursors from different sectors are required to address O3 and PM2.5 pollution in China.

The southeastern Atlantic is home to an expansive smoke aerosol plume overlying a large cloud deck for approximately a third of the year. The aerosol plume is mainly attributed to the extensive biomass burning activities that occur in southern Africa. Current Earth system models (ESMs) reveal significant differences in their estimates of regional aerosol radiative effects over this region. Such large differences partially stem from uncertainties in the vertical distribution of aerosols in the troposphere. These uncertainties translate into different aerosol optical depths (AODs) in the planetary boundary layer (PBL) and the free troposphere (FT). This study examines differences of AOD fraction in the FT and AOD differences among ESMs (WRF-CAM5, WRF-FINN, GEOS-Chem, EAM-E3SM, ALADIN, GEOS-FP, and MERRA-2) and aircraft-based measurements from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign. Models frequently define the PBL as the well-mixed surface-based layer, but this definition misses the upper parts of decoupled PBLs, in which most low-level clouds occur. To account for the presence of decoupled boundary layers in the models, the height of maximum vertical gradient of specific humidity profiles from each model is used to define PBL heights. Results indicate that the monthly mean contribution of AOD in the FT to the total-column AOD ranges from 44 % to 74 % in September 2016 and from 54 % to 71 % in August 2017 within the region bounded by 25∘ S–0∘ N–S and 15∘ W–15∘ E (excluding land) among the ESMs. ALADIN and GEOS-Chem show similar aerosol plume patterns to a derived above-cloud aerosol product from the Moderate Resolution Imaging Spectroradiometer (MODIS) during September 2016, but none of the models show a similar above-cloud plume pattern to MODIS in August 2017. Using the second-generation High Spectral Resolution Lidar (HSRL-2) to derive an aircraft-based constraint on the AOD and the fractional AOD, we found that WRF-CAM5 produces 40 % less AOD than those from the HSRL-2 measurements, but it performs well at separating AOD fraction between the FT and the PBL. AOD fractions in the FT for GEOS-Chem and EAM-E3SM are, respectively, 10 % and 15 % lower than the AOD fractions from the HSRL-2. Their similar mean AODs reflect a cancellation of high and low AOD biases. Compared with aircraft-based observations, GEOS-FP, MERRA-2, and ALADIN produce 24 %–36 % less AOD and tend to misplace more aerosols in the PBL. The models generally underestimate AODs for measured AODs that are above 0.8, indicating their limitations at reproducing high AODs. The differences in the absolute AOD, FT AOD, and the vertical apportioning of AOD in different models highlight the need to continue improving the accuracy of modeled AOD distributions. These differences affect the sign and magnitude of the net aerosol radiative forcing, especially when aerosols are in contact with clouds.

Vehicle emissions are regarded as a primary contributor to air pollution and related adverse health impacts. Heavy traffic congestion increases traffic flow and thus produces more O3 precursor emissions, leading to more adverse air quality issues. Although the development of a vehicle emission inventory has received great concern and continuous efforts, limitations still exist. For example, real-time diurnal variations and increases in emission rates due to traffic congestion are not well understood. In this study, we developed a new temporal allocation approach in transportation emissions to investigate the impact on air quality and health burden due to traffic congestion in China in 2020. Both real-time congestion-level data and emission correction factors were considered in the approach. Results show that traffic congestion aggravates air pollution and health burden across China, especially in the urban clusters such as the North China Plain and Sichuan Basin. In these regions, the average annual increases in fine particulate matter (PM2.5) and ozone (O3) could be up to 3.5 µg m−3 and 1.1 ppb, respectively. The excess PM2.5 and O3 attributed to the traffic congestion also induce an additional 20 000 and 5000 premature deaths in China, respectively. In major cities, the increased rate of premature mortality caused by traffic congestion may reach 17.5 %. Therefore, more effective and comprehensive vehicle emission control policies or better planning of the road network should be established to reduce traffic congestion and improve air quality in China.

A new open biomass burning inventory is presented that relies on the fire radiative power data from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi NPP satellite. This VIIRS-based Fire Emission Inventory (VFEI) provides emission data from early 2012 to 2019 for more than 40 species of gases and aerosols at spatial resolutions of around 500 m. We found that VFEI produces similar results when compared to other major inventories in many regions of the world. Additionally, we conducted regional simulations using VFEI with the Weather Research and Forecasting (WRF) model with chemistry (WRF-Chem) for Southern Africa (September 2016) and North America (July–August 2019). We compared aerosol optical depth (AOD) from the model against two observational datasets: the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) product and AErosol RObotic NETwork (AERONET) stations. Results showed good agreement between both simulations and the datasets, with mean AOD biases of around +0.03 for Southern Africa and −0.01 for North America. Both simulations were not only able to reproduce the AOD magnitudes accurately, but also the inter-diurnal variations of smoke concentration. In addition, we made use of the airborne data from the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES; Southern Africa) and the Fire Influence on Regional to Global Environments Experiment and Air Quality (FIREX-AQ; North America) campaigns to evaluate the simulations. In Southern Africa, results showed correlations higher than 0.77 when comparing carbon monoxide and black carbon. In North America, correlations were lower and biases higher. However, this is because the model was not able to reproduce the timing, shape, and location of individual plumes over complex terrain (Rocky Mountains) during the FIREX-AQ campaign period.

Unprecedented urbanization in China has led to serious urban heat island (UHI) issues, exerting intense heat stress on urban residents. Based on the observed temperature and PM2.5 concentrations in Beijing over 2016–2020, we find diverse influences of aerosol pollution on urban heat island intensity (UHII) under different circulations. When northerly winds are prevalent in urban Beijing, UHII tends to be much higher in both daytime and nighttime and it is less affected by aerosol concentrations. However, when southerly and westerly winds are dominant in rural Beijing, UHII is significantly reduced by aerosol pollution. Using coupled aerosol-radiation weather simulations, we demonstrate the underlying physical mechanism which is associated with local circulation and resulting spatial distribution of aerosols. Our results also highlight the role of black carbon in aggravating UHI, especially during nighttime. It could thus be targeted for cooperative management of heat islands and aerosol pollution.

Ozone (O3) dry deposition is a major O3 sink. As a follow-up study of Huang et al. (2021), we quantify the impact of satellite soil moisture (SM) on model representations of this process when different dry-deposition parameterizations are implemented, based on which the implications for interpreting O3 air pollution levels and assessing the O3 impacts on human and ecosystem health are provided. The SM data from NASA's Soil Moisture Active Passive mission are assimilated into the Noah-Multiparameterization (Noah-MP) land surface model within the NASA Land Information System framework, semicoupled with Weather Research and Forecasting model with online Chemistry (WRF-Chem) regional-scale simulations covering the southeastern US. Major changes in the modeling system used include enabling the dynamic vegetation option, adding the irrigation process, and updating the scheme for the surface exchange coefficient. Two dry-deposition schemes are implemented, i.e., the Wesely scheme and a “dynamic” scheme, in the latter of which dry-deposition parameterization is coupled with photosynthesis and vegetation dynamics. It is demonstrated that, when the dynamic scheme is applied, the simulated O3 dry-deposition velocities vd and their stomatal and cuticular portions, as well as the total O3 fluxes Ft, are larger overall; vd and Ft are 2–3 times more sensitive to the SM changes due to the data assimilation (DA). Further, through case studies at two forested sites with different soil types and hydrological regimes, we highlight that, applying the Community Land Model type of SM factor controlling stomatal resistance (i.e., β factor) scheme in replacement of the Noah-type β factor scheme reduced the vd sensitivity to SM changes by ∼75 % at one site, while it doubled this sensitivity at the other site. Referring to multiple evaluation datasets, which may be associated with variable extents of uncertainty, the model performance of vegetation, surface fluxes, weather, and surface O3 concentrations shows mixed responses to the DA, some of which display land cover dependency. Finally, using model-derived concentration- and flux-based policy-relevant O3 metrics as well as their matching exposure–response functions, the relative biomass/crop yield losses for several types of vegetation/crops are estimated to be within a wide range of 1 %–17 %. Their sensitivities to the model's dry-deposition scheme and the implementation of SM DA are discussed.

Biomass burning smoke is advected over the southeastern Atlantic Ocean between July and October of each year. This smoke plume overlies and mixes into a region of persistent low marine clouds. Model calculations of climate forcing by this plume vary significantly in both magnitude and sign. NASA EVS-2 (Earth Venture Suborbital-2) ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) had deployments for field campaigns off the west coast of Africa in 3 consecutive years (September 2016, August 2017, and October 2018) with the goal of better characterizing this plume as a function of the monthly evolution by measuring the parameters necessary to calculate the direct aerosol radiative effect. Here, this dataset and satellite retrievals of cloud properties are used to test the representation of the smoke plume and the underlying cloud layer in two regional models (WRF-CAM5 and CNRM-ALADIN) and two global models (GEOS and UM-UKCA). The focus is on the comparisons of those aerosol and cloud properties that are the primary determinants of the direct aerosol radiative effect and on the vertical distribution of the plume and its properties. The representativeness of the observations to monthly averages are tested for each field campaign, with the sampled mean aerosol light extinction generally found to be within 20 % of the monthly mean at plume altitudes. When compared to the observations, in all models, the simulated plume is too vertically diffuse and has smaller vertical gradients, and in two of the models (GEOS and UM-UKCA), the plume core is displaced lower than in the observations. Plume carbon monoxide, black carbon, and organic aerosol masses indicate underestimates in modeled plume concentrations, leading, in general, to underestimates in mid-visible aerosol extinction and optical depth. Biases in mid-visible single scatter albedo are both positive and negative across the models. Observed vertical gradients in single scatter albedo are not captured by the models, but the models do capture the coarse temporal evolution, correctly simulating higher values in October (2018) than in August (2017) and September (2016). Uncertainties in the measured absorption Ångstrom exponent were large but propagate into a negligible (<4 %) uncertainty in integrated solar absorption by the aerosol and, therefore, in the aerosol direct radiative effect. Model biases in cloud fraction, and, therefore, the scene albedo below the plume, vary significantly across the four models. The optical thickness of clouds is, on average, well simulated in the WRF-CAM5 and ALADIN models in the stratocumulus region and is underestimated in the GEOS model; UM-UKCA simulates cloud optical thickness that is significantly too high. Overall, the study demonstrates the utility of repeated, semi-random sampling across multiple years that can give insights into model biases and how these biases affect modeled climate forcing. The combined impact of these aerosol and cloud biases on the direct aerosol radiative effect (DARE) is estimated using a first-order approximation for a subset of five comparison grid boxes. A significant finding is that the observed grid box average aerosol and cloud properties yield a positive (warming) aerosol direct radiative effect for all five grid boxes, whereas DARE using the grid-box-averaged modeled properties ranges from much larger positive values to small, negative values. It is shown quantitatively how model biases can offset each other, so that model improvements that reduce biases in only one property (e.g., single scatter albedo but not cloud fraction) would lead to even greater biases in DARE. Across the models, biases in aerosol extinction and in cloud fraction and optical depth contribute the largest biases in DARE, with aerosol single scatter albedo also making a significant contribution.

Wildfire smoke is one of the most significant concerns of human and environmental health, associated with its substantial impacts on air quality, weather, and climate. However, biomass burning emissions and smoke remain among the largest sources of uncertainties in air quality forecasts. In this study, we evaluate the smoke emissions and plume forecasts from 12 state-of-the-art air quality forecasting systems during the Williams Flats fire in Washington State, US, August 2019, which was intensively observed during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Model forecasts with lead times within 1 d are intercompared under the same framework based on observations from multiple platforms to reveal their performance regarding fire emissions, aerosol optical depth (AOD), surface PM2.5, plume injection, and surface PM2.5 to AOD ratio. The comparison of smoke organic carbon (OC) emissions suggests a large range of daily totals among the models, with a factor of 20 to 50. Limited representations of the diurnal patterns and day-to-day variations of emissions highlight the need to incorporate new methodologies to predict the temporal evolution and reduce uncertainty of smoke emission estimates. The evaluation of smoke AOD (sAOD) forecasts suggests overall underpredictions in both the magnitude and smoke plume area for nearly all models, although the high-resolution models have a better representation of the fine-scale structures of smoke plumes. The models driven by fire radiative power (FRP)-based fire emissions or assimilating satellite AOD data generally outperform the others. Additionally, limitations of the persistence assumption used when predicting smoke emissions are revealed by substantial underpredictions of sAOD on 8 August 2019, mainly over the transported smoke plumes, owing to the underestimated emissions on 7 August. In contrast, the surface smoke PM2.5 (sPM2.5) forecasts show both positive and negative overall biases for these models, with most members presenting more considerable diurnal variations of sPM2.5. Overpredictions of sPM2.5 are found for the models driven by FRP-based emissions during nighttime, suggesting the necessity to improve vertical emission allocation within and above the planetary boundary layer (PBL). Smoke injection heights are further evaluated using the NASA Langley Research Center's Differential Absorption High Spectral Resolution Lidar (DIAL-HSRL) data collected during the flight observations. As the fire became stronger over 3–8 August, the plume height became deeper, with a day-to-day range of about 2–9 km a.g.l. However, narrower ranges are found for all models, with a tendency of overpredicting the plume heights for the shallower injection transects and underpredicting for the days showing deeper injections. The misrepresented plume injection heights lead to inaccurate vertical plume allocations along the transects corresponding to transported smoke that is 1 d old. Discrepancies in model performance for surface PM2.5 and AOD are further suggested by the evaluation of their ratio, which cannot be compensated for by solely adjusting the smoke emissions but are more attributable to model representations of plume injections, besides other possible factors including the evolution of PBL depths and aerosol optical property assumptions. By consolidating multiple forecast systems, these results provide strategic insight on pathways to improve smoke forecasts.

This study evaluates the impact of satellite soil moisture (SM) data assimilation (DA) on regional weather and ozone (O3) modeling over the southeastern US during the summer. Satellite SM data are assimilated into the Noah land surface model using an ensemble Kalman filter approach within National Aeronautics and Space Administration's Land Information System framework, which is semicoupled with the Weather Research and Forecasting model with online Chemistry (WRF-Chem; standard version 3.9.1.1). The DA impacts on the model performance of SM, weather states, and energy fluxes show strong spatiotemporal variability. Dense vegetation and water use from human activities unaccounted for in the modeling system are among the factors impacting the effectiveness of the DA. The daytime surface O3 responses to the DA can largely be explained by the temperature-driven changes in biogenic emissions of volatile organic compounds and soil nitric oxide, chemical reaction rates, and dry deposition velocities. On a near-biweekly timescale, the DA modified the mean daytime and daily maximum 8 h average surface O3 by up to 2–3 ppbv, with the maximum impacts occurring in areas where daytime surface air temperature most strongly (i.e., by ∼2 K) responded to the DA. The DA impacted WRF-Chem upper tropospheric O3 (e.g., for its daytime-mean, by up to 1–1.5 ppbv) partially via altering the transport of O3 and its precursors from other places as well as in situ chemical production of O3 from lightning and other emissions. Case studies during airborne field campaigns suggest that the DA improved the model treatment of convective transport and/or lightning production. In the cases that the DA improved the modeled SM, weather fields, and some O3-related processes, its influences on the model's O3 performance at various altitudes are not always as desirable. This is in part due to the uncertainty in the model's key chemical inputs, such as anthropogenic emissions, and the model representation of stratosphere–troposphere exchanges. This can also be attributable to shortcomings in model parameterizations (e.g., chemical mechanism, natural emission, photolysis and deposition schemes), including those related to representing water availability impacts. This study also shows that the WRF-Chem upper tropospheric O3 response to the DA has comparable magnitudes with its response to the estimated US anthropogenic emission changes within 2 years. As reductions in anthropogenic emissions in North America would benefit the mitigation of O3 pollution in its downwind regions, this analysis highlights the important role of SM in quantifying air pollutants' source–receptor relationships between the US and its downwind areas. It also emphasizes that using up-to-date anthropogenic emissions is necessary for accurately assessing the DA impacts on the model performance of O3 and other pollutants over a broad region. This work will be followed by a Noah-Multiparameterization (with dynamic vegetation)-based study over the southeastern US, in which selected processes including photosynthesis and O3 dry deposition will be the foci.

Light absorption and radiative forcing of black carbon (BC) is influenced by both BC itself and its interactions with other aerosol chemical compositions. Although the changes in BC concentrations in response to emission reduction measures have been well documented, the influence of emission reductions on the light absorption properties of BC and its influence on BC-boundary-layer interactions has been less explored. In this study, we used the online coupled WRF-Chem model to examine how emission control measures during the Asia-Pacific Economic Cooperation (APEC) summit affect the mixing state and light absorption of BC, and the associated implications for BC-PBL interactions. We found that both the mass concentration of BC and the BC coating materials declined during the APEC week, which reduced the light absorption and light absorption enhancement (Eab) of BC. The reduced absorption aerosol optical depth (AAOD) during APEC was caused by both the decline in the mass concentration of BC itself (52.0 %), and the lensing effect of BC (48.0 %). The reduction in coating materials (39.4 %) contributed the most to the influence of the lensing effect, and the reduced light absorption capability (Eab) contributed 3.2 % to the total reduction in AAOD. Reduced light absorption of BC due to emission control during APEC enhanced planetary boundary layer height (PBLH) by 8.2 m. PM2.5 and O3 were found to have different responses to the changes in the light absorption of BC. Reduced light absorption of BC due to emission reductions decreased near-surface PM2.5 concentrations but near-surface O3 concentrations were enhanced in the North China Plain. These results suggest that current measures to control SO2, NOx, etc. would be effective in reducing the absorption enhancement of BC and in inhibiting the feedback of BC on the boundary layer. However, enhanced ground O3 might be a side effect of current emission control strategies. How to control emissions to offset this side effect of current emission control measures on O3 should be an area of further focus.

Asia has attracted research attention because it has the highest anthropogenic emissions in the world, and the Model Inter-Comparison Study for Asia (MICS-Asia) phase III was carried out to foster our understanding of the status of air quality over Asia. This study analyzed wet deposition in southeast Asian countries (Myanmar, Thailand, Lao People's Democratic Republic (PDR), Cambodia, Vietnam, the Philippines, Malaysia, and Indonesia) with the aim of providing insights into the seasonal variation of wet deposition. Southeast Asia was not fully considered in MICS-Asia phase II due to a lack of observational data; however, the analysis period of MICS-Asia III, namely the year 2010, is covered by ground observations of the Acid Deposition Monitoring Network in East Asia (EANET), and the coordinated simulation domain was extended to cover these observation sites. The analyzed species are wet depositions of S (sulfate aerosol, sulfur dioxide (SO2), and sulfuric acid (H2SO4)), N (nitrate aerosol, nitrogen monoxide (NO), nitrogen dioxide (NO2), and nitric acid (HNO3)), and A (ammonium aerosol and ammonia (NH3)). The wet deposition simulated with seven models driven by a unified meteorological model in MICS-Asia III was used with the ensemble approach, which effectively modulates the differences in performance among models. By comparison with EANET observations, although the seven models generally captured the wet depositions of S, N, and A, there were difficulties capturing these in some cases. Considering the model performance for ambient aerosol concentrations over southeast Asia, this failure of models is considered to be related to the difficulty in capturing the precipitation in southeast Asia, especially during the dry and wet seasons. Generally, meteorological fields overestimate the precipitation during the dry season, which leads to the overestimation of wet deposition during this season. To overcome this, a precipitation-adjusted approach that scaled the modeled precipitation to the observed value was applied, and it was demonstrated that the model performance was improved. Satellite measurements were also used to adjust for precipitation data, which adequately accounted for the spatiotemporal precipitation patterns, especially in the dry season. As the statistical scores were mostly improved by this adjustment, the estimation of wet deposition with precipitation adjustment was considered to be superior. To utilize satellite measurements, the spatial distribution of wet deposition was revised. Based on this revision, it was found that Vietnam, Malaysia, and Indonesia were upward corrected, and Myanmar, Thailand, Lao PDR, Cambodia, and the Philippines were downward-corrected; these corrections were up to ±40 %. The improved accuracy of precipitation amount was key to estimating wet deposition in this study. These results suggest that the precipitation-adjusted approach has the potential to obtain accurate estimates of wet deposition through the fusion of models and observations.

Southern Africa produces almost a third of the Earth's biomass burning (BB) aerosol particles, yet the fate of these particles and their influence on regional and global climate is poorly understood. ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) is a 5-year NASA EVS-2 (Earth Venture Suborbital-2) investigation with three intensive observation periods designed to study key atmospheric processes that determine the climate impacts of these aerosols. During the Southern Hemisphere winter and spring (June–October), aerosol particles reaching 3–5 km in altitude are transported westward over the southeast Atlantic, where they interact with one of the largest subtropical stratocumulus (Sc) cloud decks in the world. The representation of these interactions in climate models remains highly uncertain in part due to a scarcity of observational constraints on aerosol and cloud properties, as well as due to the parameterized treatment of physical processes. Three ORACLES deployments by the NASA P-3 aircraft in September 2016, August 2017, and October 2018 (totaling ∼350 science flight hours), augmented by the deployment of the NASA ER-2 aircraft for remote sensing in September 2016 (totaling ∼100 science flight hours), were intended to help fill this observational gap. ORACLES focuses on three fundamental science themes centered on the climate effects of African BB aerosols: (a) direct aerosol radiative effects, (b) effects of aerosol absorption on atmospheric circulation and clouds, and (c) aerosol–cloud microphysical interactions. This paper summarizes the ORACLES science objectives, describes the project implementation, provides an overview of the flights and measurements in each deployment, and highlights the integrative modeling efforts from cloud to global scales to address science objectives. Significant new findings on the vertical structure of BB aerosol physical and chemical properties, chemical aging, cloud condensation nuclei, rain and precipitation statistics, and aerosol indirect effects are emphasized, but their detailed descriptions are the subject of separate publications. The main purpose of this paper is to familiarize the broader scientific community with the ORACLES project and the dataset it produced.

Ammonia (NH3) emissions have large impacts on air quality and nitrogen deposition, influencing human health and the well-being of sensitive ecosystems. Large uncertainties exist in the “bottom-up” NH3 emission inventories due to limited source information and a historical lack of measurements, hindering the assessment of NH3-related environmental impacts. The increasing capability of satellites to measure NH3 abundance and the development of modeling tools enable us to better constrain NH3 emission estimates at high spatial resolution. In this study, we constrain the NH3 emission estimates from the widely used 2011 National Emissions Inventory (2011 NEI) in the US using Infrared Atmospheric Sounding Interferometer NH3 column density measurements (IASI-NH3) gridded at a 36 km by 36 km horizontal resolution. With a hybrid inverse modeling approach, we use the Community Multiscale Air Quality Modeling System (CMAQ) and its multiphase adjoint model to optimize NH3 emission estimates in April, July, and October. Our optimized emission estimates suggest that the total NH3 emissions are biased low by 26 % in 2011 NEI in April with overestimation in the Midwest and underestimation in the Southern States. In July and October, the estimates from NEI agree well with the optimized emission estimates, despite a low bias in hotspot regions. Evaluation of the inversion performance using independent observations shows reduced underestimation in simulated ambient NH3 concentration in all 3 months and reduced underestimation in NH4+ wet deposition in April. Implementing the optimized NH3 emission estimates improves the model performance in simulating PM2.5 concentration in the Midwest in April. The model results suggest that the estimated contribution of ammonium nitrate would be biased high in a priori NEI-based assessments. The higher emission estimates in this study also imply a higher ecological impact of nitrogen deposition originating from NH3 emissions.

A 6-year-long high-resolution Chinese air quality reanalysis (CAQRA) dataset is presented in this study obtained from the assimilation of surface observations from the China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and Nested Air Quality Prediction Modeling System (NAQPMS).This dataset contains surface fields of six conventional air pollutants in China (i.e. PM2.5, PM10, SO2, NO2, CO, and O3) for the period 2013–2018 at high spatial (15 km×15 km) and temporal (1 h) resolutions. This paper aims to document this dataset by providing detailed descriptions of the assimilation system and the first validation results for the above reanalysis dataset. The 5-fold cross-validation (CV) method is adopted to demonstrate the quality of the reanalysis. The CV results show that the CAQRA yields an excellent performance in reproducing the magnitude and variability of surface air pollutants in China from 2013 to 2018 (CV R2=0.52–0.81, CV root mean square error (RMSE) =0.54 mg/m3 for CO, and CV RMSE =16.4–39.3 µg/m3 for the other pollutants on an hourly scale). Through comparison to the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) dataset produced by the European Centre for Medium-Range Weather Forecasts (ECWMF), we show that CAQRA attains a high accuracy in representing surface gaseous air pollutants in China due to the assimilation of surface observations. The fine horizontal resolution of CAQRA also makes it more suitable for air quality studies on a regional scale. The PM2.5 reanalysis dataset is further validated against the independent datasets from the US Department of State Air Quality Monitoring Program over China, which exhibits a good agreement with the independent observations (R2=0.74–0.86 and RMSE =16.8–33.6 µg/m3 in different cities). Furthermore, through the comparison to satellite-estimated PM2.5 concentrations, we show that the accuracy of the PM2.5 reanalysis is higher than that of most satellite estimates. The CAQRA is the first high-resolution air quality reanalysis dataset in China that simultaneously provides the surface concentrations of six conventional air pollutants, which is of great value for many studies, such as health impact assessment of air pollution, investigation of air quality changes in China, model evaluation and satellite calibration, optimization of monitoring sites, and provision of training data for statistical or artificial intelligence (AI)-based forecasting. All datasets are freely available at https://doi.org/10.11922/sciencedb.00053 (Tang et al., 2020a), and a prototype product containing the monthly and annual means of the CAQRA dataset has also been released at https://doi.org/10.11922/sciencedb.00092 (Tang et al., 2020b) to facilitate the evaluation of the CAQRA dataset by potential users.

The Indo-Gangetic Plain (IGP) experienced an intensive air pollution episode during November 2017. Weather Research and Forecasting model coupled to Chemistry (WRF-Chem), a coupled meteorology–chemistry model, was used to simulate this episode. In order to capture PM2.5 peaks, we modified input chemical boundary conditions and biomass burning emissions. The Community Atmosphere Model with Chemistry (CAM-chem) and Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) global models provided gaseous and aerosol chemical boundary conditions, respectively. We also incorporated Visible Infrared Imaging Radiometer Suite (VIIRS) active fire points to fill in missing fire emissions in the Fire INventory from NCAR (FINN) and scaled by a factor of 7 for an 8 d period. Evaluations against various observations indicated the model captured the temporal trend very well although missed the peaks on 7, 8, and 10 November. Modeled aerosol composition in Delhi showed secondary inorganic aerosols (SIAs) and secondary organic aerosols (SOAs) comprised 30 % and 27 % of total PM2.5 concentration, respectively, during November, with a modeled OC/BC ratio of 2.72. Back trajectories showed agricultural fires in Punjab were the major source for extremely polluted days in Delhi. Furthermore, high concentrations above the boundary layers in vertical profiles suggested either the plume rise in the model released the emissions too high or the model did not mix the smoke down fast enough. Results also showed long-range-transported dust did not affect Delhi's air quality during the episode. Spatial plots showed averaged aerosol optical depth (AOD) of 0.58 (±0.4) over November. The model AODs were biased high over central India and low over the eastern IGP, indicating improving emissions in the eastern IGP can significantly improve the air quality predictions. We also found high ozone concentrations over the domain, which indicates ozone should be considered in future air quality management strategies alongside particulate matter.

In the southeast Atlantic, well-defined smoke plumes from Africa advect over marine boundary layer cloud decks; both are most extensive around September, when most of the smoke resides in the free troposphere. A framework is put forth for evaluating the performance of a range of global and regional atmospheric composition models against observations made during the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) airborne mission in September 2016. A strength of the comparison is a focus on the spatial distribution of a wider range of aerosol composition and optical properties than has been done previously. The sparse airborne observations are aggregated into approximately 2∘ grid boxes and into three vertical layers: 3–6 km, the layer from cloud top to 3 km, and the cloud-topped marine boundary layer. Simulated aerosol extensive properties suggest that the flight-day observations are reasonably representative of the regional monthly average, with systematic deviations of 30 % or less. Evaluation against observations indicates that all models have strengths and weaknesses, and there is no single model that is superior to all the others in all metrics evaluated. Whereas all six models typically place the top of the smoke layer within 0–500 m of the airborne lidar observations, the models tend to place the smoke layer bottom 300–1400 m lower than the observations. A spatial pattern emerges, in which most models underestimate the mean of most smoke quantities (black carbon, extinction, carbon monoxide) on the diagonal corridor between 16∘ S, 6∘ E, and 10∘ S, 0∘ E, in the 3–6 km layer, and overestimate them further south, closer to the coast, where less aerosol is present. Model representations of the above-cloud aerosol optical depth differ more widely. Most models overestimate the organic aerosol mass concentrations relative to those of black carbon, and with less skill, indicating model uncertainties in secondary organic aerosol processes. Regional-mean free-tropospheric model ambient single scattering albedos vary widely, between 0.83 and 0.93 compared with in situ dry measurements centered at 0.86, despite minimal impact of humidification on particulate scattering. The modeled ratios of the particulate extinction to the sum of the black carbon and organic aerosol mass concentrations (a mass extinction efficiency proxy) are typically too low and vary too little spatially, with significant inter-model differences. Most models overestimate the carbonaceous mass within the offshore boundary layer. Overall, the diversity in the model biases suggests that different model processes are responsible. The wide range of model optical properties requires further scrutiny because of their importance for radiative effect estimates.

We present the development of a multiphase adjoint for the Community Multiscale Air Quality (CMAQ) model, a widely used chemical transport model. The adjoint model provides location- and time-specific gradients that can be used in various applications such as backward sensitivity analysis, source attribution, optimal pollution control, data assimilation, and inverse modeling. The science processes of the CMAQ model include gas-phase chemistry, aerosol dynamics and thermodynamics, cloud chemistry and dynamics, diffusion, and advection. Discrete adjoints are implemented for all the science processes, with an additional continuous adjoint for advection. The development of discrete adjoints is assisted with algorithmic differentiation (AD) tools. Particularly, the Kinetic PreProcessor (KPP) is implemented for gas-phase and aqueous chemistry, and two different automatic differentiation tools are used for other processes such as clouds, aerosols, diffusion, and advection. The continuous adjoint of advection is developed manually. For adjoint validation, the brute-force or finite-difference method (FDM) is implemented process by process with box- or column-model simulations. Due to the inherent limitations of the FDM caused by numerical round-off errors, the complex variable method (CVM) is adopted where necessary. The adjoint model often shows better agreement with the CVM than with the FDM. The adjoints of all science processes compare favorably with the FDM and CVM. In an example application of the full multiphase adjoint model, we provide the first estimates of how emissions of particulate matter (PM2.5) affect public health across the US.

KORUS-AQ was an international cooperative air quality field study in South Korea that measured local and remote sources of air pollution affecting the Korean Peninsula during May–June 2016. Some of the largest aerosol mass concentrations were measured during a Chinese haze transport event (24 May). Air quality forecasts using the WRF-Chem model with aerosol optical depth (AOD) data assimilation captured AOD during this pollution episode but overpredicted surface particulate matter concentrations in South Korea, especially PM2.5, often by a factor of 2 or larger. Analysis revealed multiple sources of model deficiency related to the calculation of optical properties from aerosol mass that explain these discrepancies. Using in situ observations of aerosol size and composition as inputs to the optical properties calculations showed that using a low-resolution size bin representation (four bins) underestimates the efficiency with which aerosols scatter and absorb light (mass extinction efficiency). Besides using finer-resolution size bins (8–16 bins), it was also necessary to increase the refractive indices and hygroscopicity of select aerosol species within the range of values reported in the literature to achieve better consistency with measured values of the mass extinction efficiency (6.7 m2 g−1 observed average) and light-scattering enhancement factor (f(RH)) due to aerosol hygroscopic growth (2.2 observed average). Furthermore, an evaluation of the optical properties obtained using modeled aerosol properties revealed the inability of sectional and modal aerosol representations in WRF-Chem to properly reproduce the observed size distribution, with the models displaying a much wider accumulation mode. Other model deficiencies included an underestimate of organic aerosol density (1.0 g cm−3 in the model vs. observed average of 1.5 g cm−3) and an overprediction of the fractional contribution of submicron inorganic aerosols other than sulfate, ammonium, nitrate, chloride, and sodium corresponding to mostly dust (17 %–28 % modeled vs. 12 % estimated from observations). These results illustrate the complexity of achieving an accurate model representation of optical properties and provide potential solutions that are relevant to multiple disciplines and applications such as air quality forecasts, health impact assessments, climate projections, solar power forecasts, and aerosol data assimilation.

This study compares the performance of 12 regional chemical transport models (CTMs) from the third phase of the Model Inter-Comparison Study for Asia (MICS-Asia III) on simulating the particulate matter (PM) over East Asia (EA) in 2010. The participating models include the Weather Research and Forecasting model coupled with Community Multiscale Air Quality (WRF-CMAQ; v4.7.1 and v5.0.2), the Regional Atmospheric Modeling System coupled with CMAQ (RAMS-CMAQ; v4.7.1 and v5.0.2), the Weather Research and Forecasting model coupled with chemistry (WRF-Chem; v3.6.1 and v3.7.1), Goddard Earth Observing System coupled with chemistry (GEOS-Chem), a non-hydrostatic model coupled with chemistry (NHM-Chem), the Nested Air Quality Prediction Modeling System (NAQPMS) and the NASA-Unified WRF (NU-WRF). This study investigates three model processes as the possible reasons for different model performances on PM. (1) Models perform very differently in the gas–particle conversion of sulfur (S) and oxidized nitrogen (N). The model differences in sulfur oxidation ratio (50 %) are of the same magnitude as that in SO42- concentrations. The gas–particle conversion is one of the main reasons for different model performances on fine mode PM. (2) Models without dust emission modules can perform well on PM10 at non-dust-affected sites but largely underestimate (up to 50 %) the PM10 concentrations at dust sites. The implementation of dust emission modules in the models has largely improved the model accuracies at dust sites (reduce model bias to −20 %). However, both the magnitude and distribution of dust pollution are not fully captured. (3) The amounts of modeled depositions vary among models by 75 %, 39 %, 21 % and 38 % for S wet, S dry, N wet and N dry depositions, respectively. Large inter-model differences are found in the washout ratios of wet deposition (at most 170 % in India) and dry deposition velocities (generally 0.3–2 cm s−1 differences over inland regions).

The Model Inter-Comparison Study for Asia (MICS-Asia) phase III was conducted to promote understanding of regional air quality and climate change in Asia, which have received growing attention due to the huge amount of anthropogenic emissions worldwide. This study provides an overview of acid deposition. Specifically, dry and wet deposition of the following species was analyzed: S (sulfate aerosol, sulfur dioxide (SO2), and sulfuric acid (H2SO4)), N (nitrate aerosol, nitrogen monoxide (NO), nitrogen dioxide (NO2), and nitric acid (HNO3)), and A (ammonium aerosol and ammonia (NH3)). The wet deposition simulated by a total of nine models was analyzed and evaluated using ground observation data from the Acid Deposition Monitoring Network in East Asia (EANET). In the phase III study, the number of observation sites was increased from 37 in the phase II study to 54, and southeast Asian countries were newly added. Additionally, whereas the analysis period was limited to representative months of each season in MICS-Asia phase II, the phase III study analyzed the full year of 2010. The scope of this overview mainly focuses on the annual accumulated deposition. In general, models can capture the observed wet deposition over Asia but underestimate the wet deposition of S and A, and show large differences in the wet deposition of N. Furthermore, the ratio of wet deposition to the total deposition (the sum of dry and wet deposition) was investigated in order to understand the role of important processes in the total deposition. The general dominance of wet deposition over Asia and attributions from dry deposition over land were consistently found in all models. Then, total deposition maps over 13 countries participating in EANET were produced, and the balance between deposition and anthropogenic emissions was calculated. Excesses of deposition, rather than of anthropogenic emissions, were found over Japan, northern Asia, and southeast Asia, indicating the possibility of long-range transport within and outside of Asia, as well as other emission sources. To improve the ability of models to capture the observed wet deposition, two approaches were attempted, namely, ensemble and precipitation adjustment. The ensemble approach was effective at modulating the differences in performance among models, and the precipitation-adjusted approach demonstrated that the model performance for precipitation played a key role in better simulating wet deposition. Finally, the lessons learned from the phase III study and future perspectives for phase IV are summarized.

Severe wintertime PM2.5 pollution in Beijing has been receiving increasing worldwide attention, yet the decadal variations remain relatively unexplored. Combining field measurements and model simulations, we quantified the relative influences of anthropogenic emissions and meteorological conditions on PM2.5 concentrations in Beijing over the winters of 2002–2016. Between the winters of 2011 and 2016, stringent emission control measures resulted in a 21 % decrease in mean mass concentrations of PM2.5 in Beijing, with 7 fewer haze days per winter on average. Given the overestimation of PM2.5 by the model, the effectiveness of stringent emission control measures might have been slightly overstated. With fixed emissions, meteorological conditions over the study period would have led to an increase in haze in Beijing, but the strict emission control measures have suppressed the unfavorable influences of the recent climate. The unfavorable meteorological conditions are attributed to the weakening of the East Asia winter monsoon associated particularly with an increase in pressure associated with the Aleutian Low.

Horizontal grid resolution has a profound effect on model performances on meteorology and air quality simulations. In contribution to MICS-Asia Phase III, one of whose goals was to identify and reduce model uncertainty in air quality prediction, this study examined the impact of grid resolution on meteorology and air quality simulation over East Asia, focusing on the North China Plain (NCP) region. The NASA Unified Weather Research and Forecasting (NU-WRF) model has been applied with the horizontal resolutions at 45, 15, and 5 km. The results revealed that, in comparison with ground observations, no single resolution can yield the best model performance for all variables across all stations. From a regional average perspective (i.e., across all monitoring sites), air temperature modeling was not sensitive to the grid resolution but wind and precipitation simulation showed the opposite. NU-WRF with the 5 km grid simulated the wind speed best, while the 45 km grid yielded the most realistic precipitation as compared to the site observations. For air quality simulations, finer resolution generally led to better comparisons with observations for O3, CO, NOx, and PM2.5. However, the improvement of model performance on air quality was not linear with the resolution increase. The accuracy of modeled surface O3 of the 15 km grid was greatly improved over the one from the 45 km grid. A further increase in grid resolution to 5 km, however, showed diminished impact on model performance improvement on O3 prediction. In addition, a 5 km resolution grid showed large advantage in better capturing the frequency of high-pollution occurrences. This was important for the assessment of noncompliance with ambient air quality standards, which was key to air quality planning and management. Balancing the modeling accuracy and resource limitation, a 15 km grid resolution was suggested for future MICS-Asia air quality modeling activity if the research region remained unchanged. This investigation also found a large overestimate of ground-level O3 and an underestimate of surface NOx and CO, likely due to missing emissions of NOx and CO.

Despite the significant progress in improving chemical transport models (CTMs), applications of these modeling endeavors are still subject to large and complex model uncertainty. The Model Inter-Comparison Study for Asia III (MICS-Asia III) has provided the opportunity to assess the capability and uncertainty of current CTMs in East Asian applications. In this study, we have evaluated the multi-model simulations of nitrogen dioxide (NO2), carbon monoxide (CO) and ammonia (NH3) over China under the framework of MICS-Asia III. A total of 13 modeling results, provided by several independent groups from different countries and regions, were used in this study. Most of these models used the same modeling domain with a horizontal resolution of 45 km and were driven by common emission inventories and meteorological inputs. New observations over the North China Plain (NCP) and Pearl River Delta (PRD) regions were also available in MICS-Asia III, allowing the model evaluations over highly industrialized regions. The evaluation results show that most models captured the monthly and spatial patterns of NO2 concentrations in the NCP region well, though NO2 levels were slightly underestimated. Relatively poor performance in NO2 simulations was found in the PRD region, with larger root-mean-square error and lower spatial correlation coefficients, which may be related to the coarse resolution or inappropriate spatial allocations of the emission inventories in the PRD region. All models significantly underpredicted CO concentrations in both the NCP and PRD regions, with annual mean concentrations that were 65.4 % and 61.4 % underestimated by the ensemble mean. Such large underestimations suggest that CO emissions might be underestimated in the current emission inventory. In contrast to the good skills for simulating the monthly variations in NO2 and CO concentrations, all models failed to reproduce the observed monthly variations in NH3 concentrations in the NCP region. Most models mismatched the observed peak in July and showed negative correlation coefficients with the observations, which may be closely related to the uncertainty in the monthly variations in NH3 emissions and the NH3 gas–aerosol partitioning. Finally, model intercomparisons have been conducted to quantify the impacts of model uncertainty on the simulations of these gases, which are shown to increase with the reactivity of species. Models contained more uncertainty in the NH3 simulations. This suggests that for some highly active and/or short-lived primary pollutants, like NH3, model uncertainty can also take a great part in the forecast uncertainty in addition to the emission uncertainty. Based on these results, some recommendations are made for future studies.

Topic 3 of the Model Inter-Comparison Study for Asia (MICS-Asia) Phase III examines how online coupled air quality models perform in simulating wintertime haze events in the North China Plain region and evaluates the importance of aerosol radiative feedbacks. This paper discusses the estimates of aerosol radiative forcing, aerosol feedbacks, and possible causes for the differences among the participating models. Over the Beijing–Tianjin–Hebei (BTH) region, the ensemble mean of estimated aerosol direct radiative forcing (ADRF) at the top of atmosphere, inside the atmosphere, and at the surface are −1.1, 7.7, and −8.8 W m−2 during January 2010, respectively. Subdivisions of direct and indirect aerosol radiative forcing confirm the dominant role of direct forcing. During severe haze days (17–19 January 2010), the averaged reduction in near-surface temperature for the BTH region can reach 0.3–1.6 ∘C. The responses of wind speeds at 10 m (WS10) inferred from different models show consistent declines in eastern China. For the BTH region, aerosol–radiation feedback-induced daytime changes in PM2.5 concentrations during severe haze days range from 6.0 to 12.9 µg m−3 ( %). Sensitivity simulations indicate the important effect of aerosol mixing states on the estimates of ADRF and aerosol feedbacks. Besides, black carbon (BC) exhibits a large contribution to atmospheric heating and feedbacks although it accounts for a small share of mass concentration of PM2.5.

Spatiotemporal variations of ozone (O3) and nitrogen oxide (NOx) mixing ratios from 14 state-of-the-art chemical transport models (CTMs) are intercompared and evaluated with O3 observations in East Asia, within the framework of the Model Inter-Comparison Study for Asia Phase III (MICS-Asia III). This study was designed to evaluate the capabilities and uncertainties of current CTMs simulations for Asia and to provide multi-model estimates of pollutant distributions. These models were run by 14 independent groups working in China, Japan, South Korea, the United States and other countries/regions. Compared with the previous phase of MICS-Asia (MICS-Asia II), the evaluation with observations was extended from 4 months to 1 full year across China and the western Pacific Rim. In general, model performance levels for O3 varied widely by region and season. Most models captured the key patterns of monthly and diurnal variation of surface O3 and its precursors in the North China Plain and western Pacific Rim but failed to do so for the Pearl River Delta. A significant overestimation of surface O3 was evident from May to September/October and from January to May over the North China Plain, the western Pacific Rim and the Pearl River Delta. Comparisons drawn from observations show that the considerable diversity in O3 photochemical production partly contributed to this overestimation and to high levels of inter-model variability in O3 for North China. In terms of O3 soundings, the ensemble average of models reproduced the vertical structure for the western Pacific, but overestimated O3 levels to below 800 hPa in the summer. In the industrialized Pearl River Delta, the ensemble average presented an overestimation for the lower troposphere and an underestimation for the middle troposphere. The ensemble average of 13 models for O3 did not always exhibit superior performance compared with certain individual models in contrast with its superior value for Europe. This finding suggests that the spread of ensemble-model values does not represent all of the uncertainties of O3 or that most MICS-Asia III models missed key processes. This study improved the performance of modeling O3 in March at Japanese sites compared with MICS-Asia II. However, it overpredicted surface O3 concentrations for western Japan in July, which was not found by MICS-Asia II. Major challenges still remain with regard to identifying the sources of bias in surface O3 over East Asia in CTMs.

The impacts of black carbon (BC) and particulate matter with aerodynamic diameters less than 2.5 µm (PM2.5) emissions from different source sectors (e.g., transportation, power, industry, residential, and biomass burning) and geographic source regions (e.g., Europe, North America, China, Russia, central Asia, south Asia, and the Middle East) to Arctic BC and PM2.5 concentrations are investigated through a series of annual sensitivity simulations using the Weather Research and Forecasting – sulfur transport and deposition model (WRF-STEM) modeling framework. The simulations are validated using observations at two Arctic sites (Alert and Barrow Atmospheric Baseline Observatory), the Interagency Monitoring of Protected Visual Environments (IMPROVE) surface sites over the US, and aircraft observations over the Arctic during spring and summer 2008. Emissions from power, industrial, and biomass burning sectors are found to be the main contributors to the Arctic PM2.5 surface concentration, with contributions of ∼ 30 %, ∼ 25 %, and ∼ 20 %, respectively. In contrast, the residential and transportation sectors are identified as the major contributors to Arctic BC, with contributions of ∼ 38 % and ∼ 30 %. Anthropogenic emissions are the most dominant contributors (∼ 88 %) to the BC surface concentration over the Arctic annually; however, the contribution from biomass burning is significant over the summer (up to ∼ 50 %). Among all geographical regions, Europe and China have the highest contributions to the BC surface concentrations, with contributions of ∼ 46 % and ∼ 25 %, respectively. Industrial and power emissions had the highest contributions to the Arctic sulfate (SO4) surface concentration, with annual contributions of ∼ 43 % and ∼ 41 %, respectively. Further sensitivity runs show that, among various economic sectors of all geographic regions, European and Chinese residential sectors contribute to ∼ 25 % and ∼ 14 % of the Arctic average surface BC concentration. Emissions from the Chinese industry sector and European power sector contribute ∼ 12 % and ∼ 18 % of the Arctic surface sulfate concentration. For Arctic PM2.5, the anthropogenic emissions contribute > ∼ 75 % at the surface annually, with contributions of ∼ 25 % from Europe and ∼ 20 % from China; however, the contributions of biomass burning emissions are significant in particular during spring and summer. The contributions of each geographical region to the Arctic PM2.5 and BC vary significantly with altitude. The simulations show that the BC from China is transported to the Arctic in the midtroposphere, while BC from European emission sources are transported near the surface under 5 km, especially during winter.

Spatial distribution of diurnal variations of aerosol properties in South Korea, both long term and short term, is studied by using 9 AERONET (AErosol RObotic NETwork) sites from 1999 to 2017 and an additional 10 sites during the KORUS-AQ (Korea–United States Air Quality) field campaign in May and June of 2016. The extent to which the WRF-Chem (Weather Research and Forecasting coupled with Chemistry) model and the GOCI (Geostationary Ocean Color Imager) satellite retrieval can describe these variations is also analyzed. On a daily average, aerosol optical depth (AOD) at 550 nm is 0.386 and shows a diurnal variation of 20 to −30 % in inland sites, which is larger than the AOD of 0.308 and diurnal variation of ±20 % seen in coastal sites. For all the inland and coastal sites, AERONET, GOCI, and WRF-Chem, and observed PM2.5 (particulate matter with aerodynamic diameter less than 2.5 µm) data generally show dual peaks for both AOD and PM2.5, one in the morning (often at ∼ 08:00–10:00 KST, Korea Standard Time, especially for PM2.5) and another in the early afternoon ( ∼ 14:00 KST, albeit for PM2.5 this peak is smaller and sometimes insignificant). In contrast, Ångström exponent values in all sites are between 1.2 and 1.4 with the exception of the inland rural sites having smaller values near 1.0 during the early morning hours. All inland sites experience a pronounced increase in the Ångström exponent from morning to evening, reflecting an overall decrease in particle size in daytime. To statistically obtain the climatology of diurnal variation of AOD, a minimum requirement of ∼ 2 years of observation is needed in coastal rural sites, twice as long as that required for the urban sites, which suggests that the diurnal variation of AOD in an urban setting is more distinct and persistent. While Korean GOCI satellite retrievals are able to consistently capture the diurnal variation of AOD (although it has a systematically low bias of 0.04 on average and up to 0.09 in later afternoon hours), WRF-Chem clearly has a deficiency in describing the relative change of peaks and variations between the morning and afternoon, suggesting further studies for the diurnal profile of emissions. Furthermore, the ratio between PM2.5 and AOD in WRF-Chem is persistently larger than the observed counterparts by 30 %–50 % in different sites, but spatially no consistent diurnal variation pattern of this ratio can be found. Overall, the relatively small diurnal variation of PM2.5 is in high contrast with large AOD diurnal variation, which suggests the large diurnal variation of AOD–PM2.5 relationships (with the PM2.5 ∕ AOD ratio being largest in the early morning, decreasing around noon, and increasing in late afternoon) and, therefore, the need to use AOD from geostationary satellites to constrain either modeling or estimate of surface PM2.5 for air quality application.

Topic 3 of the Model Inter-Comparison Study for Asia (MICS-Asia) Phase III examines how online coupled air quality models perform in simulating high aerosol pollution in the North China Plain region during wintertime haze events and evaluates the importance of aerosol radiative and microphysical feedbacks. A comprehensive overview of the MICS-Asia III Topic 3 study design, including descriptions of participating models and model inputs, the experimental designs, and results of model evaluation, are presented. Six modeling groups from China, Korea and the United States submitted results from seven applications of online coupled chemistry–meteorology models. Results are compared to meteorology and air quality measurements, including data from the Campaign on Atmospheric Aerosol Research Network of China (CARE-China) and the Acid Deposition Monitoring Network in East Asia (EANET). The correlation coefficients between the multi-model ensemble mean and the CARE-China observed near-surface air pollutants range from 0.51 to 0.94 (0.51 for ozone and 0.94 for PM2.5) for January 2010. However, large discrepancies exist between simulated aerosol chemical compositions from different models. The coefficient of variation (SD divided by the mean) can reach above 1.3 for sulfate in Beijing and above 1.6 for nitrate and organic aerosols in coastal regions, indicating that these compositions are less consistent from different models. During clean periods, simulated aerosol optical depths (AODs) from different models are similar, but peak values differ during severe haze events, which can be explained by the differences in simulated inorganic aerosol concentrations and the hygroscopic growth efficiency (affected by varied relative humidity). These differences in composition and AOD suggest that future models can be improved by including new heterogeneous or aqueous pathways for sulfate and nitrate formation under hazy conditions, a secondary organic aerosol (SOA) formation chemical mechanism with new volatile organic compound (VOCs) precursors, yield data and approaches, and a more detailed evaluation of the dependence of aerosol optical properties on size distribution and mixing state. It was also found that using the ensemble mean of the models produced the best prediction skill. While this has been shown for other conditions (for example, the prediction of high-ozone events in the US (McKeen et al., 2005)), this is to our knowledge the first time it has been shown for heavy haze events.

Land and atmospheric initial conditions of the Weather Research and Forecasting (WRF) model are often interpolated from a different model output. We perform case studies during NASA's SEAC4RS and DISCOVER-AQ Houston airborne campaigns, demonstrating that using land initial conditions directly downscaled from a coarser resolution dataset led to significant positive biases in the coupled NASA-Unified WRF (NUWRF, version 7) surface and near-surface air temperature and planetary boundary layer height (PBLH) around the Missouri Ozarks and Houston, Texas, as well as poorly partitioned latent and sensible heat fluxes. Replacing land initial conditions with the output from a long-term offline Land Information System (LIS) simulation can effectively reduce the positive biases in NUWRF surface air temperature by ∼ 2 °C. We also show that the LIS land initialization can modify surface air temperature errors almost 10 times as effectively as applying a different atmospheric initialization method. The LIS-NUWRF-based isoprene emission calculations by the Model of Emissions of Gases and Aerosols from Nature (MEGAN, version 2.1) are at least 20 % lower than those computed using the coarser resolution data-initialized NUWRF run, and are closer to aircraft-observation-derived emissions. Higher resolution MEGAN calculations are prone to amplified discrepancies with aircraft-observation-derived emissions on small scales. This is possibly a result of some limitations of MEGAN's parameterization and uncertainty in its inputs on small scales, as well as the representation error and the neglect of horizontal transport in deriving emissions from aircraft data. This study emphasizes the importance of proper land initialization to the coupled atmospheric weather modeling and the follow-on emission modeling. We anticipate it to also be critical to accurately representing other processes included in air quality modeling and chemical data assimilation. Having more confidence in the weather inputs is also beneficial for determining and quantifying the other sources of uncertainties (e.g., parameterization, other input data) of the models that they drive.

The recent update on the US National Ambient Air Quality Standards (NAAQS) of the ground-level ozone (O3) can benefit from a better understanding of its source contributions in different US regions during recent years. In the Hemispheric Transport of Air Pollution experiment phase 1 (HTAP1), various global models were used to determine the O3 source–receptor (SR) relationships among three continents in the Northern Hemisphere in 2001. In support of the HTAP phase 2 (HTAP2) experiment that studies more recent years and involves higher-resolution global models and regional models' participation, we conduct a number of regional-scale Sulfur Transport and dEposition Model (STEM) air quality base and sensitivity simulations over North America during May–June 2010. STEM's top and lateral chemical boundary conditions were downscaled from three global chemical transport models' (i.e., GEOS-Chem, RAQMS, and ECMWF C-IFS) base and sensitivity simulations in which the East Asian (EAS) anthropogenic emissions were reduced by 20 %. The mean differences between STEM surface O3 sensitivities to the emission changes and its corresponding boundary condition model's are smaller than those among its boundary condition models, in terms of the regional/period-mean (3 sensitivities to the size of the emission perturbation is spatially varying, and the full (i.e., based on a 100 % emission reduction) source contribution obtained from linearly scaling the North American mean O3 sensitivities to a 20 % reduction in the EAS anthropogenic emissions may be underestimated by at least 10 %. The three boundary condition models' mean O3 sensitivities to the 20 % EAS emission perturbations are ∼ 8 % (May–June 2010)/∼ 11 % (2010 annual) lower than those estimated by eight global models, and the multi-model ensemble estimates are higher than the HTAP1 reported 2001 conditions. GEOS-Chem sensitivities indicate that the EAS anthropogenic NOx emissions matter more than the other EAS O3 precursors to the North American O3, qualitatively consistent with previous adjoint sensitivity calculations. In addition to the analyses on large spatial–temporal scales relative to the HTAP1, we also show results on subcontinental and event scales that are more relevant to the US air quality management. The EAS pollution impacts are weaker during observed O3 exceedances than on all days in most US regions except over some high-terrain western US rural/remote areas. Satellite O3 (TES, JPL–IASI, and AIRS) and carbon monoxide (TES and AIRS) products, along with surface measurements and model calculations, show that during certain episodes stratospheric O3 intrusions and the transported EAS pollution influenced O3 in the western and the eastern US differently. Free-running (i.e., without chemical data assimilation) global models underpredicted the transported background O3 during these episodes, posing difficulties for STEM to accurately simulate the surface O3 and its source contribution. Although we effectively improved the modeled O3 by incorporating satellite O3 (OMI and MLS) and evaluated the quality of the HTAP2 emission inventory with the Royal Netherlands Meteorological Institute–Ozone Monitoring Instrument (KNMI–OMI) nitrogen dioxide, using observations to evaluate and improve O3 source attribution still remains to be further explored.

The MIX inventory is developed for the years 2008 and 2010 to support the Model Inter-Comparison Study for Asia (MICS-Asia) and the Task Force on Hemispheric Transport of Air Pollution (TF HTAP) by a mosaic of up-to-date regional emission inventories. Emissions are estimated for all major anthropogenic sources in 29 countries and regions in Asia. We conducted detailed comparisons of different regional emission inventories and incorporated the best available ones for each region into the mosaic inventory at a uniform spatial and temporal resolution. Emissions are aggregated to five anthropogenic sectors: power, industry, residential, transportation, and agriculture. We estimate the total Asian emissions of 10 species in 2010 as follows: 51.3 Tg SO2, 52.1 Tg NOx, 336.6 Tg CO, 67.0 Tg NMVOC (non-methane volatile organic compounds), 28.8 Tg NH3, 31.7 Tg PM10, 22.7 Tg PM2.5, 3.5 Tg BC, 8.3 Tg OC, and 17.3 Pg CO2. Emissions from China and India dominate the emissions of Asia for most of the species. We also estimated Asian emissions in 2006 using the same methodology of MIX. The relative change rates of Asian emissions for the period of 2006–2010 are estimated as follows: −8.1 % for SO2, +19.2 % for NOx, +3.9 % for CO, +15.5 % for NMVOC, +1.7 % for NH3, −3.4 % for PM10, −1.6 % for PM2.5, +5.5 % for BC, +1.8 % for OC, and +19.9 % for CO2. Model-ready speciated NMVOC emissions for SAPRC-99 and CB05 mechanisms were developed following a profile-assignment approach. Monthly gridded emissions at a spatial resolution of 0.25° × 0.25° are developed and can be accessed from http://www.meicmodel.org/dataset-mix.

The winter haze is a growing problem in North China, but the causes are not well understood. The chemistry version of the Weather Research and Forecasting model (WRF-Chem) was applied in North China to examine how PM2.5 concentrations change in response to changes in emissions (sulfur dioxide (SO2), black carbon (BC), organic carbon (OC), ammonia (NH3), and nitrogen oxides (NOx)), as well as meteorology (temperature, relative humidity (RH), and wind speeds) changes in winter. From 1960 to 2010, the dramatic changes in emissions lead to +260 % increases in sulfate, +320 % increases in nitrate, +300 % increases in ammonium, +160 % increases in BC, and +50 % increases in OC. The responses of PM2.5 to individual emission species indicate that the simultaneous increases in SO2, NH3, and NOx emissions dominated the increases in PM2.5 concentrations. PM2.5 shows more notable increases in response to changes in SO2 and NH3 as compared to increases in response to changes in NOx emissions. In addition, OC also accounts for a large fraction in PM2.5 changes. These results provide some implications for haze pollution control. The responses of PM2.5 concentrations to temperature increases are dominated by changes in wind fields and mixing heights. PM2.5 shows relatively smaller changes in response to temperature increases and RH decreases compared to changes in response to changes in wind speed and aerosol feedbacks. From 1960 to 2010, aerosol feedbacks have been significantly enhanced due to higher aerosol loadings. The discussions in this study indicate that dramatic changes in emissions are the main cause of increasing haze events in North China, and long-term trends in atmospheric circulations may be another important cause since PM2.5 is shown to be substantially affected by wind speed and aerosol feedbacks. More studies are necessary to get a better understanding of the aerosol–circulation interactions.

The online coupled Weather Research and Forecasting-Chemistry (WRF-Chem) model was applied to simulate a haze event that happened in January 2010 in the North China Plain (NCP), and was validated against various types of measurements. The evaluations indicate that WRF-Chem provides reliable simulations for the 2010 haze event in the NCP. This haze event was mainly caused by high emissions of air pollutants in the NCP and stable weather conditions in winter. Secondary inorganic aerosols also played an important role and cloud chemistry had important contributions. Air pollutants outside Beijing contributed about 64.5 % to the PM2.5 levels in Beijing during this haze event, and most of them are from south Hebei, Tianjin city, Shandong and Henan provinces. In addition, aerosol feedback has important impacts on surface temperature, relative humidity (RH) and wind speeds, and these meteorological variables affect aerosol distribution and formation in turn. In Shijiazhuang, Planetary Boundary Layer (PBL) decreased about 278.2 m and PM2.5 increased more than 20 µg m−3 due to aerosol feedback. It was also shown that black carbon (BC) absorption has significant impacts on meteorology and air quality changes, indicating more attention should be paid to BC from both air pollution control and climate change perspectives.

Accurate estimates of the emissions and distribution of black carbon (BC) in the region referred to here as Southeastern Asia (70–150° E, 11° S–55° N) are critical to studies of the atmospheric environment and climate change. Analysis of modeled BC concentrations compared to in situ observations indicates levels are underestimated over most of Southeast Asia when using any of four different emission inventories. We thus attempt to reduce uncertainties in BC emissions and improve BC model simulations by developing top-down, spatially resolved, estimates of BC emissions through assimilation of OMI (Ozone Monitoring Instrument) observations of aerosol absorption optical depth (AAOD) with the GEOS-Chem (Goddard Earth Observing System – chemistry) model and its adjoint for April and October 2006. Overwhelming enhancements, up to 500 %, in anthropogenic BC emissions are shown after optimization over broad areas of Southeast Asia in April. In October, the optimization of anthropogenic emissions yields a slight reduction (1–5 %) over India and parts of southern China, while emissions increase by 10–50 % over eastern China. Observational data from in situ measurements and AERONET (Aerosol Robotic Network) observations are used to evaluate the BC inversions and assess the bias between OMI and AERONET AAOD. Low biases in BC concentrations are improved or corrected in most eastern and central sites over China after optimization, while the constrained model still underestimates concentrations in Indian sites in both April and October, possibly as a consequence of low prior emissions. Model resolution errors may contribute up to a factor of 2.5 to the underestimation of surface BC concentrations over northern India. We also compare the optimized results using different anthropogenic emission inventories and discuss the sensitivity of top-down constraints on anthropogenic emissions with respect to biomass burning emissions. In addition, the impacts of brown carbon, the formulation of the observation operator, and different a priori constraints on the optimization are investigated. Overall, despite these limitations and uncertainties, using OMI AAOD to constrain BC sources improves model representation of BC distributions, particularly over China.

Atmospheric mercury (Hg) is a toxic pollutant and can be transported over the whole globe due to its long lifetime in the atmosphere. For the purpose of assessing Hg hemispheric transport and better characterizing regional Hg pollution, a global nested atmospheric Hg transport model (GNAQPMS-Hg – Global Nested Air Quality Prediction Modeling System for Hg) has been developed. In GNAQPMS-Hg, the gas- and aqueous-phase Hg chemistry representing the transformation among three forms of Hg: elemental mercury (Hg(0)), divalent mercury (Hg(II)), and primary particulate mercury (Hg(P)) are calculated. A detailed description of the model, including mercury emissions, gas- and aqueous-phase chemistry, and dry and wet deposition is given in this study. Worldwide observations including extensive data in China have been collected for model evaluation. Comparison results show that the model reasonably simulates the global mercury budget and the spatiotemporal variation of surface mercury concentrations and deposition. Overall, model predictions of annual total gaseous mercury (TGM) and wet deposition agree with observations within a factor of 2, and within a factor of 5 for oxidized mercury and dry deposition. The model performs significantly better in North America and Europe than in East Asia. This can probably be attributed to the large uncertainties in emission inventories, coarse model resolution and to the inconsistency between the simulation and observation periods in East Asia. Compared to the global simulation, the nested simulation shows improved skill at capturing the high spatial variability of surface Hg concentrations and deposition over East Asia. In particular, the root mean square error (RMSE) of simulated Hg wet deposition over East Asia is reduced by 24 % in the nested simulation. Model sensitivity studies indicate that Chinese primary anthropogenic emissions account for 30 and 62 % of surface mercury concentrations and deposition over China, respectively. Along the rim of the western Pacific, the contributions from Chinese sources are 11 and 15.2 % over the Korean Peninsula, 10.4 and 8.2 % over Southeast Asia, and 5.7 and 5.9 % over Japan. But for North America, Europe and western Asia, the contributions from China are all below 5 %.

Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM.

This study examines differences in the surface black carbon (BC) aerosol loading between the Bay of Bengal (BoB) and the Arabian Sea (AS) and identifies dominant sources of BC in South Asia and surrounding regions during March–May 2006 (Integrated Campaign for Aerosols, Gases and Radiation Budget, ICARB) period. A total of 13 BC tracers are introduced in the Weather Research and Forecasting Model coupled with Chemistry to address these objectives. The model reproduced the temporal and spatial variability of BC distribution observed over the AS and the BoB during the ICARB ship cruise and captured spatial variability at the inland sites. In general, the model underestimates the observed BC mass concentrations. However, the model–observation discrepancy in this study is smaller compared to previous studies. Model results show that ICARB measurements were fairly well representative of the AS and the BoB during the pre-monsoon season. Elevated BC mass concentrations in the BoB are due to 5 times stronger influence of anthropogenic emissions on the BoB compared to the AS. Biomass burning in Burma also affects the BoB much more strongly than the AS. Results show that anthropogenic and biomass burning emissions, respectively, accounted for 60 and 37% of the average ± standard deviation (representing spatial and temporal variability) BC mass concentration (1341 ± 2353 ng m−3) in South Asia. BC emissions from residential (61%) and industrial (23%) sectors are the major anthropogenic sources, except in the Himalayas where vehicular emissions dominate. We find that regional-scale transport of anthropogenic emissions contributes up to 25% of BC mass concentrations in western and eastern India, suggesting that surface BC mass concentrations cannot be linked directly to the local emissions in different regions of South Asia.

Particulate matter (PM) mass concentrations, seasonal cycles, source sector, and source region contributions in Central Asia (CA) are analyzed for the period April 2008–July 2009 using the Sulfur Transport and dEposition Model (STEM) chemical transport model and modeled meteorology from the Weather Research and Forecasting (WRF) model. Predicted aerosol optical depth (AOD) values (annual mean value ~0.2) in CA vary seasonally, with lowest values in the winter. Surface PM2.5 concentrations (annual mean value ~10 μg m−3) also exhibit a seasonal cycle, with peak values and largest variability in the spring/summer, and lowest values and variability in the winter (hourly values from 2 to 90 μg m−3). Surface concentrations of black carbon (BC) (mean value ~0.1 μg m−3) show peak values in the winter. The simulated values are compared to surface measurements of AOD as well as PM2.5, PM10, BC, and organic carbon (OC) mass concentrations at two regional sites in Kyrgyzstan (Lidar Station Teplokluchenka (LST) and Bishkek). The predicted values of AOD and PM mass concentrations and their seasonal cycles are fairly well captured. The carbonaceous aerosols are underpredicted in winter, and analysis suggests that the winter heating emissions are underestimated in the current inventory. Dust, from sources within and outside CA, is a significant component of the PM mass and drives the seasonal cycles of PM and AOD. On an annual basis, the power and industrial sectors are found to be the most important contributors to the anthropogenic portion of PM2.5. Residential combustion and transportation are shown to be the most important sectors for BC. Biomass burning within and outside the region also contributes to elevated PM and BC concentrations. The analysis of the transport pathways and the variations in particulate matter mass and composition in CA demonstrates that this region is strategically located to characterize regional and intercontinental transport of pollutants. Aerosols at these sites are shown to reflect dust, biomass burning, and anthropogenic sources from Europe; South, East, and Central Asia; and Russia depending on the time period. Simulations for a reference 2030 emission scenario based on pollution abatement measures already committed to in current legislation show that PM2.5 and BC concentrations in the region increase, with BC growing more than PM2.5 on a relative basis. This suggests that both the health impacts and the climate warming associated with these particles may increase over the next decades unless additional control measures are taken. The importance of observations in CA to help characterize the changes that are rapidly taking place in the region are discussed.

A diverse collection of models are used to simulate the marine boundary layer in the southeast Pacific region during the period of the October–November 2008 VOCALS REx (VAMOS Ocean Cloud Atmosphere Land Study Regional Experiment) field campaign. Regional models simulate the period continuously in boundary-forced free-running mode, while global forecast models and GCMs (general circulation models) are run in forecast mode. The models are compared to extensive observations along a line at 20° S extending westward from the South American coast. Most of the models simulate cloud and aerosol characteristics and gradients across the region that are recognizably similar to observations, despite the complex interaction of processes involved in the problem, many of which are parameterized or poorly resolved. Some models simulate the regional low cloud cover well, though many models underestimate MBL (marine boundary layer) depth near the coast. Most models qualitatively simulate the observed offshore gradients of SO2, sulfate aerosol, CCN (cloud condensation nuclei) concentration in the MBL as well as differences in concentration between the MBL and the free troposphere. Most models also qualitatively capture the decrease in cloud droplet number away from the coast. However, there are large quantitative intermodel differences in both means and gradients of these quantities. Many models are able to represent episodic offshore increases in cloud droplet number and aerosol concentrations associated with periods of offshore flow. Most models underestimate CCN (at 0.1% supersaturation) in the MBL and free troposphere. The GCMs also have difficulty simulating coastal gradients in CCN and cloud droplet number concentration near the coast. The overall performance of the models demonstrates their potential utility in simulating aerosol–cloud interactions in the MBL, though quantitative estimation of aerosol–cloud interactions and aerosol indirect effects of MBL clouds with these models remains uncertain.

Particles containing black carbon (BC), a strong absorbing substance, exert a rather uncertain direct and indirect radiative forcing in the atmosphere. To investigate the mass concentration and absorption properties of BC particles over central Europe, the model WRF-Chem was used at a resolution of 12 km in conjunction with a high-resolution BC emission inventory (EUCAARI 42-Pan-European Carbonaceous Aerosol Inventory; 1/8° × 1/16°). The model simulation was evaluated using measurements of equivalent soot carbon, absorption coefficients and particle number concentrations at seven sites within the German Ultrafine Aerosol Network, PM mass concentrations from the dense measurement network of the German Federal Environmental Agency at 392 monitoring stations, and aerosol optical depth from MODIS and AERONET. A distinct time period (25 March to 10 April 2009) was chosen, during which the clean marine air mass prevailed in the first week and afterwards the polluted continental air mass mainly from the southeast dominated with elevated daily average BC concentration of up to 4 μ g m−3. The simulated PM mass concentration, aerosol number concentration and optical depth were in good agreement with the observations, while the modelled BC mass concentrations were found to be a factor of 2 lower than the observations. Together with back trajectories, detailed model bias analyses suggested that the current BC emission in countries to the east and south of Germany might be underestimated by a factor of 5, at least for the simulation period. Running the model with upscaled BC emissions in these regions led to a smaller model bias and a better correlation between model and measurement. In contrast, the particle absorption coefficient was positively biased by about 20% even when the BC mass concentration was underestimated by around 50%. This indicates that the internal mixture treatment of BC in the WRF-Chem optical calculation is unrealistic in our case, which overamplifies the light absorption by BC-containing particles. By adjusting the modelled mass absorption cross-section towards the measured values, the simulation of particle light absorption of BC was improved as well. Finally, the positive direct radiative forcing of BC particles at the top of the atmosphere was estimated to be in the range of 0 to +4 W m−2 over Germany for the model run with improved BC mass concentration and adjusted BC light absorption cross-section. This adjustment lowered the positive forcing of BC by up to 70%, compared with the internal mixing treatment of BC in the model simulation.

In order to improve our understanding of air quality in Southeast Asia, the anthropogenic emissions inventory must be well represented. In this work, we apply different anthropogenic emission inventories in the Weather Research and Forecasting Model with Chemistry (WRF-Chem) version 3.3 using Model for Ozone and Related Chemical Tracers (MOZART) gas-phase chemistry and Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) aerosols to examine the differences in predicted carbon monoxide (CO) and ozone (O3) surface mixing ratios for Southeast Asia in March and December 2008. The anthropogenic emission inventories include the Reanalysis of the TROpospheric chemical composition (RETRO), the Intercontinental Chemical Transport Experiment-Phase B (INTEX-B), the MACCity emissions (adapted from the Monitoring Atmospheric Composition and Climate and megacity Zoom for the Environment projects), the Southeast Asia Composition, Cloud, Climate Coupling Regional Study (SEAC4RS) emissions, and a combination of MACCity and SEAC4RS emissions. Biomass-burning emissions are from the Fire Inventory from the National Center for Atmospheric Research (NCAR) (FINNv1) model. WRF-Chem reasonably predicts the 2 m temperature, 10 m wind, and precipitation. In general, surface CO is underpredicted by WRF-Chem while surface O3 is overpredicted. The NO2 tropospheric column predicted by WRF-Chem has the same magnitude as observations, but tends to underpredict the NO2 column over the equatorial ocean and near Indonesia. Simulations using different anthropogenic emissions produce only a slight variability of O3 and CO mixing ratios, while biomass-burning emissions add more variability. The different anthropogenic emissions differ by up to 30% in CO emissions, but O3 and CO mixing ratios averaged over the land areas of the model domain differ by ~4.5% and ~8%, respectively, among the simulations. Biomass-burning emissions create a substantial increase for both O3 and CO by ~29% and ~16%, respectively, when comparing the March biomass-burning period to the December period with low biomass-burning emissions. The simulations show that none of the anthropogenic emission inventories are better than the others for predicting O3 surface mixing ratios. However, the simulations with different anthropogenic emission inventories do differ in their predictions of CO surface mixing ratios producing variations of ~30% for March and 10–20% for December at Thai surface monitoring sites.