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Fine particulate matter with aerodynamic diameters ≤2.5 µm (PM2.5) has adverse effects on human health and the atmospheric environment. The estimation of surface PM2.5 concentrations has made intensive use of satellite-derived aerosol products. However, it has been a great challenge to obtain high-quality and high-resolution PM2.5 data from both ground and satellite observations, which is essential to monitor air pollution over small-scale areas such as metropolitan regions. Here, the space–time extremely randomized trees (STET) model was enhanced by integrating updated spatiotemporal information and additional auxiliary data to improve the spatial resolution and overall accuracy of PM2.5 estimates across China. To this end, the newly released Moderate Resolution Imaging Spectroradiometer Multi-Angle Implementation of Atmospheric Correction AOD product, along with meteorological, topographical and land-use data and pollution emissions, was input to the STET model, and daily 1 km PM2.5 maps for 2018 covering mainland China were produced. The STET model performed well, with a high out-of-sample (out-of-station) cross-validation coefficient of determination (R2) of 0.89 (0.88), a low root-mean-square error of 10.33 (10.93) µg m−3, a small mean absolute error of 6.69 (7.15) µg m−3 and a small mean relative error of 21.28 % (23.69 %). In particular, the model captured well the PM2.5 concentrations at both regional and individual site scales. The North China Plain, the Sichuan Basin and Xinjiang Province always featured high PM2.5 pollution levels, especially in winter. The STET model outperformed most models presented in previous related studies, with a strong predictive power (e.g., monthly R2=0.80), which can be used to estimate historical PM2.5 records. More importantly, this study provides a new approach for obtaining high-resolution and high-quality PM2.5 dataset across mainland China (i.e., ChinaHighPM2.5), important for air pollution studies focused on urban areas.

This research introduces a numerically efficient aerosol activation scheme and evaluates it by using stratus and stratocumulus cloud data sampled during multiple aircraft campaigns in Canada, Chile, Brazil, and China. The scheme employs a Quasi-steady state approximation of the cloud Droplet Growth Equation (QDGE) to efficiently simulate aerosol activation, the vertical profile of supersaturation, and the activated cloud droplet number concentration (CDNC) near the cloud base. We evaluate the QDGE scheme by specifying observed environmental thermodynamic variables and aerosol information from 31 cloud cases as input and comparing the simulated CDNC with cloud observations. The average of mean relative error of the simulated CDNC for cloud cases in each campaign ranges from 17.30 % in Brazil to 25.90 % in China, indicating that the QDGE scheme successfully reproduces observed variations in CDNC over a wide range of different meteorological conditions and aerosol regimes. Additionally, we carried out an error analysis by calculating the Maximum Information Coefficient (MIC) between the mean relative error (MRE) and input variables for the individual campaigns and all cloud cases. MIC values are then sorted by aerosol properties, pollution level, environmental humidity, and dynamic condition according to their relative importance to MRE . Based on the error analysis we found that the magnitude of MRE is more relevant to the specification of input aerosol pollution level in marine regions and aerosol hygroscopicity in continental regions than to other variables in the simulation.

The next-generation geostationary meteorological Himawari-8 satellite carrying the Advanced Himawari Imager (AHI) allows frequent observations of the atmosphere, the surface, and oceans every 10 min. With its retrieval algorithms recently updated, Himawari-8/AHI Version 2 Level 2 aerosol products are now available. However, these retrievals have not yet undergone a quality assessment. This study aims to comprehensively validate the official aerosol optical properties derived from Himawari-8/AHI over land and ocean. Aerosol Robotic Network and Sun-Sky Radiometer Observation Network ground-based measurements at 98 stations in the Himawari-domain region are used to validate aerosol optical depth (AOD, or τ) retrievals at 500 nm and Angstrom exponent (AE) retrievals at 440–675 nm from the year 2016. The AOD retrievals agree well with surface observations (i.e., from linear regression, slope = 0.876, intercept = 0.076, and correlation coefficient = 0.756) with a mean absolute error and a root-mean-square error of 0.168 and 0.293, respectively. On site and regional scales, large uncertainties are seen, especially in Australia (significant overestimation) and South Asia (significant underestimation). The AOD retrievals can correctly capture daily variations and show the best (worst) performance in summer (spring). The AE performance is poorer on all scales, showing overall underestimations, especially in Australia, Southeast Asia, and China. The data quality of AOD retrievals improves as the vegetation coverage and the AE increases. This suggests that the official aerosol retrieval algorithm still faces great challenges over bright surfaces and under coarse-particle-dominated conditions. In general, approximately 61% and 64% of the AOD matchups meet the newly defined expected errors of [0.330 × τ + 0.024; −0.132 × τ − 0.125] and [0.519 × τ + 0.005; −0.007 × τ − 0.194] determined by ground measurements and aerosol retrievals, respectively. The highly variable accuracy of aerosol retrievals raises a concern about the reliability of the current product under different environmental conditions and underlying surfaces. It also sheds light on what future improvements need implementing to the aerosol retrieval algorithm.

Moderate resolution imaging spectroradiometer (MODIS) has been widely used in related aerosol studies because of its long data records. However, operational aerosol optical depth (AOD) products at coarse spatial resolutions limit their applications on small and medium scales. Thus, high-spatial-resolution AOD products are needed. In this paper, a regionally robust high-resolution aerosol retrieval algorithm is developed for MODIS images over Eastern China which has complex surfaces and severe air pollution. Several major challenges in aerosol retrieval are resolved including: 1) surface reflectance by correcting for the effects of surface bidirectional reflectance distribution function using the RossThick-LiSparse model; 2) aerosol models assumed by time-series data analysis with historical aerosol optical properties measurements from the Aerosol Robotic Network (AERONET) sites; and 3) cloud screening using the proposed universal dynamic threshold cloud detection algorithm. Moreover, gas (i.e., ozone and water vapor) absorption is also corrected. Finally, our AOD retrievals are compared with the newest AERONET Version 3 Level 2.0 AOD ground-based measurements, latest MODIS Collection 6.1 AOD products at 3- and 10-km resolutions, and multiangle implementation of the atmospheric correction (MAIAC) AOD product at a 1-km resolution. The results suggest that our algorithm performs well over dark vegetated and bright urban surfaces and that 78.56% of the retrievals meet the acceptable expected error of ±(0.05% + 20%) with a mean absolute error and a root-mean-square error of 0.074 and 0.125, respectively. Comparison results indicate that the newly generated 1-km AOD data set is much better than the routine MOD04 3- and 10-km dark target data sets, and slightly better than the 10-km deep blue (with lower resolution) and 1-km MAIAC (with narrower space coverage) AOD products. This attests to the robustness of our algorithm that generates an AOD product with a more continuous coverage and finer resolution over complex surfaces.

This study evaluates the long-term Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) Level 3 atmospheric aerosol products over land. For this purpose, three monthly aerosol optical depth (AOD) datasets, including Dark Target (DT), Deep Blue (DB) and combined DT and DB (DTB) during 2003–2017, are collected. Aerosol Robotic Network (AERONET) Version 2 Level 2.0 (cloud-screened and quality-assured) monthly measurements at 431 sites around the world are selected for comparison. This study attempts to provide a better understanding of the different MODIS products for their applicability at multiple spatial scales and their suitability for representing the long-term trend of aerosol characteristics. Experiments are performed with direct comparisons between MODIS retrievals and AERONET measurements at global, local to site scales. Meanwhile, the spatial and temporal variations are also compared and discussed. Our results illustrate that C6.1 MODIS AOD retrievals are well correlated with AERONET AOD measurements globally, while the DTB product performs best at most regions, yet the DB product is superior at the site scale. In general, Terra AOD products always overestimate while Aqua AOD products are more accurate in describing the annual mean AOD loadings over land. For long-term aerosol trends, there are small differences between Terra and Aqua aerosol products. Among the three aerosol datasets, neither one can consistently outperform the others in both spatial and temporal aerosol variations over land. In general, DTB products can more accurately capture the correct aerosol changes and are strongly recommended for selection in related aerosol studies at the global scale.

Satellite-derived aerosol products provide long-term and large-scale observations for analysing aerosol distributions and variations, climate-scale aerosol simulations, and aerosol–climate interactions. Therefore, a better understanding of the consistencies and differences among multiple aerosol products is important. The objective of this study is to compare 11 global monthly aerosol optical depth (AOD) products, which are the European Space Agency Climate Change Initiative (ESA-CCI) Advanced Along-Track Scanning Radiometer (AATSR), Advanced Very High Resolution Radiometer (AVHRR), Multi-angle Imaging SpectroRadiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Visible Infrared Imaging Radiometer (VIIRS), and POLarization and Directionality of the Earth's Reflectance (POLDER) products. AErosol RObotic NEtwork (AERONET) Version 3 Level 2.0 monthly measurements at 308 sites around the world are selected for comparison. Our results illustrate that the spatial distributions and temporal variations of most aerosol products are highly consistent globally but exhibit certain differences on regional and site scales. In general, the AATSR Dual View (ADV) and SeaWiFS products show the lowest spatial coverage with numerous missing values, while the MODIS products can cover most areas (average of 87 %) of the world. The best performance is observed in September–October–November (SON) and the worst is in June–July–August (JJA). All the products perform unsatisfactorily over northern Africa and Middle East, southern and eastern Asia, and their coastal areas due to the influence from surface brightness and human activities. In general, the MODIS products show the best agreement with the AERONET-based AOD values on different spatial scales among all the products. Furthermore, all aerosol products can capture the correct aerosol trends at most cases, especially in areas where aerosols change significantly. The MODIS products perform best in capturing the global temporal variations in aerosols. These results provide a reference for users to select appropriate aerosol products for their particular studies.

Our previous study illustrated that the operational Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) Dark Target (DT) and Deep Blue (DB) combined products (denoted as DTB0) are not always the best in most regions due to its unsuitable merge approach. Therefore, the objective of this study is to develop an improved merge approach to increase the spatiotemporal data coverage and reduce the estimation uncertainty. For this, three tests, i.e., a land-use-type test, a surface-relief test, and an aerosol-type test are performed according to the strengths and weakness of the performances of the DT and DB algorithms with their high-quality assurance retrievals (QA = 3 for DT and QA ≥ 2 for DB) against the newest Aerosol Robotic Network (AERONET) Version 3 Level 2.0 measurements. Based on this, new merged DT and DB products (denoted as DTB1) are generated. The Terra and Aqua DTB1 products are then validated against AERONET measurements at 286 sites on site, continental, and global scales, and for varying underlying surfaces and elevated terrains from 2013 to 2017. The DTB0 products for the same period are collected for comparison. More than 90% of the sites now have more data points, and the performances of the DTB1 products are improved with an increased percentage of the data falling within the expected error [±(0.05 + 15%)] envelope and reduced mean absolute errors and root-mean-square errors compared with DTB0 products at most sites. Separate- and equal-number comparisons show that the DTB1 products significantly improve both the data coverage and data quality. The new merged products are more accurate and less affected by varying surface structures than the operational products. These results suggest that the improved merge approach is more robust and can be used for generating more accurate global aerosol products.

Recently, the newest Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) aerosol optical depth (AOD) products were available with various refinements and improvements made to both the radiation calibration and Dark Target (DT) and Deep Blue (DB) algorithms. A combined DT and DB dataset (DTB) was also added based on piecewise fixed thresholds using the Normalized Difference Vegetation Index (NDVI) for taking advantage of one's merits. This study provides a cross-comparison and evaluation of these Terra MODIS aerosol products with reference to the enhanced ground-based AOD measurements by the Aerosol Robotic Network (AERONET) Level 3 Version 2.0 data at 384 ground stations. Their absolute and relative performance are evaluated in the period of 2013–2017 among the products, as well as between the current (C6.1) and previous (C6) releases. In general, the C6.1 aerosol products are found to be superior over the C6 products for three datasets from all scales, but the differences and improvements are rather non-uniform that varies with region. Overall, the DB AOD products show the best performance in most regions at about half of the sites, especially in Europe and North America. Meanwhile, besides bright surfaces (i.e., deserts and arid/semi-arid areas), DB products match more closely with the AERONET AODs than that of DT over medium or densely vegetated areas. The dependences of retrieval errors illustrate that the performance of three datasets deteriorates as surface reflectance, elevation and aerosol loading increase. However, the DB algorithm remains relatively more stable and less affected by changes in atmospheric and surface conditions. While the merged product using NDVI has some improvements over individual ones in general, worse performance is also shown in many cases. A more optimal method is thus wanting.

This research summarizes a numerically efficient aerosol activation scheme and evaluates it by using stratus and stratocumulus cloud data sampled during multiple aircraft campaigns in China, Canada, Brazil, and Chile. The scheme employs a Quasi-steady state approximation of the cloud Droplet Growth Equation (QDGE) to efficiently simulate aerosol activation and vertical profiles of supersaturation and cloud droplet number concentration (CDNC) near the cloud base. We evaluated the scheme by specifying observed environmental thermodynamic variables and aerosol properties from 36 cloud cases as input and comparing the simulated CDNC and other simulated variables with cloud microphysical observations. The relative error (RE) of the mean simulated CDNC ranges from 15.27 % for Chile to 23.97 % for China, with an average of 19.69 %, indicating that the scheme successfully reproduces observed variations in CDNC over a wide range of different meteorological conditions and aerosol regimes. Subsequently, we carried out an error analysis by calculating the Maximum Information Coefficient (MIC) values between RE and individual input variables and sorted them by aerosol properties, pollution degree, environmental humidity, and dynamic condition according to their importance. Based on this analysis we find that the magnitude of the RE is sensitive to the specification of aerosol chemical composition and updraft velocity in the simulation, which can partly explain differences between simulated and observed CDNC in some of the regions.

Dust size distributions from the two-mode scheme and the continuous scheme are compared with AERONET observations. The two-mode dust scheme contains fine and coarse modes with fixed effective radius and effective variance for each mode. The continuous scheme takes into account the size distribution with a prognostic variation of effective radius and effective variance. The size distribution of the two-mode scheme changes with the fine/coarse mode mass ratio. However, it is impossible to make both effective radius and effective variance to match with the observed result by adjusting the fine/coarse mode mass ratio. The size distribution from the continuous scheme can be much closer to the observations. A new parameterization for dust optical properties is proposed based on the continuous scheme. The shape of non-spherical dust particles is approximated using a rotational symmetric spheroid in optical property calculations. This parameterization can also be used for the two-mode scheme. The parameterization is applied to a set of idealized one-dimensional radiative transfer calculations to investigate the sensitivity of dust radiative forcing to various attributes, including dust loading, dust solar heating rate, surface albedo, effective radius and variance. The parameterization is also evaluated in a climate model. Though at each selected region the two-mode scheme can always be tuned to approximate the results of the continuous scheme, on global scales, however, the two-mode dust scheme cannot produce similar radiative forcing as the continuous scheme and the relative error can be over 30% in several desert regions. The accurate results in radiative forcing can not be achieved by tuning the mass ratio.

Aerosol-induced increase of relative dispersion of cloud droplet size distribution ε exerts a warming effect and partly offsets the cooling of aerosol indirect radiative forcing (AIF) associated with increased droplet concentration by increasing the cloud droplet effective radius (Re) and enhancing the cloud-to-rain autoconversion rate (Au) (labeled as the dispersion effect), which can help reconcile global climate models (GCMs) with the satellite observations. However, the total dispersion effects on both Re and Au are not fully considered in most GCMs, especially in different versions of the Community Atmospheric Model (CAM). In order to accurately evaluate the dispersion effect on AIF, the new complete cloud parameterizations of Re and Au explicitly accounting for ε are implemented into the CAM version 5.1 (CAM5.1), and a suite of sensitivity experiments is conducted with different representations of ε reported in the literature. It is shown that the shortwave cloud radiative forcing is much better simulated with the new cloud parameterizations as compared to the standard scheme in CAM5.1, whereas the influences on longwave cloud radiative forcing and surface precipitation are minimal. Additionally, consideration of the dispersion effect can significantly reduce the changes induced by anthropogenic aerosols in the cloud-top effective radius and the liquid water path, especially in the Northern Hemisphere. The corresponding AIF with the dispersion effect considered can also be reduced substantially by a range of 0.10 to 0.21 W m−2 at the global scale and by a much bigger margin of 0.25 to 0.39 W m−2 for the Northern Hemisphere in comparison with that of fixed relative dispersion, mainly dependent on the change of relative dispersion and droplet concentrations (Δε∕ΔNc).

In this study, aerosol indirect effects suffer from large uncertainty in climate models and among observations. This study focuses on two plausible factors: regime dependence of aerosol-cloud interactions and the effect of cloud droplet spectral shape. We show, using a new parcel model, that combined consideration of droplet number concentration (Nc) and relative dispersion (e, ratio of standard deviation to mean radius of the cloud droplet size distribution) better characterizes the regime dependence of aerosol-cloud interactions than considering Nc alone. Given updraft velocity (w), e increases with increasing aerosol number concentration (Na) in the aerosol-limited regime, peaks in the transitional regime, and decreases with further increasing Na in the updraft-limited regime. This new finding further reconciles contrasting observations in literature and reinforces the compensating role of dispersion effect. The nonmonotonic behavior of e further quantifies the relationship between the transitional Na and w that separates the aerosol- and updraft-limited regimes.

Recently, NASA has released the newest Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra Collection 6.1 (C6.1) aerosol product generated at a higher spatial resolution of 3 km (i.e., MOD04_3K) with main updates in the radiation calibration and the Dark Target (DT) aerosol algorithm. This product is of great importance for air pollution studies at small to medium scales but has not been fully evaluated. This paper aims to provide a comprehensive validation and error analysis of the MOD04_3K C6.1 aerosol optical depth (AOD) data set against Aerosol Robotic Network (AERONET) Version 3 AOD measurements at different spatiotemporal scales from 2013 to 2017 over land and ocean. Results suggest that the data quality of the MOD04_3K AOD data set is overall improved at different spatial scales after quality control. In general, the highest-quality 3 km AOD retrievals (quality assurance = 3) and ground-based measurements are highly correlated (correlation coefficient = 0.81), with a mean absolute error of 0.08 and a root-mean-square error of 0.12. About 63% of the data samples fall within the expected error envelope on a global scale. Nevertheless, there is large spatial heterogeneity in the performance at regional and site scales, with the worse accuracy generally observed in areas covered by bright surfaces or dominated by human activities. This is mainly due to the difficulties in estimating surface reflectance and the aerosol-type assumption. In addition, aerosol retrievals are overall overestimated, especially over North America, Europe, and East Asia. Furthermore, the estimation errors and uncertainties are highly related to varying surface and atmospheric aerosol conditions, which become larger with increases in surface brightness, aerosol loading, Angstrom exponent and single scattering albedo. The MODIS 3 km AOD data set is generally less accurate than its coarser-spatial-resolution (10 km) counterpart due to a decrease in the opportunity to discard marginal pixels from the retrieval. Therefore, further algorithm improvements are needed to reduce the estimation uncertainty, especially for heterogeneous urban and bright surfaces.

Satellite-derived aerosol products provide long-term and large-scale observations for analysing aerosol distributions and variations, climate-scale aerosol simulations, and aerosol-climate interactions. Therefore, a better understanding of the consistencies and differences among multiple aerosol products is of great importance. The objective of this study is to evaluate nine operational global aerosol optical depth (AOD) products, including the European Space Agency Climate Change Initiative (ESA-CCI) Advanced Along-Track Scanning Radiometer (AATSR), Advanced Very High Resolution Radiometer (AVHRR), Multi-angle Imaging Spectro Radiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) over the land and oceans during the period from 1997 to 2017. Aerosol Robotic Network (AERONET) Version 3 Level 2.0 (cloud screened and quality assured) measurements at 243 sites around the world are selected for validation purposes. Our results illustrate that noticeable inconsistencies exist in spatial coverages, variations, and temporal trends among different aerosol products. Large annual mean AOD differences greater than 0.15 are mainly found in North Africa, Central Africa, the Middle East, and East Asia, as well as in the coastal areas of western North America and Asia. In general, the AATSR Swansea University (SU) product appears to have the best accuracy, the SeaWiFS product obviously underestimates aerosol loadings over the land and ocean, and the MODIS products seriously overestimate aerosol loadings over the land and ocean. For temporal trends, more than 77% and 72% of land and ocean sites, respectively, show the same trends direction as the ground observations. However, numerical differences are non-negligible in North Africa, the Middle East, and South Asia and in most open oceans. MODIS products are generally more accurate in describing the temporal trends over the land and ocean. The aerosol loadings trend changed from positive to negative over the past two decades (2000−2017) in some continental areas. These results suggest that multiple AOD products show significant numerical inconsistencies in describing spatial distributions and temporal trends, which should be carefully considered when users choose aerosol products for their studies.

Aerosol-induced increase of relative dispersion of cloud droplet size distribution ε exerts a warming effect and partly offsets the cooling of aerosol indirect radiative forcing (AIF) associated with increased droplet concentration by increasing the cloud droplet effective radius (Re) and enhancing the cloud-to-rain autoconversion rate (Au) (labeled as dispersion effect), which can help reconcile global climate models (GCMs) with the satellite observations. However, the total dispersion effects on both Re and Au are not fully considered in most GCMs, especially in different versions of the Community Atmospheric Model (CAM). In order to accurately evaluate the dispersion effect on AIF, the new complete cloud parameterizations of Re and Au explicitly accounting for ε are implemented into the CAM version 5.1 (CAM5.1), and a suite of sensitivity experiments is conducted with different representations of ε reported in literature. It is shown that the shortwave cloud radiative forcing is much better simulated with the new cloud parameterizations as compared to the standard scheme in CAM5.1, whereas the influences on longwave cloud radiative forcing and surface precipitation are minimal. Additionally, consideration of dispersion effect can significantly reduce the changes induced by anthropogenic aerosols in the cloud top effective radius and the liquid water path, especially in Northern Hemisphere. The corresponding AIF with dispersion effect considered can also be reduced substantially, by a range of 0.10 to 0.21 W m−2 at global scale, and by a much bigger margin of 0.25 to 0.39 W m−2 for the Northern Hemisphere in comparison with that fixed relative dispersion, mainly dependent on the change of relative dispersion and droplet concentrations (Δε / ΔNc).

The aerosol effects on clouds and precipitation in deep convective cloud systems are investigated using the Weather Research and Forecast (WRF) model with the Morrison two-moment bulk microphysics scheme. Considering positive or negative relationships between the cloud droplet number concentration ( N c ) and spectral dispersion ( ɛ ), a suite of sensitivity experiments are performed using an initial sounding data of the deep convective cloud system on 31 March 2005 in Beijing under either a maritime (‘clean’) or continental (‘polluted’) background. Numerical experiments in this study indicate that the sign of the surface precipitation response induced by aerosols is dependent on the ɛ − N c relationships, which can influence the autoconversion processes from cloud droplets to rain drops. When the spectral dispersion ɛ is an increasing function of N c , the domain-average cumulative precipitation increases with aerosol concentrations from maritime to continental background. That may be because the existence of large-sized rain drops can increase precipitation at high aerosol concentration. However, the surface precipitation is reduced with increasing concentrations of aerosol particles when ɛ is a decreasing function of N c . For the ɛ − N c negative relationships, smaller spectral dispersion suppresses the autoconversion processes, reduces the rain water content and eventually decreases the surface precipitation under polluted conditions. Although differences in the surface precipitation between polluted and clean backgrounds are small for all the ɛ − N c relationships, additional simulations show that our findings are robust to small perturbations in the initial thermal conditions. Keywords: aerosol indirect effects, cloud droplet spectral dispersion, autoconversion parameterization, deep convective systems, two-moment bulk microphysics scheme (Published: 15 February 2013) Citation: Tellus B 2013, 65 , 19054, http://dx.doi.org/10.3402/tellusb.v65i0.19054

A new size-resolved dust scheme based on the numerical method of piecewise log-normal approximation (PLA) was developed and implemented in the fourth generation of the Canadian Atmospheric Global Climate Model with the PLA Aerosol Model (CanAM4-PAM). The total simulated annual global dust emission is 2500 Tg yr−1, and the dust mass load is 19.3 Tg for year 2000. Both are consistent with estimates from other models. Results from simulations are compared with multiple surface measurements near and away from dust source regions, validating the generation, transport and deposition of dust in the model. Most discrepancies between model results and surface measurements are due to unresolved aerosol processes. Biases in long-range transport are also contributing. Radiative properties of dust aerosol are derived from approximated parameters in two size modes using Mie theory. The simulated aerosol optical depth (AOD) is compared with satellite and surface remote sensing measurements and shows general agreement in terms of the dust distribution around sources. The model yields a dust AOD of 0.042 and dust aerosol direct radiative forcing (ADRF) of −1.24 W m−2 respectively, which show good consistency with model estimates from other studies.

Formulation of the dust emission scheme in the global aerosol-climate modeling system ECHAM5-HAM has been improved. Modifications on the surface aerodynamic roughness length, soil moisture and East-Asian soil properties are included in the parameterization, which result in a large impact on the threshold wind friction velocity for aeolian erosion and thus influence the simulated dust emission amount. The annual global mean of dust emission in the year 2000 is reduced by 76.5% and 2.2%, respectively, due to changes in the aerodynamic roughness length and the soil moisture. An inclusion of detailed East-Asian soil properties leads to an increase of 16.6% in the annual global mean of dust emission, which exhibits mainly in the arid and semi-arid areas of northern China and southern Mongolia. Measurements of the surface dust concentrations are collected in remote marine sites globally and in dust source regions of East Asia. The averaged relative differences between model results and measurements are reduced from 17% to 12% in global remote marine sites and from 69% to 30% in East Asia, by including the improvements. Comparisons between model results and available measurements verify a more realistic dust distribution with the improved emission scheme.

The aerosol dispersion effect ( the influence of an increasing number of aerosol particles on the width of the cloud droplet size distribution) has been observed in maritime clouds [ e. g., Liu and Daum, 2002]. Climate model simulations show that the dispersion effect at least partially compensates the first indirect aerosol effect. The application of observational data from maritime stratus/stratocumulus clouds into an adiabatic parcel model allows to analyze the role of the aerosol activation process for the dispersion effect in order to better understand the microphysical mechanism of the dispersion effect in the early stage of cloud formation. When the total aerosol number concentration is increased, the parcel model simulations show that the higher number of aerosol particles at cloud base reduces the supersaturation, which results in a slower particle growth rate, and thus more cloud droplets remain small. This extends the droplet spectra toward the smaller size end and increases the spectral width. The broadening effect partially offsets the reduction of droplet radius because of an enhanced number of aerosol particles, leading to a positive aerosol dispersion effect. Sensitivity studies show that the dispersion effect decreases for increasing updraft velocities. When the updraft velocity approaches 55 cm s(-1), the dispersion effect almost vanishes for maritime stratus clouds. Aerosols composed of sulfate or of less soluble organics increase the dispersion effect as compared to sea-salt aerosols, whereas condensation of gaseous nitric acid on aerosols decreases the dispersion effect in marine stratus clouds.

[1] Eleven cloud cases through marine stratus, obtained during two field experiments in the North Atlantic Ocean, are used to study the sensitivity of cloud droplet nucleation to the vertical gust velocity. Selected cloud microphysical data, size-distributed aerosol properties and particle chemistry are applied in an adiabatic parcel model. The nucleated cloud droplet number concentrations (N) predicted using the probability density function (PDF) of the measured in-cloud vertical velocities are compared to predictions using a characteristic velocity value. In this study, the model-predicted N from the PDF of the measured in-cloud vertical velocities agrees with the observed maximum N (Nmax )t o within 8.6%. The average N (Navg) can be related to Nmax using a power law (Leaitch et al., 1996). If a relationship between Nmax and Navg based on the measurements is applied to obtain the average N from the model-predicted N, then the model-predicted average N agrees with the observed average N to within 13.3%. When a characteristic vertical velocity (0.8 times the standard deviation of the vertical velocity distribution in this study) is used in the parcel model to simulate N, the model-predicted N agrees with the observed maximum N to within 5.7% and the model-predicted average N agrees with the observed average N within 8.8%. This indicates that using a characteristic value of the vertical velocity distribution instead of its PDF is a good approximation for simulating the nucleated cloud droplet number of marine stratus on a cloud scale.

[1] To study the influence of anthropogenic aerosols on the shape of the cloud droplet size spectra (dispersion effect), we analyze observed liquid water cloud data during two Canadian field studies. Scaled by the parameter β, which is a function of the relative dispersion of cloud droplet spectra, the calculated cloud albedo shows better agreement with the independently measured cloud albedo than the cloud albedo calculated without scaling. The scaling factor β is positively correlated with the cloud droplet number concentration. A linear relationship between β and the cloud droplet number concentration obtained from different field studies is applied to the ECHAM4 general circulation model. The global mean indirect aerosol effect at the top of atmosphere including the dispersion effect is reduced by 0.2 W m−2 as compared to the reference simulation. This accounts for about 1/3 of the reduction that needed to be imposed on the simulated anthropogenic indirect aerosol effect by Lohmann and Lesins [2002].

This work examines the influences of relative dispersion (the ratio of the standard deviation to the mean radius of the cloud droplet size distribution) on cloud albedo and cloud radiative forcing, derives an analytical formulation that accounts explicitly for the contribution from droplet concentration and relative dispersion, and presents a new approach to parameterize relative dispersion in climate models. It is shown that inadequate representation of relative dispersion in climate models leads to an overestimation of cloud albedo, resulting in a negative bias of global mean shortwave cloud radiative forcing that can be comparable to the warming caused by doubling CO2 in magnitude, and that this dispersion bias is likely near its maximum for ambient clouds. Relative dispersion is empirically expressed as a function of the quotient between cloud liquid water content and droplet concentration (i.e., water per droplet), yielding an analytical formulation for the first aerosol indirect effect. Further analysis of the new expression reveals that the dispersion effect not only offsets the cooling from the Twomey effect, but is also proportional to the Twomey effect in magnitude. These results suggest that unrealistic representation of relative dispersion in cloud parameterization in general, and evaluation of aerosol indirect effects in particular, is at least in part responsible for several outstanding puzzles of the aerosol‐cloud conundrum: for example, overestimation of cloud radiative cooling by climate models compared to satellite observations; large uncertainty and discrepancy in estimates of the aerosol indirect effect; and the lack of interhemispheric difference in cloud albedo.