This study investigated the air pollution characteristics of synoptic-scale circulation in the Beijing megacity, and provided quantitative evaluation of the impacts of circulation patterns on air quality during the 2008 Beijing Summer Olympics. Nine weather circulation types (CTs) were objectively identified over the North China region during 2000–2009, using obliquely rotated T-mode principal component analysis (PCA). The resulting CTs were examined in relation to the local meteorology, regional transport pathways, and air quality parameters, respectively. The FLEXPART-WRF model was used to calculate 48-h backward plume trajectories for each CT. Each CT was characterized with distinct local meteorology and air mass origin. CT 1 (high pressure to the west with a strong pressure gradient) was characterized by a northwestern air mass origin, with the smallest local and southeasterly air mass sources, and CT 6 (high pressure to the northwest) had air mass sources mostly from the north and east. On the contrary, CTs 5, 8, and 9 (weak pressure field, high pressure to the east, and low pressure to the northwest, respectively) were characterized by southern and southeastern trajectories, which indicated a greater influence of high pollutant emission sources. In turn, poor air quality in Beijing (high loadings of PM₁₀, BC, SO₂, NO₂, NO_x, O₃, AOD, and low visibility) was associated with these CTs. Good air quality in Beijing was associated with CTs 1 and 6. The average visibilities (with ±1σ) in Beijing for CTs 1 and 6 during 2000–2009 were 18.5 ± 8.3 km and 14.3 ± 8.5 km, respectively. In contrast, low visibility values of 6.0 ± 3.5 km, 6.6 ± 3.7 km, and 6.7 ± 3.6 km were found in CTs 5, 8, and 9, respectively. The mean concentrations of PM₁₀ for CTs 1, 6, 5, 8, and 9 during 2005–2009 were 90.3 ± 76.3 μg m⁻³, 111.7 ± 89.6 μg m⁻³, 173.4 ± 105.8 μg m⁻³, 158.4 ± 90.0 μg m⁻³, and 151.2 ± 93.1 μg m⁻³, respectively. Analysis of the relationship between circulation pattern and air quality during the emission control period suggests that CTs are the primary drivers of day-to-day variations in pollutant concentrations over Beijing and its vicinity. During the Olympics period, the frequency of CT 6 was twice that of the mean in August from 2000 to 2009. This CT had northerly transport pathways and favorable meteorological conditions (e.g. frequent precipitation) for clean air during the Olympics. Assuming that relationships between CTs and air quality parameters in the same season are fixed in different years, the relative contributions of synoptic circulation to decreases in PM₁₀, BC, SO₂, NO₂, NO_x, CO, and horizontal light extinction during the Olympics were estimated as 19 ± 14%, 18 ± 13%, 41 ± 36%, 12 ± 7%, 10 ± 5%, 19 ± 11%, and 54 ± 25%, respectively.

The radiative effects of Saharan dust aerosols are investigated in the NASA GEOS‐5 atmospheric general circulation model. A sectional aerosol microphysics model (CARMA) is run online in GEOS‐5. CARMA treats the dust aerosol lifecycle, and its tracers are radiatively coupled to GEOS‐5. A series of AMIP‐style simulations are performed, in which input dust optical properties (particle shape and refractive index) are varied. Simulated dust distributions for summertime Saharan dust compare well to observations, with best results found when the most absorbing dust optical properties are assumed. Dust absorption leads to a strengthening of the summertime Hadley cell circulation, increased dust lofting to higher altitudes, and a strengthening of the African easterly jet, resulting in increased dust atmospheric lifetime and farther northward and westward transport. We find a positive feedback of dust radiative forcing on emissions, in contrast with previous studies, which we attribute to our having a relatively strong longwave forcing caused by our simulating larger effective particle sizes. This longwave forcing reduces the magnitude of midday net surface cooling relative to other studies, and leads to a nighttime warming that results in higher nighttime wind speeds and dust emissions. The radiative effects of dust particle shape have only minor impact on transport and emissions, with small (~5%) impact on top of atmosphere shortwave forcing, in line with previous studies, but relatively more pronounced effects on shortwave atmospheric heating and surface forcing (~20% increase in atmospheric forcing for spheroids). Shape effects on longwave heating terms are of order ~10%.

Ice clouds influence the climate system by changing the radiation budget and large-scale circulation. Therefore, climate models need to have an accurate representation of ice clouds and their radiative effects. In this paper, new broadband parameterizations for ice cloud bulk scattering properties are developed for severely roughened ice particles. The parameterizations are based on a general habit mixture that includes nine habits (droxtals, hollow/solid columns, plates, solid/hollow bullet rosettes, aggregate of solid columns, and small/large aggregates of plates). The scattering properties for these individual habits incorporate recent advances in light-scattering computations. The influence of ice particle surface roughness on the ice cloud radiative effect is determined through simulations with the Fu–Liou and the GCM version of the Rapid Radiative Transfer Model (RRTMG) codes and the National Center for Atmospheric Research Community Atmosphere Model (CAM, version 5.1). The differences in shortwave (SW) and longwave (LW) radiative effect at both the top of the atmosphere and the surface are determined for smooth and severely roughened ice particles. While the influence of particle roughening on the single-scattering properties is negligible in the LW, the results indicate that ice crystal roughness can change the SW forcing locally by more than 10 W m−2 over a range of effective diameters. The global-averaged SW cloud radiative effect due to ice particle surface roughness is estimated to be roughly 1–2 W m−2. The CAM results indicate that ice particle roughening can result in a large regional SW radiative effect and a small but nonnegligible increase in the global LW cloud radiative effect.

With the development and the improvement of meteorological satellites, different instruments have significantly enhanced the ability to observe clouds over large spatial regions. Recent geostationary satellite radiometers, e.g., Advanced Himawari Imager (AHI) and Advanced Geosynchronous Radiation Imager (AGRI) onboard the Himawari-8 and the Fengyun-4A satellite, respectively, provide observations over similar regions at higher spatial and temporal resolutions for cloud and atmosphere studies. To better understand the reliability of AHI and AGRI retrieval products, we compare their cloud products with collocated Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products, especially in terms of the cloud optical thickness (COT) and cloud effective radius (CER). Our comparison indicates that cloud mask and cloud phase of these instruments are reasonably consistent, while clear differences are noticed for COT and CER results. The average relative differences (RDs) between AHI and AGRI ice COT and that of MODIS are both over 40%, and the RDs of ice CER are less than 20%. The consistency between AHI and MODIS water cloud results is much better, with the RDs of COT and CER being 29% and 9%, respectively, whereas the RDs of AGRI COT and CER are still larger than 30%. Many factors such as observation geometry, cloud horizontal homogeneity, and retrieval system (e.g., retrieval algorithm, forward model, and assumptions) may contribute to these differences. The RDs of COTs from different instruments for homogeneous clouds are about one-third smaller than the corresponding RDs for inhomogeneous clouds. By applying unified retrieval systems based on the forward radiative transfer models designed for each particular band, we find that 30% to 70% of the differences among the results from different instruments are caused by the retrieval system (e.g., different treatments or assumptions for the retrievals), and the rest may be due to sub-pixel inhomogeneity, parallax errors, and calibration.