Journal of Geophysical Research D: Atmospheres

China experienced severe haze pollution in January 2013. Here we have a detailed characterization of the sources and evolution mechanisms of this haze pollution with a focus on four haze episodes that occurred during 10–14 January in Beijing. The main source of data analyzed is from submicron aerosol measurements by an Aerodyne Aerosol Chemical Speciation Monitor. The average PM1 mass concentration during the four haze episodes ranged from 144 to 300 µg m⁻³, which was more than 10 times higher than that observed during clean periods. All submicron aerosol species showed substantial increases during haze episodes with sulfate being the largest. Secondary inorganic species played enhanced roles in the haze formation as suggested by their elevated contributions during haze episodes. Positive matrix factorization analysis resolved six organic aerosol (OA) factors including three primary OA (POA) factors from traffic, cooking, and coal combustion emissions, respectively, and three secondary OA (SOA) factors. Overall, SOA contributed 41–59% of OA with the rest being POA. Coal combustion OA (CCOA) was the largest primary source, on average accounting for 20–32% of OA, and showed the most significant enhancement during haze episodes. A regional SOA (RSOA) was resolved for the first time which showed a pronounced peak only during the record‐breaking haze episode (Ep3) on 12–13 January. The regional contributions estimated based on the steep evolution of air pollutants were found to play dominant roles for the formation of Ep3, on average accounting for 66% of PM1 during the peak of Ep3 with sulfate, CCOA, and RSOA being the largest fractions (> ~ 75%). Our results suggest that stagnant meteorological conditions, coal combustion, secondary production, and regional transport are four main factors driving the formation and evolution of haze pollution in Beijing during wintertime.

We present a new data set of sulfur dioxide (SO₂) vertical columns from observations of the Ozone Monitoring Instrument (OMI)/AURA instrument between 2004 and 2013. The retrieval algorithm used is an advanced Differential Optical Absorption Spectroscopy (DOAS) scheme combined with radiative transfer calculation. It is developed in preparation for the operational processing of SO₂ data product for the upcoming TROPOspheric Monitoring Instrument/Sentinel 5 Precursor mission. We evaluate the SO₂ column results with those inferred from other satellite retrievals such as Infrared Atmospheric Sounding Interferometer and OMI (Linear Fit and Principal Component Analysis algorithms). A general good agreement between the different data sets is found for both volcanic and anthropogenic SO₂ emission scenarios. We show that our algorithm produces SO₂ columns with low noise and is able to provide accurate estimates of SO₂. This conclusion is supported by important validation results over the heavily polluted site of Xianghe (China). Nearly 4 years of OMI and ground‐based multiaxis DOAS SO₂ columns are compared, and an excellent match is found. We also highlight the improved performance of the algorithm in capturing weak SO₂ sources by detecting shipping SO₂ emissions in long‐term averaged data, an unreported measurement from space.

Assessing the accuracy of the aerosol‐above‐cloud (AAC) properties derived by CALIOP (the Cloud‐Aerosol Lidar with Orthogonal Polarization) is challenged by the shortage of accurate global validation measurements. We have used measurements of aerosol vertical profiles from the NASA Langley airborne High Spectral Resolution Lidar (HSRL‐1) in 86 CALIOP‐coincident flights to evaluate CALIOP AAC detection, classification, and retrieval. Our study shows that CALIOP detects ~23% of the HSRL‐detected AAC. According to our CALIOP‐HSRL data set, the majority of AAC aerosol optical depth (AOD) values are < 0.1 at 532 nm over North America. Our analyses show that the standard CALIOP retrieval algorithm substantially underestimates the occurrence frequency of AAC when optical depths are less than ~0.02. Those aerosols with low AOD values can still have a consequent radiative forcing effect depending on the underlying cloud cover and overlying aerosol absorption properties. We find essentially no correlation between CALIOP and HSRL AAC AOD (R2 = 0.27 and N = 151). We show that the CALIOP underestimation of AAC is mostly due to tenuous aerosol layers with backscatter less than the CALIOP detection threshold. The application of an alternate CALIOP AAC retrieval method (depolarization ratio) to our data set yields very few coincident cases. We stress the need for more extensive suborbital CALIOP validation campaigns to acquire a process‐level understanding of AAC implications and further evaluate CALIOP's AAC detection and retrieval capability, especially over the ocean and in different parts of the world where AAC are more frequently observed and show higher values of AOD.

Convective cold pools and the breakdown of nocturnal low‐level jets (NLLJs) are key meteorological drivers of dust emission over summertime West Africa, the world's largest dust source. This study is the first to quantify their relative contributions and physical interrelations using objective detection algorithms and an off‐line dust emission model applied to convection‐permitting simulations from the Met Office Unified Model. The study period covers 25 July to 02 September 2006. All estimates may therefore vary on an interannual basis. The main conclusions are as follows: (a) approximately 40% of the dust emissions are from NLLJs, 40% from cold pools, and 20% from unidentified processes (dry convection, land‐sea and mountain circulations); (b) more than half of the cold‐pool emissions are linked to a newly identified mechanism where aged cold pools form a jet above the nocturnal stable layer; (c) 50% of the dust emissions occur from 1500 to 0200 LT with a minimum around sunrise and after midday, and 60% of the morning‐to‐noon emissions occur under clear skies, but only 10% of the afternoon‐to‐nighttime emissions, suggesting large biases in satellite retrievals; (d) considering precipitation and soil moisture effects, cold‐pool emissions are reduced by 15%; and (e) models with parameterized convection show substantially less cold‐pool emissions but have larger NLLJ contributions. The results are much more sensitive to whether convection is parameterized or explicit than to the choice of the land‐surface characterization, which generally is a large source of uncertainty. This study demonstrates the need of realistically representing moist convection and stable nighttime conditions for dust modeling.

Deposition of mineral dust into ocean fertilizes ecosystems and influences biogeochemical cycles and climate. In situ observations of dust deposition are scarce, and model simulations depend on the highly parameterized representations of dust processes with few constraints. By taking advantage of satellites' routine sampling on global and decadal scales, we estimate African dust deposition flux and loss frequency (a ratio of deposition flux to mass loading) along the trans‐Atlantic transit using the three‐dimensional distributions of aerosol retrieved by spaceborne lidar (Cloud‐Aerosol Lidar with Orthogonal Polarization [CALIOP]) and radiometers (Moderate Resolution Imaging Spectroradiometer [MODIS], Multiangle Imaging Spectroradiometer [MISR], and Infrared Atmospheric Sounding Interferometer [IASI]). On the basis of a 10‐year (2007‐2016) and basin‐scale average, the amount of dust deposition into the tropical Atlantic Ocean is estimated at 136‐222 Tg/year. The 65‐83% of satellite‐based estimates agree with the in situ climatology within a factor of 2. The magnitudes of dust deposition are highest in boreal summer and lowest in fall, whereas the interannual variability as measured by the normalized standard deviation with mean is largest in spring (28‐41%) and smallest (7‐15%) in summer. The dust deposition displays high spatial heterogeneity, revealing that the meridional shifts of major dust deposition belts are modulated by the seasonal migration of the intertropical convergence zone. On the basis of the annual and basin mean, the dust loss frequency derived from the satellite observations ranges from 0.078 to 0.100 day^‐1, which is lower than model simulations by up to factors of 2 to 5. The most efficient loss of dust occurs in winter, consistent with the higher possibility of low‐altitude transported dust in southern trajectories being intercepted by rainfall associated with the intertropical convergence zone. The satellite‐based estimates of dust deposition can be used to fill the geographical gaps and extend time span of in situ measurements, study the dust‐ocean interactions, and evaluate model simulations of dust processes.

Global Navigation Satellite System (GNSS)‐based radio occultation (RO) is a satellite remote sensing technique providing accurate profiles of the Earth's atmosphere for weather and climate applications. Above about 30 km altitude, however, statistical optimization is a critical process for initializing the RO bending angles in order to optimize the climate monitoring utility of the retrieved atmospheric profiles. Here we introduce an advanced dynamic statistical optimization algorithm, which uses bending angles from multiple days of European Centre for Medium‐range Weather Forecasts (ECMWF) short‐range forecast and analysis fields, together with averaged‐observed bending angles, to obtain background profiles and associated error covariance matrices with geographically varying background uncertainty estimates on a daily updated basis. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.4 (OPSv5.4) algorithm, using several days of simulated MetOp and observed CHAMP and COSMIC data, for January and July conditions. We find the following for the new method's performance compared to OPSv5.4: 1.) it significantly reduces random errors (standard deviations), down to about half their size, and leaves less or about equal residual systematic errors (biases) in the optimized bending angles; 2.) the dynamic (daily) estimate of the background error correlation matrix alone already improves the optimized bending angles; 3.) the subsequently retrieved refractivity profiles and atmospheric (temperature) profiles benefit by improved error characteristics, especially above about 30 km. Based on these encouraging results, we work to employ similar dynamic error covariance estimation also for the observed bending angles and to apply the method to full months and subsequently to entire climate data records.

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%.

We characterized the chemical composition and optical properties of particulate matter (PM) emitted by a marine diesel engine operated on heavy fuel oil (HFO), marine gas oil (MGO), and diesel fuel (DF). For all three fuels, ∼80% of submicron PM was organic (and sulfate, for HFO at higher engine loads). Emission factors varied only slightly with engine load. Refractory black carbon (rBC) particles were not thickly coated for any fuel; rBC was therefore externally mixed from organic and sulfate PM. For MGO and DF PM, rBC particles were lognormally distributed in size (mode at d_rBC≈120 nm). For HFO, much larger rBC particles were present. Combining the rBC mass concentrations with in situ absorption measurements yielded an rBC mass absorption coefficient MAC_BC,780nm of 7.8 ± 1.8 m²/g at 780 nm for all three fuels. Using positive deviations of the absorption Ångström exponent (AAE) from unity to define brown carbon (brC), we found that brC absorption was negligible for MGO or DF PM (AAE(370,880 nm)≈1.0 ± 0.1) but typically 50% of total 370‐nm absorption for HFO PM. Even at 590 nm, ∼20  of the total absorption was due to brC. Using absorption at 880 nm as a reference for BC absorption and normalizing to organic PM mass, we obtained a MAC_OM,370nm of 0.4 m²/g at typical operating conditions. Furthermore, we calculated an imaginary refractive index of (0.045 ± 0.025)(λ/370nm)⁻³ for HFO PM at 370 nm>λ > 660 nm, more than twofold greater than previous recommendations. Climate models should account for this substantial brC absorption in HFO PM.

The absorption Ångström exponent (AAE) describes the spectral dependence of light absorption by aerosols. AAE is typically used to differentiate between different aerosol types for example., black carbon, brown carbon, and dust particles. In this study, the variation of AAE was investigated mainly in fresh aerosol emissions from different fuel and combustion types, including emissions from ships, buses, coal‐fired power plants, and residential wood burning. The results were assembled to provide a compendium of AAE values from different emission sources. A dual‐spot aethalometer (AE33) was used in all measurements to obtain the light absorption coefficients at seven wavelengths (370–950 nm). AAE_470/950 varied greatly between the different emission sources, ranging from −0.2 ± 0.7 to 3.0 ± 0.8. The correlation between the AAE_470/950 and AAE_370‐950 results was good (R² = 0.95) and the mean bias error between these was 0.02. In the ship engine exhaust emissions, the highest AAE_470/950 values (up to 2.0 ± 0.1) were observed when high sulfur content heavy fuel oil was used, whereas low sulfur content fuels had the lowest AAE_470/950 (0.9–1.1). In the diesel bus exhaust emissions, AAE_470/950 increased in the order of acceleration (0.8 ± 0.1), deceleration (1.1 ± 0.1), and steady driving (1.2 ± 0.1). In the coal‐fired power plant emissions, the variation of AAE_470/950 was substantial (from −0.1 ± 2.1 to 0.9 ± 1.6) due to the differences in the fuels and flue gas cleaning conditions. Fresh wood‐burning derived aerosols had AAE_470/950 from 1.1 ± 0.1 (modern masonry heater) to 1.4 ± 0.1 (pellet boiler), lower than typically associated with wood burning, while the burn cycle phase affected AAE variation.