Quantifying the structural uncertainty of the aerosol mixing state representation in a modal model

Aerosol mixing state is an important emergent property that affects aerosol radiative forcing and aerosol–cloud interactions, but it has not been easy to constrain this property globally. This study aims to verify the global distribution of aerosol mixing state represented by modal models. To quantify the aerosol mixing state, we used the aerosol mixing state indices for submicron aerosol based on the mixing of optically absorbing and non-absorbing species (χo), the mixing of primary carbonaceous and non-primary carbonaceous species (χc), and the mixing of hygroscopic and non-hygroscopic species (χh). To achieve a spatiotemporal comparison, we calculated the mixing state indices using output from the Community Earth System Model with the four-mode version of the Modal Aerosol Module (MAM4) and compared the results with the mixing state indices from a benchmark machine-learned model trained on high-detail particle-resolved simulations from the particle-resolved stochastic aerosol model PartMC-MOSAIC. The two methods yielded very different spatial patterns of the mixing state indices. In some regions, the yearly averaged χ value computed by the MAM4 model differed by up to 70 percentage points from the benchmark values. These errors tended to be zonally structured, with the MAM4 model predicting a more internally mixed aerosol at low latitudes and a more externally mixed aerosol at high latitudes compared to the benchmark. Our study quantifies potential model bias in simulating mixing state in different regions and provides insights into potential improvements to model process representation for a more realistic simulation of aerosols towards better quantification of radiative forcing and aerosol–cloud interactions.

Measurement report: Comparison of airborne, in situ measured, lidar-based, and modeled aerosol optical properties in the central European background – identifying sources of deviations

A unique data set derived from remote sensing, airborne, and ground-based in situ measurements is presented. This measurement report highlights the known complexity of comparing multiple aerosol optical parameters examined with different approaches considering different states of humidification and atmospheric aerosol concentrations. Mie-theory-based modeled aerosol optical properties are compared with the respective results of airborne and ground-based in situ measurements and remote sensing (lidar and photometer) performed at the rural central European observatory at Melpitz, Germany. Calculated extinction-to-backscatter ratios (lidar ratios) were in the range of previously reported values. However, the lidar ratio is a function of the aerosol type and the relative humidity. The particle lidar ratio (LR) dependence on relative humidity was quantified and followed the trend found in previous studies. We present a fit function for the lidar wavelengths of 355, 532, and 1064 nm with an underlying equation of fLR(RH, γ(λ))=fLR(RH=0,λ)×(1-RH)-γ(λ), with the derived estimates of γ(355 nm) = 0.29 (±0.01), γ(532 nm) = 0.48 (±0.01), and γ(1064 nm) = 0.31 (±0.01) for central European aerosol. This parameterization might be used in the data analysis of elastic-backscatter lidar observations or lidar-ratio-based aerosol typing efforts. Our study shows that the used aerosol model could reproduce the in situ measurements of the aerosol particle light extinction coefficients (measured at dry conditions) within 13 %. Although the model reproduced the in situ measured aerosol particle light absorption coefficients within a reasonable range, we identified many sources for significant uncertainties in the simulations, such as the unknown aerosol mixing state, brown carbon (organic material) fraction, and the unknown aerosol mixing state wavelength-dependent refractive index. The modeled ambient-state aerosol particle light extinction and backscatter coefficients were smaller than the measured ones. However, depending on the prevailing aerosol conditions, an overlap of the uncertainty ranges of both approaches was achieved.

Quantifying the structural uncertainty of the aerosol mixing state representation in a modal model

Aerosol mixing state is an important emergent property that affects aerosol radiative forcing and aerosol-cloud interactions, but it has not been easy to constrain this property globally. This study aims to verify the global distribution of aerosol mixing state represented by modal models. To quantify the aerosol mixing state, we used the aerosol mixing state indices for submicron aerosol based on the mixing of optically absorbing and non-absorbing species (χo), the mixing of primary carbonaceous and non-primary carbonaceous species (χc), and the mixing of hygroscopic and non-hygroscopic species (χh). To achieve a spatiotemporal comparison, we calculated the mixing state indices using output from the Community Earth System Model with the modal MAM4 aerosol module, and compared the results with the mixing state indices from a benchmark machine-learned model trained on high-detail particle-resolved simulations from the particle-resolved stochastic aerosol model PartMC-MOSAIC. The two methods yielded very different spatial patterns of the mixing state indices. In some regions, the yearly-averaged χ value computed by the MAM4 model differed by up to 70 percentage points from the benchmark values. These errors tended to be zonally structured, with the MAM4 model predicting a more internally mixed aerosol at low latitudes, and a more externally mixed aerosol at high latitudes, compared to the benchmark. Our study quantifies potential model bias in simulating mixing state in different regions, and provides insights into potential improvements to model process representation for a more realistic simulation of aerosols.

Characterizing the volatility and mixing state of ambient fine particles in summer and winter of urban Beijing

Understanding the volatility and mixing state of atmospheric aerosols is important for elucidating the formation of fine particles and to help determining their effect on environment and climate. In this study, the volatility of the fine particles is characterized by the size-dependent volatility shrink factor (VSF) for summer and winter in the urban area of Beijing using measurements of a volatility tandem differential mobility analyzer (VTDMA). We show the volatility of aerosols is always with one high-volatile and one less- or non-volatile mode both in the summer and winter. On average, the particles are more volatile in the summer (with mean VSF of 0.3) than in the winter (with mean VSF of 0.6). The outstanding high-volatile mode around noontime illustrates the role of nucleation in producing more volatile particles in the summer. We further retrieve the mixing state of the ambient fine particles from the size-resolved VSF and find that the non-black carbon (BC) particles that formed from nucleation processes accounted for 52–69 % of the total number concentration in the summer. While, particles containing a refractory core that is thought to be BC-containing particles dominate and contribute 67–77 % toward the total number concentration in the winter. The diurnal cycles of the retrieved aerosol mixing state for the summer further supports the conclusion that nucleation process is the main contributors to non-BC particles. In addition, the extent of aging of BC particles was characterized as the ratio of the BC diameter before and after heating at 300 °C (Dp/Dc), showing that the average ratio of ~2.2 in the winter is higher than the average of ~1.5 in the summer, which indicates that BC aging is more efficient in wintertime, with resulting differences in light absorption enhancement between cold and warm seasons.

Secondary aerosol formation alters CCN activity in the North China Plain

<p>The formation of secondary aerosols (SA, including secondary organic and inorganic aerosols, SOA and SIA) were the dominant sources of aerosol particles in the North China Plain and can result in significant variations of particle size distribution (PNSD) and hygroscopicity. Earlier studies have shown that the mechanism of SA formation can be affected by relative humidity (RH), and thus has different influences on the aerosol hygroscopicity and PNSD under different RH conditions. Based on the measurements of size-resolved particle activation ratio (SPAR), hygroscopicity distribution (GF-PDF), PM<sub>2.5</sub> chemical composition, PNSD, meteorology and gaseous pollutants in a recent field campaign McFAN (Multiphase chemistry experiment in Fogs and Aerosols in the North China Plain) conducted at Gucheng site from November 16<sup>th</sup> to December 16<sup>th</sup> in 2018, the influences of SA formation on CCN activity and CCN number concentration (N<sub>CCN</sub>) calculation at super-saturation of 0.05% under different RH conditions were studied. Measurements showed that during daytime, SA formation could lead to a significant increase in N<sub>CCN</sub> and a strong diurnal variation in CCN activity. During periods with daytime minimum RH exceeding 50% (high RH conditions), SA formation significantly contributed to the particle mass/size changes in wide particle size range of 150 nm to 1000 nm, and led to an increase of N<sub>CCN</sub> in particle size range of 200 nm to 300 nm, while increases in particle mass concentration mainly occurred within particle sizes larger than 300nm. During periods with daytime minimum RH below 30% in (low RH conditions), SA formation mainly contributed to the particle mass/size and N<sub>CCN</sub> changes in particle sizes smaller than 300 nm. As a result, under the same amount SA formation induced mass increase, the increase of N<sub>CCN</sub> was weaker under high RH conditions, while stronger under low RH conditions. Moreover, the diurnal variations of aerosol mixing state (inferred from CCN measurements) due to SA formation was different under different RH conditions. If the variations of the aerosol mixing state were not considered, estimations of N<sub>CCN</sub> would bear significant deviations. By applying aerosol mixing state estimated by number fraction of hygroscopic particles from measurements of particle hygroscopicity or mass fraction of SA from measurements of particle chemical compositions, N<sub>CCN</sub> calculation can be largely improved with relative deviation within 30%. This study improves the understanding of the impact of SA formation on CCN activity and N<sub>CCN</sub> calculation, which is of great significance for improving parameterization of SA formation in aerosol models and CCN calculation in climate models.</p>

Biomass burning aerosols in most climate models are too absorbing

Uncertainty in the representation of biomass burning (BB) aerosol composition and optical properties in climate models contributes to a range in modeled aerosol effects on incoming solar radiation. Depending on the model, the top-of-the-atmosphere BB aerosol effect can range from cooling to warming. By relating aerosol absorption relative to extinction and carbonaceous aerosol composition from 12 observational datasets to nine state-of-the-art Earth system models/chemical transport models, we identify varying degrees of overestimation in BB aerosol absorptivity by these models. Modifications to BB aerosol refractive index, size, and mixing state improve the Community Atmosphere Model version 5 (CAM5) agreement with observations, leading to a global change in BB direct radiative effect of −0.07 W m−2, and regional changes of −2 W m−2 (Africa) and −0.5 W m−2 (South America/Temperate). Our findings suggest that current modeled BB contributes less to warming than previously thought, largely due to treatments of aerosol mixing state.

Secondary aerosol formation alters CCN activity in the North China Plain

The formation of secondary aerosols (SA, including secondary organic and inorganic aerosols, SOA and SIA) is the dominant source of aerosol particles in the North China Plain and has a significant impact on the variations of particle size distribution (PNSD) and hygroscopicity. Previous studies have shown that the mechanism of SA formation can be affected by relative humidity (RH), and thus has different influences on the aerosol hygroscopicity and PNSD under different RH conditions. Based on the measurements of size-resolved particle activation ratio (SPAR), hygroscopicity distribution (GF-PDF), PM2.5 chemical composition, PNSD, meteorology and gaseous pollutants in a recent field campaign McFAN (Multiphase chemistry experiment in Fogs and Aerosols in the North China Plain) conducted at Gucheng site from 16th November to 16th December in 2018, the influences of SA formation on CCN activity and CCN number concentration (NCCN) calculation under different RH conditions were studied. Measurements showed that during daytime the SA formation can lead to a significant increase of NCCN and a strong diurnal variation of CCN activity. During periods with minimum RH higher than 50 % in daytime (high RH conditions), SA formation significantly contributed to the particle mass/size changes in wide particle size range of 150 nm to 1000 nm, and lead to the increase of NCCN in particle size range of 200 nm to 300 nm while increase of particle mass concentration mainly in particle size larger than 300 nm. During periods with minimum RH lower than 30 % in daytime (low RH conditions), SA formation mainly contributed to the particle mass/size changes in particle size smaller than 300 nm and so did the increases of both NCCN and particle mass concentration. As a result, upon the same amount of mass increase through SA formation, the increase of NCCN is weaker under high RH conditions while stronger under low RH conditions. Moreover, the diurnal variations of aerosol mixing state (inferred from CCN measurements) due to SA formation was different under different RH conditions, which contributed one of the largest uncertainties in NCCN predictions. By applying aerosol mixing state estimated by number fraction of hygroscopic particles from measurements of particle hygroscopicity or mass fraction of SA from measurements of particle chemical compositions, the NCCN prediction was largely improved with relative deviations smaller than 30 %. This study highlights the impact of SA formation on CCN activity and NCCN calculation, which is of great significance for improving parameterization of SA formation in chemical-transport models and CCN predictions in climate models.