A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011–2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of −24 % and −35 % for particles with dry diameters >50 and >120 nm, as well as −36 % and −34 % for CCN at supersaturations of 0.2 % and 1.0 %, respectively. However, they seem to behave differently for particles activating at very low supersaturations (<0.1 %) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N₃ (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2 % (CCN_0.2) compared to that for N₃, maximizing over regions where new particle formation is important. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120 nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40 % during winter and 20 % in summer. In contrast to the large spread in simulated aerosol particle and CCN number concentrations, the CDNC derived from simulated CCN spectra is less diverse and in better agreement with CDNC estimates consistently derived from the observations (average NMB −13 % and −22 % for updraft velocities 0.3 and 0.6 m s⁻¹, respectively). In addition, simulated CDNC is in slightly better agreement with observationally derived values at lower than at higher updraft velocities (index of agreement 0.64 vs. 0.65). The reduced spread of CDNC compared to that of CCN is attributed to the sublinear response of CDNC to aerosol particle number variations and the negative correlation between the sensitivities of CDNC to aerosol particle number concentration ($\partial N_{\mathrm{d}}/\partial N_{\mathrm{a}}$ ) and to updraft velocity ($\partial N_{\mathrm{d}}/\partial w$ ). Overall, we find that while CCN is controlled by both aerosol particle number and composition, CDNC is sensitive to CCN at low and moderate CCN concentrations and to the updraft velocity when CCN levels are high. Discrepancies are found in sensitivities $\partial N_{\mathrm{d}}/\partial N_{\mathrm{a}}$ and $\partial N_{\mathrm{d}}/\partial w$ ; models may be predisposed to be too “aerosol sensitive” or “aerosol insensitive” in aerosol–cloud–climate interaction studies, even if they may capture average droplet numbers well. This is a subtle but profound finding that only the sensitivities can clearly reveal and may explain inter-model biases on the aerosol indirect effect.

Many of the next generation of global climate models will include aerosol schemes which explicitly simulate the microphysical processes that determine the particle size distribution. These models enable aerosol optical properties and cloud condensation nuclei (CCN) concentrations to be determined by fundamental aerosol processes, which should lead to a more physically based simulation of aerosol direct and indirect radiative forcings. This study examines the global variation in particle size distribution simulated by 12 global aerosol microphysics models to quantify model diversity and to identify any common biases against observations. Evaluation against size distribution measurements from a new European network of aerosol supersites shows that the mean model agrees quite well with the observations at many sites on the annual mean, but there are some seasonal biases common to many sites. In particular, at many of these European sites, the accumulation mode number concentration is biased low during winter and Aitken mode concentrations tend to be overestimated in winter and underestimated in summer. At high northern latitudes, the models strongly underpredict Aitken and accumulation particle concentrations compared to the measurements, consistent with previous studies that have highlighted the poor performance of global aerosol models in the Arctic. In the marine boundary layer, the models capture the observed meridional variation in the size distribution, which is dominated by the Aitken mode at high latitudes, with an increasing concentration of accumulation particles with decreasing latitude. Considering vertical profiles, the models reproduce the observed peak in total particle concentrations in the upper troposphere due to new particle formation, although modelled peak concentrations tend to be biased high over Europe. Overall, the multi-model-mean data set simulates the global variation of the particle size distribution with a good degree of skill, suggesting that most of the individual global aerosol microphysics models are performing well, although the large model diversity indicates that some models are in poor agreement with the observations. Further work is required to better constrain size-resolved primary and secondary particle number sources, and an improved understanding of nucleation and growth (e.g. the role of nitrate and secondary organics) will improve the fidelity of simulated particle size distributions.

Gaseous amines have attracted increasing attention due to their potential role in enhancing particle nucleation and growth and affecting secondary organic aerosol formation. Here we study with a chemistry transport model the global distributions of the most common and abundant amines in the air: monomethylamine (MMA), dimethylamine (DMA), and trimethylamine (TMA). We show that gas phase oxidation and aerosol uptakes are dominant sinks for these methylamines. The oxidation alone (i.e., no aerosol uptake) leads to methylamine lifetimes of 5–10 h in most parts of low and middle latitude regions. The uptake by secondary species with uptake coefficient (γ) of 0.03 (corresponding to the uptake by sulfuric acid particles) reduces the lifetime by ~30% over oceans and much more over the major continents, resulting in a methylamine lifetime of less than 1–2 h over central Europe, eastern Asia, and eastern US. With the estimated global emission flux, from the literature, our simulations indicate that [DMA] in the model surface layer over major continents is generally in the range of 0.1–2 ppt (parts per trillion) when γ = 0.03 and 0.2–10 ppt when γ = 0, and decreases quickly with altitude. [DMA] over oceans is below 0.05 ppt and over polar regions it is below 0.01 ppt. The simulated [MMA] is about a factor of ~2.5 higher while [TMA] is a factor of ~8 higher than [DMA]. The modeled concentrations of methylamines are substantially lower than the limited observed values available, with normalized mean bias ranging from −57 (γ = 0) to −88% (γ = 0.03) for MMA and TMA, and from −78 (γ = 0) to −93% (γ = 0.03) for DMA.

The widely used community model GEOS-Chem 12.0.0 and previous versions have been recognized to significantly overestimate the concentrations of gaseous nitric acid, aerosol nitrate, and aerosol ammonium over the United States. The concentrations of nitric acid are also significantly overpredicted in most global models participating in a recent model intercomparison study. In this study, we show that most or all of this overestimation issue appears to be associated with wet scavenging processes. The replacement of constant in-cloud condensation water (ICCW) assumed in GEOS-Chem standard versions with one varying with location and time from the assimilated meteorology significantly reduces mass loadings of nitrate and ammonium during the wintertime, while the employment of an empirical washout rate for nitric acid significantly decreases mass concentrations of nitric acid and ammonium during the summertime. Compared to the standard version, GEOS-Chem with updated ICCW and washout rate significantly reduces the simulated annual mean mass concentrations of nitric acid, nitrate, and ammonium at surface monitoring network sites in the US from 2.04 to 1.03, 1.89 to 0.88, and 1.09 to 0.68 µg m−3, respectively, in much better agreement with corresponding observed values of 0.83, 0.70, and 0.60 µg m−3, respectively. In addition, the agreement of model-simulated seasonal variations of corresponding species with measurements is also improved. The updated wet scavenging scheme improves the skill of the model in predicting nitric acid, nitrate, and ammonium concentrations, which are important species for air quality and climate.

Wet processes, including aqueous-phase chemistry, wet scavenging, and wet surface uptake during dry deposition, are important for global modeling of aerosols and aerosol precursors. In this study, we improve the treatments of these wet processes in the Goddard Earth Observing System with chemistry (GEOS-Chem) v12.6.0, including pH calculations for cloud, rain, and wet surfaces, the fraction of cloud available for aqueous-phase chemistry, rainout efficiencies for various types of clouds, empirical washout by rain and snow, and wet surface uptake during dry deposition. We compare simulated surface mass concentrations of aerosols and aerosol precursors with surface monitoring networks over the United States, European, Asian, and Arctic regions, and show that model results with updated wet processes agree better with measurements for most species. With the implementation of these updates, normalized mean biases (NMBs) of surface nitric acid, nitrate, and ammonium are reduced from 78 %, 126 %, and 45 % to 0.9 %, 15 %, and 4.1 % over the US sites, from 107 %, 127 %, and 90 % to −0.7 %, 4.2 %, and 16 % over European sites, and from 121 %, 269 %, and 167 % to −21 %, 37 %, and 86 % over Asian remote region sites. Comparison with surface measured SO₂, sulfate, and black carbon at four Arctic sites indicated that those species simulated with the updated wet processes match well with observations except for a large underestimate of black carbon at one of the sites. We also compare our model simulation with aircraft measurement of nitric acid and aerosols during the Atmospheric Tomography Mission (ATom)-1 and ATom-2 periods and found a significant improvement of modeling skill of nitric acid, sulfate, and ammonium in the Northern Hemisphere during wintertime. The NMBs of these species are reduced from 163 %, 78 %, and 217 % to −13 %, −1 %, and 10 %, respectively. The investigation of impacts of updated wet process treatments on surface mass concentrations indicated that the updated wet processes have strong impacts on the global means of nitric acid, sulfate, nitrate, and ammonium and relative small impacts on the global means of sulfur dioxide, dust, sea salt, black carbon, and organic carbon.

Geostationary satellite measurements of aerosol optical depth (AOD) over East Asia from the Geostationary Ocean Color Imager (GOCI) and Advanced Himawari Imager (AHI) instruments can augment surface monitoring of fine particulate matter (PM_2.5) air quality, but this requires better understanding of the AOD–PM_2.5 relationship. Here we use the GEOS-Chem chemical transport model to analyze the critical variables determining the AOD–PM_2.5 relationship over East Asia by simulation of observations from satellite, aircraft, and ground-based datasets. This includes the detailed vertical aerosol profiling over South Korea from the KORUS-AQ aircraft campaign (May–June 2016) with concurrent ground-based PM_2.5 composition, PM₁₀, and AERONET AOD measurements. The KORUS-AQ data show that 550 nm AOD is mainly contributed by sulfate–nitrate–ammonium (SNA) and organic aerosols in the planetary boundary layer (PBL), despite large dust concentrations in the free troposphere, reflecting the optically effective size and high hygroscopicity of the PBL aerosols. We updated SNA and organic aerosol size distributions in GEOS-Chem to represent aerosol optical properties over East Asia by using in situ measurements of particle size distributions from KORUS-AQ. We find that SNA and organic aerosols over East Asia have larger size (number median radius of 0.11 µm with geometric standard deviation of 1.4) and 20 % larger mass extinction efficiency as compared to aerosols over North America (default setting in GEOS-Chem). Although GEOS-Chem is successful in reproducing the KORUS-AQ vertical profiles of aerosol mass, its ability to link AOD to PM_2.5 is limited by under-accounting of coarse PM and by a large overestimate of nighttime PM_2.5 nitrate. The GOCI–AHI AOD data over East Asia in different seasons show agreement with AERONET AODs and a spatial distribution consistent with surface PM_2.5 network data. The AOD observations over North China show a summer maximum and winter minimum, opposite in phase to surface PM_2.5. This is due to low PBL depths compounded by high residential coal emissions in winter and high relative humidity (RH) in summer. Seasonality of AOD and PM_2.5 over South Korea is much weaker, reflecting weaker variation in PBL depth and lack of residential coal emissions.