Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation.

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 > 120nm, 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% (CCN0.2) 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 ms-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 (∂N d/∂N a) and to updraft velocity (∂N d/∂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 ∂N d/∂N a and ∂N d/∂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.

Global distribution of particle phase state in atmospheric secondary organic aerosols.

Secondary organic aerosols (SOA) are a large source of uncertainty in our current understanding of climate change and air pollution. The phase state of SOA is important for quantifying their effects on climate and air quality, but its global distribution is poorly characterized. We developed a method to estimate glass transition temperatures based on the molar mass and molecular O:C ratio of SOA components, and we used the global chemistry climate model EMAC with the organic aerosol module ORACLE to predict the phase state of atmospheric SOA. For the planetary boundary layer, global simulations indicate that SOA are mostly liquid in tropical and polar air with high relative humidity, semi-solid in the mid-latitudes and solid over dry lands. We find that in the middle and upper troposphere SOA should be mostly in a glassy solid phase state. Thus, slow diffusion of water, oxidants and organic molecules could kinetically limit gas-particle interactions of SOA in the free and upper troposphere, promote ice nucleation and facilitate long-range transport of reactive and toxic organic pollutants embedded in SOA.

Response of fine particulate matter to emission changes of oxides of nitrogen and anthropogenic volatile organic compounds in the eastern United States.

A three-dimensional chemical transport model (Particulate Matter Comprehensive Air Quality Model with Extensions [PMCAMx]) is used to investigate changes in fine particle (PM2.5) concentrations in response to 50% emissions changes of oxides of nitrogen (NOx) and anthropogenic volatile organic compounds (VOCs) during July 2001 and January 2002 in the eastern United States. The reduction of NOx emissions by 50% during the summer results in lower average oxidant levels and lowers PM2.5 (8% on average), mainly because of reductions of sulfate (9-11%), nitrate (45-58%), and ammonium (7-11%). The organic particulate matter (PM) slightly decreases in rural areas, whereas it increases in cities by a few percent when NOx is reduced. Reduction of NOx during winter causes an increase of the oxidant levels and a rather complicated response of the PM components, leading to small net changes. Sulfate increases (8-17%), nitrate decreases (18-42%), organic PM slightly increases, and ammonium either increases or decreases a little. The reduction of VOC emissions during the summer causes on average a small increase of the oxidant levels and a marginal increase in PM2.5. This small net change is due to increases in the inorganic components and decreases of the organic ones. Reduction of VOC emissions during winter results in a decrease of the oxidant levels and a 5-10% reduction of PM2.5 because of reductions in nitrate (4-19%), ammonium (4-10%), organic PM (12-14%), and small reductions in sulfate. Although sulfur dioxide (SO2) reduction is the single most effective approach for sulfate control, the coupled decrease of SO2 and NOx emissions in both seasons is more effective in reducing total PM2.5 mass than the SO2 reduction alone.

Response of inorganic fine particulate matter to emission changes of sulfur dioxide and ammonia: the eastern United States as a case study.

A three-dimensional chemical transport model (PMCAMx) was used to investigate changes in fine particle (PM2.5) concentrations in response to changes in sulfur dioxide (SO2) and ammonia (NH3) emissions during July 2001 and January 2002 in the eastern United States. A uniform 50% reduction in SO2 emissions was predicted to produce an average decrease of PM2.5 concentrations by 26% during July but only 6% during January. A 50% reduction of NH3 emissions leads to an average 4 and 9% decrease in PM2.5 in July and January, respectively. During the summer, the highest concentration of sulfate is in South Indiana (12.8 microg x m(-3)), and the 50% reduction of SO2 emissions results in a 5.7 microg x m(-3) (44%) sulfate decrease over this area. During winter, the SO2 emissions reduction results in a 1.5 microg x m(-3) (29%) decrease of the peak sulfate levels (5.2 microg x m(-3)) over Southeast Georgia. The maximum nitrate and ammonium concentrations are predicted to be over the Midwest (1.9 (-3)g x m(-3) in Ohio and 5.3 microg x m(-3) in South Indiana, respectively) in the summer whereas in the winter these concentrations are higher over the Northeast (3 microg x m(-3) of nitrate in Connecticut and 2.7 microg x m(-3) of ammonium in New York). The 50% NH3 emissions reduction is more effective for controlling nitrate, compared with SO2 reductions, producing a 1.1 microg x m(-3) nitrate decrease over Ohio in July and a 1.2 microg x m(-3) decrease over Connecticut in January. Ammonium decreases significantly when either SO2 or NH3 emissions are decreased. However, the SO2 control strategy has better results in July when ammonium decreases, up to 2 microg x m(-3) (37%), are predicted in South Indiana. The NH3 control strategy has better results in January (ammonium decreases up to 0.4 microg x m(-3) in New York). The spatial and temporal characteristics of the effectiveness of these emission control strategies during the summer and winter seasons are discussed.