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 > 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 N-3 (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 N-3, 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 derivative N-d/partial derivative N-a) and to updraft velocity (partial derivative N-d/partial derivative 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 derivative N-d/partial derivative N-a and partial derivative N-d/partial derivative 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 intermodel biases on the aerosol indirect effect.Peer reviewe

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% (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 120nm). 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

How alkaline compounds control atmospheric aerosol particle acidity

The acidity of atmospheric particulate matter regulates its mass, composition, and toxicity and has important consequences for public health, ecosystems and climate. Despite these broad impacts, the global distribution and evolution of aerosol particle acidity are unknown. We used the comprehensive atmospheric multiphase chemistry–climate model EMAC (ECHAM5/MESSy Atmospheric Chemistry) to investigate the main factors that control aerosol particle acidity and uncovered remarkable variability and unexpected trends during the past 50 years in different parts of the world. Aerosol particle acidity decreased strongly over Europe and North America during the past decades while at the same time it increased over Asia. Our simulations revealed that these particle acidity trends are strongly related to changes in the phase partitioning of nitric acid, production of sulfate in aqueous aerosols, and the aerosol hygroscopicity. It is remarkable that the aerosol hygroscopicity (κ) has increased in many regions following the particle pH. Overall, we find that alkaline compounds, notably ammonium and to a lesser extent crustal cations, regulate the particle pH on a global scale. Given the importance of aerosol particles for the atmospheric energy budget, cloud formation, pollutant deposition, and public health, alkaline species hold the key to control strategies for air quality and climate change

Weaker cooling by aerosols due to dust–pollution interactions

The interactions between aeolian dust and anthropogenic air pollution, notably chemical ageing of mineral dust and coagulation of dust and pollution particles, modify the atmospheric aerosol composition and burden. Since the aerosol particles can act as cloud condensation nuclei, this affects the radiative transfer not only directly via aerosol–radiation interactions, but also indirectly through cloud adjustments. We study both radiative effects using the global ECHAM/MESSy atmospheric chemistry-climate model (EMAC) which combines the Modular Earth Submodel System (MESSy) with the European Centre/Hamburg (ECHAM) climate model. Our simulations show that dust–pollution–cloud interactions reduce the condensed water path and hence the reflection of solar radiation. The associated climate warming outweighs the cooling that the dust–pollution interactions exert through the direct radiative effect. In total, this results in a net warming by dust–pollution interactions which moderates the negative global anthropogenic aerosol forcing at the top of the atmosphere by (0.2 ± 0.1) W m−2

Mineral dust aerosol impacts on global climate and climate change

Mineral dust aerosols impact the energy budget of Earth through interactions with radiation, clouds, atmospheric chemistry, the cryosphere and biogeochemistry. In this Review, we summarize these interactions and assess the resulting impacts of dust, and of changes in dust, on global climate and climate change. The total effect of dust interactions on the global energy budget of Earth — the dust effective radiative effect — is −0.2 ± 0.5 W m−2 (90% confidence interval), suggesting that dust net cools the climate. Global dust mass loading has increased 55 ± 30% since pre-industrial times, driven largely by increases in dust from Asia and North Africa, leading to changes in the energy budget of Earth. Indeed, this increase in dust has produced a global mean effective radiative forcing of −0.07 ± 0.18 W m−2, somewhat counteracting greenhouse warming. Current climate models and climate assessments do not represent the historical increase in dust and thus omit the resulting radiative forcing, biasing climate change projections and assessments of climate sensitivity. Climate model simulations of future changes in dust diverge widely and are very uncertain. Further work is thus needed to constrain the radiative effects of dust on climate and to improve the representation of dust in climate models

Implementation of a comprehensive ice crystal formation parameterization for cirrus and mixed-phase clouds in the EMAC model (based on MESSy 2.53)

A comprehensive ice nucleation parameterization has been implemented in the global chemistry-climate model EMAC to improve the representation of ice crystal number concentrations (ICNCs). The parameterization of Barahona and Nenes (2009, hereafter BN09) allows for the treatment of ice nucleation taking into account the competition for water vapour between homogeneous and heterogeneous nucleation in cirrus clouds. Furthermore, the influence of chemically heterogeneous, polydisperse aerosols is considered by applying one of the multiple ice nucleating particle parameterizations which are included in BN09 to compute the heterogeneously formed ice crystals. BN09 has been modified in order to consider the pre-existing ice crystal effect and implemented to operate both in the cirrus and in the mixed-phase regimes. Compared to the standard EMAC parameterizations, BN09 produces fewer ice crystals in the upper troposphere but higher ICNCs in the middle troposphere, especially in the Northern Hemisphere where ice nucleating mineral dust particles are relatively abundant. Overall, ICNCs agree well with the observations, especially in cold cirrus clouds (at temperatures below 205K), although they are underestimated between 200 and 220K. As BN09 takes into account processes which were previously neglected by the standard version of the model, it is recommended for future EMAC simulations

Global Distribution of the Phase State and Mixing Times Within Secondary Organic Aerosol Particles in the Troposphere Based on Room-Temperature Viscosity Measurements

Information on the global distributions of secondary organic aerosol (SOA) phase state and mixing times within SOA is needed to predict the impact of SOA on air quality, climate, and atmospheric chemistry; nevertheless, such information is rare. In this study, we developed parameterizations for viscosity as a function of relative humidity (RH) and temperature based on room-temperature viscosity data for simulated pine tree SOA and toluene SOA. The viscosity parameterizations were then used together with tropospheric RH and temperature fields to predict the SOA phase state and mixing times of water and organic molecules within SOA in the troposphere for 200 nm particles. Based on our results, the glassy state can often occur, and the mixing times of water can often exceed 1 h within SOA at altitudes \u3e6 km. Furthermore, the mixing times of organic molecules within SOA can often exceed 1 h throughout most of the free troposphere (i.e., ≳1 km in altitude). In most of the planetary boundary layer (i.e., ≲1 km in altitude), the glassy state is not important, and the mixing times of water and organic molecules are less than 1 h. Our results are qualitatively consistent with the results from Shiraiwa et al. (Nat. Commun., 2017), although there are quantitative differences. Additional studies are needed to better understand the reasons for these differences

The atmospheric chemistry box model CAABA/MECCA-4.0gmdd

We present version 4.0gmdd of the atmospheric chemistry box model CAABA/MECCA which now includes a number of new features: (i) skeletal mechanism reduction, (ii) the MOM chemical mechanism for volatile organic compounds, (iii) an option to include reactions from the Master Chemical Mechanism (MCM) and other chemical mechanisms, (iv) updated isotope tagging, and (v) improved and new photolysis modules (JVAL, RADJIMT, DISSOC). Further, when MECCA is connected to a global model, the new feature of coexisting multiple chemistry mechanisms (PolyMECCA/CHEMGLUE) can be used. Additional changes have been implemented to make the code more user-friendly and to facilitate the analysis of the model results. Like earlier versions, CAABA/MECCA-4.0gmdd is a community model published under the GNU General Public License

Uptake of Water‐soluble Gas‐phase Oxidation Products Drives Organic Particulate Pollution in Beijing

Despite the recent decrease in pollution events in Chinese urban areas, the World Health Organization air quality guideline values are still exceeded. Observations from monitoring networks show a stronger decrease of organic aerosol directly emitted to the atmosphere relative to secondary organic aerosol (SOA) generated from oxidation processes. Here, the uptake of water-soluble gas-phase oxidation products is reported as a major SOA contribution to particulate pollution in Beijing, triggered by the increase of aerosol liquid water. In pollution episodes, this pathway is enough to explain the increase in SOA mass, with formaldehyde, acetaldehyde, glycolaldehyde, formic acid, and acetic acid alone explaining 15%–25% of the SOA increase. Future mitigation strategies to reduce non-methane volatile organic compound emissions should be considered to reduce organic particulate pollution in China