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

Roles of saltation, sandblasting, and wind speed variability on mineral dust aerosol size distribution during the Puerto Rican Dust Experiment (PRIDE)

Recent field observations demonstrate that a significant discrepancy exists between models and measurements of large dust aerosol particles at remote sites. We assess the fraction of this bias explained by assumptions involving four different dust production processes. These include dust source size distribution (constant or dynamically changing according to saltation and sandblasting theory), wind speed distributions (using mean wind or a probability density function (PDF)), parent soil aggregate size distribution, and the discretization (number of bins) in the dust size distribution. The Dust Entrainment and Deposition global model is used to simulate the measurements from the Puerto Rican Dust Experiment (PRIDE) (2000). Using wind speed PDFs from observed National Centers for Environmental Prediction winds results in small changes in downwind size distribution for the production which neglects sandblasting, but it results in significant changes when production includes sandblasting. Saltation-sandblasting generally produces more large dust particles than schemes which neglect sandblasting. Parent soil aggregate size distribution is an important factor when calculating size-distributed dust emissions. Changing from a soil with large grains to a soil with smaller grains increases by 50% the fraction of large aerosols (D &gt;5 μm) modeled at Puerto Rico. Assuming that the coarse medium sand typical of West Africa dominates all source regions produces the best agreement with PRIDE observations

Roles of saltation, sandblasting, and wind speed variability on mineral dust aerosol size distribution during the Puerto Rican Dust Experiment

Abstract. Recent field observations demonstrate that a significant discrepancy exists between models and measurements of large dust aerosol particles at remote sites. We assess the fraction of this bias explained by assumptions involving four different dust production processes. These include dust source size distribution (constant or dynamically changing according to saltation and sandblasting theory), wind speed distributions (using mean wind or a probability density function (PDF)), parent soil aggregate size distribution, and the discretization (number of bins) in the dust size distribution. The Dust Entrainment and Deposition (DEAD) global model is used to simulate the measurements from the Puerto Rican Dust Experiment, PRIDE (2000). Using wind speed PDFs from observed (NCEP) winds results in small changes in downwind size distribution for the production which neglects sandblasting, but significant changes when production includes sandblasting. Saltation-sandblasting generally produces more large dust particles than schemes which neglect sandblasting. Parent soil aggregate size distribution is an important factor when calculating size distributed dust emissions. Changing from a soil with large grains to a soil with smaller grains increases by 50 % the fraction of large aerosols (D&gt; 5 μm) modeled at Puerto Rico. Assuming that the coarse medium sand typical of West Africa dominates all source regions produces the best agreement with PRIDE observations. 1

Puerto Rican Dust Experiment (PRIDE)

mineral dust aerosol size distribution during th

Saltation Sandblasting behavior during mineral dust aerosol production

The dominant process in producing fine dust aerosols during saltation is thought to be sandblasting. Recent studies claim that due to competing physical processes, emission efficiencies of dust aerosols oscillate with increasing wind friction speed. These oscillations can result in order of magnitude changes in dust mass emissions. Our work shows that emission efficiencies, and hence emissions of dust aerosols are smooth functions of the wind friction speed for natural soil size distributions. This rules out oscillations as an explanation for scatter in experimental data. We show and explain the reasons for the oscillations

Model simulations of dust sources and transport in the global atmosphere: Effects of soil erodibility and wind speed variability

Global atmospheric dust is simulated using the Dust Entrainment and Deposition (DEAD) model in combination with the global-scale Oslo chemical transport model CTM2 using meteorological data for 1996. Dust sources are calculated using both mean wind speeds with model resolution T63 and subgrid wind speeds. Different data sets are used to describe soil erodibility. We explain how the different assumptions about dust production affect atmospheric dust burden and deposition. Some aspects of the annual dust cycle, such as the east Asian dust emissions, are largely dependent on the data used to determine soil erodibility. Other aspects, such as the timing of the maximum in the African plume at Northern Hemisphere summer, are well modeled with all data sets applied here. We show that the daily variation in optical depth at Cape Verde on the west coast of Africa is well simulated when we assume that erodibility is correlated with surface reflectivity from Moderate-Resolution Imaging Spetroradiometer (MODIS) satellite data. Using a subgrid probability density function of wind speed to drive the dust sources facilitates dust emissions in areas with low wind speeds. Dust concentrations in remote areas are sensitive to the parameterization of wet deposition. Our results point out the need for a detailed soil erodibility data set for global dust modeling, and they suggest that surface reflectivity is potentially valuable for producing or evaluating such data sets

High-latitude volcanic eruptions in the Norwegian Earth System Model: the effect of different initial conditions and of the ensemble size

Large volcanic eruptions have strong impacts on both atmospheric and ocean dynamics that can last for decades. Numerical models have attempted to reproduce the effects of major volcanic eruptions on climate; however, there are remarkable inter-model disagreements related to both short-term dynamical response to volcanic forcing and long-term oceanic evolution. The lack of robust simulated behaviour is related to various aspects from model formulation to simulated background internal variability to the eruption details. Here, we use the Norwegian Earth System Model version 1 to calculate interactively the volcanic aerosol loading resulting from SO2 emissions of the second largest high-latitude volcanic eruption in historical time (the Laki eruption of 1783). We use two different approaches commonly used interchangeably in the literature to generate ensembles. The ensembles start from different background initial states, and we show that the two approaches are not identical on short-time scales (<1 yr) in discerning the volcanic effects on climate, depending on the background initial state in which the simulated eruption occurred. Our results also show that volcanic eruptions alter surface climate variability (in general increasing it) when aerosols are allowed to realistically interact with circulation: Simulations with fixed volcanic aerosol show no significant change in surface climate variability. Our simulations also highlight that the change in climate variability is not a linear function of the amount of the volcanic aerosol injected. We then provide a tentative estimation of the ensemble size needed to discern a given volcanic signal on surface temperature from the natural internal variability on regional scale: At least 20–25 members are necessary to significantly detect seasonally averaged anomalies of 0.5°C; however, when focusing on North America and in winter, a higher number of ensemble members (35–40) is necessary