The effect of anthropogenic aerosols on cloud droplet concentrations and radiative properties is the source of one of the largest uncertainties in the radiative forcing of climate over the industrial ...period. This uncertainty affects our ability to estimate how sensitive the climate is to greenhouse gas emissions. Here we perform a sensitivity analysis on a global model to quantify the uncertainty in cloud radiative forcing over the industrial period caused by uncertainties in aerosol emissions and processes. Our results show that 45 per cent of the variance of aerosol forcing since about 1750 arises from uncertainties in natural emissions of volcanic sulphur dioxide, marine dimethylsulphide, biogenic volatile organic carbon, biomass burning and sea spray. Only 34 per cent of the variance is associated with anthropogenic emissions. The results point to the importance of understanding pristine pre-industrial-like environments, with natural aerosols only, and suggest that improved measurements and evaluation of simulated aerosols in polluted present-day conditions will not necessarily result in commensurate reductions in the uncertainty of forcing estimates.
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Dostopno za:
DOBA, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Aerosol–cloud interaction effects are a major source of uncertainty in climate models so it is important to quantify the sources of uncertainty and thereby direct research efforts. However, the ...computational expense of global aerosol models has prevented a full statistical analysis of their outputs. Here we perform a variance-based analysis of a global 3-D aerosol microphysics model to quantify the magnitude and leading causes of parametric uncertainty in model-estimated present-day concentrations of cloud condensation nuclei (CCN). Twenty-eight model parameters covering essentially all important aerosol processes, emissions and representation of aerosol size distributions were defined based on expert elicitation. An uncertainty analysis was then performed based on a Monte Carlo-type sampling of an emulator built for each model grid cell. The standard deviation around the mean CCN varies globally between about ±30% over some marine regions to ±40–100% over most land areas and high latitudes, implying that aerosol processes and emissions are likely to be a significant source of uncertainty in model simulations of aerosol–cloud effects on climate. Among the most important contributors to CCN uncertainty are the sizes of emitted primary particles, including carbonaceous combustion particles from wildfires, biomass burning and fossil fuel use, as well as sulfate particles formed on sub-grid scales. Emissions of carbonaceous combustion particles affect CCN uncertainty more than sulfur emissions. Aerosol emission-related parameters dominate the uncertainty close to sources, while uncertainty in aerosol microphysical processes becomes increasingly important in remote regions, being dominated by deposition and aerosol sulfate formation during cloud-processing. The results lead to several recommendations for research that would result in improved modelling of cloud–active aerosol on a global scale.
We use a global aerosol microphysics model in combination with an offline radiative transfer model to quantify the radiative effect of biogenic secondary organic aerosol (SOA) in the present-day ...atmosphere. Through its role in particle growth and ageing, the presence of biogenic SOA increases the global annual mean concentration of cloud condensation nuclei (CCN; at 0.2% supersaturation) by 3.6-21.1%, depending upon the yield of SOA production from biogenic volatile organic compounds (BVOCs), and the nature and treatment of concurrent primary carbonaceous emissions. This increase in CCN causes a rise in global annual mean cloud droplet number concentration (CDNC) of 1.9-5.2%, and a global mean first aerosol indirect effect (AIE) of between +0.01 W m-2 and -0.12 W m-2. The radiative impact of biogenic SOA is far greater when biogenic oxidation products also contribute to the very early stages of new particle formation; using two organically mediated mechanisms for new particle formation, we simulate global annual mean first AIEs of -0.22 W m-2 and -0.77 W m-2. The inclusion of biogenic SOA substantially improves the simulated seasonal cycle in the concentration of CCN-sized particles observed at three forested sites. The best correlation is found when the organically mediated nucleation mechanisms are applied, suggesting that the first AIE of biogenic SOA could be as large as -0.77 W m-2. The radiative impact of SOA is sensitive to the presence of anthropogenic emissions. Lower background aerosol concentrations simulated with anthropogenic emissions from 1750 give rise to a greater fractional CCN increase and a more substantial first AIE from biogenic SOA. Consequently, the anthropogenic indirect radiative forcing between 1750 and the present day is sensitive to assumptions about the amount and role of biogenic SOA. We also calculate an annual global mean direct radiative effect of between -0.08 W m-2 and -0.78 W m-2 in the present day, with uncertainty in the amount of SOA produced from the oxidation of BVOCs accounting for most of this range.
Uncertainty in pre-industrial natural aerosol emissions is a major component of the overall uncertainty in the radiative forcing of climate. Improved characterisation of natural emissions and their ...radiative effects can therefore increase the accuracy of global climate model projections. Here we show that revised assumptions about pre-industrial fire activity result in significantly increased aerosol concentrations in the pre-industrial atmosphere. Revised global model simulations predict a 35% reduction in the calculated global mean cloud albedo forcing over the Industrial Era (1750-2000 CE) compared to estimates using emissions data from the Sixth Coupled Model Intercomparison Project. An estimated upper limit to pre-industrial fire emissions results in a much greater (91%) reduction in forcing. When compared to 26 other uncertain parameters or inputs in our model, pre-industrial fire emissions are by far the single largest source of uncertainty in pre-industrial aerosol concentrations, and hence in our understanding of the magnitude of the historical radiative forcing due to anthropogenic aerosol emissions.
The new global anthropogenic emission inventory (EDGAR-CIRCE) of gas and aerosol pollutants has been incorporated in the chemistry general circulation model EMAC (ECHAM5/MESSy Atmospheric Chemistry). ...A relatively high horizontal resolution simulation is performed for the years 2005-2008 to evaluate the capability of the model and the emissions to reproduce observed aerosol concentrations and aerosol optical depth (AOD) values. Model output is compared with observations from different measurement networks (CASTNET, EMEP and EANET) and AODs from remote sensing instruments (MODIS and MISR). A good spatial agreement of the distribution of sulfate and ammonium aerosol is found when compared to observations, while calculated nitrate aerosol concentrations show some discrepancies. The simulated temporal development of the inorganic aerosols is in line with measurements of sulfate and nitrate aerosol, while for ammonium aerosol some deviations from observations occur over the USA, due to the wrong temporal distribution of ammonia gas emissions. The calculated AODs agree well with the satellite observations in most regions, while negative biases are found for the equatorial area and in the dust outflow regions (i.e. Central Atlantic and Northern Indian Ocean), due to an underestimation of biomass burning and aeolian dust emissions, respectively. Aerosols and precursors budgets for five different regions (North America, Europe, East Asia, Central Africa and South America) are calculated. Over East-Asia most of the emitted aerosols (precursors) are also deposited within the region, while in North America and Europe transport plays a larger role. Further, it is shown that a simulation with monthly varying anthropogenic emissions typically improves the temporal correlation by 5-10% compared to one with constant annual emissions.
In the most advanced aerosol-climate models it is common to represent the aerosol particle size distribution in terms of several log-normal modes. This approach, motivated by computational ...efficiency, makes assumptions about the shape of the particle distribution that may not always capture the properties of global aerosol. Here, a global modal aerosol microphysics module (GLOMAP-mode) is evaluated and improved by comparing against a sectional version (GLOMAP-bin) and observations in the same 3-D global offline chemistry transport model. With both schemes, the model captures the main features of the global particle size distribution, with sub-micron aerosol approximately unimodal in continental regions and bi-modal in marine regions. Initial bin-mode comparisons showed that the current values for two size distribution parameter settings in the modal scheme (mode widths and inter-modal separation sizes) resulted in clear biases compared to the sectional scheme. By adjusting these parameters in the modal scheme, much better agreement is achieved against the bin scheme and observations. Annual mean surface-level mass of sulphate, sea-salt, black carbon (BC) and organic carbon (OC) are within 25% in the two schemes in nearly all regions. Surface level concentrations of condensation nuclei (CN), cloud condensation nuclei (CCN), surface area density and condensation sink also compare within 25% in most regions. However, marine CCN concentrations between 30° N and 30° S are systematically 25-60% higher in the modal model, which we attribute to differences in size-resolved particle growth or cloud-processing. Larger differences also exist in regions or seasons dominated by biomass burning and in free-troposphere and high-latitude regions. Indeed, in the free-troposphere, GLOMAP-mode BC is a factor 2-4 higher than GLOMAP-bin, likely due to differences in size-resolved scavenging. Nevertheless, in most parts of the atmosphere, we conclude that bin-mode differences are much less than model-observation differences, although some processes are missing in these runs which may pose a bigger challenge to modal schemes (e.g., boundary layer nucleation and ultra-fine sea-spray). The findings here underline the need for a spectrum of complexity in global models, with size-resolved aerosol properties predicted by modal schemes needing to be continually benchmarked and improved against freely evolving sectional schemes and observations.
In the last two IPCC assessments aerosol radiative forcings have been given the largest uncertainty range of all forcing agents assessed. This forcing range is really a diversity of simulated ...forcings in different models. An essential step towards reducing model uncertainty is to quantify and attribute the sources of uncertainty at the process level. Here, we use statistical emulation techniques to quantify uncertainty in simulated concentrations of July-mean cloud condensation nuclei (CCN) from a complex global aerosol microphysics model. CCN was chosen because it is the aerosol property that controls cloud drop concentrations, and therefore the aerosol indirect radiative forcing effect. We use Gaussian process emulation to perform a full variance-based sensitivity analysis and quantify, for each model grid box, the uncertainty in simulated CCN that results from 8 uncertain model parameters. We produce global maps of absolute and relative CCN sensitivities to the 8 model parameter ranges and derive probability density functions for simulated CCN. The approach also allows us to include the uncertainty from interactions between these parameters, which cannot be quantified in traditional one-at-a-time sensitivity tests. The key findings from our analysis are that model CCN in polluted regions and the Southern Ocean are mostly only sensitive to uncertainties in emissions parameters but in all other regions CCN uncertainty is driven almost exclusively by uncertainties in parameters associated with model processes. For example, in marine regions between 30° S and 30° N model CCN uncertainty is driven mainly by parameters associated with cloud-processing of Aitken-sized particles whereas in polar regions uncertainties in scavenging parameters dominate. In these two regions a single parameter dominates but in other regions up to 50% of the variance can be due to interaction effects between different parameters. Our analysis provides direct quantification of the reduction in variance that would result if a parameter could be specified precisely. When extended to all process parameters the approach presented here will therefore provide a clear global picture of how improved knowledge of aerosol processes would translate into reduced model uncertainty.
In this study we use the ECHAM/MESSy Atmospheric Chemistry (EMAC) model to simulate global fields of the effective hygroscopicity parameter κ which approximately describes the influence of chemical ...composition on the cloud condensation nucleus (CCN) activity of aerosol particles. The obtained global mean values of κ at the Earth's surface are 0.27±0.21 for continental and 0.72±0.24 for marine regions (arithmetic mean ± standard deviation). These values are the internally mixed κ calculated across the Aitken and accumulation modes. The mean κ values are in good agreement with previous estimates based on observational data, but the model standard deviation for continental regions is higher. Over the continents, the regional distribution appears fairly uniform, with κ values mostly in the range of 0.1–0.4. Lower values over large arid regions and regions of high organic loading lead to reduced continental average values for Africa and South America (0.15–0.17) compared to the other continents (0.21–0.36). Marine regions show greater variability with κ values ranging from 0.9–1.0 in remote regions to 0.4–0.6 in continental outflow regions where the highly hygroscopic sea spray aerosol mixes with less hygroscopic continental aerosol. Marine κ values as low as 0.2–0.3 are simulated in the outflow from the Sahara desert. At the top of the planetary boundary layer the κ values can deviate substantially from those at the surface (up to 30%) – especially in marine and coastal regions. In moving from the surface to the height of the planetary boundary layer, the global average marine κ value reduces by 20%. Thus, surface observations may not always be representative for the altitudes where cloud formation mostly occurs. In a pre-industrial model scenario, the κ values tend to be higher over marine regions and lower over the continents, because the anthropogenic particulate matter is on average less hygroscopic than sea-spray but more hygroscopic than the natural continental background aerosol (dust and organic matter). In regions influenced by desert dust the particle hygroscopicity has increased strongly as the mixing of air pollutants with mineral particles typically enhances the κ values by a factor of 2–3 above the initial value of ≈0.005.
This paper evaluates the current status of global modeling of the organic aerosol (OA) in the troposphere and analyzes the differences between models as well as between models and observations. ...Thirty-one global chemistry transport models (CTMs) and general circulation models (GCMs) have participated in this intercomparison, in the framework of AeroCom phase II. The simulation of OA varies greatly between models in terms of the magnitude of primary emissions, secondary OA (SOA) formation, the number of OA species used (2 to 62), the complexity of OA parameterizations (gas-particle partitioning, chemical aging, multiphase chemistry, aerosol microphysics), and the OA physical, chemical and optical properties. The diversity of the global OA simulation results has increased since earlier AeroCom experiments, mainly due to the increasing complexity of the SOA parameterization in models, and the implementation of new, highly uncertain, OA sources. Diversity of over one order of magnitude exists in the modeled vertical distribution of OA concentrations that deserves a dedicated future study. Furthermore, although the OA / OC ratio depends on OA sources and atmospheric processing, and is important for model evaluation against OA and OC observations, it is resolved only by a few global models. The median global primary OA (POA) source strength is 56 Tg a−1 (range 34–144 Tg a−1) and the median SOA source strength (natural and anthropogenic) is 19 Tg a−1 (range 13–121 Tg a−1). Among the models that take into account the semi-volatile SOA nature, the median source is calculated to be 51 Tg a−1 (range 16–121 Tg a−1), much larger than the median value of the models that calculate SOA in a more simplistic way (19 Tg a−1; range 13–20 Tg a−1, with one model at 37 Tg a−1). The median atmospheric burden of OA is 1.4 Tg (24 models in the range of 0.6–2.0 Tg and 4 between 2.0 and 3.8 Tg), with a median OA lifetime of 5.4 days (range 3.8–9.6 days). In models that reported both OA and sulfate burdens, the median value of the OA/sulfate burden ratio is calculated to be 0.77; 13 models calculate a ratio lower than 1, and 9 models higher than 1. For 26 models that reported OA deposition fluxes, the median wet removal is 70 Tg a−1 (range 28–209 Tg a−1), which is on average 85% of the total OA deposition. Fine aerosol organic carbon (OC) and OA observations from continuous monitoring networks and individual field campaigns have been used for model evaluation. At urban locations, the model–observation comparison indicates missing knowledge on anthropogenic OA sources, both strength and seasonality. The combined model–measurements analysis suggests the existence of increased OA levels during summer due to biogenic SOA formation over large areas of the USA that can be of the same order of magnitude as the POA, even at urban locations, and contribute to the measured urban seasonal pattern. Global models are able to simulate the high secondary character of OA observed in the atmosphere as a result of SOA formation and POA aging, although the amount of OA present in the atmosphere remains largely underestimated, with a mean normalized bias (MNB) equal to −0.62 (−0.51) based on the comparison against OC (OA) urban data of all models at the surface, −0.15 (+0.51) when compared with remote measurements, and −0.30 for marine locations with OC data. The mean temporal correlations across all stations are low when compared with OC (OA) measurements: 0.47 (0.52) for urban stations, 0.39 (0.37) for remote stations, and 0.25 for marine stations with OC data. The combination of high (negative) MNB and higher correlation at urban stations when compared with the low MNB and lower correlation at remote sites suggests that knowledge about the processes that govern aerosol processing, transport and removal, on top of their sources, is important at the remote stations. There is no clear change in model skill with increasing model complexity with regard to OC or OA mass concentration. However, the complexity is needed in models in order to distinguish between anthropogenic and natural OA as needed for climate mitigation, and to calculate the impact of OA on climate accurately.
Combustion of fuels in the residential sector for cooking and heating results in the emission of aerosol and aerosol precursors impacting air quality, human health, and climate. Residential emissions ...are dominated by the combustion of solid fuels. We use a global aerosol microphysics model to simulate the impact of residential fuel combustion on atmospheric aerosol for the year 2000. The model underestimates black carbon (BC) and organic carbon (OC) mass concentrations observed over Asia, Eastern Europe, and Africa, with better prediction when carbonaceous emissions from the residential sector are doubled. Observed seasonal variability of BC and OC concentrations are better simulated when residential emissions include a seasonal cycle. The largest contributions of residential emissions to annual surface mean particulate matter (PM2.5) concentrations are simulated for East Asia, South Asia, and Eastern Europe. We use a concentration response function to estimate the human health impact due to long-term exposure to ambient PM2.5 from residential emissions. We estimate global annual excess adult (> 30 years of age) premature mortality (due to both cardiopulmonary disease and lung cancer) to be 308 000 (113 300–497 000, 5th to 95th percentile uncertainty range) for monthly varying residential emissions and 517 000 (192 000–827 000) when residential carbonaceous emissions are doubled. Mortality due to residential emissions is greatest in Asia, with China and India accounting for 50 % of simulated global excess mortality. Using an offline radiative transfer model we estimate that residential emissions exert a global annual mean direct radiative effect between −66 and +21 mW m−2, with sensitivity to the residential emission flux and the assumed ratio of BC, OC, and SO2 emissions. Residential emissions exert a global annual mean first aerosol indirect effect of between −52 and −16 mW m−2, which is sensitive to the assumed size distribution of carbonaceous emissions. Overall, our results demonstrate that reducing residential combustion emissions would have substantial benefits for human health through reductions in ambient PM2.5 concentrations.
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