Explore the vast range of titles available.

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. It has been assumed that a better understanding of the effects of anthropogenic aerosols will greatly reduce the large uncertainties associated with our predictions of the radiative forcing effects of aerosols on climate; however, this study shows that nearly half of the uncertainty in the radiative effect of aerosols on clouds derives from uncertainties in pre-industrial natural aerosols. Natural versus anthropogenic aerosols in climate forcing Firmly establishing the influence of aerosols on cloud albedo — their forcing effect on climate in essence — is one of the greatest challenges of modern climate science. It is often tacitly assumed that the continued high uncertainties are linked mainly to anthropogenic emissions. In other words, if the anthropogenic effects could be better understood, so would the overall effect. Now Ken Carslaw and colleagues present an analysis of 28 parameters representing aerosol and precursor gas emissions and other factors that could influence cloud brightness. They find that only 34 per cent of the variance in aerosol forcing since pre-industrial times (around 1750) is associated with anthropogenic emissions, with 45 per cent of the variance linked to natural emissions of volcanic sulphur dioxide, marine dimethylsulphide and other natural sources. This work casts doubts on the degree of progress that can be made solely through advances in the understanding of anthropogenic aerosols and suggests that we need to discover more about the workings of the pre-industrial environment, when natural aerosols were predominant.

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. Several lines of evidence suggest that fire activity was much greater in the preindustrial era than currently assumed in climate models. Here the authors show that greater emission of aerosols from fires leads to a substantial reduction in the magnitude of aerosol radiative forcing over the Industrial Era.

Long-term exposure to ambient particulate matter (PM2.5, mass of particles with an aerodynamic dry diameter of < 2.5 μm) is a major risk factor to the global burden of disease. Previous studies have focussed on present day or future health burdens attributed to ambient PM2.5. Few studies have estimated changes in PM2.5 and attributable health burdens over the last few decades, a period where air quality has changed rapidly. Here we used the HadGEM3-UKCA coupled chemistry-climate model, integrated exposure-response relationships, demographic and background disease data to provide the first estimate of the changes in global and regional ambient PM2.5 concentrations and attributable health burdens over the period 1960 to 2009. Over this period, global mean population-weighted PM2.5 concentrations increased by 38%, dominated by increases in China and India. Global attributable deaths increased by 89% to 124% over the period 1960 to 2009, dominated by large increases in China and India. Population growth and ageing contributed mostly to the increases in attributable deaths in China and India, highlighting the importance of demographic trends. In contrast, decreasing PM2.5 concentrations and background disease dominated the reduction in attributable health burden in Europe and the United States. Our results shed light on how future projected trends in demographics and uncertainty in the exposure-response relationship may provide challenges for future air quality policy in Asia.

Observations and models have shown that continuously degassing volcanoes have a potentially large effect on the natural background aerosol loading and the radiative state of the atmosphere. We use a global aerosol microphysics model to quantify the impact of these volcanic emissions on the cloud albedo radiative forcing under pre-industrial (PI) and present-day (PD) conditions. We find that volcanic degassing increases global annual mean cloud droplet number concentrations by 40% under PI conditions, but by only 10% under PD conditions. Consequently, volcanic degassing causes a global annual mean cloud albedo effect of −1.06 W m−2 in the PI era but only −0.56 W m−2 in the PD era. This non-equal effect is explained partly by the lower background aerosol concentrations in the PI era, but also because more aerosol particles are produced per unit of volcanic sulphur emission in the PI atmosphere. The higher sensitivity of the PI atmosphere to volcanic emissions has an important consequence for the anthropogenic cloud radiative forcing because the large uncertainty in volcanic emissions translates into an uncertainty in the PI baseline cloud radiative state. Assuming a −50/+100% uncertainty range in the volcanic sulphur flux, we estimate the annual mean anthropogenic cloud albedo forcing to lie between −1.16 W m−2 and −0.86 W m−2. Therefore, the volcanically induced uncertainty in the PI baseline cloud radiative state substantially adds to the already large uncertainty in the magnitude of the indirect radiative forcing of climate.

The seasonal cycle in Arctic aerosol is typified by high concentrations of large aged anthropogenic particles transported from lower latitudes in the late Arctic winter and early spring followed by a sharp transition to low concentrations of locally sourced smaller particles in the summer. However, multi-model assessments show that many models fail to simulate a realistic cycle. Here, we use a global aerosol microphysics model (GLOMAP) and surface-level aerosol observations to understand how wet scavenging processes control the seasonal variation in Arctic black carbon (BC) and sulphate aerosol. We show that the transition from high wintertime concentrations to low concentrations in the summer is controlled by the transition from ice-phase cloud scavenging to the much more efficient warm cloud scavenging in the late spring troposphere. This seasonal cycle is amplified further by the appearance of warm drizzling cloud in the late spring and summer boundary layer. Implementing these processes in GLOMAP greatly improves the agreement between the model and observations at the three Arctic ground-stations Alert, Barrow and Zeppelin Mountain on Svalbard. The SO4 model-observation correlation coefficient (R) increases from: −0.33 to 0.71 at Alert (82.5° N), from −0.16 to 0.70 at Point Barrow (71.0° N) and from −0.42 to 0.40 at Zeppelin Mountain (78° N). The BC model-observation correlation coefficient increases from −0.68 to 0.72 at Alert and from −0.42 to 0.44 at Barrow. Observations at three marginal Arctic sites (Janiskoski, Oulanka and Karasjok) indicate a far weaker aerosol seasonal cycle, which we show is consistent with the much smaller seasonal change in the frequency of ice clouds compared to higher latitude sites. Our results suggest that the seasonal cycle in Arctic aerosol is driven by temperature-dependent scavenging processes that may be susceptible to modification in a future climate.

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 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.

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.