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Moisture evaporated from the land contributing to precipitation in a given area is known as precipitation recycling and needs to be accurately represented in climate models. The Amazon and Congo basins are reported to have the highest precipitation recycling rates globally, but model representation has not yet been assessed over these regions. We evaluated recycling over the Amazon and Congo in 45 Coupled Model Intercomparison Project Phase 6 models. Regional annual means from models and reanalyzes agreed well over both basins. Models captured seasonal variation in recycling over the Congo but there was a large-scale underestimation of recycling during the Amazon dry-to-wet transition season relative to ERA5, caused by models underestimating Amazon evapotranspiration and overestimating incoming wind speed and associated water vapor imports. Both regions show robust declines in precipitation recycling over the next century under future climate-change scenarios. Our results suggest models may underestimate impacts of deforestation on regional precipitation in the Amazon.

Woodlands can reduce the risk of rainfall-generated flooding through increased interception, soil infiltration and available storage. Despite growing evidence, there is still low confidence in using woodlands as a flood mitigation method due to limited empirical data, particularly for broadleaf woodlands. We measured soil properties and streamflow for nine small (

Marine birds are strongly exposed to weather conditions at sea but to date, few studies have investigated the influences of wind or rainfall on their time-activity budgets or foraging routines. Here, we used data from GPS and pressure loggers to investigate the effects of wind speed and direction and rainfall on the 3-dimensional foraging behaviour of gannets Morus bassanus breeding at Bass Rock, Scotland. We found that birds spent more time actively foraging during stronger winds, but there was no subsequent increase in overall trip duration because individuals compensated by decreasing the time they spent on the water during stronger winds. Birds returned more quickly from distant foraging grounds, and those encountering head winds spent less time on the water and so were able to compensate to some extent for an adverse effect of head winds on speed of travel over the return leg. These data strongly suggest that by reducing time spent on the water, birds were able to buffer trip durations against adverse effects of strong winds encountered during both commuting and active foraging. Birds also commuted at greater heights with increasing tail wind speed and at lower heights with increasing head wind speed, potentially providing an additional behavioural buffer against the adverse effects of strong head winds during foraging trips. There was no discernible effect of rain on foraging, but the behavioural flexibility recorded here is likely to be critical to maintaining nest attendance patterns and food provisioning rates of chicks across variable environmental conditions encountered at sea.

Sustainable development requires knowledge of trade-offs and synergies between environmental and non-environmental goals and targets. Understanding the ways in which positive progress in matters of development not directly concerned with the environment can affect the natural environment, whether for better or for worse, can allow policymakers and development agencies to avoid the negative impacts of their actions, while capitalising on mutually beneficial opportunities. Through a systematic review of the literature, we consider the impacts of UN Sustainable Development Goal (SDG) targets on forest ecosystems, and identify 63 targets associated with potentially beneficial, damaging or mixed (i.e. damaging and/or beneficial depending on context or location) impacts. Types of impact are not uniform within SDGs, nor necessarily within individual targets. Targets relating to energy and infrastructure are among the most damaging and best studied, while targets expected to potentially result in beneficial outcomes, typically associated with social progress and well-being, have been investigated to a much lesser degree, especially in the context of external interventions. Thirty-eight targets have some variation in the direction of their impacts (i.e. at least one record with mixed impacts, or two or more records with different directions), suggesting the potential to achieve beneficial over damaging impacts in many cases. We provide illustrative examples of a range of impacts and use our findings to provide recommendations for researchers, development agencies and policymakers.

We use a regional chemistry transport model (Weather Research and Forecasting model coupled with chemistry, WRF-Chem) in conjunction with surface observations of tropospheric ozone and Ozone Monitoring Instrument (OMI) satellite retrievals of tropospheric column NO2 to evaluate processes controlling the regional distribution of tropospheric ozone over western Siberia for late spring and summer in 2011. This region hosts a range of anthropogenic and natural ozone precursor sources, and it serves as a gateway for near-surface transport of Eurasian pollution to the Arctic. However, there is a severe lack of in situ observations to constrain tropospheric ozone sources and sinks in the region. We show widespread negative bias in WRF-Chem tropospheric column NO2 when compared to OMI satellite observations from May–August, which is reduced when using ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants) v5a emissions (fractional mean bias (FMB) = −0.82 to −0.73) compared with the EDGAR (Emissions Database for Global Atmospheric Research)-HTAP (Hemispheric Transport of Air Pollution) v2.2 emissions data (FMB = −0.80 to −0.70). Despite the large negative bias, the spatial correlations between model and observed NO2 columns suggest that the spatial pattern of NOx sources in the region is well represented. Scaling transport and energy emissions in the ECLIPSE v5a inventory by a factor of 2 reduces column NO2 bias (FMB = −0.66 to −0.35), but with overestimates in some urban regions and little change to a persistent underestimate in background regions. Based on the scaled ECLIPSE v5a emissions, we assess the influence of the two dominant anthropogenic emission sectors (transport and energy) and vegetation fires on surface NOx and ozone over Siberia and the Russian Arctic. Our results suggest regional ozone is more sensitive to anthropogenic emissions, particularly from the transport sector, and the contribution from fire emissions maximises in June and is largely confined to latitudes south of 60∘ N. Ozone dry deposition fluxes from the model simulations show that the dominant ozone dry deposition sink in the region is to forest vegetation, averaging 8.0 Tg of ozone per month, peaking at 10.3 Tg of ozone deposition during June. The impact of fires on ozone dry deposition within the domain is small compared to anthropogenic emissions and is negligible north of 60∘ N. Overall, our results suggest that surface ozone in the region is controlled by an interplay between seasonality in atmospheric transport patterns, vegetation dry deposition, and a dominance of transport and energy sector emissions.

Air pollution is a serious environmental issue and leading contributor to disease burden in China. Rapid reductions in fine particulate matter (PM2.5) concentrations and increased ozone concentrations occurred across China during 2015 to 2017. We used measurements of particulate matter with a diameter  µm (PM2.5) and ozone (O3) from more than 1000 stations across China along with Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) regional air quality simulations, to explore the drivers and impacts of observed trends. The measured nationwide median PM2.5 trend of -3.4µgm-3yr-1 was well simulated by the model (-3.5µgm-3yr-1). With anthropogenic emissions fixed at 2015 levels, the simulated trend was much weaker (-0.6µgm-3yr-1), demonstrating that interannual variability in meteorology played a minor role in the observed PM2.5 trend. The model simulated increased ozone concentrations in line with the measurements but underestimated the magnitude of the observed absolute trend by a factor of 2. We combined simulated trends in PM2.5 concentrations with an exposure–response function to estimate that reductions in PM2.5 concentrations over this period have reduced PM2.5-attributable premature mortality across China by 150 000 deaths yr−1.

The outbreak of Coronavirus Disease 2019 (COVID-19) in China in January 2020 prompted substantial control measures including social distancing measures, suspension of public transport and industry, and widespread cordon sanitaires ('lockdowns'), that have led to a decrease in industrial activity and air pollution emissions over a prolonged period. We use a 5 year dataset from China's air quality monitoring network to assess the impact of control measures on air pollution. Pollutant concentration time series are decomposed to account for the inter-annual trend, seasonal cycles and the effect of Lunar New Year, which coincided with the COVID-19 outbreak. Over 2015–2019, there were significant negative trends in particulate matter (PM2.5, −6% yr−1) and sulphur dioxide (SO2, −12% yr−1) and nitrogen dioxide (NO2, −2.2% yr−1) whereas there were positive trends in ozone (O3, + 2.8% yr−1). We quantify the change in air quality during the LNY holiday week, during which pollutant concentrations increase on LNY's day, followed by reduced concentrations in the rest of the week. After accounting for interannual trends and LNY we find NO2 and PM concentrations were significantly lower during the lockdown period than would be expected, but there were no significant impacts on O3. Largest reductions occurred in NO2, with concentrations 27.0% lower on average across China, during the lockdown. Average concentrations of PM2.5 and PM10 across China were respectively 10.5% and 21.4% lower during the lockdown period. The largest reductions were in Hubei province, where NO2 concentrations were 50.5% lower than expected during the lockdown. Concentrations of affected pollutants returned to expected levels during April, after control measures were relaxed.

The climate impact of deforestation depends on the relative strength of several biogeochemical and biogeophysical effects. In addition to affecting the exchange of carbon dioxide (CO2) and moisture with the atmosphere and surface albedo, vegetation emits biogenic volatile organic compounds (BVOCs) that alter the formation of short-lived climate forcers (SLCFs), which include aerosol, ozone and methane. Here we show that a scenario of complete global deforestation results in a net positive radiative forcing (RF; 0.12Wm-2) from SLCFs, with the negative RF from decreases in ozone and methane concentrations partially offsetting the positive aerosol RF. Combining RFs due to CO2, surface albedo and SLCFs suggests that global deforestation could cause 0.8 K warming after 100 years, with SLCFs contributing 8% of the effect. However, deforestation as projected by the RCP8.5 scenario leads to zero net RF from SLCF, primarily due to nonlinearities in the aerosol indirect effect.

To improve poor air quality in Asia and inform effective emission-reduction strategies, it is vital to understand the contributions of different pollution sources and their associated human health burdens. In this study, we use the WRF-Chem regional atmospheric model to explore the air quality and human health benefits of eliminating emissions from six different anthropogenic sectors (transport, industry, shipping, electricity generation, residential combustion, and open biomass burning) over South and East Asia in 2014. We evaluate WRF-Chem against measurements from air quality monitoring stations across the region and find the model captures the spatial distribution and magnitude of PM2.5 (particulate matter with an aerodynamic diameter of no greater than 2.5 µm). We find that eliminating emissions from residential energy use, industry, or open biomass burning yields the largest reductions in population-weighted PM2.5 concentrations across the region. The largest human health benefit is achieved by eliminating either residential or industrial emissions, averting 467 000 (95 % uncertainty interval (95UI): 409 000–542 000) or 283 000 (95UI: 226 000–358 000) annual premature mortalities, respectively, in India, China, and South-east Asia, with fire prevention averting 28 000 (95UI: 24 000–32 000) annual premature mortalities across the region. We compare our results to previous sector-specific emission studies. Across these studies, residential emissions are the dominant cause of particulate pollution in India, with a multi-model mean contribution of 42 % to population-weighted annual mean PM2.5. Residential and industrial emissions cause the dominant contributions in China, with multi-model mean contributions of 29 % for both sectors to population-weighted annual mean PM2.5. Future work should focus on identifying the most effective options within the residential, industrial, and open biomass-burning emission sectors to improve air quality across South and East Asia.

Tropical forests have an important regulating influence on local and regional climate, through modulating the exchange of moisture and energy between the land and the atmosphere. Deforestation disrupts this exchange, though the climatic consequences of progressive, patch-scale deforestation of formerly intact forested landscapes have not previously been assessed. Remote sensing datasets of land surface and atmospheric variables were used to compare the climate responses of Amazon evergreen broadleaf forests that lost their intact status between 2000 and 2013. Clear gradients in environmental change with increasing disturbance were observed. Leaf area index (LAI) showed progressively stronger reductions as forest loss increased, with evapotranspiration (ET) showing a comparative decline. These changes in LAI and ET were related to changes in temperature (T), with increased warming as deforestation increased. Severe deforestation of intact Amazon forest, defined as areas where canopy cover was reduced below 70 %, was shown to have increased daytime land surface T by 0.44 °C over the study period. Differences between intact and disturbed forests were most pronounced during the dry season, with severely deforested areas warming as much as 1.5 °C. Maintenance of canopy cover was identified as an important factor in minimising the impacts of disturbance. Overall, the results highlight the climate benefits provided by intact tropical forests, providing further evidence that protecting intact forests is of utmost importance.

São Paulo, Brazil has experienced severe water shortages and record low levels of its water reservoirs in 2013–2014. We evaluate the contributions of Amazon deforestation and climate change to low precipitation levels using a modelling approach, and address whether similar precipitation anomalies might occur more frequently in a warming world. Precipitation records from INMET show that the dry anomaly extended over a fairly large region to the north of São Paulo. Unique features of this event were anomalous sea surface temperature (SST) patterns in the Southern Atlantic, an extension of the sub tropical high into the São Paulo region and moisture flux divergence over São Paulo. The SST anomalies were very similar in 2013/14 and 2014/15, suggesting they played a major role in forcing the dry conditions. The SST anomalies consisted of three zonal bands: a cold band in the tropics, a warm band to the south of São Paulo and another cold band poleward of 40 S. We performed ensemble climate simulations with observed SSTs prescribed, vegetation cover either fixed at 1870 levels or varying over time, and greenhouse gases (GHGs) either fixed at pre-industrial levels (280 ppm CO₂) or varying over time. These simulations exhibit similar precipitation deficits over the São Paulo region in 2013/14. From this, we infer that SST patterns and the associated large-scale state of the atmosphere were important factors in determining the precipitation anomalies, while deforestation and increased GHGs only weakly modulated the signal. Finally, analyses of future climate simulations from CMIP5 models indicate that the frequency of such precipitation anomalies is not likely to change in a warmer climate.

Exposure to ambient fine particulate matter (PM2.5) is a leading contributor to diseases in India. Previous studies analysing emission source attributions were restricted by coarse model resolution and limited PM2.5 observations. We use a regional model informed by new observations to make the first high-resolution study of the sector-specific disease burden from ambient PM2.5 exposure in India. Observed annual mean PM2.5 concentrations exceed 100 μg m−3 and are well simulated by the model. We calculate that the emissions from residential energy use dominate (52%) population-weighted annual mean PM2.5 concentrations, and are attributed to 511,000 (95UI: 340,000–697,000) premature mortalities annually. However, removing residential energy use emissions would avert only 256,000 (95UI: 162,000–340,000), due to the non-linear exposure–response relationship causing health effects to saturate at high PM2.5 concentrations. Consequently, large reductions in emissions will be required to reduce the health burden from ambient PM2.5 exposure in India.

Designs of "improved" stoves are introduced recently to benefit the solid fuel consumption of cooking activities in developing countries, but the uncertainties concerning the combustion processes and particulate emissions remain poorly characterized. To help understand this, combustion in three examples of "improved" African cookstoves was investigated in the laboratory. A typical European heating stove was included for comparison purpose. Detailed aerosol emissions were studied in real-time with an Aerosol Mass Spectrometer and Single Particle Soot Photometer, to explore interactions between black carbon (BC) and organic carbon aerosols, which were parametrized according to modified combustion efficiency (MCE), a common metric used within the atmospheric emission community. Greater than 50% of the total organic matter (OM) was found in BC-containing particles when MCE was >0.95 for dry oak and coal fuels, whereas at lower MCE, over 80% of the total OM for most of the fuels existed in particles without detectable BC. When the OM mass fraction of total particulate matter (PM 1) > 0.9, the mass ratio of OM to refractory BC in BC-containing particles was about 2-3, but only ∼0.8 when OM mass fraction

Novel methodologies to quantify model uncertainty are combined with an extensive new database of in-situ aerosol microphysical and chemical measurements to reduce uncertainty in aerosol effects on climate. The largest uncertainty in the historical radiative forcing of climate is caused by changes in aerosol particles due to anthropogenic activity. Sophisticated aerosol microphysics processes have been included in many climate models in an effort to reduce the uncertainty. However, the models are very challenging to evaluate and constrain because they require extensive in-situ measurements of the particle size distribution, number concentration and chemical composition that are not available from global satellite observations. The Global Aerosol Synthesis and Science Project (GASSP) aims to improve the robustness of global aerosol models by combining new methodologies for quantifying model uncertainty, an extensive global dataset of aerosol in-situ microphysical and chemical measurements, and new ways to assess the uncertainty associated with comparing sparse point measurements with low resolution models. GASSP has assembled over 45,000 hours of measurements from ships and aircraft as well as data from over 350 ground stations. The measurements have been harmonized into a standardized format that is easily used by modellers and non-specialist users. Available measurements are extensive, but they biased to polluted regions of the northern hemisphere, leaving large pristine regions and many continental areas poorly sampled. The aerosol radiative forcing uncertainty can be reduced using a rigorous model-data synthesis approach. Nevertheless, our research highlights significant remaining challenges because of the difficulty of constraining many interwoven model uncertainties simultaneously. Although the physical realism of global aerosol models still needs to be improved, the uncertainty in aerosol radiative forcing will be reduced most effectively by systematically and rigorously constraining the models using extensive syntheses of measurements.

Irrigated agriculture accounts for 20% of global cropland area and may alter climate locally and globally, but feedbacks on clouds and rainfall remain highly uncertain, particularly in arid regions. Nonrenewable groundwater in arid regions accounts for 20% of global irrigation water demand, and quantifying these feedbacks is crucial for the prediction of long-term water use in a changing climate. Here, satellite data are used to show how irrigated crops in an arid environment alter land surface properties, cloud cover, and rainfall patterns. Land surface temperatures (LSTs) over the cropland are 5-7 K lower than their surroundings, despite a lower albedo, suggesting that Bowen ratio is strongly reduced (and latent heat fluxes increased) over the irrigated cropland. Daytime cloud cover is increased by up to 15% points (a relative increase of 60%), with increased cloud development in the morning and a greater afternoon peak in cloud. Cloud cover is significantly correlated with interannual variations in vegetation and LST. Afternoon rainfall also appears to be enhanced around the irrigation. The cloud feedback is the opposite of what has been previously observed in tropical and semiarid regions, suggesting different processes drive land-atmosphere feedbacks in very dry environments. Increased cloud and rainfall, and associated increases in diffuse radiation and reductions in temperature, are likely to benefit vegetation growth. Predictions of changes in crop productivity due to climate change and the impacts of global land-use change on climate and the use of water resources would therefore benefit from including these effects.

The northern Eurasian regions and Arctic Ocean will very likely undergo substantial changes during the next decades. The Arctic-boreal natural environments play a crucial role in the global climate via albedo change, carbon sources and sinks as well as atmospheric aerosol production from biogenic volatile organic compounds. Furthermore, it is expected that global trade activities, demographic movement, and use of natural resources will be increasing in the Arctic regions. There is a need for a novel research approach, which not only identifies and tackles the relevant multi-disciplinary research questions, but also is able to make a holistic system analysis of the expected feedbacks. In this paper, we introduce the research agenda of the Pan-Eurasian Experiment (PEEX), a multi-scale, multi-disciplinary and international program started in 2012 (https://www.atm.helsinki.fi/peex/). PEEX sets a research approach by which large-scale research topics are investigated from a system perspective and which aims to fill the key gaps in our understanding of the feedbacks and interactions between the land-Atmosphere-Aquatic-society continuum in the northern Eurasian region. We introduce here the state of the art for the key topics in the PEEX research agenda and present the future prospects of the research, which we see relevant in this context.

Vegetation and peatland fires cause poor air quality and thousands of premature deaths across densely populated regions in Equatorial Asia. Strong El-Niño and positive Indian Ocean Dipole conditions are associated with an increase in the frequency and intensity of wildfires in Indonesia and Borneo, enhancing population exposure to hazardous concentrations of smoke and air pollutants. Here we investigate the impact on air quality and population exposure of wildfires in Equatorial Asia during Fall 2015, which were the largest over the past two decades. We performed high-resolution simulations using the Weather Research and Forecasting model with Chemistry based on a new fire emission product. The model captures the spatio-temporal variability of extreme pollution episodes relative to space- and ground-based observations and allows for identification of pollution sources and transport over Equatorial Asia. We calculate that high particulate matter concentrations from fires during Fall 2015 were responsible for persistent exposure of 69 million people to unhealthy air quality conditions. Short-term exposure to this pollution may have caused 11,880 (6,153-17,270) excess mortalities. Results from this research provide decision-relevant information to policy makers regarding the impact of land use changes and human driven deforestation on fire frequency and population exposure to degraded air quality.

Aerosols are short lived so their geographical distribution and impact on climate depends on where they are emitted. Previous model studies have shown that the mass of sulfate aerosol produced per unit sulfur emission (the sulfate burden potential) and the associated direct radiative forcing vary regionally because of differences in meteorology and photochemistry. Using a global model of aerosol microphysics, we show that the total number of aerosol particles produced per unit sulfur emission (the aerosol number potential) has a different regional variation to that of sulfate mass. The aerosol number potential of N. American and Asian emissions is calculated to be a factor of 3 to 4 times greater than that of European emissions, even though Europe has a higher sulfate burden potential. Pollution from North America and Asia tends to reach higher altitudes than European pollution so forms more new particles through nucleation. These regional differences in particle production mean that sulfur emissions from N. America and E. Asia produce cloud condensation nuclei up to 70% more efficiently than Europe. Taking account of the higher sulfate burden potential of Europe in these simulations shows that E. Asian sulfate produces CCN twice as effectively as European sulfate. The impact of regional sulfur emissions on particle concentrations is also much more widely spread than the impact on sulfate mass, due to efficient particle production in the free troposphere during long range transport. These results imply that regional sulfur emissions will have different climate forcing potentials through changes in cloud drop number.

We completed a meta-analysis of regional and global climate model simulations (n = 96) of the impact of Amazonian deforestation on Amazon basin rainfall. Across all simulations, mean (±1σ) change in annual mean Amazon basin rainfall was -12 ± 11%. Variability in simulated rainfall was not explained by differences in model resolution or surface parameters. Across all simulations we find a negative linear relationship between rainfall and deforestation extent, although individual studies often simulate a nonlinear response. Using the linear relationship, we estimate that deforestation in 2010 has reduced annual mean rainfall across the Amazon basin by 1.8 ± 0.3%, less than the interannual variability in observed rainfall. This may explain why a reduction in Amazon rainfall has not consistently been observed. We estimate that business-as-usual deforestation (based on deforestation rates prior to 2004) would lead to an 8.1 ± 1.4% reduction in annual mean Amazon basin rainfall by 2050, greater than natural variability.

Aviation emissions impact both air quality and climate. Using a coupled tropospheric chemistry-aerosol microphysics model we investigate the effects of varying aviation fuel sulfur content (FSC) on premature mortality from long-term exposure to aviation-sourced PM2.5 (particulate matter with a dry diameter of < 2.5 μm) and on the global radiation budget due to changes in aerosol and tropospheric ozone. We estimate that present-day non-CO2 aviation emissions with a typical FSC of 600 ppm result in 3597 (95 % CI: 1307–5888) annual mortalities globally due to increases in cases of cardiopulmonary disease and lung cancer, resulting from increased surface PM2.5 concentrations. We quantify the global annual mean combined radiative effect (REcomb) of non-CO2 aviation emissions as −13.3 mW m−2; from increases in aerosols (direct radiative effect and cloud albedo effect) and tropospheric ozone. Ultra-low sulfur jet fuel (ULSJ; FSC =15 ppm) has been proposed as an option to reduce the adverse health impacts of aviation-induced PM2.5. We calculate that swapping the global aviation fleet to ULSJ fuel would reduce the global aviation-induced mortality rate by 624 (95 % CI: 227–1021) mortalities a−1 and increase REcomb by +7.0 mW m−2. We explore the impact of varying aviation FSC between 0–6000 ppm. Increasing FSC increases annual mortality, while enhancing climate cooling through increasing the aerosol cloud albedo effect (aCAE). We explore the relationship between the injection altitude of aviation emissions and the resulting climate and air quality impacts. Compared to the standard aviation emissions distribution, releasing aviation emissions at the ground increases global aviation-induced mortality and produces a net warming effect, primarily through a reduced aCAE. Aviation emissions injected at the surface are 5 times less effective at forming cloud condensation nuclei, reducing the aviation-induced aCAE by a factor of 10. Applying high FSCs at aviation cruise altitudes combined with ULSJ fuel at lower altitudes result in reduced aviation-induced mortality and increased negative RE compared to the baseline aviation scenario.

Airborne observations of fluorescent aerosol were made aboard an airship during CloudLab, a series of flights that took place in September and October of 2013 and covered a wideband of longitude across the continental U.S. between Florida and California and between 28 and 37-N latitudes. Sampling occurred from near the surface to 1000-m above the ground. A Wideband Integrated Bioaerosol Sensor (WIBS-4) measured average concentrations of supermicron fluorescent particles aloft (1-μm to 10-μm), revealing number concentrations ranging from 2.1-±-0.8 to 8.7-±-2.2-×-104 particles m-3 and representing up to 24% of total supermicron particle number. We observed distinct variations in size distributions and fluorescent characteristics in different regions, and attribute these to geographically diverse bioaerosol. Fluorescent aerosol detected in the east is largely consistent with mold spores observed in a laboratory setting, while a shift to larger sizes associated with different fluorescent patterns is observed in the west. Fluorescent bioaerosol loadings in the desert west were as high as those near the Gulf of Mexico, suggesting that bioaerosol is a substantial component of supermicron aerosol both in humid and arid environments. The observations are compared to model fungal and bacterial loading predictions, and good agreement in both particle size and concentrations is observed in the east. In the west, the model underestimated observed concentrations by a factor between 2 and 4 and the prescribed particle sizes are smaller than the observed fluorescent aerosol. A classification scheme for use with WIBS data is also presented. Key Points Fluorescent supermicron aerosol loads are reported across the southern U.S. Regional variations in fluorescent behavior and particle size are observed Comparison to modeled emissions shows an underestimate in the west

A global model of aerosol microphysics is used to simulate a large East Asian dust storm during the ACE-Asia experiment. We use the model together with size resolved measurements of aerosol number concentration and composition to examine how dust modified the production of sulfate aerosol and the particle size distribution in East Asian outflow. Simulated size distributions and mass concentrations of dust, sub- and super-micron sulfate agree well with observations from the C-130 aircraft. Modeled mass concentrations of fine sulfate (DpDp>1.0 μm) by more than an order of magnitude, but total sulfate concentrations increase by less than 2% because decreases in fine sulfate have a compensating effect. Our analysis shows that the sulfate associated with dust can be explained largely by the uptake of H2SO4 rather than reaction of SO2 on the dust surface, which we assume is suppressed once the particles are coated in sulfate. We suggest that many previous model investigations significantly overestimated SO2 oxidation on East Asian dust, possibly due to the neglect of surface saturation effects. We extend previous model experiments by examining how dust modified existing particle concentrations in Asian outflow. Total particle concentrations (condensation nuclei, CN) modeled in the dust-pollution plume are reduced by up to 20%, but we predict that dust led to less than 10% depletion in particles large enough to act as cloud condensation nuclei (CCN). Our analysis suggests that E. Asian dust storms have only a minor impact on sulfate particles present at climate-relevant sizes.

We implement the TwO-Moment Aerosol Sectional (TOMAS) microphysics module into GEOS-CHEM, a CTM driven by assimilated meteorology. TOMAS has 30 size sections covering 0.01–10 μm diameter with conservation equations for both aerosol mass and number. The implementation enables GEOS-CHEM to simulate aerosol microphysics, size distributions, mass and number concentrations. The model system is developed for sulfate and sea-salt aerosols, a year-long simulation has been performed, and results are compared to observations. Additionally model intercomparison was carried out involving global models with sectional microphysics: GISS GCM-II' and GLOMAP. Comparison with marine boundary layer observations of CN10 and CCN(0.2%) shows that all models perform well with average errors of 30–50%. However, all models underpredict CN10 by up to 42% between 15° S and 45° S while overpredicting CN10 up to 52% between 45° N and 60° N, which could be due to the sea-salt emission parameterization and the assumed size distribution of primary sulfate emission, in each case respectively. Model intercomparison at the surface shows that GISS GCM-II' and GLOMAP, each compared against GEOS-CHEM, both predict 40% higher CN10 and predict 20% and 30% higher CCN(0.2%) on average, respectively. Major discrepancies are due to different emission inventories and transport. Budget comparison shows GEOS-CHEM predicts the lowest global CCN(0.2%) due to microphysical growth being a factor of 2 lower than other models because of lower SO2 availability. These findings stress the need for accurate meteorological inputs, updated emission inventories, and realistic clouds and oxidant fields when evaluating global aerosol microphysics models.

Empirical relationships that link cloud droplet number (CDN) to aerosol number or mass are commonly used to calculate global fields of CDN for climate forcing assessments. In this work we use a sectional global model of sulfate and sea-salt aerosol coupled to a mechanistic aerosol activation scheme to explore the limitations of this approach. We find that a given aerosol number concentration produces a wide range of CDN concentrations due to variations in the shape of the aerosol size distribution. On a global scale, the dependence of CDN on the size distribution results in regional biases in predicted CDN (for a given aerosol number). Empirical relationships between aerosol number and CDN are often derived from regional data but applied to the entire globe. In an analogous process, we derive regional "correlation-relations" between aerosol number and CDN and apply these regional relations to calculations of CDN on the global scale. The global mean percentage error in CDN caused by using regionally derived CDN-aerosol relations is 20 to 26%, which is about half the global mean percentage change in CDN caused by doubling the updraft velocity. However, the error is as much as 25–75% in the Southern Ocean, the Arctic and regions of persistent stratocumulus when an aerosol-CDN correlation relation from the North Atlantic is used. These regions produce much higher CDN concentrations (for a given aerosol number) than predicted by the globally uniform empirical relations. CDN-aerosol number relations from different regions also show very different sensitivity to changing aerosol. The magnitude of the rate of change of CDN with particle number, a measure of the aerosol efficacy, varies by a factor 4. CDN in cloud processed regions of persistent stratocumulus is particularly sensitive to changing aerosol number. It is therefore likely that the indirect effect will be underestimated in these important regions.