[1] The volatility basis set, a computationally efficient framework for the description of organic aerosol partitioning and chemical aging, is implemented in the Goddard Institute for Space Studies General Circulation Model II′ for a coupled global circulation and chemical transport model to simulate secondary organic aerosol (SOA) formation. The latest smog chamber information about the yields of anthropogenic and biogenic precursors is incorporated in the model. SOA formation from monoterpenes, sesquiterpenes, isoprene, and anthropogenic precursors is estimated as 17.2, 3.9, 6.5, and 1.6 Tg yr −1 , respectively. Reducing water solubility of secondary organic gas from 10 5 to 10 3 mol L −1 atm −1 (1 atm = 1.01325 × 10 5 N m −2 ) leads to a 90% increase in SOA production and an increase of over 340% in total atmospheric burden, from 0.54 to 2.4 Tg. Increasing the temperature sensitivity of SOA leads to a 30% increase in production, to 38.2 Tg yr −1. Since the additional SOA is formed at high altitude, where deposition time scales are longer, the average lifetime is doubled from 6.8 to 14.3 days, resulting in a near tripling of atmospheric burden to 1.5 Tg. Chemical aging of anthropogenic SOA by gas-phase reaction of the SOA components with the hydroxyl radical adds an additional 2.7-9.3 Tg yr −1 of anthropogenic SOA to the above production rates and nearly doubles annual average total SOA burdens. The possibility of such high anthropogenic SOA production rates challenges the assumption that anthropogenic volatile organic compounds are not important SOA precursors on a global scale. Model predictions with and without SOA aging are compared with data from two surface observation networks: the Interagency Monitoring of Protected Visual Environments for the United States and the European Monitoring and Evaluation Programme.Citation: Farina, S. C., P. J. Adams, and S. N. Pandis (2010), Modeling global secondary organic aerosol formation and processing with the volatility basis set: Implications for anthropogenic secondary organic aerosol,

Abstract. Chemical transport models have historically struggled to accurately simulate the magnitude and variability of observed organic aerosol (OA), with previous studies demonstrating that models significantly underestimate observed concentrations in the troposphere. In this study, we explore two different model OA schemes within the standard GEOS-Chem chemical transport model and evaluate the simulations against a suite of 15 globally distributed airborne campaigns from 2008 to 2017, primarily in the spring and summer seasons. These include the ATom, KORUS-AQ, GoAmazon, FRAPPE, SEAC4RS, SENEX, DC3, CalNex, OP3, EUCAARI, ARCTAS and ARCPAC campaigns and provide broad coverage over a diverse set of atmospheric composition regimes – anthropogenic, biogenic, pyrogenic and remote. The schemes include significant differences in their treatment of the primary and secondary components of OA – a “simple scheme” that models primary OA (POA) as non-volatile and takes a fixed-yield approach to secondary OA (SOA) formation and a “complex scheme” that simulates POA as semi-volatile and uses a more sophisticated volatility basis set approach for non-isoprene SOA, with an explicit aqueous uptake mechanism to model isoprene SOA. Despite these substantial differences, both the simple and complex schemes perform comparably across the aggregate dataset in their ability to capture the observed variability (with an R2 of 0.41 and 0.44, respectively). The simple scheme displays greater skill in minimizing the overall model bias (with a normalized mean bias of 0.04 compared to 0.30 for the complex scheme). Across both schemes, the model skill in reproducing observed OA is superior to previous model evaluations and approaches the fidelity of the sulfate simulation within the GEOS-Chem model. However, there are significant differences in model performance across different chemical source regimes, classified here into seven categories. Higher-resolution nested regional simulations indicate that model resolution is an important factor in capturing variability in highly localized campaigns, while also demonstrating the importance of well-constrained emissions inventories and local meteorology, particularly over Asia. Our analysis suggests that a semi-volatile treatment of POA is superior to a non-volatile treatment. It is also likely that the complex scheme parameterization overestimates biogenic SOA at the global scale. While this study identifies factors within the SOA schemes that likely contribute to OA model bias (such as a strong dependency of the bias in the complex scheme on relative humidity and sulfate concentrations), comparisons with the skill of the sulfate aerosol scheme in GEOS-Chem indicate the importance of other drivers of bias, such as emissions, transport and deposition, that are exogenous to the OA chemical scheme.

Abstract.Semi-volatile and reactive primary organic aerosols are modeled on a global scale using the GISS GCM II' "unified" climate model. We employ the volatility basis set framework to simulate emissions, chemical reactions and phase partitioning of primary and secondary organic aerosol (POA and SOA). The model also incorporates the emissions and reactions of intermediate volatility organic compounds (IVOCs) as a source of organic aerosol (OA), one that has been missing in most prior work. Model predictions are evaluated against a broad set of observational constraints including mass concentrations, degree of oxygenation, volatility and isotopic composition. A traditional model that treats POA as non-volatile and non-reactive is also compared to the same set of observations to highlight the progress made in this effort. The revised model predicts a global dominance of SOA and brings the POA/SOA split into better agreement with ambient measurements. This change is due to traditionally defined POA evaporating and the evaporated vapors oxidizing to form non-traditional SOA. IVOCs (traditionally not included in chemical transport models) oxidize to form condensable products that account for a third of total OA, suggesting that global models have been missing a large source of OA. Predictions of the revised model for the SOA fraction at 17 different locations compared much better to observations than predictions from the traditional model. Modelpredicted volatility is compared with thermodenuder data collected at three different different field campaigns: FAME-2008, MILAGRO-2006 and SOAR-2005. The revised model predicts the OA volatility much more closely than the traditional model. When compared against monthly averaged OA Correspondence to: P. J. Adams (peter.adams@cmu.edu) mass concentrations measured by the IMPROVE network, predictions of the revised model lie within a factor of two in summer and mostly within a factor of five during winter. A sensitivity analysis indicates that the winter comparison can be improved either by increasing POA emissions or lowering the volatility of those emissions. Model predictions of the isotopic composition of OA are compared against those computed via a radiocarbon isotope analysis of field samples. The contemporary fraction, on average, is slightly under-predicted (20 %) during the summer months but is a factor of two lower during the winter months. We hypothesize that the large wintertime under-prediction of surface OA mass concentrations and the contemporary fraction is due to an under-representation of biofuel (particularly, residential wood burning) emissions in the emissions inventory. Overall, the model evaluation highlights the importance of treating POA as semi-volatile and reactive in order to predict accurately the sources, composition and properties of ambient OA.

Abstract. Aerosol emissions from biofuel combustion impact both health and climate; however, while reducing emissions through improvements to combustion technologies will improve health, the net effect on climate is largely unconstrained. In this study, we examine sensitivities in global aerosol concentration, direct radiative climate effect, and cloud-albedo aerosol indirect climate effect to uncertainties in biofuel emission factors, optical mixing state, and model nucleation and background secondary organic aerosol (SOA). We use the Goddard Earth Observing System global chemical-transport model (GEOS-Chem) with TwO Moment Aerosol Sectional (TOMAS) microphysics. The emission factors include amount, composition, size, and hygroscopicity, as well as optical mixing-state properties. We also evaluate emissions from domestic coal use, which is not biofuel but is also frequently emitted from homes. We estimate the direct radiative effect assuming different mixing states (homogeneous, core-shell, and external) with and without absorptive organic aerosol (brown carbon). We find the global-mean direct radiative effect of biofuel emissions ranges from −0.02 to +0.06 W m−2 across all simulation/mixing-state combinations with regional effects in source regions ranging from −0.2 to +0.8 W m−2. The global-mean cloud-albedo aerosol indirect effect (AIE) ranges from +0.01 to −0.02 W m−2 with regional effects in source regions ranging from −1.0 to −0.05 W m−2. The direct radiative effect is strongly dependent on uncertainties in emissions mass, composition, emissions aerosol size distributions, and assumed optical mixing state, while the indirect effect is dependent on the emissions mass, emissions aerosol size distribution, and the choice of model nucleation and secondary organic aerosol schemes. The sign and magnitude of these effects have a strong regional dependence. We conclude that the climate effects of biofuel aerosols are largely unconstrained, and the overall sign of the aerosol effects is unclear due to uncertainties in model inputs. This uncertainty limits our ability to introduce mitigation strategies aimed at reducing biofuel black carbon emissions in order to counter warming effects from greenhouse gases. To better understand the climate impact of particle emissions from biofuel combustion, we recommend field/laboratory measurements to narrow constraints on (1) emissions mass, (2) emission size distribution, (3) mixing state, and (4) ratio of black carbon to organic aerosol.

<p><strong>Abstract.</strong> Chemical transport models have historically struggled to accurately simulate the magnitude and variability of observed organic aerosol (OA), with previous studies demonstrating that models significantly underestimate observed concentrations in the troposphere. In this study, we explore two different model OA schemes within the standard GEOS-Chem chemical transport model and evaluate the simulations against a suite of 15 globally-distributed airborne campaigns from 2008–2017. These include the ATom, KORUS-AQ, GoAmazon, FRAPPE, SEAC4RS, SENEX, DC3, CalNex, OP3, EUCAARI, ARCTAS and ARCPAC campaigns and provide broad coverage over a diverse set of atmospheric-composition regimes – anthropogenic, biogenic, pyrogenic and remote. The schemes include significant differences in their treatment of the primary and secondary components of OA – a <q>simple scheme</q> that models primary OA (POA) as non-volatile and takes a fixed-yield approach to secondary OA (SOA) formation, and a <q>complex scheme</q> that simulates POA as semi-volatile and uses a more sophisticated volatility basis set approach for non-isoprene SOA, with an explicit aqueous uptake mechanism to model isoprene SOA. Despite these substantial differences, both the simple and complex schemes perform comparably across the aggregate dataset in their ability to capture the observed variability (with an R<sup>2</sup> of 0.41 and 0.44 respectively). The simple scheme displays greater skill in minimizing the overall model-bias (with a NMB of 0.04, compared to 0.29 for the complex scheme). Across both schemes, the model skill in reproducing observed OA is superior to previous model evaluations and approaches the fidelity of the sulfate simulation within GEOS-Chem. However, there are significant differences in model performance across different chemical source regimes, classified here into 7 categories. Higher-resolution nested regional simulations indicate that model resolution is an important factor in capturing variability in highly-localized campaigns, while also demonstrating the importance of well-constrained emissions inventories and local meteorology, particularly over Asia. A comparison of the POA loadings from the complex scheme with SOA loadings from the simple scheme (and vice versa) also suggests that a semi-volatile treatment of POA is superior to a non-volatile treatment. While this study identifies factors within the SOA schemes that likely contribute to OA model bias (such as a strong dependency of the bias in the complex scheme on relative humidity and sulfate concentrations), comparisons with the skill of the sulfate aerosol scheme in GEOS-Chem indicate the importance of other drivers of bias such as emissions, transport, and deposition that are exogenous to the OA chemical scheme.</p>

Abstract. Aerosol emissions from biofuel combustion impact both health and climate; however, while reducing emissions through improvements to combustion technologies will improve health, the net effect on climate is largely unconstrained. In this study, we examine sensitivities in global aerosol concentration, direct radiative climate effect, and cloud-albedo aerosol indirect climate effect to uncertainties in biofuel emission factors, optical mixing-state, and model nucleation and background SOA. We use the Goddard Earth Observing System global chemical-transport model (GEOS-Chem) with TwO Moment Aerosol Sectional (TOMAS) microphysics. The emission factors include: amount, composition, size and hygroscopicity, as well as optical mixing-state properties. We also evaluate emissions from domestic coal use, which is not biofuel but is also frequently emitted from homes. We estimate the direct radiative effect assuming different mixing states (internal, core-shell, and external) with and without absorptive organic aerosol (brown carbon). We find the global-mean direct radiative effect of biofuel emissions ranges from −0.02 to +0.06 W m−2 across all simulation/mixing state combinations with regional effects in source regions ranging from −0.2 to +1.2 W m−2. The global-mean cloud-albedo aerosol indirect effect ranges from +0.01 to −0.02 W m−2 with regional effects in source regions ranging from −1.0 to −0.05 W m−2. The direct radiative effect is strongly dependent on uncertainties in emissions mass, composition, emissions aerosol size distributions and assumed optical mixing state, while the indirect effect is dependent on the emissions mass, emissions aerosol size distribution and the choice of model nucleation and secondary organic aerosol schemes. The sign and magnitude of these effects have a strong regional dependence. We conclude that the climate effects of biofuel aerosols are largely unconstrained, and the overall sign of the aerosol effects is unclear due to uncertainties in model inputs. This uncertainty limits our ability to introduce mitigation strategies aimed at reducing biofuel black carbon emissions in order to counter warming effects from greenhouse-gases. To better understand the climate impact of particle emissions from biofuel combustion, we recommend field/laboratory measurements to narrow constraints on: (1) emissions mass, (2) emission size distribution, (3) mixing state, and (4) ratio of black carbon to organic aerosol.

The installation of roofing materials with increased solar reflectance (i.e., "cool roofs") can mitigate the urban heat island effect and reduce energy use. In addition, meteorological changes, along with the possibility of enhanced UV reflection from these surfaces, can have complex impacts on ozone and PM 2.5 concentrations. We aim to evaluate the air-quality impacts of widespread cool-roof installations prescribed by California's Title 24 building energy efficiency standards within the heavily populated and polluted South Coast Air Basin (SoCAB). Development of a comprehensive rooftop area database and evaluation of spectral reflectance measurements of roofing materials allows us to project potential future changes in solar and UV reflectance for simulations using the Weather Research Forecast and Community Multiscale Air Quality (CMAQ) models. 2012 meteorological simulations indicate a decrease in daily maximum temperatures, daily maximum boundary layer heights, and ventilation coefficients throughout the SoCAB upon widespread installation of cool roofs. CMAQ simulations show significant increases in PM 2.5 concentrations and policyrelevant design values. Changes in 8-h ozone concentrations depend on the potential change in UV reflectance, ranging from a decrease in population-weighted concentrations when UV reflectance remains unchanged to an increase when changes in UV reflectance are at an upper bound. However, 8-h policy-relevant ozone design values increase in all cases. Although the other benefits of cool roofs could outweigh small air-quality penalties, UV reflectance standards for cool roofing materials could mitigate these negative consequences. Results of this study motivate the careful consideration of future rooftop and pavement solar reflectance modification policies. With 16.8 million people, the SoCAB is the second most populous urban area in the United States. A fossil-fuel-dependent transit and goods movement infrastructure along with a welldeveloped industrial presence within the 27,824-km 2 SoCAB generates significant emissions of oxides of nitrogen (NOx), volatile organic compounds (VOCs), directly emitted primary particulate matter (PM), and secondary PM precursors. Persistent highpressure systems, ample photochemistry, infrequent rainfall, and ventilation-inhibiting topography also contribute to severe airquality problems. The SoCAB currently does not attain federal air-quality standards for 8-h O 3 , 1-h O 3 , annual-averaged PM 2.5 , and 24-h PM 2.5 . Ozone levels within the SoCAB are often the highest in the nation (1).The hot and sunny conditions typically experienced within the SoCAB make urban surface modification a useful strategy to reduce urban temperatures. Meteorological impacts of roofing materials with enhanced solar reflectance (SR, synonymous with "albedo"), colloquially referred to as "cool roofs," are wellstudied and indicate several benefits in urban areas. The replacement of darker materials with high-reflectance surfaces within cities can help mitigate the urban heat...

Semi-volatile and reactive primary organic aerosols are modeled on a global scale using the GISS GCM II' "unified" climate model. We employ the volatility basis set framework to simulate emissions, chemical reactions and phase partitioning of primary and secondary organic aerosol (POA and SOA). The model also incorporates the emissions and reactions of intermediate volatility organic compounds (IVOCs) as a source of OA, one that has been missing in most prior work. Model predictions are evaluated against a broad set of observational constraints including mass concentrations, degree of oxygenation, volatility and isotopic composition. A traditional model that treats POA as non-volatile and non-reactive is also compared to the same set of observations to highlight the progress made in this effort. The revised model predicts a global dominance of SOA and brings the POA/SOA split into better agreement with ambient measurements. This change is due to traditionally defined POA evaporating and the evaporated vapors oxidizing to form non-traditional SOA. IVOCs (traditionally not included in chemical transport models) oxidize to form condensable products that account for a third of total OA, suggesting that global models have been missing a large source of OA. Predictions of the revised model for the oxygenated OA (OOA) fraction at 17 different locations and OA volatility at 3 different locations compared much better to observations than predictions from the traditional model. When compared against monthly averaged OA mass concentrations measured by the IMPROVE network, model predictions lied within a factor of two in summer and mostly within a factor of five during winter. A sensitivity analysis indicates that the winter comparison can be improved either by increasing POA emissions or lowering the volatility of those emissions. Model predictions of the isotopic composition of OA are compared against those computed via a radiocarbon isotope analysis of field samples. The contemporary fraction, on average, is slightly under-predicted (20%) during the summer months but is a factor of two lower during the winter months. We hypothesize that the large wintertime under-prediction of surface OA mass concentrations and the contemporary fraction is due to an under-representation of biofuel (particularly, residential wood burning) emissions in the emission inventory. Overall, the model evaluation highlights the importance of treating POA as semi-volatile and reactive in order to predict accurately the sources, composition and properties of ambient OA