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Ambient fine particulate matter (PM2.5) is the leading global environmental determinant of mortality. However, large gaps exist in ground-based PM2.5 monitoring. Satellite remote sensing of aerosol optical depth (AOD) offers information to help fill these gaps worldwide when augmented with a modeled PM2.5 –AOD relationship. This study aims to understand the spatial pattern and driving factors of this relationship by examining η (PM2.5AOD) using both observations and modeling. A global observational estimate of η for the year 2019 is inferred from 6870 ground-based PM2.5 measurement sites and satellite-retrieved AOD. The global chemical transport model GEOS-Chem, in its high-performance configuration (GCHP), is used to interpret the observed spatial pattern of annual mean η. Measurements and the GCHP simulation consistently identify a global population-weighted mean η value of 96–98 µgm-3 , with regional values ranging from 59.8 µgm-3 in North America to more than 190 µgm-3 in Africa. The highest η value is found in arid regions, where aerosols are less hygroscopic due to mineral dust, followed by regions strongly influenced by surface aerosol sources. Relatively low η values are found over regions distant from strong aerosol sources. The spatial correlation of observed η values with meteorological fields, aerosol vertical profiles, and aerosol chemical composition reveals that spatial variation in η is strongly influenced by aerosol composition and aerosol vertical profiles. Sensitivity tests with globally uniform parameters quantify the effects of aerosol composition and aerosol vertical profiles on spatial variability in η , exhibiting a population-weighted mean difference in aerosol composition of 12.3 µgm-3 , which reflects the determinant effects of composition on aerosol hygroscopicity and aerosol optical properties, and a population-weighted mean difference in the aerosol vertical profile of 8.4 µgm-3 , which reflects spatial variation in the column–surface relationship. [ABSTRACT FROM AUTHOR]

Earth system models (ESMs) participating in the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) simulate various components of fine particulate matter (PM2.5) as major climate forcers. Yet the model performance for PM2.5 components remains little evaluated due in part to a lack of observational data. Here, we evaluate near-surface concentrations of PM2.5 and its five main components over China as simulated by 14 CMIP6 models, including organic carbon (OC; available in 14 models), black carbon (BC; 14 models), sulfate (14 models), nitrate (4 models), and ammonium (5 models). For this purpose, we collect observational data between 2000 and 2014 from a satellite-based dataset for total PM2.5 and from 2469 measurement records in the literature for PM2.5 components. Seven models output total PM2.5 concentrations, and they all underestimate the observed total PM2.5 over eastern China, with GFDL-ESM4 (- 1.5 %) and MPI-ESM-1-2-HAM (- 1.1 %) exhibiting the smallest biases averaged over the whole country. The other seven models, for which we recalculate total PM2.5 from the available component output, underestimate the total PM2.5 concentrations partly because of the missing model representations of nitrate and ammonium. Concentrations of the five individual components are underestimated in almost all models, except that sulfate is overestimated in MPI-ESM-1-2-HAM by 12.6 % and in MRI-ESM2-0 by 24.5 %. The underestimation is the largest for OC (by - 71.2 % to - 37.8 % across the 14 models) and the smallest for BC (- 47.9 % to - 12.1 %). The multi-model mean (MMM) reproduces the observed spatial pattern for OC (R = 0.51), sulfate (R = 0.57), nitrate (R = 0.70) and ammonium (R = 0.74) fairly well, yet the agreement is poorer for BC (R = 0.39). The varying performances of ESMs on total PM2.5 and its components have important implications for the modeled magnitude and spatial pattern of aerosol radiative forcing. [ABSTRACT FROM AUTHOR]

Accurate representation of the hourly variation of the NO2 column-to-surface relationship is needed to interpret geostationary constellation observations of tropospheric NO2 columns. Prior work has revealed inconsistency in the hourly variation in NO2 columns and surface concentrations. In this study, we use the high-performance configuration of the GEOS-Chem model (GCHP) to interpret the daytime hourly variation in NO2 total columns and surface concentrations during summer. We use summer-time Pandora sun photometers and aircraft measurements during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) field campaign over Maryland, Texas, and Colorado as well as 50 sites (31: contiguous USA, 10: Europe, 9: Asia) from the Pandonia Global Network (PGN). We correct the Pandora columns for 1) hourly variation in the column effective temperature driven by the fractional boundary layer contribution to the total column, and 2) change in local solar time along the line-of-sight of the Pandora instrument. The corrected Pandora observations are increased by about 5-6 × 1014 molecules cm-2 at 9 AM and 6 PM across all Pandora sites. We conduct fine resolution (~12 km) simulations over the contiguous US, Europe, and East Asia using the stretched grid capability of GCHP. We also examine the effect of planetary boundary layer height (PBLH) corrections on the total columns. We first evaluate the GCHP simulated absolute NO2 concentration with Pandora and aircraft observations. We find that fine resolution simulations at 12 km compared with moderate resolution ~55 km reduce the Normalized Bias (NB) versus Pandora total columns (19% to 10%) and versus aircraft measurements (25% to 13%) over Maryland, Texas, and Colorado. Fine resolution simulations at 12 km compared with moderate resolution at 55 km reduce the NB versus Pandora total columns over the eastern US (17% to 9%), western US (22% to 14%), Europe (24% to 15%), and Asia (29% to 21%). We next use the 12 km simulation to examine the hourly variation in the NO2 column and surface concentrations. We explain the weaker hourly variation in NO2 columns than at the surface as a function of 1) hourly variation in the column effective temperature, 2) hourly variation in the local solar time along the Pandora line-of sight and 3) the integral of weakly connected layers; with the lowest 500 m exhibiting greater NO2 concentrations in morning and evening than midday, while the residual column above 500 m dominates the total column with weaker variability. [ABSTRACT FROM AUTHOR]

Ambient fine particulate matter (PM2.5) is the leading global environmental determinant of mortality. However, large gaps exist in ground-based PM2.5 monitoring. Satellite remote sensing of aerosol optical depth (AOD) offers information to fill these gaps worldwide, when augmented with a modeled PM2.5 to AOD relationship (η). This study aims to understand the spatial pattern and driving factors of η from both observations and modeling. A global observational estimate of η for the year 2019 is inferred from 6,118 ground-based PM2.5 measurement sites and satellite retrieved AOD from the MAIAC algorithm. A global chemical transport model, GEOS-Chem, in its high performance configuration (GCHP), is used to interpret the observed spatial pattern of annual mean η. Measurements and the GCHP simulation consistently identify a global population-weighted mean η of 92 – 100 μg/m3, with regional values ranging from 60.3 μg/m3 for North America to more than 130 μg/m3 in Africa. The highest η is found in arid regions where aerosols are less hygroscopic due to mineral dust, followed by regions strongly influenced by surface aerosol sources. Relatively low η is found over regions distant from strong aerosol sources. The spatial variation of η is strongly influenced by aerosol composition driven by its effects on aerosol hygroscopicity. Sensitivity tests with globally uniform parameters reveal that aerosol composition leads to the strongest η spatial variability, with a population-weighted normalized mean difference of 12.3 μg/m3, higher than that from aerosol vertical profile (8.4 μg/m3), reflecting the determinant composition effects on aerosol hygroscopicity and aerosol optical properties. [ABSTRACT FROM AUTHOR]

Earth system models (ESMs) participating in the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) simulate various components of fine particulate matter (PM2.5) as major climate forcers. Yet the model performance for PM2.5 components remains little evaluated due in part to lack of observational data. Here, we evaluate near-surface concentrations of PM2.5 and its five main components over China as simulated by fourteen CMIP6 models, including organic carbon (OC, available in 14 models), black carbon (BC, 14 models), sulfate (14 models), nitrate (4 models), and ammonium (5 models). For this purpose, we collect observational data between 2000 and 2014 from a satellite-based dataset for total PM2.5 and from 2469 measurement records in the literature for PM2.5 components. Seven models output total PM2.5 concentrations, and they all underestimate the observed total PM2.5 over eastern China, with GFDL-ESM4 (–1.5 %) and MPI-ESM-1-2-HAM (–1.1 %) exhibiting the smallest biases averaged over the whole country. The other seven models, for which we recalculate total PM2.5 from the available components output, underestimate the total PM2.5 concentrations, partly because of the missing model representations of nitrate and ammonium. Concentrations of the five individual components are underestimated in almost all models, except that sulfate is overestimated in MPI-ESM-1-2-HAM by 12.6 % and in MRI-ESM2-0 by 24.5 %. The underestimation is the largest for OC (by –71.2 % to –37.8 % across the 14 models) and the smallest for BC (–47.9 % to –12.1 %). The multi-model mean (MMM) reproduces fairly well the observed spatial pattern for OC (R = 0.51), sulfate (R = 0.57), nitrate (R = 0.70) and ammonium (R = 0.75), yet the agreement is poorer for BC (R = 0.39). The varying performances of ESMs on total PM2.5 and its components have important implications for the modeled magnitude and spatial pattern of aerosol radiative forcing. [ABSTRACT FROM AUTHOR]

Earth system models (ESMs) participating in the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) simulate various components of fine particulate matter (PM2.5) as major climate forcers. Yet the model performance for PM2.5 components remains little evaluated due in part to lack of observational data. Here, we evaluate near-surface concentrations of PM2.5 and its five main components over China as simulated by fourteen CMIP6 models, including organic carbon (OC, available in 14 models), black carbon (BC, 14 models), sulfate (14 models), nitrate (4 models), and ammonium (5 models). For this purpose, we collect observational data between 2000 and 2014 from a satellite-based dataset for total PM2.5 and from 2469 measurement records in the literature for PM2.5 components. Seven models output total PM2.5 concentrations, and they all underestimate the observed total PM2.5 over eastern China, with GFDL-ESM4 (–1.5 %) and MPI-ESM-1-2-HAM (–1.1 %) exhibiting the smallest biases averaged over the whole country. The other seven models, for which we recalculate total PM2.5 from the available components output, underestimate the total PM2.5 concentrations, partly because of the missing model representations of nitrate and ammonium. Concentrations of the five individual components are underestimated in almost all models, except that sulfate is overestimated in MPI-ESM-1-2-HAM by 12.6 % and in MRI-ESM2-0 by 24.5 %. The underestimation is the largest for OC (by –71.2 % to –37.8 % across the 14 models) and the smallest for BC (–47.9 % to –12.1 %). The multi-model mean (MMM) reproduces fairly well the observed spatial pattern for OC (R = 0.51), sulfate (R = 0.57), nitrate (R = 0.70) and ammonium (R = 0.75), yet the agreement is poorer for BC (R = 0.39). The varying performances of ESMs on total PM2.5 and its components have important implications for the modeled magnitude and spatial pattern of aerosol radiative forcing. [ABSTRACT FROM AUTHOR]

The capability of chemical transport models to represent fine particulate matter (PM 2.5) over the course of a day is of vital importance for air quality simulation and assessment. In this work, we used the nested GEOS-Chem model at 0.25∘×0.3125∘ resolution to simulate the diel (24 h) variation in PM 2.5 mass concentrations over the contiguous United States (US) in 2016. We evaluate the simulations with in situ measurements from a national monitoring network. Our base case simulation broadly reproduces the observed morning peak, afternoon dip, and evening peak of PM 2.5 , matching the timings of these features within 1–3 h. However, the simulated PM 2.5 diel amplitude in our base case was 106 % biased high, relative to observations. We find that temporal resolution of emissions, subgrid vertical gradient between surface model-level center and observations, and biases in boundary layer mixing and aerosol nitrate are the major causes for this inconsistency. We applied an hourly anthropogenic emission inventory, converted the PM 2.5 mass concentrations from the model-level center to the height of surface measurements by correcting for aerodynamic resistance, adjusted the boundary layer heights in the driving meteorological fields using aircraft observations, and constrained nitrate concentrations using in situ measurements. The bias in the PM 2.5 diel amplitude was reduced to - 12 % in the improved simulation. Gridded hourly emissions rather than diel scaling factors applied to monthly emissions reduced biases in simulated PM 2.5 overnight. Resolving the subgrid vertical gradient in the surface model level aided the capturing of the timings of the PM 2.5 morning peak and afternoon minimum. Based on the improved model, we find that the mean observed diel variation in PM 2.5 for the contiguous US is driven by (1) building up of PM 2.5 by 10 % in early morning (04:00–08:00 local time, LT), due to increasing anthropogenic emissions into a shallow mixed layer; (2) decreasing PM 2.5 by 22 % from mid-morning (08:00 LT) through afternoon (15:00 LT), associated with mixed-layer growth; (3) increasing PM 2.5 by 30 % from mid-afternoon (15:00 LT) though evening (22:00 LT) as emissions persist into a collapsing mixed layer; and (4) decreasing PM 2.5 by 10 % overnight (22:00–04:00 LT) as emissions diminish. [ABSTRACT FROM AUTHOR]

The capability of chemical transport models to represent fine particulate matter (PM2.5) over the course of a day is of vital importance for air quality simulation and assessment. In this work, we used the nested GEOS-Chem model at 0.25° x 0.3125° resolution to simulate the diel (24 h) variation in PM2.5 mass concentrations over the United States (US) in 2016. We evaluate the simulations with in situ measurements from a national monitoring network. Our base case simulation broadly reproduces the observed morning peak, afternoon dip and evening peak of PM2.5, matching the timings of these features within 1-3 hours. However, the simulated PM2.5 diel amplitude in our base case was 105% biased high relative to observations. We find that temporal resolution of emissions, differences in vertical representativeness between model and observations, as well as boundary layer mixing are the major causes for this inconsistency. We applied an hourly anthropogenic emission inventory and converted the PM2.5 masses from model level center to the height of surface measurements by correcting for aerodynamic resistance. The biases in the PM2.5 diel amplitude were reduced to 25% in the improved simulation and the timing of diel variations were better captured. In addition, notable sensitivity of the simulated diel amplitude of PM2.5 (8%) on the boundary layer height in the driving met fields were identified. Based on the improved model, we find that the diel variation in PM2.5 is driven by 1) building up of PM2.5 in early morning due to increasing anthropogenic emissions into a shallow mixed layer, 2) decreasing PM2.5 from mid-morning through afternoon associated with mixed layer growth, 3) increasing PM2.5 from mid-afternoon though evening as emissions persist into a collapsing mixed layer, and 4) decreasing PM2.5 overnight as emissions diminish. [ABSTRACT FROM AUTHOR]

Accurate representation of aerosol optical properties is essential for the modeling and remote sensing of atmospheric aerosols. Although aerosol optical properties are strongly dependent upon the aerosol size distribution, the use of detailed aerosol microphysics schemes in global atmospheric models is inhibited by associated computational demands. Computationally efficient parameterizations for aerosol size are needed. In this study, airborne measurements over the United States (DISCOVER-AQ) and South Korea (KORUS-AQ) are interpreted with a global chemical transport model (GEOS-Chem) to investigate the variation in aerosol size when organic matter (OM) and sulfate–nitrate–ammonium (SNA) are the dominant aerosol components. The airborne measurements exhibit a strong correlation (r=0.83) between dry aerosol size and the sum of OM and SNA mass concentration (MSNAOM). A global microphysical simulation (GEOS-Chem-TOMAS) indicates that MSNAOM and the ratio between the two components (OM/SNA) are the major indicators for SNA and OM dry aerosol size. A parameterization of the dry effective radius (Reff) for SNA and OM aerosol is designed to represent the airborne measurements (R2=0.74 ; slope = 1.00) and the GEOS-Chem-TOMAS simulation (R2=0.72 ; slope = 0.81). When applied in the GEOS-Chem high-performance model, this parameterization improves the agreement between the simulated aerosol optical depth (AOD) and the ground-measured AOD from the Aerosol Robotic Network (AERONET; R2 from 0.68 to 0.73 and slope from 0.75 to 0.96). Thus, this parameterization offers a computationally efficient method to represent aerosol size dynamically. [ABSTRACT FROM AUTHOR]

The lifetime and concentration of nitrogen oxides (NO x) are susceptible to nonlinear production and loss and to the resolution of a chemical transport model (CTM). This is due to the strong spatial gradients of NO x and the dependence of its own chemical loss on such gradients. In this study, we use the GEOS-Chem CTM in its high-performance implementation (GCHP) to investigate NO x simulations over the eastern United States across a wide range of spatial model resolutions (six different horizontal grids from 13 to 181 km). Following increasing grid size, afternoon surface NO x mixing ratios over July 2015 generally decrease over the Great Lakes region (GL) and increase over the southern states of the US region (SS), yielding regional differences (181 km vs. 13 km) of -16 % (in the GL) to 7 % (in the SS); meanwhile, hydrogen oxide radicals (HO x) increase over both regions, consistent with their different chemical regimes (i.e., NO x -saturated in the GL and NO x -limited in the SS). Nighttime titration of ozone by surface nitric oxide (NO) was found to be more efficient at coarser resolutions, leading to longer NO x lifetimes and higher surface mixing ratios of nitrogen dioxide (NO 2) over the GL in January 2015. The tropospheric NO 2 column density at typical afternoon satellite overpass time has spatially more coherent negative biases (e.g., -8 % over the GL) at coarser resolutions in July, which reversed the positive biases of surface NO x over the SS. The reduced NO x aloft (>1 km altitude) at coarser resolutions was attributable to the enhanced HO x that intrudes into the upper troposphere. Application of coarse-resolution simulations for interpreting satellite NO 2 columns will generally underestimate surface NO 2 over the GL and overestimate surface NO 2 over the SS in summer, but it will uniformly overestimate NO x emissions over both regions. This study significantly broadens understanding of factors contributing to NO x resolution effects and the role of fine-resolution data in accurately simulating and interpreting NO x and its relevance to air quality. [ABSTRACT FROM AUTHOR]

We describe a new generation of the high-performance GEOS-Chem (GCHP) global model of atmospheric composition developed as part of the GEOS-Chem version 13 series. GEOS-Chem is an open-source grid-independent model that can be used online within a meteorological simulation or offline using archived meteorological data. GCHP is an offline implementation of GEOS-Chem driven by NASA Goddard Earth Observing System (GEOS) meteorological data for massively parallel simulations. Version 13 offers major advances in GCHP for ease of use, computational performance, versatility, resolution, and accuracy. Specific improvements include (i) stretched-grid capability for higher resolution in user-selected regions, (ii) more accurate transport with new native cubed-sphere GEOS meteorological archives including air mass fluxes at hourly temporal resolution with spatial resolution up to C720 (∼ 12 km), (iii) easier build with a build system generator (CMake) and a package manager (Spack), (iv) software containers to enable immediate model download and configuration on local computing clusters, (v) better parallelization to enable simulation on thousands of cores, and (vi) multi-node cloud capability. The C720 data are now part of the operational GEOS forward processing (GEOS-FP) output stream, and a C180 (∼ 50 km) consistent archive for 1998–present is now being generated as part of a new GEOS-IT data stream. Both of these data streams are continuously being archived by the GEOS-Chem Support Team for access by GCHP users. Directly using horizontal air mass fluxes rather than inferring from wind data significantly reduces global mean error in calculated surface pressure and vertical advection. A technical performance demonstration at C720 illustrates an attribute of high resolution with population-weighted tropospheric NO 2 columns nearly twice those at a common resolution of 2 ∘ × 2.5 ∘. [ABSTRACT FROM AUTHOR]

Accurate representation of aerosol optical properties is essential for modeling and remote sensing of atmospheric aerosols. Although aerosol optical properties are strongly dependent upon the aerosol size distribution, use of detailed aerosol microphysics schemes in global atmospheric models is inhibited by associated computational demands. Computationally efficient parameterizations for aerosol size are needed. In this study, airborne measurements over the United States (DISCOVER-AQ) and South Korea (KORUS-AQ) are interpreted with a global chemical transport model (GEOS-Chem) to investigate the variation in aerosol size when organic matter (OM) and sulfate-nitrate-ammonium (SNA) are the dominant aerosol components. The airborne measurements exhibit a strong correlation (r = 0.83) between dry aerosol size and the sum of OM and SNA mass concentration (MSNAOM). A global microphysical simulation (GEOS-Chem-TOMAS) indicates that MSNAOM, and the ratio between the two components (OM/SNA) are the major indicators for SNA and OM dry aerosol size. A parameterization of dry effective radius (Reff) for SNA and OM aerosol is proposed, which well represents the airborne measurements (R² = 0.74, slope = 1.00) and the GEOS-Chem-TOMAS simulation (R² = 0.72, slope = 0.81). When applied in the GEOS-Chem high-performance model, this parameterization improves the agreement between the simulated aerosol optical depth (AOD) and the ground-measured AOD from the Aerosol Robotic Network (AERONET; R² from 0.68 to 0.73, slope from 0.75 to 34 0.96). Thus, this parameterization offers a computationally efficient method to represent aerosol size dynamically. [ABSTRACT FROM AUTHOR]

Accurate representation of aerosol optical properties is essential for modeling and remote sensing of atmospheric aerosols. Although aerosol optical properties are strongly dependent upon the aerosol size distribution, use of detailed aerosol microphysics schemes in global atmospheric models is inhibited by associated computational demands. Computationally efficient parameterizations for aerosol size are needed. In this study, airborne measurements over the United States (DISCOVER-AQ) and South Korea (KORUS-AQ) are interpreted with a global chemical transport model (GEOS-Chem) to investigate the variation in aerosol size when organic matter (OM) and sulfate-nitrate-ammonium (SNA) are the dominant aerosol components. The airborne measurements exhibit a strong correlation (r = 0.83) between dry aerosol size and the sum of OM and SNA mass concentration (MSNAOM). A global microphysical simulation (GEOS-Chem-TOMAS) indicates that MSNAOM, and the ratio between the two components are the major indicators for SNA and OM dry aerosol size. A parameterization of dry effective radius (Reff) for SNA and OM aerosol is proposed, which well represents the airborne measurements (R2 = 0.74, slope = 1.00) and the GEOS-Chem-TOMAS simulation (R2 = 0.72, slope = 0.81). When applied in the GEOS-Chem high-performance model, this parameterization improves the agreement between the simulated aerosol optical depth (AOD) and the ground-measured AOD from the Aerosol Robotic Network (AERONET; R2 from 0.68 to 0.73, slope from 0.75 to 0.96). Thus, this parameterization offers a computationally efficient method to represent aerosol size dynamically. [ABSTRACT FROM AUTHOR]

The lifetime and concentration of nitrogen oxides (NOx) are susceptible to non-linear production and loss, and con- sequently to the resolution of a chemical transport model (CTM). Here we use the GEOS-Chem CTM in its high performance implementation (GCHP) to investigate NOx simulations over the eastern United States across a wide range of resolutions (13–181 km). Following increasing grid size, daytime surface NOx concentrations over July 2015 generally decrease over the Great Lakes (GL) region and increase over the Southern States (SS), yielding regional biases (181 km vs. 13 km) of −18 % to 9 %; meanwhile hydrogen oxide radicals (HOx) increase over both regions, consistent with their different chemical regimes. Night- time titration of ozone by surface nitric oxide (NO) was found to be more efficient at coarser resolutions, leading to longer NOx lifetimes and higher surface concentrations of nitrogen dioxide (NO2) over the GL in January 2015. The tropospheric NO2 column density at typical afternoon satellite overpass time has spatially more coherent negative biases (e.g., −10 % over the GL) at coarser resolutions in July, which reversed the positive biases of surface NOx over the SS. The reduced NO2 aloft (> 1 km altitude) at coarser resolutions was attributable to the enhanced HOx that intrudes into the upper troposphere. Application of coarse resolution simulations for interpreting satellite NO2 columns will generally underestimate surface NO2 over the GL and overestimate surface NO2 over the SS in summer, while uniformly overestimating NOx emissions over both regions. This study significantly broadens understanding of factors contributing to NOx resolution effects, and the role of fine resolution to accurately simulate and interpret NOx and its relevance to air quality. [ABSTRACT FROM AUTHOR]

Geostationary satellite measurements of aerosol optical depth (AOD) over East Asia from the Geostationary Ocean Color Imager (GOCI) and Advanced Himawari Imager (AHI) instruments can augment surface monitoring of fine particulate matter (PM 2.5) air quality, but this requires better understanding of the AOD–PM 2.5 relationship. Here we use the GEOS-Chem chemical transport model to analyze the critical variables determining the AOD–PM 2.5 relationship over East Asia by simulation of observations from satellite, aircraft, and ground-based datasets. This includes the detailed vertical aerosol profiling over South Korea from the KORUS-AQ aircraft campaign (May–June 2016) with concurrent ground-based PM 2.5 composition, PM 10 , and AERONET AOD measurements. The KORUS-AQ data show that 550 nm AOD is mainly contributed by sulfate–nitrate–ammonium (SNA) and organic aerosols in the planetary boundary layer (PBL), despite large dust concentrations in the free troposphere, reflecting the optically effective size and high hygroscopicity of the PBL aerosols. We updated SNA and organic aerosol size distributions in GEOS-Chem to represent aerosol optical properties over East Asia by using in situ measurements of particle size distributions from KORUS-AQ. We find that SNA and organic aerosols over East Asia have larger size (number median radius of 0.11 µ m with geometric standard deviation of 1.4) and 20 % larger mass extinction efficiency as compared to aerosols over North America (default setting in GEOS-Chem). Although GEOS-Chem is successful in reproducing the KORUS-AQ vertical profiles of aerosol mass, its ability to link AOD to PM 2.5 is limited by under-accounting of coarse PM and by a large overestimate of nighttime PM 2.5 nitrate. The GOCI–AHI AOD data over East Asia in different seasons show agreement with AERONET AODs and a spatial distribution consistent with surface PM 2.5 network data. The AOD observations over North China show a summer maximum and winter minimum, opposite in phase to surface PM 2.5. This is due to low PBL depths compounded by high residential coal emissions in winter and high relative humidity (RH) in summer. Seasonality of AOD and PM 2.5 over South Korea is much weaker, reflecting weaker variation in PBL depth and lack of residential coal emissions. [ABSTRACT FROM AUTHOR]

Modeling atmospheric chemistry at fine resolution globally is computationally expensive; the capability to focus on specific geographic regions using a multiscale grid is desirable. Here, we develop, validate, and demonstrate stretched grids in the GEOS-Chem atmospheric chemistry model in its high-performance implementation (GCHP). These multiscale grids are specified at runtime by four parameters that offer users nimble control of the region that is refined and the resolution of the refinement. We validate the stretched-grid simulation versus global cubed-sphere simulations. We demonstrate the operation and flexibility of stretched-grid simulations with two case studies that compare simulated tropospheric NO 2 column densities from stretched-grid and cubed-sphere simulations to retrieved column densities from the TROPOspheric Monitoring Instrument (TROPOMI). The first case study uses a stretched grid with a broad refinement covering the contiguous US to produce simulated columns that perform similarly to a C180 (∼ 50 km) cubed-sphere simulation at less than one-ninth the computational expense. The second case study experiments with a large stretch factor for a global stretched-grid simulation with a highly localized refinement with ∼ 10 km resolution for California. We find that the refinement improves spatial agreement with TROPOMI columns compared to a C90 cubed-sphere simulation of comparable computational demands. Overall, we find that stretched grids in GEOS-Chem are a practical tool for fine-resolution regional- or continental-scale simulations of atmospheric chemistry. Stretched grids are available in GEOS-Chem version 13.0.0. [ABSTRACT FROM AUTHOR]

Geostationary satellite sensors over East Asia (GOCI and AHI) are now providing continuous mapping of aerosol optical depth (AOD) at 550 nm to improve monitoring of fine particulate matter (PM2.5) air quality. Here we evaluate our understanding of the physical relationships between AOD and PM2.5 over East Asia by using the GEOS-Chem atmospheric chemistry model to simulate observations from multiple sources: 1) the joint NASA-NIER Korea - United States Air Quality aircraft campaign over South Korea (KORUS-AQ; May-June 2016); 2) AODs from the AERONET ground-based network; 3) AOD from a new GOCI/AHI fused product; and 4) surface PM2.5 networks in South Korea and China. The KORUS-AQ data show that 550 nm AOD is mainly contributed by sulfate-nitrate-ammonium (SNA) and organic aerosols in the planetary boundary layer (PBL), despite large dust concentrations in the free troposphere, reflecting the optically effective size and the high hygroscopicity of the PBL aerosols. Although GEOS-Chem is successful in reproducing the KORUS-AQ vertical profiles of aerosol mass, its ability to link AOD to PM2.5 is limited by under-accounting of coarse PM and by a large overestimate of nighttime PM2.5 nitrate. A broader analysis of the GOCI/AHI AOD data over East Asia in different seasons shows agreement with AERONET AODs and a spatial distribution consistent with surface PM2.5 network data. The AOD observations over North China show a summer maximum and winter minimum, opposite in phase to surface PM2.5. This is due to low PBL depths compounded by high residential coal emissions in winter, and high relative humidity (RH) in summer. Seasonality of AOD and PM2.5 over South Korea is much weaker, reflecting weaker variation of PBL depth and lack of residential coal emissions. Physical interpretation of the satellite AOD data in terms of surface PM2.5 is sensitive to accurate information on aerosol size distributions, PBL depths, RH, the role of coarse particles, and diurnal variation of PM2.5. [ABSTRACT FROM AUTHOR]

The nonlinear dependence of the dust saltation process on wind speed poses a challenge for models of varying resolutions. This challenge is of particular relevance for the next generation of chemical transport models with nimble capability for multiple resolutions. We develop and apply a method to harmonize dust emissions across simulations of different resolutions by generating offline grid-independent dust emissions driven by native high-resolution meteorological fields. We implement into the GEOS-Chem chemical transport model a high-resolution dust source function to generate updated offline dust emissions. These updated offline dust emissions based on high-resolution meteorological fields strengthen dust emissions over relatively weak dust source regions, such as in southern South America, southern Africa and the southwestern United States. Identification of an appropriate dust emission strength is facilitated by the resolution independence of offline emissions. We find that the performance of simulated aerosol optical depth (AOD) versus measurements from the AERONET network and satellite remote sensing improves significantly when using the updated offline dust emissions with the total global annual dust emission strength of 2000 Tgyr-1 rather than the standard online emissions in GEOS-Chem. The updated simulation also better represents in situ measurements from a global climatology. The offline high-resolution dust emissions are easily implemented in chemical transport models. The source code and global offline high-resolution dust emission inventory are publicly available. [ABSTRACT FROM AUTHOR]

Aerosols over Earth's remote and spatially extensive ocean surfaces have important influences on planetary climate. However, these aerosols and their effects remain poorly understood, in part due to the remoteness and limited observations over these regions. In this study, we seek to understand factors that shape marine aerosol size distributions and composition in the northwest Atlantic Ocean region. We use the GEOS-Chem model with the TwO-Moment Aerosol Sectional (TOMAS) microphysics algorithm model to interpret measurements collected from ship and aircraft during the four seasonal campaigns of the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) conducted between 2015 and 2018. Observations from the NAAMES campaigns show enhancements in the campaign-median number of aerosols with diameters larger than 3 nm in the lower troposphere (below 6 km), most pronounced during the phytoplankton bloom maxima (May/June) below 2 km in the free troposphere. Our simulations, combined with NAAMES ship and aircraft measurements, suggest several key factors that contribute to aerosol number and size in the northwest Atlantic lower troposphere, with significant regional-mean (40–60 ∘ N and 20–50 ∘ W) cloud-albedo aerosol indirect effect (AIE) and direct radiative effect (DRE) processes during the phytoplankton bloom. These key factors and their associated simulated radiative effects in the region include the following: (1) particle formation near and above the marine boundary layer (MBL) top (AIE: - 3.37 Wm-2 , DRE: - 0.62 Wm-2); (2) particle growth due to marine secondary organic aerosol (MSOA) as the nascent particles subside into the MBL, enabling them to become cloud-condensation-nuclei-sized particles (AIE: - 2.27 Wm-2 , DRE: - 0.10 Wm-2); (3) particle formation and growth due to the products of dimethyl sulfide, above and within the MBL (- 1.29 Wm-2 , DRE: - 0.06 Wm-2); (4) ship emissions (AIE: - 0.62 Wm-2 , DRE: - 0.05 Wm-2); and (5) primary sea spray emissions (AIE: + 0.04 Wm-2 , DRE: - 0.79 Wm-2). Our results suggest that a synergy of particle formation in the lower troposphere (particularly near and above the MBL top) and growth by MSOA contributes strongly to cloud-condensation-nuclei-sized particles with significant regional radiative effects in the northwest Atlantic. To gain confidence in radiative effect magnitudes, future work is needed to understand (1) the sources and temperature dependence of condensable marine vapors forming MSOA, (2) primary sea spray emissions, and (3) the species that can form new particles in the lower troposphere and grow these particles as they descend into the marine boundary layer. [ABSTRACT FROM AUTHOR]

Modeling atmospheric chemistry at fine resolution globally is computationally expensive; the capability to focus on specific geographic regions using a multiscale grid is desirable. Here, we develop, validate, and demonstrate stretched-grids in the GEOS-Chem atmospheric chemistry model in its high performance implementation (GCHP). These multiscale grids are specified at runtime by four parameters that offer users nimble control of the region that is refined and the resolution of the refinement. We validate the stretched-grid simulation versus global cubed-sphere simulations. We demonstrate the operation and flexibility of stretched-grid simulations with two case studies that compare simulated tropospheric NO2 column densities from stretched-grid and cubed-sphere simulations to retrieved column densities from the TROPOspheric Monitoring Instrument (TROPOMI). The first case study uses a stretched-grid with a broad refinement covering the contiguous US to produce simulated columns that perform similarly to a C180 (~50 km) cubed-sphere simulation at less than one-ninth the computational expense. The second case study experiments with a large stretch-factor for a global stretched-grid simulation with a highly localized refinement with ~10 km resolution for California. We find that the refinement improves spatial agreement with TROPOMI columns compared to a C90 cubed-sphere simulation of comparable computational demands, despite conducting the simulation at a finer resolution than parent meteorological fields. Overall we find that stretched-grids in GEOS-Chem are a practical tool for fine resolution regional- or continental-scale simulations of atmospheric chemistry. Stretched-grids are available in GEOS-Chem version 13.0.0. [ABSTRACT FROM AUTHOR]

The nonlinear dependence of the dust saltation process on wind speed poses a challenge for models of varying resolutions. This challenge is of particular relevance for the next generation of chemical transport models with nimble capability for multiple resolutions. We develop and apply a method to harmonize dust emissions across simulations of different resolutions by generating offline grid-independent dust emissions driven by native high-resolution meteorological fields. We implement into the GEOS-Chem chemical transport model a high-resolution dust source function to generate updated offline dust emissions. These updated offline dust emissions based on high-resolution meteorological fields can better resolve weak dust source regions, such as in southern South America, southern Africa, and the southwestern United States. Identification of an appropriate dust emission strength is facilitated by the resolution independence of offline emissions. We find that the performance of simulated aerosol optical depth (AOD) versus measurements from the AERONET network and satellite remote sensing improves significantly when using the updated offline dust emissions with the total global annual dust emission strength of 2,000 Tg yr-1 rather than the standard online emissions in GEOS-Chem. The offline high-resolution dust emissions are easily implemented in chemical transport models. The source code is available online through GitHub: https://github.com/Jun-Meng/geos-chem/tree/v11-01-Patches-UniCF-vegetation. The global offline high resolution dust emission inventory is freely available (see Code and Data Availability section). [ABSTRACT FROM AUTHOR]

Global anthropogenic emission inventories remain vital for understanding the sources of atmospheric pollution and the associated impacts on the environment, human health, and society. Rapid changes in today's society require that these inventories provide contemporary estimates of multiple atmospheric pollutants with both source sector and fuel type information to understand and effectively mitigate future impacts. To fill this need, we have updated the open-source Community Emissions Data System (CEDS) (Hoesly et al., 2019) to develop a new global emission inventory, CEDS GBD-MAPS. This inventory includes emissions of seven key atmospheric pollutants (NO x ; CO; SO 2 ; NH 3 ; non-methane volatile organic compounds, NMVOCs; black carbon, BC; organic carbon, OC) over the time period from 1970–2017 and reports annual country-total emissions as a function of 11 anthropogenic sectors (agriculture; energy generation; industrial processes; on-road and non-road transportation; separate residential, commercial, and other sectors (RCO); waste; solvent use; and international shipping) and four fuel categories (total coal, solid biofuel, the sum of liquid-fuel and natural-gas combustion, and remaining process-level emissions). The CEDS GBD-MAPS inventory additionally includes monthly global gridded (0.5 ∘ × 0.5 ∘) emission fluxes for each compound, sector, and fuel type to facilitate their use in earth system models. CEDS GBD-MAPS utilizes updated activity data, updates to the core CEDS default scaling procedure, and modifications to the final procedures for emissions gridding and aggregation. Relative to the previous CEDS inventory (Hoesly et al., 2018), these updates extend the emission estimates from 2014 to 2017 and improve the overall agreement between CEDS and two widely used global bottom-up emission inventories. The CEDS GBD-MAPS inventory provides the most contemporary global emission estimates to date for these key atmospheric pollutants and is the first to provide global estimates for these species as a function of multiple fuel types and source sectors. Dominant sources of global NO x and SO 2 emissions in 2017 include the combustion of oil, gas, and coal in the energy and industry sectors as well as on-road transportation and international shipping for NO x. Dominant sources of global CO emissions in 2017 include on-road transportation and residential biofuel combustion. Dominant global sources of carbonaceous aerosol in 2017 include residential biofuel combustion, on-road transportation (BC only), and emissions from the waste sector. Global emissions of NO x , SO 2 , CO, BC, and OC all peak in 2012 or earlier, with more recent emission reductions driven by large changes in emissions from China, North America, and Europe. In contrast, global emissions of NH 3 and NMVOCs continuously increase between 1970 and 2017, with agriculture as a major source of global NH 3 emissions and solvent use, energy, residential, and the on-road transport sectors as major sources of global NMVOCs. Due to similar development methods and underlying datasets, the CEDS GBD-MAPS emissions are expected to have consistent sources of uncertainty as other bottom-up inventories. The CEDS GBD-MAPS source code is publicly available online through GitHub: https://github.com/emcduffie/CEDS/tree/CEDS%5fGBD-MAPS (last access: 1 December 2020). The CEDS GBD-MAPS emission inventory dataset (both annual country-total and monthly global gridded files) is publicly available under 10.5281/zenodo.3754964 (McDuffie et al., 2020c). [ABSTRACT FROM AUTHOR]

Aerosols over Earth's remote and spatially extensive ocean surfaces have important influences on planetary climate. However, these aerosols and their effects remain poorly understood, in part due to the remoteness and limited observations over these regions. In this study, we seek to understand factors that shape marine aerosol size distributions and composition in the Northwest Atlantic Ocean region. We use the GEOS-Chem-TOMAS model to interpret measurements collected from ship and aircraft during the four seasonal campaigns of the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) conducted between 2015 and 2018. Observations from the NAAMES campaigns show enhancements in aerosol total number concentration at atmospheric altitudes of about 1 km, most pronounced during the phytoplankton bloom maxima (May/June). Our simulations, combined with NAAMES ship and aircraft measurements, suggest several key factors contribute to aerosol number and size in the Northwest Atlantic lower troposphere, with significant regional-mean (40-60° N, 20-50° W) aerosol-cloud albedo indirect effects (AIE) and direct radiative effects (DRE) during the phytoplankton bloom. These key factors and their associated radiative effects in the region are: (1) particle formation above/near the marine boundary layer (MBL) top (AIE: -3.37 W m-2, DRE: -0.62 W m-2), (2) particle growth due to marine secondary organic aerosol (MSOA) as the nascent particles subside into the MBL, enabling them to become cloud-condensation-nuclei-size particles (AIE: -2.27 W m-2, DRE: -0.10 W m-2), (3) particle formation/growth due to the products of dimethyl sulfide, above/within the MBL (-1.29 W m-2, DRE: -0.06 W m-2), and (4) ship emissions (AIE: -0.62 W m-2, DRE: -0.05 W m-2). Our results suggest a synergy of particle formation near the MBL top and growth by MSOA that contributes strongly to cloud-condensation-nuclei-sized particles with significant regional radiative effects in the Northwest Atlantic. Future work is needed to understand the sources and temperature-dependence of condensable marine vapors forming MSOA and to understand the species that can form new particles at the boundary layer top and grow these particles as they descend into the marine boundary layer. [ABSTRACT FROM AUTHOR]

A critical step in satellite retrievals of trace gas columns is the calculation of the air mass factor (AMF) used to convert observed slant columns to vertical columns. This calculation requires a priori information on the shape of the vertical profile. As a result, comparisons between satellite-retrieved and model-simulated column abundances are influenced by the a priori profile shape. We examine how differences between the shape of the simulated and a priori profiles can impact the interpretation of satellite retrievals by performing an adjoint-based four-dimensional variational (4D-Var) assimilation of synthetic NO2 observations for constraining NOx emissions. We use the GEOS-Chem adjoint model to perform assimilations using a variety of AMFs to examine how a posteriori emission estimates are affected if the AMF is calculated using an a priori shape factor that is inconsistent with the simulated profile. In these tests, an inconsistent a priori shape factor increased root mean square errors in a posteriori emission estimates by up to 30% for realistic conditions over polluted regions. As the difference between the simulated profile shape and the a priori profile shape increases, so do the corresponding assimilated emission errors. This reveals the importance of using simulated profile information for AMF calculations when comparing that simulated output to satellite-retrieved columns. [ABSTRACT FROM AUTHOR]

A critical step in satellite retrievals of trace gas columns is the calculation of the air mass factor (AMF) used to convert observed slant columns to vertical columns. This calculation requires a priori information on the shape of the vertical profile. As a result, comparisons between satellite-retrieved and model-simulated column abundances are influenced by the a priori profile shape. We examine how differences between the shape of the simulated and a priori profiles can impact the interpretation of satellite retrievals by performing an adjoint-based four-dimensional variational (4D-Var) assimilation of synthetic NO2 observations for constraining NOx emissions. We use the GEOS-Chem adjoint model to perform assimilations using a variety of AMFs to examine how a posteriori emission estimates are affected if the AMF is calculated using an a priori shape factor that is inconsistent with the simulated profile. In these tests, an inconsistent a priori shape factor increased root mean square errors in a posteriori emission estimates by up to 30 % for realistic conditions over polluted regions. As the difference between the simulated profile shape and the a priori profile shape increases, so do the corresponding assimilated emission errors. This reveals the importance of using simulated profile information for AMF calculations when comparing that simulated output to satellite-retrieved columns. [ABSTRACT FROM AUTHOR]

Air quality models have not been able to reproduce the magnitude of the observed concentrations of fine particulate matter (PM 2.5) during wintertime Chinese haze events. The discrepancy has been at least partly attributed to low biases in modeled sulfate production rates, due to the lack of heterogeneous sulfate production on aerosols in the models. In this study, we explicitly implement four heterogeneous sulfate formation mechanisms into a regional chemical transport model, in addition to gas-phase and in-cloud sulfate production. We compare the model results with observations of sulfate concentrations and oxygen isotopes, Δ17O (SO42-), in the winter of 2014–2015, the latter of which is highly sensitive to the relative importance of different sulfate production mechanisms. Model results suggest that heterogeneous sulfate production on aerosols accounts for about 20 % of sulfate production in clean and polluted conditions, partially reducing the modeled low bias in sulfate concentrations. Model sensitivity studies in comparison with the Δ17O (SO42-) observations suggest that heterogeneous sulfate formation is dominated by transition metal ion-catalyzed oxidation of SO2. [ABSTRACT FROM AUTHOR]

Summertime Arctic aerosol size distributions are strongly controlled by natural regional emissions. Within this context, we use a chemical transport model with size-resolved aerosol microphysics (GEOS-Chem-TOMAS) to interpret measurements of aerosol size distributions from the Canadian Arctic Archipelago during the summer of 2016, as part of the "NETwork on Climate and Aerosols: Addressing key uncertainties in Remote Canadian Environments" (NETCARE) project. Our simulations suggest that condensation of secondary organic aerosol (SOA) from precursor vapors emitted in the Arctic and near Arctic marine (ice-free seawater) regions plays a key role in particle growth events that shape the aerosol size distributions observed at Alert (82.5 ∘ N, 62.3 ∘ W), Eureka (80.1 ∘ N, 86.4 ∘ W), and along a NETCARE ship track within the Archipelago. We refer to this SOA as Arctic marine SOA (AMSOA) to reflect the Arctic marine-based and likely biogenic sources for the precursors of the condensing organic vapors. AMSOA from a simulated flux (500 µgm-2day-1 , north of 50 ∘ N) of precursor vapors (with an assumed yield of unity) reduces the summertime particle size distribution model–observation mean fractional error 2- to 4-fold, relative to a simulation without this AMSOA. Particle growth due to the condensable organic vapor flux contributes strongly (30 %–50 %) to the simulated summertime-mean number of particles with diameters larger than 20 nm in the study region. This growth couples with ternary particle nucleation (sulfuric acid, ammonia, and water vapor) and biogenic sulfate condensation to account for more than 90 % of this simulated particle number, which represents a strong biogenic influence. The simulated fit to summertime size-distribution observations is further improved at Eureka and for the ship track by scaling up the nucleation rate by a factor of 100 to account for other particle precursors such as gas-phase iodine and/or amines and/or fragmenting primary particles that could be missing from our simulations. Additionally, the fits to the observed size distributions and total aerosol number concentrations for particles larger than 4 nm improve with the assumption that the AMSOA contains semi-volatile species: the model–observation mean fractional error is reduced 2- to 3-fold for the Alert and ship track size distributions. AMSOA accounts for about half of the simulated particle surface area and volume distributions in the summertime Canadian Arctic Archipelago, with climate-relevant simulated summertime pan-Arctic-mean top-of-the-atmosphere aerosol direct (-0.04 Wm-2) and cloud-albedo indirect (-0.4 Wm-2) radiative effects, which due to uncertainties are viewed as an order of magnitude estimate. Future work should focus on further understanding summertime Arctic sources of AMSOA. [ABSTRACT FROM AUTHOR]

Aerosol mass scattering efficiency affects climate forcing calculations, atmospheric visibility, and the interpretation of satellite observations of aerosol optical depth. We evaluated the representation of aerosol mass scattering efficiency (αsp) in the GEOS-Chem chemical transport model over North America using collocated measurements of aerosol scatter and mass from IMPROVE network sites between 2000 and 2010. We found a positive bias in mass scattering efficiency given current assumptions of aerosol size distributions and particle hygroscopicity in the model. We found that overestimation of mass scattering efficiency was most significant in dry (RH <35 %) and midrange humidity (35 % < RH <65 %) conditions, with biases of 82 % and 40 %, respectively. To address these biases, we investigated assumptions surrounding the two largest contributors to fine aerosol mass, organic (OA) and secondary inorganic aerosols (SIA). Inhibiting hygroscopic growth of SIA below 35 % RH and decreasing the dry geometric mean radius, from 0.069 µ m for SIA and 0.073 µ m for OA to 0.058 µ m for both aerosol types, significantly decreased the overall bias observed at IMPROVE sites in dry conditions from 82 % to 9 %. Implementation of a widely used alternative representation of hygroscopic growth following κ -Kohler theory for secondary inorganic (hygroscopicity parameter κ=0.61) and organic (κ=0.10) aerosols eliminated the remaining overall bias in αsp. Incorporating these changes in aerosol size and hygroscopicity into the GEOS-Chem model resulted in an increase of 16 % in simulated annual average αsp over North America, with larger increases of 25 % to 45 % in northern regions with high RH and hygroscopic aerosol fractions, and decreases in αsp up to 15 % in the southwestern U.S. where RH is low. [ABSTRACT FROM AUTHOR]

Health impact analyses are increasingly tapping the broad spatial coverage of satellite aerosol optical depth (AOD) products to estimate human exposure to fine particulate matter (PM 2.5). We use a forward geophysical approach to derive ground-level PM 2.5 distributions from satellite AOD at 1 km 2 resolution for 2011 over the northeastern US by applying relationships between surface PM 2.5 and column AOD (calculated offline from speciated mass distributions) from a regional air quality model (CMAQ; 12×12 km 2 horizontal resolution). Seasonal average satellite-derived PM 2.5 reveals more spatial detail and best captures observed surface PM 2.5 levels during summer. At the daily scale, however, satellite-derived PM 2.5 is not only subject to measurement uncertainties from satellite instruments, but more importantly to uncertainties in the relationship between surface PM 2.5 and column AOD. Using 11 ground-based AOD measurements within 10 km of surface PM 2.5 monitors, we show that uncertainties in modeled PM 2.5/AOD can explain more than 70 % of the spatial and temporal variance in the total uncertainty in daily satellite-derived PM 2.5 evaluated at PM 2.5 monitors. This finding implies that a successful geophysical approach to deriving daily PM 2.5 from satellite AOD requires model skill at capturing day-to-day variations in PM 2.5/AOD relationships. Overall, we estimate that uncertainties in the modeled PM 2.5/AOD lead to an error of 11 µ g m -3 in daily satellite-derived PM 2.5 , and uncertainties in satellite AOD lead to an error of 8 µ g m -3. Using multi-platform ground, airborne, and radiosonde measurements, we show that uncertainties of modeled PM 2.5/AOD are mainly driven by model uncertainties in aerosol column mass and speciation, while model representation of relative humidity and aerosol vertical profile shape contributes some systematic biases. The parameterization of aerosol optical properties, which determines the mass extinction efficiency, also contributes to random uncertainty, with the size distribution being the largest source of uncertainty and hygroscopicity of inorganic salt the second largest. Future efforts to reduce uncertainty in geophysical approaches to derive surface PM 2.5 from satellite AOD would thus benefit from improving model representation of aerosol vertical distribution and aerosol optical properties, to narrow uncertainty in satellite-derived PM 2.5. [ABSTRACT FROM AUTHOR]

Air quality models have not been able to reproduce the magnitude of the observed concentrations of fine particulate matter (PM2.5) during wintertime Chinese haze events. The discrepancy has been at least partly attributed to low biases in modeled sulfate production rates due to the lack of heterogeneous sulfate production on aerosols in the models. In this study, we explicitly implement four heterogeneous sulfate formation mechanisms into a regional chemical transport model, in addition to gas-phase and in-cloud sulfate production. We compare the model results with observations of sulfate concentrations and oxygen isotopes (Δ17O(SO42-)) in the winter of 2014ndash;2015, the latter of which is highly sensitive to the relative importance of different sulfate production mechanisms. Model results suggest that heterogeneous sulfate production on aerosols accounts for about 20% of sulfate production in clean and polluted conditions, partially reducing the modeled low bias in sulfate concentrations. Model sensitivity studies in comparison with the Δ17O(SO42-) observations suggest that heterogeneous sulfate formation is dominated by transition metal ion catalyzed oxidation of SO2 [ABSTRACT FROM AUTHOR]

Separating the stratospheric and tropospheric contributions in satellite retrievals of atmospheric NO2 column abundance is a crucial step in the interpretation and application of the satellite observations. A variety of stratosphere–troposphere separation algorithms have been developed for sun-synchronous instruments in low Earth orbit (LEO) that benefit from global coverage, including broad clean regions with negligible tropospheric NO2 compared to stratospheric NO2. These global sun-synchronous algorithms need to be evaluated and refined for forthcoming geostationary instruments focused on continental regions, which lack this global context and require hourly estimates of the stratospheric column. Here we develop and assess a spatial filtering algorithm for the upcoming TEMPO geostationary instrument that will target North America. Developments include using independent satellite observations to identify likely locations of tropospheric enhancements, using independent LEO observations for spatial context, consideration of diurnally varying partial fields of regard, and a filter based on stratospheric to tropospheric air mass factor ratios. We test the algorithm with LEO observations from the OMI instrument with an afternoon overpass, and from the GOME-2 instrument with a morning overpass. We compare our TEMPO field of regard algorithm against an identical global algorithm to investigate the penalty resulting from the limited spatial coverage in geostationary orbit, and find excellent agreement in the estimated mean daily tropospheric NO2 column densities (R2=0.999 , slope=1.009 for July and R2=0.998 , slope=0.999 for January). The algorithm performs well even when only small parts of the continent are observed by TEMPO. The algorithm is challenged the most by east coast morning retrievals in the wintertime (e.g., R2=0.995 , slope=1.038 at 14:00 UTC). We find independent global LEO observations (corrected for time of day) provide important context near the field-of-regard edges. We also test the performance of the TEMPO algorithm without these supporting global observations. Most of the continent is unaffected (R2=0.924 and slope=0.973 for July and R2=0.996 and slope=1.008 for January), with 90 % of the pixels having differences of less than ±0.2×1015 moleculescm-2 between the TEMPO tropospheric NO2 column density and the global algorithm. For near-real-time retrieval, even a climatological estimate of the stratospheric NO2 surrounding the field of regard would improve this agreement. In general, the additional penalty of a limited field of regard from TEMPO introduces no more error than normally expected in most global stratosphere–troposphere separation algorithms. Overall, we conclude that hourly near-real-time stratosphere–troposphere separation for the retrieval of NO2 tropospheric column densities by the TEMPO geostationary instrument is both feasible and robust, regardless of the diurnally varying limited field of regard. [ABSTRACT FROM AUTHOR]

Health impact analyses are increasingly tapping the broad spatial coverage of satellite aerosol optical depth (AOD) products to estimate human exposure to fine particulate matter (PM2.5). We use a forward geophysical approach to derive ground-level PM2.5 distributions from satellite AOD at 1 km² resolution for 2011 over the Northeast USA by applying relationships between surface PM2.5 and column AOD (calculated offline from speciated mass distributions) from a regional air quality model (CMAQ; 12 x 12 km² horizontal resolution). Seasonal average satellite-derived PM2.5 reveals more spatial detail and best captures observed surface PM2.5 levels during summer. At the daily scale, however, satellite-derived PM2.5 is not only subject to measurement uncertainties from satellite instruments, but more importantly, to uncertainties in the relationship between surface PM2.5 and column AOD. Using 11 ground-based AOD measurements within 10 km of surface PM2.5 monitors, we show that uncertainties in modeled PM2.5/AOD can explain more than 70 % of the spatial and temporal variance in the total uncertainty in daily satellite-derived PM2.5 evaluated at PM2.5 monitors. This finding implies that a successful geophysical approach to deriving daily PM2.5 from satellite AOD requires model skill at capturing day-to-day variations in PM2.5/AOD relationships. Overall, we estimate that uncertainties in the modeled PM2.5/AOD lead to an error of 11 μg/m³ in daily satellite-derived PM2.5, and uncertainties in satellite AOD lead to an error of 8 μg/m³. Using multi-platform ground, airborne and radiosonde measurements, we show that uncertainties of modeled PM2.5/AOD are mainly driven by model uncertainties in aerosol column mass and speciation, while model uncertainties of relative humidity and aerosol vertical profile shape contribute some systematic biases. The parameterization of aerosol optical properties, which determines the mass-extinction efficiency, also contributes to random uncertainty, with the size distribution the largest source of uncertainty, and hygroscopicity of inorganic salt the second. Future efforts to reduce uncertainty in geophysical approaches to derive surface PM2.5 from satellite AOD would thus benefit from improving model representation of aerosol vertical distribution and aerosol optical properties, to narrow uncertainty in satellite-derived PM2.5. [ABSTRACT FROM AUTHOR]

Summertime Arctic aerosol size distributions are strongly controlled by natural regional emissions. Within this context, we use a chemical transport model with size-resolved aerosol microphysics (GEOS-Chem-TOMAS) to interpret measurements of aerosol size distributions from the Canadian Arctic Archipelago during the summer of 2016, as part of the NETwork on Climate and Aerosols: addressing key uncertainties in Remote Canadian Environments (NETCARE). Our simulations suggest that condensation of secondary organic aerosol (SOA) from precursor vapors emitted in the Arctic and near Arctic marine (open ocean and coastal) regions plays a key role in particle growth events that shape the aerosol size distributions observed at Alert (82.5° N, 62.3° W), Eureka (80.1° N, 86.4° W), and along a NETCARE ship track within the Archipelago. We refer to this SOA as Arctic marine SOA (Arctic MSOA) to reflect the Arctic marine-based and likely biogenic sources for the precursors of the condensing organic vapors. Arctic MSOA from a simulated flux (500 μg m-2 d-1, north of 50° N) of precursor vapors (assumed yield of unity) reduces the summertime particle size distribution model-observation mean fractional error by 2- to 4-fold, relative to a simulation without this Arctic MSOA. Particle growth due to the condensable organic vapor flux contributes strongly (30-50 %) to the simulated summertime-mean number of particles with diameters larger than 20 nm in the study region, and couples with ternary particle nucleation (sulfuric acid, ammonia, and water vapor) and biogenic sulfate condensation to account for more than 90 % of this simulated particle number, a strong biogenic influence. The simulated fit to summertime size-distribution observations is further improved at Eureka and for the ship track by scaling up the nucleation rate by a factor of 100 to account for other particle precursors such as gas-phase iodine and/or amines and/or fragmenting primary particles that could be missing from our simulations. Additionally, the fits to observed size distributions and total aerosol number concentrations for particles larger than 4 nm improve with the assumption that the Arctic MSOA contains semi-volatile species; reducing model-observation mean fractional error by 2- to 3-fold for the Alert and ship track size distributions. Arctic MSOA accounts for more than half of the simulated total particulate organic matter mass concentrations in the summertime Canadian Arctic Archipelago, and this Arctic MSOA has strong simulated summertime pan-Arctic-mean top-of-the-atmosphere aerosol direct (-0.04 W m-2) and cloud-albedo indirect (-0.4 W m-2) radiative effects. Future work should focus on further understanding summertime Arctic sources of Arctic MSOA. [ABSTRACT FROM AUTHOR]

Aerosol mass scattering efficiency affects climate forcing calculations, atmospheric visibility, and the interpretation of satellite observations of aerosol optical depth. We evaluated the representation of aerosol mass scattering efficiency (αsp) in the GEOS-Chem chemical transport model over North America using collocated measurements of aerosol scatter and mass from IMPROVE network sites between 2000–2015. We found a positive bias in mass scattering efficiency given current assumptions of aerosol size distributions and particle hygroscopicity in the model. We found that overestimation of mass scattering efficiency was most significant in dry (RH<35%) and midrange humidity (35%sp. Simulated average αsp over North America increased by 12%, with larger increases of 20% to 40% in northern regions with high RH and hygroscopic aerosol fractions, and decreases in αsp up to 15% in the southwestern U.S. where RH is low. [ABSTRACT FROM AUTHOR]

Global modeling of atmospheric chemistry is a grand computational challenge because of the need to simulate large coupled systems of ~ 100-1000 chemical species interacting with transport on all scales. Offline chemical transport models (CTMs), where the chemical continuity equations are solved using meteorological data as input, have usability advantages and are important vehicles for developing atmospheric chemistry knowledge that can then be transferred to Earth system models. However, they have generally not been designed to take advantage of massively parallel computing architectures. Here, we develop such a highperformance capability for GEOS-Chem (GCHP), a CTM driven by meteorological data from the NASA Goddard Earth Observation System (GEOS) and used by hundreds of research groups worldwide. GCHP is a grid-independent implementation of GEOS-Chem using the Earth System Modeling Framework (ESMF) that permits the same standard model to operate in a distributed-memory framework for massive parallelization. GCHP also allows GEOS-Chem to take advantage of the native GEOS cubed-sphere grid for greater accuracy and computational efficiency in simulating transport. GCHP enables GEOS-Chem simulations to be conducted with high computational scalability up to at least 500 cores, so that global simulations of stratosphere-troposphere oxidant-aerosol chemistry at C180 spatial resolution (~ 0:5° x 0:625°) or finer become routinely feasible. [ABSTRACT FROM AUTHOR]

Observations of aerosol scattering and absorption offer valuable information about aerosol composition. We apply a simulation of the Ultraviolet Aerosol Index (UVAI), a method of detecting aerosol absorption from satellite observations, to interpret UVAI values observed by the Ozone Monitoring Instrument (OMI) from 2005 to 2015 to understand global trends in aerosol composition. We conduct our simulation using the vector radiative transfer model VLIDORT with aerosol fields from the global chemical transport model GEOS-Chem. We examine the 2005–2015 trends in individual aerosol species from GEOS-Chem and apply these trends to the UVAI simulation to calculate the change in simulated UVAI due to the trends in individual aerosol species. We find that global trends in the UVAI are largely explained by trends in absorption by mineral dust, absorption by brown carbon, and scattering by secondary inorganic aerosol. Trends in absorption by mineral dust dominate the simulated UVAI trends over North Africa, the Middle East, East Asia, and Australia. The UVAI simulation resolves observed negative UVAI trends well over Australia, but underestimates positive UVAI trends over North Africa and Central Asia near the Aral Sea and underestimates negative UVAI trends over East Asia. We find evidence of an increasing dust source from the desiccating Aral Sea that may not be well represented by the current generation of models. Trends in absorption by brown carbon dominate the simulated UVAI trends over biomass burning regions. The UVAI simulation reproduces observed negative trends over central South America and West Africa, but underestimates observed UVAI trends over boreal forests. Trends in scattering by secondary inorganic aerosol dominate the simulated UVAI trends over the eastern United States and eastern India. The UVAI simulation slightly overestimates the observed positive UVAI trends over the eastern United States and underestimates the observed negative UVAI trends over India. Quantitative simulation of the OMI UVAI offers new insight into global trends in aerosol composition. [ABSTRACT FROM AUTHOR]

India is currently experiencing degraded air quality, and future economic development will lead to challenges for air quality management. Scenarios of sectoral emissions of fine particulate matter and its precursors were developed and evaluated for 2015–2050, under specific pathways of diffusion of cleaner and more energy-efficient technologies. The impacts of individual source sectors on PM2.5 concentrations were assessed through systematic simulations of spatially and temporally resolved particulate matter concentrations, using the GEOS-Chem model, followed by population-weighted aggregation to national and state levels. We find that PM2.5 pollution is a pan-India problem, with a regional character, and is not limited to urban areas or megacities. Under present-day emissions, levels in most states exceeded the national PM2.5 annual standard (40 μgm-3). Sources related to human activities were responsible for the largest proportion of the present-day population exposure to PM2.5 in India. About 60% of India’s mean population-weighted PM2.5 concentrations come from anthropogenic source sectors, while the remainder are from “other” sources, windblown dust and extra-regional sources. Leading contributors are residential biomass combustion, power plant and industrial coal combustion and anthropogenic dust (including coal fly ash, fugitive road dust and waste burning). Transportation, brick production and distributed diesel were other contributors to PM2.5. Future evolution of emissions under regulations set at current levels and promulgated levels caused further deterioration of air quality in 2030 and 2050. Under an ambitious prospective policy scenario, promoting very large shifts away from traditional biomass technologies and coal-based electricity generation, significant reductions in PM2.5 levels are achievable in 2030 and 2050. Effective mitigation of future air pollution in India requires adoption of aggressive prospective regulation, currently not formulated, for a three-pronged switch away from (i) biomass-fuelled traditional technologies, (ii) industrial coal-burning and (iii) open burning of agricultural residue. Future air pollution is dominated by industrial process emissions, reflecting larger expansion in industrial, rather than residential energy demand. However, even under the most active reductions envisioned, the 2050 mean exposure, excluding any impact from windblown mineral dust, is estimated to be nearly 3 times higher than the WHO Air Quality Guideline. [ABSTRACT FROM AUTHOR]

Accurate representation of surface reflectivity is essential to tropospheric trace gas retrievals from solar backscatter observations. Surface snow cover presents a significant challenge due to its variability and thus snowcovered scenes are often omitted from retrieval data sets; however, the high reflectance of snow is potentially advantageous for trace gas retrievals. We first examine the implications of surface snow on retrievals from the upcoming TEMPO geostationary instrument for North America. We use a radiative transfer model to examine how an increase in surface reflectivity due to snow cover changes the sensitivity of satellite retrievals to NO2 in the lower troposphere. We find that a substantial fraction (> 50 %) of the TEMPO field of regard can be snow covered in January and that the average sensitivity to the tropospheric NO2 column substantially increases (doubles) when the surface is snow covered. We then evaluate seven existing satellite-derived or reanalysis snow extent products against ground station observations over North America to assess their capability of informing surface conditions for TEMPO retrievals. The Interactive Multisensor Snow and Ice Mapping System (IMS) had the best agreement with ground observations (accuracy of 93 %, precision of 87 %, recall of 83 %). Multiangle Implementation of Atmospheric Correction (MAIAC) retrievals of MODIS-observed radiances had high precision (90% for Aqua and Terra), but underestimated the presence of snow (recall of 74% for Aqua, 75% for Terra). MAIAC generally outperforms the standard MODIS products (precision of 51 %, recall of 43% for Aqua; precision of 69 %, recall of 45% for Terra). The Near-real-time Ice and Snow Extent (NISE) product had good precision (83 %) but missed a significant number of snow-covered pixels (recall of 45 %). The Canadian Meteorological Centre (CMC) Daily Snow Depth Analysis Data set had strong performance metrics (accuracy of 91 %, precision of 79 %, recall of 82 %). We use the Fscore, which balances precision and recall, to determine overall product performance (F = 85 %, 82 (82) %, 81 %, 58 %, 46 (54)% for IMS, MAIAC Aqua (Terra), CMC, NISE, MODIS Aqua (Terra), respectively) for providing snow cover information for TEMPO retrievals from solar backscatter observations. We find that using IMS to identify snow cover and enable inclusion of snow-covered scenes in clear-sky conditions across North America in January can increase both the number of observations by a factor of 2.1 and the average sensitivity to the tropospheric NO2 column by a factor of 2.7. [ABSTRACT FROM AUTHOR]

In his study, we use a combination of multivariate statistical methods to understand the relationships of PM2.5 with local meteorology and synoptic weather patterns in different regions of China across various timescales. Using June 2014 to May 2017 daily total PM2.5 observations from  ∼  1500 monitors, all deseasonalized and detrended to focus on synoptic-scale variations, we find strong correlations of daily PM2.5 with all selected meteorological variables (e.g., positive correlation with temperature but negative correlation with sea-level pressure throughout China; positive and negative correlation with relative humidity in northern and southern China, respectively). The spatial patterns suggest that the apparent correlations with individual meteorological variables may arise from common association with synoptic systems. Based on a principal component analysis of 1998–2017 meteorological data to diagnose distinct meteorological modes that dominate synoptic weather in four major regions of China, we find strong correlations of PM2.5 with several synoptic modes that explain 10 to 40 % of daily PM2.5 variability. These modes include monsoonal flows and cold frontal passages in northern and central China associated with the Siberian High, onshore flows in eastern China, and frontal rainstorms in southern China. Using the Beijing–Tianjin–Hebei (BTH) region as a case study, we further find strong interannual correlations of regionally averaged satellite-derived annual mean PM2.5 with annual mean relative humidity (RH; positive) and springtime fluctuation frequency of the Siberian High (negative). We apply the resulting PM2.5-to-climate sensitivities to the Intergovernmental Panel on Climate Change (IPCC) Coupled Model Intercomparison Project Phase 5 (CMIP5) climate projections to predict future PM2.5 by the 2050s due to climate change, and find a modest decrease of  ∼  0.5 µg m−3 in annual mean PM2.5 in the BTH region due to more frequent cold frontal ventilation under the RCP8.5 future, representing a small climate benefit, but the RH-induced PM2.5 change is inconclusive due to the large inter-model differences in RH projections. [ABSTRACT FROM AUTHOR]

Global modeling of atmospheric composition is a grand computational challenge because of the need to simulate large coupled systems of chemical species interacting with transport on all scales. Off-line chemical transport models (CTMs), where the chemical continuity equations are solved using meteorological data as input, have the advantages of simplicity and reproducibility, and are important vehicles for developing knowledge that can then be transferred to Earth system models. However, they have generally not been designed to take advantage of massively parallel computing architectures. Here we develop such a high-performance capability (GCHP) for GEOS-Chem, a CTM driven by GEOS meteorological data from the NASA Goddard Earth Observation System (GEOS) and used by hundreds of research groups worldwide. GCHP is a grid-independent implementation of GEOS-Chem using the Earth System Modeling Framework (ESMF) that permits the same standard model to be run in a distributed-memory framework, scalable from six cores on a single node up to hundreds of cores distributed across a network. GCHP also allows GEOS-Chem to take advantage of the native GEOS cubed-sphere grid for greater accuracy and computational efficiency in simulating transport. GCHP enables GEOS-Chem simulations to be conducted with high computational scalability up to at least 500 cores, so that global simulations of stratosphere-troposphere oxidant-aerosol chemistry at C180 spatial resolution (~0.5° × 0.625°) or finer become routinely feasible. [ABSTRACT FROM AUTHOR]

Bottom-up emission inventories provide primary understanding of sources of air pollution and essential input of chemical transport models. Focusing on SO2 and NOx, we conducted a comprehensive evaluation of two widely used anthropogenic emission inventories over China, ECLIPSE and MIX, to explore the potential sources of uncertainties and find clues to improve emission inventories.We first compared the activity rates and emission factors used in two inventories and investigated the reasons of differences and the impacts on emission estimates. We found that SO2 emission estimates are consistent between two inventories (with 1% differences), while NOx emissions in ECLIPSE's estimates are 16% lower than those of MIX. The FGD (flue-gas desulfurization) device penetration rate and removal efficiency, LNB (low-NOx burner) application rate and abatement ef- ficiency in power plants, emission factors of industrial boilers and various vehicle types, and vehicle fleet need further verification. Diesel consumptions are quite uncertain in current inventories. Discrepancies at the sectorial and provincial levels are much higher than those of the national total. We then examined the impacts of different inventories on model performance by using the nested GEOS-Chem model. We finally derived top-down emissions by using the retrieved columns from the Ozone Monitoring Instrument (OMI) compared with the bottom-up estimates. High correlations were observed for SO2 between model results and OMI columns. For NOx, negative biases in bottom-up gridded emission inventories (-21% for MIX, -39% for ECLIPSE) were found compared to the satellite-based emissions. The emission trends from 2005 to 2010 estimated by two inventories were both consistent with satellite observations. The inventories appear to be fit for evaluation of the policies at an aggregated or national level; more work is needed in specific areas in order to improve the accuracy and robustness of outcomes at finer spatial and also technological levels. To our knowledge, this is the first work in which source comparisons detailed to technology-level parameters are made along with the remote sensing retrievals and chemical transport modeling. Through the comparison between bottom-up emission inventories and evaluation with top-down information, we identified potential directions for further improvement in inventory development. [ABSTRACT FROM AUTHOR]

Observations of aerosol scattering and absorption offer valuable information about aerosol composition. We apply a simulation of the Ultraviolet Aerosol Index (UVAI), a method of detecting aerosol absorption from satellite observations, to interpret UVAI values observed by the Ozone Monitoring Instrument (OMI) over 2005–2015 to understand global trends in aerosol composition. We conduct our simulation using the vector radiative transfer model VLIDORT with aerosol fields from the global chemical transport model GEOS-Chem. We examine the 2005–2015 trends in individual aerosol species from GEOS-Chem, and apply these trends to the UVAI simulation to calculate the change in simulated UVAI due to the trends in individual aerosol species. We find that global trends in the UVAI are largely explained by trends in absorption by mineral dust, absorption by brown carbon, and scattering by secondary inorganic aerosol. Trends in absorption by mineral dust dominate the simulated UVAI trends over North Africa, the Middle-East, East Asia, and Australia. The UVAI simulation well resolves observed negative UVAI trends over Australia, but underestimates positive UVAI trends over North Africa and Central Asia near the Aral Sea, and underestimates negative UVAI trends over East Asia. We find evidence of an increasing dust source from the desiccating Aral Sea, that may not be well represented by the current generation of models. Trends in absorption by brown carbon dominate the simulated UVAI trends over biomass burning regions. The UVAI simulation reproduces observed negative trends over central South America and West Africa, but underestimates observed UVAI trends over boreal forests. Trends in scattering by secondary inorganic aerosol dominate the simulated UVAI trends over the eastern United States and eastern India. The UVAI simulation slightly overestimates the observed positive UVAI trends over the eastern United States, and underestimates the observed negative UVAI trends over India. Quantitative simulation of the OMI UVAI offers new insight into global trends in aerosol composition. [ABSTRACT FROM AUTHOR]

Accurate representation of surface reflectivity is essential to tropospheric trace gas retrievals from solar backscatter observations. Surface snow cover presents a significant challenge due to its variability; however, the high reflectance of snow is advantageous for trace gas retrievals. We first examine the implications of surface snow on retrievals from the upcoming TEMPO geostationary instrument for North America. We use a radiative transfer model to examine how an increase in surface reflectivity due to snow cover changes the sensitivity of satellite retrievals to NO2 in the lower troposphere. We find that a substantial fraction (> 50 %) of the TEMPO field of regard can be snow covered in January, and that the average sensitivity to the tropospheric NO2 column substantially increases (doubles) when the surface is snow covered. We then evaluate seven existing satellite-derived or reanalysis snow extent products against ground station observations over North America to assess their capability of informing surface conditions for TEMPO retrievals. The Interactive Multisensor Snow and Ice Mapping System (IMS) had the best agreement with ground observations (accuracy = 93 %, precision = 87 %, recall = 83 %, F = 85 %). Multiangle Implementation of Atmospheric Correction (MAIAC) retrievals of MODIS observed radiances had high precision (90 % for Aqua and Terra), but underestimated the presence of snow (recall = 74 % for Aqua, 75 % for Terra). MAIAC generally outperforms the standard MODIS products (precision = 51 %, recall = 43 % for Aqua; precision = 69 %, recall = 45 % for Terra). The Near-real-time Ice and Snow Extent (NISE) product had good precision (83 %) but missed a significant number of snow covered pixels (recall = 45 %). The Canadian Meteorological Centre (CMC) Daily Snow Depth Analysis Data set had strong performance metrics (accuracy = 91 %, precision = 79 %, recall = 82 %, F = 81 %). We use the F score, which balances precision and recall, to determine overall product performance (F = 85 %, 82(82) %, 81 %, 58 %, 46(54) % for IMS, MAIAC Aqua(Terra), CMC, NISE, MODIS Aqua(Terra) respectively) for providing snow cover information for TEMPO retrievals from solar backscatter observations. [ABSTRACT FROM AUTHOR]

India currently experiences degraded air quality, with future economic development leading to challenges for air quality management. Scenarios of sectoral emissions of fine particulate matter and its precursors were developed and evaluated for 2015-2050, under specific pathways of diffusion of cleaner and more energy efficiency technologies. The impacts of individual source-sectors on PM2.5 concentrations were assessed through GEOS-Chem model simulations of spatially and temporally resolved particulate matter concentrations, followed by population-weighted aggregation to national and state levels. PM2.5 pollution is a pan-India problem, with a regional character, not limited to urban areas or megacities. Under present-day emissions, levels in most states exceeded the national PM2.5 standard (40g/m3). Future evolution of emissions under current regulation or under promulgated or proposed regulation, yield deterioration in future air-quality in 2030 and 2050. Only under a scenario where more ambitious measures are introduced, promoting a total shift away from traditional biomass technologies and a very large shift (80-85%) to non-fossil electricity generation was an overall reduction in PM2.5 concentrations below 2015 levels achieved. In this scenario, concentrations in 20 states and six union territories would fall below the national standard. However, even under this ambitious scenario, 10 states (including Delhi) would fail to comply with the national standard through to 2050. Under present day (2015) emissions, residential biomass fuel use for cooking and heating is the largest single sector influencing outdoor air pollution across most of India. Agricultural residue burning is the next most important source, especially in north-west and north India, while in eastern and peninsular India, coal burning in thermal power plants and industry are important contributors. The relative influence of anthropogenic dust and total dust is projected to increase in all future scenarios, largely from decreases in the influence of other PM2.5 sources. Overall, the findings suggest a large regional background of PM2.5 pollution (from residential biomass, agricultural residue burning and power plant and industrial coal), underlying that from local sources (transportation, brick kiln, distributed diesel) in highly polluted areas. [ABSTRACT FROM AUTHOR]

Black carbon (BC) contributes to Arctic warming, yet sources of Arctic BC and their geographic contributions remain uncertain. We interpret a series of recent airborne (NETCARE 2015; PAMARCMiP 2009 and 2011 campaigns) and ground-based measurements (at Alert, Barrow and Ny-Âlesund) from multiple methods (thermal, laser incandescence and light absorption) with the GEOS-Chem global chemical transport model and its adjoint to attribute the sources of Arctic BC. This is the first comparison with a chemical transport model of refractory BC (rBC) measurements at Alert. The springtime airborne measurements performed by the NETCARE campaign in 2015 and the PAMARCMiP campaigns in 2009 and 2011 offer BC vertical profiles extending to above 6 km across the Arctic and include profiles above Arctic ground monitoring stations. Our simulations with the addition of seasonally varying domestic heating and of gas flaring emissions are consistent with ground-based measurements of BC concentrations at Alert and Barrow in winter and spring (rRMSE< 13 %) and with airborne measurements of the BC vertical profile across the Arctic (rRMSED 17 %) except for an underestimation in the middle troposphere (500-700 hPa). Sensitivity simulations suggest that anthropogenic emissions in eastern and southern Asia have the largest effect on the Arctic BC column burden both in spring (56 %) and annually (37 %), with the largest contribution in the middle troposphere (400-700 hPa). Anthropogenic emissions from northern Asia contribute considerable BC (27% in spring and 43% annually) to the lower troposphere (below 900 hPa). Biomass burning contributes 20% to the Arctic BC column annually. At the Arctic surface, anthropogenic emissions from northern Asia (40-45 %) and eastern and southern Asia (20-40 %) are the largest BC contributors in winter and spring, followed by Europe (16-36 %). Biomass burning from North America is the most important contributor to all stations in summer, especially at Barrow. Our adjoint simulations indicate pronounced spatial heterogeneity in the contribution of emissions to the Arctic BC column concentrations, with noteworthy contributions from emissions in eastern China (15 %) and western Siberia (6.5 %). Although uncertain, gas flaring emissions from oilfields in western Siberia could have a striking impact (13 %) on Arctic BC loadings in January, comparable to the total influence of continental Europe and North America (6.5% each in January). Emissions from as far as the Indo-Gangetic Plain could have a substantial influence (6.3% annually) on Arctic BC as well. [ABSTRACT FROM AUTHOR]

In this study, we use a combination of multivariate statistical methods to understand the relationships of PM2.5 with local meteorology and synoptic weather patterns in different regions of China across various timescales. Using June 2014 to May 2017 daily total PM2.5 observations from ~1500 monitors, all deseasonalized and detrended to focus on synoptic-scale variations, we find strong correlations of daily PM2.5 with all selected meteorological variables (e.g., positive correlation with temperature but negative correlation with sea-level pressure throughout China; positive and negative correlation with relative humidity in northern and southern China, respectively). The spatial patterns suggest that the apparent correlations with individual meteorological variables may arise from common association with synoptic systems. Based on a principal component analysis on 1998-2017 meteorological data to diagnose distinct meteorological modes that dominate synoptic weather in four major regions of China, we find strong correlations of PM2.5 with several synoptic modes that explain 10% to 40% of daily PM2.5 variability. These modes include monsoonal flows and cold frontal passages in northern and central China associated with the Siberian high, onshore flows in eastern China, and frontal rainstorms in southern China. Using the Beijing-Tianjin-Hebei (BTH) region as a case study, we further find strong interannual correlations of regionally averaged satellite-derived annual mean PM2.5 with annual mean relative humidity (positive) and springtime fluctuation frequency of the Siberian high (negative). We apply the resulting PM2.5 to climate sensitivities to IPCC Coupled Model Intercomparison Project Phase 5 (CMIP5) climate projections to predict future PM2.5 by 2050s due to climate change, and find a modest decrease of ~0.1 µg m-3 in annual mean PM2.5 in the BTH region, which represents the compensating effects of enhanced relative humidity and synoptic frequency. [ABSTRACT FROM AUTHOR]

Reactive nitrogen oxides (NOy) are a major constituent of the nitrogen deposited from the atmosphere, but observational constraints on their deposition are limited by poor or nonexistent measurement coverage in many parts of the world. Here we apply NO2 observations from multiple satellite instruments (GOME, SCIAMACHY, and GOME-2) to constrain the global deposition of NOy over the last 2 decades. We accomplish this by producing top-down estimates of NOx emissions from inverse modeling of satellite NO2 columns over 1996-2014, and including these emissions in the GEOS-Chem chemical transport model to simulate chemistry, transport, and deposition of NOy. Our estimates of long-term mean wet nitrate (NO-3) deposition are highly consistent with available measurements in North America, Europe, and East Asia combined (r=0.83, normalized mean bias = -7 %, N =136). Likewise, our calculated trends in wet NO-3 deposition are largely consistent with the measurements, with 129 of the 136 gridded model-data pairs sharing overlapping 95% confidence intervals. We find that global mean NOy deposition over 1996-2014 is 56.0 TgNyr-1, with a minimum in 2006 of 50.5 TgN and a maximum in 2012 of 60.8 Tg N. Regional trends are large, with opposing signs in different parts of the world. Over 1996 to 2014, NOy deposition decreased by up to 60% in eastern North America, doubled in regions of East Asia, and declined by 20% in parts of western Europe. About 40% of the global NOy deposition occurs over oceans, with deposition to the North Atlantic Ocean declining and deposition to the northwestern Pacific Ocean increasing. Using the residual between NOx emissions and NOy deposition over specific land regions, we investigate how NOx export via atmospheric transport has changed over the last 2 decades. Net export from the continental United States decreased substantially, from 2.9 TgNyr-1 in 1996 to 1.5 TgNyr-1 in 2014. Export from China more than tripled between 1996 and 2011 (from 1.0 to 3.5 TgNyr-1), before a striking decline to 2.5 TgNyr-1 by 2014. We find that declines in NOx export from some western European countries have counteracted increases in emissions from neighboring countries to the east. A sensitivity study indicates that simulated NOy deposition is robust to uncertainties in NH3 emissions with a few exceptions. Our novel long-term study provides timely context on the rapid redistribution of atmospheric nitrogen transport and subsequent deposition to ecosystems around the world. [ABSTRACT FROM AUTHOR]

Bottom-up emission inventories provide primary understanding of sources of air pollution and essential input of chemical transport models. Focusing on SO2 and NOx, we conducted a comprehensive evaluation of two widely-used anthropogenic emission inventories over China, ECLIPSE and MIX, to explore the potential sources of uncertainties and find the clues in improving emission inventories. We first compared the activity rates and emission factors used in two inventories, and investigated the reasons of differences and the impacts on emission estimates. We found that SO2 emission estimates are consistent between two inventories (with 1 % differences), while NOx emissions in ECLIPSE's estimates are 16 % lower than those of MIX. Discrepancies at sectorial and provincial level are much higher. We then examined the impacts of different inventories on model performance, by using the nested GEOS-Chem model. We finally derived top-down NOx emissions by using the NO2 columns from the Ozone Monitoring Instrument (OMI) and compared with the bottom-up estimates. To our knowledge, this is the first work where source-sector comparisons are made along with the remote sensing retrievals and chemical transport modeling. Through the comparison between bottom-up emission inventories and evaluation with top-down information, we summarized the potential directions for further improvement in inventory development. [ABSTRACT FROM AUTHOR]