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Ozone pollution in the Southeast US involves complex chemistry driven by emissions of anthropogenic nitrogen oxide radicals (NO(x) triple bond NO + NO2) and biogenic isoprene. Model estimates of surface ozone concentrations tend to be biased high in the region and this is of concern for designing effective emission control strategies to meet air quality standards. We use detailed chemical observations from the SEAC(exp 4)RS aircraft campaign in August and September 2013, interpreted with the GEOS-Chem chemical transport model at 0.25 deg x 0.3125 deg horizontal resolution, to better understand the factors controlling surface ozone in the Southeast US. We find that the National Emission Inventory (NEI) for NO(x) from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEAC(exp 4)RS observations of NO(x) and its oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO2 columns. Our results indicate that NEI NO(x) emissions from mobile and industrial sources must be reduced by 30-60%, dependent on the assumption of the contribution by soil NO(x) emissions. Upper-tropospheric NO2 from lightning makes a large contribution to satellite observations of tropospheric NO2 that must be accounted for when using these data to estimate surface NO(x) emissions. We find that only half of isoprene oxidation proceeds by the high-NO(x) pathway to produce ozone; this fraction is only moderately sensitive to changes in NO(x) emissions because isoprene and NO(x) emissions are spatially segregated. GEOS-Chem with reduced NO(x) emissions provides an unbiased simulation of ozone observations from the aircraft and reproduces the observed ozone production efficiency in the boundary layer as derived from a regression of ozone and NO(x) oxidation products. However, the model is still biased high by 6 plus or minus 14 ppb relative to observed surface ozone in the Southeast US. Ozonesondes launched during midday hours show a 7 ppb ozone decrease from 1.5 km to the surface that GEOS-Chem does not capture. This bias may reflect a combination of excessive vertical mixing and net ozone production in the model boundary layer.

Surface and satellite observations of atmospheric methane show smooth seasonal behavior in the Southern Hemisphere driven by loss from the hydroxyl (OH) radical. However, observations in the Northern Hemisphere show a sharp mid‐summer increase that is asymmetric with the Southern Hemisphere and not captured by the default configuration of the GEOS‐Chem chemical transport model. Using an ensemble of 22 OH model estimates and 24 wetland emission inventories in GEOS‐Chem, we show that the magnitude, latitudinal distribution, and seasonality of Northern Hemisphere wetland emissions are critical for reproducing the observed seasonality of methane in that hemisphere, with the interhemispheric OH ratio playing a lesser role. Reproducing the observed seasonality requires a wetland emission inventory with ∼80 Tg a−1 poleward of 10°N including significant emissions in South Asia, and an August peak in boreal emissions persisting into autumn. In our 24‐member wetland emission ensemble, only the LPJ‐wsl MERRA‐2 inventory has these attributes. Plain Language Summary The amount of methane, a powerful greenhouse gas, has been growing in Earth's atmosphere during the last decade, and scientists disagree about which methane sources and sinks are responsible for the growth. One clue into understanding methane's sources and sinks is their seasonality—their month‐to‐month cycles that happen every year. Measurements of atmospheric methane taken at the Earth's surface and using satellite instruments show a steep increase each summer in the Northern Hemisphere that is not replicated when methane is simulated in a global chemical transport model, indicating missing information about source and sink seasonalities. To investigate, we use that model to simulate 24 representations of methane's largest source, emissions from wetlands, and 22 representations of its largest sink, chemical loss by the hydroxyl radical (OH). We find that OH is unlikely to cause the summer increase and model bias, but the amount, spatial distribution, and seasonal cycles of global wetland emissions are the strongest drivers. We suggest that these characteristics are linked to the underlying mechanisms determining wetland area and methane production in wetland models. The results unveil the role of global wetlands in driving methane's seasonality and inform research to analyze methane's long‐term trends. Key Points Northern Hemisphere atmospheric methane shows a summer increase not replicated by the GEOS‐Chem model with its default sources and sinks The summer increase's timing and magnitude is determined by the magnitude, seasonality, and spatial distribution of NH wetland emissions Inversions of atmospheric methane observations should use a suitable wetland emission inventory and optimize hemispheric OH concentrations

Kinetic integration of large and stiff chemical mechanisms is a computational bottleneck in models of atmospheric chemistry. It requires implicit solution of the coupled system of kinetic differential equations with time‐consuming construction and inversion of the Jacobian matrix. We present here a new version of the Kinetic Pre‐Processor (KPP 3.0.0) for fast integration of chemical kinetics featuring a range of improvements over previous versions in performance, diagnostics, versatility, and community openness. KPP 3.0.0 includes a new adaptive auto‐reduction solver to decrease the size of any mechanism locally and on the fly under conditions where full complexity is not needed, by partitioning species as “fast” or “slow” based on their local production and loss rates. Previous implementations of this adaptive solver suffered from excessive overhead in the repeated construction of the local Jacobian matrix or were hard‐wired to specific mechanisms. Here we retain the general applicability of the method to any mechanism and avoid overhead by using pre‐computed Jacobian matrix terms for the full mechanism and cropping the matrix locally to remove the slow species with no change in memory allocation. We apply this adaptive solver within KPP 3.0.0 to the GEOS‐Chem global 3‐D model of atmospheric chemistry and demonstrate a 32% reduction in solver time while maintaining a mean error lower than 1% for key species in the troposphere. Plain Language Summary Calculating chemical evolution in global atmospheric chemistry models is computationally expensive because the chemical mechanisms typically include hundreds of species to account for all conditions from urban to remote. However, the full chemical complexity is not needed under most conditions. Here we have developed an adaptive auto‐reduction chemical solver that reduces any mechanism on the fly depending on local conditions and without significant computational overhead. We apply this adaptive solver as an option in a new version 3.0.0 of the Kinetic Pre‐Processor (KPP) chemical solver software package that also includes a number of updates relative to previous versions. The adaptive solver achieves a 32% reduction in solver time in a global model simulation while incurring less than 1% average errors for key species. Key Points An updated version 3.0.0 of the Kinetic Pre‐Processor (KPP) integrator of chemical kinetics for atmospheric models has been developed KPP 3.0.0 features an adaptive solver option for reducing chemical mechanisms locally and on the fly where full complexity is not needed The adaptive solver implemented in the global GEOS‐Chem model shows a 32% speedup with errors less than 1% for key tropospheric species

Smoke from wildfires presents one of the greatest threats to air quality, public health, and ecosystems in the United States, especially in the West. Here we quantify the efficacy of prescribed burning as an intervention for mitigating smoke exposure downwind of wildfires across the West during the 2018 and 2020 fire seasons. Using the adjoint of the GEOS‐Chem chemical transport model, we calculate the sensitivities of population‐weighted smoke concentrations in receptor regions, including states and rural environmental justice communities, to fire emissions upwind of the receptors. We find that the population‐weighted smoke exposure across the West during the September 2020 fires was 44 μg/m3 but would have been 20%–30% greater had these wildfires occurred in October or November. We further simulate a set of prescribed burn scenarios and find that controlled burning interventions in northern California and the Pacific Northwest could have reduced the population‐weighted smoke exposure across the western United States by 21 μg/m3 in September 2020, while doing so in all other states would have reduced smoke exposure by only 1.5 μg/m3. Satellite records of large, prescribed burns (>1,000 acres, or 4 km2) reveal that northern California and western Oregon conducted only seven such prescribed fires over a 6‐year period (2015–2020), even though these regions have a disproportionate impact on smoke exposure for rural environmental justice communities and population centers across the West. Our analysis suggests that prioritizing northern California and the Pacific Northwest for prescribed burns might mitigate future smoke exposure. Plain Language Summary Catastrophic wildfires pose substantial risk to public health, infrastructures, and ecosystems in the western United States. As these large and costly wildfires become more common, methods to identify locations for prescribed burning are needed to mitigate impacts on affected populations and ecosystems. Here we investigate the effectiveness of prescribed burning for abating potential wildfire smoke exposures in states and rural environmental justice communities across the western United States during the destructive 2018 and 2020 fire seasons. We find that due in part to prevailing wind patterns, wildfires in the coastal states contribute more to overall smoke exposure in the West compared to wildfires in other states in both 2018 and 2020. We show that implementing prescribed burns in the heavily forested northern California and the Pacific Northwest would yield large net benefits for the entire western United States, while doing so in other states would have relatively smaller impacts. Our work suggests that land managers should prioritize northern California, western Oregon, and eastern Washington for prescribed burns to mitigate future smoke exposure as these regions have a disproportionate impact on smoke exposure for rural environmental justice communities and population centers across the West. Key Points The West Coast both experiences the largest smoke exposures and contributes most to the burden of smoke PM2.5 in the western US Applying prescribed burns on the coast yields large benefits for the West, while doing so in other states has relatively smaller impacts Larger prescribed burns may reduce smoke impacts from future large wildfires, but few such burns have occurred in key areas

Cloud computing platforms can facilitate the use of Earth science models by providing immediate access to fully configured software, massive computing power, and large input data sets. However, slow internode communication performance has previously discouraged the use of cloud platforms for massively parallel simulations. Here we show that recent advances in the network performance on the Amazon Web Services cloud enable efficient model simulations with over a thousand cores. The choices of Message Passing Interface library configuration and internode communication protocol are critical to this success. Application to the Goddard Earth Observing System (GEOS)‐Chem global 3‐D chemical transport model at 50‐km horizontal resolution shows efficient scaling up to at least 1,152 cores, with performance and cost comparable to the National Aeronautics and Space Administration Pleiades supercomputing cluster. Plain Language Summary Earth science model simulations are computationally expensive, typically requiring the use of high‐end supercomputing clusters that are managed by universities or national laboratories. Commercial cloud computing offers an alternative. However, past work found that cloud computing platforms were not efficient for large‐scale simulations on over 100 CPU cores, because the network communication performance on the cloud was slow compared to local clusters. Here we show that recent advances in the cloud network performance enable efficient model simulations with over a thousand cores, and cloud platforms can now serve as a viable alternative to local clusters for simulations at large scale. Computing on the cloud has extensive advantages, such as providing immediate access to fully configured model code and large data sets for any users, allowing full reproducibility of model simulation results, offering quick access to novel hardware that might not be available on local clusters, and being able to scale to virtually unlimited amounts of compute and storage resources. Those benefits will help advance Earth science modeling research. Key Points Recent advances in network performance enable efficient model simulations with over a thousand cores on the Amazon Web Services (AWS) cloud Performance and cost of cloud can be comparable to local supercomputing cluster The GEOS‐Chem chemical transport model is now available and documented for massively parallel simulations on the AWS cloud

Particle number concentration in the troposphere is an important parameter controlling the climate and health impacts of atmospheric aerosols. We show that nucleation rates and total particle number concentrations in the troposphere, predicted by different nucleation schemes, differ significantly. Our extensive comparisons of simulated results with land‐, ship‐, and aircraft‐based measurements indicate that, among six widely used nucleation schemes involving sulfuric acid, only the ion‐mediated nucleation (IMN) scheme can reasonably account for both absolute values (within a factor of ∼2) and spatial distributions of particle number concentrations in the whole troposphere. Binary homogeneous nucleation (BHN) schemes significantly underpredict particle number concentration in the lower troposphere (below ∼500 mbar), especially in the boundary layer over major continents (by a factor of up to ∼10). BHN is also insignificant in the upper troposphere based on a recent kinetically self‐consistent nucleation model constrained by multiple independent laboratory data. Previous conclusions about the importance of BHN in the upper troposphere should be revisited. Empirical activation and kinetic nucleation formulas significantly overpredict the particle number concentrations over tropical and subtropical oceans (by a factor of up to ∼10 in the boundary layer), and the overpredictions extend from ocean surface to around ∼400 mbar. This study represents the first comprehensive comparison of global particle number simulations with relevant measurements that have a 3‐D global spatial coverage. Our results suggest that ion‐mediated H2SO4‐H2O nucleation appears to dominate over neutral H2SO4‐H2O nucleation, not only in the lower troposphere but also in the middle and upper troposphere.

We use the Global Modeling Initiative (GMI) modeling framework to assess the utility of cosmogenic beryllium-7 (7Be), a natural aerosol tracer, for evaluating cross-tropopause transport in global models. The GMI chemical transport model (CTM) was used to simulate atmospheric 7Be distributions using four different meteorological data sets (GEOS1-STRAT DAS, GISS II′ GCM, fvGCM, and GEOS4-DAS), featuring significantly different stratosphere–troposphere exchange (STE) characteristics. The simulations were compared with the upper troposphere and/or lower stratosphere (UT/LS) 7Be climatology constructed from  ∼  25 years of aircraft and balloon data, as well as climatological records of surface concentrations and deposition fluxes. Comparison of the fraction of surface air of stratospheric origin estimated from the 7Be simulations with observationally derived estimates indicates excessive cross-tropopause transport at mid-latitudes in simulations using GEOS1-STRAT and at high latitudes using GISS II′ meteorological data. These simulations also overestimate 7Be deposition fluxes at mid-latitudes (GEOS1-STRAT) and at high latitudes (GISS II′), respectively. We show that excessive cross-tropopause transport of 7Be corresponds to overestimated stratospheric contribution to tropospheric ozone. Our perspectives on STE in these meteorological fields based on 7Be simulations are consistent with previous modeling studies of tropospheric ozone using the same meteorological fields. We conclude that the observational constraints for 7Be and observed 7Be total deposition fluxes can be used routinely as a first-order assessment of cross-tropopause transport in global models.

Surface ozone pollution in South Korea has increased over the past 2 decades, despite efforts to decrease emissions, and is pervasively in exceedance of the maximum daily 8 h average (MDA8) standard of 60 ppb. Here, we investigate the 2015–2019 trends in surface ozone and NO2 concentrations over South Korea and the Seoul metropolitan area (SMA), focusing on the 90th percentile MDA8 ozone as an air quality metric. We use a random forest algorithm to remove the effect of meteorological variability on the 2015–2019 trends and find an ozone increase of up to 1.5 ppb a−1 in April–May, while NO2 decreases by 22 %. Global 3-D atmospheric chemistry model simulations including recent chemical updates can successfully simulate surface ozone over South Korea and China as well as the very high free-tropospheric ozone observed above 2 km altitude (mean 75 ppb in May–June) and can reproduce the observed 2015–2019 emission-driven ozone trend over the SMA including its seasonality. Further investigation of the model trend for May, when meteorology-corrected ozone and its increase are the highest, reveals that a decrease in South Korea NOx emissions is the main driver for the SMA ozone increase. Although this result implies that decreasing volatile organic compound (VOC) emissions is necessary to decrease ozone, we find that ozone would still remain above 80 ppb even if all anthropogenic emissions in South Korea were shut off. China contributes only 8 ppb to this elevated South Korea background, and ship emissions contribute only a few parts per billion. Zeroing out all anthropogenic emissions in East Asia in the model indicates a remarkably high external background of 56 ppb, consistent with the high concentrations observed in the free troposphere, implying that the air quality standard in South Korea is not practically achievable unless this background external to East Asia can be decreased.

We use satellite (GOSAT) and in situ (GLOBALVIEWplus CH4 ObsPack) observations of atmospheric methane in a joint global inversion of methane sources, sinks, and trends for the 2010–2017 period. The inversion is done by analytical solution to the Bayesian optimization problem, yielding closed-form estimates of information content to assess the consistency and complementarity (or redundancy) of the satellite and in situ data sets. We find that GOSAT and in situ observations are to a large extent complementary, with GOSAT providing a stronger overall constraint on the global methane distributions, but in situ observations being more important for northern midlatitudes and for relaxing global error correlations between methane emissions and the main methane sink (oxidation by OH radicals). The in-situ-only and the GOSAT-only inversions alone achieve 113 and 212 respective independent pieces of information (DOFS) for quantifying mean 2010–2017 anthropogenic emissions on 1009 global model grid elements, and respective DOFS of 67 and 122 for 2010–2017 emission trends. The joint GOSAT+ in situ inversion achieves DOFS of 262 and 161 for mean emissions and trends, respectively. Thus, the in situ data increase the global information content from the GOSAT-only inversion by 20 %–30 %. The in-situ-only and GOSAT-only inversions show consistent corrections to regional methane emissions but are less consistent in optimizing the global methane budget. The joint inversion finds that oil and gas emissions in the US and Canada are underestimated relative to the values reported by these countries to the United Nations Framework Convention on Climate Change (UNFCCC) and used here as prior estimates, whereas coal emissions in China are overestimated. Wetland emissions in North America are much lower than in the mean WetCHARTs inventory used as a prior estimate. Oil and gas emissions in the US increase over the 2010–2017 period but decrease in Canada and Europe. The joint inversion yields a global methane emission of 551 Tg a−1 averaged over 2010–2017 and a methane lifetime of 11.2 years against oxidation by tropospheric OH (86 % of the methane sink).

The global chemical transport model GEOS‐Chem, implemented with a dust‐iron dissolution scheme, was used to analyze the magnitude and spatial distribution of mineral dust and soluble‐iron (sol‐Fe) deposition to the South Atlantic Ocean (SAO). The comparison of model results with remotely sensed data shows that GEOS‐Chem can capture dust source regions in Patagonia and characterize the temporal variability of dust outflow. For a year‐long model simulation, 22 Tg of mineral dust and 4 Gg of sol‐Fe were deposited to the surface waters of the entire SAO region, with roughly 30% of this dust and sol‐Fe predicted to be deposited to possible high nitrate low chlorophyll oceanic regions. Model‐predicted dissolved iron fraction of mineral dust over the SAO was small, on average only accounting for 0.57% of total iron. Simulations suggest that the primary reason for such a small fraction of sol‐Fe is the low ambient concentrations of acidic trace gases available for mixing with dust plumes. Overall, the amount of acid added to the deliquesced aerosol solution was not enough to overcome the alkalinity buffer of Patagonian dust and initiate considerable acid dissolution of mineral‐iron. Sensitivity studies show that the amount of sol‐Fe deposited to the SAO was largely controlled by the initial amount of sol‐Fe at the source region, with limited contribution from the spatial variability of Patagonian‐desert topsoil mineralogy and natural sources of acidic trace gases. Simulations suggest that Patagonian dust should have a minor effect on biological productivity in the SAO.