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The NASA Atmospheric Tomography (ATom) mission built a photochemical climatology of air parcels based on in situ measurements with the NASA DC-8 aircraft along objectively planned profiling transects through the middle of the Pacific and Atlantic oceans. In this paper we present and analyze a data set of 10s (2km) merged and gap-filled observations of the key reactive species driving the chemical budgets of O3 and CH4 (O3, CH4, CO, H2O, HCHO, H2O2, CH3OOH, C2H6, higher alkanes, alkenes, aromatics, NOx, HNO3, HNO4, peroxyacetyl nitrate, and other organic nitrates), consisting of 146494 distinct air parcels from ATom deployments 1 through 4. Six models calculated the O3 and CH4 photochemical tendencies from this modeling data stream for ATom 1. We find that 80%-90% of the total reactivity lies in the top 50% of the parcels and 25%-35% in the top 10%, supporting previous model-only studies that tropospheric chemistry is driven by a fraction of all the air. Surprisingly, the probability densities of species and reactivities averaged on a model scale (100 km) differ only slightly from the 2km ATom 10s data, indicating that much of the heterogeneity in tropospheric chemistry can be captured with current global chemistry models. Comparing the ATom reactivities over the tropical oceans with climatological statistics from six global chemistry models, we find generally good agreement with the reactivity rates for O3 and CH4. Models distinctly underestimate O3 production below 2km relative to the mid-troposphere, and this can be traced to lower NOx levels than observed. Attaching photochemical reactivities to measurements of chemical species allows for a richer, yet more constrained-to-what-matters, set of metrics for model evaluation. This paper presents a corrected version of the paper published under the same authors and title (sans "corrected") as 10.5194/acp-21-13729-2021.

Abstract. The Community Atmosphere Model (CAM6.1), the atmospheric componentof the Community Earth System Model (CESM; version 2.1), simulates thelife cycle (emission, transport, and deposition) of mineral dust and itsinteractions with physio-chemical components to quantify the impacts of duston climate and the Earth system. The accuracy of such quantifications relieson how well dust-related processes are represented in the model. Here weupdate the parameterizations for the dust module, including those on thedust emission scheme, the aerosol dry deposition scheme, the sizedistribution of transported dust, and the treatment of dust particle shape.Multiple simulations were undertaken to evaluate the model performanceagainst diverse observations, and to understand how each update alters themodeled dust cycle and the simulated dust direct radiative effect. Themodel–observation comparisons suggest that substantially improved modelrepresentations of the dust cycle are achieved primarily through the newmore physically-based dust emission scheme. In comparison, the othermodifications induced small changes to the modeled dust cycle andmodel–observation comparisons, except the size distribution of dust in thecoarse mode, which can be even more influential than that of replacing thedust emission scheme. We highlight which changes introduced here areimportant for which regions, shedding light on further dust modeldevelopments required for more accurately estimating interactions betweendust and climate.

The Korea-United States Air Quality (KORUS-AQ) field study was conducted during May-June 2016. The effort was jointly sponsored by the National Institute of Environmental Research of South Korea and the National Aeronautics and Space Administration of the United States. KORUS-AQ offered an unprecedented, multi-perspective view of air quality conditions in South Korea by employing observations from three aircraft, an extensive ground-based network, and three ships along with an array of air quality forecast models. Information gathered during the study is contributing to an improved understanding of the factors controlling air quality in South Korea. The study also provided a valuable test bed for future air quality-observing strategies involving geostationary satellite instruments being launched by both countries to examine air quality throughout the day over Asia and North America. This article presents details on the KORUS-AQ observational assets, study execution, data products, and air quality conditions observed during the study. High-level findings from companion papers in this special issue are also summarized and discussed in relation to the factors controlling fine particle and ozone pollution, current emissions and source apportionment, and expectations for the role of satellite observations in the future. Resulting policy recommendations and advice regarding plans going forward are summarized. These results provide an important update to early feedback previously provided in a Rapid Science Synthesis Report produced for South Korean policy makers in 2017 and form the basis for the Final Science Synthesis Report delivered in 2020.

The NASA Atmospheric Tomography (ATom) mission built a photochemical climatology of air parcels based on in situ measurements with the NASA DC-8 aircraft along objectively planned profiling transects through the middle of the Pacific and Atlantic oceans. In this paper we present and analyze a data set of 10s (2km) merged and gap-filled observations of the key reactive species driving the chemical budgets of O3 and CH4 (O3, CH4, CO, H2O, HCHO, H2O2, CH3OOH, C2H6, higher alkanes, alkenes, aromatics, NOx, HNO3, HNO4, peroxyacetyl nitrate, other organic nitrates), consisting of 146494 distinct air parcels from ATom deployments 1 through 4. Six models calculated the O3 and CH4 photochemical tendencies from this modeling data stream for ATom 1. We find that 80%-90% of the total reactivity lies in the top 50% of the parcels and 25%-35% in the top 10%, supporting previous model-only studies that tropospheric chemistry is driven by a fraction of all the air. In other words, accurate simulation of the least reactive 50% of the troposphere is unimportant for global budgets. Surprisingly, the probability densities of species and reactivities averaged on a model scale (100km) differ only slightly from the 2km ATom data, indicating that much of the heterogeneity in tropospheric chemistry can be captured with current global chemistry models. Comparing the ATom reactivities over the tropical oceans with climatological statistics from six global chemistry models, we find excellent agreement with the loss of O3 and CH4 but sharp disagreement with production of O3. The models sharply underestimate O3 production below 4km in both Pacific and Atlantic basins, and this can be traced to lower NOx levels than observed. Attaching photochemical reactivities to measurements of chemical species allows for a richer, yet more constrained-to-what-matters, set of metrics for model evaluation.

Halogenated very short lived substances (VSLS) affect the ozone budget in the atmosphere. Brominated VSLS are naturally emitted from the ocean, and current oceanic emission inventories vary dramatically. We present a new global oceanic emission inventory of Br-VSLS (bromoform and dibromomethane), considering the physical forcing in the ocean and the atmosphere, as well as the ocean biogeochemistry control. A data-oriented machine-learning emulator was developed to couple the air-sea exchange with the ocean biogeochemistry. The predicted surface seawater concentrations and the surface atmospheric mixing ratios of Br-VSLS are evaluated with long-term, global-scale observations; and the predicted vertical distributions of Br-VSLS are compared to the global airborne observations in both boreal summer and winter. The global marine emissions of bromoform and dibromomethane are estimated to be 385 and 54 Gg Br per year, respectively. The new oceanic emission inventory of Br-VSLS is more skillful than the widely used top-down approaches for representing the seasonal/spatial variations and the annual means of atmospheric concentrations. The new approach improves the model predictability for the coupled Earth system model and can be used as a basis for investigating the past and future ocean emissions and feedbacks under climate change. This model framework can be used to calculate the bidirectional oceanic fluxes for other compounds of interest.

We report airborne measurements of acetaldehyde (CH3CHO) during the first and second deployments of the National Aeronautics and Space Administration (NASA) Atmospheric Tomography Mission (ATom). The budget of CH3CHO is examined using the Community Atmospheric Model with chemistry (CAM-chem), with a newly-developed online air-sea exchange module. The upper limit of the global ocean net emission of CH3CHO is estimated to be 34 Tg a-1 (42 Tg a-1 if considering bubble-mediated transfer), and the ocean impacts on tropospheric CH3CHO are mostly confined to the marine boundary layer. Our analysis suggests that there is an unaccounted CH3CHO source in the remote troposphere and that organic aerosols can only provide a fraction of this missing source. We propose that peroxyacetic acid (PAA) is an ideal indicator of the rapid CH3CHO production in the remote troposphere. The higher-than-expected CH3CHO measurements represent a missing sink of hydroxyl radicals (and halogen radical) in current chemistry-climate models.

We investigate regional sources contributing to CO during the Korea United States Air Quality (KORUS-AQ) campaign conducted over Korea (1 May to 10 June 2016) using 17 tagged CO simulations from the Community Atmosphere Model with chemistry (CAM-chem). The simulations use three spatial resolutions, three anthropogenic emission inventories, two meteorological fields, and nine emission scenarios. These simulations are evaluated against measurements from the DC-8 aircraft and Measurements Of Pollution In The Troposphere (MOPITT). Results show that simulations using bottom-up emissions are consistently lower (bias: −34 to −39%) and poorer performing (Taylor skill: 0.38–0.61) than simulations using alternative anthropogenic emissions (bias: −6 to −33%; Taylor skill: 0.48–0.86), particularly for enhanced Asian CO and volatile organic compound (VOC) emission scenarios, suggesting underestimation in modeled CO background and emissions in the region. The ranges of source contributions to modeled CO along DC-8 aircraft from Korea and southern (90°E to 123°E, 20°N to 29°N), middle (90°E to 123°E, 29°N to 38.5°N), and northern (90°E to 131.5°E, 38.5°N to 45°N) East Asia (EA) are 6–13%, ~5%, 16–28%, and 9–18%, respectively. CO emissions from middle and northern EA can reach Korea via transport within the boundary layer, whereas those from southern EA are transported to Korea mainly through the free troposphere. Emission contributions from middle EA dominate during continental outflow events (29–51%), while Korean emissions play an overall more important role for ground sites (up to 25–49%) and plumes within the boundary layer (up to 25–44%) in Korea. Finally, comparisons with four other source contribution approaches (FLEXPART 9.1 back trajectory calculations driven by Weather Research and Forecasting (WRF) WRF inert tracer, China signature VOCs, and CO to CO2 enhancement ratios) show general consistency with CAM-chem.

China's pursuing the carbon neutrality goal could affect future air quality not only by reducing anthropogenic emissions but also by modulating aerosol‐planetary boundary layer (PBL) interactions. However, contributions of aerosol‐PBL interactions to future air quality benefits remain unclear. Here we conduct ensemble experiments using the variable‐resolution (VR) Community Atmosphere Model with full chemistry based on the scalable spectral element (SE) dynamical core with East Asia refined to ∼28 km (SE_VR). Additional simulations at a uniform resolution of ∼111 km (SE_UR) are conducted to help demonstrate SE_VR's advantages in projecting future air quality. Results of SE_VR show that the mean PM2.5 concentrations in China would drop to below 10 μg/m3, especially in Sichuan Basin (SCB) where the frequencies of moderate and severe air pollution events are predicted to decrease from 60.7% and 11.3% to nearly zero respectively when achieving carbon neutrality. The aerosol‐PBL interactions would be substantially weakened with anthropogenic emission reductions. At SCB and Eastern China (EC), the weakened radiative effects of black carbon (BC) would contribute 34.3% and 71.6% to the increase in PBL height (PBLH). Consequently, these weakened BC‐PBL interactions reduce the surface PM2.5 concentrations by 16.1 μg/m3 (18.9%) in SCB and 4.6 μg/m3 (16.4%) in EC. Notably, SE_VR outperforms SE_UR in projecting the frequency decrease of future air pollution events for its better reproducing current levels, particularly those caused by BC aerosols. This study highlights the importance of weakened aerosol‐PBL feedbacks on future air quality improvement and demonstrates the added values of variable‐resolution global models in air quality projections. Plain Language Summary: Numerous efforts have been devoted to assessing the future air quality changes following the emission reductions in China, with a predominant focus on the direct benefits of aerosol reductions. However, contributions of changes in aerosol‐PBL (planetary boundary layer) interactions to the future air quality improvement still remain unclear, despite the crucial role of aerosol‐PBL interactions in the formation of haze events. To address this issue, we conduct ensemble experiments using a variable‐resolution global chemistry model to explore the future air quality improvement when achieving carbon neutrality in China. Results show that achieving carbon neutrality would significantly improve the future air quality and weaken the aerosol‐PBL interactions. The weakened interactions between black carbon (BC) and PBL would contribute about 18.9% and 16.4% of the total air quality improvement at Sichuan Basin (SCB) and Eastern China (EC). Notably, the model with higher horizontal resolution at ∼28 km shows advantages in projecting the frequency changes in future air pollution events, particularly those induced by BC, compared to the uniform coarser resolution ∼111 km model. Our work highlights the crucial role of aerosol‐PBL interaction changes in future air quality improvement, and demonstrates the added‐value of the regional refinement global model in future climate and air quality projections. Key Points: Achieving carbon neutrality would lead to significant changes in both air quality and the regional climate in winterThe weakened BC‐PBL interactions play an essential role in future air quality improvementFiner grids show added values in projecting probability changes of extreme air pollution events, particularly those induced by BC aerosols [ABSTRACT FROM AUTHOR]

The detection of meteorological, chemical, or other signals in modeled or observed air quality data - such as an estimate of a temporal trend in surface ozone data, or an estimate of the mean ozone of a particular region during a particular season - is a critical component of modern atmospheric chemistry. However, the magnitude of a surface air quality signal is generally small compared to the magnitude of the underlying chemical, meteorological, and climatological variabilities (and their interactions) that exist both in space and in time, and which include variability in emissions and surface processes. This can present difficulties for both policymakers and researchers as they attempt to identify the influence or signal of climate trends (e.g., any pauses in warming trends), the impact of enacted emission reductions policies (e.g., United States NOx State Implementation Plans), or an estimate of the mean state of highly variable data (e.g., summertime ozone over the northeastern United States). Here we examine the scale dependence of the variability of simulated and observed surface ozone data within the United States and the likelihood that a particular choice of temporal or spatial averaging scales produce a misleading estimate of a particular ozone signal. Our main objective is to develop strategies that reduce the likelihood of overconfidence in simulated ozone estimates. We find that while increasing the extent of both temporal and spatial averaging can enhance signal detection capabilities by reducing the noise from variability, a strategic combination of particular temporal and spatial averaging scales can maximize signal detection capabilities over much of the continental US. For signals that are large compared to the meteorological variability (e.g., strong emissions reductions), shorter averaging periods and smaller spatial averaging regions may be sufficient, but for many signals that are smaller than or comparable in magnitude to the underlying meteorological variability, we recommend temporal averaging of 10-15 years combined with some level of spatial averaging (up to several hundred kilometers). If this level of averaging is not practical (e.g., the signal being examined is at a local scale), we recommend some exploration of the spatial and temporal variability to provide context and confidence in the robustness of the result. These results are consistent between simulated and observed data, as well as within a single model with different sets of parameters. The strategies selected in this study are not limited to surface ozone data and could potentially maximize signal detection capabilities within a broad array of climate and chemical observations or model output.

We investigate the composition of 63 C2-C10 nonmethane hydrocarbons (NMHCs), methane (CH4) and carbon monoxide (CO), in Jeddah, Mecca, and Madina (Saudi Arabia), in Lahore, (Pakistan), and in Singapore. We established a database with which to compare and contrast NMHCs in regions where ambient levels and emissions are poorly characterized, but where conditions are favorable to the formation of tropospheric ozone, and where measurements are essential for improving emission inventories and modeling. This dataset will also serve as a base for further analysis of air pollution in Western Saudi Arabia including, but not limited to, the estimation of urban emissions and long range pollution transport from these regions. The measured species showed enhanced levels in all Saudi Arabian cities compared to the local background but were generally much lower than in Lahore. In Madina, vehicle exhaust was the dominant NMHC source, as indicated by enhanced levels of combustion products and by the good correlation between NMHCs and CO, while in Jeddah and Mecca a combination of sources needs to be considered. Very high NMHC levels were measured in Lahore, and elevated levels of CH4 in Lahore were attributed to natural gas. When we compared our results with 2010 emissions from the MACCity global inventory, we found discrepancies in the relative contribution of NMHCs between the measurements and the inventory. In all cities, alkenes (especially ethene and propene) dominated the hydroxyl radical (OH) reactivity (kOH) because of their great abundance and their relatively fast reaction rates with OH.

The Geostationary Environment Monitoring Spectrometer (GEMS) over Asia is the first geostationary Earth orbit instrument in the virtual constellation of sensors for atmospheric chemistry and composition air quality research and applications. For the first time, the hourly observations enable studies of diurnal variation in several important trace gas and aerosol pollutants including nitrogen dioxide (NO2), which is the focus of this work. NO2 is a regulated pollutant and an indicator of anthropogenic emissions in addition to being involved in tropospheric ozone chemistry and particulate matter formation. We present new quantitative measures of NO2 tropospheric column diurnal variation which can be greater than 50 % of the column amount, especially in polluted environments. The NO2 distribution is seen to change hourly and can be quite different from what would be seen by a once-a-day low-Earth-orbit satellite observation. We use GEMS data in combination with TROPOspheric Monitoring Instrument (TROPOMI) satellite and Pandora ground-based remote sensing measurements and Multi-Scale Infrastructure for Chemistry and Aerosols (Version 0, MUSICAv0) 3D chemical transport model analysis to examine the NO2 diurnal variation in January and June 2023 over Northeast Asia and Seoul, South Korea, study regions to distinguish the different emissions, chemistry, and meteorological processes that drive the variation. Understanding the relative importance of these processes will be key to including pollutant diurnal variation in models aimed at determining true pollutant exposure levels for air quality studies. The work presented here also provides a path for investigating similar NO2 diurnal cycles in the new Earth Venture Instrument-1 Tropospheric Emissions: Monitoring Pollution (TEMPO) data over North America, and later over Europe with Sentinel-4. [ABSTRACT FROM AUTHOR]

Tropospheric ozone is a major air pollutant and greenhouse gas. It is also the primary precursor of OH, the main tropospheric oxidant. Global atmospheric chemistry models show large differences in their simulations of tropospheric ozone budgets. Here we implement the widely used GEOS-Chem atmospheric chemistry module as an alternative to CAM-chem within the Community Earth System Model version 2 (CESM2). We compare the resulting GEOS-Chem and CAM-chem simulations of tropospheric ozone and related species within CESM2 to observations from ozonesondes, surface sites, the ATom-1 aircraft campaign over the Pacific and Atlantic, and the KORUS-AQ aircraft campaign over the Seoul Metropolitan Area. We find that GEOS-Chem and CAM-chem within CESM2 have similar tropospheric ozone budgets and concentrations usually within 5 ppb but important differences in the underlying processes including (1) photolysis scheme (no aerosol effects in CAM-chem), (2) aerosol nitrate photolysis, (3) N2O5 cloud uptake, (4) tropospheric halogen chemistry, and (5) ozone deposition to the oceans. Global tropospheric OH concentrations are the same in both models, but there are large regional differences reflecting the above processes. Carbon monoxide is lower in CAM-chem (and lower than observations), at least in part because of higher OH concentrations in the Northern Hemisphere and insufficient production from isoprene oxidation in the Southern Hemisphere. CESM2 does not scavenge water-soluble gases in convective updrafts, leading to some upper-tropospheric biases. Comparison to KORUS-AQ observations shows an overestimate of ozone above 4 km altitude in both models, which at least in GEOS-Chem is due to inadequate scavenging of particulate nitrate in convective updrafts in CESM2, leading to excessive NO production from nitrate photolysis. The KORUS-AQ comparison also suggests insufficient boundary layer mixing in CESM2. This implementation and evaluation of GEOS-Chem in CESM2 contribute to the MUSICA vision of modularizing tropospheric chemistry in Earth system models. [ABSTRACT FROM AUTHOR]

The Community Earth System Model (CESM1) CAM4-chem has been used to perform the Chemistry Climate Model Initiative (CCMI) reference and sensitivity simulations. In this model, the Community Atmospheric Model version 4 (CAM4) is fully coupled to tropospheric and stratospheric chemistry. Details and specifics of each configuration, including new developments and improvements are described. CESM1 CAM4-chem is a low-top model that reaches up to approximately 40km and uses a horizontal resolution of 1.9° latitude and 2.5° longitude. For the specified dynamics experiments, the model is nudged to Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis. We summarize the performance of the three reference simulations suggested by CCMI, with a focus on the last 15 years of the simulation when most observations are available. Comparisons with selected data sets are employed to demonstrate the general performance of the model. We highlight new data sets that are suited for multi-model evaluation studies. Most important improvements of the model are the treatment of stratospheric aerosols and the corresponding adjustments for radiation and optics, the updated chemistry scheme including improved polar chemistry and stratospheric dynamics and improved dry deposition rates. These updates lead to a very good representation of tropospheric ozone within 20% of values from available observations for most regions. In particular, the trend and magnitude of surface ozone is much improved compared to earlier versions of the model. Furthermore, stratospheric column ozone of the Southern Hemisphere in winter and spring is reasonably well represented. All experiments still underestimate CO most significantly in Northern Hemisphere spring and show a significant underestimation of hydrocarbons based on surface observations.

© 2015. American Geophysical Union. All Rights Reserved. We show the results and evaluation with independent measurements from assimilating both MOPITT (Measurements Of Pollution In The Troposphere) and IASI (Infrared Atmospheric Sounding Interferometer) retrieved profiles into the Community Earth System Model (CESM). We used the Data Assimilation Research Testbed ensemble Kalman filter technique, with the full atmospheric chemistry CESM component Community Atmospheric Model with Chemistry. We first discuss the methodology and evaluation of the current data assimilation system with coupled meteorology and chemistry data assimilation. The different capabilities of MOPITT and IASI retrievals are highlighted, with particular attention to instrument vertical sensitivity and coverage and how these impact the analyses. MOPITT and IASI CO retrievals mostly constrain the CO fields close to the main anthropogenic, biogenic, and biomass burning CO sources. In the case of IASI CO assimilation, we also observe constraints on CO far from the sources. During the simulation time period (June and July 2008), CO assimilation of both instruments strongly improves the atmospheric CO state as compared to independent observations, with the higher spatial coverage of IASI providing better results on the global scale. However, the enhanced sensitivity of multispectral MOPITT observations to near surface CO over the main source regions provides synergistic effects at regional scales.

Satellite retrievals of carbon monoxide (CO) are routinely assimilated in atmospheric chemistry models to improve air quality forecasts, produce reanalyzes and to estimate emissions. This study applies the quantile‐conserving ensemble filter framework, a novel assimilation algorithm that can deal with non‐Gaussian and modestly nonlinear distributions. Instead of assuming normal distributions like the Ensemble Adjustments Kalman Filter (EAKF), we now apply a bounded normal rank histogram (BNRH) distribution for the prior. The goal is to efficiently estimate bounded quantities such as CO atmospheric mixing ratios and emission fluxes while maintaining the good performance achieved by the EAKF. We contrast assimilating meteorological and MOPITT (Measurement of Pollution in the Troposphere) observations for May 2018. We evaluate the results with the fourth deployment of the NASA Atmospheric Tomography Mission (ATom‐4) airborne field campaign. We also compare simulations with CO tropospheric columns from the network for the detection of atmospheric composition change and surface in‐situ observations from NOAA carbon cycle greenhouse gases. While the differences remain small, the BNRH approach clearly works better than the EAKF in comparison to all observation data sets. Plain Language Summary: The MOPITT instrument on the NASA/Terra satellite can detect carbon monoxide (CO) pollution in the lower and mid‐tropospheric atmosphere but cannot accurately differentiate small changes in the altitude of pollution plumes. Such satellite observations are assimilated in numerical model predictions to improve the spatial and temporal distribution of CO in the atmosphere and to estimate emission fluxes. We present a novel method that does not require assumptions about the model and the observations, leading to a more efficient and accurate assimilation of the satellite observations. Key Points: A novel non Gaussian and nonlinear ensemble data assimilation (DA) framework is applied to MOPITT joint state/flux optimizationThe new method performs better than the Ensemble Adjustment Kalman Filter in comparison to independent observationsMOPITT observations indicate that CAMS‐GLOB‐ANT_v5.3 emission fluxes are underestimated across the mid‐latitudes in May 2018 [ABSTRACT FROM AUTHOR]

Few field campaigns with extensive aerosol measurements have been conducted over continental areas in the southern hemisphere. To address this data gap and better understand the interactions of convective clouds and the surrounding environment, extensive in situ and remote sensing measurements were collected during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign conducted between October 2018 and April 2019 over the Sierras de Córdoba range of central Argentina. This study describes measurements of aerosol number, size, composition, mixing state, and cloud condensation nuclei (CCN) collected at the ground and from a research aircraft during seven weeks of the campaign. Large spatial and multi-day variations in aerosol number, size, composition, and CCN were observed due to transport from upwind sources controlled by mesoscale to synoptic-scale meteorological conditions. Large vertical wind shears, back trajectories, single particle measurements, and chemical transport model predictions indicate that different types of emissions and source regions, including biogenic emissions and biomass burning from the Amazon and anthropogenic emissions from Chile and eastern Argentina, contribute to aerosols observed during CACTI. Repeated aircraft measurements near the boundary layer top reveal strong spatial and temporal variations in CCN and demonstrate that understanding the complex co-variability of aerosol properties and clouds is critical to quantify the impact of aerosol-cloud interactions. In addition to quantifying aerosol properties in this data-sparse region, these measurements will be valuable to evaluate predictions over the mid latitudes of South America and improve parameterized aerosol processes in local, regional, and global models. [ABSTRACT FROM AUTHOR]

The Measurements Of Pollution In The Troposphere (MOPITT) is an ideal instrument to understand the impact of (1) assimilating multispectral and joint retrievals versus single spectral products, (2) assimilating satellite profile products versus column products, and (3) assimilating multispectral and joint retrievals versus assimilating individual products separately. We use the Community Atmosphere Model with chemistry with the Data Assimilation Research Testbed (CAM-chem + DART) to assimilate different MOPITT carbon monoxide (CO) products to address these three questions. Both anthropogenic and fire CO emissions are optimized in the data assimilation experiments. The results are compared with independent CO observations from TROPOspheric Monitoring Instrument (TROPOMI), the Total Carbon Column Observing Network (TCCON), NOAA Carbon Cycle Greenhouse Gases (CCGG) sites, In-service Aircraft for a Global Observing System (IAGOS), and Western wildfire Experiment for Cloud chemistry, Aerosol absorption and Nitrogen (WE-CAN). We find that (1) assimilating the MOPITT joint (multispectral; near-IR and thermal IR) column product leads to better model–observation agreement at and near the surface than assimilating the MOPITT thermal-IR-only column retrieval. (2) Assimilating column products has a larger impact and improvement for background and large-scale CO compared to assimilating profile products due to vertical localization in profile assimilation. However, profile assimilation can outperform column assimilations in fire-impacted regions and near the surface. (3) Assimilating multispectral and joint products results in similar or slightly better agreement with observations compared to assimilating the single spectral products separately. [ABSTRACT FROM AUTHOR]

During the operational phase of the ICARTT field experiment in 2004, the regional air quality model STEM showed a strong positive surface bias and a negative upper troposphere bias (compared to observed DC-8 and WP-3 observations) with respect to ozone. After updating emissions from NEI 1999 to NEI 2001 (with a 2004 large point sources inventory update), and modifying boundary conditions, low-level model bias decreases from 11.21 to 1.45 ppbv for the NASA DC-8 observations and from 8.26 to -0.34 for the NOAA WP-3. Improvements in boundary conditions provided by global models decrease the upper troposphere negative ozone bias, while accounting for biomass burning emissions improved model performance for CO. The covariances of ozone bias were highly correlated to NOz, NOy, and HNO3 biases. Interpolation of bias information through kriging showed that decreasing emissions in SE United States would reduce regional ozone model bias and improve model correlation coefficients. The spatial distribution of forecast errors was analyzed using kriging, which identified distinct features, which when compared to errors in postanalysis simulations, helped document improvements. Changes in dry deposition to crops were shown to reduce substantially high bias in the forecasts in the Midwest, while updated emissions were shown to account for decreases in bias in the eastern United States. Observed and modeled ozone production efficiencies for the DC-8 Were calculated and shown to be very similar (7.8) suggesting that recurring ozone bias is due to overestimation of NOx, emissions. Sensitivity studies showed that ozone formation in the United States is most sensitive to NOx, emissions, followed by VOCs and CO. PAN as a reservoir of NOx, can contribute to a significant amount of surface ozone through thermal decomposition. Copyright 2007 by the American Geophysical Union.

The Geostationary Environment Monitoring Spectrometer (GEMS) over Asia is the first geostationary Earth orbit instrument in the virtual constellation of sensors for atmospheric chemistry and composition air quality research and applications. For the first time, the hourly observations enable studies of diurnal variation of several important trace gas and aerosol pollutants including nitrogen dioxide (NO2) which is the focus of this work. NO2 is a regulated pollutant and an indicator of anthropogenic emissions in addition to being involved in tropospheric ozone chemistry and particulate matter formation. We present new quantitative measures of NO2 tropospheric column diurnal variation which can be greater than 50 % of the column amount especially in polluted environments. The NO2 distribution is seen to change hourly and can be quite different from what would be seen by a once-a-day low Earth orbit satellite observation. We use GEMS data in combination with TROPOMI satellite and Pandora ground-based remote sensing measurements and MUSICAv0 3D chemical transport model analysis to examine the NO2 diurnal variation in January and June 2023 over Northeast Asia and Seoul, South Korea, study regions to distinguish the different emissions, chemistry, and meteorological processes that drive the variation. Understanding the relative importance of these processes will be important for including pollutant diurnal variation in models aimed at determining true pollutant exposure levels for air quality studies. The work presented here also provides a path for investigating similar NO2 diurnal cycles in the new TEMPO data over North America, and later over Europe with S-4. [ABSTRACT FROM AUTHOR]

The Measurements Of Pollution In The Troposphere (MOPITT) is an ideal instrument to understand the impact of (1) assimilating multispectral/joint retrievals versus single-spectral products, (2) assimilating satellite profile products versus column products, and (3) assimilating multispectral/joint retrievals versus assimilating individual products separately. We use the Community Atmosphere Model with chemistry with the Data Assimilation Research Testbed (CAM-chem+DART) to assimilate different MOPITT CO products to address these three questions. Both anthropogenic and fire CO emissions are optimized in the data assimilation experiments. The results are compared with independent CO observations from TROPOspheric Monitoring Instrument (TROPOMI), the Total Carbon Column Observing Network (TCCON), NOAA Carbon Cycle Greenhouse Gases (CCGG) sites, In-service Aircraft for a Global Observing System (IAGOS), and Western wildfire Experiment for Cloud chemistry, Aerosol absorption and Nitrogen (WE-CAN). We find that (1) assimilating the MOPITT joint (multispectral Near-IR and Thermal-IR) column product leads to better model-observation agreement at and near the surface than assimilating the MOPITT Thermal-IR-only column retrieval. (2) Assimilating column products has a larger impact and improvement for background and large-scale CO compared to assimilating profile products due to vertical localization in profile assimilation. However, profile assimilation can out-perform column assimilations in fire-impacted regions and near the surface. (3) Assimilating multispectral/joint products results in similar or slightly better agreement with observations compared to assimilating the single-spectral products separately. [ABSTRACT FROM AUTHOR]

Surficial enhanced rock weathering (ERW) is a land-based carbon dioxide removal (CDR) strategy that involves applying crushed silicate rock (e.g., basalt) to agricultural soils. However, unintended biogeochemical interactions with the nitrogen cycle may arise through ERW increasing soil pH as basalt grains undergo dissolution that may reinforce, counteract, or even offset the climate benefits from carbon sequestration. Increases in soil pH could drive changes in the soil emissions of key non-CO 2 greenhouse gases, e.g., nitrous oxide (N 2 O), and trace gases, e.g., nitric oxide (NO) and ammonia (NH 3), that affect air quality and crop and human health. We present the development and implementation of a new improved nitrogen cycling scheme for the Community Land Model v5 (CLM5), the land component of the Community Earth System Model, allowing evaluation of ERW effects on soil gas emissions. We base the new parameterizations on datasets derived from soil pH responses of N 2 O, NO, and NH 3 in ERW field trial and mesocosm experiments with crushed basalt. These new capabilities involve the direct implementation of routines within the CLM5 N cycle framework, along with asynchronous coupling of soil pH changes estimated through an ERW model. We successfully validated simulated "control" (i.e., no ERW) seasonal cycles of soil N 2 O, NO, and NH 3 emissions against a wide range of global emission inventories. We benchmark simulated mitigation of soil N 2 O fluxes in response to ERW against a subset of data from ERW field trials in the US Corn Belt. Using the new scheme, we provide a specific example of the effect of large-scale ERW deployment with croplands on soil nitrogen fluxes across five key regions with high potential for CDR with ERW (North America, Brazil, Europe, India, and China). Across these regions, ERW implementation led to marked reductions in N 2 O and NO (both 18 %), with moderate increases in NH 3 (2 %). While further developments are still required in our implementations when additional ERW data become available, our improved N cycle scheme within CLM5 has utility for investigating the potential of ERW point-source and regional effects of soil N 2 O, NO, and NH 3 fluxes in response to current and future climates. This framework also provides the basis for assessing the implications of ERW for air quality given the role of NO in tropospheric ozone formation, as well as both NO and NH 3 in inorganic aerosol formation. [ABSTRACT FROM AUTHOR]