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We present the Fire Inventory from National Center for Atmospheric Research (NCAR) version 2.5 (FINNv2.5), a fire emissions inventory that provides publicly available emissions of trace gases and aerosols for various applications, including use in global and regional atmospheric chemistry modeling. FINNv2.5 includes numerous updates to the FINN version 1 framework to better represent burned area, vegetation burned, and chemicals emitted. Major changes include the use of active fire detections from the Visible Infrared Imaging Radiometer Suite (VIIRS) at 375 m spatial resolution, which allows smaller fires to be included in the emissions processing. The calculation of burned area has been updated such that a more rigorous approach is used to aggregate fire detections, which better accounts for larger fires and enables using multiple satellite products simultaneously for emissions estimates. Fuel characterization and emissions factors have also been updated in FINNv2.5. Daily fire emissions for many trace gases and aerosols are determined for 2002–2019 (Moderate Resolution Imaging Spectroradiometer (MODIS)-only fire detections) and 2012–2019 (MODIS + VIIRS fire detections). The non-methane organic gas emissions are allocated to the species of several commonly used chemical mechanisms. We compare FINNv2.5 emissions against other widely used fire emissions inventories. The performance of FINNv2.5 emissions as inputs to a chemical transport model is assessed with satellite observations. Uncertainties in the emissions estimates remain, particularly in Africa and South America during August–October and in southeast and equatorial Asia in March and April. Recommendations for future evaluation and use are given.

The Multi-Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0) is a new community modeling infrastructure that enables the study of atmospheric composition and chemistry across all relevant scales. We develop a MUSICAv0 grid with Africa refinement (~28 km × 28 km over Africa). We evaluate the MUSICAv0 simulation for 2017 with in situ observations and compare the model results to satellite products over Africa. A simulation from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), a regional model that is widely used in Africa studies, is also included in the analyses as a reference. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Both models underestimate carbon monoxide (CO) compared to in situ observations and satellite CO column retrievals from the Measurements of Pollution in the Troposphere (MOPITT) satellite instrument. MUSICAv0 tends to overestimate ozone (O3), likely due to overestimated stratosphere-to-troposphere flux of ozone. Both models significantly underestimate fine particulate matter (PM2.5) at two surface sites in East Africa. The MUSICAv0 simulation agrees better with aerosol optical depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) and tropospheric nitrogen dioxide (NO2) column retrievals from the Ozone Monitoring Instrument (OMI) than WRF-Chem. MUSICAv0 has a consistently lower tropospheric formaldehyde (HCHO) column than OMI retrievals. Based on model-satellite discrepancies between MUSICAv0 and WRF-Chem and MOPITT CO, MODIS AOD, and OMI tropospheric NO2, we find that future field campaign(s) and more in situ observations in an East African region (30° E – 45° E, 5° S – 5° N) could substantially improve the predictive skill of atmospheric chemistry model(s). This suggested focus region exhibits the largest model-in situ observation discrepancies, as well as targets for high population density, land cover variability, and anthropogenic pollution sources.

We quantify future changes in wildfire burned area and carbon emissions in the 21st century under four Shared Socioeconomic Pathways (SSPs) scenarios and two SSP5-8.5-based solar geoengineering scenarios with a target surface temperature defined by SSP2-4.5 – solar irradiance reduction (G6solar) and stratospheric sulfate aerosol injections (G6sulfur) – and explore the mechanisms that drive solar geoengineering impacts on fires. This study is based on fully coupled climate–chemistry simulations with simulated occurrence of fires (burned area and carbon emissions) using the Whole Atmosphere Community Climate Model version 6 (WACCM6) as the atmospheric component of the Community Earth System Model version 2 (CESM2). Globally, total wildfire burned area is projected to increase over the 21st century under scenarios without geoengineering and decrease under the two geoengineering scenarios. By the end of the century, the two geoengineering scenarios have lower burned area and fire carbon emissions than not only their base-climate scenario SSP5-8.5 but also the targeted-climate scenario SSP2-4.5. Geoengineering reduces wildfire occurrence by decreasing surface temperature and wind speed and increasing relative humidity and soil water, with the exception of boreal regions where geoengineering increases the occurrence of wildfires due to a decrease in relative humidity and soil water compared with the present day. This leads to a global reduction in burned area and fire carbon emissions by the end of the century relative to their base-climate scenario SSP5-8.5. However, geoengineering also yields reductions in precipitation compared with a warming climate, which offsets some of the fire reduction. Overall, the impacts of the different driving factors are larger on burned area than fire carbon emissions. In general, the stratospheric sulfate aerosol approach has a stronger fire-reducing effect than the solar irradiance reduction approach.

Measurements of Pollution In The Troposphere (MOPITT) is an instrument on NASA's Terra satellite that has measured tropospheric carbon monoxide (CO) from early 2000 to the present day. Validation of data from satellite instruments like MOPITT is often conducted using ground-based measurements to ensure the continued accuracy of the space-based instrument's measurements and its scientific results. Previous MOPITT validation studies generally found a larger bias in the MOPITT data poleward of 60∘ N. In this study, we use data from 2006 to 2019 from the Bruker IFS 125HR Fourier Transform Infrared spectrometer (FTIR) located at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut, Canada, to validate the MOPITT version 8 (V8) retrievals. These comparisons utilize mid- and near-infrared FTIR measurements made as part of the Network for the Detection for Atmospheric Composition Change (NDACC) and the Total Carbon Column Observing Network (TCCON), respectively. All MOPITT version 8 retrievals within a radius of 110 km (1∘) from the PEARL Ridge Laboratory and within a 24 h time interval are used in this validation study. MOPITT retrieval products include those from the near-infrared (NIR) channel, the thermal infrared (TIR) channel, and a joint product from the thermal and near-infrared (TIR–NIR) channels. Each channel's detector has 4 pixels. We calculated the MOPITT pixel-to-pixel biases for each pixel, which were found to vary based on the season and surface type (land or water). The systematic bias for pixel 1 over land is larger than that for other pixels, which can reach up to 20 ppb. We use a small-region approximation method to find filtering criteria. We then apply the filters to the MOPITT dataset to minimize the MOPITT pixel bias and the number of outliers in the dataset. The sensitivity of each MOPITT pixel and each product is examined over the Canadian high Arctic. We then follow the methodologies recommended by NDACC and TCCON for the comparison between the FTIR and satellite total column retrievals. MOPITT averaging kernels are used to weight the NDACC and TCCON retrievals and take into account the different vertical sensitivities between the satellite and PEARL FTIR measurements. We use a modified Taylor diagram to present the comparison results from each pixel for each product over land and water with NDACC and TCCON measurements. Our results show overall consistency between MOPITT and the NDACC and TCCON measurements. When compared to the FTIR, the NIR MOPITT retrievals have a positive bias of 3 %–10 % depending on the pixel. The bias values are negative for the TIR product, with values between −5 % and 0 %. The joint TIR–NIR products show differences of −4 % to 7 %. The drift in MOPITT biases (in units of % yr−1) relative to NDACC and TCCON varies by MOPITT data product. In the NIR, drifts vs. TCCON are smaller than those vs. NDACC; however, this scenario is reversed for the MOPITT TIR and joint TIR–NIR products. Overall, this study aims to provide detailed validation for MOPITT version 8 measurements in the Canadian high Arctic.

International audience; A key Earth system science question is the role of atmospheric deposition in supplying vital nutrients to the phytoplankton that form the base of marine food webs. Industrial and vehicular pollution, wildfires, volcanoes, biogenic debris, and desert dust all carry nutrients within their plumes throughout the globe. In remote ocean ecosystems, aerosol deposition represents an essential new source of nutrients for primary production. The large spatiotemporal variability in aerosols from myriad sources combined with the differential responses of marine biota to changing fluxes makes it crucially important to understand where, when, and how much nutrients from the atmosphere enter marine ecosystems. This review brings together existing literature, experimental evidence of impacts, and new atmospheric nutrient observations that can be compared with atmospheric and ocean biogeochemistry modeling. We evaluate the contribution and spatiotemporal variability of nutrient-bearing aerosols from desert dust, wildfire, volcanic, and anthropogenic sources, including the organic component, deposition fluxes, and oceanic impacts. Expected final online publication date for the Annual Review of Marine Science, Volume 14 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

International audience; This commentary paper from the recently formed International Global Atmospheric Chemistry (IGAC) Southern Hemisphere Working Group outlines key issues in atmospheric composition research that particularly impact the Southern Hemisphere. In this article, we present a broad overview of many of the challenges for understanding atmospheric chemistry in the Southern Hemisphere, before focusing in on the most significant factors that differentiate it from the Northern Hemisphere. We present sections on the importance of biogenic emissions and fires in the Southern Hemisphere, showing that these emissions often dominate over anthropogenic emissions in many regions. We then describe how these and other factors influence air quality in different parts of the Southern Hemisphere. Finally, we describe the key role of the Southern Ocean in influencing atmospheric chemistry and conclude with a description of the aims and scope of the newly formed IGAC Southern Hemisphere Working Group.

Observations of carbon monoxide (CO) from the Measurements Of Pollution In The Troposphere (MOPITT) instrument aboard the Terra spacecraft were expected to have an accuracy of 10 % prior to the launch in 1999. Here we evaluate MOPITT Version 7 joint (V7J) thermal-infrared and near-infrared (TIR–NIR) retrieval accuracy and precision and suggest ways to further improve the accuracy of the observations. We take five steps involving filtering or bias corrections to reduce scatter and bias in the data relative to other MOPITT soundings and ground-based measurements. (1) We apply a preliminary filtering scheme in which measurements over snow and ice are removed. (2) We find a systematic pairwise bias among the four MOPITT along-track detectors (pixels) on the order of 3–4 ppb with a small temporal trend, which we remove on a global scale using a temporally trended bias correction. (3) Using a small-region approximation (SRA), a new filtering scheme is developed and applied based on additional quality indicators such as the signal-to-noise ratio (SNR). After applying these new filters, the root-mean-squared error computed using the local median from the SRA over 16 years of global observations decreases from 3.84 to 2.55 ppb. (4) We also use the SRA to find variability in MOPITT retrieval anomalies that relates to retrieval parameters. We apply a bias correction to one parameter from this analysis. (5) After applying the previous bias corrections and filtering, we compare the MOPITT results with the GGG2014 ground-based Total Carbon Column Observing Network (TCCON) observations to obtain an overall global bias correction. These comparisons show that MOPITT V7J is biased high by about 6 %–8 %, which is similar to past studies using independent validation datasets on V6J. When using TCCON spectrometric column retrievals without the standard airmass correction or scaling to aircraft (WMO scale), the ground- and satellite-based observations overall agree to better than 0.5 %. GEOS-Chem data assimilations are used to estimate the influence of filtering and scaling to TCCON on global CO and tend to pull concentrations away from the prior fluxes and closer to the truth. We conclude with suggestions for further improving the MOPITT data products.

Australian fires are a primary driver of variability in Australian atmospheric composition and contribute significantly to regional and global carbon budgets. However, biomass burning emissions fro...

Multiple 50-member ensemble simulations with the Community Earth System Model version 2 are performed to estimate the coupled climate responses to the 2019-2020 Australian wildfires and COVID-19 pandemic policies. The climate response to the pandemic is found to be weak generally, with global-mean net top-of-atmosphere radiative anomalies of +0.23 +/- 0.14 W m(-2) driving a gradual global warming of 0.05 +/- 0.04 K by the end of 2022. While regional anomalies are detectable in aerosol burdens and clear-sky radiation, few significant anomalies exist in other fields due to internal variability. In contrast, the simulated response to Australian wildfires is a strong and rapid cooling, peaking globally at -0.95 +/- 0.15 W m(-2) in late 2019 with a global cooling of 0.06 +/- 0.04 K by mid-2020. Transport of fire aerosols throughout the Southern Hemisphere increases albedo and drives a strong interhemispheric radiative contrast, with simulated responses that are consistent generally with those to a Southern Hemisphere volcanic eruption.

Following past studies to quantify decadal trends in global carbon monoxide (CO) using satellite observations, we update estimates and find a CO trend in column amounts of about −0.50 % per year between 2002 to 2018, which is a deceleration compared to analyses performed on shorter records that found −1 % per year. Aerosols are co-emitted with CO from both fires and anthropogenic sources but with a shorter lifetime than CO. A combined trend analysis of CO and aerosol optical depth (AOD) measurements from space helps to diagnose the drivers of regional differences in the CO trend. We use the long-term records of CO from the Measurements of Pollution in the Troposphere (MOPITT) and AOD from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. Other satellite instruments measuring CO in the thermal infrared, AIRS, TES, IASI, and CrIS, show consistent hemispheric CO variability and corroborate results from the trend analysis performed with MOPITT CO. Trends are examined by hemisphere and in regions for 2002 to 2018, with uncertainties quantified. The CO and AOD records are split into two sub-periods (2002 to 2010 and 2010 to 2018) in order to assess trend changes over the 16 years. We focus on four major population centers: Northeast China, North India, Europe, and Eastern USA, as well as fire-prone regions in both hemispheres. In general, CO declines faster in the first half of the record compared to the second half, while AOD trends show more variability across regions. We find evidence of the atmospheric impact of air quality management policies. The large decline in CO found over Northeast China is initially associated with an improvement in combustion efficiency, with subsequent additional air quality improvements from 2010 onwards. Industrial regions with minimal emission control measures such as North India become more globally relevant as the global CO trend weakens. We also examine the CO trends in monthly percentile values to understand seasonal implications and find that local changes in biomass burning are sufficiently strong to counteract the global downward trend in atmospheric CO, particularly in late summer., SCOPUS: ar.j, info:eu-repo/semantics/published

Atmospheric carbon monoxide (CO) concentrations have decreased since the beginning of the century, and the rate of decrease depends on the region. Depending on how regions are chosen, their boundaries may not always align with where there are differences in trends. To address this, we calculate trends within 0.4° × 0.4° grid cells independently throughout the globe using satellite CO retrievals from the Measurements Of Pollution In The Troposphere (MOPITT) satellite instrument from 2002 to 2017. These trends are found with the caveat that there are large singular biomass burning events somewhere nearly every year, and we include examples of large column CO anomalies during sporadic but large burning events in the North American and Eurasian boreal forests, the Amazon, Africa, and Indonesia. CO trends behave similarly within regions that span about a few thousand kilometers. Using TransCom region definitions, we find average trends between −0.9 and 0.1 ppb year⁻¹ (about −0.9 to 0.1% year⁻¹) for 2002–2017. Over 5‐year subsets, trends in TransCom regions vary between −3.6 and 1.8 ppb year⁻¹. This substantial spatial and temporal variability in trends is in agreement with other studies. With an understanding of regional trends, we compare with trends from urban areas. Generally, CO trends within urban areas are indistinguishable from regional trends. This may be because of a combination of noise in the data, the large footprint for MOPITT, or because anthropogenic CO reduction measures were implemented before the MOPITT record began. We provide case studies for a few cities, such as Los Angeles, and find long‐term variation in the rate of change of column CO.

Sub-grid variability (SGV) of atmospheric trace gases within satellite pixels is a key issue in satellite design, and interpretation and validation of retrieval products. However, characterizing this variability is challenging due to the lack of independent high-resolution measurements. Here we use tropospheric NO2 vertical column (VC) measurements from the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument with a spatial resolution of about 250 m 250 m to quantify the normalized SGV (i.e., the standard deviation of the sub-grid GeoTASO values within the sampled satellite pixel divided by their mean of the sub-grid GeoTASO values within the sampled satellite pixel) for different satellite pixel sizes. We use the GeoTASO measurements over the Seoul Metropolitan Area (SMA) and Busan region of South Korea during the 2016 KORUS‐AQ field campaign, and over the Los Angeles Basin, USA during the 2017 SARP field campaign. We find that the normalized SGV of NO2 VC increases with increasing satellite pixel sizes (from ~10 % for 0.5 km × 0.5 km pixel size to ~35 % for 25 km × 25 km pixel size), and this relationship holds for the three study regions, which are also within the domains of upcoming geostationary satellite air quality missions. We also quantify the temporal variability of the retrieved NO2 VC within the same satellite pixels (represented by the difference of retrieved values at two different times of a day). For a given satellite pixel size, the temporal variability within the same satellite pixels increases with the sampling time difference over SMA. For a given small (e.g., 2 VC increases with the increasing spatial resolution over the SMA, Busan region, and the Los Angeles basin. The results of this study have implications for future satellite design and retrieval interpretation, and validation when comparing pixel data with local observations. In addition, the analyses presented in this study are equally applicable in model evaluation when comparing model grid values to local observations. Results from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model indicate that the normalized satellite SGV of tropospheric NO2 VC calculated in this study could serve as an upper bound to the satellite SGV of other species (e.g., CO and SO2) that share common source(s) with NO2 but have relatively longer lifetime.

Rising emissions from wildfires over recent decades in the Pacific Northwest are known to counteract the reductions in human-produced aerosol pollution over North America. Since amplified Pacific Northwest wildfires are predicted under accelerating climate change, it is essential to understand both local and transported contributions to air pollution in North America. Here, we find corresponding increases for carbon monoxide emitted from the Pacific Northwest wildfires and observe significant impacts on both local and down-wind air pollution. Between 2002 and 2018, the Pacific Northwest atmospheric carbon monoxide abundance increased in August, while other months showed decreasing carbon monoxide, so modifying the seasonal pattern. These seasonal pattern changes extend over large regions of North America, to the Central USA and Northeast North America regions, indicating that transported wildfire pollution could potentially impact the health of millions of people.

While the complementarity of CO data in monitoring CO2 from fossil-fuel combustion (ffCO2) is widely known, a rigorous demonstration of its use in reducing uncertainties on top-down regional ffCO2 emissions is still warranted. Here, we report a case study investigating the regional covariation of observed and modeled abundances of CO, CO2, and ffCO2 and demonstrating its implication to joint CO:CO2 inversions. We use data from a recent aircraft field campaign (KORUS-AQ) conducted over Korea and neighboring regions on May 2016 for this case study. We use the Community Atmosphere Model with Chemistry (CAM-Chem) to simulate CO, CO2, ffCO2 and associated source tags, using a posteriori fluxes from global CO2 flux inversions and CO emissions independently calibrated against CO data. Among other model-data comparisons, CAM-Chem simulations show an underestimation in CO2 (1 ppm), CO (24 ppb) and ffCO2 (1 ppm) against aircraft measurements. These are all within the range of model and data uncertainties. Although the overall observed enhancement ratio, ΔCO⁄ΔCO2 (~ 13.3 &om; 0.21 ppb/ppm), is well captured by CAM-Chem (~ 13.8 ± 0.23 ppb/ppm), we find an overestimation (29 ppb/ppm) for air samples between 2 to 3 km, where East Asian influence is substantial (35 %). The contribution of ffCO2 from Korea and Japan is smaller (30 %) and localized below 3 km, suggesting that regional ffCO2 and background and non-ffCO2 cannot be neglected in interpreting observed enhancements in this region. These spatial variations translate in the joint CO:CO2 inversion to increases in a posteriori ffCO2 estimates from East Asia (27 % ± 24 %) and Korea and Japan (9 % ± 17 %). This is consistent (albeit larger in 1-sigma uncertainty) with our estimate using 14CO2 data (27 % ± 9 % and 10 % ± 3 %, respectively). In contrast, the inversion using only CO2 data shows a decrease by ~ 5 % ± 27 % in East Asia and ~ 6 % ± 19 % in Korea and Japan. Our results show that inversions using both CO2 and CO can be an effective approach in constraining ffCO2 when the regional variations of CO and CO2 relationships are appropriately accounted for. Although this further points to the potential of augmenting current observing system of CO2 with CO for global inverse analyses of ffCO2 from different regions of the globe, we highlight the need to verify the spatiotemporal distribution of the covariation of CO with CO2 in both regional and global models. We caution its use for constraining local ffCO2, unless the spatiotemporal a priori flux distribution and surface processes are reasonably represented, as they may confound the analysis. These have important implications on inversion studies using columnar data from satellite observations, especially for regions lacking necessary verification measurements.

The Community Earth System Model version 2 (CESM2) includes a detailed representation of chemistry throughout the atmosphere in the Community Atmosphere Model with chemistry and Whole Atmosphere Community Climate Model configurations. These model configurations use the Model for Ozone and Related chemical Tracers (MOZART) family of chemical mechanisms, covering the troposphere, stratosphere, mesosphere, and lower thermosphere. The new MOZART tropospheric chemistry scheme (T1) has a number of updates over the previous version (MOZART‐4) in CESM, including improvements to the oxidation of isoprene and terpenes, organic nitrate speciation, and aromatic speciation and oxidation and thus improved representation of ozone and secondary organic aerosol precursors. An evaluation of the present‐day simulations of CESM2 being provided for Climate Model Intercomparison Project round 6 (CMIP6) is presented. These simulations, using the anthropogenic and biomass burning emissions from the inventories specified for CMIP6, as well as online calculation of emissions of biogenic compounds, lightning NO, dust, and sea salt, indicate an underestimate of anthropogenic emissions of a variety of compounds, including carbon monoxide and hydrocarbons. The simulation of surface ozone in the southeast United States is improved over previous model versions, largely due to the improved representation of reactive nitrogen and organic nitrate compounds resulting in a lower ozone production rate than in CESM1 but still overestimates observations in summer. The simulation of tropospheric ozone agrees well with ozonesonde observations in many parts of the globe. The comparison of NOx and PAN to aircraft observations indicates the model simulates the nitrogen budget well.

The performance of the Measurements of Pollution in the Troposphere (MOPITT) retrievals over urban regions has not been validated systematically, even though MOPITT observations are widely used to study CO over urban regions. Here we validate MOPITT products over urban regions using aircraft measurements from DISCOVER-AQ, SEAC4RS, ARIAs, A-FORCE, and KORUS-AQ campaigns. Overall, MOPITT performs reasonably well over both urban and non-urban regions, overall biases for V8J and V8T vary from −0.7 % to 0.0 %, and from 2.0 % to 3.5 %, respectively. The evaluation statistics of MOPITT V8J and V8T over non-urban regions are better than that over urban regions with smaller biases and higher correlation coefficients. We find that the performance of MOPITT V8J and V8T at high CO concentrations is not as good as that at low CO concentrations, although CO variability may tend to exaggerate retrieval biases in heavily-polluted scenes. We test the sensitivities of validation results to assumptions and data filters applied during the comparisons of MOPITT retrievals and in-situ profiles. The results at the surface are insensitive to the model-based profile extension (required due to aircraft altitude limitations) whereas the results at levels with limited aircraft observations are more sensitive to the model-based profile extension. The validation results are insensitive to the allowed maximum time difference as criteria for co-location (12 hours, 6 hours, 3 hours, and 1 hour), and are generally insensitive to the radius for co-location, except for the case where the radius is small (25 km) and hence the MOPITT retrievals included in the validation become very small. Daytime MOPITT products have overall smaller biases than nighttime MOPITT products when comparing both MOPITT daytime and nighttime retrievals to the daytime aircraft observations. However, it would be premature to draw conclusions on the performance of MOPITT nighttime retrievals without nighttime aircraft observations. Applying signal-to-noise ratio (SNR) filters does not necessarily improve the overall agreement between MOPITT retrievals and in-situ profiles, likely due to the reduced number of MOPITT retrievals that result for comparison. Comparisons of MOPITT retrievals and in-situ profiles over complex urban or polluted regimes are inherently challenging due to spatial and temporal variabilities of CO within MOPITT retrieval pixels (i.e., footprints). We demonstrate the some of that errors are due to CO representativeness with these sensitivity tests, but further quantification of validation errors due to CO variability within the MOPITT footprint will require future work.

Multiple tropospheric gases are analyzed in ten years of observations (2010–2019) using a high-resolution ground-based solar absorption Fourier Transform InfraRed (FTIR) instrument in the Colorado Northern Front Range (CNFR). The first year of measurements in 2010 coincides with the start of the remarkable increase of oil and natural (O&NG) extraction in the region. We show seasonal variations and trends of atmospheric gases related to O&NG (C2H6), cattle feedlot activities (NH3), urban emissions (CO, C2H2), biomass burning (HCN), and volatile organic compounds related to photochemistry and ozone production (H2CO, HCOOH). The long-term time series of C2H6 shows clear episodic peak-enhancements related to local O&NG emissions that contribute to a positive trend of 0.9 ± 0.3% ⋅yr−1. NH3 also shows episodic enhancements and has the greatest rate of change for the gases studied here (2.7 ± 0.7% ⋅yr−1). Simulations of all gases are presented using the Community Atmosphere Model with chemistry (CAM-chem) within the Community Earth System Model (CESM) framework. Modeled gases are compared to the observations using different combinations of global emissions in order to determine the best combination for the CNFR. For most gases, using best emissions, the annual rate of change obtained with CAM-chem agrees with the FTIR observations, except for NH3, which is underestimated by a factor of 6. Nevertheless, simulations of NH3 show that the positive trend in NH3 is due to a decrease in its removal via reaction with H2SO4 from a reduction in coal power plant emissions. The seasonal variations of all gases are generally well represented in the model, although magnitudes are often underestimated. The anthropogenic emissions of CO, C2H2, and C2H6 are underestimated by about 20%, 40%, and 65%, respectively, independent of emission inventories. While NH3 summer values are underestimated significantly, other months show low relative mean difference between FTIR observations and CAM-chem simulations. Excellent agreement is found for H2CO, but for HCOOH a factor of 2–3 is needed in the simulations to match observations, pointing to a significant missing source. HCN, a tracer for biomass burning emissions, is well reproduced by the NCAR Fire Inventory (FINN). Furthermore, we show here a simple approach to identify local enhancements of gases related to nearby O&NG and concentrated animal feeding operations. These enhancements above ambient levels are used to estimate emission ratios (ER) of C2H6, H2CO, and HCOOH relative to CO from O&NG air masses. A median ER (and standard deviation) of 0.48 ± 0.41, 0.26 ± 0.15, and 0.13 ± 0.09 [ppb/ppb] are estimated for C2H6, H2CO, and HCOOH, respectively and are representative of the ten years of observations. ER can be used to estimate emission factors (EF), although CO emissions from O&NG need to be well-known. To complement the FTIR observations, we show results derived with airborne observations during The Front Range Air Pollution and Photochemical Experiment (FRAPPE) in 2014. Even though FTIR and FRAPPE observations can not be compared easily they both yield similar median ER. Finally, plumes of fires originated in the western United States in August 2015 were identified and EF are estimated.

Volatile organic compounds (VOCs) are important precursors to the formation of ozone and fine particulate matter, the two pollutants of most concern in Sydney, Australia. Despite this importance, there are very few published measurements of ambient VOC concentrations in Australia. In this paper, we present mole fractions of several important VOCs measured during the campaign known as MUMBA (Measurements of Urban, Marine and Biogenic Air) in the Australian city of Wollongong (34&deg, S). We particularly focus on measurements made during periods when clean marine air impacted the measurement site and on VOCs of biogenic origin. Typical unpolluted marine air mole fractions during austral summer 2012-2013 at latitude 34&deg, S were established for CO2 (391.0 &plusmn, 0.6 ppm), CH4 (1760.1 &plusmn, 0.4 ppb), N2O (325.04 &plusmn, 0.08 ppb), CO (52.4 &plusmn, 1.7 ppb), O3 (20.5 &plusmn, 1.1 ppb), acetaldehyde (190 &plusmn, 40 ppt), acetone (260 &plusmn, 30 ppt), dimethyl sulphide (50 &plusmn, 10 ppt), benzene (20 &plusmn, 10 ppt), toluene (30 &plusmn, 20 ppt), C8H10 aromatics (23 &plusmn, 6 ppt) and C9H12 aromatics (36 &plusmn, 7 ppt). The MUMBA site was frequently influenced by VOCs of biogenic origin from a nearby strip of forested parkland to the east due to the dominant north-easterly afternoon sea breeze. VOCs from the more distant densely forested escarpment to the west also impacted the site, especially during two days of extreme heat and strong westerly winds. The relative amounts of different biogenic VOCs observed for these two biomes differed, with much larger increases of isoprene than of monoterpenes or methanol during the hot westerly winds from the escarpment than with cooler winds from the east. However, whether this was due to different vegetation types or was solely the result of the extreme temperatures is not entirely clear. We conclude that the clean marine air and biogenic signatures measured during the MUMBA campaign provide useful information about the typical abundance of several key VOCs and can be used to constrain chemical transport model simulations of the atmosphere in this poorly sampled region of the world.

Observations of carbon monoxide (CO) from the Measurements Of Pollution In The Troposphere (MOPITT) instrument onboard the Terra spacecraft were expected to have an accuracy of 10 % prior to launch in 1999. Here we evaluate MOPITT version 7 joint TIR-NIR (V7J) accuracy and precision, and suggest ways to further improve the accuracy of the observations. We take five steps involving filtering or bias corrections to reduce scatter and bias in the data relative to other MOPITT soundings, and ground based measurements. 1) We apply a preliminary filtering scheme in which measurements over snow and ice are removed. 2) We find a systematic pairwise bias among the four MOPITT along-track detectors (pixels) on the order of 3–4 ppb with a small temporal trend, which we remove on a global scale using a temporally trended bias correction. 3) Using a small region approximation (SRA) a new filtering scheme is developed and applied based on additional quality indicators such as signal-to-noise. After applying these new filters, the root mean squared error computed using the local median from the SRA over 16 years of global observations decreases from 3.84 ppb to 2.55 ppb. 4) We also use the SRA to find variability in MOPITT retrieval anomalies that relates to retrieval parameters. We apply a bias correction to one parameter from this analysis. 5) After applying the previous bias corrections and filtering, we compare the MOPITT results with the GGG2014 ground-based Total Carbon Column Observing Network (TCCON) observations to obtain an overall global bias correction. These comparisons show that MOPITT V7J is biased high by about 6–8 %, which is similar to past studies using independent validation datasets on V6J. When using TCCON spectrometric column retrievals without the standard airmass correction or scaling to aircraft (WMO scale), the ground and satellite based observations overall agree to better than 0.5 %. GEOS-Chem data assimilations are used to estimate the influence of filtering and scaling to TCCON on global CO, and tend to pull concentrations away from the prior, and closer to the truth. We conclude with suggestions for further improving the MOPITT data products.

Retrievals of vertical profiles of key atmospheric gases provide a critical long-term record from ground-based Fourier transform infrared (FTIR) solar absorption measurements. However, the characterization of the retrieved vertical profile structure can be difficult to validate, especially for gases with large vertical gradients and spatial–temporal variability such as water vapor. In this work, we evaluate the accuracy of the most common water vapor isotope (H216O, hereafter WV) FTIR retrievals in the lower and upper troposphere–lower stratosphere. Coincident high-quality vertically resolved WV profile measurements obtained from 2010 to 2016 with balloon-borne NOAA frost point hygrometers (FPHs) are used as reference to evaluate the performance of the retrieved profiles at two sites: Boulder (BLD), Colorado, and at the mountaintop observatory of Mauna Loa (MLO), Hawaii. For a meaningful comparison, the spatial–temporal variability has been investigated. We present results of comparisons among FTIR retrievals with unsmoothed and smoothed FPH profiles to assess WV vertical gradients. Additionally, we evaluate the quantitative impact of different a priori profiles in the retrieval of WV. An orthogonal linear regression analysis shows the best correlation among tropospheric layers using ERA-Interim (ERA-I) a priori profiles and biases are lower for unsmoothed comparisons. In Boulder, we found a negative bias of 0.02±1.9 % (r=0.95) for the 1.5–3 km layer. A larger negative bias of 11.1±3.5 % (r=0.97) was found in the lower free troposphere layer of 3–5 km attributed to rapid vertical change of WV, which is not always captured by the retrievals. The bias improves in the 5–7.5 km layer (1.0±5.3 %, r=0.94). The bias remains at about 13 % for layers above 7.5 km but below 13.5 km. At MLO the spatial mismatch is significantly larger due to the launch of the sonde being farther from the FTIR location. Nevertheless, we estimate a negative bias of 5.9±4.6 % (r=0.93) for the 3.5–5.5 km layer and 9.9±3.7 % (r=0.93) for the 5.5–7.5 km layer, and we measure positive biases of 6.2±3.6 % (r=0.95) for the 7.5–10 km layer and 12.6 % and greater values above 10 km. The agreement for the first layer is significantly better at BLD because the air masses are similar for both FTIR and FPH. Furthermore, for the first time we study the influence of different WV a priori profiles in the retrieval of selected gas profiles. Using NDACC standard retrievals we present results for hydrogen cyanide (HCN), carbon monoxide (CO), and ethane (C2H6) by taking NOAA FPH profiles as the ground truth and evaluating the impact of other WV profiles. We show that the effect is minor for C2H6 (bias  % for all WV sources) among all vertical layers. However, for HCN we found significant biases between 6 % for layers close to the surface and 2 % for the upper troposphere depending on the WV profile source. The best results (reduced bias and precision and r values closer to unity) are always found for pre-retrieved WV. Therefore, we recommend first retrieving WV to use in subsequent retrieval of gases.

Fire emissions are a major contributor to atmospheric composition, affecting atmospheric oxidizing capacity and air quality. Transported amounts from Northern Hemisphere boreal fires can reach the ...

Author(s): Tang, Wenfu; Emmons, Louisa K; Jr, Arellano Avelino F; Gaubert, Benjamin; Knote, Christoph; Tilmes, Simone; Buchholz, Rebecca R; Pfister, Gabriele G; Diskin, Glenn S; Blake, Donald R; Blake, Nicola J; Meinardi, Simone; DiGangi, Joshua P; Choi, Yonghoon; Woo, Jung-Hun; He, Cenlin; Schroeder, Jason R; Suh, Inseon; Lee, Hyo-Jung; Jo, Hyun-Young; Kanaya, Yugo; Jung, Jinsang; Lee, Youngjae; Kim, Danbi

We present findings from the Measurements of Urban, Marine and Biogenic Air (MUMBA) campaign, which took place in the coastal city of Wollongong in New South Wales, Australia. We focus on a few key air quality indicators, along with a comparison to regional scale chemical transport model predictions at a spatial resolution of 1 km by 1 km. We find that the CSIRO chemical transport model provides accurate simulations of ozone concentrations at most times, but underestimates the ozone enhancements that occur during extreme temperature events. The model also meets previously published performance standards for fine particulate matter less than 2.5 microns in diameter (PM2.5), and the larger aerosol fraction (PM10). We explore the observed composition of the atmosphere within this urban air-shed during the MUMBA campaign and discuss the different influences on air quality in the city. Our findings suggest that further improvements to our ability to simulate air quality in this coastal city can be made through more accurate anthropogenic and biogenic emissions inventories and better understanding of the impact of extreme temperatures on air quality. The challenges in modelling air quality within the urban air-shed of Wollongong, including difficulties in accurate simulation of the local meteorology, are likely to be replicated in many other coastal cities in the Southern Hemisphere.

This paper studies the seasonal variation of surface and column CO at three different sites (Paris, Jungfraujoch and Wollongong), with an emphasis on establishing a link between the CO vertical distribution and the nature of CO emission sources. We find the first evidence of a time lag between surface and free tropospheric CO seasonal variations in the Northern Hemisphere. The CO seasonal variability obtained from the total columns and free tropospheric partial columns shows a maximum around March-April and a minimum around September-October in the Northern Hemisphere (Paris and Jungfraujoch). In the Southern Hemisphere (Wollongong) this seasonal variability is shifted by about 6 months. Satellite observations by the IASI-MetOp (Infrared Atmospheric Sounding Interferometer) and MOPITT (Measurements Of Pollution In The Troposphere) instruments confirm this seasonality. Ground-based FTIR (Fourier transform infrared) measurements provide useful complementary information due to good sensitivity in the boundary layer. In situ surface measurements of CO volume mixing ratios at the Paris and Jungfraujoch sites reveal a time lag of the near-surface seasonal variability of about 2 months with respect to the total column variability at the same sites. The chemical transport model GEOS-Chem (Goddard Earth Observing System chemical transport model) is employed to interpret our observations. GEOS-Chem sensitivity runs identify the emission sources influencing the seasonal variation of CO. At both Paris and Jungfraujoch, the surface seasonality is mainly driven by anthropogenic emissions, while the total column seasonality is also controlled by air masses transported from distant sources. At Wollongong, where the CO seasonality is mainly affected by biomass burning, no time shift is observed between surface measurements and total column data., SCOPUS: ar.j, info:eu-repo/semantics/published