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One of the scientific objectives of the Asian Summer Monsoon Chemical & CLimate Impact Project (ACCLIP) project was to sample the chemical content of air processed in the Asian Summer Monsoon (ASM) to ascertain its role in UTLS ozone chemistry and re-distributing active species and short-lived climate forcers. Whole Air Samplers were used on the NSF/NCAR GV and NASA WB-57 aircraft to collect samples for analysis of a range of organic compounds, including organic halogens and hydrocarbons. From these samples we determined vertical distributions of individual species as well as correlations between species. We also determined total organic chlorine and bromine values and subdivided those into long-lived and short-lived contributions. In addition, we characterized the composition of samples that were collected within the ASM, within the ASM outflow, and within the background air. These results will be compared with model output from ACCLIP.

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 10 s (2 km) 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 146 494 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 2 km ATom 10 s 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 2 km 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 https://doi.org/10.5194/acp-21-13729-2021.

The latest operational National Air Quality Forecast Capability (NAQFC) has been advanced to use the Community Multiscale Air Quality (CMAQ) model (version 5.3.1) with the CB6r3 (Carbon Bond 6 revision 3) AERO7 (version 7 of the aerosol module) chemical mechanism and is driven by the Finite-Volume Cubed-Sphere (FV3) Global Forecast System, version 16 (GFSv16). This update has been accomplished via the development of the meteorological preprocessor, NOAA-EPA Atmosphere–Chemistry Coupler (NACC), adapted from the existing Meteorology–Chemistry Interface Processor (MCIP). Differing from the typically used Weather Research and Forecasting (WRF) CMAQ system in the air quality research community, the interpolation-based NACC can use various meteorological outputs to drive the CMAQ model (e.g., FV3-GFSv16), even though they are on different grids. In this study, we compare and evaluate GFSv16-CMAQ and WRFv4.0.3-CMAQ using observations over the contiguous United States (CONUS) in summer 2019 that have been verified with surface meteorological and AIRNow observations. During this period, the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign was performed, and we compare the two models with airborne measurements from the NASA DC-8 aircraft. The GFS-CMAQ and WRF-CMAQ systems show similar performance overall with some differences for certain events, species and regions. The GFSv16 meteorology tends to have a stronger diurnal variability in the planetary boundary layer height (higher during daytime and lower at night) than WRF over the US Pacific coast, and it also predicted lower nighttime 10 m winds. In summer 2019, the GFS-CMAQ system showed better surface ozone (O3) than WRF-CMAQ at night over the CONUS domain; however, the models' fine particulate matter (PM2.5) predictions showed mixed verification results: GFS-CMAQ yielded better mean biases but poorer correlations over the Pacific coast. These results indicate that using global GFSv16 meteorology with NACC to directly drive CMAQ via interpolation is feasible and yields reasonable results compared to the commonly used WRF approach.

Clouds remain one of the largest uncertainties connected to future climate predictions, due partly to existing gaps in the understanding of cloud microphysical processes. Airborne in situ observations provide relevant data sets to investigate these cloud microphysical processes. However, the process of selecting and classifying cloud sequences can be very time consuming if done manually and algorithms developed by the community are numerous and prone to misclassification, particularly when coarse aerosol is present.We developed a novel cloud indicating algorithm, automatically detecting and classifying clouds in airborne in situ observations, based on data of three international airborne field campaigns, including ATom (Atmospheric Tomography; 2016-2018), A-LIFE (Absorbing aerosol layers in a changing climate: aging, lifetime and dynamics; 2017) and FIREX-AQ (Fire Influence on Regional to Global Environments and Air Quality; 2019). The algorithm automatically detects flight sequences in clouds and classifies the cloud type using size distribution measurements, combined with measurements of relative humidity and temperature. In addition, the cloud indicator algorithm was tuned and evaluated to successfully differentiate cloud sequences to those with enhanced concentrations of coarse mode particles (e.g. mineral dust, sea salt, or biomass burning layers).In this study, we introduce the cloud indicator algorithm and present its ability to differentiate aerosol layers from clouds with a detailed analysis of unique measurements of ice clouds imbedded in a Saharan dust layer. Furthermore, we show first results of the analysis of the combined global-scale data set of ATom, A-LIFE and FIREX-AQ with regard to properties of aerosol-impacted cirrus clouds as well as properties of cirrus clouds in pristine environments.

Characteristics of the Version 9 (V9) MOPITT ("Measurements of Pollution in the Troposphere") satellite retrieval product for tropospheric carbon monoxide (CO) are described. The new V9 product includes many CO retrievals over land which, in previous MOPITT product versions, would have been discarded by the cloud detection algorithm. Globally, the number of daytime MOPITT retrievals over land has increased by 30–40 % relative to the Version 8 product, although the increase in retrieval coverage exhibits significant geographical variability. Areas benefiting from the improved cloud detection performance include (but are not limited to) source regions often characterized by high aerosol concentrations. The V9 MOPITT product also incorporates a modified calibration strategy for the MOPITT near-infrared (NIR) CO channels, resulting in greater temporal consistency for the NIR-only and thermal infrared-near infrared (TIR-NIR) retrieval variants. Validation results based on in-situ CO profiles acquired from aircraft in a variety of contexts indicate that retrieval biases for V9 are typically within the range of ±5 % and are generally comparable to results for the V8 product.

Aircraft observations and ozonesonde profiles collected on July 14 and 27, 2011, during the Maryland month-long DISCOVER-AQ campaign, indicate the presence of stratospheric air just above the planetary boundary layer (PBL). This raises the question of whether summer stratospheric intrusions (SIs) elevate surface ozone levels and to what degree they influence background ozone levels and contribute to ozone production. We used idealized stratospheric air tracers, along with observations, to determine the frequency and extent of SIs in Maryland during July 2011. On 4 of 14 flight days, SIs were detected in layers that the aircraft encountered above the PBL from the coincidence of enhanced ozone, moderate CO, and low moisture. Satellite observations of lower tropospheric humidity confirmed the occurrence of synoptic scale influence of SIs as do simulations with the GEOS-5 Atmospheric General Circulation Model. The evolution of GEOS-5 stratospheric air tracers agree with the timing and location of observed stratospheric influence and indicate that more than 50% of air in SI layers above the PBL had resided in the stratosphere within the previous 14 days. Despite having a strong influence in the lower free troposphere, these events did not significantly affect surface ozone, which remained low on intrusion days. The model indicates similar frequencies of stratospheric influence during all summers from 2009–2013. GEOS-5 results suggest that, over Maryland, the strong inversion capping the summer PBL limits downward mixing of stratospheric air during much of the day, helping to preserve low surface ozone associated with frontal passages that precede SIs.

The exchange of trace gases between the Earth’s surface and its atmosphere drives atmospheric composition. Airborne eddy covariance can provide observational constraints on surface fluxes at regional scales, helping to bridge the gap between top-down and bottom-up flux estimates and offering novel insights into biophysical and biogeochemical processes. The NASA Carbon Airborne Flux Experiment (CARAFE) utilizes the NASA C-23 Sherpa aircraft with a suite of commercial and custom instrumentation to acquire fluxes of carbon dioxide, methane, sensible heat, and latent heat at high spatial resolution. Key components of the CARAFE payload are described, including the meteorological, greenhouse gas, water vapor, and surface imaging systems. Continuous wavelet transforms deliver spatially-resolved fluxes along aircraft flight tracks. Flux analysis methodology is discussed in depth, with special emphasis on evaluation of uncertainties and vertical flux divergence. CARAFE has successfully flown two missions in the Eastern U.S. in 2016 and 2017, quantifying fluxes over forest, cropland, wetlands, and water. Results from these campaigns highlight the performance of this system and its potential to further our understanding of ecosystem exchange.