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Measurement of light absorption of solar radiation by aerosols is vital for assessing direct aerosol radiative forcing, which affects local and global climate. Low-cost and easy-to-operate filter-based instruments, such as the Particle Soot Absorption Photometer (PSAP), that collect aerosols on a filter and measure light attenuation through the filter are widely used to infer aerosol light absorption. However, filter-based absorption measurements are subject to artifacts that are difficult to quantify. These artifacts are associated with the presence of the filter medium and the complex interactions between the filter fibers and accumulated aerosols. Various correction algorithms have been introduced to correct for the filter-based absorption coefficient measurements toward predicting the particle-phase absorption coefficient (Babs). However, the inability of these algorithms to incorporate into their formulations the complex matrix of influencing parameters such as particle asymmetry parameter, particle size, and particle penetration depth results in prediction of particle-phase absorption coefficients with relatively low accuracy. The analytical forms of corrections also suffer from a lack of universal applicability: different corrections are required for rural and urban sites across the world. In this study, we analyzed and compared 3 months of high-time-resolution ambient aerosol absorption data collected synchronously using a three-wavelength photoacoustic absorption spectrometer (PASS) and PSAP. Both instruments were operated on the same sampling inlet at the Department of Energy's Atmospheric Radiation Measurement program's Southern Great Plains (SGP) user facility in Oklahoma. We implemented the two most commonly used analytical correction algorithms, namely, Virkkula (2010) and the average of Virkkula (2010) and Ogren (2010)–Bond et al. (1999) as well as a random forest regression (RFR) machine learning algorithm to predict Babs values from the PSAP's filter-based measurements. The predicted Babs was compared against the reference Babs measured by the PASS. The RFR algorithm performed the best by yielding the lowest root mean square error of prediction. The algorithm was trained using input datasets from the PSAP (transmission and uncorrected absorption coefficient), a co-located nephelometer (scattering coefficients), and the Aerosol Chemical Speciation Monitor (mass concentration of non-refractory aerosol particles). A revised form of the Virkkula (2010) algorithm suitable for the SGP site has been proposed; however, its performance yields approximately 2-fold errors when compared to the RFR algorithm. To generalize the accuracy and applicability of our proposed RFR algorithm, we trained and tested it on a dataset of laboratory measurements of combustion aerosols. Input variables to the algorithm included the aerosol number size distribution from the Scanning Mobility Particle Sizer, absorption coefficients from the filter-based Tricolor Absorption Photometer, and scattering coefficients from a multiwavelength nephelometer. The RFR algorithm predicted Babs values within 5 % of the reference Babs measured by the multiwavelength PASS during the laboratory experiments. Thus, we show that machine learning approaches offer a promising path to correct for biases in long-term filter-based absorption datasets and accurately quantify their variability and trends needed for robust radiative forcing determination.

Aerosol particles dynamically evolve in the atmosphere by physicochemical interactions with sunlight, trace chemical species, and water. Current modeling approaches fix properties such as aerosol refractive index, introducing spatial and temporal errors in the radiative impacts. Further progress requires a process-level description of the refractive indices as the particles age and experience physicochemical transformations. We present two multivariate modeling approaches of light absorption by brown carbon (BrC). The initial approach was to extend the modeling framework of the refractive index at 589 nm (

The Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over its first 11 years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inversion systems (models) which do not assimilate satellite-derived CO2. In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter, and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2×106 out of 37×106) were assigned a “good” XCO2 quality flag, as compared to 3.9 % in v7.3 (

Satellite-based observations of atmospheric carbon dioxide (CO 2 ) provide measurements in remote regions, such as the biologically sensitive but undersampled northern high latitudes, and are progressing toward true global data coverage. Recent improvements in satellite retrievals of total column-averaged dry air mole fractions of CO 2 ( X CO 2 ) from the NASA Orbiting Carbon Observatory 2 (OCO-2) have allowed for unprecedented data coverage of northern high-latitude regions, while maintaining acceptable accuracy and consistency relative to ground-based observations, and finally providing sufficient data in spring and autumn for analysis of satellite-observed X CO 2 seasonal cycles across a majority of terrestrial northern high-latitude regions. Here, we present an analysis of X CO 2 seasonal cycles calculated from OCO-2 data for temperate, boreal, and tundra regions, subdivided into 5 ∘ latitude by 20 ∘ longitude zones. We quantify the seasonal cycle amplitudes (SCAs) and the annual half drawdown day (HDD). OCO-2 SCAs are in good agreement with ground-based observations at five high-latitude sites, and OCO-2 SCAs show very close agreement with SCAs calculated for model estimates of X CO 2 from the Copernicus Atmosphere Monitoring Services (CAMS) global inversion-optimized greenhouse gas flux model v19r1 and the CarbonTracker2019 model (CT2019B). Model estimates of X CO 2 from the GEOS-Chem CO 2 simulation version 12.7.2 with underlying biospheric fluxes from CarbonTracker2019 (GC-CT2019) yield SCAs of larger magnitude and spread over a larger range than those from CAMS, CT2019B, or OCO-2; however, GC-CT2019 SCAs still exhibit a very similar spatial distribution across northern high-latitude regions to that from CAMS, CT2019B, and OCO-2. Zones in the Asian boreal forest were found to have exceptionally large SCA and early HDD, and both OCO-2 data and model estimates yield a distinct longitudinal gradient of increasing SCA from west to east across the Eurasian continent. In northern high-latitude regions, spanning latitudes from 47 to 72 ∘ N, longitudinal gradients in both SCA and HDD are at least as pronounced as latitudinal gradients, suggesting a role for global atmospheric transport patterns in defining spatial distributions of X CO 2 seasonality across these regions. GEOS-Chem surface contact tracers show that the largest X CO 2 SCAs occur in areas with the greatest contact with land surfaces, integrated over 15–30 d. The correlation of X CO 2 SCA with these land surface contact tracers is stronger than the correlation of X CO 2 SCA with the SCA of CO 2 fluxes or the total annual CO 2 flux within each 5 ∘ latitude by 20 ∘ longitude zone. This indicates that accumulation of terrestrial CO 2 flux during atmospheric transport is a major driver of regional variations in X CO 2 SCA.

During the first phase of the Biomass Burn Operational Project (BBOP) field campaign, conducted in the Pacific Northwest, the DOE G-1 aircraft was used to follow the time evolution of wildfire smoke from near the point of emission to locations 2–3.5 h downwind. In nine flights we made repeated transects of wildfire plumes at varying downwind distances and could thereby follow the plume's time evolution. On average there was little change in dilution-normalized aerosol mass concentration as a function of downwind distance. This consistency hides a dynamic system in which primary aerosol particles are evaporating and secondary ones condensing. Organic aerosol is oxidized as a result. On all transects more than 90 % of aerosol is organic. In freshly emitted smoke aerosol, NH4+ is approximately equivalent to NO3. After 2 h of daytime aging, NH4+ increased and is approximately equivalent to the sum of Cl, SO42, and NO3. Particle size increased with downwind distance, causing particles to be more efficient scatters. Averaged over nine flights, mass scattering efficiency (MSE) increased in ∼ 2 h by 56 % and doubled in one flight. Mechanisms for redistributing mass from small to large particles are discussed. Coagulation is effective at moving aerosol from the Aitken to accumulation modes but yields only a minor increase in MSE. As absorption remained nearly constant with age, the time evolution of single scatter albedo was controlled by age-dependent scattering. Near-fire aerosol had a single scatter albedo (SSA) of 0.8–0.9. After 1 to 2 h of aging SSAs were typically 0.9 and greater. Assuming global-average surface and atmospheric conditions, the observed age dependence in SSA would change the direct radiative effect of a wildfire plume from near zero near the fire to a cooling effect downwind.

Seasonal CO2 exchange in the boreal forest plays an important role in the global carbon budget and in driving interannual variability in seasonal cycles of atmospheric CO2 . Satellite-based observations from polar orbiting satellites like the Orbiting Carbon Observatory-2 (OCO-2) offer an opportunity to characterize boreal forest seasonal cycles across longitudes with a spatially and temporally rich data set, but data quality controls and biases still require vetting at high latitudes. With the objective of improving data availability at northern, terrestrial high latitudes, this study evaluates quality control methods and biases of OCO-2 retrievals of atmospheric column-averaged dry air mole fractions of CO2 ( X CO 2 ) in boreal forest regions. In addition to the standard quality control (QC) filters recommended for the Atmospheric Carbon Observations from Space (ACOS) B8 (B8 QC) and ACOS B9 (B9 QC) OCO-2 retrievals, a third set of quality control filters were specifically tailored to boreal forest observations (boreal QC) with the goal of increasing data availability at high latitudes without sacrificing data quality. Ground-based reference measurements of X CO 2 include observations from two sites in the Total Carbon Column Observing Network (TCCON) at East Trout Lake, Saskatchewan, Canada, and Sodankyla, Finland. OCO-2 retrievals were also compared to ground-based observations from two Bruker EM27/SUN Fourier transform infrared spectrometers (FTSs) at Fairbanks, Alaska, USA. The EM27/SUN spectrometers that were deployed in Fairbanks were carefully monitored for instrument performance and were bias corrected to TCCON using observations at the Caltech TCCON site. The B9 QC were found to pass approximately twice as many OCO-2 retrievals over land north of 50 ∘ N than the B8 QC, and the boreal QC were found to pass approximately twice as many retrievals in May, August, and September as the B9 QC. While boreal QC results in a substantial increase in passable retrievals, this is accompanied by increases in the standard deviations in biases at boreal forest sites from ∼1.4 parts per million (ppm) with B9 QC to ∼1.6 ppm with boreal QC. Total average biases for coincident OCO-2 retrievals at the three sites considered did not consistently increase or decrease with different QC methods, and instead, responses to changes in QC varied according to site and satellite viewing geometries. Regardless of the quality control method used, seasonal variability in biases was observed, and this variability was more pronounced at Sodankyla and East Trout Lake than at Fairbanks. Long-term coincident observations from TCCON, EM27/SUN, and satellites from multiple locations would be necessary to determine whether the reduced seasonal variability in bias at Fairbanks is due to geography or instrumentation. Monthly average biases generally varied between −1 and +1 ppm at the three sites considered, with more negative biases in spring (March, April, and May – MAM) and autumn (September and October – SO) but more positive biases in the summer months (June, July, and August – JJA). Monthly standard deviations in biases ranged from approximately 1.0 to 2.0 ppm and did not exhibit strong seasonal dependence, apart from exceptionally high standard deviation observed with all three QC methods at Sodankyla in June. There was no evidence found to suggest that seasonal variability in bias is a direct result of air mass dependence in ground-based retrievals or of proximity bias from coincidence criteria, but there were a number of retrieval parameters used as quality control filters that exhibit seasonality and could contribute to seasonal dependence in OCO-2 bias. Furthermore, it was found that OCO-2 retrievals of X CO 2 without the standard OCO-2 bias correction exhibit almost no perceptible seasonal dependence in average monthly bias at these boreal forest sites, suggesting that seasonal variability in bias is introduced by the bias correction. Overall, we found that modified quality controls can allow for significant increases in passable OCO-2 retrievals with only marginal compromises in data quality, but seasonal dependence in biases still warrants further exploration.

Aerosols alter Earth’s radiative budget both directly and indirectly through interaction with clouds. Continuous observations are required to reduce the uncertainties in climate models associated with atmospheric processing and the interactions between aerosols and clouds. Field observations of aerosols are a central component of the Atmospheric Radiation Measurement (ARM) Facility’s global measurements. The ARM mission goal is to “provide the climate research community with strategically located in situ and remote sensing observatories designed to improve the understanding and representation, in climate and earth system models, of clouds and aerosols as well as their interactions and coupling with the Earth’s surface.” Since 1996, ARM has met this goal by operating Aerosol Observing Systems (AOS) for in situ measurement of aerosols. Currently the five ARM AOSs are the most comprehensive field deployable aerosol systems in the United States. The AOS suite includes seven measurement classes: number concentration, size distribution, chemical composition, radiative and optical properties, hygroscopicity, trace gases, and supporting meteorological conditions. AOSs are designed as standardized measurement platforms to enable intercomparison across the ARM Facility for regional process studies within a global context. The instrumentation and measurement capabilities of the ARM AOSs, along with a history of their design and field deployments are presented here.

Retrievals of total column NO2 (TCNO2) are compared for 14 sites from the Ozone Measuring Instrument (OMI using OMNO2-NASA v3.1) on the AURA satellite and from multiple ground-based PANDORA spectrometer instruments making direct-sun measurements. While OMI accurately provides the daily global distribution of retrieved TCNO2, OMI almost always underestimates the local amount of TCNO2 by 50 % to 100 % in polluted areas, while occasionally the daily OMI value exceeds that measured by PANDORA at very clean sites. Compared to local ground-based or aircraft measurements, OMI cannot resolve spatially variable TCNO2 pollution within a city or urban areas, which makes it less suitable for air quality assessments related to human health. In addition to systematic underestimates in polluted areas, OMI's selected 13:30 Equator crossing time polar orbit causes it to miss the frequently much higher values of TCNO2 that occur before or after the OMI overpass time. Six discussed Northern Hemisphere PANDORA sites have multi-year data records (Busan, Seoul, Washington DC, Waterflow, New Mexico, Boulder, Colorado, and Mauna Loa), and one site in the Southern Hemisphere (Buenos Aires, Argentina). The first four of these sites and Buenos Aires frequently have high TCNO2 (TCNO2 > 0.5 DU). Eight additional sites have shorter-term data records in the US and South Korea. One of these is a 1-year data record from a highly polluted site at City College in New York City with pollution levels comparable to Seoul, South Korea. OMI-estimated air mass factor, surface reflectivity, and the OMI 24 km × 13 km FOV (field of view) are three factors that can cause OMI to underestimate TCNO2. Because of the local inhomogeneity of NOx emissions, the large OMI FOV is the most likely factor for consistent underestimates when comparing OMI TCNO2 to retrievals from the small PANDORA effective FOV (measured in m2) calculated from the solar diameter of 0.5∘.

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.

The dairy industry in the San Joaquin Valley (SJV) is one of California’s largest methane (CH4) sources. Reducing dairy emissions is a priority for the state’s climate change plans. Observations of current dairy CH4 emissions are key to monitoring actions taken toward this goal. To help support this, we present new ground-based measurements of atmospheric column-averaged CH4 mixing ratio (XCH4) gradients across a group of 600 dairies in the central SJV using EM27/SUN solar spectrometers. We used measurements from the 2019 summer and 2020 winter seasons for a top-down emission inversion based on the WRF-STILT model. Our top-down estimates of the region’s dairy emissions range from 90% to 183% of the current CALGEM inventory’s emissions of 277Gg/yr. In contrast to the strong temperature dependence found by earlier dairy CH4 emission studies, we also find that our top-down emissions during the winter measurement days are comparable to the summer measurement days, possibly due to seasonal changes in dairy management practices and meteorological conditions. Furthermore, we find significant interday variability in our measurements and find that our emission estimates overlap with earlier top-down studies and bottom-up inventories in this region. Our study demonstrates how analysis of ground-based remotely sensed CH4 gradient observations can help improve our understanding of CH4 sources at scales relevant to mitigation policy. It also reflects the need for long-term monitoring of CH4 emissions in the region and at individual facilities to better understand their emissions.

We present indirect measurements of size-resolved ultrafine particle composition conducted during the Ocean-Atmosphere-Sea Ice-Snowpack (OASIS) Campaign in Utqiagvik, Alaska, during March 2009. This study focuses on measurements of size-resolved particle hygroscopicity and volatility measured over two periods of the campaign. During a period that represents background conditions in this location, particle hygroscopic growth factors (HGF) at 90% relative humidity ranged from 1.45 to 1.51, which combined with volatility measurements suggest a mixture of ∼30% ammoniated sulfates and ∼70% oxidized organics. Two separate regional ultrafine particle growth events were also observed during this campaign. Event 1 coincided with elevated levels of H2SO4 and solar radiation. These particles were highly hygroscopic (HGF=2.1 for 35nm particles), but were almost fully volatilized at 160°C. The air masses associated with both events originated over the Arctic Ocean. Event 1 was influenced by the upper marine boundary layer (200–350m AGL), while Event 2 spent more time closer to the surface (50–150m AGL) and over open ocean leads, suggesting marine influence in growth processes. Event 2 particles were slightly less hygroscopic (HGF=1.94 for 35nm and 1.67 for 15nm particles), and similarly volatile. We hypothesize that particles formed during both events contained 60–70% hygroscopic salts by volume, with the balance for Event 1 being sulfates and oxidized organics for Event 2. These observations suggest that primary sea spray may be an important initiator of ultrafine particle formation events in the Arctic late-winter, but a variety of processes may be responsible for condensational growth.

High explosive (HE) detonations reach pressures and temperatures that extend beyond normal environmental conditions, thereby permitting access to various carbon and metal allotropes of different morphologies, sizes and surface structures. The products of HE detonations are dependent on multiple parameters, including the chemical and physical properties of the starting material and atmospheric conditions (i.e. oxygen). One important factor is the HE oxygen balance, which is the extent to which the material can be oxidized. Insensitive HEs are designed to resist external stimuli that would cause detonation in conventional HEs. The insensitive HEs are negatively oxygen balanced and therefore produce not only gaseous species but solid carbon products during detonation. Insensitive HEs were studied, Composition B-3 and PBX 9501, with steady and overdriven geometries in an oxygen-free atmosphere that reached different pressure and temperature regimes. Small angle x-ray scattering provided the size and surface structure of the resulting particulates. Composition B-3 primary particles were 157.0 ± 0.3 Å and 199.5 ± 0.3 Å for steady and overdriven detonations; where PBX 9501 primary particles were larger than Composition B-3 at 300 ± 6 Å and 334.5 ± 0.3 Å for steady and overdriven detonations. The two compounds formed contrasting primary particles with different cluster structures, in the Composition B-3 steady detonation the particles were agglomerated into a surface fractal with rough surfaces where as the PBX 9501 was a mass fractal cluster with smooth surface primary particles. In the overdriven detonation the primary particles were reversed, Composition B-3 was agglomerated into a mass fractal structure with smooth surfaces and PBX 9501 had a surface fractal structure with a rough surface primary particles. Scanning electron microscopy provided a snapshot of the morphology of the materials on the micron length scale, supporting the observation of x-ray scattering that the Composition B-3 particulates/agglomerates are smaller than the PBX 9501. Raman spectroscopy provided information as to the carbon bonding of the detonation soot, showing significantly more product variation in Composition B-3 than PBX 9501, likely due the poor oxygen balance of Composition B-3 leading to more complex carbon bonding formations. Finally, x-ray photoelectron spectroscopy showed how the difference in the oxygen balance of the HE fuel directly relates to the amount of carbon-oxygen bonding that is present in the final products, where PBX 9501 had significantly more oxygen on the surface of the particulates. We used two HEs to understand the detonation pathways for both synthesis and atmospheric processes; where the chemical constituents of the particulates can promote processes such as self-lofting and aerosol-cloud interactions after the particles are launched into the troposphere or stratosphere during detonation.

Cities represent a large and concentrated portion of global greenhouse gas emissions, including methane. Quantifying methane emissions from urban areas is difficult, and inventories made using bottom-up accounting methods often differ greatly from top-down estimates generated from atmospheric observations. Emissions from leaks in natural gas infrastructure are difficult to predict and are therefore poorly constrained in bottom-up inventories. Natural gas infrastructure leaks and emissions from end uses can be spread throughout the city, and this diffuse source can represent a significant fraction of a city's total emissions. We investigated diffuse methane emissions of the city of Indianapolis, USA, during a field campaign in May 2016. A network of five portable solar-tracking Fourier transform infrared (FTIR) spectrometers was deployed throughout the city. These instruments measure the mole fraction of methane in a total column of air, giving them sensitivity to larger areas of the city than in situ sensors at the surface. We present an innovative inversion method to link these total column concentrations to surface fluxes. This method combines a Lagrangian transport model with a Bayesian inversion framework to estimate surface emissions and their uncertainties, together with determining the concentrations of methane in the air flowing into the city. Variations exceeding 10 ppb were observed in the inflowing air on a typical day, which is somewhat larger than the enhancements due to urban emissions ( ppb downwind of the city). We found diffuse methane emissions of 73(±22) mol s−1, which is about 50 % of the urban total and 68 % higher than estimated from bottom-up methods, although it is somewhat smaller than estimates from studies using tower and aircraft observations. The measurement and model techniques developed here address many of the challenges present when quantifying urban greenhouse gas emissions and will help in the design of future measurement schemes in other cities.

The Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over it's first eleven years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inverse modeling systems (models). In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million (M) soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2M out of 37M) were assigned a “good” XCO2 quality flag, as compared to 3.9 % in v7.3 (

The Nano-satellite Atmospheric Chemistry Hyperspectral Observation System (NACHOS) is a high-throughput (f/2.9), high spectral resolution (~1.3 nm optical resolution, 0.6 nm sampling) Offner-design hyperspectral imager operating in the 300-500 nm spectral region. The 1.5U instrument payload (1U optical system, 0.5U electronics module) is hosted by a 1.5U LANL-designed CubeSat bus to comprise a 3U complete satellite. Spectroscopically similar to NASA’s Ozone Monitoring Instrument (OMI), which provides wide-field global mapping of ozone and other gases at coarse spatial resolution, NACHOS fills the complementary niche of targeted measurements at much higher spatial resolution. With 350 across-track spatial pixels and a 15-degree across-track field of view, NACHOS will provide spectral imaging at roughly 0.4 km per pixel from 500 km altitude. NACHOS incorporates highly streamlined gas-retrieval algorithms for rapid onboard processing, alleviating the need to routinely downlink massive hyperspectral data cubes. We will discuss the instrument design, challenges in achieving mechanical robustness to launch vibration in such a compact instrument, the onboard calibration system, and gas-retrieval data downlink strategy. We will also discuss potential science missions, including monitoring of NO2 as an easily detected proxy for anthropogenic fossil-fuel greenhouse gases, monitoring lowlevel SO2 degassing at pre-eruptive volcanoes, H2CO from wildfires, and characterization of aerosols. The long-term vision is for a many-satellite constellation that could provide both high spatial resolution and frequent revisits for selected targets of interest. As an initial technology demonstration of this vision, the NACHOS project is currently slated to launch two CubeSats in early 2022.

Hyperspectral imaging with sufficient resolution and sensitivity for scientifically useful space-based mapping of trace gases has long required large and expensive satellite instruments. Miniaturizing this capability to a CubeSat configuration is a major challenge, but opens up more agile and far less expensive observing strategies. A major step in this direction is our development of NACHOS, an ultra-compact (1.5U instrument, 3U complete CubeSat) hyperspectral imager covering the 300-500nm spectral range in 400 channels. Here we describe laboratory and field performance characterization of this new instrument. Laboratory tests demonstrate spatial and spectral resolutions of

Tar balls are frequently found in slightly aged biomass burning plumes. They are spherical in shape, have diameters between ∼100 and 300 nm, are amorphous and composed mostly of oxygen and carbon. Tar balls are light absorbing and considered to be a component of brown carbon. Tar balls have been typically reported and analyzed as individual spheres; however, in a recent study, we reported the presence of significant fractions of fractal-like aggregates made of several tar balls in fire plumes from different geographical locations. Aggregation affects the optical properties of particles; therefore, we use T-Matrix and Lorenz-Mie simulations to explore the effects of aggregation on the tar balls’ optical properties in the 350 – 1150 nm wavelength range. We also evaluate the effects of different refractive indices available from the literature, different monomer numbers, and monomer sizes, as these are key factors determining the aggregates optical properties. Furthermore, we estimate the simple forcing efficiency for low and high surface albedos. Aggregates have a single scattering albedo (SSA) higher than that of individual tar balls (ΔSSA550 nm up to 0.22). The hemispherical upscatter fraction of individual tar balls is more than 100% larger than for tar ball aggregates in many cases. The top of the atmosphere simple forcing efficiency over dark surfaces shows large variabilities with an increase up to ∼53% for tar ball aggregates compared to individual tar balls. These results demonstrate that aggregation of tar balls can have a significant impact on their optical properties and radiative forcing.

Airborne measurements of upper troposphere and lower stratosphere biomass burning smoke show a large size mode at 350nm radius. Furthermore, very thickly coated black carbon (300-400nm radius) is observed in 2 month aged Pyro-cumulonimbus (PyroCb) smoke in the lower stratosphere. Finally, the stratospheric aerosol mass injections from the 2017 British Columbia (BC17) PyroCbs are much larger than fuel loading predicts. We propose a secondary organic aerosol (SOA) production mechanism where volatile organic compounds (VOCs) emitted by fires condense in the cold convective PyroCb updrafts to explain the aforementioned data. Observations supporting this mechanism present in FIREX-AQ, ATOM and CARIBEC airborne data are synthesized. The condensation, evaporation and coagulation mechanisms are implemented into LANL’s large eddy cloud resolving model called HIGRAD. Our simulations provide insights into the vertical distribution of SOA in the BC17 PyroCb and the role of warm and ice clouds in lofting it into the lower stratosphere. We show that SOA formation can increase aerosols by a factor of 2-3 and latent heat from warm and ice clouds adds 5 km to the injection height of BC17 fire smoke. The fate, transport and impacts of smoke from BC17 and 2020 Australian fires are examined using climate model (CESM) simulations.

Particle and trace gas emissions can undergo rapid changes in the atmosphere as a result of evaporation, condensation, and coagulation processes that are driven by dynamics and photochemistry. Here we analyze the fate of non-methane hydrocarbons (NMHCs) gases emitted by intense fires associated with pyroCbs towers that rise and cool rapidly and undergo relatively little dilution as they loft smoke into the upper troposphere and lower stratosphere. We use airborne observations of plumes from the Williams Flats Fire over the continental United States taken during 2019 FIREX-AQ campaign. Trace gas data from both fresh boundary smoke and PyroCb-lofted smoke from this fire are compared to that from smoke plumes that stayed at lower altitudes to constrain the roles of condensation, cloud processing and photochemistry in the outflow of pyroCbs. In the pyroCb outflows we observe lower CO normalized NMHCs mixing ratios with low vapor pressure compared to the boundary layer samples while for high vapor pressure compounds there is little difference between the CO normalized NMHC mixing ratios observed in the different fresh smoke plumes. Associated with this decrease in condensable NMHCs we find an increase in particle concentrations, specifically at large sizes (~350nm). These multiple observational facts are used to estimate the secondary organic aerosol production by NMHC condensation in pyroCb. Further analysis of FIREX-AQ data will be used to elucidate the roles of solubility and photochemistry on SOA formation in pyroCbs. These FIREX-AQ results will be used to inform cloud resolving large eddy simulation (LES, HIGRAD) to examine deep convective fire impacts on long range smoke impacts on climate and air quality.

Methane (CH4) is a potent greenhouse gas; therefore, accurate measurement of its sources is important for climate research. Because of the diversity of methane sources, identifying and apportioning different sources is essential. We demonstrate our ability characterize a specific source using top-down atmospheric observations downwind of a coal mine vent shaft, a large natural gas source, in San Juan, NM. To facilitate a field campaign in December of 2020, a mobile platform was developed to make simultaneous in situ observations of methane and ethane (C2H6) with an Aeris mid-IR spectrometer and wind velocities with a Trisonica mini 3-D anemometer. Total column methane was also measured during the campaign using an EM27/SUN mobile solar Fourier transform spectrometer (FTS) and compared with column methane and ethane measured in March of 2013 using higher resolution FTS instruments at a TCCON station near the site1. Our in situ data shows a unique and stable C2H6:CH4 ratio of 1-2% in the vent plume that agrees well with the 1.5% ratio measured by the TCCON FTS instruments in 2013, demonstrating that consistent attribution can be made using both in situ and remote methods. Furthermore, we infer the mass flux of methane and ethane from the vent shaft using a simple plume dispersion model and multiple measurements around the vent shaft. This direct source inversion is compared to results from a trained neural network code we have developed for source location and quantification (ALFaLDS)2. Our results demonstrate how multiscale measurements, inverse modeling, and machine learning can be used to better attribute and constrain methane emissions.1 Lindenmaier, R. et al.: Multiscale observations of CO2, 13CO2, and pollutants at Four Corners for emission verification and attribution, Proc. Natl. Acad. Sci., 111 (23), 8386-8391, https://doi.org/10.1073/pnas.1321883111, 2014.2 Travis, B., Dubey, M. and Sauer J.: Neural networks to locate and quantify fugitive natural gas leaks for a MIR detection system, Atmos. Environ: X, 8, (2020) 100092, https://doi.org/10.1016/j.aeaoa.2020.100092, 2020.