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In this study, we demonstrate the utility of available correlative measurements of carbon species to identify regional and local air mass characteristics as well as their associated source types. In particular, we combine different regression techniques and enhancement ratio algorithms with carbon monoxide (CO), carbon dioxide (CO2), and methane (CH4) total column abundance from 11 sites of the Total Carbon Column Observing Network (TCCON) to infer relative contributions of regional and local sources to each of these sites. The enhancement ratios provide a viable alternative to univariate measures of relationships between the trace gases that are insufficient in capturing source-type and transport signatures. Regional enhancements are estimated from the difference between bivariate regressions across a specific time window of observed total abundance of these species (BERr for bulk enhancement regression ratio) and inferred anomalies (AERr for anomaly enhancement regression ratio) associated with a site-specific background. Since BERr and AERr represent the bulk and local species enhancement ratio, respectively, its difference simply represents the site-specific regional component of these ratios. We can then compare these enhancements for CO2 and CH4 with CO to differentiate between combustion and non-combustion air masses. Our results show that while the regional and local influences in enhancements vary across sites, dominant characteristics are found to be consistent with previous studies over these sites and with bottom-up anthropogenic and fire emission inventories. The site in Pasadena shows a dominant local influence (> 60 %) across all species enhancement ratios, which appear to come from a mixture of biospheric and combustion activities. In contrast, Anmyeondo shows more regionally influenced (> 60 %) air masses associated with high-temperature and/or biofuel combustion activities. Ascension Island appears to only show a large regional influence (> 80 %) on CO / CO2 and CO / CH4, which is indicative of transported and combustion-related CO from the nearby African region, consistent with a sharp rise in column CO (3.51 ± 0.43 % ppb yr−1) at this site. These methods have important applications to source analysis using spaceborne column retrievals of these species.

Methane (CH4) is a powerful greenhouse gas that is produced by a diverse set of natural and anthropogenic emission sources. Biogenic methane sources generally involve anaerobic decay processes such as those occurring in wetlands, melting permafrost, or the digestion of organic matter in the guts of ruminant animals. Thermogenic CH4 sources originate from the breakdown of organic material at high temperatures and pressure within the Earth's crust, a process which also produces more complex trace hydrocarbons such as ethane (C2H6). Here, we present the development and deployment of an uncrewed aerial system (UAS) that employs a fast (1 Hz) and sensitive (1–0.5 ppb s−1) CH4 and C2H6 sensor and ultrasonic anemometer. The UAS platform is a vertical-takeoff, hexarotor drone (DJI Matrice 600 Pro, M600P) capable of vertical profiling to 120 m altitude and plume sampling across scales up to 1 km. Simultaneous measurements of CH4 and C2H6 concentrations, vector winds, and positional data allow for source classification (biogenic versus thermogenic), differentiation, and emission rates without the need for modeling or a priori assumptions about winds, vertical mixing, or other environmental conditions. The system has been used for direct quantification of methane point sources, such as orphan wells, and distributed emitters, such as landfills and wastewater treatment facilities. With detectable source rates as low as 0.04 and up to ∼ 1500 kg h−1, this UAS offers a direct and repeatable method of horizontal and vertical profiling of emission plumes at scales that are complementary to regional aerial surveys and localized ground-based monitoring.

A high-resolution soot particle aerosol mass spectrometer (SP-AMS) was used to selectively measure refractory black carbon (rBC) and its associated coating material using both the ensemble size-resolved mass spectral mode and the event trigger single particle (ETSP) mode in Houston, Texas, in summer 2022. This study was conducted as part of the Department of Energy Atmospheric Radiation Measurement (ARM) program's TRacking Aerosol Convection interactions ExpeRiment (TRACER) field campaign. The study revealed an average (±1σ) rBC concentration of 103 ± 176 ng m−3. Additionally, the coatings on the BC particles were primarily composed of organics (59 %; 219 ± 260 ng m−3) and sulfate (26 %; 94 ± 55 ng m−3). Positive matrix factorization (PMF) analysis of the ensemble mass spectra of BC-containing particles resolved four distinct types of soot aerosol, including an oxidized organic aerosol (OOABC,PMF) factor associated with processed primary organic aerosol, an inorganic sulfate factor (SO4,BC,PMF), an oxidized rBC factor (O-BCPMF), and a mixed mineral dust–biomass burning aerosol factor with significant contribution from potassium (K-BBBC,PMF). Additionally, K-means clustering analysis of the single-particle mass spectra identified eight different clusters, including soot particles enriched in hydrocarbon-like organic aerosol (HOABC,ETSP), sulfate (SO4,BC,ETSP), two types of rBC, OOA (OOABC,ETSP), chloride (ClBC,ETSP), and nitrate (NO3,BC,ETSP). The single-particle measurements demonstrate substantial variation in BC coating thickness with coating-to-rBC mass ratios ranging from 0.1 to 100. The mixing state index (χ), which denotes the degree of homogeneity of the soot aerosol, varied from 4 % to 94 % with a median of 40 %, indicating that the aerosol population lies in between internal and external mixing but has large temporal and source type variability. In addition, a significant fraction of BC-containing particles, a majority enriched with oxidized organics and sulfate, exhibit sufficiently high κ values and diameters conducive to activation as cloud nuclei under atmospherically relevant supersaturation conditions. This finding bears significance in comprehending the aging processes of rBC-containing particles and their activation into cloud droplets. Our analysis highlights the complex nature of soot aerosol and underscores the need to comprehend its variability across different environments for accurate assessment of climate change.

Accurate accounting of emissions and removals of CO2 is critical for the planning and verification of emission reduction targets in support of the Paris Agreement. Here, we present a pilot dataset of country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) and terrestrial carbon stock changes aimed at informing countries' carbon budgets. These estimates are based on “top-down” NCE outputs from the v10 Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP), wherein an ensemble of inverse modeling groups conducted standardized experiments assimilating OCO-2 column-averaged dry-air mole fraction (XCO2) retrievals (ACOS v10), in situ CO2 measurements or combinations of these data. The v10 OCO-2 MIP NCE estimates are combined with “bottom-up” estimates of fossil fuel emissions and lateral carbon fluxes to estimate changes in terrestrial carbon stocks, which are impacted by anthropogenic and natural drivers. These flux and stock change estimates are reported annually (2015–2020) as both a global 1∘ × 1∘ gridded dataset and a country-level dataset and are available for download from the Committee on Earth Observation Satellites' (CEOS) website: https://doi.org/10.48588/npf6-sw92 (Byrne et al., 2022). Across the v10 OCO-2 MIP experiments, we obtain increases in the ensemble median terrestrial carbon stocks of 3.29–4.58 Pg CO2 yr−1 (0.90–1.25 Pg C yr−1). This is a result of broad increases in terrestrial carbon stocks across the northern extratropics, while the tropics generally have stock losses but with considerable regional variability and differences between v10 OCO-2 MIP experiments. We discuss the state of the science for tracking emissions and removals using top-down methods, including current limitations and future developments towards top-down monitoring and verification systems.

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.

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 ( ×106 out of 24×106). After quality filtering and bias correction, the differences in XCO2 between ACOS GOSAT v9 and both TCCON and models have a scatter (1σ) of approximately 1 ppm for ocean-glint observations and 1 to 1.5 ppm for land observations. Global mean biases against TCCON and models are less than approximately 0.2 ppm. Seasonal mean biases relative to the v10 OCO-2 XCO2 product are of the order of 0.1 ppm for observations over land. However, for ocean-glint observations, seasonal mean biases relative to OCO-2 range from 0.2 to 0.6 ppm, with substantial variation in time and latitude. The ACOS GOSAT v9 XCO2 data are available on the NASA Goddard Earth Science Data and Information Services Center (GES-DISC) in both the per-orbit full format (https://doi.org/10.5067/OSGTIL9OV0PN, OCO-2 Science Team et al., 2019b) and in the per-day lite format (https://doi.org/10.5067/VWSABTO7ZII4, OCO-2 Science Team et al., 2019a). In addition, a new set of monthly super-lite files, containing only the most essential variables for each satellite observation, has been generated to provide entry level users with a light-weight satellite product for initial exploration (CaltechDATA, https://doi.org/10.22002/D1.2178, Eldering, 2021). The v9 ACOS Data User's Guide (DUG) describes best-use practices for the GOSAT data (O'Dell et al., 2020). The GOSAT v9 data set should be especially useful for studies of carbon cycle phenomena that span a full decade or more and may serve as a useful complement to the shorter OCO-2 v10 data set, which begins in September 2014.

Satellite-based observations of atmospheric carbon dioxide (CO2) 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 CO2 (XCO2) 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 XCO2 seasonal cycles across a majority of terrestrial northern high-latitude regions. Here, we present an analysis of XCO2 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 XCO2 from the Copernicus Atmosphere Monitoring Services (CAMS) global inversion-optimized greenhouse gas flux model v19r1 and the CarbonTracker2019 model (CT2019B). Model estimates of XCO2 from the GEOS-Chem CO2 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 XCO2 seasonality across these regions. GEOS-Chem surface contact tracers show that the largest XCO2 SCAs occur in areas with the greatest contact with land surfaces, integrated over 15–30 d. The correlation of XCO2 SCA with these land surface contact tracers is stronger than the correlation of XCO2 SCA with the SCA of CO2 fluxes or the total annual CO2 flux within each 5∘ latitude by 20∘ longitude zone. This indicates that accumulation of terrestrial CO2 flux during atmospheric transport is a major driver of regional variations in XCO2 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 (XCO2) 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 XCO2 include observations from two sites in the Total Carbon Column Observing Network (TCCON) at East Trout Lake, Saskatchewan, Canada, and Sodankylä, 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 Sodankylä 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 Sodankylä 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 XCO2 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.

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.

Optical properties of flame-generated black carbon (BC) containing soot particles were quantified at multiple wavelengths for particles produced using two different flames: a methane diffusion flame and an ethylene premixed flame. Measurements were made for (i) nascent soot particles, (ii) thermally denuded nascent particles, and (iii) particles that were coated and then thermally denuded, leading to the collapse of the initially lacy, fractal-like morphology. The measured mass absorption coefficients (MACs) depended on soot maturity and generation but were similar between flames for similar conditions. For mature soot, here corresponding to particles with volume-equivalent diameters > ∼ 160 nm, the MAC and absorption Ångström exponent (AAE) values were independent of particle collapse while the single-scatter albedo increased. The MAC values for these larger particles were also size-independent. The mean MAC value at 532 nm for larger particles was 9.1±1.1 m2 g−1, about 17 % higher than that recommended by Bond and Bergstrom (2006), and the AAE was close to unity. Effective, theory-specific complex refractive index (RI) values are derived from the observations with two widely used methods: Lorenz–Mie theory and the Rayleigh–Debye–Gans (RDG) approximation. Mie theory systematically underpredicts the observed absorption cross sections at all wavelengths for larger particles (with x > 0.9) independent of the complex RI used, while RDG provides good agreement. (The dimensionless size parameter x = πdp∕λ, where dp is particle diameter and λ is wavelength.) Importantly, this implies that the use of Mie theory within air quality and climate models, as is common, likely leads to underpredictions in the absorption by BC, with the extent of underprediction depending on the assumed BC size distribution and complex RI used. We suggest that it is more appropriate to assume a constant, size-independent (but wavelength-specific) MAC to represent absorption by uncoated BC particles within models.

We validate the Ozone Monitoring Instrument (OMI) Ozone Profile (PROFOZ) product from October 2004 through December 2014 retrieved by the Smithsonian Astrophysical Observatory (SAO) algorithm against ozonesonde observations. We also evaluate the effects of OMI row anomaly (RA) on the retrieval by dividing the dataset into before and after the occurrence of serious OMI RA, i.e., pre-RA (2004–2008) and post-RA (2009–2014). The retrieval shows good agreement with ozonesondes in the tropics and midlatitudes and for pressure ∼ 50 hPa after applying OMI averaging kernels to ozonesonde data. The MBs of the stratospheric ozone column (SOC, the ozone column from the tropopause pressure to the ozonesonde burst pressure) are within 2 % with SDs of ∼ 50 hPa. The SOC MBs increase up to 3 % with SDs as great as 6 % and the TOC SDs increase up to 30 %. The comparison generally degrades at larger solar zenith angles (SZA) due to weaker signals and additional sources of error, leading to worse performance at high latitudes and during the midlatitude winter. Agreement also degrades with increasing cloudiness for pressure > ∼ 100 hPa and varies with cross-track position, especially with large MBs and SDs at extreme off-nadir positions. In the tropics and midlatitudes, the post-RA comparison is considerably worse with larger SDs reaching 2 % in the stratosphere and 8 % in the troposphere and up to 6 % in TOC. There are systematic differences that vary with latitude compared to the pre-RA comparison. The retrieval comparison demonstrates good long-term stability during the pre-RA period but exhibits a statistically significant trend of 0.14–0.7 % year−1 for pressure −1 in SOC, and −0. 33 DU year−1 in TOC during the post-RA period. The spatiotemporal variation of retrieval performance suggests the need to improve OMI's radiometric calibration especially during the post-RA period to maintain the long-term stability and reduce the latitude/season/SZA and cross-track dependency of retrieval quality.

NASA's Orbiting Carbon Observatory-2 (OCO-2) has been measuring carbon dioxide column-averaged dry-air mole fraction, XCO2, in the Earth's atmosphere for over 2 years. In this paper, we describe the comparisons between the first major release of the OCO-2 retrieval algorithm (B7r) and XCO2 from OCO-2's primary ground-based validation network: the Total Carbon Column Observing Network (TCCON). The OCO-2 XCO2 retrievals, after filtering and bias correction, agree well when aggregated around and coincident with TCCON data in nadir, glint, and target observation modes, with absolute median differences less than 0.4 ppm and RMS differences less than 1.5 ppm. After bias correction, residual biases remain. These biases appear to depend on latitude, surface properties, and scattering by aerosols. It is thus crucial to continue measurement comparisons with TCCON to monitor and evaluate the OCO-2 XCO2 data quality throughout its mission.

We estimate the amount of methane (CH4) emitted by the largest dairies in the southern California region by combining measurements from four mobile solar-viewing ground-based spectrometers (EM27/SUN), in situ isotopic 13∕12CH4 measurements from a CRDS analyzer (Picarro), and a high-resolution atmospheric transport simulation with a Weather Research and Forecasting model in large-eddy simulation mode (WRF-LES). The remote sensing spectrometers measure the total column-averaged dry-air mole fractions of CH4 and CO2 (XCH4 and XCO2) in the near infrared region, providing information on total emissions of the dairies at Chino. Differences measured between the four EM27/SUN ranged from 0.2 to 22 ppb (part per billion) and from 0.7 to 3 ppm (part per million) for XCH4 and XCO2, respectively. To assess the fluxes of the dairies, these differential measurements are used in conjunction with the local atmospheric dynamics from wind measurements at two local airports and from the WRF-LES simulations at 111 m resolution. Our top-down CH4 emissions derived using the Fourier transform spectrometers (FTS) observations of 1.4 to 4.8 ppt s−1 are in the low end of previous top-down estimates, consistent with reductions of the dairy farms and urbanization in the domain. However, the wide range of inferred fluxes points to the challenges posed by the heterogeneity of the sources and meteorology. Inverse modeling from WRF-LES is utilized to resolve the spatial distribution of CH4 emissions in the domain. Both the model and the measurements indicate heterogeneous emissions, with contributions from anthropogenic and biogenic sources at Chino. A Bayesian inversion and a Monte Carlo approach are used to provide the CH4 emissions of 2.2 to 3.5 ppt s−1 at Chino.

The Total Carbon Column Observing Network (TCCON) has become the standard for long-term column-averaged measurements of CO2 and CH4. Here, we use a pair of portable spectrometers to test for intra-network bias among the four currently operating TCCON sites in the United States (US). A previous analytical error analysis has suggested that the maximum 2σ site-to-site relative (absolute) bias of TCCON should be less than 0.2 % (0.8 ppm) in XCO2 and 0.4 % (7 ppb) in XCH4. We find here experimentally that the 95 % confidence intervals for maximum pairwise site-to-site bias among the four US TCCON sites are 0.05–0.14 % for XCO2 and 0.08–0.24 % for XCH4. This is close to the limit of the bias we can detect using this methodology.

The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier transform spectrometers (FTSs) that record near-infrared (NIR) spectra of the sun. From these spectra, accurate and precise observations of CO2 column-averaged dry-air mole fractions (denoted XCO2) are retrieved. TCCON FTS observations have previously been used to validate satellite estimations of XCO2; however, our knowledge of the short-term spatial and temporal variations in XCO2 surrounding the TCCON sites is limited. In this work, we use the National Institute for Environmental Studies (NIES) Eulerian three-dimensional transport model and the FLEXPART (FLEXible PARTicle dispersion model) Lagrangian particle dispersion model (LPDM) to determine the footprints of short-term variations in XCO2 observed by operational, past, future and possible TCCON sites. We propose a footprint-based method for the collocation of satellite and TCCON XCO2 observations and estimate the performance of the method using the NIES model and five GOSAT (Greenhouse Gases Observing Satellite) XCO2 product data sets. Comparison of the proposed approach with a standard geographic method shows a higher number of collocation points and an average bias reduction up to 0.15 ppm for a subset of 16 stations for the period from January 2010 to January 2014. Case studies of the Darwin and Reunion Island sites reveal that when the footprint area is rather curved, non-uniform and significantly different from a geographical rectangular area, the differences between these approaches are more noticeable. This emphasises that the collocation is sensitive to local meteorological conditions and flux distributions.

Bruker™ EM27/SUN instruments are commercial mobile solar-viewing near-IR spectrometers. They show promise for expanding the global density of atmospheric column measurements of greenhouse gases and are being marketed for such applications. They have been shown to measure the same variations of atmospheric gases within a day as the high-resolution spectrometers of the Total Carbon Column Observing Network (TCCON). However, there is little known about the long-term precision and uncertainty budgets of EM27/SUN measurements. In this study, which includes a comparison of 186 measurement days spanning 11 months, we note that atmospheric variations of Xgas within a single day are well captured by these low-resolution instruments, but over several months, the measurements drift noticeably. We present comparisons between EM27/SUN instruments and the TCCON using GGG as the retrieval algorithm. In addition, we perform several tests to evaluate the robustness of the performance and determine the largest sources of errors from these spectrometers. We include comparisons of XCO2, XCH4, XCO, and XN2O. Specifically we note EM27/SUN biases for January 2015 of 0.03, 0.75, –0.12, and 2.43 % for XCO2, XCH4, XCO, and XN2O respectively, with 1σ running precisions of 0.08 and 0.06 % for XCO2 and XCH4 from measurements in Pasadena. We also identify significant error caused by nonlinear sensitivity when using an extended spectral range detector used to measure CO and N2O.

We demonstrate the use of compact solar-tracking Fourier transform spectrometers (Bruker EM27/SUN) for differential measurements of the column-averaged dry-air mole fractions of CH4 and CO2 within urban areas. Using Allan variance analysis, we show that the differential column measurement has a precision of 0.01 % for XCO2 and XCH4 with an optimum integration time of 10 min, corresponding to Allan deviations of 0.04 ppm and 0.2 ppb, respectively. The sensor system is very stable over time and after relocation across the continent. We report tests of the differential column measurement, and its sensitivity to emission sources, by measuring the downwind-minus-upwind column difference ΔXCH4 across dairy farms in the Chino area, California, and using the data to verify emissions reported in the literature. Ratios of spatial column differences ΔXCH4∕ΔXCO2 were observed across Pasadena within the Los Angeles basin, indicating values consistent with regional emission ratios from the literature. Our precise, rapid measurements allow us to determine significant short-term variations (5–10 min) of XCO2 and XCH4 and to show that they represent atmospheric phenomena.Overall, this study helps establish a range of new applications for compact solar-viewing Fourier transform spectrometers. By accurately measuring the small differences in integrated column amounts across local and regional sources, we directly observe the mass loading of the atmosphere due to the influence of emissions in the intervening locale. The inference of the source strength is much more direct than inversion modeling using only surface concentrations and less subject to errors associated with small-scale transport phenomena.

This study presents results from the European Centre for Medium-Range Weather Forecasts (ECMWF) carbon dioxide (CO2) analysis system where the atmospheric CO2 is controlled through the assimilation of column-averaged dry-air mole fractions of CO2 (XCO2) from the Greenhouse gases Observing Satellite (GOSAT). The analysis is compared to a free-run simulation (without assimilation of XCO2), and they are both evaluated against XCO2 data from the Total Carbon Column Observing Network (TCCON). We show that the assimilation of the GOSAT XCO2 product from the Bremen Optimal Estimation Differential Optical Absorption Spectroscopy (BESD) algorithm during the year 2013 provides XCO2 fields with an improved mean absolute error of 0.6 parts per million (ppm) and an improved station-to-station bias deviation of 0.7 ppm compared to the free run (1.1 and 1.4 ppm, respectively) and an improved estimated precision of 1 ppm compared to the GOSAT BESD data (3.3 ppm). We also show that the analysis has skill for synoptic situations in the vicinity of frontal systems, where the GOSAT retrievals are sparse due to cloud contamination. We finally computed the 10-day forecast from each analysis at 00:00 UTC, and we demonstrate that the CO2 forecast shows synoptic skill for the largest-scale weather patterns (of the order of 1000 km) even up to day 5 compared to its own analysis.

Consistent validation of satellite CO2 estimates is a prerequisite for using multiple satellite CO2 measurements for joint flux inversion, and for establishing an accurate long-term atmospheric CO2 data record. Harmonizing satellite CO2 measurements is particularly important since the differences in instruments, observing geometries, sampling strategies, etc. imbue different measurement characteristics in the various satellite CO2 data products. We focus on validating model and satellite observation attributes that impact flux estimates and CO2 assimilation, including accurate error estimates, correlated and random errors, overall biases, biases by season and latitude, the impact of coincidence criteria, validation of seasonal cycle phase and amplitude, yearly growth, and daily variability. We evaluate dry-air mole fraction (XCO2) for Greenhouse gases Observing SATellite (GOSAT) (Atmospheric CO2 Observations from Space, ACOS b3.5) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) (Bremen Optimal Estimation DOAS, BESD v2.00.08) as well as the CarbonTracker (CT2013b) simulated CO2 mole fraction fields and the Monitoring Atmospheric Composition and Climate (MACC) CO2 inversion system (v13.1) and compare these to Total Carbon Column Observing Network (TCCON) observations (GGG2012/2014). We find standard deviations of 0.9, 0.9, 1.7, and 2.1 ppm vs. TCCON for CT2013b, MACC, GOSAT, and SCIAMACHY, respectively, with the single observation errors 1.9 and 0.9 times the predicted errors for GOSAT and SCIAMACHY, respectively. We quantify how satellite error drops with data averaging by interpreting according to error2 = a2 + b2/n (with n being the number of observations averaged, a the systematic (correlated) errors, and b the random (uncorrelated) errors). a and b are estimated by satellites, coincidence criteria, and hemisphere. Biases at individual stations have year-to-year variability of ∼ 0.3 ppm, with biases larger than the TCCON-predicted bias uncertainty of 0.4 ppm at many stations. We find that GOSAT and CT2013b underpredict the seasonal cycle amplitude in the Northern Hemisphere (NH) between 46 and 53° N, MACC overpredicts between 26 and 37° N, and CT2013b underpredicts the seasonal cycle amplitude in the Southern Hemisphere (SH). The seasonal cycle phase indicates whether a data set or model lags another data set in time. We find that the GOSAT measurements improve the seasonal cycle phase substantially over the prior while SCIAMACHY measurements improve the phase significantly for just two of seven sites. The models reproduce the measured seasonal cycle phase well except for at Lauder_125HR (CT2013b) and Darwin (MACC). We compare the variability within 1 day between TCCON and models in JJA; there is correlation between 0.2 and 0.8 in the NH, with models showing 10–50 % the variability of TCCON at different stations and CT2013b showing more variability than MACC. This paper highlights findings that provide inputs to estimate flux errors in model assimilations, and places where models and satellites need further investigation, e.g., the SH for models and 45–67° N for GOSAT and CT2013b.

The composition of PM1 (particulate matter with diameter less than 1 µm) in the greater London area was characterized during the Clean Air for London (ClearfLo) project in winter 2012. Two high-resolution time-of-flight aerosol mass spectrometers (HR-ToF-AMS) were deployed at a rural site (Detling, Kent) and an urban site (North Kensington, London). The simultaneous and high-temporal resolution measurements at the two sites provide a unique opportunity to investigate the spatial distribution of PM1. We find that the organic aerosol (OA) concentration is comparable between the rural and urban sites, but the contribution from different sources is distinctly different between the two sites. The concentration of solid fuel OA at the urban site is about twice as high as at the rural site, due to elevated domestic heating in the urban area. While the concentrations of oxygenated OA (OOA) are well-correlated between the two sites, the OOA concentration at the rural site is almost twice that of the urban site. At the rural site, more than 70 % of the carbon in OOA is estimated to be non-fossil, which suggests that OOA is likely related to aged biomass burning considering the small amount of biogenic SOA in winter. Thus, it is possible that the biomass burning OA contributes a larger fraction of ambient OA in wintertime than what previous field studies have suggested. A suite of instruments was deployed downstream of a thermal denuder (TD) to investigate the volatility of PM1 species at the rural Detling site. After heating at 250 °C in the TD, 40 % of the residual mass is OA, indicating the presence of non-volatile organics in the aerosol. Although the OA associated with refractory black carbon (rBC; measured by a soot-particle aerosol mass spectrometer) only accounts for

Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate-related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY on-board ENVISAT (March 2002–April 2012) and TANSO-FTS on-board GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System. We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between SCIAMACHY and GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT–SCIAMACHY (linear correlation coefficient r=0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT–TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY–TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (± 2 h, 10° x 10° around TCCON sites), i.e. the observed air masses are not exactly identical but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by also using data from other missions (e.g. OCO-2, GOSAT-2, CarbonSat) in the future.

We use 2009–2011 space-borne methane observations from the Greenhouse Gases Observing SATellite (GOSAT) to estimate global and North American methane emissions with 4° × 5° and up to 50 km × 50 km spatial resolution, respectively. GEOS-Chem and GOSAT data are first evaluated with atmospheric methane observations from surface and tower networks (NOAA/ESRL, TCCON) and aircraft (NOAA/ESRL, HIPPO), using the GEOS-Chem chemical transport model as a platform to facilitate comparison of GOSAT with in situ data. This identifies a high-latitude bias between the GOSAT data and GEOS-Chem that we correct via quadratic regression. Our global adjoint-based inversion yields a total methane source of 539 Tg a−1 with some important regional corrections to the EDGARv4.2 inventory used as a prior. Results serve as dynamic boundary conditions for an analytical inversion of North American methane emissions using radial basis functions to achieve high resolution of large sources and provide error characterization. We infer a US anthropogenic methane source of 40.2–42.7 Tg a−1, as compared to 24.9–27.0 Tg a−1 in the EDGAR and EPA bottom-up inventories, and 30.0–44.5 Tg a−1 in recent inverse studies. Our estimate is supported by independent surface and aircraft data and by previous inverse studies for California. We find that the emissions are highest in the southern–central US, the Central Valley of California, and Florida wetlands; large isolated point sources such as the US Four Corners also contribute. Using prior information on source locations, we attribute 29–44 % of US anthropogenic methane emissions to livestock, 22–31 % to oil/gas, 20 % to landfills/wastewater, and 11–15 % to coal. Wetlands contribute an additional 9.0–10.1 Tg a−1.

The Bio-hydro-atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics & Nitrogen (BEACHON) project seeks to understand the feedbacks and inter-relationships between hydrology, biogenic emissions, carbon assimilation, aerosol properties, clouds and associated feedbacks within water-limited ecosystems. The Manitou Experimental Forest Observatory (MEFO) was established in 2008 by the National Center for Atmospheric Research to address many of the BEACHON research objectives, and it now provides a fixed field site with significant infrastructure. MEFO is a mountainous, semi-arid ponderosa pine-dominated forest site that is normally dominated by clean continental air but is periodically influenced by anthropogenic sources from Colorado Front Range cities. This article summarizes the past and ongoing research activities at the site, and highlights some of the significant findings that have resulted from these measurements. These activities include - soil property measurements; - hydrological studies; - measurements of high-frequency turbulence parameters; - eddy covariance flux measurements of water, energy, aerosols and carbon dioxide through the canopy; - determination of biogenic and anthropogenic volatile organic compound emissions and their influence on regional atmospheric chemistry; - aerosol number and mass distributions; - chemical speciation of aerosol particles; - characterization of ice and cloud condensation nuclei; - trace gas measurements; and - model simulations using coupled chemistry and meteorology. In addition to various long-term continuous measurements, three focused measurement campaigns with state-of-the-art instrumentation have taken place since the site was established, and two of these studies are the subjects of this special issue: BEACHON-ROCS (Rocky Mountain Organic Carbon Study, 2010) and BEACHON-RoMBAS (Rocky Mountain Biogenic Aerosol Study, 2011).

The prospective future adoption of molecular hydrogen (H2) to power the road transportation sector could greatly improve tropospheric air quality but also raises the question of whether the adoption would have adverse effects on the stratospheric ozone. The possibility of undesirable impacts must be fully evaluated to guide future policy decisions. Here we evaluate the possible impact of a future (2050) H2-based road transportation sector on stratospheric composition and chemistry, especially on the stratospheric ozone, with the MOZART (Model for OZone And Related chemical Tracers) model. Since future growth is highly uncertain, we evaluate the impact of two world evolution scenarios, one based on an IPCC (Intergovernmental Panel on Climate Change) high-emitting scenario (A1FI) and the other on an IPCC low-emitting scenario (B1), as well as two technological options: H2 fuel cells and H2 internal combustion engines. We assume a H2 leakage rate of 2.5% and a complete market penetration of H2 vehicles in 2050. The model simulations show that a H2-based road transportation sector would reduce stratospheric ozone concentrations as a result of perturbed catalytic ozone destruction cycles. The magnitude of the impact depends on which growth scenario evolves and which H2 technology option is applied. For the evolution growth scenario, stratospheric ozone decreases more in the H2 fuel cell scenarios than in the H2 internal combustion engine scenarios because of the NOx emissions in the latter case. If the same technological option is applied, the impact is larger in the A1FI emission scenario. The largest impact, a 0.54% decrease in annual average global mean stratospheric column ozone, is found with a H2 fuel cell type road transportation sector in the A1FI scenario; whereas the smallest impact, a 0.04% increase in stratospheric ozone, is found with applications of H2 internal combustion engine vehicles in the B1 scenario. The impacts of the other two scenarios fall between the above two boundary scenarios. However, the magnitude of these changes is much smaller than the increases in 2050 stratospheric ozone projected, as stratospheric ozone is expected to recover due to the limits in ozone depleting substance emissions imposed in the Montreal Protocol.

Vehicles burning fossil fuel emit a number of substances that change the composition and chemistry of the atmosphere, and contribute to global air and water pollution and climate change. For example, nitrogen oxides and volatile organic compounds (VOCs) emitted as byproducts of fossil fuel combustion are key precursors to ground-level ozone and aerosol formation. In addition, on-road vehicles are major CO2 emitters. In order to tackle these problems, molecular hydrogen (H2) has been proposed as an energy carrier to substitute for fossil fuels in the future. However, before implementing any such strategy it is crucial to evaluate its potential impacts on air quality and climate. Here, we evaluate the impact of a future (2050) H2-based road transportation sector on tropospheric chemistry and air quality for several possible growth and technology adoption scenarios. The growth scenarios are based on the high and low emissions Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios, A1FI and B1, respectively. The technological adoption scenarios include H2 fuel cell and H2 internal combustion engine options. The impacts are evaluated with the Community Atmospheric Model Chemistry global chemistry transport model (CAM-Chem). Higher resolution simulations focusing on the contiguous United States are also carried out with the Community Multiscale Air Quality Modeling System (CMAQ) regional chemistry transport model. For all scenarios future air quality improves with the adoption of a H2-based road transportation sector; however, the magnitude and type of improvement depend on the scenario. Model results show that the adoption of H2 fuel cells would decrease tropospheric burdens of ozone (7%), CO (14%), NOx (16%), soot (17%), sulfate aerosol (4%), and ammonium nitrate aerosol (12%) in the A1FI scenario, and would decrease those of ozone (5%), CO (4%), NOx (11%), soot (7%), sulfate aerosol (4%), and ammonium nitrate aerosol (9%) in the B1 scenario. The adoption of H2 internal combustion engines would decrease tropospheric burdens of ozone (1%), CO (18%), soot (17%), and sulfate aerosol (3%) in the A1FI scenario, and would decrease those of ozone (1%), CO (7%), soot (7%), and sulfate aerosol (3%) in the B1 scenario. In the future, people residing in the contiguous United States could expect to experience significantly fewer days of elevated levels of pollution if a H2 fuel cell road transportation sector were to be adopted. Health benefits of transitioning to a H2 economy for citizens in developing nations, like China and India, will be much more dramatic, particularly in megacities with severe, intensifying air-quality problems.

Carbonaceous aerosols including organic carbon and black carbon have significant implications for both climate and air quality. In the current global climate or chemical transport models, a fixed hydrophobic-to-hydrophilic conversion lifetime for carbonaceous aerosol (τ) is generally assumed, which is usually around one day. We have implemented a new detailed aging scheme for carbonaceous aerosols in a chemical transport model (GEOS-Chem) to account for both the chemical oxidation and the physical condensation-coagulation effects, where τ is affected by local atmospheric environment including atmospheric concentrations of water vapor, ozone, hydroxyl radical and sulfuric acid. The updated τ exhibits large spatial and temporal variations with the global average (up to 11 km altitude) calculated to be 2.6 days. The chemical aging effects are found to be strongest over the tropical regions driven by the low ozone concentrations and high humidity there. The τ resulted from chemical aging generally decreases with altitude due to increases in ozone concentration and decreases in humidity. The condensation-coagulation effects are found to be most important for the high-latitude areas, in particular the polar regions, where the τ values are calculated to be up to 15 days. When both the chemical aging and condensation-coagulation effects are considered, the total atmospheric burdens and global average lifetimes of BC, black carbon, (OC, organic carbon) are calculated to increase by 9% (3%) compared to the control simulation, with considerable enhancements of BC and OC concentrations in the Southern Hemisphere. Model evaluations against data from multiple datasets show that the updated aging scheme improves model simulations of carbonaceous aerosols for some regions, especially for the remote areas in the Northern Hemisphere. The improvement helps explain the persistent low model bias for carbonaceous aerosols in the Northern Hemisphere reported in literature. Further model sensitivity simulations focusing on the continental outflow of carbonaceous aerosols demonstrate that previous studies using the old aging scheme could have significantly underestimated the intercontinental transport of carbonaceous aerosols.

An Aerodyne high resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) was deployed during the Carbonaceous Aerosols and Radiative Effects Study (CARES) that took place in northern California in June 2010. We present results obtained at Cool (denoted as the T1 site of the project) in the foothills of the Sierra Nevada Mountains, where intense biogenic emissions are periodically mixed with urban outflow transported by daytime southwesterly winds from the Sacramento metropolitan area. During this study, the average mass loading of submicrometer particles (PM1) was 3.0 μg m−3, dominated by organics (80%) and sulfate (9.9%). The organic aerosol (OA) had a nominal formula of C1H1.38N0.004OM0.44, thus an average organic mass-to-carbon (OM/OC) ratio of 1.70. Two distinct oxygenated OA factors were identified via Positive matrix factorization (PMF) of the high-resolution mass spectra of organics. The more oxidized MO-OOA (O/C = 0.54) was interpreted as a surrogate for secondary OA (SOA) influenced by biogenic emissions whereas the less oxidized LO-OOA (O/C = 0.42) was found to represent SOA formed in photochemically processed urban emissions. LO-OOA correlated strongly with ozone and MO-OOA correlated well with two 1st generation isoprene oxidation products (methacrolein and methyl vinyl ketone), indicating that both SOAs were relatively fresh. A hydrocarbon like OA (HOA) factor was also identified, representing primary emissions mainly due to local traffic. On average, SOA (= MO-OOA + LO-OOA) accounted for 91% of the total OA mass and 72% of the PM1 mass observed at Cool. Twenty three periods of urban plumes from T0 (Sacramento) to T1 (Cool) were identified using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The average PM1 mass loading was considerably higher in urban plumes than in air masses dominated by biogenic SOA. The change in OA mass relative to CO (ΔOA/ΔCO) varied in the range of 5-196 μg m−3 ppm−1, reflecting large variability in SOA production. The highest ΔOA/ΔCO was reached when air masses were dominated by anthropogenic emissions in the presence of a high concentration of biogenic volatile organic compounds (BVOCs). This ratio, which was 97 μg m−3 ppm−1 on average, was much higher than when urban plumes arrived in a low BVOC environment (~36 μg m−3 ppm−1) or during other periods dominated by biogenic SOA (35 μg m−3 ppm−1). These results demonstrate that SOA formation is enhanced when anthropogenic emissions interact with biogenic precursors.

Substantial uncertainties still exist in the scientific understanding of the possible interactions between urban and natural (biogenic) emissions in the production and transformation of atmospheric aerosol and the resulting impact on climate change. The US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program's Carbonaceous Aerosol and Radiative Effects Study (CARES) carried out in June 2010 in Central Valley, California, was a comprehensive effort designed to improve this understanding. The primary objective of the field study was to investigate the evolution of secondary organic and black carbon aerosols and their climate-related properties in the Sacramento urban plume as it was routinely transported into the forested Sierra Nevada foothills area. Urban aerosols and trace gases experienced significant physical and chemical transformations as they mixed with the reactive biogenic hydrocarbons emitted from the forest. Two heavily-instrumented ground sites – one within the Sacramento urban area and another about 40 km to the northeast in the foothills area – were set up to characterize the evolution of meteorological variables, trace gases, aerosol precursors, aerosol size, composition, and climate-related properties in freshly polluted and "aged" urban air. On selected days, the DOE G-1 aircraft was deployed to make similar measurements upwind and across the evolving Sacramento plume in the morning and again in the afternoon. The NASA B-200 aircraft, carrying remote sensing instruments, was also deployed to characterize the vertical and horizontal distribution of aerosols and aerosol optical properties within and around the plume. This overview provides: (a) the scientific background and motivation for the study, (b) the operational and logistical information pertinent to the execution of the study, (c) an overview of key observations and initial findings from the aircraft and ground-based sampling platforms, and (d) a roadmap of planned data analyses and focused modeling efforts that will facilitate the integration of new knowledge into improved representations of key aerosol processes and properties in climate models.

Tunable diode laser absorption (TDL) and cavity ring-down spectroscopic (CRDS) sensors for atmospheric carbon dioxide were co-deployed during summer and fall of 2010 in field and laboratory conditions at Los Alamos National Laboratory. Both sensors were characterized for accuracy and precision for ambient carbon dioxide measurements at ground level and compared using both laboratory and ambient field data. After post-processing that included water vapor correction and calibration to WMO reference standards, overall mean [12C16O2] = 392.05 ± 8.92 ppm and [12C16O2] = 392.22 ± 9.05 ppm were observed between 29 July and 16 August 2010. The mean difference between the CRDS and TDL data for 12CO2 was 0.04 ± 1.80 ppm (±1σ in 60 s) for ambient field data, demonstrating the sensors meet the WMO/IAEA compatibility standard. The observations show over the 19-day period the [CO2]CRDS'/[CO2]TDL ratio exhibits a Gaussian distribution centered at x0 = 1.003 ± 3.38 × 10−5 (±1σ), indicating the ratio is dominated by random noise as opposed to a bias in the output of either sensor. The CRDS sensor is capable of measuring [12C16O2] to a precision of 23 ppb in 1 min and decreases to 6.5 ppb in 58 min. At one and 58-min, the TDL exhibits precisions of 29 ppb and 53 ppb. The CRDS is compact, fast, and stable; the TDL is larger and requires frequent calibrations to maintain its precision. The sensors also exhibit consistent hourly averaged diurnal values underscoring the interplay of biological, anthropogenic, and transport processes regulating CO2 at the site.

We describe the synoptic and regional-scale meteorological conditions that affected the transport and mixing of trace gases and aerosols in the vicinity of Sacramento, California during June 2010 when the Carbonaceous Aerosol and Radiative Effects Study (CARES) was conducted. The meteorological measurements collected by various instruments deployed during the campaign and the performance of the chemistry version of the Weather Research and Forecasting model (WRF-Chem) are both discussed. WRF-Chem was run daily during the campaign to forecast the spatial and temporal variation of carbon monoxide emitted from 20 anthropogenic source regions in California to guide aircraft sampling. The model is shown to reproduce the overall circulations and boundary-layer characteristics in the region, although errors in the upslope wind speed and boundary-layer depth contribute to differences in the observed and simulated carbon monoxide. Thermally-driven upslope flows that transported pollutants from Sacramento over the foothills of the Sierra Nevada occurred every afternoon, except during three periods when the passage of mid-tropospheric troughs disrupted the regional-scale flow patterns. The meteorological conditions after the passage of the third trough were the most favorable for photochemistry and likely formation of secondary organic aerosols. Meteorological measurements and model forecasts indicate that the Sacramento pollutant plume was likely transported over a downwind site that collected trace gas and aerosol measurements during 23 time periods; however, direct transport occurred during only eight of these periods. The model also showed that emissions from the San Francisco Bay area transported by intrusions of marine air contributed a large fraction of the carbon monoxide in the vicinity of Sacramento, suggesting that this source likely affects local chemistry. Contributions from other sources of pollutants, such as those in the Sacramento Valley and San Joaquin Valley, were relatively low. Aerosol layering in the free troposphere was observed during the morning by an airborne Lidar. WRF-Chem forecasts showed that mountain venting processes contributed to aged pollutants aloft in the valley atmosphere that are then entrained into the growing boundary layer the subsequent day.

Transport of aerosols in pollution plumes from the mainland Asian continent was observed in situ at Jeju, South Korea during the Cheju Asian Brown Cloud Plume-Asian Monsoon Experiment (CAPMEX) field campaign throughout August and September 2008 using a 3-laser photoacoustic spectrometer (PASS-3), chemical filter analysis, and size distributions. The PASS-3 directly measures the effects of morphology (e.g. coatings) on light absorption that traditional filter-based instruments are unable to address. Transport of mixed sulfate, carbonaceous, and nitrate aerosols from various Asian pollution plumes to Jeju accounted for 74% of the deployment days, showing large variations in their measured chemical and optical properties. Analysis of eight distinct episodes, spanning wide ranges of chemical composition, optical properties, and source regions, reveals that episodes with higher organic carbon (OC)/sulfate (SO42−) and nitrate (NO3−)/SO42− composition ratios exhibit lower single scatter albedo at shorter wavelengths (ω405). We infer complex refractive indices (n–ik) as a function of wavelength for the high, intermediate, and low OC/SO42− pollution episodes by using the observed particle size distributions and the measured optical properties. The smallest mean particle diameter corresponds to the high OC/SO42− aerosol episode. The imaginary part of the refractive index (k) is greater for the high OC/SO42− episode at all wavelengths. A distinct, sharp increase in k at short wavelength implies enhanced light absorption by OC, which accounts for 50% of the light absorption at 405 nm, in the high OC/SO42− episode. Idealized analysis indicates increased absorption at 781 nm by factors greater than 3 relative to denuded black carbon in the laboratory. We hypothesize that coatings of black carbon cores are the mechanism of this enhancement. This implies that climate warming and atmospheric heating rates from black carbon particles can be significantly larger than have been estimated previously. The results of this study demonstrate ways in which atmospheric processing and mixing can amplify particle light absorption for carbonaceous aerosol, significantly at short wavelength, underscoring the need to understand and predict chemical composition effects on optical properties to accurately estimate the climate radiative forcing by mixed carbonaceous aerosols.

We introduce a method for deriving aerosol spectral radiative forcing along with single scattering albedo, asymmetry parameter, and surface albedo from airborne vertical profile measurements of shortwave spectral irradiance and spectral aerosol optical thickness. The new method complements the traditional, direct measurement of aerosol radiative forcing efficiency from horizontal flight legs below gradients of aerosol optical thickness, and is particularly useful over heterogeneous land surfaces and for homogeneous aerosol layers where the horizontal gradient method is impractical. Using data collected by the Solar Spectral Flux Radiometer (SSFR) and the Ames Airborne Tracking Sunphotometer (AATS-14) during the MILAGRO (Megacity Initiative: Local and Global Research Observations) experiment, we validate an over-ocean spectral aerosol forcing efficiency from the new method by comparing with the traditional method. Retrieved over-land aerosol optical properties are compared with in-situ measurements and AERONET retrievals. The spectral forcing efficiencies over ocean and land are remarkably similar and agree with results from other field experiments.

We report the direct observation of laboratory production of spherical, carbonaceous particles – "tar balls" – from smoldering combustion of two commonly occurring dry mid-latitude fuels. Real-time measurements of spectrally varying absorption Ångström coefficients (AAC) indicate that a class of light absorbing organic carbon (OC) with wavelength dependent imaginary part of its refractive index – optically defined as "brown carbon" – is an important component of tar balls. The spectrum of the imaginary parts of their complex refractive indices can be described with a Lorentzian-like model with an effective resonance wavelength in the ultraviolet (UV) spectral region. Sensitivity calculations for aerosols containing traditional OC (no absorption at visible and UV wavelengths) and brown carbon suggest that accounting for near-UV absorption by brown carbon leads to an increase in aerosol radiative forcing efficiency and increased light absorption. Since particles from smoldering combustion account for nearly three-fourths of the total carbonaceous aerosol mass emitted globally, inclusion of the optical properties of tar balls into radiative forcing models has significance for the Earth's radiation budget, optical remote sensing, and understanding of anomalous UV absorption in the troposphere.

Simulations using the fully coupled WRF/Chem (Weather Research and Forecasting – Chemistry) model at 3-km resolution in Mexico City have been performed to examine the temperature, relative humidity, wind, and gaseous criteria pollutants (CO, O3, NO, NO2, and NOy) during the MCMA-2006/MILAGRO field campaign. Comparison of the model simulations with measurements from the ground-based air quality monitoring network (RAMA) is presented. The model resolves reasonably well the observed surface temperature, relative humidity and wind speed; however, large discrepancies are identified between the simulated and the observed surface wind direction for wind speeds below 2 m s−1. The simulated chemical species concentrations (CO, O3, NO, NO2, and NOy) compare favorably with the observations. Simulated O3 concentrations agree especially well with the observations. The simulated 10 VOC species compare generally favorably with the observations at the T0 supersite although lower correlation coefficients and larger biases exist for propene, acetone and propanal, isoprene, and c10-aromatics when compared to the other VOC species. The model performs much better during daytime than nighttime for both chemical species and meteorological variables, although the model tends to underestimate daytime temperature and relative humidity. Simulations using combinations of the available PBL schemes and land surface models (LSMs) do not show a preferred combination in reproducing the observations. The simulated meteorological fields under the O3-South, O3-North and EI Norte weather episodes exhibit similar correlation coefficients and biases for the same variable. However, the model performs well for the O3-South episode but inferiorly for the El Norte events in resolving the observed chemical species.