Based on a uniquely dense network of surface towers measuring continuously the atmospheric concentrations of greenhouse gases (GHGs), we developed the first comprehensive monitoring systems of CO 2 emissions at high resolution over the city of Indianapolis. The urban inversion evaluated over the 2012-2013 dormant season showed a statistically significant increase of about 20% (from 4.5 to 5.7 MtC ± 0.23 MtC) compared to the Hestia CO 2 emission estimate, a state-of-the-art building-level emission product. Spatial structures in prior emission errors, mostly undetermined, appeared to affect the spatial pattern in the inverse solution and the total carbon budget over the entire area by up to 15%, while the inverse solution remains fairly insensitive to the CO 2 boundary inflow and to the different prior emissions (i.e., ODIAC). Preceding the surface emission optimization, we improved the atmospheric simulations using a meteorological data assimilation system also informing our Bayesian inversion system through updated observations error variances. Finally, we estimated the uncertainties associated with undetermined parameters using an ensemble of inversions. The total CO 2 emissions based on the ensemble mean and quartiles (5.26-5.91 MtC) were statistically different compared to the prior total emissions (4.1 to 4.5 MtC). Considering the relatively small sensitivity to the different parameters, we conclude that atmospheric inversions are potentially able to constrain the carbon budget of the city, assuming sufficient data to measure the inflow of GHG over the city, but additional information on prior emission error structures are required to determine the spatial structures of urban emissions at high resolution.

The large concentrations of ultrafine particles consistently observed at high altitudes over the tropics represent one of the world’s largest aerosol reservoirs, which may be providing a globally important source of cloud condensation nuclei. However, the sources and chemical processes contributing to the formation of these particles remain unclear. Here we investigate new particle formation (NPF) mechanisms in the Amazon free troposphere by integrating insights from laboratory measurements, chemical transport modeling, and field measurements. To account for organic NPF, we develop a comprehensive model representation of the temperature-dependent formation chemistry and thermodynamics of extremely low volatility organic compounds as well as their roles in NPF processes. We find that pure-organic NPF driven by natural biogenic emissions dominates in the uppermost troposphere above 13 km and accounts for 65 to 83% of the column total NPF rate under relatively pristine conditions, while ternary NPF involving organics and sulfuric acid dominates between 8 and 13 km. The large organic NPF rates at high altitudes mainly result from decreased volatility of organics and increased NPF efficiency at low temperatures, somewhat counterbalanced by a reduced chemical formation rate of extremely low volatility organic compounds. These findings imply a key role of naturally occurring organic NPF in high-altitude preindustrial environments and will help better quantify anthropogenic aerosol forcing from preindustrial times to the present day.

Our knowledge about synoptic‐scale variations of atmospheric‐CO2 produced by interactions between midlatitude cyclones and regional‐scale surface fluxes remains limited due to the scarcity of observations. We synthesized observations of greenhouse gases (GHGs) with respect to frontal passages to understand how GHG distributions change vertically and horizontally during a synoptic event. We use the airborne in situ measurements of GHGs collected during the Atmospheric Carbon and Transport‐America summer 2016 field campaign. Using these measurements, we defined three metrics, (1) the differences in the GHG mole fractions across frontal boundaries in the atmospheric boundary layer (BL) and free troposphere (FT), (2) differences in the vertical contrasts in GHGs in warm and cold sectors, and (3) the size and magnitude of enhanced CO2 in the vicinity of frontal boundary. We found that frontal structures are clearly associated with spatially coherent and significant changes in GHG composition. Warm sector CO2 mole fractions [CO2] are higher than those in the cold sector. The cross‐frontal [CO2] contrasts are largest in the BL (5–30 ppm) with smaller differences in the FT (3–5 ppm). We found higher [CH4] in the BL in the warm sector than in the cold sector for 5 out of 11 cases. Analyses of vertical profiles revealed higher [CO2] in the FT than in the BL in the cold sector while opposite pattern in the warm sector. Average BL‐to‐FT [CO2] differences are 12 and −6 ppm in the warm and cold sectors, respectively. The observational analyses presented define new metrics involving horizontal and vertical GHG contrasts across fronts during summer which will be used to evaluate simulations of GHG transport.

Abstract. We estimate the amount of methane (CH 4 ) 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 13/12 CH 4 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 CH 4 and CO 2 (X CH 4 and X CO 2 ) 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 X CH 4 and X CO 2 , 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 CH 4 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 CH 4 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 CH 4 emissions of 2.2 to 3.5 ppt s −1 at Chino.

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We present a high-resolution atmospheric inversion system combining a Lagrangian Particle Dispersion Model (LPDM) and the Weather Research and Forecasting model (WRF), and test the impact of assimilating meteorological observation on transport accuracy. A Four Dimensional Data Assimilation (FDDA) technique continuously assimilates meteorological observations from various observing systems into the transport modeling system, and is coupled to the high resolution CO 2 emission product Hestia to simulate the atmospheric mole fractions of CO 2 . For the Indianapolis Flux Experiment (INFLUX) project, we evaluated the impact of assimilating different meteorological observation systems on the linearized adjoint solutions and the CO 2 inverse fluxes estimated using observed CO 2 mole fractions from 11 out of 12 communications towers over Indianapolis for the Sep. ). Wind speed MAE and ME are larger in daytime than in nighttime. PBL depth MAE is reduced by ~1 0%, with little bias reduction. The inverse results indicate that the spatial distribution of CO 2 inverse fluxes were affected by the model performance while the overall flux estimates changed little across WRF simulations when aggregated over the entire domain. Our results show that PBL wind observations are a potent tool for increasing the precision of urban meteorological reanalyses, but that the impact on inverse flux estimates is dependent on the specific urban environment.

The Weather Research and Forecasting (WRF) model is evaluated by conducting various sensitivity experiments over central California including the San Francisco Bay Area (SFBA), with the goal of establishing a WRF model configuration to be used by the Bay Area Air Quality Management District (BAAQMD) for its air quality applications. For the two selected cases, a winter particulate matter case and a summer ozone case, WRF solutions are evaluated both quantitatively by comparing the error statistics and qualitatively by analyzing the model-simulated mesoscale features. Model evaluation is also performed for the SFBA, Sacramento Valley, and San Joaquin Valley subregions. The recommended WRF configuration includes use of the Rapid Radiative Transfer Model/Dudhia (or RRTMG) radiation schemes and the Pleim-Xiu land surface physics, combined with a multiscale four-dimensional data assimilation strategy throughout the simulation period to assimilate the available observations, including standard observations from the World Meteorological Organization and local special observations. With the recommended model configuration, WRF is able to simulate the meteorological variables with reasonable error, with the added value, although relatively small, of assimilating the additional BAAQMD local special observations. Mesoscale features, simulated reasonably well for both cases, include the upslope and downslope flows that occur along the mountains that surround the Central Valley of California, as well as the mesoscale eddies that develop within the valley.

Combining unique high-altitude aircraft measurements and detailed regional model simulations, we show that inplant biochemistry plays a central but previously unidentified role in fine particulate-forming processes and atmosphere−biosphere− climate interactions over the Amazon rainforest. Isoprene epoxydiol secondary organic aerosols (IEPOX-SOA) are key components of sub-micrometer aerosol particle mass throughout the troposphere over the Amazon rainforest and are traditionally thought to form by multiphase chemical pathways. Here, we show that these pathways are strongly inhibited by the solid thermodynamic phase state of aerosol particles and lack of particle and cloud liquid water in the upper troposphere. Strong diffusion limitations within organic aerosol coatings prevailing at low temperatures and low relative humidity in the upper troposphere strongly inhibit the reactive uptake of IEPOX to inorganic aerosols. We find that direct emissions of 2-methyltetrol gases formed by in-plant biochemical oxidation and/or oxidation of deposited IEPOX gases on the surfaces of soils and leaves and their transport by cloud updrafts followed by their condensation at low temperatures could explain over 90% of the IEPOX-SOA mass concentrations in the upper troposphere. Our simulations indicate that even near the surface, direct emissions of 2-methyltetrol gases represent a ubiquitous, but previously unaccounted for, source of IEPOX-SOA. Our results provide compelling evidence for new pathways related to land surface−aerosol−cloud interactions that have not been considered previously.