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The age of air is an important transport diagnostic that can be derived from trace gas measurements and compared to global chemistry climate model output. We describe a new technique to calculate the age of air, measuring transport times from the Earth's surface to any location in the atmosphere based on simultaneous in situ measurements of multiple key long-lived trace gases. The primary benefits of this new technique include (1) optimized ages of air consistent with simultaneously measured SF6 and CO2; (2) age of air from the upper troposphere through the stratosphere; (3) estimates of the second moment of age spectra that have not been well constrained from measurements; and (4) flexibility to be used with measurements across multiple instruments, platforms, and decades. We demonstrate the technique on aircraft and balloon measurements from the 1990s, the last period of extensive stratospheric in situ sampling, and several recent missions from the 2020s, and compare the results with previously published and modeled values.

Accurate and comprehensive quantification of oil and gas methane emissions is pivotal in informing effective methane mitigation policies while also supporting the assessment and tracking of progress towards emissions reduction targets set by governments and industry. While national bottom-up source-level inventories are useful for understanding the sources of methane emissions, they are often unrepresentative across spatial scales, and their reliance on generic emission factors produces underestimations when compared with measurement-based inventories. Here, we compile and analyze previously reported ground-based facility-level methane emissions measurements (n=1540) in the major US oil- and gas-producing basins and develop representative methane emission profiles for key facility categories in the US oil and gas supply chain, including well sites, natural-gas compressor stations, processing plants, crude-oil refineries, and pipelines. We then integrate these emissions data with comprehensive spatial data on national oil and gas activity to estimate each facility's mean total methane emissions and uncertainties for the year 2021, from which we develop a mean estimate of annual national methane emissions resolved at 0.1° × 0.1° spatial scales (∼ 10 km × 10 km). From this measurement-based methane emissions inventory (EI-ME), we estimate total US national oil and gas methane emissions of approximately 16 Tg (95 % confidence interval of 14–18 Tg) in 2021, which is ∼ 2 times greater than the EPA Greenhouse Gas Inventory. Our estimate represents a mean gas-production-normalized methane loss rate of 2.6 %, consistent with recent satellite-based estimates. We find significant variability in both the magnitude and spatial distribution of basin-level methane emissions, ranging from production-normalized methane loss rates of < 1 % in the gas-dominant Appalachian and Haynesville regions to > 3 %–6 % in oil-dominant basins, including the Permian, Bakken, and the Uinta. Additionally, we present and compare novel comprehensive wide-area airborne remote-sensing data and results for total area methane emissions and the relative contributions of diffuse and concentrated methane point sources as quantified using MethaneAIR in 2021. The MethaneAIR assessment showed reasonable agreement with independent regional methane quantification results in sub-regions of the Permian and Uinta basins and indicated that diffuse area sources accounted for the majority of the total oil and gas emissions in these two regions. Our assessment offers key insights into plausible underlying drivers of basin-to-basin variabilities in oil and gas methane emissions, emphasizing the importance of integrating measurement-based data when developing high-resolution spatially explicit methane inventories in support of accurate methane assessment, attribution, and mitigation. The high-resolution spatially explicit EI-ME inventory is publicly available at https://doi.org/10.5281/zenodo.10734299 (Omara, 2024).

Reducing methane (CH4) emissions from the oil and gas (O&G) sector is crucial for mitigating climate change in the near term. MethaneSAT is an upcoming satellite mission designed to monitor basin-wide O&G emissions globally, providing estimates of emission rates and helping identify the underlying processes leading to methane release in the atmosphere. MethaneSAT data will support advocacy and policy efforts by helping to track methane reduction commitments and targets set by countries and industries. Here, we introduce a CH4 retrieval algorithm for MethaneSAT based on the CO2 proxy method. We apply the algorithm to observations from the maiden campaign of MethaneAIR, an airborne precursor to the satellite that has similar instrument specifications. The campaign was conducted during winter 2019 and summer 2021 over three major US oil and gas basins. Analysis of MethaneAIR data shows that measurement precision is typically better than 2 % at a 20×20 m2 pixel resolution, exhibiting no strong dependence on geophysical variables, e.g., surface reflectance. We show that detector focus drifts over the course of each flight, likely due to thermal gradients that develop across the optical bench. The impacts of this drift on retrieved CH4 can mostly be mitigated by including a parameter that squeezes the laboratory-derived, tabulated instrument spectral response function (ISRF) in the spectral fit. Validation against coincident EM27/SUN retrievals shows that MethaneAIR values are generally within 1 % of the retrievals. MethaneAIR retrievals were also intercompared with retrievals from the TROPOspheric Monitoring Instrument (TROPOMI). We estimate that the mean bias between the instruments is 2.5 ppb, and the latitudinal gradients for the two data sets are in good agreement. We evaluate the accuracy of MethaneAIR estimates of point-source emissions using observations recorded over the Permian Basin, an O&G basin, based on the integrated-mass-enhancement approach coupled with a plume-masking algorithm that uses total variational denoising. We estimate that the median point-source detection threshold is 100–150 kg h−1 at the aircraft's nominal above-surface observation altitude of 12 km. This estimate is based on an ensemble of Weather Research and Forecasting (WRF) large-eddy simulations used to mimic the campaign's conditions, with the threshold for quantification set at approximately twice the detection threshold. Retrievals from repeated basin surveys indicate the presence of both persistent and intermittent sources, and we highlight an example from each case. For the persistent source, we infer emissions from a large O&G processing facility and estimate a leak rate between 1.6 % and 2.1 %, higher than any previously reported emission levels from a facility of its size. We also identify a ruptured pipeline that could increase total basin emissions by 2 % if left unrepaired; this pipeline was discovered 2 weeks before it was found by its operator, highlighting the importance of regular monitoring by future satellite missions. The results showcase MethaneAIR's capability to make highly accurate, precise measurements of methane dry-air mole fractions in the atmosphere, with a fine spatial resolution (∼ 20×20 m2) mapped over large swaths (∼ 100×100 km2) in a single flight. The results provide confidence that MethaneSAT can make such measurements at unprecedentedly fine scales from space (∼ 130×400 m2 pixel size over a target area measuring ∼ 200×200 km2), thereby delivering quantitative data on basin-wide methane emissions.

The Total Carbon Column Observing Network (TCCON) measures column-average mole fractions of several greenhouse gases (GHGs), beginning in 2004, from over 30 current or past measurement sites around the world using solar absorption spectroscopy in the near-infrared (near-IR) region. TCCON GHG data have been used extensively for multiple purposes, including in studies of the carbon cycle and anthropogenic emissions, as well as to validate and improve observations from space-based sensors. Here, we describe an update to the retrieval algorithm used to process the TCCON near-IR solar spectra and to generate the associated data products. This version, called GGG2020, was initially released in April 2022. It includes updates and improvements to all steps of the retrieval, including but not limited to the conversion of the original interferograms into spectra, the spectroscopic information used in the column retrieval, post hoc air mass dependence correction, and scaling to align with the calibration scales of in situ GHG measurements. All TCCON data are available through https://tccondata.org/ (last access: 22 April 2024) and are hosted on CaltechDATA (https://data.caltech.edu/, last access: 22 April 2024). Each TCCON site has a unique DOI for its data record. An archive of all the sites' data is also available with the DOI https://doi.org/10.14291/TCCON.GGG2020 (Total Carbon Column Observing Network (TCCON) Team, 2022). The hosted files are updated approximately monthly, and TCCON sites are required to deliver data to the archive no later than 1 year after acquisition. Full details of data locations are provided in the “Code and data availability” section.

The MethaneSAT satellite instrument and its aircraft precursor, MethaneAIR, are imaging spectrometers designed to measure methane concentrations with wide spatial coverage, fine spatial resolution, and high precision compared to currently deployed remote sensing instruments. At 12 960 m cruise altitude above ground (13 850 m above sea level), MethaneAIR datasets have a 4.5 km swath gridded to 10 m × 10 m pixels with 17–20 ppb standard deviation on a flat scene. MethaneAIR was deployed in the summer of 2021 in the Permian Basin to test the accuracy of the retrieved methane concentrations and emission rates using the algorithms developed for MethaneSAT. We report here point source emissions obtained during a single-blind volume-controlled release experiment, using two methods. (1) The modified integrated mass enhancement (mIME) method estimates emission rates using the total mass enhancement of methane in an observed plume combined with winds obtained from Weather Research Forecast driven by High-Resolution Rapid Refresh meteorological data in Large Eddy Simulations mode (WRF-LES-HRRR). WRF-LES-HRRR simulates winds in stochastic eddy-scale (100–1000 m) variability, which is particularly important for low-wind conditions and informing the error budget. The mIME can estimate emission rates of plumes of any size that are detectable by MethaneAIR. (2) The divergence integral (DI) method applies Gauss's theorem to estimate the flux divergence fields through a series of closed surfaces enclosing the sources. The set of boxes grows from the upwind side of the plume through the core of each plume and downwind. No selection of inflow concentration, as used in the mIME, is required. The DI approach can efficiently determine fluxes from large sources and clusters of sources but cannot resolve small point emissions. These methods account for the effects of eddy-scale variation in different ways: the DI averages across many eddies, whereas the mIME re-samples many eddies from the LES simulation. The DI directly uses HRRR winds, while mIME uses WRF-LES-HRRR wind products. Emissions estimates from both the mIME and DI methods agreed closely with the single-blind volume-controlled experiments (N = 21). The York regression between the estimated emissions and the released emissions has a slope of 0.96 [0.84, 1.08], R = 0.83 and N = 21, with 30 % mean percentage error for the whole dataset, which indicates that MethaneAIR can quantify point sources emitting more than 200 kg h−1 for the mIME and 500 kg h−1 for the DI method. The two methods also agreed on methane emission estimates from various uncontrolled sources in the Permian Basin. The experiment thus demonstrates the powerful potential of the MethaneAIR instrument and suggests that the quantification method should be transferable to MethaneSAT if it meets the design specifications.

Reducing oil and gas methane emissions is crucially important for limiting the rate of human-induced climate warming. As the capacity of multi-scale measurements of global oil and gas methane emissions has advanced in recent years, including the emerging ecosystem of satellite and airborne remote sensing platforms, a clear need for an openly accessible and regularly updated global inventory of oil and gas infrastructure has emerged as an important tool for characterizing and tracking methane emission sources. In this study, we develop a spatially explicit database of global oil and gas infrastructure, focusing on the acquisition, curation, and integration of public-domain geospatial datasets reported by official government sources and by industry, academic research institutions, and other non-government entities. We focus on the major oil and gas facility types that are key sources of measured methane emissions, including production wells, offshore production platforms, natural gas compressor stations, processing facilities, liquefied natural gas facilities, crude oil refineries, and pipelines. The first version of this global geospatial database (Oil and Gas Infrastructure Mapping database, OGIM_v1) contains a total of ∼ 6 million features, including 2.6 million point locations of major oil and gas facility types and over 2.6×106 km of pipelines globally. For each facility record, we include key attributes – such as facility type, operational status, oil and gas production and capacity information, operator names, and installation dates – which enable detailed methane source assessment and attribution analytics. Using the OGIM database, we demonstrate facility-level source attribution for multiple airborne remote-sensing-detected methane point sources from the Permian Basin, which is the largest oil-producing basin in the United States. In addition to source attribution, we present other major applications of this oil and gas infrastructure database in relation to methane emission assessment, including the development of an improved bottom-up methane emission inventory at high resolution (1 km × 1 km). We also discuss the tracking of changes in basin-level oil and gas activity and the development of policy-relevant analytics and insights for targeted methane mitigation. This work and the OGIM database, which we anticipate updating on a regular cadence, help fulfill a crucial oil and gas geospatial data need, in support of the assessment, attribution, and mitigation of global oil and gas methane emissions at high resolution. OGIM_v1 is publicly available at https://doi.org/10.5281/zenodo.7466757 (Omara et al., 2022a).

The NASA Atmospheric Tomography (ATom) mission built a photochemical climatology of air parcels based on in situ measurements with the NASA DC-8 aircraft along objectively planned profiling transects through the middle of the Pacific and Atlantic oceans. In this paper we present and analyze a data set of 10 s (2 km) merged and gap-filled observations of the key reactive species driving the chemical budgets of O3 and CH4 (O3, CH4, CO, H2O, HCHO, H2O2, CH3OOH, C2H6, higher alkanes, alkenes, aromatics, NOx, HNO3, HNO4, peroxyacetyl nitrate, and other organic nitrates), consisting of 146 494 distinct air parcels from ATom deployments 1 through 4. Six models calculated the O3 and CH4 photochemical tendencies from this modeling data stream for ATom 1. We find that 80 %–90 % of the total reactivity lies in the top 50 % of the parcels and 25 %–35 % in the top 10 %, supporting previous model-only studies that tropospheric chemistry is driven by a fraction of all the air. Surprisingly, the probability densities of species and reactivities averaged on a model scale (100 km) differ only slightly from the 2 km ATom 10 s data, indicating that much of the heterogeneity in tropospheric chemistry can be captured with current global chemistry models. Comparing the ATom reactivities over the tropical oceans with climatological statistics from six global chemistry models, we find generally good agreement with the reactivity rates for O3 and CH4. Models distinctly underestimate O3 production below 2 km relative to the mid-troposphere, and this can be traced to lower NOx levels than observed. Attaching photochemical reactivities to measurements of chemical species allows for a richer, yet more constrained-to-what-matters, set of metrics for model evaluation. This paper presents a corrected version of the paper published under the same authors and title (sans “corrected”) as https://doi.org/10.5194/acp-21-13729-2021.

An optimal estimation-based algorithm is developed to retrieve the number density of excited oxygen (O2) molecules that generate airglow emissions near 0.76 µm (b1Σg+ or A band) and 1.27 µm (a1Δg or 1Δ band) in the upper atmosphere. Both oxygen bands are important for the remote sensing of greenhouse gases. The algorithm is applied to the limb spectra observed by the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument in both the nominal (tangent heights below ∼ 90 km) and mesosphere–lower thermosphere (MLT) modes (tangent heights spanning 50–150 km). The number densities of emitting O2 in the a1Δg band are retrieved in an altitude range of 25–100 km near-daily in 2010, providing a climatology of O2 a1Δg-band airglow emission. This climatology will help disentangle the airglow from backscattered light in nadir remote sensing of the a1Δg band. The global monthly distributions of the vertical column density of emitting O2 in a1Δg state show mainly latitudinal dependence without other discernible geographical patterns. Temperature profiles are retrieved simultaneously from the spectral shapes of the a1Δg-band airglow emission in the nominal limb mode (valid altitude range of 40–100 km) and from both a1Δg- and b1Σg+-band airglow emissions in the MLT mode (valid range of 60–105 km). The temperature retrievals from both airglow bands are consistent internally and in agreement with independent observations from the Atmospheric Chemistry Experiment Fourier transform spectrometer (ACE-FTS) and the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), with the absolute mean bias near or below 5 K and root mean squared error (RMSE) near or below 10 K. The retrieved emitting O2 number density and temperature provide a unique dataset for the remote sensing of greenhouse gases and constraining the chemical and physical processes in the upper atmosphere.

UCATS (the UAS Chromatograph for Atmospheric Trace Species) was designed and built for observations of important atmospheric trace gases from unmanned aircraft systems (UAS) in the upper troposphere and lower stratosphere (UTLS). Initially it measured major chlorofluorocarbons (CFCs) and the stratospheric transport tracers nitrous oxide (N2O) and sulfur hexafluoride (SF6), using gas chromatography with electron capture detection. Compact commercial absorption spectrometers for ozone (O3) and water vapor (H2O) were added to enhance its capabilities on platforms with relatively small payloads. UCATS has since been reconfigured to measure methane (CH4), carbon monoxide (CO), and molecular hydrogen (H2) instead of CFCs and has undergone numerous upgrades to its subsystems. It has served as part of large payloads on stratospheric UAS missions to probe the tropical tropopause region and transport of air into the stratosphere; in piloted aircraft studies of greenhouse gases, transport, and chemistry in the troposphere; and in 2021 is scheduled to return to the study of stratospheric ozone and halogen compounds, one of its original goals. Each deployment brought different challenges, which were largely met or resolved. The design, capabilities, modifications, and some results from UCATS are shown and described here, including changes for future missions.

In situ measurements of aerosol microphysical, chemical, and optical properties were made during global-scale flights from 2016–2018 as part of the Atmospheric Tomography Mission (ATom). The NASA DC-8 aircraft flew from ∼ 84∘ N to ∼ 86∘ S latitude over the Pacific, Atlantic, Arctic, and Southern oceans while profiling nearly continuously between altitudes of ∼ 160 m and ∼ 12 km. These global circuits were made once each season. Particle size distributions measured in the aircraft cabin at dry conditions and with an underwing probe at ambient conditions were combined with bulk and single-particle composition observations and measurements of water vapor, pressure, and temperature to estimate aerosol hygroscopicity and hygroscopic growth factors and calculate size distributions at ambient relative humidity. These reconstructed, composition-resolved ambient size distributions were used to estimate intensive and extensive aerosol properties, including single-scatter albedo, the asymmetry parameter, extinction, absorption, Ångström exponents, and aerosol optical depth (AOD) at several wavelengths, as well as cloud condensation nuclei (CCN) concentrations at fixed supersaturations and lognormal fits to four modes. Dry extinction and absorption were compared with direct in situ measurements, and AOD derived from the extinction profiles was compared with remotely sensed AOD measurements from the ground-based Aerosol Robotic Network (AERONET); this comparison showed no substantial bias. The purpose of this work is to describe the methodology by which ambient aerosol properties are estimated from the in situ measurements, provide statistical descriptions of the aerosol characteristics of different remote air mass types, examine the contributions to AOD from different aerosol types in different air masses, and provide an entry point to the ATom aerosol database. The contributions of different aerosol types (dust, sea salt, biomass burning, etc.) to AOD generally align with expectations based on location of the profiles relative to continental sources of aerosols, with sea salt and aerosol water dominating the column extinction in most remote environments and dust and biomass burning (BB) particles contributing substantially to AOD, especially downwind of the African continent. Contributions of dust and BB aerosols to AOD were also significant in the free troposphere over the North Pacific. Comparisons of lognormally fitted size distribution parameters to values in the Optical Properties of Aerosols and Clouds (OPAC) database commonly used in global models show significant differences in the mean diameters and standard deviations for accumulation-mode particles and coarse-mode dust. In contrast, comparisons of lognormal parameters derived from the ATom data with previously published shipborne measurements in the remote marine boundary layer show general agreement. The dataset resulting from this work can be used to improve global-scale representation of climate-relevant aerosol properties in remote air masses through comparison with output from global models and assumptions used in retrievals of aerosol properties from both ground-based and satellite remote sensing.

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.

We measured the global distribution of tropospheric N2O mixing ratios during the NASA airborne Atmospheric Tomography (ATom) mission. ATom measured concentrations of ∼ 300 gas species and aerosol properties in 647 vertical profiles spanning the Pacific, Atlantic, Arctic, and much of the Southern Ocean basins, nearly from pole to pole, over four seasons (2016–2018). We measured N2O concentrations at 1 Hz using a quantum cascade laser spectrometer (QCLS). We introduced a new spectral retrieval method to account for the pressure and temperature sensitivity of the instrument when deployed on aircraft. This retrieval strategy improved the precision of our ATom QCLS N2O measurements by a factor of three (based on the standard deviation of calibration measurements). Our measurements show that most of the variance of N2O mixing ratios in the troposphere is driven by the influence of N2O-depleted stratospheric air, especially at mid- and high latitudes. We observe the downward propagation of lower N2O mixing ratios (compared to surface stations) that tracks the influence of stratosphere–troposphere exchange through the tropospheric column down to the surface. The highest N2O mixing ratios occur close to the Equator, extending through the boundary layer and free troposphere. We observed influences from a complex and diverse mixture of N2O sources, with emission source types identified using the rich suite of chemical species measured on ATom and the geographical origin calculated using an atmospheric transport model. Although ATom flights were mostly over the oceans, the most prominent N2O enhancements were associated with anthropogenic emissions, including from industry (e.g., oil and gas), urban sources, and biomass burning, especially in the tropical Atlantic outflow from Africa. Enhanced N2O mixing ratios are mostly associated with pollution-related tracers arriving from the coastal area of Nigeria. Peaks of N2O are often associated with indicators of photochemical processing, suggesting possible unexpected source processes. In most cases, the results show how difficult it is to separate the mixture of different sources in the atmosphere, which may contribute to uncertainties in the N2O global budget. The extensive data set from ATom will help improve the understanding of N2O emission processes and their representation in global models.

We evaluate the uncertainties of methane optimal estimation retrievals from single-footprint thermal infrared observations from the Atmospheric Infrared Sounder (AIRS). These retrievals are primarily sensitive to atmospheric methane in the mid-troposphere through the lower stratosphere (∼2 to ∼17 km). We compare them to in situ observations made from aircraft during the HIAPER Pole to Pole Observations (HIPPO) and Atmospheric Tomography Mission (ATom) campaigns, and from the NOAA GML aircraft network, between the surface and 5–13 km, across a range of years, latitudes between 60∘ S to 80∘ N, and over land and ocean. After a global, pressure-dependent bias correction, we find that the land and ocean have similar biases and that the reported observation error (combined measurement and interference errors) of ∼27 ppb is consistent with the SD between aircraft and individual AIRS observations. A single observation has measurement (noise related) uncertainty of ∼17 ppb, a ∼20 ppb uncertainty from radiative interferences (e.g., from water or temperature), and ∼30 ppb due to “smoothing error”, which is partially removed when making comparisons to in situ measurements or models in a way that accounts for this regularization. We estimate a 10 ppb validation uncertainty because the aircraft typically did not measure methane at altitudes where the AIRS measurements have some sensitivity, e.g., the stratosphere, and there is uncertainty in the truth that we validate against. Daily averaging only partly reduces the difference between aircraft and satellite observation, likely because of correlated errors introduced into the retrieval from temperature and water vapor. For example, averaging nine observations only reduces the aircraft–model difference to ∼17 ppb vs. the expected ∼10 ppb. Seasonal averages can reduce this ∼17 ppb uncertainty further to ∼10 ppb, as determined through comparison with NOAA aircraft, likely because uncertainties related to radiative effects of temperature and water vapor are reduced when averaged over a season.

Accurate simulation of atmospheric circulation, particularly in the lower stratosphere, is challenging due to unresolved wave–mean flow interactions and limited high-resolution observations for validation. Gravity-induced pressure gradients lead to a small but measurable separation of heavy and light gases by molecular diffusion in the stratosphere. Because the relative abundance of Ar to N2 is exclusively controlled by physical transport, the argon-to-nitrogen ratio (Ar∕N2) provides an additional constraint on circulation and the age of air (AoA), i.e., the time elapsed since entry of an air parcel into the stratosphere. Here we use airborne measurements of N2O and Ar∕N2 from nine campaigns with global coverage spanning 2008–2018 to calculate AoA and to quantify gravitational separation in the lowermost stratosphere. To this end, we develop a new N2O–AoA relationship using a Markov chain Monte Carlo algorithm. We observe that gravitational separation increases systematically with increasing AoA for samples with AoA between 0 and 3 years. These observations are compared to a simulation of the TOMCAT/SLIMCAT 3-D chemical transport model, which has been updated to include gravitational fractionation of gases. We demonstrate that although AoA at old ages is slightly underestimated in the model, the relationship between Ar∕N2 and AoA is robust and agrees with the observations. This highlights the potential of Ar∕N2 to become a new AoA tracer that is subject only to physical transport phenomena and can supplement the suite of available AoA indicators.

Ozone is a key constituent of the troposphere, where it drives photochemical processes, impacts air quality, and acts as a climate forcer. Large-scale in situ observations of ozone commensurate with the grid resolution of current Earth system models are necessary to validate model outputs and satellite retrievals. In this paper, we examine measurements from the Atmospheric Tomography (ATom; four deployments in 2016–2018) and the HIAPER Pole-to-Pole Observations (HIPPO; five deployments in 2009–2011) experiments, two global-scale airborne campaigns covering the Pacific and Atlantic basins. ATom and HIPPO represent the first global-scale, vertically resolved measurements of O3 distributions throughout the troposphere, with HIPPO sampling the atmosphere over the Pacific and ATom sampling both the Pacific and Atlantic. Given the relatively limited temporal resolution of these two campaigns, we first compare ATom and HIPPO ozone data to longer-term observational records to establish the representativeness of our dataset. We show that these two airborne campaigns captured on average 53 %, 54 %, and 38 % of the ozone variability in the marine boundary layer, free troposphere, and upper troposphere–lower stratosphere (UTLS), respectively, at nine well-established ozonesonde sites. Additionally, ATom captured the most frequent ozone concentrations measured by regular commercial aircraft flights in the northern Atlantic UTLS. We then use the repeated vertical profiles from these two campaigns to confirm and extend the existing knowledge of tropospheric ozone spatial and vertical distributions throughout the remote troposphere. We highlight a clear hemispheric gradient, with greater ozone in the Northern Hemisphere, consistent with greater precursor emissions and consistent with previous modeling and satellite studies. We also show that the ozone distribution below 8 km was similar in the extra-tropics of the Atlantic and Pacific basins, likely due to zonal circulation patterns. However, twice as much ozone was found in the tropical Atlantic as in the tropical Pacific, due to well-documented dynamical patterns transporting continental air masses over the Atlantic. Finally, we show that the seasonal variability of tropospheric ozone over the Pacific and the Atlantic basins is driven year-round by transported continental plumes and photochemistry, and the vertical distribution is driven by photochemistry and mixing with stratospheric air. This new dataset provides additional constraints for global climate and chemistry models to improve our understanding of both ozone production and loss processes in remote regions, as well as the influence of anthropogenic emissions on baseline ozone.

China has pledged reduction of carbon dioxide (CO2) emissions per unit of gross domestic product (GDP) by 60 %–65 % relative to 2005 levels, and to peak carbon emissions overall by 2030. However, the lack of observational data and disagreement among the many available inventories makes it difficult for China to track progress toward these goals and evaluate the efficacy of control measures. To demonstrate the value of atmospheric observations for constraining CO2 inventories we track the ability of CO2 concentrations predicted from three different CO2 inventories to match a unique multi-year continuous record of atmospheric CO2. Our analysis time window includes the key commitment period for the Paris Agreement (2005) and the Beijing Olympics (2008). One inventory is China-specific and two are spatial subsets of global inventories. The inventories differ in spatial resolution, basis in national or subnational statistics, and reliance on global or China-specific emission factors. We use a unique set of historical atmospheric observations from 2005 to 2009 to evaluate the three CO2 emissions inventories within China's heavily industrialized and populated northern region accounting for ∼33 %–41 % of national emissions. Each anthropogenic inventory is combined with estimates of biogenic CO2 within a high-resolution atmospheric transport framework to model the time series of CO2 observations. To convert the model–observation mismatch from mixing ratio to mass emission rates we distribute it over a region encompassing 90 % of the total surface influence in seasonal (annual) averaged back-trajectory footprints (L_0.90 region). The L_0.90 region roughly corresponds to northern China. Except for the peak growing season, where assessment of anthropogenic emissions is entangled with the strong vegetation signal, we find the China-specific inventory based on subnational data and domestic field studies agrees significantly better with observations than the global inventories at all timescales. Averaged over the study time period, the unscaled China-specific inventory reports substantially larger annual emissions for northern China (30 %) and China as a whole (20 %) than the two unscaled global inventories. Our results, exploiting a robust time series of continuous observations, lend support to the rates and geographic distribution in the China-specific inventory Though even long-term observations at a single site reveal differences among inventories, exploring inventory discrepancy over all of China requires a denser observational network in future efforts to measure and verify CO2 emissions for China both regionally and nationally. We find that carbon intensity in the northern China region has decreased by 47 % from 2005 to 2009, from approximately 4 kg of CO2 per USD (note that all references to USD in this paper refer to USD adjusted for purchasing power parity, PPP) in 2005 to about 2 kg of CO2 per USD in 2009 (Fig. 9c). However, the corresponding 18 % increase in absolute emissions over the same time period affirms a critical point that carbon intensity targets in emerging economies can be at odds with making real climate progress. Our results provide an important quantification of model–observation mismatch, supporting the increased use and development of China-specific inventories in tracking China's progress as a whole towards reducing emissions. We emphasize that this work presents a methodology for extending the analysis to other inventories and is intended to be a comparison of a subset of anthropogenic CO2 emissions rates from inventories that were readily available at the time this research began. For this study's analysis time period, there was not enough spatially distinct observational data to conduct an optimization of the inventories. The primary intent of the comparisons presented here is not to judge specific inventories, but to demonstrate that even a single site with a long record of high-time-resolution observations can identify major differences among inventories that manifest as biases in the model–data comparison. This study provides a baseline analysis for evaluating emissions from a small but important region within China, as well a guide for determining optimal locations for future ground-based measurement sites.

Earth system models (ESMs) have been developed to represent the role of terrestrial ecosystems on the energy, water, and carbon cycles. However, many ESMs still lack representation of within-ecosystem heterogeneity and diversity. In this paper, we present the Ecosystem Demography model version 2.2 (ED-2.2). In ED-2.2, the biophysical and physiological processes account for the horizontal and vertical heterogeneity of the ecosystem: the energy, water, and carbon cycles are solved separately for a series of vegetation cohorts (groups of individual plants of similar size and plant functional type) distributed across a series of spatially implicit patches (representing collections of micro-environments that have a similar disturbance history). We define the equations that describe the energy, water, and carbon cycles in terms of total energy, water, and carbon, which simplifies the differential equations and guarantees excellent conservation of these quantities in long-term simulation (< 0.1 % error over 50 years). We also show examples of ED-2.2 simulation results at single sites and across tropical South America. These results demonstrate the model's ability to characterize the variability of ecosystem structure, composition, and functioning both at stand and continental scales. A detailed model evaluation was conducted and is presented in a companion paper (Longo et al., 2019a). Finally, we highlight some of the ongoing model developments designed to improve the model's accuracy and performance and to include processes hitherto not represented in the model.

The Ecosystem Demography model version 2.2 (ED-2.2) is a terrestrial biosphere model that simulates the biophysical, ecological, and biogeochemical dynamics of vertically and horizontally heterogeneous terrestrial ecosystems. In a companion paper (Longo et al., 2019a), we described how the model solves the energy, water, and carbon cycles, and verified the high degree of conservation of these properties in long-term simulations that include long-term (multi-decadal) vegetation dynamics. Here, we present a detailed assessment of the model's ability to represent multiple processes associated with the biophysical and biogeochemical cycles in Amazon forests. We use multiple measurements from eddy covariance towers, forest inventory plots, and regional remote-sensing products to assess the model's ability to represent biophysical, physiological, and ecological processes at multiple timescales, ranging from subdaily to century long. The ED-2.2 model accurately describes the vertical distribution of light, water fluxes, and the storage of water, energy, and carbon in the canopy air space, the regional distribution of biomass in tropical South America, and the variability of biomass as a function of environmental drivers. In addition, ED-2.2 qualitatively captures several emergent properties of the ecosystem found in observations, specifically observed relationships between aboveground biomass, mortality rates, and wood density; however, the slopes of these relationships were not accurately captured. We also identified several limitations, including the model's tendency to overestimate the magnitude and seasonality of heterotrophic respiration and to overestimate growth rates in a nutrient-poor tropical site. The evaluation presented here highlights the potential of incorporating structural and functional heterogeneity within biomes in Earth system models (ESMs) and to realistically represent their impacts on energy, water, and carbon cycles. We also identify several priorities for further model development.

Abstract Methyl ethyl ketone (MEK) is a relatively abundant but understudied oxygenated volatile organic compound that can serve as a source of both HOx and PAN when photooxidized. We use aircraft observations of MEK from the remote marine troposphere to show that the ocean serves as a source of MEK to the atmosphere during both meteorological winter and summer. There is pronounced seasonality in the MEK profiles in the extratropical troposphere, with higher MEK mixing ratios observed in summer than in winter. MEK in clean air over the remote oceans correlates with both acetone and acetaldehyde, whose primary sources in the ocean water are the photooxidation of organic material. We show that even a small (>1 nM) concentration of MEK in surface waters is sufficient to allow the ocean to be a net source of MEK to the atmosphere over ocean basins across multiple seasons.

Accurately quantifying the spatiotemporal distribution of the biological component of CO2 surface–atmosphere exchange is necessary to improve top-down constraints on China's anthropogenic CO2 emissions. We provide hourly fluxes of CO2 as net ecosystem exchange (NEE; µmol CO2 m−2 s−1) on a 0.25° × 0.25° grid by adapting the Vegetation, Photosynthesis, and Respiration Model (VPRM) to the eastern half of China for the time period from 2005 to 2009; the minimal empirical parameterization of the VPRM-CHINA makes it well suited for inverse modeling approaches. This study diverges from previous VPRM applications in that it is applied at a large scale to China's ecosystems for the first time, incorporating a novel processing framework not previously applied to existing VPRM versions. In addition, the VPRM-CHINA model prescribes methods for addressing dual-cropping regions that have two separate growing-season modes applied to the same model grid cell. We evaluate the VPRM-CHINA performance during the growing season and compare to other biospheric models. We calibrate the VPRM-CHINA with ChinaFlux and FluxNet data and scale up regionally using Weather Research and Forecasting (WRF) Model v3.6.1 meteorology and MODIS surface reflectances. When combined with an anthropogenic emissions model in a Lagrangian particle transport framework, we compare the ability of VPRM-CHINA relative to an ensemble mean of global hourly flux models (NASA CMS – Carbon Monitoring System) to reproduce observations made at a site in northern China. The measurements are heavily influenced by the northern China administrative region. Modeled hourly time series using vegetation fluxes prescribed by VPRM-CHINA exhibit low bias relative to measurements during the May–September growing season. Compared to NASA CMS subset over the study region, VPRM-CHINA agrees significantly better with measurements. NASA CMS consistently underestimates regional uptake in the growing season. We find that during the peak growing season, when the heavily cropped North China Plain significantly influences measurements, VPRM-CHINA models a CO2 uptake signal comparable in magnitude to the modeled anthropogenic signal. In addition to demonstrating efficacy as a low-bias prior for top-down CO2 inventory optimization studies using ground-based measurements, high spatiotemporal resolution models such as the VPRM are critical for interpreting retrievals from global CO2 remote-sensing platforms such as OCO-2 and OCO-3 (planned). Depending on the satellite time of day and season of crossover, efforts to interpret the relative contribution of the vegetation and anthropogenic components to the measured signal are critical in key emitting regions such as northern China – where the magnitude of the vegetation CO2 signal is shown to be equivalent to the anthropogenic signal.

In Amazon forests, the relative contributions of climate, phenology, and disturbance to net ecosystem exchange of carbon (NEE) are not well understood. To partition influences across various timescales, we use a statistical model to represent eddy-covariance-derived NEE in an evergreen eastern Amazon forest as a constant response to changing meteorology and phenology throughout a decade. Our best fit model represented hourly NEE variations as changes due to sunlight, while seasonal variations arose from phenology influencing photosynthesis and from rainfall influencing ecosystem respiration, where phenology was asynchronous with dry-season onset. We compared annual model residuals with biometric forest surveys to estimate impacts of drought disturbance. We found that our simple model represented hourly and monthly variations in NEE well (R2 = 0.81 and 0.59, respectively). Modeled phenology explained 1 % of hourly and 26 % of monthly variations in observed NEE, whereas the remaining modeled variability was due to changes in meteorology. We did not find evidence to support the common assumption that the forest phenology was seasonally light- or water-triggered. Our model simulated annual NEE well, with the exception of 2002, the first year of our data record, which contained 1.2 MgC ha−1 of residual net emissions, because photosynthesis was anomalously low. Because a severe drought occurred in 1998, we hypothesized that this drought caused a persistent, multi-year depression of photosynthesis. Our results suggest drought can have lasting impacts on photosynthesis, possibly via partial damage to still-living trees.

Methane (CH4) is the second most important greenhouse gas but its emissions from northern regions are still poorly constrained. In this study, we analyze a subset of in situ CH4 aircraft observations made over Alaska during the growing seasons of 2012–2014 as part of the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). Net surface CH4 fluxes are estimated using a Lagrangian particle dispersion model which quantitatively links surface emissions from Alaska and the western Yukon with observations of enhanced CH4 in the mixed layer. We estimate that between May and September, net CH4 emissions from the region of interest were 2.2 ± 0.5 Tg, 1.9 ± 0.4 Tg, and 2.3 ± 0.6 Tg of CH4 for 2012, 2013, and 2014, respectively. If emissions are only attributed to two biogenic eco-regions within our domain, then tundra regions were the predominant source, accounting for over half of the overall budget despite only representing 18 % of the total surface area. Boreal regions, which cover a large part of the study region, accounted for the remainder of the emissions. Simple multiple linear regression analysis revealed that, overall, CH4 fluxes were largely driven by soil temperature and elevation. In regions specifically dominated by wetlands, soil temperature and moisture at 10 cm depth were important explanatory variables while in regions that were not wetlands, soil temperature and moisture at 40 cm depth were more important, suggesting deeper methanogenesis in drier soils. Although similar environmental drivers have been found in the past to control CH4 emissions at local scales, this study shows that they can be used to generate a statistical model to estimate the regional-scale net CH4 budget.

An approach for analysis and modeling of global atmospheric chemistry is developed for application to measurements that provide a tropospheric climatology of those heterogeneously distributed, reactive species that control the loss of methane and the production and loss of ozone. We identify key species (e.g., O3, NOx, HNO3, HNO4, C2H3NO5, H2O, HOOH, CH3OOH, HCHO, CO, CH4, C2H6, acetaldehyde, acetone) and presume that they can be measured simultaneously in air parcels on the scale of a few km horizontally and a few tenths of a km vertically. As a first step, six global models have prepared such climatologies sampled at the modeled resolution for August with emphasis on the vast central Pacific Ocean basin. Objectives of this paper are to identify and characterize differences in model-generated reactivities as well as species covariances that could readily be discriminated with an unbiased climatology. A primary tool is comparison of multidimensional probability densities of key species weighted by the mass of such parcels or frequency of occurrence as well as by the reactivity of the parcels with respect to methane and ozone. The reactivity-weighted probabilities tell us which parcels matter in this case, and this method shows skill in differentiating among the models' chemistry. Testing 100 km scale models with 2 km measurements using these tools also addresses a core question about model resolution and whether fine-scale atmospheric structures matter to the overall ozone and methane budget. A new method enabling these six global chemistry–climate models to ingest an externally sourced climatology and then compute air parcel reactivity is demonstrated. Such an objective climatology containing these key species is anticipated from the NASA Atmospheric Tomography (ATom) aircraft mission (2015–2020), executing profiles over the Pacific and Atlantic Ocean basins. This modeling study addresses a core part of the design of ATom.

Stomatal conductance influences both photosynthesis and transpiration, thereby coupling the carbon and water cycles and affecting surface–atmosphere energy exchange. The environmental response of stomatal conductance has been measured mainly on the leaf scale, and theoretical canopy models are relied on to upscale stomatal conductance for application in terrestrial ecosystem models and climate prediction. Here we estimate stomatal conductance and associated transpiration in a temperate deciduous forest directly on the canopy scale via two independent approaches: (i) from heat and water vapor exchange and (ii) from carbonyl sulfide (OCS) uptake. We use the eddy covariance method to measure the net ecosystem–atmosphere exchange of OCS, and we use a flux-gradient approach to separate canopy OCS uptake from soil OCS uptake. We find that the seasonal and diurnal patterns of canopy stomatal conductance obtained by the two approaches agree (to within ±6 % diurnally), validating both methods. Canopy stomatal conductance increases linearly with above-canopy light intensity (in contrast to the leaf scale, where stomatal conductance shows declining marginal increases) and otherwise depends only on the diffuse light fraction, the canopy-average leaf-to-air water vapor gradient, and the total leaf area. Based on stomatal conductance, we partition evapotranspiration (ET) and find that evaporation increases from 0 to 40 % of ET as the growing season progresses, driven primarily by rising soil temperature and secondarily by rainfall. Counterintuitively, evaporation peaks at the time of year when the soil is dry and the air is moist. Our method of ET partitioning avoids concerns about mismatched scales or measurement types because both ET and transpiration are derived from eddy covariance data. Neither of the two ecosystem models tested predicts the observed dynamics of evaporation or transpiration, indicating that ET partitioning such as that provided here is needed to further model development and improve our understanding of carbon and water cycling.

Canopy and aerodynamic conductances (gC and gA) are two of the key land surface biophysical variables that control the land surface response of land surface schemes in climate models. Their representation is crucial for predicting transpiration (λET) and evaporation (λEE) flux components of the terrestrial latent heat flux (λE), which has important implications for global climate change and water resource management. By physical integration of radiometric surface temperature (TR) into an integrated framework of the Penman–Monteith and Shuttleworth–Wallace models, we present a novel approach to directly quantify the canopy-scale biophysical controls on λET and λEE over multiple plant functional types (PFTs) in the Amazon Basin. Combining data from six LBA (Large-scale Biosphere-Atmosphere Experiment in Amazonia) eddy covariance tower sites and a TR-driven physically based modeling approach, we identified the canopy-scale feedback-response mechanism between gC, λET, and atmospheric vapor pressure deficit (DA), without using any leaf-scale empirical parameterizations for the modeling. The TR-based model shows minor biophysical control on λET during the wet (rainy) seasons where λET becomes predominantly radiation driven and net radiation (RN) determines 75 to 80 % of the variances of λET. However, biophysical control on λET is dramatically increased during the dry seasons, and particularly the 2005 drought year, explaining 50 to 65 % of the variances of λET, and indicates λET to be substantially soil moisture driven during the rainfall deficit phase. Despite substantial differences in gA between forests and pastures, very similar canopy–atmosphere "coupling" was found in these two biomes due to soil moisture-induced decrease in gC in the pasture. This revealed the pragmatic aspect of the TR-driven model behavior that exhibits a high sensitivity of gC to per unit change in wetness as opposed to gA that is marginally sensitive to surface wetness variability. Our results reveal the occurrence of a significant hysteresis between λET and gC during the dry season for the pasture sites, which is attributed to relatively low soil water availability as compared to the rainforests, likely due to differences in rooting depth between the two systems. Evaporation was significantly influenced by gA for all the PFTs and across all wetness conditions. Our analytical framework logically captures the responses of gC and gA to changes in atmospheric radiation, DA, and surface radiometric temperature, and thus appears to be promising for the improvement of existing land–surface–atmosphere exchange parameterizations across a range of spatial scales.

Wetlands are the largest global natural methane (CH4) source, and emissions between 50 and 70° N latitude contribute 10–30 % to this source. Predictive capability of land models for northern wetland CH4 emissions is still low due to limited site measurements, strong spatial and temporal variability in emissions, and complex hydrological and biogeochemical dynamics. To explore this issue, we compare wetland CH4 emission predictions from the Community Land Model 4.5 (CLM4.5-BGC) with site- to regional-scale observations. A comparison of the CH4 fluxes with eddy flux data highlighted needed changes to the model's estimate of aerenchyma area, which we implemented and tested. The model modification substantially reduced biases in CH4 emissions when compared with CarbonTracker CH4 predictions. CLM4.5 CH4 emission predictions agree well with growing season (May–September) CarbonTracker Alaskan regional-level CH4 predictions and site-level observations. However, CLM4.5 underestimated CH4 emissions in the cold season (October–April). The monthly atmospheric CH4 mole fraction enhancements due to wetland emissions are also assessed using the Weather Research and Forecasting-Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) model coupled with daily emissions from CLM4.5 and compared with aircraft CH4 mole fraction measurements from the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) campaign. Both the tower and aircraft analyses confirm the underestimate of cold-season CH4 emissions by CLM4.5. The greatest uncertainties in predicting the seasonal CH4 cycle are from the wetland extent, cold-season CH4 production and CH4 transport processes. We recommend more cold-season experimental studies in high-latitude systems, which could improve the understanding and parameterization of ecosystem structure and function during this period. Predicted CH4 emissions remain uncertain, but we show here that benchmarking against observations across spatial scales can inform model structural and parameter improvements.

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.

In recent years, space-borne observations of atmospheric carbon dioxide (CO2) have been increasingly used in global carbon-cycle studies. In order to obtain added value from space-borne measurements, they have to suffice stringent accuracy and precision requirements, with the latter being less crucial as it can be reduced by just enhanced sample size. Validation of CO2 column-averaged dry air mole fractions (XCO2) heavily relies on measurements of the Total Carbon Column Observing Network (TCCON). Owing to the sparseness of the network and the requirements imposed on space-based measurements, independent additional validation is highly valuable. Here, we use observations from the High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole-to-Pole Observations (HIPPO) flights from 01/2009 through 09/2011 to validate CO2 measurements from satellites (Greenhouse Gases Observing Satellite – GOSAT, Thermal Emission Sounder – TES, Atmospheric Infrared Sounder – AIRS) and atmospheric inversion models (CarbonTracker CT2013B, Monitoring Atmospheric Composition and Climate (MACC) v13r1). We find that the atmospheric models capture the XCO2 variability observed in HIPPO flights very well, with correlation coefficients (r2) of 0.93 and 0.95 for CT2013B and MACC, respectively. Some larger discrepancies can be observed in profile comparisons at higher latitudes, in particular at 300 hPa during the peaks of either carbon uptake or release. These deviations can be up to 4 ppm and hint at misrepresentation of vertical transport. Comparisons with the GOSAT satellite are of comparable quality, with an r2 of 0.85, a mean bias μ of −0.06 ppm, and a standard deviation σ of 0.45 ppm. TES exhibits an r2 of 0.75, μ of 0.34 ppm, and σ of 1.13 ppm. For AIRS, we find an r2 of 0.37, μ of 1.11 ppm, and σ of 1.46 ppm, with latitude-dependent biases. For these comparisons at least 6, 20, and 50 atmospheric soundings have been averaged for GOSAT, TES, and AIRS, respectively. Overall, we find that GOSAT soundings over the remote Pacific Ocean mostly meet the stringent accuracy requirements of about 0.5 ppm for space-based CO2 observations.

Northern high-latitude carbon sources and sinks, including those resulting from degrading permafrost, are thought to be sensitive to the rapidly warming climate. Because the near-surface atmosphere integrates surface fluxes over large ( ∼ 500–1000 km) scales, atmospheric monitoring of carbon dioxide (CO2) and methane (CH4) mole fractions in the daytime mixed layer is a promising method for detecting change in the carbon cycle throughout boreal Alaska. Here we use CO2 and CH4 measurements from a NOAA tower 17 km north of Fairbanks, AK, established as part of NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE), to investigate regional fluxes of CO2 and CH4 for 2012–2014. CARVE was designed to use aircraft and surface observations to better understand and quantify the sensitivity of Alaskan carbon fluxes to climate variability. We use high-resolution meteorological fields from the Polar Weather Research and Forecasting (WRF) model coupled with the Stochastic Time-Inverted Lagrangian Transport model (hereafter, WRF-STILT), along with the Polar Vegetation Photosynthesis and Respiration Model (PolarVPRM), to investigate fluxes of CO2 in boreal Alaska using the tower observations, which are sensitive to large areas of central Alaska. We show that simulated PolarVPRM–WRF-STILT CO2 mole fractions show remarkably good agreement with tower observations, suggesting that the WRF-STILT model represents the meteorology of the region quite well, and that the PolarVPRM flux magnitudes and spatial distribution are generally consistent with CO2 mole fractions observed at the CARVE tower. One exception to this good agreement is that during the fall of all 3 years, PolarVPRM cannot reproduce the observed CO2 respiration. Using the WRF-STILT model, we find that average CH4 fluxes in boreal Alaska are somewhat lower than flux estimates by Chang et al. (2014) over all of Alaska for May–September 2012; we also find that enhancements appear to persist during some wintertime periods, augmenting those observed during the summer and fall. The possibility of significant fall and winter CO2 and CH4 fluxes underscores the need for year-round in situ observations to quantify changes in boreal Alaskan annual carbon balance.

The upper troposphere and lower stratosphere (UTLS) represents a transition region between the more dynamically active troposphere and more stably stratified stratosphere. The region is characterized by strong gradients in the distribution of long-lived tracers, whose representation in models is sensitive to discrepancies in transport. We evaluate the GEOS-Chem model in the UTLS using carbon dioxide (CO2) and ozone (O3) observations from the HIAPER (The High-Performance Instrumented Airborne Platform for Environmental Research) Pole-to-Pole Observations (HIPPO) campaign in March 2010. GEOS-Chem CO2/O3 correlation suggests that there is a discrepancy in mixing across the tropopause in the model, which results in an overestimate of CO2 and an underestimate of O3 in the Arctic lower stratosphere. We assimilate stratospheric O3 data from the Optical Spectrograph and InfraRed Imager System (OSIRIS) and use the assimilated O3 fields together with the HIPPO CO2/O3 correlations to obtain an adjustment to the modeled CO2 profile in the Arctic UTLS (primarily between the 320 and 360 K isentropic surfaces). The HIPPO-derived adjustment corresponds to a sink of 0.60 Pg C for March–August 2010 in the Arctic. Imposing this adjustment results in a reduction in the CO2 sinks inferred from GOSAT observations for temperate North America, Europe, and tropical Asia of 19, 13, and 49 %, respectively. Conversely, the inversion increased the source of CO2 from tropical South America by 23 %. We find that the model also underestimates CO2 in the upper tropical and subtropical troposphere. Correcting for the underestimate in the model relative to HIPPO in the tropical upper troposphere leads to a reduction in the source from tropical South America by 77 %, and produces an estimated sink for tropical Asia that is only 19 % larger than the standard inversion (without the imposed source and sink). Globally, the inversion with the Arctic and tropical adjustment produces a sink of −6.64 Pg C, which is consistent with the estimate of −6.65 Pg C in the standard inversion. However, the standard inversion produces a stronger northern land sink by 0.98 Pg C to account for the CO2 overestimate in the high-latitude UTLS, suggesting that this UTLS discrepancy can impact the latitudinal distribution of the inferred sources and sinks. We find that doubling the model resolution from 4° × 5° to 2° × 2.5° enhances the CO2 vertical gradient in the high-latitude UTLS, and reduces the overestimate in CO2 in the extratropical lower stratosphere. Our results illustrate that discrepancies in the CO2 distribution in the UTLS can affect CO2 flux inversions and suggest the need for more careful evaluation of model errors in the UTLS.

We describe a new 4D-Var inversion framework for nitrous oxide (N2O) based on the GEOS-Chem chemical transport model and its adjoint, and apply it in a series of observing system simulation experiments to assess how well N2O sources and sinks can be constrained by the current global observing network. The employed measurement ensemble includes approximately weekly and quasi-continuous N2O measurements (hourly averages used) from several long-term monitoring networks, N2O measurements collected from discrete air samples onboard a commercial aircraft (Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrument Container; CARIBIC), and quasi-continuous measurements from the airborne HIAPER Pole-to-Pole Observations (HIPPO) campaigns. For a 2-year inversion, we find that the surface and HIPPO observations can accurately resolve a uniform bias in emissions during the first year; CARIBIC data provide a somewhat weaker constraint. Variable emission errors are much more difficult to resolve given the long lifetime of N2O, and major parts of the world lack significant constraints on the seasonal cycle of fluxes. Current observations can largely correct a global bias in the stratospheric sink of N2O if emissions are known, but do not provide information on the temporal and spatial distribution of the sink. However, for the more realistic scenario where source and sink are both uncertain, we find that simultaneously optimizing both would require unrealistically small errors in model transport. Regardless, a bias in the magnitude of the N2O sink would not affect the a posteriori N2O emissions for the 2-year timescale used here, given realistic initial conditions, due to the timescale required for stratosphere–troposphere exchange (STE). The same does not apply to model errors in the rate of STE itself, which we show exerts a larger influence on the tropospheric burden of N2O than does the chemical loss rate over short (< 3 year) timescales. We use a stochastic estimate of the inverse Hessian for the inversion to evaluate the spatial resolution of emission constraints provided by the observations, and find that significant, spatially explicit constraints can be achieved in locations near and immediately upwind of surface measurements and the HIPPO flight tracks; however, these are mostly confined to North America, Europe, and Australia. None of the current observing networks are able to provide significant spatial information on tropical N2O emissions. There, averaging kernels (describing the sensitivity of the inversion to emissions in each grid square) are highly smeared spatially and extend even to the midlatitudes, so that tropical emissions risk being conflated with those elsewhere. For global inversions, therefore, the current lack of constraints on the tropics also places an important limit on our ability to understand extratropical emissions. Based on the error reduction statistics from the inverse Hessian, we characterize the atmospheric distribution of unconstrained N2O, and identify regions in and downwind of South America, central Africa, and Southeast Asia where new surface or profile measurements would have the most value for reducing present uncertainty in the global N2O budget.

The exchanges of carbon, water and energy between the atmosphere and the Amazon basin have global implications for the current and future climate. Here, the global atmospheric inversion system of the Monitoring of Atmospheric Composition and Climate (MACC) service is used to study the seasonal and interannual variations of biogenic CO2 fluxes in Amazonia during the period 2002–2010. The system assimilated surface measurements of atmospheric CO2 mole fractions made at more than 100 sites over the globe into an atmospheric transport model. The present study adds measurements from four surface stations located in tropical South America, a region poorly covered by CO2 observations. The estimates of net ecosystem exchange (NEE) optimized by the inversion are compared to an independent estimate of NEE upscaled from eddy-covariance flux measurements in Amazonia. They are also qualitatively evaluated against reports on the seasonal and interannual variations of the land sink in South America from the scientific literature. We attempt at assessing the impact on NEE of the strong droughts in 2005 and 2010 (due to severe and longer-than-usual dry seasons) and the extreme rainfall conditions registered in 2009. The spatial variations of the seasonal and interannual variability of optimized NEE are also investigated. While the inversion supports the assumption of strong spatial heterogeneity of these variations, the results reveal critical limitations of the coarse-resolution transport model, the surface observation network in South America during the recent years and the present knowledge of modelling uncertainties in South America that prevent our inversion from capturing the seasonal patterns of fluxes across Amazonia. However, some patterns from the inversion seem consistent with the anomaly of moisture conditions in 2009.

This study analyzes in situ airborne measurements from the 2008 Stratosphere–Troposphere Analyses of Regional Transport (START08) experiment to characterize gravity waves in the extratropical upper troposphere and lower stratosphere (ExUTLS). The focus is on the second research flight (RF02), which took place on 21–22 April 2008. This was the first airborne mission dedicated to probing gravity waves associated with strong upper-tropospheric jet–front systems. Based on spectral and wavelet analyses of the in situ observations, along with a diagnosis of the polarization relationships, clear signals of mesoscale variations with wavelengths ~ 50–500 km are found in almost every segment of the 8 h flight, which took place mostly in the lower stratosphere. The aircraft sampled a wide range of background conditions including the region near the jet core, the jet exit and over the Rocky Mountains with clear evidence of vertically propagating gravity waves of along-track wavelength between 100 and 120 km. The power spectra of the horizontal velocity components and potential temperature for the scale approximately between ~ 8 and ~ 256 km display an approximate −5/3 power law in agreement with past studies on aircraft measurements, while the fluctuations roll over to a −3 power law for the scale approximately between ~ 0.5 and ~ 8 km (except when this part of the spectrum is activated, as recorded clearly by one of the flight segments). However, at least part of the high-frequency signals with sampled periods of ~ 20–~ 60 s and wavelengths of ~ 5–~ 15 km might be due to intrinsic observational errors in the aircraft measurements, even though the possibilities that these fluctuations may be due to other physical phenomena (e.g., nonlinear dynamics, shear instability and/or turbulence) cannot be completely ruled out.

This paper describes the atmospheric modeling that underlies the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) science analysis, including its meteorological and atmospheric transport components (polar variant of the Weather Research and Forecasting (WRF) and Stochastic Time Inverted Lagrangian Transport (STILT) models), and provides WRF validation for May–October 2012 and March–November 2013 – the first 2 years of the aircraft field campaign. A triply nested computational domain for WRF was chosen so that the innermost domain with 3.3 km grid spacing encompasses the entire mainland of Alaska and enables the substantial orography of the state to be represented by the underlying high-resolution topographic input field. Summary statistics of the WRF model performance on the 3.3 km grid indicate good overall agreement with quality-controlled surface and radiosonde observations. Two-meter temperatures are generally too cold by approximately 1.4 K in 2012 and 1.1 K in 2013, while 2 m dewpoint temperatures are too low (dry) by 0.2 K in 2012 and too high (moist) by 0.6 K in 2013. Wind speeds are biased too low by 0.2 m s−1 in 2012 and 0.3 m s−1 in 2013. Model representation of upper level variables is very good. These measures are comparable to model performance metrics of similar model configurations found in the literature. The high quality of these fine-resolution WRF meteorological fields inspires confidence in their use to drive STILT for the purpose of computing surface influences ("footprints") at commensurably increased resolution. Indeed, footprints generated on a 0.1° grid show increased spatial detail compared with those on the more common 0.5° grid, better allowing for convolution with flux models for carbon dioxide and methane across the heterogeneous Alaskan landscape. Ozone deposition rates computed using STILT footprints indicate good agreement with observations and exhibit realistic seasonal variability, further indicating that WRF-STILT footprints are of high quality and will support accurate estimates of CO2 and CH4 surface–atmosphere fluxes using CARVE observations.

Errors in the spectroscopic parameters used in the forward radiative transfer model can introduce spatially, temporally, and altitude-dependent biases in trace gas retrievals. For well-mixed trace gases such as methane, where the variability of tropospheric mixing ratios is relatively small, reducing such biases is particularly important. We use aircraft observations from all five missions of the HIAPER Pole-to-Pole Observations (HIPPO) of the Carbon Cycle and Greenhouse Gases Study to evaluate the impact of updates to spectroscopic parameters for methane (CH4), water vapor (H2O), and nitrous oxide (N2O) on thermal infrared retrievals of methane from the NASA Aura Tropospheric Emission Spectrometer (TES). We find that updates to the spectroscopic parameters for CH4 result in a substantially smaller mean bias in the retrieved CH4 when compared with HIPPO observations. After an N2O-based correction, the bias in TES methane upper tropospheric representative values for measurements between 50° S and 50° N decreases from 56.9 to 25.7 ppbv, while the bias in the lower tropospheric representative value increases only slightly (from 27.3 to 28.4 ppbv). For retrievals with less than 1.6 degrees of freedom for signal (DOFS), the bias is reduced from 26.8 to 4.8 ppbv. We also find that updates to the spectroscopic parameters for N2O reduce the errors in the retrieved N2O profile.

At the beginning of 2009 new space-borne observations of dry-air column-averaged mole fractions of atmospheric methane (XCH4) became available from the Thermal And Near infrared Sensor for carbon Observations–Fourier Transform Spectrometer (TANSO-FTS) instrument on board the Greenhouse Gases Observing SATellite (GOSAT). Until April 2012 concurrent {methane (CH4) retrievals} were provided by the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) instrument on board the ENVironmental SATellite (ENVISAT). The GOSAT and SCIAMACHY XCH4 retrievals can be compared during the period of overlap. We estimate monthly average CH4 emissions between January 2010 and December 2011, using the TM5-4DVAR inverse modelling system. In addition to satellite data, high-accuracy measurements from the Cooperative Air Sampling Network of the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA ESRL) are used, providing strong constraints on the remote surface atmosphere. We discuss five inversion scenarios that make use of different GOSAT and SCIAMACHY XCH4 retrieval products, including two sets of GOSAT proxy retrievals processed independently by the Netherlands Institute for Space Research (SRON)/Karlsruhe Institute of Technology (KIT), and the University of Leicester (UL), and the RemoTeC "Full-Physics" (FP) XCH4 retrievals available from SRON/KIT. The GOSAT-based inversions show significant reductions in the root mean square (rms) difference between retrieved and modelled XCH4, and require much smaller bias corrections compared to the inversion using SCIAMACHY retrievals, reflecting the higher precision and relative accuracy of the GOSAT XCH4. Despite the large differences between the GOSAT and SCIAMACHY retrievals, 2-year average emission maps show overall good agreement among all satellite-based inversions, with consistent flux adjustment patterns, particularly across equatorial Africa and North America. Over North America, the satellite inversions result in a significant redistribution of CH4 emissions from North-East to South-Central United States. This result is consistent with recent independent studies suggesting a systematic underestimation of CH4 emissions from North American fossil fuel sources in bottom-up inventories, likely related to natural gas production facilities. Furthermore, all four satellite inversions yield lower CH4 fluxes across the Congo basin compared to the NOAA-only scenario, but higher emissions across tropical East Africa. The GOSAT and SCIAMACHY inversions show similar performance when validated against independent shipboard and aircraft observations, and XCH4 retrievals available from the Total Carbon Column Observing Network (TCCON).

The Regional Carbon Balance in Amazonia (BARCA) campaign provided the first Amazon Basin-wide aircraft measurements of ozone (O3) during both the dry-to-wet (November and December 2008) and wet-to-dry (May 2009) transition seasons. Extremely low background values (< 20 ppb) were observed to the west and north of Manaus in both seasons and in all regions during the wet-to-dry transition. On the other hand, elevated O3 levels (40–60 ppb) were seen during the dry-to-wet transition to the east and south of Manaus, where biomass burning emissions of O3 precursors were present. Chemistry simulations with the CCATT-BRAMS and WRF-Chem models are within the error bars of the observed O3 profiles in the boundary layer (0–3 km a.s.l.) in polluted conditions. However, the models overestimate O3 in the boundary layer in clean conditions, despite lacking the predominant NO source from soil. In addition, O3 simulated by the models was either within the error bars or lower than BARCA observations in mid-levels (3–5 km a.s.l.), and lower than total tropospheric O3 retrieved from the OMI/MLS instruments, which is primarily comprised of middle troposphere O3 and thus reflects long-range transport processes. Therefore, the models do a relatively poor job of representing the free troposphere-boundary layer gradient in O3 compared with aircraft and satellite observations, which could be due to missing long-range and convective transport of O3 at mid-levels. Additional simulations with WRF-Chem showed that the model O3 production is very sensitive to both the O3 deposition velocities and the NOx emissions, which were both about one-half of observed values. These results indicate the necessity of more realistic model representations of emissions, deposition, and convective processes for accurate monitoring and prediction of increases in O3 production in the Amazon Basin as the regional population grows.

We present field test results for a new spectroscopic instrument to measure atmospheric carbon dioxide (CO2) with high precision (0.02 μmol mol−1, or ppm at 1 Hz) and demonstrate high stability (within 0.1 ppm over more than 8 months), without the need for hourly, daily, or even monthly calibration against high-pressure gas cylinders. The technical novelty of this instrument (ABsolute Carbon dioxide, ABC) is the spectral null method using an internal quartz reference cell with known CO2 column density. Compared to a previously described prototype, the field instrument has better stability and benefits from more precise thermal control of the optics and more accurate pressure measurements in the sample cell (at the mTorr level). The instrument has been deployed at a long-term ecological research site (the Harvard Forest, USA), where it has measured for 8 months without on-site calibration and with minimal maintenance, showing drift bounds of less than 0.1 ppm. Field measurements agree well with those of a commercially available cavity ring-down CO2 instrument (Picarro G2301) run with a standard calibration protocol. This field test demonstrates that ABC is capable of performing high-accuracy, unattended, continuous field measurements with minimal use of reference gas cylinders.

The Measurements of Pollution in the Troposphere (MOPITT) Version 6 (V6) product for carbon monoxide (CO) incorporates several enhancements which will benefit many users of MOPITT data. V6 algorithm improvements are described in detail, and V6 validation results are presented. First, a geolocation bias related to the orientation of the MOPITT instrument relative to the TERRA platform was characterized and eliminated. Second, the variable a priori for CO concentrations for V6 is based on simulations performed with the chemical transport model Community Atmosphere Model with Chemistry (CAM-chem) for the years 2000–2009 instead of the model-derived climatology for 1997–2004 used for V5. Third, meteorological fields required for V6 retrieval processing are extracted from the MERRA (Modern-Era Retrospective Analysis For Research And Applications) reanalysis. Finally, a significant latitude-dependent retrieval bias in the upper troposphere in Version 5 products has been substantially reduced.

Our understanding of biosphere–atmosphere exchange has been considerably enhanced by eddy covariance measurements. However, there remain many trace gases, such as molecular hydrogen (H2), that lack suitable analytical methods to measure their fluxes by eddy covariance. In such cases, flux-gradient methods can be used to calculate ecosystem-scale fluxes from vertical concentration gradients. The budget of atmospheric H2 is poorly constrained by the limited available observations, and thus the ability to quantify and characterize the sources and sinks of H2 by flux-gradient methods in various ecosystems is important. We developed an approach to make nonintrusive, automated measurements of ecosystem-scale H2 fluxes both above and below the forest canopy at the Harvard Forest in Petersham, Massachusetts, for over a year. We used three flux-gradient methods to calculate the fluxes: two similarity methods that do not rely on a micrometeorological determination of the eddy diffusivity, K, based on (1) trace gases or (2) sensible heat, and one flux-gradient method that (3) parameterizes K. We quantitatively assessed the flux-gradient methods using CO2 and H2O by comparison to their simultaneous independent flux measurements via eddy covariance and soil chambers. All three flux-gradient methods performed well in certain locations, seasons, and times of day, and the best methods were trace gas similarity for above the canopy and K parameterization below it. Sensible heat similarity required several independent measurements, and the results were more variable, in part because those data were only available in the winter, when heat fluxes and temperature gradients were small and difficult to measure. Biases were often observed between flux-gradient methods and the independent flux measurements, and there was at least a 26% difference in nocturnal eddy-derived net ecosystem exchange (NEE) and chamber measurements. H2 fluxes calculated in a summer period agreed within their uncertainty and pointed to soil uptake as the main driver of H2 exchange at Harvard Forest, with H2 deposition velocities ranging from 0.04 to 0.10 cm s−1.

We apply a continental-scale inverse modeling system for North America based on the GEOS-Chem model to optimize California methane emissions at 1/2° × 2/3° horizontal resolution using atmospheric observations from the CalNex aircraft campaign (May–June 2010) and from satellites. Inversion of the CalNex data yields a best estimate for total California methane emissions of 2.86 ± 0.21 Tg a−1, compared with 1.92 Tg a−1 in the EDGAR v4.2 emission inventory used as a priori and 1.51 Tg a−1 in the California Air Resources Board (CARB) inventory used for state regulations of greenhouse gas emissions. These results are consistent with a previous Lagrangian inversion of the CalNex data. Our inversion provides 12 independent pieces of information to constrain the geographical distribution of emissions within California. Attribution to individual source types indicates dominant contributions to emissions from landfills/wastewater (1.1 Tg a−1), livestock (0.87 Tg a−1), and gas/oil (0.64 Tg a−1). EDGAR v4.2 underestimates emissions from livestock, while CARB underestimates emissions from landfills/wastewater and gas/oil. Current satellite observations from GOSAT can constrain methane emissions in the Los Angeles Basin but are too sparse to constrain emissions quantitatively elsewhere in California (they can still be qualitatively useful to diagnose inventory biases). Los Angeles Basin emissions derived from CalNex and GOSAT inversions are 0.42 ± 0.08 and 0.31 ± 0.08 Tg a−1 that the future TROPOMI satellite instrument (2015 launch) will be able to constrain California methane emissions at a detail comparable to the CalNex aircraft campaign. Geostationary satellite observations offer even greater potential for constraining methane emissions in the future.

We present an evaluation of aircraft observations of the carbon and greenhouse gases CO2, CH4, N2O, and CO using a direct-absorption pulsed quantum cascade laser spectrometer (QCLS) operated during the HIPPO and CalNex airborne experiments. The QCLS made continuous 1 Hz measurements with 1σ Allan precisions of 20, 0.5, 0.09, and 0.15 ppb for CO2, CH4, N2O, and CO, respectively, over > 500 flight hours on 79 research flights. The QCLS measurements are compared to two vacuum ultraviolet (VUV) CO instruments (CalNex and HIPPO), a cavity ring-down spectrometer (CRDS) measuring CO2 and CH4 (CalNex), two broadband non-dispersive infrared (NDIR) spectrometers measuring CO2 (HIPPO), two onboard gas chromatographs measuring a variety of chemical species including CH4, N2O, and CO (HIPPO), and various flask-based measurements of all four species. QCLS measurements are tied to NOAA and WMO standards using an in-flight calibration system, and mean differences when compared to NOAA CCG flask data over the 59 HIPPO research flights were 100, 1, 1, and 2 ppb for CO2, CH4, N2O, and CO, respectively. The details of the end-to-end calibration procedures and the data quality assurance and quality control (QA/QC) are presented. Specifically, we discuss our practices for the traceability of standards given uncertainties in calibration cylinders, isotopic and surface effects for the long-lived greenhouse gas tracers, interpolation techniques for in-flight calibrations, and the effects of instrument linearity on retrieved mole fractions.

We have examined the utility of retrieved column-averaged, dry-air mole fractions of CO2 (XCO2) from the Greenhouse Gases Observing Satellite (GOSAT) for quantifying monthly, regional flux estimates of CO2, using the GEOS-Chem four-dimensional variational (4D-Var) data assimilation system. We focused on assessing the potential impact of biases in the GOSAT CO2 data on the regional flux estimates. Using different screening and bias correction approaches, we selected three different subsets of the GOSAT XCO2 data for the 4D-Var inversion analyses, and found that the inferred global fluxes were consistent across the three XCO2 inversions. However, the GOSAT observational coverage was a challenge for the regional flux estimates. In the northern extratropics, the inversions were more sensitive to North American fluxes than to European and Asian fluxes due to the lack of observations over Eurasia in winter and over eastern and southern Asia in summer. The regional flux estimates were also sensitive to the treatment of the residual bias in the GOSAT XCO2 data. The largest differences obtained were for temperate North America and temperate South America, for which the largest spread between the inversions was 1.02 and 0.96 Pg C, respectively. In the case of temperate North America, one inversion suggested a strong source, whereas the second and third XCO2 inversions produced a weak and strong sink, respectively. Despite the discrepancies in the regional flux estimates between the three XCO2 inversions, the a posteriori CO2 distributions were in good agreement (with a mean difference between the three inversions of typically less than 0.5 ppm) with independent data from the Total Carbon Column Observing Network (TCCON), the surface flask network, and from the HIAPER Pole-to-Pole Observations (HIPPO) aircraft campaign. The discrepancy in the regional flux estimates from the different inversions, despite the agreement of the global flux estimates suggests the need for additional work to determine the minimum spatial scales at which we can reliably quantify the fluxes using GOSAT XCO2. The fact that the a posteriori CO2 from the different inversions were in good agreement with the independent data although the regional flux estimates differed significantly, suggests that innovative ways of exploiting existing data sets, and possibly additional observations, are needed to better evaluate the inferred regional flux estimates.

Mid-upper tropospheric methane (CH4), as an operational product at NOAA's (National Oceanic and Atmospheric Administration) Comprehensive Large Array-data Stewardship System (CLASS), has been retrieved from the Infrared Atmospheric Sounding Interferometer (IASI) since 2008. This paper provides a description of the retrieval method and the validation using 596 CH4 vertical profiles from aircraft measurements by the HIAPER Pole-to-Pole Observations (HIPPO) program over the Pacific Ocean. The number of degrees of freedom for the CH4 retrieval is mostly less than 1.5, and it decreases under cloudy conditions. The retrievals show greatest sensitivity between 100–600 hPa in the tropics and 200–750 hPa in the mid- to high latitude. Validation is accomplished using aircraft measurements (convolved by applying the monthly mean averaging kernels) collocated with all the retrieved profiles within 200 km and on the same day, and the results show that, on average, a larger error of CH4 occurs at 300–500 hPa. The bias in the trapezoid of 374–477 hPa is −1.74% with a residual standard deviation of 1.20%, and at layer 596–753 hPa the bias is −0.69% with a residual standard deviation of 1.07%. The retrieval error is relatively larger in the high northern latitude regions and/or under cloudy conditions. The main reasons for this negative bias include the uncertainty in the spectroscopy near the methane Q branch and/or the empirical bias correction, plus residual cloud contamination in the cloud-cleared radiances. It is expected for NOAA to generate the CH4 product for 20 + years using a similar algorithm from three similar thermal infrared sensors: Atmospheric Infrared Sounder (AIRS), IASI and the Cross-track Infrared Sounder (CrIS). Such a unique product will provide a supplementary to the current ground-based observation network, particularly in the Arctic, for monitoring the CH4 cycle, its transport and trend associated with climate change.

We present a novel spectral method to measure atmospheric carbon dioxide (CO2) with high precision and stability without resorting to calibration tanks during long-term operation. This spectral null method improves precision by reducing spectral proportional noise associated with laser emission instabilities. We employ sealed quartz cells with known CO2 column densities to serve as the permanent internal references in the null method, which improve the instrument's stability and accuracy. A prototype instrument – ABsolute Carbon dioxide (ABC) is developed using this new approach. The instrument has a one-second precision of 0.02 ppm, which averages down to 0.007 ppm within one minute. Long-term stability of within 0.1 ppm is achieved without any calibrations for over a one-month period. These results have the potential for eliminating the need for calibration cylinders for high accuracy field measurements of carbon dioxide.

We present top-down estimates of anthropogenic CO, NOx and CO2 surface fluxes at mesoscale using a Lagrangian model in combination with three different WRF model configurations, driven by data from aircraft flights during the CALNEX campaign in southern California in May–June 2010. The US EPA National Emission Inventory 2005 (NEI 2005) was the prior in the CO and NOx inversion calculations. The flux ratio inversion method, based on linear relationships between chemical species, was used to calculate the CO2 inventory without prior knowledge of CO2 surface fluxes. The inversion was applied to each flight to estimate the variability of single-flight-based flux estimates. In Los Angeles (LA) County, the uncertainties on CO and NOx fluxes were 10% and 15%, respectively. Compared with NEI 2005, the CO posterior emissions were lower by 43% in LA County and by 37% in the South Coast Air Basin (SoCAB). NOx posterior emissions were lower by 32% in LA County and by 27% in the SoCAB. NOx posterior emissions were 40% lower on weekends relative to weekdays. The CO2 posterior estimates were 183 Tg yr−1 in SoCAB. A flight during ITCT (Intercontinental Transport and Chemical Transformation) in 2002 was used to estimate emissions in the LA Basin in 2002. From 2002 to 2010, the CO and NOx posterior emissions decreased by 41% and 37%, respectively, in agreement with previous studies. Over the same time period, CO2 emissions increased by 10% in LA County but decreased by 4% in the SoCAB, a statistically insignificant change. Overall, the posterior estimates were in good agreement with the California Air Resources Board (CARB) inventory, with differences of 15% or less. However, the posterior spatial distribution in the basin was significantly different from CARB for NOx emissions. WRF-Chem mesoscale chemical-transport model simulations allowed an evaluation of differences in chemistry using different inventory assumptions, including NEI 2005, a gridded CARB inventory and the posterior inventories derived in this study. The biases in WRF-Chem ozone were reduced and correlations were increased using the posterior from this study compared with simulations with the two bottom-up inventories, suggesting that improving the spatial distribution of ozone precursor surface emissions is also important in mesoscale chemistry simulations.

Thermal infrared radiances from the Tropospheric Emission Spectrometer (TES) between 10 and 15 μm contain significant carbon dioxide (CO2) information, however the CO2 signal must be separated from radiative interference from temperature, surface and cloud parameters, water, and other trace gases. Validation requires data sources spanning the range of TES CO2 sensitivity, which is approximately 2.5 to 12 km with peak sensitivity at about 5 km and the range of TES observations in latitude (40° S to 40° N) and time (2005–2011). We therefore characterize Tropospheric Emission Spectrometer (TES) CO2 version 5 biases and errors through comparisons to ocean and land-based aircraft profiles and to the CarbonTracker assimilation system. We compare to ocean profiles from the first three Hiaper Pole-to-Pole Observations (HIPPO) campaigns between 40° S and 40° N with measurements between the surface and 14 km and find that TES CO2 estimates capture the seasonal and latitudinal gradients observed by HIPPO CO2 measurements. Actual errors range from 0.8–1.8 ppm, depending on the campaign and pressure level, and are approximately 1.6–2 times larger than the predicted errors. The bias of TES versus HIPPO is within 1 ppm for all pressures and datasets; however, several of the sub-tropical TES CO2 estimates are lower than expected based on the calculated errors. Comparisons to land aircraft profiles from the United States Southern Great Plains (SGP) Atmospheric Radiation Measurement (ARM) between 2005 and 2011 measured from the surface to 5 km to TES CO2 show good agreement with an overall bias of −0.3 ppm to 0.1 ppm and standard deviations of 0.8 to 1.0 ppm at different pressure levels. Extending the SGP aircraft profiles above 5 km using AIRS or CONTRAIL measurements improves comparisons with TES. Comparisons to CarbonTracker (version CT2011) show a persistent spatially dependent bias pattern and comparisons to SGP show a time-dependent bias of −0.2 ppm yr−1. We also find that the predicted sensitivity of the TES CO2 estimates is too high, which results from using a multi-step retrieval for CO2 and temperature. We find that the averaging kernel in the TES product corrected by a pressure-dependent factor accurately reflects the sensitivity of the TES CO2 product.

Monitoring the global distribution and long-term variations of CO2 sources and sinks is required for characterizing the global carbon budget. Total column measurements are useful for estimating regional-scale fluxes; however, model transport remains a significant error source, particularly for quantifying local sources and sinks. To improve the capability of estimating regional fluxes, we estimate lower tropospheric CO2 concentrations from ground-based near-infrared (NIR) measurements with space-based thermal infrared (TIR) measurements. The NIR measurements are obtained from the Total Carbon Column Observing Network (TCCON) of solar measurements, which provide an estimate of the total CO2 column amount. Estimates of tropospheric CO2 that are co-located with TCCON are obtained by assimilating Tropospheric Emission Spectrometer (TES) free tropospheric CO2 estimates into the GEOS-Chem model. We find that quantifying lower tropospheric CO2 by subtracting free tropospheric CO2 estimates from total column estimates is a linear problem, because the calculated random uncertainties in total column and lower tropospheric estimates are consistent with actual uncertainties as compared to aircraft data. For the total column estimates, the random uncertainty is about 0.55 ppm with a bias of −5.66 ppm, consistent with previously published results. After accounting for the total column bias, the bias in the lower tropospheric CO2 estimates is 0.26 ppm with a precision (one standard deviation) of 1.02 ppm. This precision is sufficient for capturing the winter to summer variability of approximately 12 ppm in the lower troposphere; double the variability of the total column. This work shows that a combination of NIR and TIR measurements can profile CO2 with the precision and accuracy needed to quantify lower tropospheric CO2 variability.

We present the results of airborne measurements of carbon monoxide (CO) and aerosol particle number concentration (CN) made during the Balanço Atmosférico Regional de Carbono na Amazônia (BARCA) program. The primary goal of BARCA is to address the question of basin-scale sources and sinks of CO2 and other atmospheric carbon species, a central issue of the Large-scale Biosphere-Atmosphere (LBA) program. The experiment consisted of two aircraft campaigns during November–December 2008 (BARCA-A) and May–June 2009 (BARCA-B), which covered the altitude range from the surface up to about 4500 m, and spanned most of the Amazon Basin. Based on meteorological analysis and measurements of the tracer, SF6, we found that airmasses over the Amazon Basin during the late dry season (BARCA-A, November 2008) originated predominantly from the Southern Hemisphere, while during the late wet season (BARCA-B, May 2009) low-level airmasses were dominated by northern-hemispheric inflow and mid-tropospheric airmasses were of mixed origin. In BARCA-A we found strong influence of biomass burning emissions on the composition of the atmosphere over much of the Amazon Basin, with CO enhancements up to 300 ppb and CN concentrations approaching 10 000 cm−3; the highest values were in the southern part of the Basin at altitudes of 1–3 km. The ΔCN/ΔCO ratios were diagnostic for biomass burning emissions, and were lower in aged than in fresh smoke. Fresh emissions indicated CO/CO2 and CN/CO emission ratios in good agreement with previous work, but our results also highlight the need to consider the residual smoldering combustion that takes place after the active flaming phase of deforestation fires. During the late wet season, in contrast, there was little evidence for a significant presence of biomass smoke. Low CN concentrations (300–500 cm−3) prevailed basinwide, and CO mixing ratios were enhanced by only ~10 ppb above the mixing line between Northern and Southern Hemisphere air. There was no detectable trend in CO with distance from the coast, but there was a small enhancement of CO in the boundary layer suggesting diffuse biogenic sources from photochemical degradation of biogenic volatile organic compounds or direct biological emission. Simulations of CO distributions during BARCA-A using a range of models yielded general agreement in spatial distribution and confirm the important contribution from biomass burning emissions, but the models evidence some systematic quantitative differences compared to observed CO concentrations. These mismatches appear to be related to problems with the accuracy of the global background fields, the role of vertical transport and biomass smoke injection height, the choice of model resolution, and reliability and temporal resolution of the emissions data base.