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The paper reviews and evaluates a 30-year-long atmospheric CO2 data series measured at the Hegyhátsál tall-tower greenhouse gas monitoring site, a member of the World Meteorological Organization (WMO) Global Atmosphere Watch (GAW), US National Oceanic and Atmospheric Administration (NOAA), and pan-European Integrated Carbon Observation System (ICOS) networks. The paper also gives the technical description of the monitoring system and its changes over time and introduces the environment of the station. This low-elevation (248 m above mean sea level – m a.m.s.l.), mid-continental central European site shows a 3.90 ± 0.83 µmolmol-1 offset relative to the latitudinally representative marine boundary layer reference concentration, presumably due to European net anthropogenic emissions. The long-term trend (2.20 µmolmol-1yr-1) closely follows the global tendencies. In the concentration growth rate, the ENSO effect is clearly detectable with a 6–7-month lag time. The summer diurnal concentration amplitude is slightly decreasing due to the faster-than-average increase in the nighttime concentrations, which is related to the warming climate. The warming climate also caused a 0.96 ± 0.41 dyr-1 advance at the beginning of the summer CO2 -deficit season in the first half of the measurement period, which did not continue later. The summer CO2 -deficit season was extended by 9.0 ± 6.1 d during the measurement period. [ABSTRACT FROM AUTHOR]

The Geostationary Carbon Cycle Observatory (GeoCarb) was selected as NASA's second Earth Venture Mission (EVM-2). The scientific objectives of GeoCarb were to advance our knowledge of the carbon cycle, in particular, land–atmosphere fluxes of the greenhouse gases carbon dioxide (CO 2) and methane (CH 4) and the effects of these fluxes on the Earth's radiation budget. GeoCarb would retrieve column-integrated dry-air mole fractions of CO 2 (XCO2), CH 4 (XCH4) and CO (XCO), important for understanding tropospheric chemistry), in addition to solar-induced fluorescence (SIF), from hyperspectral resolution measurements in the O 2 A-band at 0.76 µ m, the weak CO 2 band at 1.6 µ m, the strong CO 2 band at 2.06 µ m, and a CH 4 /CO band at 2.32 µ m. Unlike its predecessors (OCO-2/3, GOSAT-1/2, TROPOMI), GeoCarb would be in a geostationary orbit with a sub-satellite point centered over the Americas. This orbital configuration combined with its high-spatial-resolution imaging capabilities would provide an unprecedented view of these quantities on spatial and temporal scales accurate enough to resolve sources and sinks to improve land–atmosphere CO 2 and CH 4 flux calculations and reduce the uncertainty of these fluxes. This paper will present a description of the GeoCarb instrument and the L2 retrieval algorithms which will be followed by simulation experiments to determine an error budget for each target gas. Several sources of uncertainty will be explored, including that from the instrument calibration parameters for radiometric gain, the instrument line shape (ILS), the polarization, and the geolocation pointing, in addition to forward model parameters including meteorology and spectroscopy, although there are some other instrument-related sources of uncertainty that are left out for this study, including that from "smile", the keystone effect, stray light, detector persistence, and scene inhomogeneity. The results indicate that the errors (1 σ) are less than the instrument's multi-sounding precision requirements of 1.2 ppm, 10 ppb, and 12 ppb (10 %), for XCO2 , XCH4 , and XCO , respectively. In particular, when considering the sources of uncertainty separately and in combination (all sources included), we find overall RMSEs of 1.06 ppm for XCO2 , 8.2 ppb for XCH4 , and 2.5 ppb for XCO , respectively. Additionally, we find that, as expected, errors in XCO2 and XCH4 are dominated by forward model and other systematic errors, while errors in XCO are dominated by measurement noise. It is important to note that the GeoCarb mission was canceled by NASA; however, the instrument is still in development and will be delivered to NASA, in full, with the hope that it will eventually be adopted in a future mission proposal. [ABSTRACT FROM AUTHOR]

Since 1999, Environment and Climate Change Canada (ECCC) has been coordinating a multi-laboratory comparison of measurements of long-lived greenhouse gases in whole air samples collected at the Global Atmosphere Watch (GAW) Alert Observatory located in the Canadian High Arctic (82 ∘ 28 ′ N, 62 ∘ 30 ′ W). In this paper, we evaluate the measurement agreement of atmospheric CO 2 , CH 4 , N 2 O, SF 6 , and stable isotopes of CO 2 (δ13 C, δ18 O) between leading laboratories from seven independent international institutions. The measure of success is linked to target goals for network compatibility outlined by the World Meteorological Organization's (WMO) GAW greenhouse gas measurement community. Overall, based on ∼ 8000 discrete flask samples, we find that the co-located atmospheric CO 2 and CH 4 measurement records from Alert by CSIRO, MPI-BGC, SIO, UHEI-IUP, and ECCC versus NOAA (the designated reference laboratory) are generally consistent with the WMO compatibility goals of ± 0.1 ppm CO 2 and ± 2 ppb CH 4 over the 17-year period (1999–2016), although there are periods where differences exceed target levels and persist as systematic bias for months or years. Consistency with the WMO goals for N 2 O, SF 6 , and stable isotopes of CO 2 (δ13 C, δ18 O) has not been demonstrated. Additional analysis of co-located comparison measurements between CSIRO and SIO versus NOAA or INSTAAR (for the isotopes of CO 2) at other geographical sites suggests that the findings at Alert for CO 2 , CH 4 , N 2 O, and δ13 C–CO 2 could be extended across the CSIRO, SIO, and NOAA observing networks. The primary approach to estimate an overall measurement agreement level was carried out by pooling the differences of all individual laboratories versus the designated reference laboratory and determining the 95th percentile range of these data points. Using this approach over the entire data record, our best estimate of the measurement agreement range is -0.51 to + 0.53 ppm for CO 2 , -0.09 ‰ to + 0.07 ‰ for δ13 C, -0.50 ‰ to + 0.58 ‰ for δ18 O, -4.86 to + 6.16 ppb for CH 4 , -0.75 to + 1.20 ppb for N 2 O, and -0.14 to + 0.09 ppt for SF 6. A secondary approach of using the average of 2 standard deviations of the means for all flask samples taken in each individual sampling episode provided similar results. These upper and lower limits represent our best estimate of the measurement agreement at the 95 % confidence level for these individual laboratories, providing more confidence for using these datasets in various scientific applications (e.g., long-term trend analysis). [ABSTRACT FROM AUTHOR]

The Thermal and Near-Infrared Sensor for Carbon Observation Fourier-Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse Gases Observing SATellite-2 (GOSAT-2) observes a wide spectral region of the atmosphere, from the ShortWave-InfraRed (SWIR) to the longwave Thermal InfraRed radiation (TIR) with 0.2 cm -1 spectral sampling, and the corresponding spectral resolution (full width at half maximum, FWHM) of TIR region is less than 0.27 cm -1. TANSO-FTS-2 has operated nominally since February 2019, and the atmospheric radiance spectra it has acquired have been released to the public. This paper describes an updated model for spectral radiance calibration and its validation. The model applies to the version v210210 TIR products of TANSO-FTS-2 and integrates polarization sensitivity correction for the internal optics and the pointing mirror thermal emission. These correction parameters are characterized by an optimization that depends on the difference between the spectral radiance of TANSO-FTS-2 and coincident nadir observation data from the Infrared Atmospheric Sounding Interferometer (IASI) on METOP-B. To validate the updated spectral radiance product against other satellite products, temporally and spatially coincident observation points were considered for the simultaneous nadir overpass (SNO) from February 2019 to March 2021 from the Atmospheric Infrared Sounder (AIRS) on Aqua, IASI on METOP-B, and TANSO-FTS on GOSAT. The agreement of brightness temperatures between TANSO-FTS-2 and AIRS and IASI was better than 0.3 K (1 σ) from 180 to 330 K for the 680 cm -1 CO 2 spectral range. The brightness temperatures between TANSO-FTS-2 and TANSO-FTS of version v230231, which implemented a new polarization reflectivity of the pointing mirror and was released in June 2021, generally agree from 220 to 320 K. However, there is a discrepancy at lower brightness temperatures, pronounced for CO 2 spectral ranges at high latitudes. To characterize the spectral radiance bias for along-track and cross-track angles, a 2-orthogonal simultaneous off-nadir overpass (2O-SONO) is now done for TANSO-FTS-2 and IASI, TANSO-FTS-2 and AIRS, and TANSO-FTS-2 and TANSO-FTS. The 2O-SONO comparison results indicate that the TIR product for TANSO-FTS-2 has a bias that exceeds 0.5 K in the CO 2 spectral range for scenes with forward and backward viewing angles greater than 20 ∘. These multi-satellite sensor and multi-angle comparison results suggest that the calibration of spectral radiance for TANSO-FTS-2 TIR, version v210210, is superior to that of the previous version in its consistency of multi-satellite sensor data. In addition, the paper identifies the remaining challenging issues in current TIR products. [ABSTRACT FROM AUTHOR]

The UNFCCC (United Nations Framework Convention on Climate Change) requires the nations of the world to report their carbon dioxide (CO2) emissions. The independent verification of these reported emissions is a cornerstone for advancing towards the emission accounting and reduction measures agreed upon in the Paris Agreement. In this paper, we present the concept and first performance assessment of a compact spaceborne imaging spectrometer with a spatial resolution of 50×50 m2 that could contribute to the "monitoring, verification and reporting" (MVR) of CO2 emissions worldwide. CO2 emissions from medium-sized power plants (1–10 MtCO2yr-1), currently not targeted by other spaceborne missions, represent a significant part of the global CO2 emission budget. In this paper we show that the proposed instrument concept is able to resolve emission plumes from such localized sources as a first step towards corresponding CO2 flux estimates. Through radiative transfer simulations, including a realistic instrument noise model and a global trial ensemble covering various geophysical scenarios, it is shown that an instrument noise error of 1.1 ppm (1σ) can be achieved for the retrieval of the column-averaged dry-air mole fraction of CO2 (X CO2). Despite a limited amount of information from a single spectral window and a relatively coarse spectral resolution, scattering by atmospheric aerosol and cirrus can be partly accounted for in the X CO2 retrieval, with deviations of at most 4.0 ppm from the true abundance for two-thirds of the scenes in the global trial ensemble. We further simulate the ability of the proposed instrument concept to observe CO2 plumes from single power plants in an urban area using high-resolution CO2 emission and surface albedo data for the city of Indianapolis. Given the preliminary instrument design and the corresponding instrument noise error, emission plumes from point sources with an emission rate down to the order of 0.3 MtCO2yr-1 can be resolved, i.e., well below the target source strength of 1 MtCO2yr-1. This leaves a significant margin for additional error sources, like scattering particles and complex meteorology, and shows the potential for subsequent CO2 flux estimates with the proposed instrument concept. [ABSTRACT FROM AUTHOR]

The Orbiting Carbon Observatory-3 (OCO-3) is NASA's next instrument dedicated to extending the record of the dry-air mole fraction of column carbon dioxide (XCO2) and solar-induced fluorescence (SIF) measurements from space. The current schedule calls for a launch from the Kennedy Space Center no earlier than April 2019 via a Space-X Falcon 9 and Dragon capsule. The instrument will be installed as an external payload on the Japanese Experimental Module Exposed Facility (JEM-EF) of the International Space Station (ISS) with a nominal mission lifetime of 3 years. The precessing orbit of the ISS will allow for viewing of the Earth at all latitudes less than approximately 52 ∘ , with a ground repeat cycle that is much more complicated than the polar-orbiting satellites that so far have carried all of the instruments capable of measuring carbon dioxide from space. The grating spectrometer at the core of OCO-3 is a direct copy of the OCO-2 spectrometer, which was launched into a polar orbit in July 2014. As such, OCO-3 is expected to have similar instrument sensitivity and performance characteristics to OCO-2, which provides measurements of XCO2 with precision better than 1 ppm at 3 Hz, with each viewing frame containing eight footprints approximately 1.6 km by 2.2 km in size. However, the physical configuration of the instrument aboard the ISS, as well as the use of a new pointing mirror assembly (PMA), will alter some of the characteristics of the OCO-3 data compared to OCO-2. Specifically, there will be significant differences from day to day in the sampling locations and time of day. In addition, the flexible PMA system allows for a much more dynamic observation-mode schedule. This paper outlines the science objectives of the OCO-3 mission and, using a simulation of 1 year of global observations, characterizes the spatial sampling, time-of-day coverage, and anticipated data quality of the simulated L1b. After application of cloud and aerosol prescreening, the L1b radiances are run through the operational L2 full physics retrieval algorithm, as well as post-retrieval filtering and bias correction, to examine the expected coverage and quality of the retrieved XCO2 and to show how the measurement objectives are met. In addition, results of the SIF from the IMAP–DOAS algorithm are analyzed. This paper focuses only on the nominal nadir–land and glint–water observation modes, although on-orbit measurements will also be made in transition and target modes, similar to OCO-2, as well as the new snapshot area mapping (SAM) mode. [ABSTRACT FROM AUTHOR]

The Integrated Carbon Observation System (ICOS) is a pan-European research infrastructure which provides harmonized and high-precision scientific data on the carbon cycle and the greenhouse gas budget. All stations have to undergo a rigorous assessment before being labeled, i.e., receiving approval to join the network. In this paper, we present the labeling process for the ICOS atmosphere network through the 23 stations that were labeled between November 2017 and November 2019. We describe the labeling steps, as well as the quality controls, used to verify that the ICOS data (CO 2 , CH 4 , CO and meteorological measurements) attain the expected quality level defined within ICOS. To ensure the quality of the greenhouse gas data, three to four calibration gases and two target gases are measured: one target two to three times a day, the other gases twice a month. The data are verified on a weekly basis, and tests on the station sampling lines are performed twice a year. From these high-quality data, we conclude that regular calibrations of the CO 2 , CH 4 and CO analyzers used here (twice a month) are important in particular for carbon monoxide (CO) due to the analyzer's variability and that reducing the number of calibration injections (from four to three) in a calibration sequence is possible, saving gas and extending the calibration gas lifespan. We also show that currently, the on-site water vapor correction test does not deliver quantitative results possibly due to environmental factors. Thus the use of a drying system is strongly recommended. Finally, the mandatory regular intake line tests are shown to be useful in detecting artifacts and leaks, as shown here via three different examples at the stations. [ABSTRACT FROM AUTHOR]

We report in-plume carbon dioxide (CO 2) concentrations and carbon isotope ratios during the 2021 eruption of Tajogaite volcano, island of La Palma, Spain. CO 2 measurements inform our understanding of volcanic contributions to the global climate carbon cycle and the role of CO 2 in eruptions. Traditional ground-based methods of CO 2 collection are difficult and dangerous, and as a result only about 5 % of volcanoes have been directly surveyed. We demonstrate that unpiloted aerial system (UAS) surveys allow for fast and relatively safe measurements. Using CO 2 concentration profiles we estimate the total flux during several measurements in November 2021 to be 1.76±0.20×103 to 2.23±0.26×104 t d -1. Carbon isotope ratios of plume CO 2 indicate a deep magmatic source, consistent with the intensity of the eruption. Our work demonstrates the feasibility of UASs for CO 2 surveys during active volcanic eruptions, particularly for deriving rapid emission estimates. [ABSTRACT FROM AUTHOR]

By utilizing progress in millijoule-level pulsed fiber lasers operating in the 1.96 µm spectral range, we introduce a concept utilizing a spaceborne differential absorption barometric lidar designed to operate within the 1.96 µm CO2 absorption band for remote sensing of Martian atmospheric properties. Our focus is on the online wavelength situated in the trough region of two absorption lines, selected due to its insensitivity to laser frequency variations, thus mitigating the necessity for stringent laser frequency stability. Our investigation revolves around a compact lidar configuration, featuring reduced telescope dimensions and lower laser pulse energies. These adjustments are geared towards minimizing costs for potential forthcoming Mars missions. The core measurement objectives encompass the determination of column CO2 absorption optical depth, columnar CO2 abundance, surface atmospheric pressure, and vertical distributions of dust and cloud layers. Through the amalgamation of surface pressure data with atmospheric temperature insights garnered from sounders and utilizing the barometric formula, the prospect of deducing atmospheric pressure profiles becomes feasible. Simulation studies validate the viability of our approach. Notably, the precision of Martian surface pressure measurements is projected to surpass 1 Pa when the aerial dust optical depth is projected to be under 0.7, a typical airborne dust scenario on Mars, considering a horizontal averaging span of 10 km. [ABSTRACT FROM AUTHOR]

We present the first results of a ground-based imaging experiment using a shortwave infrared spectral camera to quantify carbon dioxide (CO 2) emissions from a coal-fired power plant in Mannheim, Germany. The power plant emits more than 4.9 Mt CO 2 yr -1 and is a validation opportunity for the emission estimation technique. The camera is a hyperspectral imaging spectrometer that covers the spectral range from 900 to 2500 nm with a spectral resolution of 7 nm. We identify CO 2 enhancements from hourly averaged images using an iterative matched filter retrieval using the 2000 nm absorption band of CO 2. We present 11 plume images from 5 d in 2021 and 2022 covering a variety of ambient conditions. We design a forward model based on a three-dimensional, bent-over Gaussian plume rise simulation and compare our observed emission plumes with the forward model. The model depends on the parameters ambient wind velocity, wind direction, plume dispersion, and emission rate. We retrieve the emission rate by minimizing the least-squares difference between the measured and the simulated images. We find an overall reasonable agreement between the retrieved and expected emissions for power plant emission rates between 223 and 587 t CO 2 h -1. The retrieved emissions average 84 % of the expected emissions and have a mean relative uncertainty of 24 %. The technique works at wind speeds down to 1.4 m s -1 and can follow diurnal emission dynamics. We also include observations with unfavorable ambient conditions, such as background heterogeneity and acute observation angles. These conditions are shown to produce considerable biases in the retrieved emission rates, yet they can be filtered out reliably in most cases. Thus, this emission estimation technique is a promising tool for independently verifying reported emissions from large point sources and provides complementary information to existing monitoring techniques. [ABSTRACT FROM AUTHOR]

Mitigating the global climate crisis and its consequences, such as more frequent and severe droughts, is one of the major challenges for future agriculture. Therefore, identifying land use systems and management practices that reduce greenhouse gas (GHG) emissions and promote water use efficiency (WUE) is crucial. This, however, requires accurate and precise measurements of carbon dioxide (CO 2) fluxes and evapotranspiration (ET). Despite that, commercial systems to measure CO 2 and ET fluxes are expensive and thus often exclude research in ecosystems within the Global South. This is especially true for research and data of agroecosystems in these areas, which are to date still widely underrepresented. Here, we present a newly developed low-cost, non-dispersive infrared (NDIR)-based CO 2 and ET flux measurement device (∼ EUR 200) that provides reliable, accurate and precise CO 2 and ET flux measurements in conjunction with manually operated closed chambers. To validate the system, laboratory and field validation experiments were performed, testing multiple different low-cost sensors. We demonstrate that the system delivers accurate and precise CO 2 and ET flux measurements using the K30 FR NDIR (CO 2) and SHT31 (RH, relative humidity) sensor. An additional field trial application demonstrated its longer-term stability (> 3 months) and ability to obtain valid net ecosystem C balances (NECBs) and WUE. This was the case, even though environmental conditions at the field trial application site in sub-Saharan Africa were rather challenging (e.g., extremely high temperatures, humidity and rainfall). Consequently, the developed low-cost CO 2 and ET flux measurement device not only provides reasonable results but also might help with democratizing science and closing current data gaps. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas. Its atmospheric concentration has increased by almost 50 % since the beginning of the industrial era, causing climate change. Fossil fuel combustion is responsible for most of the atmospheric CO2 increase, which originates to a large extent from localized sources such as power stations. Independent estimates of the emissions from these sources are key to tracking the effectiveness of implemented climate policies to mitigate climate change. We developed an automatic procedure to quantify CO2 emissions from localized sources based on a cross-sectional mass-balance approach and applied it to infer CO2 emissions from the Bełchatów Power Station (Poland) using atmospheric observations from the Orbiting Carbon Observatory 3 (OCO-3) in its snapshot area map (SAM) mode. As a result of the challenge of identifying CO2 emission plumes from satellite data with adequate accuracy, we located and constrained the shape of emission plumes using TROPOspheric Monitoring Instrument (TROPOMI) NO2 column densities. We automatically analysed all available OCO-3 overpasses over the Bełchatów Power Station from July 2019 to November 2022 and found a total of nine that were suitable for the estimation of CO2 emissions using our method. The mean uncertainty in the obtained estimates was 5.8 Mt CO 2 yr -1 (22.0 %), mainly driven by the dispersion of the cross-sectional fluxes downwind of the source, e.g. due to turbulence. This dispersion uncertainty was characterized using a semivariogram, made possible by the OCO-3 imaging capability over a target region in SAM mode, which provides observations containing plume information up to several tens of kilometres downwind of the source. A bottom-up emission estimate was computed based on the hourly power-plant-generated power and emission factors to validate the satellite-based estimates. We found that the two independent estimates agree within their 1σ uncertainty in eight out of nine analysed overpasses and have a high Pearson's correlation coefficient of 0.92. Our results confirm the potential to monitor large localized CO2 emission sources from space-based observations and the usefulness of NO2 estimates for plume detection. They also illustrate the potential to improve CO2 monitoring capabilities with the planned Copernicus Anthropogenic CO2 Monitoring (CO2M) satellite constellation, which will provide simultaneously retrieved XCO2 and NO2 maps. [ABSTRACT FROM AUTHOR]

The development in uncrewed aerial vehicle (UAV) technologies over the past decade has led to a plethora of platforms that can potentially enable greenhouse gas emission quantification. Here, we report the development of a new air sampler, consisting of a pumped stainless coiled tube of 150 m in length with controlled time stamping, and its deployment from an industrial UAV to quantify CO 2 and CH 4 emissions from the main coking plant stacks of a major steel maker in eastern China. Laboratory tests show that the time series of CO 2 and CH 4 measured using the sampling system is smoothed when compared to online measurement by the cavity ring-down spectrometer (CRDS) analyzer. Further analyses show that the smoothing is akin to a convolution of the true time series signals with a heavy-tailed digital filter. For field testing, the air sampler was mounted on the UAV and flown in virtual boxes around two stacks in the coking plant of the Shagang Group (steel producer). Mixing ratios of CO 2 and CH 4 in air and meteorological parameters were measured from the UAV during the test flight. A mass-balance computational algorithm was used on the data to estimate the CO 2 and CH 4 emission rates from the stacks. Using this algorithm, the emission rates for the two stacks from the coking plant were calculated to be 0.12±0.014 t h -1 for CH 4 and 110±18 t h -1 for CO 2 , the latter being in excellent agreement with material-balance-based estimates. A Gaussian plume inversion approach was also used to derive the emission rates, and the results were compared with those derived using the mass-balance algorithm, showing a good agreement between the two methods. [ABSTRACT FROM AUTHOR]

Monitoring the atmospheric concentrations of the greenhouse gases (GHG) carbon dioxide (CO2) and methane (CH4) is a key ingredient for fostering our understanding of the mechanisms behind the sources and sinks of these gases and for verifying and quantitatively attributing their anthropogenic emissions. Here, we present the instrumental setup and performance evaluation of an open-path GHG observatory in the city of Heidelberg, Germany. The observatory measures path-averaged concentrations of CO2 and CH4 along a 1.55 km path in the urban boundary layer above the city. We combine these open-path data with local in situ measurements to evaluate the representativeness of these observation types on the kilometer scale. This representativeness is necessary to accurately quantify emissions, since atmospheric models tasked with this job typically operate on kilometer-scale horizontal grids. For the operational period between 8 February and 11 July 2023, we find a precision of 2.7 ppm (0.58 %) and 18 ppb (0.89 %) for the dry-air mole fractions of CO2 (xCO2) and CH4 (xCH4) in 5 min measurements, respectively. After bias correction, the open-path measurements show excellent agreement with the local in situ data under atmospheric background conditions. Both datasets show clear signals of traffic CO2 emissions in the diurnal xCO2 cycle. However, there are particular situations, such as under southeasterly wind conditions, in which the in situ and open-path data reveal distinct differences up to 20 ppm in xCO2 , most likely related to their different sensitivity to local emission and transport patterns. Our setup is based on a Bruker IFS 125HR Fourier transform spectrometer, which offers a spacious and modular design providing ample opportunities for future refinements of the technique with respect to finer spectral resolution and wider spectral coverage to provide information on gases such as carbon monoxide and nitrogen dioxide. [ABSTRACT FROM AUTHOR]

The identification of spikes (i.e., short and high variability in the measured signals due to very local emissions occurring in the proximity of a measurement site) is of interest when using continuous measurements of atmospheric greenhouse gases (GHGs) in different applications like the determination of long-term trends and/or spatial gradients, inversion experiments devoted to the top-down quantification of GHG surface–atmosphere fluxes, the characterization of local emissions, or the quality control of GHG measurements. In this work, we analyzed the results provided by two automatic spike identification methods (i.e., the standard deviation of the background (SD) and the robust extraction of baseline signal (REBS)) for a 2-year dataset of 1 min in situ observations of CO 2 , CH 4 and CO at 10 different atmospheric sites spanning different environmental conditions (remote, continental, urban). The sensitivity of the spike detection frequency and its impact on the averaged mole fractions on method parameters was investigated. Results for both methods were compared and evaluated against manual identification by the site principal investigators (PIs). The study showed that, for CO 2 and CH 4 , REBS identified a larger number of spikes than SD and it was less "site-sensitive" than SD. This led to a larger impact of REBS on the time-averaged values of the observed mole fractions for CO 2 and CH 4. Further, it could be shown that it is challenging to identify one common algorithm/configuration for all the considered sites: method-dependent and setting-dependent differences in the spike detection were observed as a function of the sites, case studies and considered atmospheric species. Neither SD nor REBS appeared to provide a perfect identification of the spike events. The REBS tendency to over-detect the spike occurrence shows limitations when adopting REBS as an operational method to perform automatic spike detection. REBS should be used only for specific sites, mostly affected by frequent very nearby local emissions. SD appeared to be more selective in identifying spike events, and the temporal variabilities in CO 2 , CH 4 and CO were more consistent with those of the original datasets. Further activities are needed for better consolidating the fitness for purpose of the two proposed methods and to compare them with other spike detection techniques. [ABSTRACT FROM AUTHOR]

Current understanding of land–atmosphere exchange fluxes is limited by the fact that available observational techniques mainly quantify net fluxes, which are the sum of generally larger, bidirectional fluxes that partially cancel out. As a consequence, validation of gas exchange fluxes applied in models is challenging due to the lack of ecosystem-scale exchange flux measurements partitioned into soil, plant, and atmospheric components. One promising experimental method to partition measured turbulent fluxes uses the exchange-process-dependent isotopic fractionation of molecules like CO2 and H2O. When applying this method at a field scale, an isotope flux (δ flux) needs to be measured. Here, we present and discuss observations made during the LIAISE (Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment) 2021 field campaign using an eddy covariance (EC) system coupled to two laser spectrometers for high-frequency measurement of the isotopic composition of H2O and CO2. This campaign took place in the summer of 2021 in the irrigated Ebro River basin near Mollerussa, Spain, embedded in a semi-arid region. We present a systematic procedure to scrutinise and analyse measurements of the δ -flux variable, which plays a central role in flux partitioning. Our experimental data indicated a larger relative signal loss in the δ fluxes of H2O compared to the net ecosystem flux of H2O , while this was not true for CO2. Furthermore, we find that mole fractions and isotope ratios measured with the same instrument can be offset in time by more than a minute for the H2O isotopologues due to the isotopic memory effect. We discuss how such artefacts can be detected and how they impact flux partitioning. We argue that these effects are likely due to condensation of water on a cellulose filter in our inlet system. Furthermore, we show that these artefacts can be resolved using physically sound corrections for inlet delays and high-frequency loss. Only after such corrections and verifications are made can ecosystem-scale fluxes be partitioned using isotopic fluxes as constraints, which in turn allows for conceptual land–atmosphere exchange models to be validated. [ABSTRACT FROM AUTHOR]

We show new results from an updated version of the Fast atmOspheric traCe gAs retrievaL (FOCAL) retrieval method applied to measurements of the Greenhouse gases Observing SATellite (GOSAT) and its successor GOSAT-2. FOCAL was originally developed for estimating the total column carbon dioxide mixing ratio (XCO2) from spectral measurements made by the Orbiting Carbon Observatory-2 (OCO-2). However, depending on the available spectral windows, FOCAL also successfully retrieves total column amounts for other atmospheric species and their uncertainties within one single retrieval. The main focus of the current paper is on methane (XCH4 ; full-physics and proxy product), water vapour (XH2O) and the relative ratio of semi-heavy water (HDO) to water vapour (δD). Due to the extended spectral range of GOSAT-2, it is also possible to derive information on carbon monoxide (XCO) and nitrous oxide (XN2O) for which we also show first results. We also present an update on XCO2 from both instruments. For XCO2 , the new FOCAL retrieval (v3.0) significantly increases the number of valid data compared with the previous FOCAL retrieval version (v1) by 50 % for GOSAT and about a factor of 2 for GOSAT-2 due to relaxed pre-screening and improved post-processing. All v3.0 FOCAL data products show reasonable spatial distribution and temporal variations. Comparisons with the Total Carbon Column Observing Network (TCCON) result in station-to-station biases which are generally in line with the reported TCCON uncertainties. With this updated version of the GOSAT-2 FOCAL data, we provide a first total column average XN2O product. Global XN2O maps show a gradient from the tropics to higher latitudes on the order of 15 ppb , which can be explained by variations in tropopause height. The new GOSAT-2 XN2O product compares well with TCCON. Its station-to-station variability is lower than 2 ppb , which is about the magnitude of the typical N2O variations close to the surface. However, both GOSAT-2 and TCCON measurements show that the seasonal variations in the total column average XN2O are on the order of 8 ppb peak-to-peak, which can be easily resolved by the GOSAT-2 FOCAL data. Noting that only few XN2O measurements from satellites exist so far, the GOSAT-2 FOCAL product will be a valuable contribution in this context. [ABSTRACT FROM AUTHOR]

A data set containing more than 6 years (February 2009 to present) of radiance spectra for carbon dioxide (CO2) and methane (CH4) observations has been acquired by the Greenhouse gases Observing SATellite (GOSAT, available at http://data.gosat.nies.go.jp/GosatUserInterfaceGateway/guig/GuigPage/open.do), nicknamed "Ibuki", Thermal And Near infrared Sensor for carbon Observation Fourier Transform Spectrometer (TANSOFTS). This paper provides updates on the performance of the satellite and TANSO-FTS sensor and describes important changes to the data product, which has recently been made available to users. With these changes the typical accuracy of retrieved column-averaged dry air mole fractions of CO2 and CH4 (XCO2 and XCH4, respectively) are 2 ppm or 0.5% and 13 ppb or 0.7 %, respectively. Three major anomalies of the satellite system affecting TANSO-FTS are reported: a failure of one of the two solar paddles in May 2014, a switch to the secondary pointing system in January 2015, and most recently a cryocooler shutdown and restart in August 2015. The Level 1A (L1A) (raw interferogram) and the Level 1B (L1B) (radiance spectra) of version V201 described here have longterm uniform quality and provide consistent retrieval accuracy even after the satellite system anomalies. In addition, we discuss the unique observation abilities of GOSAT made possible by an agile pointing mechanism, which allows for optimization of global sampling patterns. [ABSTRACT FROM AUTHOR]

The thermal And near infrared sensor for carbon observations Fourier transform spectrometer (TANSO-FTS) on board the Greenhouse Gases Observing Satellite (GOSAT) applies the normal nadir mode above the land ("land data") and sun glint mode over the ocean ("ocean data") to provide global distributions of column-averaged dry-air mole fractions of CO2 and CH4, or XCO2 and XCH4. Several algorithms have been developed to obtain highly accurate greenhouse gas concentrations from TANSO-FTS/GOSAT spectra. So far, all the retrieval algorithms have been validated with the measurements from ground-based Fourier transform spectrometers from the Total Carbon Column Observing Network (TCCON), but limited to the land data. In this paper, the ocean data of the SRPR, SRFP (the proxy and full-physics versions 2.3.5 of SRON/KIT's RemoTeC algorithm), NIES (National Institute for Environmental Studies operational algorithm version 02.21) and ACOS (NASA's Atmospheric CO2 Observations from Space version 3.5) are compared with FTIR measurements from five TCCON sites and nearby GOSAT land data. For XCO2, both land and ocean data of NIES, SRFP and ACOS show good agreement with TCCON measurements. Averaged over all TCCON sites, the relative biases of ocean data and land data are -0.33 and -0.13 % for NIES, 0.03 and 0.04% for SRFP, 0.06 and -0.03 % for ACOS, respectively. The relative scatter ranges between 0.31 and 0.49%. For XCH4, the relative bias of ocean data is even less than that of the land data for the NIES (0.02 vs. -0.35 %), SRFP (0.04 vs. 0.20%) and SRPR (-0.02 vs. 0.06%) algorithms. Compared to the results for XCO2, the XCH4 retrievals show larger relative scatter (0.65-0.81 %). [ABSTRACT FROM AUTHOR]

Planetary boundary layer (PBL) CO2 mole fraction data are needed by transport models and carbon budget models as both input and reference for validation. The height of in situ CO2 mole fraction measurements is usually different from that of the model levels where the data are needed; data from short towers, in particular, are difficult to utilize in atmospheric models that do not simulate the surface layer well. Tall-tower CO2 mole fraction measurements observed at heights ranging from 10 to 115m above ground level at a rural site in Hungary and regular airborne vertical mole fraction profile measurements (136 vertical profiles) above the tower allowed us to estimate how well a tower of a given height could estimate the CO2 mole fraction above the tower in the PBL. The statistical evaluation of the height-dependent bias between the real PBL CO2 mole fraction profile (measured by the aircraft) and the measurement at a given elevation above the ground was performed separately for the summer and winter half years to take into account the different dynamics of the lower troposphere and the different surface CO2 flux in the different seasons. The paper presents (1) how accurately the vertical distribution of CO2 in the PBL can be estimated from the measurements on the top of a tower of height H; (2) how tall of a tower would be needed for the satisfaction of different requirements on the accuracy of the estimation of the CO2 vertical distribution; (3) how accurate of a CO2 vertical distribution estimation can be expected from the existing towers; and (4) how much improvement can be achieved in the accuracy of the estimation of CO2 vertical distribution by applying the virtual tall-tower concept. [ABSTRACT FROM AUTHOR]

Mapping the greenhouse gases (GHGs) carbon dioxide (CO 2) and methane (CH 4) above source regions such as urban areas can deliver insights into the distribution and dynamics of local emission patterns. Here, we present the prototype development and an initial performance evaluation of a portable spectrometer that allows for measuring CO 2 and CH 4 concentrations integrated along a long (>10 km) horizontal path component through the atmospheric boundary layer above a target region. To this end, the spectrometer is positioned at an elevated site from which it points downward at reflection targets in the region, collecting the reflected sunlight at shallow viewing angles. The path-integrated CO 2 and CH 4 concentrations are inferred from the absorption fingerprint in the shortwave–infrared (SWIR) spectral range. While mimicking the concept of the stationary California Laboratory for Atmospheric Remote Sensing – Fourier Transform Spectrometer (CLARS-FTS) in Los Angeles, our portable setup requires minimal infrastructure and is straightforward to duplicate and to operate in various locations. For performance evaluation, we deployed the instrument, termed EM27/SCA, side by side with the CLARS-FTS at the Mt. Wilson Observatory (1670 m a.s.l.) above Los Angeles for a 1-month period in April/May 2022. We determined the relative precision of the retrieved slant column densities (SCDs) for urban reflection targets to be 0.36 %–0.55 % for O 2 , CO 2 and CH 4 , where O 2 is relevant for light path estimation. For the partial vertical column (VCD) below instrument level, which is the quantity carrying emission information, the propagated precision errors amount to 0.75 %–2 % for the three gases depending on the distance to the reflection target and solar zenith angle. The comparison to simultaneous CLARS-FTS measurements shows good consistency, but the observed diurnal patterns highlight the need to take light scattering into account to enable detection of emission patterns. [ABSTRACT FROM AUTHOR]

Vehicle chase measurements used for studying real-world emissions apply various methods for calculating emission factors. Currently available methods are typically based on the dilution of emitted carbon dioxide (CO2) and the assumption that other emitted pollutants dilute similarly. A problem with the current methods arises when the studied vehicle is not producing CO2 , e.g. during engine-motoring events, such as on downhill sections. This problem is also encountered when studying non-exhaust particulate emissions, e.g. from electric vehicles. In this study, we compare multiple methods previously applied for determining the dilution ratios. Additionally, we present a method applying multivariate adaptive regression splines and a new method called near-wake dilution. We show that emission factors for particulate emissions calculated with both methods are in line with the current methods for vehicles producing CO2. In downhill sections, the new methods were more robust to low CO2 concentrations than some of the current methods. The methods introduced in this study can hence be applied in chase measurements with changing driving conditions and be possibly extended to estimate non-exhaust emissions in the future. [ABSTRACT FROM AUTHOR]

Many modern instruments generate more data than may be fully processed in a timely manner. For some atmospheric sounders, much of the raw data cannot be processed into meaningful observations due to suboptimal viewing conditions, such as the presence of clouds. Conventional solutions are quick, empirical-threshold filters hand-created by domain experts to weed out unlikely or unreasonable observations, coupled with randomized down sampling when the data volume is still too high. In this paper, we describe a method for the construction of a subsampling and ordering solution that maximizes the likelihood that a requested data subset will be usefully processed. The method can be used for any metadata-rich source and implicitly discerns informative vs. non-informative data features while still permitting user feedback into the final features selected for filter implementation. We demonstrate the method by creating a selector for the spectra of the Japanese GOSAT satellite designed to measure column averaged mixing ratios of greenhouse gases including carbon dioxide (CO2). This is done within the Atmospheric CO2 Measurements from Space (ACOS) NASA project with the intention of eventual use during the early Orbiting Carbon Observatory-2 (OCO-2) mission. OCO-2 will have a 1.5 orders of magnitude larger data volume than ACOS, requiring intelligent pre-filtration. [ABSTRACT FROM AUTHOR]

Traditional techniques for measuring the mole fractions of greenhouse gases in the well-mixed atmosphere have required dry sample gas streams (dew point <-25 °C) to achieve the inter-laboratory compatibility goals set forth by the Global Atmosphere Watch programme of the World Meteorological Organisation (WMO/GAW) for carbon dioxide (±0.1 ppm in the Northern Hemisphere and ±0.05 ppm in the Southern Hemisphere) and methane (±2 ppb). Drying the sample gas to low levels of water vapour can be expensive, time-consuming, and/or problematic, especially at remote sites where access is difficult. Recent advances in optical measurement techniques, in particular cavity ring down spectroscopy, have led to the development of greenhouse gas analysers capable of simultaneous measurements of carbon dioxide, methane and water vapour. Unlike many older technologies, which can suffer from significant uncorrected interference from water vapour, these instruments permit accurate and precise greenhouse gas measurements that can meet the WMO/GAW inter-laboratory compatibility goals (WMO, 2011a) without drying the sample gas. In this paper, we present laboratory methodology for empirically deriving the water vapour correction factors, and we summarise a series of in-situ validation experiments comparing the measurements in humid gas streams to well-characterised dry-gas measurements. By using the manufacturer-supplied correction factors, the dry-mole fraction measurements have been demonstrated to be well within the GAWcompatibility goals up to a water vapour concentration of at least 1 %. By determining the correction factors for individual instruments once at the start of life, this water vapour concentration range can be extended to at least 2% over the life of the instrument, and if the correction factors are determined periodically over time, the evidence suggests that this range can be extended up to and even above 4% water vapour concentrations. [ABSTRACT FROM AUTHOR]

High-quality, long-time-series measurements of atmospheric greenhouse gases show interannual variability in the measured seasonal cycles. These changes can be analyzed to better understand the carbon cycle and the impact of climate drivers. However, nearly all discrete measurement records contain gaps and have noise due to the influence of local fluxes or synoptic variability. To facilitate analysis, filtering and curve-fitting techniques are often applied to these time series. Previous studies have recognized that there is an inherent uncertainty associated with this curve fitting, and the choice of a given mathematical method might introduce biases. Since uncertainties are seldom propagated to the metrics under study, this can lead to misinterpretation of the signal. In this study, we use an ensemble-based approach to quantify the uncertainty of the derived seasonal cycle metrics. We apply it to CO 2 dry-air mole fraction time series from flask measurements in the Northern Hemisphere. We use this ensemble-based approach to analyze the carbon uptake period (CUP: the time of the year when the CO 2 uptake is greater than the CO 2 release): its onset, termination and duration. Previous studies have diagnosed CUP based on the dates on which the detrended, zero-centered seasonal cycle curve switches from positive to negative (the downward zero-crossing date, DZCD) and vice versa (upward zero-crossing date, UZCD). However, the UZCD is sensitive to the skewness of the CO 2 seasonal cycle during the net carbon release period. Hence, we develop an alternative method proposed by to estimate the onset and termination of the CUP based on a threshold defined in terms of the first derivative of the CO 2 seasonal cycle. Using the ensemble approach, we arrive at a tighter constraint to the threshold by considering the annual uncertainty; we call this the ensemble of first derivative (EFD) method. Further, using the EFD approach and an additional curve-fitting algorithm, we show that (a) the uncertainty of the studied metrics is smaller using the EFD method than when approximated using the timing of the zero-crossing date (ZCD), and (b) the onset and termination dates derived with the EFD method provide more robust results, irrespective of the curve-fitting method applied to the data. [ABSTRACT FROM AUTHOR]

The version 10 (v10) Atmospheric Carbon Observations from Space (ACOS) Level 2 full-physics (L2FP) retrieval algorithm has been applied to multiyear records of observations from NASA's Orbiting Carbon Observatory 2 and 3 sensors (OCO-2 and OCO-3, respectively) to provide estimates of the carbon dioxide (CO 2) column-averaged dry-air mole fraction (XCO 2). In this study, a number of improvements to the ACOS v10 L2FP algorithm are described. The post-processing quality filtering and bias correction of the XCO 2 estimates against multiple truth proxies are also discussed. The OCO v10 data volumes and XCO 2 estimates from the two sensors for the time period of August 2019 through February 2022 are compared, highlighting differences in spatiotemporal sampling but demonstrating broad agreement between the two sensors where they overlap in time and space. A number of evaluation sources applied to both sensors suggest they are broadly similar in data and error characteristics. Mean OCO-3 differences relative to collocated OCO-2 data are approximately 0.2 and - 0.3 ppm for land and ocean observations, respectively. Comparison of XCO 2 estimates to collocated Total Carbon Column Observing Network (TCCON) measurements shows root mean squared errors (RMSEs) of approximately 0.8 and 0.9 ppm for OCO-2 and OCO-3, respectively. An evaluation against XCO 2 fields derived from atmospheric inversion systems that assimilated only near-surface CO 2 observations, i.e., did not assimilate satellite CO 2 measurements, yielded RMSEs of 1.0 and 1.1 ppm for OCO-2 and OCO-3, respectively. Evaluation of uncertainties in XCO 2 over small areas, as well as XCO 2 biases across land–ocean crossings, also indicates similar behavior in the error characteristics of both sensors. Taken together, these results demonstrate a broad consistency of OCO-2 and OCO-3 XCO 2 measurements, suggesting they may be used together for scientific analyses. [ABSTRACT FROM AUTHOR]

We describe an approach for determining limited information about the vertical distribution of carbon monoxide (CO) and carbon dioxide (CO2) from total column ground-based Total Carbon Column Observation Network (TCCON) observations. For CO and CO2 , it has been difficult to retrieve information about their vertical distribution from spectral line shapes because of the errors in the spectroscopy and the atmospheric temperature profile that mask the effects of variations in their mixing ratio with altitude. For CO2 the challenge is especially difficult given that these variations are typically 2 % or less. Nevertheless, if sufficient accuracy can be obtained, such information would be highly valuable for evaluation of retrievals from satellites and more generally for improving the estimate of surface sources and sinks of these trace gases. We present here the Temporal Atmospheric Retrieval Determining Information from Secondary Scaling (TARDISS) retrieval algorithm. TARDISS uses several simultaneously obtained total column observations of the same gas from different absorption bands with distinctly different vertical averaging kernels. The different total column retrievals are combined in TARDISS using a Bayesian approach where the weights and temporal covariance applied to the different retrievals include additional constraints on the diurnal variation in the vertical distribution for these gases. We assume that the near-surface part of the column varies rapidly over the course of a day (from surface sources and sinks, for example) and that the upper part of the column has a larger temporal covariance over the course of a day. Using measurements from the five North American TCCON sites, we find that the retrieved lower partial column (between the surface and ∼ 800 hPa) of the CO and CO2 dry mole fractions (DMFs) have slopes of 0.999 ± 0.002 and 1.001 ± 0.003 with respect to lower column DMF from integrated in situ data measured directly from aircraft and in AirCores. The average error for our lower column CO retrieval is 1.51 ppb (∼ 2 %) while the average error for our CO2 retrieval is 5.09 ppm (∼ 1.25 %). Compared with classical line-shape-derived vertical profile retrievals, our algorithm reduces the influence of forward model errors such as imprecision in spectroscopy (line shapes and intensities) and in the instrument line shape. In addition, because TARDISS uses the existing retrieved column abundances from TCCON (which themselves are computationally much less intensive than profile retrieval algorithms), it is very fast and processes years of data in minutes. We anticipate that this approach will find broad application for use in carbon cycle science. [ABSTRACT FROM AUTHOR]

The CO, CO 2 and CH 4 mole fractions have been measured since 2002 at the Environmental Research Station Schneefernerhaus , which is located approximately 300 m below the summit of Zugspitze in the German Alps. Although the station is located remotely at an altitude of 2650 m a.s.l., local pollution events by snow blowers and snow groomers can be detected in the high temporal-resolution time series of seconds or minutes. Therefore, a time-consuming flagging process, carried out manually by the station manager, is necessary. To examine local influences and the effectiveness of data flagging, a 290 m long intake line to the higher Zugspitze ridge was used to measure CO, CO 2 and CH 4 mole fractions at a potentially less polluted location between October 2018 and October 2020. The comparison of these two time series shows that the mountain ridge measurement is almost unaffected by local pollution. It also demonstrates that the influence of local pollution events on the Schneefernerhaus measurements is successfully removed by the station manager. Only a small deviation of up to 0.24 ppm can be observed during the day between the CO 2 time series of the Schneefernerhaus and the mountain ridge in winter, probably due to anthropogenic sources. [ABSTRACT FROM AUTHOR]

Tropospheric helium variations are tightly linked to CO2 due to the co-emission of He and CO2 from natural-gas burning. Recently, Birner et al. (2022a) showed that the global consumption of natural gas has measurably increased the He content of the atmosphere. Like CO2 , He is also predicted to exhibit complex spatial and temporal variability on shorter timescales, but measurements of these short-term variations are lacking. Here, we present the development of an improved gas delivery and purification system for the semi-continuous mass spectrometric measurement of the atmospheric He -to-nitrogen ratio (He/N2). The method replaces the chemical getter used previously by Birner et al. (2021, 2022a) to preconcentrate He in an air stream with a cryogenic trap which can be more simply regenerated by heating and which improves the precision of the measurement to 22 permeg (i.e., 0.022 ‰) in 10 min (1 σ). Using this "cryo-enrichment" method, we measured the He/N2 ratios in ambient air at La Jolla (California, USA) over 5 weeks in 2022. During this period, He/N2 was strongly correlated with atmospheric CO2 concentrations, as expected from anthropogenic emissions, with a diurnal cycle of 450–500 permeg (max–min) caused by the sea–land breeze pattern of local winds, which modulates the influence of local pollution sources. [ABSTRACT FROM AUTHOR]

The Orbiting Carbon Observatory-2 (OCO-2) makes space-based radiance measurements in the oxygen A band and the weak and strong carbon dioxide (CO 2) bands. Using a physics-based retrieval algorithm these measurements are inverted to column-averaged atmospheric CO 2 dry-air mole fractions (X CO2). However, the retrieved X CO2 values are biased due to calibration issues and mismatches between the physics-based retrieval radiances and observed radiances. Using multiple linear regression, the biases are empirically mitigated. However, a recent analysis revealed remaining biases in the proximity of clouds caused by 3D cloud radiative effects (Massie et al., 2021) in the processing version B10. Using an interpretable non-linear machine learning approach, we develop a bias correction model to address these 3D cloud biases. The model is able to reduce unphysical variability over land and sea by 20 % and 40 %, respectively. Additionally, the 3D cloud bias-corrected X CO2 values show agreement with independent ground-based observations from the Total Carbon Column Observation Network (TCCON). Overall, we find that the published OCO-2 data record underestimates X CO2 over land by -0.3 ppm in the tropics and northward of 45 ∘ N. The approach can be expanded to a more general bias correction and is generalizable to other greenhouse gas experiments, such as GeoCarb, GOSAT-3, and CO2M. [ABSTRACT FROM AUTHOR]

We assess the theoretical capability of the upcoming France–UK MicroCarb satellite, which has a city-scan observing mode, to determine integrated urban emissions of carbon dioxide (CO 2). To achieve this we report results from a series of closed-loop numerical experiments that use an atmospheric transport model with anthropogenic and biogenic fluxes to determine the corresponding changes in atmospheric CO 2 column, accounting for changes in measurement coverage due to cloud loading. We use a maximum a posteriori inverse method to infer the CO 2 fluxes based on the measurements and the a priori information. Using an urban CO 2 inversion system, we explore the relative performance of alternative two-sweep and three-sweep city observing strategies to quantify CO 2 emissions over the cities of Paris and London in different months when biospheric fluxes vary in magnitude. We find that both the two-sweep and three-sweep observing modes are able to reduce a priori flux errors by 20 %–40 % over Paris and London. The three-sweep observing strategy, which generally outperforms the two-sweep mode by virtue of its wider scan area that typically yields more cloud-free observations, can retrieve the total emissions of the truth within 7 % over Paris and 21 % over London. The performance of the limited-domain city-mode observing strategies is sensitive to cloud coverage and particularly sensitive to the prevailing wind direction. We also find that seasonal photosynthetic uptake of CO 2 by the urban biosphere weakens atmospheric CO 2 gradients across both cities, thereby reducing the sensitivity of urban CO 2 enhancements and subsequently compromising the ability of MicroCarb to reduce bias in estimating urban CO 2 emissions. This suggests that additional trace gases co-emitted with anthropogenic CO 2 emissions, but unaffected by the land biosphere, are needed to quantify sub-city scale CO 2 emissions during months when the urban biosphere is particularly active. [ABSTRACT FROM AUTHOR]

A novel method for atmospheric network design is presented, which is based on information theory. The method does not require calculation of the posterior uncertainty (or uncertainty reduction) and is therefore computationally more efficient than methods that require this. The algorithm is demonstrated in two examples: the first looks at designing a network for monitoring CH 4 sources using observations of the stable carbon isotope ratio in CH 4 (δ13 C), and the second looks at designing a network for monitoring fossil fuel emissions of CO 2 using observations of the radiocarbon isotope ratio in CO 2 (Δ14 CO 2). [ABSTRACT FROM AUTHOR]

Black carbon (BC) aerosol typically has two major sources in the urban environment: traffic and domestic biomass burning, which has a significant contribution to urban air pollution during the heating season. Traffic emissions have been widely studied by both laboratory experiments (individual vehicle emission) and real-world measurement campaigns (fleet emission). However, emission information from biomass burning is limited, especially an insufficiency of experimental results from real-world studies. In this work, the black carbon burden in the urban atmosphere was apportioned to fossil fuel (FF) and biomass burning (BB) related components using the Aethalometer source apportionment model. Applying the BC source apportionment information, the combustion-related CO 2 was apportioned by multilinear regression analysis, supposing that both CO 2 components should be correlated with their corresponding BC component. The combination of the Aethalometer model with the multilinear regression analysis (AM-MLR) provided the source-specific emission ratios (ERs) as the slopes of the corresponding BC–CO 2 regressions. Based on the ER values, the source-specific emission factors (EFs) were determined using the carbon content of the corresponding fuel. The analysis was carried out on a 3-month-long BC and CO 2 dataset collected at three monitoring locations in Ljubljana, Slovenia, between December 2019 and March 2020. The measured mean site-specific concentration values were in the 3560–4830 ng m -3 and 458–472 ppm ranges for BC and CO 2 , respectively. The determined average EFs for BC were 0.39 and 0.16 g(kg fuel) -1 for traffic and biomass burning, respectively. It was also concluded that the traffic-related BC component dominates the black carbon concentration (55 %–64 % depending on the location), while heating has the major share in the combustion-related CO 2 (53 %–62 % depending on the location). The method gave essential information on the source-specific emission factors of BC and CO 2 , enabling better characterization of urban anthropogenic emissions and the respective measures that may change the anthropogenic emission fingerprint. [ABSTRACT FROM AUTHOR]

The Atmospheric Carbon Observations from Space (ACOS) retrieval algorithm has been delivering operational column-averaged carbon dioxide dry-air mole fraction (XCO2 ​​​​​​​) data for the Orbiting Carbon Observatory (OCO) missions since 2014. The ACOS Level 2 Full Physics (L2FP) algorithm retrieves a number of parameters, including aerosol and surface properties, in addition to atmospheric CO 2. Past analysis has shown that while the ACOS retrieval meets mission precision requirements of 0.1 %–0.5 % in XCO2 , residual biases and some sources of error remain unaccounted for. Forward model and other errors can lead to systematic biases in the retrieved XCO2 , which are often correlated with these additional retrieved parameters. The characterization of such biases is particularly essential to urban- and local-scale emissions studies, where it is critical to accurately distinguish source signals relative to background concentrations. In this study we explore algorithm-induced biases through the use of simulated OCO-3 snapshot area mapping (SAM) mode observations, which offer a unique window into these biases with their wide range of viewing geometries over a given scene. We focus on a small percentage of SAMs in the OCO-3 vEarly product which contains artificially strong across-swath XCO2 biases spanning several parts per million, related to observation geometry. We investigate the causes of swath bias by using the timing and geometry of real OCO-3 SAMs to retrieve XCO2 from custom simulated Level 1b radiance spectra. By building relatively simple scenes and testing a variety of parameters, we find that aerosol is the primary driver of swath bias, with a complex combination of viewing geometry and aerosol optical properties contributing to the strength and pattern of the bias. Finally, we seek to understand successful mitigation of swath bias in the new OCO-3 version 10 data product. Results of this study may be useful in uncovering other remaining sources of XCO2 bias and may help minimize similar retrieval biases for both present missions (GOSAT, GOSAT-2, OCO-2, OCO-3, TanSat) and future missions (e.g., MicroCarb, GeoCarb, GOSAT-GW, CO2M). [ABSTRACT FROM AUTHOR]

We evaluated carbon dioxide (CO 2) adsorption on the internal surface of the cylinder and the fractionation of CO 2 and air during the preparation of standard mixtures with atmospheric CO 2 level through multistep dilution. The CO 2 molar fractions in the standard mixtures deviated from the gravimetric values by -0.207±0.060 µmolmol-1 on average, which is larger than the compatibility goal (0.1 µmolmol-1) recommended by the World Meteorological Organization. The deviation was consistent with those calculated using two fractionation factors: one was estimated by the mother–daughter transfer experiment in which CO 2 –air mixtures were transferred from a mother cylinder to an evacuated daughter cylinder, and another was computed by applying the Rayleigh model to the change in CO 2 molar fractions in a source gas as its pressure was depleted from 11.5 to 1.1 MPa. The mother–daughter transfer experiments showed that the deviation was caused by the fractionation of CO 2 and air during the transfer of the source gas (CO 2 –air mixture with a higher CO 2 molar fraction than that in the prepared gas mixture). The CO 2 fractionation was less significant when the transfer speed decreased to less than 3 L min -1 , indicating that thermal diffusion mainly caused the fractionation. The CO 2 adsorption on the internal cylinder surface was experimentally evaluated by emitting a CO 2 –air mixture from a cylinder. When the cylinder pressure was reduced from 11.0 to 0.1 MPa, the CO 2 molar fractions in the mixture exiting the cylinder increased by 0.16±0.04 µmolmol-1. By applying the Langmuir adsorption–desorption model to the measured data, the amount of CO 2 adsorbed on the internal surfaces of a 10 L aluminum cylinder when preparing a standard mixture with atmospheric CO 2 level was estimated to be 0.027±0.004 µmolmol-1 at 11.0 MPa. [ABSTRACT FROM AUTHOR]

Various satellite imagers of the vertically integrated column of carbon dioxide (XCO2) are under development to enhance the capabilities for the monitoring of fossil fuel (FF) CO2 emissions. XCO2 images can be used to detect plumes from cities and large industrial plants and to quantify the corresponding emission using atmospheric inversions techniques. However, this potential and the ability to catch the signal from more diffuse FF CO2 sources can be hampered by the mix between these FF signals and a background signal from other types of CO2 surface fluxes, and in particular of biogenic CO2 fluxes. The deployment of dense ground-based air-sampling networks for CO2 and radiocarbon (14CO2) could complement the spaceborne imagery by supporting the separation between the fossil fuel and biogenic or biofuel (BF) CO2 signals. We evaluate this potential complementarity with a high-resolution analytical inversion system focused on northern France, western Germany, Belgium, Luxembourg, and a part of the Netherlands and with pseudo-data experiments. The inversion system controls the FF and BF emissions from the large urban areas and plants, in addition to regional budgets of more diffuse emissions or of biogenic fluxes (NEE, net ecosystem exchange), at an hourly scale over a whole day. The system provides results corresponding to the assimilation of pseudo-data from a single track of a 300 km swath XCO2 imager at 2 km resolution and from surface ground-based CO2 and/or 14CO2 networks. It represents the diversity of 14CO2 sources and sinks and not just the dilution of radiocarbon-free FF CO2 emissions. The uncertainty in the resulting FF CO2 emissions at local (urban area/plant) to regional scales is directly derived and used to assess the potential of the different combinations of observation systems. The assimilation of satellite observations yields estimates of the morning regional emissions with an uncertainty down to 10 % (1 σ) in the satellite field of view, from an assumed uncertainty of 15 % in the prior estimates. However, it does not provide direct information about emissions outside the satellite field of view or about afternoon or nighttime emissions. The co-assimilation of 14CO2 and CO2 surface observations leads to a further reduction of the uncertainty in the estimates of FF emissions. However, this further reduction is significant only in administrative regions with three or more 14CO2 and CO2 sampling sites. The uncertainty in the estimates of 1 d emission in North Rhine-Westphalia, a region with three sampling sites, decreases from 8 % to 6.6 % when assimilating the in situ 14CO2 and CO2 data in addition to the satellite data. Furthermore, this additional decrease appears to be larger when the ground stations are close to large FF emission areas, providing an additional direct constraint for the estimate of these sources rather than supporting the characterization of the background signal from the NEE and its separation from that of the FF emissions. More generally, the results indicate no amplification of the potential of each observation subsystem when they are combined into a large observation system with satellite and surface data. [ABSTRACT FROM AUTHOR]

In David et al. (2021), we introduced a neural network (NN) approach for estimating the column-averaged dry-air mole fraction of CO 2 (XCO2) and the surface pressure from the reflected solar spectra acquired by the OCO-2 instrument. The results indicated great potential for the technique as the comparison against both model estimates and independent TCCON measurements showed an accuracy and precision similar to or better than that of the operational ACOS (NASA's Atmospheric CO 2 Observations from Space retrievals – ACOS) algorithm. Yet, subsequent analysis showed that the neural network estimate often mimics the training dataset and is unable to retrieve small-scale features such as CO 2 plumes from industrial sites. Importantly, we found that, with the same inputs as those used to estimate XCO2 and surface pressure, the NN technique is able to estimate latitude and date with unexpected skill, i.e., with an error whose standard deviation is only 7 ∘ and 61 d, respectively. The information about the date mainly comes from the weak CO 2 band, which is influenced by the well-mixed and increasing concentrations of CO 2 in the stratosphere. The availability of such information in the measured spectrum may therefore allow the NN to exploit it rather than the direct CO 2 imprint in the spectrum to estimate XCO2. Thus, our first version of the NN performed well mostly because the XCO2 fields used for the training were remarkably accurate, but it did not bring any added value. Further to this analysis, we designed a second version of the NN, excluding the weak CO 2 band from the input. This new version has a different behavior as it does retrieve XCO2 enhancements downwind of emission hotspots, i.e., a feature that is not in the training dataset. The comparison against the reference Total Carbon Column Observing Network (TCCON) and the surface-air-sample-driven inversion of the Copernicus Atmosphere Monitoring Service (CAMS) remains very good, as in the first version of the NN. In addition, the difference with the CAMS model (also called innovation in a data assimilation context) for NASA Atmospheric CO 2 Observations from Space (ACOS) and the NN estimates is correlated. These results confirm the potential of the NN approach for an operational processing of satellite observations aiming at the monitoring of CO 2 concentrations and fluxes. The true information content of the neural network product remains to be properly evaluated, in particular regarding the respective input of the measured spectrum and the training dataset. [ABSTRACT FROM AUTHOR]

Several single-platform satellite missions have been designed during the past decades in order to retrieve the atmospheric concentrations of anthropogenic greenhouse gases (GHG), initiating worldwide efforts towards better monitoring of their sources and sinks. To set up a future operational system for anthropogenic GHG emission monitoring, both revisit frequency and spatial resolution need to be improved. The Space Carbon Observatory (SCARBO) project aims at significantly increasing the revisit frequency of spaceborne GHG measurements, while reaching state-of-the-art precision requirements, by implementing a concept of small satellite constellation. It would accommodate a miniaturised GHG sensor named NanoCarb coupled with an aerosol instrument, the multi-angle polarimeter SPEXone. More specifically, the NanoCarb sensor is a static Fabry–Pérot imaging interferometer with a 2.3×2.3 km 2 spatial resolution and 200 km swath. It samples a truncated interferogram at optical path differences (OPDs) optimally sensitive to all the geophysical parameters necessary to retrieve column-averaged dry-air mole fractions of CO 2 and CH 4 (hereafter XCO2 and XCH4). In this work, we present the Level 2 performance assessment of the concept proposed in the SCARBO project. We perform inverse radiative transfer to retrieve XCO2 and XCH4 directly from synthetic NanoCarb truncated interferograms and provide their systematic and random errors, column vertical sensitivities, and degrees of freedom as a function of five scattering-error-critical atmospheric and observational parameters. We show that NanoCarb XCO2 and XCH4 systematic retrieval errors can be greatly reduced with SPEXone posterior outputs used as improved prior aerosol constraints. For two-thirds of the soundings, located at the centre of the 200 km NanoCarb swath, XCO2 and XCH4 random errors span 0.5–1 ppm and 4–6 ppb, respectively, compliant with their respective 1 ppm and 6 ppb precision objectives. Finally, these Level 2 performance results are parameterised as a function of the explored scattering-error-critical atmospheric and observational parameters in order to time-efficiently compute extensive L2 error maps for future CO 2 and CH 4 flux estimation performance studies. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) provide a cost-effective way to fill in gaps between surface in situ observations and remotely sensed data from space. In this study, a novel portable CO2 measuring system suitable for operations on board small-sized UAVs has been developed and validated. It is based on a low-cost commercial nondispersive near-infrared (NDIR) CO2 sensor (Senseair AB, Sweden), with a total weight of 1058 g, including batteries. The system performs in situ measurements autonomously, allowing for its integration into various platforms. Accuracy and linearity tests in the lab showed that the precision remains within ± 1 ppm (1 σ) at 1 Hz. Corrections due to temperature and pressure changes were applied following environmental chamber experiments. The accuracy of the system in the field was validated against a reference instrument (Picarro, USA) on board a piloted aircraft and it was found to be ± 2 ppm (1 σ) at 1 Hz and ± 1 ppm (1 σ) at 1 min. Due to its fast response, the system has the capacity to measure CO2 mole fraction changes at 1 Hz, thus allowing the monitoring of CO2 emission plumes and of the characteristics of their spatial and temporal distribution. Details of the measurement system and field implementations are described to support future UAV platform applications for atmospheric trace gas measurements. [ABSTRACT FROM AUTHOR]

Soil CO 2 emissions are one of the largest contributions to the global carbon cycle, and a full understanding of processes generating them and how climate change may modify them is needed and still uncertain. Thus, a dense spatial and temporal network of CO 2 flux measurements from soil could help reduce uncertainty in the global carbon budgets. In the present study, the design, assembly, and calibration of low-cost air enquirer kits, including CO 2 and environmental parameters sensors, is presented. Different types of calibrations for the CO 2 sensors and their associated errors are calculated. In addition, for the first time, this type of sensor has been applied to design, develop, and test a new steady-state through-flow (SS-TF) chamber for simultaneous measurements of CO 2 fluxes in soil and CO 2 concentrations in air. The sensors' responses were corrected for temperature, relative humidity, and pressure conditions in order to reduce the uncertainty in the measured CO 2 values and of the following calculated CO 2 fluxes based on SS-TF. CO 2 soil fluxes measured by the proposed SS-TF and by a standard closed non-steady-state non-through-flow (NSS-NTF) chamber were briefly compared to ensure the reliability of the results. The use of a multiparametric fitting reduced the total uncertainty of the CO 2 concentration measurements by 62 %, compared with the uncertainty that occurred when a simple CO 2 calibration was applied, and by 90 %, when compared to the uncertainty declared by the manufacturer. The new SS-TF system allows the continuous measurement of CO 2 fluxes and CO 2 ambient air with low cost (EUR ∼1200), low energy demand (<5 W), and low maintenance (twice per year due to sensor calibration requirements). [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) is emitted during photosynthesis in plant leaves. It constitutes a small additional offset to reflected radiance and can be observed by sensitive instruments with high signal-to-noise ratio and spectral resolution. The Chinese global carbon dioxide monitoring satellite (TanSat) acquires measurements of greenhouse gas column densities. The advanced technical characteristics of the Atmospheric Carbon-dioxide Grating Spectrometer (ACGS) onboard TanSat enable SIF retrievals from observations in the O 2 -A band. In this study, 1-year of SIF data was retrieved from Orbiting Carbon Observatory-2 (OCO-2) and TanSat measurements using the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing (IAPCAS)/SIF algorithm. A comparison between the IAPCAS/SIF results retrieved from OCO-2 spectra and the official OCO-2 SIF product (OCO2_Level 2_Lite_SIF.8r) shows a strong linear relationship (R2 > 0.85) and suggests good reliability of the IAPCAS/SIF retrieval algorithm. Comparing global distributions of SIF retrieved by the IAPCAS/SIF from TanSat and OCO-2 shows the same spatial pattern for all seasons with a gridded SIF difference of less than 0.3 W m -2 µ m -1 sr -1. The global distributions also agree well with the official OCO-2 SIF product with a difference of less than 0.2 W m -2 µ m -1 sr -1. The retrieval uncertainty of seasonally gridded TanSat IAPCAS/SIF is less than 0.03 W m -2 µ m -1 sr -1 , whereas the uncertainty of each sounding ranges from 0.1 to 0.6 W m -2 µ m -1 sr -1. The relationship between annually averaged SIF products and FLUXCOM gross primary productivity (GPP) was also estimated for six vegetation types in a 1 ∘ × 1 ∘ grid over the globe, indicating that the SIF data from the two satellites have the same potential in quantitatively characterizing ecosystem productivity. The spatiotemporal consistency between TanSat and OCO-2 and their comparable data quality enable joint usage of the two mission products. Data supplemented by TanSat observations are expected to contribute to the development of global SIF maps with more spatiotemporal detail, which will advance global research on vegetation photosynthesis. [ABSTRACT FROM AUTHOR]

We assess the performance of an isotope ratio infrared spectrometer (IRIS) to measure carbon (δ13 C) and oxygen (δ18 O) isotope ratios in atmospheric carbon dioxide (CO 2) and report observations from a 26 d field deployment trial at Baring Head, New Zealand, NIWA's atmospheric observatory for Southern Ocean baseline air. Our study describes an operational method to improve the performance in comparison to previous publications on this analytical instrument. By using a calibration technique that reflected the principle of identical treatment of sample and reference gases, we achieved a reproducibility of 0.07 ‰ for δ13 C-CO 2 and 0.06 ‰ for δ18 O-CO 2 over multiple days. This performance is within the extended compatibility goal of 0.1 ‰ for both δ13 C-CO 2 and δ18 O-CO 2 , which was recommended by the World Meteorological Organization (WMO). Further improvement in measurement performance is desirable to also meet the WMO network compatibility goals of 0.01 ‰ for δ13 C-CO 2 and 0.05 ‰ for δ18 O-CO 2 , which is needed to resolve the small variability that is typical for background air observatories such as Baring Head. One objective of this study was to assess the capabilities and limitations of the IRIS analyser to resolve δ13 C-CO 2 and δ18 O-CO 2 variations under field conditions. Therefore, we selected multiple events within the 26 d record for Keeling plot analysis. This resolved the isotopic composition of endmembers with an uncertainty of ≤ 1 ‰ when the magnitude of CO 2 signals is larger than 10 ppm. The uncertainty of the Keeling plot analysis strongly increased for smaller CO 2 events (2–7 ppm), where the instrument performance is the limiting factor and may only allow for the distinction between very different endmembers, such as the role of terrestrial versus oceanic carbon cycle processes. [ABSTRACT FROM AUTHOR]

The WPL (Webb, Pearman, and Leuning) correction is fully accepted to correct trace gas fluxes like CO 2 for density fluctuations due to water vapour and temperature fluctuations for open-path gas analysers. It is known that this additive correction can be on the order of magnitude of the actual flux. However, this is hardly ever included in the analysis of data quality. An example from the Arctic shows the problems, because the size of the correction is a multiple of the actual flux. As a general result, we examined and tabulated the magnitude of the WPL correction for carbon dioxide flux as a function of sensible and latent heat flux. Furthermore, we propose a parameter to better estimate possible deficits in data quality and recommend integrating the quality flag derived with this parameter into the general study of small carbon dioxide fluxes. [ABSTRACT FROM AUTHOR]

Atmospheric carbon dioxide (CO2) is the most significant greenhouse gas, and its concentration is continuously increasing, mainly as a consequence of anthropogenic activities. Accurate quantification of CO2 is critical for addressing the global challenge of climate change and for designing mitigation strategies aimed at stabilizing CO2 emissions. Satellites provide the most effective way to monitor the concentration of CO2 in the atmosphere. In this study, we utilized the concentration of the column-averaged dry-air mole fraction of CO2 , i.e., XCO2 retrieved from a CO2 monitoring satellite, the Orbiting Carbon Observatory-2 (OCO-2), and the net primary productivity (NPP) provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the anthropogenic CO2 emissions using the Generalized Regression Neural Network (GRNN) over East and West Asia. OCO-2 XCO2 , MODIS NPP, and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) CO2 emission datasets for a period of 5 years (2015–2019) were used in this study. The annual XCO2 anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background CO2 concentrations and seasonal variability. The XCO2 anomaly, NPP, and ODIAC emission datasets from 2015 to 2018 were then used to train the GRNN model, and, finally, the anthropogenic CO2 emissions were estimated for 2019 based on the NPP and XCO2 anomalies derived for the same year. The estimated and the ODIAC CO2 emissions were compared, and the results showed good agreement in terms of spatial distribution. The CO2 emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions and XCO2 anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results showed that satellite-based XCO2 retrievals can be used to estimate the regional-scale anthropogenic CO2 emissions, and the accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more CO2 emission and concentration datasets. [ABSTRACT FROM AUTHOR]

Accurate monitoring of atmospheric carbon dioxide (CO 2) and its distribution is of great significance for studying the carbon cycle and predicting future climate change. Compared to the ground observational sites, the airborne observations cover a wider area and simultaneously observe a variety of surface types, which helps with effectively monitoring the distribution of CO 2 sources and sinks. In this work, an airborne experiment was carried out in March 2019 over the Shanhaiguan area, China (39–41 ∘ N, 119–121 ∘ E). An integrated path differential absorption (IPDA) light detection and ranging (lidar) system and a commercial instrument, the ultraportable greenhouse gas analyser (UGGA), were installed on an aircraft to observe the CO 2 distribution over various surface types. The pulse integration method (PIM) algorithm was used to calculate the differential absorption optical depth (DAOD) from the lidar data. The CO 2 column-averaged dry-air mixing ratio (XCO 2) was calculated over different types of surfaces including mountain, ocean, and urban areas. The concentrations of the XCO 2 calculated from lidar measurements over ocean, mountain, and urban areas were 421.11 ± 1.24, 427.67 ± 0.58, and 432.04 ± 0.74 ppm, respectively. Moreover, through the detailed analysis of the data obtained from the UGGA, the influence of pollution levels on the CO 2 concentration was also studied. During the whole flight campaign, 18 March was the most heavily polluted day with an Air Quality Index (AQI) of 175 and PM 2.5 of 131 µ g m -3. The aerosol optical depth (AOD) reported by a sun photometer installed at the Funing ground station was 1.28. Compared to the other days, the CO 2 concentration measured by UGGA at different heights was the largest on 18 March with an average value of 422.59 ± 6.39 ppm, which was about 10 ppm higher than the measurements recorded on 16 March. Moreover, the vertical profiles of Orbiting Carbon Observatory-2 (OCO-2) and CarbonTracker were also compared with the aircraft measurements. All the datasets showed a similar variation with some differences in their CO 2 concentrations, which showing a good agreement among them. [ABSTRACT FROM AUTHOR]

We present a statistical framework to identify regional signals in station-based CO 2 time series with minimal local influence. A curve-fitting function is first applied to the detrended time series to derive a harmonic describing the annual CO 2 cycle. We then combine a polynomial fit to the data with a short-term residual filter to estimate the smoothed cycle and define a seasonally adjusted noise component, equal to 2 standard deviations of the smoothed cycle about the annual cycle. Spikes in the smoothed daily data which surpass this ±2σ threshold are classified as anomalies. Examining patterns of anomalous behavior across multiple sites allows us to quantify the impacts of synoptic-scale atmospheric transport events and better understand the regional carbon cycling implications of extreme seasonal occurrences such as droughts. [ABSTRACT FROM AUTHOR]

We present a local-scale atmospheric inversion framework to estimate the location and rate of methane (CH 4) and carbon dioxide (CO 2) releases from point sources. It relies on mobile near-ground atmospheric CH 4 and CO 2 mole fraction measurements across the corresponding atmospheric plumes downwind of these sources, on high-frequency meteorological measurements, and on a Gaussian plume dispersion model. The framework exploits the scatter of the positions of the individual plume cross sections, the integrals of the gas mole fractions above the background within these plume cross sections, and the variations of these integrals from one cross section to the other to infer the position and rate of the releases. It has been developed and applied to provide estimates of brief controlled CH 4 and CO 2 point source releases during a 1-week campaign in October 2018 at the TOTAL experimental platform TADI in Lacq, France. These releases typically lasted 4 to 8 min and covered a wide range of rates (0.3 to 200 g CH 4 /s and 0.2 to 150 g CO 2 /s) to test the capability of atmospheric monitoring systems to react fast to emergency situations in industrial facilities. It also allowed testing of their capability to provide precise emission estimates for the application of climate change mitigation strategies. However, the low and highly varying wind conditions during the releases added difficulties to the challenge of characterizing the atmospheric transport over the very short duration of the releases. We present our series of CH 4 and CO 2 mole fraction measurements using instruments on board a car that drove along roads ∼50 to 150 m downwind of the 40 m × 60 m area for controlled releases along with the estimates of the release locations and rates. The comparisons of these results to the actual position and rate of the controlled releases indicate ∼10 %–40 % average errors (depending on the inversion configuration or on the series of tests) in the estimates of the release rates and ∼30 –40 m errors in the estimates of the release locations. These results are shown to be promising, especially since better results could be expected for longer releases and under meteorological conditions more favorable to local-scale dispersion modeling. However, the analysis also highlights the need for methodological improvements to increase the skill for estimating the source locations. [ABSTRACT FROM AUTHOR]

The majority of global anthropogenic CO2 emissions originate in cities. We have proposed that dense networks are a strategy for tracking changes to the processes contributing to urban CO2 emissions and suggested that a network with ∼ 2 km measurement spacing and ∼ 1 ppm node-to-node precision would be effective at constraining point, line, and area sources within cities. Here, we report on an assessment of the accuracy of the Berkeley Environmental Air-quality and CO 2 Network (BEACO 2 N) CO2 measurements over several years of deployment. We describe a new procedure for improving network accuracy that accounts for and corrects the temperature-dependent zero offset of the Vaisala CarboCap GMP343 CO2 sensors used. With this correction we show that a total error of 1.6 ppm or less can be achieved for networks that have a calibrated reference location and 3.6 ppm for networks without a calibrated reference. [ABSTRACT FROM AUTHOR]

Atmospheric ethane can be used as a tracer to distinguish methane sources, both at the local and global scale. Currently, ethane can be measured in the field using flasks or in situ analyzers. In our study, we characterized the CRDS Picarro G2201-i instrument, originally designed to measure isotopic CH 4 and CO 2 , for measurements of ethane-to-methane ratio in mobile-measurement scenarios, near sources and under field conditions. We evaluated the limitations and potential of using the CRDS G2201-i to measure the ethane-to-methane ratio, thus extending the instrument application to simultaneously measure two methane source proxies in the field: carbon isotopic ratio and the ethane-to-methane ratio. First, laboratory tests were run to characterize the instrument in stationary conditions. Subsequently, the instrument performance was tested in field conditions as part of a controlled release experiment. Finally, the instrument was tested during mobile measurements focused on gas compressor stations. The results from the field were afterwards compared with the results obtained from instruments specifically designed for ethane measurements. Our study shows the potential of using the CRDS G2201-i instrument in a mobile configuration to determine the ethane-to-methane ratio in methane plumes under measurement conditions with an ethane uncertainty of 50 ppb. Assuming typical ethane-to-methane ratios ranging between 0 and 0.1 ppb ppb -1 , we conclude that the instrument can accurately estimate the "true" ethane-to-methane ratio within 1 σ uncertainty when CH 4 enhancements are at least 1 ppm, as can be found in the vicinity of strongly emitting sites such as natural gas compressor stations and roadside gas pipeline leaks. [ABSTRACT FROM AUTHOR]

The retrieval algorithm for CO 2 column mixing ratio from measurements of a pulsed multi-wavelength integrated path differential absorption (IPDA) lidar is described. The lidar samples the shape of the 1572.33 nm CO 2 absorption line at multiple wavelengths. The algorithm uses a least-squares fit between the CO 2 line shape computed from a layered atmosphere model and that sampled by the lidar. In addition to the column-average CO 2 dry-air mole fraction (XCO 2), several other parameters are also solved simultaneously from the fit. These include the Doppler shift at the received laser signal wavelength, the product of the surface reflectivity and atmospheric transmission, and a linear trend in the lidar receiver's spectral response. The algorithm can also be used to solve for the average water vapor mixing ratio, which produces a secondary absorption in the wings of the CO 2 absorption line under humid conditions. The least-squares fit is linearized about the expected XCO 2 value, which allows the use of a standard linear least-squares fitting method and software tools. The standard deviation of the retrieved XCO 2 is obtained from the covariance matrix of the fit. The averaging kernel is also provided similarly to that used for passive trace-gas column measurements. Examples are presented of using the algorithm to retrieve XCO 2 from measurements of the NASA Goddard airborne CO 2 Sounder lidar that were made at constant altitude and during spiral-down profile maneuvers. [ABSTRACT FROM AUTHOR]