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

Spaceborne formaldehyde (HCHO) measurements constitute an excellent proxy for the sources of non-methane volatile organic compounds (NMVOCs). Past studies suggested substantial overestimations of NMVOC emissions in state-of-the-art inventories over major source regions. Here, the QA4ECV (Quality Assurance for Essential Climate Variables) retrieval of HCHO columns from OMI (Ozone Monitoring Instrument) is evaluated against (1) FTIR (Fourier-transform infrared) column observations at 26 stations worldwide and (2) aircraft in situ HCHO concentration measurements from campaigns conducted over the USA during 2012–2013. Both validation exercises show that OMI underestimates high columns and overestimates low columns. The linear regression of OMI and aircraft-based columns gives ΩOMI=0.651Ωairc+2.95×1015 molec.cm-2, with ΩOMI and Ωairc the OMI and aircraft-derived vertical columns, whereas the regression of OMI and FTIR data gives ΩOMI=0.659ΩFTIR+2.02×1015 molec.cm-2. Inverse modelling of NMVOC emissions with a global model based on OMI columns corrected for biases based on those relationships leads to much-improved agreement against FTIR data and HCHO concentrations from 11 aircraft campaigns. The optimized global isoprene emissions (∼445Tgyr-1) are 25 % higher than those obtained without bias correction. The optimized isoprene emissions bear both striking similarities and differences with recently published emissions based on spaceborne isoprene columns from the CrIS (Cross-track Infrared Sounder) sensor. Although the interannual variability of OMI HCHO columns is well understood over regions where biogenic emissions are dominant, and the HCHO trends over China and India clearly reflect anthropogenic emission changes, the observed HCHO decline over the southeastern USA remains imperfectly elucidated.

Volatile organic compounds (VOCs) are key precursors of particulate matter and tropospheric ozone. Although the terrestrial biosphere is by far the largest source of VOCs into the atmosphere, the emissions of biogenic VOCs remain poorly constrained at the regional scale. In this work, we derive top-down biogenic emissions over Europe using weekly averaged TROPOMI formaldehyde (HCHO) data from 2018 to 2021. The systematic bias of the TROPOMI HCHO columns is characterized and corrected for based on comparisons with FTIR data at seven European stations. The top-down fluxes of biogenic, pyrogenic, and anthropogenic VOC sources are optimized using an inversion framework based on the MAGRITTEv1.1 chemistry transport model and its adjoint. The inversion leads to strongly increased isoprene emissions with respect to the MEGAN–MOHYCAN inventory over the model domain (from 8.1 to 18.5 Tg yr−1), which is driven by the high observed TROPOMI HCHO columns in southern Europe. The impact of the inversion on biomass burning VOCs (+13 %) and anthropogenic VOCs (−17 %) is moderate. An evaluation of the optimized HCHO distribution against ground-based remote sensing (FTIR and MAX-DOAS) and in situ data provides generally improved agreement at stations below about 50∘ N but indicates overestimated emissions in northern Scandinavia. Sensitivity inversions show that the top-down emissions are robust with respect to changes in the inversion settings and in the model chemical mechanism, leading to differences of up to 10 % in the total emissions. However, the top-down emissions are very sensitive to the bias correction of the observed columns, as the biogenic emissions are 3 times lower when the correction is not applied. Furthermore, the use of different a priori biogenic emissions has a significant impact on the inversion results due to large differences among bottom-up inventories. The sensitivity run using CAMS-GLOB-BIOv3.1 as a priori emissions in the inversion results in 30 % lower emissions with respect to the optimization using MEGAN–MOHYCAN. In regions with large temperature and cloud cover variations, there is strong week-to-week variability in the observed HCHO columns. The top-down emissions, which are optimized at weekly increments, have a much improved capability of representing these large fluctuations than an inversion using monthly increments.

Atmospheric carbon dioxide (CO2) accounts for the largest radiative forcing among anthropogenic greenhouse gases. There is, therefore, a pressing need to understand the rate at which CO2 accumulates in the atmosphere, including the interannual variations (IAVs) in this rate. IAV in the CO2 growth rate is a small signal relative to the long-term trend and the mean annual cycle of atmospheric CO2, and IAV is tied to climatic variations that may provide insights into long-term carbon–climate feedbacks. Observations from the Orbiting Carbon Observatory-2 (OCO-2) mission offer a new opportunity to refine our understanding of atmospheric CO2 IAV since the satellite can measure over remote terrestrial regions and the open ocean, where traditional in situ CO2 monitoring is difficult, providing better spatial coverage compared to ground-based monitoring techniques. In this study, we analyze the IAV of column-averaged dry-air CO2 mole fraction (XCO2) from OCO-2 between September 2014 and June 2021. The amplitude of the IAV, which is calculated as the standard deviation of the time series, is up to 1.2 ppm over the continents and around 0.4 ppm over the open ocean. Across all latitudes, the OCO-2-detected XCO2 IAV shows a clear relationship with El Niño–Southern Oscillation (ENSO)-driven variations that originate in the tropics and are transported poleward. Similar, but smoother, zonal patterns of OCO-2 XCO2 IAV time series compared to ground-based in situ observations and with column observations from the Total Carbon Column Observing Network (TCCON) and the Greenhouse Gases Observing Satellite (GOSAT) show that OCO-2 observations can be used reliably to estimate IAV. Furthermore, the extensive spatial coverage of the OCO-2 satellite data leads to smoother IAV time series than those from other datasets, suggesting that OCO-2 provides new capabilities for revealing small IAV signals despite sources of noise and error that are inherent to remote-sensing datasets.

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

The National Institute for Environmental Studies has provided the column-averaged dry-air mole fraction of carbon dioxide and methane (XCO2 and XCH4) products (L2 products) obtained from the Greenhouse gases Observing SATellite (GOSAT) for more than a decade. Recently, we updated the retrieval algorithm used to produce the new L2 product, V03.00. The main changes from the previous version (V02) of the retrieval algorithm are the treatment of cirrus clouds, the degradation model of the Thermal And Near-infrared Spectrometer for carbon Observation–Fourier Transform Spectrometer (TANSO–FTS), solar irradiance spectra, and gas absorption coefficient tables. The retrieval results from the updated algorithm showed improvements in fitting accuracies in the O2 A, weak CO2, and CH4 bands of TANSO–FTS, although the residuals increase in the strong CO2 band over the ocean. The direct comparison of the new product obtained from the updated (V03) algorithm with the previous version V02.90/91 and the validations using the Total Carbon Column Observing Network revealed that the V03 algorithm increases the amount of data without diminishing the data qualities of XCO2 and XCH4 over land. However, the negative bias of XCO2 is larger than that of the previous version over the ocean, and bias correction is still necessary. Additionally, the V03 algorithm resolves the underestimation of the XCO2 growth rate compared with the in situ measurements over the ocean recently found using V02.90/91 and V02.95/96.

Abstract We validate formaldehyde (HCHO) vertical column densities (VCDs) from Ozone Mapping and Profiler Suite Nadir Mapper (OMPS‐NM) instruments onboard the Suomi National Polar‐orbiting Partnership (Suomi NPP) satellite for 2012–2020 and National Oceanic and Atmospheric Administration‐20 (NOAA‐20) satellite for 2018–2020, hereafter referred to as OMPS‐NPP and OMPS‐N20, with ground‐based Fourier‐Transform Infrared (FTIR) observations of the Network for the Detection of Atmospheric Composition Change (NDACC). OMPS‐NPP/N20 HCHO products reproduce seasonal variability at 24 FTIR sites. Monthly variability of OMPS‐NPP/N20 has a very good agreement with FTIR, showing correlation coefficients of 0.83 and 0.88, respectively. OMPS‐NPP (N20) biases averaged over all sites are −0.9 (4) ± 3 (6)%. However, at clean sites (with VCDs 4.0 × 1015 molecules cm−2, negative biases of −15% ± 4% appear for OMPS‐NPP, but OMPS‐N20 shows smaller bias of 0.5% ± 6% due to its smaller ground pixel footprints. Therefore, smaller satellite footprint sizes are important in distinguishing small‐scale plumes. In addition, we discuss a bias correction and provide lower limit for the monthly uncertainty of OMPS‐NPP/N20 HCHO products. The total uncertainty for OMPS‐NPP (N20) at clean sites is 0.7 (0.8) × 1015 molecules cm−2, corresponding to a relative uncertainty of 32 (30)%. In the case of HCHO VCDs > 4.0 × 1015 molecules cm−2, however, the relative uncertainty in HCHO VCDs for OMPS‐NPP (N20) decreases to 31 (18)%.

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.

In this study, an extension on the previously reported status of the COllaborative Carbon Column Observing Network's (COCCON) calibration procedures incorporating refined methods is presented. COCCON is a global network of portable Bruker EM27/SUN FTIR spectrometers for deriving column-averaged atmospheric abundances of greenhouse gases. The original laboratory open-path lamp measurements for deriving the instrumental line shape (ILS) of the spectrometer from water vapour lines have been refined and extended to the secondary detector channel incorporated in the EM27/SUN spectrometer for detection of carbon monoxide (CO). The refinements encompass improved spectroscopic line lists for the relevant water lines and a revision of the laboratory pressure measurements used for the analysis of the spectra. The new results are found to be in good agreement with those reported by Frey et al. (2019) and discussed in detail. In addition, a new calibration cell for ILS measurements was designed, constructed and put into service. Spectrometers calibrated since January 2020 were tested using both methods for ILS characterization, open-path (OP) and cell measurements. We demonstrate that both methods can detect the small variations in ILS characteristics between different spectrometers, but the results of the cell method indicate a systematic bias of the OP method. Finally, a revision and extension of the COCCON network instrument-to-instrument calibration factors for XCO2, XCO and XCH4 is presented, incorporating 47 new spectrometers (of 83 in total by now). This calibration is based on the reference EM27/SUN spectrometer operated by the Karlsruhe Institute of Technology (KIT) and spectra collected by the collocated TCCON station Karlsruhe. Variations in the instrumental characteristics of the reference EM27/SUN from 2014 to 2017 were detected, probably arising from realignment and the dual-channel upgrade performed in early 2018. These variations are considered in the evaluation of the instrument-specific calibration factors in order to keep all tabulated calibration results consistent.

The Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over its first 11 years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inversion systems (models) which do not assimilate satellite-derived CO2. In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter, and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2×106 out of 37×106) were assigned a “good” XCO2 quality flag, as compared to 3.9 % in v7.3 ( ×106 out of 24×106). After quality filtering and bias correction, the differences in XCO2 between ACOS GOSAT v9 and both TCCON and models have a scatter (1σ) of approximately 1 ppm for ocean-glint observations and 1 to 1.5 ppm for land observations. Global mean biases against TCCON and models are less than approximately 0.2 ppm. Seasonal mean biases relative to the v10 OCO-2 XCO2 product are of the order of 0.1 ppm for observations over land. However, for ocean-glint observations, seasonal mean biases relative to OCO-2 range from 0.2 to 0.6 ppm, with substantial variation in time and latitude. The ACOS GOSAT v9 XCO2 data are available on the NASA Goddard Earth Science Data and Information Services Center (GES-DISC) in both the per-orbit full format (https://doi.org/10.5067/OSGTIL9OV0PN, OCO-2 Science Team et al., 2019b) and in the per-day lite format (https://doi.org/10.5067/VWSABTO7ZII4, OCO-2 Science Team et al., 2019a). In addition, a new set of monthly super-lite files, containing only the most essential variables for each satellite observation, has been generated to provide entry level users with a light-weight satellite product for initial exploration (CaltechDATA, https://doi.org/10.22002/D1.2178, Eldering, 2021). The v9 ACOS Data User's Guide (DUG) describes best-use practices for the GOSAT data (O'Dell et al., 2020). The GOSAT v9 data set should be especially useful for studies of carbon cycle phenomena that span a full decade or more and may serve as a useful complement to the shorter OCO-2 v10 data set, which begins in September 2014.

The Sentinel-5 Precursor (S5P) mission with the TROPOspheric Monitoring Instrument (TROPOMI) on board has been measuring solar radiation backscattered by the Earth's atmosphere and surface since its launch on 13 October 2017. In this paper, we present for the first time the S5P operational methane (CH4) and carbon monoxide (CO) products' validation results covering a period of about 3 years using global Total Carbon Column Observing Network (TCCON) and Infrared Working Group of the Network for the Detection of Atmospheric Composition Change (NDACC-IRWG) network data, accounting for a priori alignment and smoothing uncertainties in the validation, and testing the sensitivity of validation results towards the application of advanced co-location criteria. We found that the S5P standard and bias-corrected CH4 data over land surface for the recommended quality filtering fulfil the mission requirements. The systematic difference of the bias-corrected total column-averaged dry air mole fraction of methane (XCH4) data with respect to TCCON data is -0.26±0.56 % in comparison to -0.68±0.74 % for the standard XCH4 data, with a correlation of 0.6 for most stations. The bias shows a seasonal dependence. We found that the S5P CO data over all surfaces for the recommended quality filtering generally fulfil the missions requirements, with a few exceptions, which are mostly due to co-location mismatches and limited availability of data. The systematic difference between the S5P total column-averaged dry air mole fraction of carbon monoxide (XCO) and the TCCON data is on average 9.22±3.45 % (standard TCCON XCO) and 2.45±3.38 % (unscaled TCCON XCO). We found that the systematic difference between the S5P CO column and NDACC CO column (excluding two outlier stations) is on average 6.5±3.54 %. We found a correlation of above 0.9 for most TCCON and NDACC stations. The study shows the high quality of S5P CH4 and CO data by validating the products against reference global TCCON and NDACC stations covering a wide range of latitudinal bands, atmospheric conditions and surface conditions.

Ethane (C2H6) is an important greenhouse gas and plays a significant role in tropospheric chemistry and climate change. This study first presents and then quantifies the variability, sources, and transport of C2H6 over densely populated and highly industrialized eastern China using ground-based high-resolution Fourier transform infrared (FTIR) remote sensing along with atmospheric modeling techniques. We obtained a retrieval error of 6.21 ± 1.2 (1σ)% and degrees of freedom (DOFS) of 1.47 ± 0.2 (1σ) in the retrieval of C2H6 tropospheric column-averaged dry-air mole fraction (troDMF) over Hefei, eastern China (32∘ N, 117∘ E; 30 ma.s.l.). The observed C2H6 troDMF reached a minimum monthly mean value of 0.36 ± 0.26 ppbv in July and a maximum monthly mean value of 1.76 ± 0.35 ppbv in December, and showed a negative change rate of −2.60 ± 1.34 % yr−1 from 2015 to 2020. The dependencies of C2H6 troDMF on meteorological and emission factors were analyzed using generalized additive models (GAMs). Generally, both meteorological and emission factors have positive influences on C2H6 troDMF in the cold season (December–January–February/March–April–May, DJF/MAM) and negative influences on C2H6 troDMF in the warm season (June–July–August/September–October–November, JJA/SON). GEOS-Chem chemical model simulation captured the observed C2H6 troDMF variability and was, thus, used for source attribution. GEOS-Chem model sensitivity simulations concluded that the anthropogenic emissions (fossil fuel plus biofuel emissions) and the natural emissions (biomass burning plus biogenic emissions) accounted for 48.1 % and 39.7 % of C2H6 troDMF variability over Hefei, respectively. The observed C2H6 troDMF variability mainly results from the emissions within China (74.1 %), where central, eastern, and northern China dominated the contribution (57.6 %). Seasonal variability in C2H6 transport inflow and outflow over the observation site is largely related to the midlatitude westerlies and the Asian monsoon system. Reduction in C2H6 abundance from 2015 to 2020 mainly results from the decrease in local and transported C2H6 emissions, which points to air quality improvement in China in recent years.

A better understanding of greenhouse gas surface sources and sinks is required in order to address the global challenge of climate change. Space-borne remote estimations of greenhouse gas atmospheric concentrations can offer the global coverage that is necessary to improve the constraint on their fluxes, thus enabling a better monitoring of anthropogenic emissions. In this work, we introduce the Adaptable 4A Inversion (5AI) inverse scheme that aims to retrieve geophysical parameters from any remote sensing observation. The algorithm is based on the Optimal Estimation algorithm, relying on the Operational version of the Automatized Atmospheric Absorption Atlas (4A/OP) radiative transfer forward model along with the Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Atmospheric Spectroscopic Information (GEISA) spectroscopic database. Here, the 5AI scheme is applied to retrieve the column-averaged dry air mole fraction of carbon dioxide (XCO2) from a sample of measurements performed by the Orbiting Carbon Observatory-2 (OCO-2) mission. Those have been selected as a compromise between coverage and the lowest aerosol content possible, so that the impact of scattering particles can be neglected, for computational time purposes. For air masses below 3.0, 5AI XCO2 retrievals successfully capture the latitudinal variations of CO2 and its seasonal cycle and long-term increasing trend. Comparison with ground-based observations from the Total Carbon Column Observing Network (TCCON) yields a bias of 1.30±1.32 ppm (parts per million), which is comparable to the standard deviation of the Atmospheric CO2 Observations from Space (ACOS) official products over the same set of soundings. These nonscattering 5AI results, however, exhibit an average difference of about 3 ppm compared to ACOS results. We show that neglecting scattering particles for computational time purposes can explain most of this difference that can be fully corrected by adding to OCO-2 measurements an average calculated–observed spectral residual correction, which encompasses all the inverse setup and forward differences between 5AI and ACOS. These comparisons show the reliability of 5AI as an optimal estimation implementation that is easily adaptable to any instrument designed to retrieve column-averaged dry air mole fractions of greenhouse gases.

Since 2009, the Greenhouse gases Observing SATellite (GOSAT) has performed radiance measurements in the near-infrared (NIR) and shortwave infrared (SWIR) spectral region. From February 2019 onward, data from GOSAT-2 have also been available. We present the first results from the application of the Fast atmOspheric traCe gAs retrievaL (FOCAL) algorithm to derive column-averaged dry-air mole fractions of carbon dioxide (XCO2) from GOSAT and GOSAT-2 radiances and their validation. FOCAL was initially developed for OCO-2 XCO2 retrievals and allows simultaneous retrievals of several gases over both land and ocean. Because FOCAL is accurate and numerically very fast, it is currently being considered as a candidate algorithm for the forthcoming European anthropogenic CO2 Monitoring (CO2M) mission to be launched in 2025. We present the adaptation of FOCAL to GOSAT and discuss the changes made and GOSAT specific additions. This particularly includes modifications in pre-processing (e.g. cloud detection) and post-processing (bias correction and filtering). A feature of the new application of FOCAL to GOSAT and GOSAT-2 is the independent use of both S- and P-polarisation spectra in the retrieval. This is not possible for OCO-2, which measures only one polarisation direction. Additionally, we make use of GOSAT's wider spectral coverage compared to OCO-2 and derive not only XCO2, water vapour (H2O), and solar-induced fluorescence (SIF) but also methane (XCH4), with the potential for further atmospheric constituents and parameters like semi-heavy water vapour (HDO). In the case of GOSAT-2, the retrieval of nitrous oxide (XN2O) and carbon monoxide (CO) may also be possible. Here, we concentrate on the new FOCAL XCO2 data products. We describe the generation of the products as well as applied filtering and bias correction procedures. GOSAT-FOCAL XCO2 data have been produced for the time interval 2009 to 2019. Comparisons with other independent GOSAT data sets reveal agreement of long-term temporal variations within about 1 ppm over 1 decade; differences in seasonal variations of about 0.5 ppm are observed. Furthermore, we obtain a station-to-station bias of the new GOSAT-FOCAL product to the ground-based Total Carbon Column Observing Network (TCCON) of 0.56 ppm with a mean scatter of 1.89 ppm. The GOSAT-2-FOCAL XCO2 product is generated in a similar way as the GOSAT-FOCAL product, but with adapted settings. All GOSAT-2 data until the end of 2019 have been processed. Because of this limited time interval, the GOSAT-2 results are considered to be preliminary only, but first comparisons show that these data compare well with the GOSAT-FOCAL results and also TCCON.

Although optical components in Fourier transform infrared (FTIR) spectrometers are preferably wedged, in practice, infrared spectra typically suffer from the effects of optical resonances (“channeling”) affecting the retrieval of weakly absorbing gases. This study investigates the level of channeling of each FTIR spectrometer within the Network for the Detection of Atmospheric Composition Change (NDACC). Dedicated spectra were recorded by more than 20 NDACC FTIR spectrometers using a laboratory mid-infrared source and two detectors. In the indium antimonide (InSb) detector domain (1900–5000 cm−1), we found that the amplitude of the most pronounced channeling frequency amounts to 0.1 ‰ to 2.0 ‰ of the spectral background level, with a mean of (0.68±0.48) ‰ and a median of 0.60 ‰. In the mercury cadmium telluride (HgCdTe) detector domain (700–1300 cm−1), we find even stronger effects, with the largest amplitude ranging from 0.3 ‰ to 21 ‰ with a mean of (2.45±4.50) ‰ and a median of 1.2 ‰. For both detectors, the leading channeling frequencies are 0.9 and 0.11 or 0.23 cm−1 in most spectrometers. The observed spectral frequencies of 0.11 and 0.23 cm−1 correspond to the optical thickness of the beam splitter substrate. The 0.9 cm−1 channeling is caused by the air gap in between the beam splitter and compensator plate. Since the air gap is a significant source of channeling and the corresponding amplitude differs strongly between spectrometers, we propose new beam splitters with the wedge of the air gap increased to at least 0.8∘. We tested the insertion of spacers in a beam splitter's air gap to demonstrate that increasing the wedge of the air gap decreases the 0.9 cm−1 channeling amplitude significantly. A wedge of the air gap of 0.8∘ reduces the channeling amplitude by about 50 %, while a wedge of about 2∘ removes the 0.9 cm−1 channeling completely. This study shows the potential for reducing channeling in the FTIR spectrometers operated by the NDACC, thereby increasing the quality of recorded spectra across the network.

This work presents the latest release (v9.0) of the University of Leicester GOSAT Proxy XCH4 dataset. Since the launch of the GOSAT satellite in 2009, these data have been produced by the UK National Centre for Earth Observation (NCEO) as part of the ESA Greenhouse Gas Climate Change Initiative (GHG-CCI) and Copernicus Climate Change Services (C3S) projects. With now over a decade of observations, we outline the many scientific studies achieved using past versions of these data in order to highlight how this latest version may be used in the future. We describe in detail how the data are generated, providing information and statistics for the entire processing chain from the L1B spectral data through to the final quality-filtered column-averaged dry-air mole fraction (XCH4) data. We show that out of the 19.5 million observations made between April 2009 and December 2019, we determine that 7.3 million of these are sufficiently cloud-free (37.6 %) to process further and ultimately obtain 4.6 million (23.5 %) high-quality XCH4 observations. We separate these totals by observation mode (land and ocean sun glint) and by month, to provide data users with the expected data coverage, including highlighting periods with reduced observations due to instrumental issues. We perform extensive validation of the data against the Total Carbon Column Observing Network (TCCON), comparing to ground-based observations at 22 locations worldwide. We find excellent agreement with TCCON, with an overall correlation coefficient of 0.92 for the 88 345 co-located measurements. The single-measurement precision is found to be 13.72 ppb, and an overall global bias of 9.06 ppb is determined and removed from the Proxy XCH4 data. Additionally, we validate the separate components of the Proxy (namely the modelled XCO2 and the XCH4∕XCO2 ratio) and find these to be in excellent agreement with TCCON. In order to show the utility of the data for future studies, we compare against simulated XCH4 from the TM5 model. We find a high degree of consistency between the model and observations throughout both space and time. When focusing on specific regions, we find average differences ranging from just 3.9 to 15.4 ppb. We find the phase and magnitude of the seasonal cycle to be in excellent agreement, with an average correlation coefficient of 0.93 and a mean seasonal cycle amplitude difference across all regions of −0.84 ppb. These data are available at https://doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb (Parker and Boesch, 2020).

TROPOMI (the TROPOspheric Monitoring Instrument), on board the Sentinel-5 Precursor (S5P) satellite, has been monitoring the Earth's atmosphere since October 2017 with an unprecedented horizontal resolution (initially 7 km2×3.5 km2, upgraded to 5.5 km2×3.5 km2 in August 2019). Monitoring air quality is one of the main objectives of TROPOMI; it obtains measurements of important pollutants such as nitrogen dioxide, carbon monoxide, and formaldehyde (HCHO). In this paper we assess the quality of the latest HCHO TROPOMI products versions 1.1.(5-7), using ground-based solar-absorption FTIR (Fourier-transform infrared) measurements of HCHO from 25 stations around the world, including high-, mid-, and low-latitude sites. Most of these stations are part of the Network for the Detection of Atmospheric Composition Change (NDACC), and they provide a wide range of observation conditions, from very clean remote sites to those with high HCHO levels from anthropogenic or biogenic emissions. The ground-based HCHO retrieval settings have been optimized and harmonized at all the stations, ensuring a consistent validation among the sites. In this validation work, we first assess the accuracy of TROPOMI HCHO tropospheric columns using the median of the relative differences between TROPOMI and FTIR ground-based data (BIAS). The pre-launch accuracy requirements of TROPOMI HCHO are 40 %–80 %. We observe that these requirements are well reached, with the BIAS found below 80 % at all the sites and below 40 % at 20 of the 25 sites. The provided TROPOMI systematic uncertainties are well in agreement with the observed biases at most of the stations except for the highest-HCHO-level site, where it is found to be underestimated. We find that while the BIAS has no latitudinal dependence, it is dependent on the HCHO concentration levels: an overestimation (+26±5 %) of TROPOMI is observed for very low HCHO levels (<2.5×1015 molec. cm−2), while an underestimation (-30.8%±1.4 %) is found for high HCHO levels (>8.0×1015 molec. cm−2). This demonstrates the great value of such a harmonized network covering a wide range of concentration levels, the sites with high HCHO concentrations being crucial for the determination of the satellite bias in the regions of emissions and the clean sites allowing a small TROPOMI offset to be determined. The wide range of sampled HCHO levels within the network allows the robust determination of the significant constant and proportional TROPOMI HCHO biases (TROPOMI =+1.10±0.05 ×1015+0.64±0.03 × FTIR; in molecules per square centimetre). Second, the precision of TROPOMI HCHO data is estimated by the median absolute deviation (MAD) of the relative differences between TROPOMI and FTIR ground-based data. The clean sites are especially useful for minimizing a possible additional collocation error. The precision requirement of 1.2×1016 molec. cm−2 for a single pixel is reached at most of the clean sites, where it is found that the TROPOMI precision can even be 2 times better (0.5–0.8×1015 molec. cm−2 for a single pixel). However, we find that the provided TROPOMI random uncertainties may be underestimated by a factor of 1.6 (for clean sites) to 2.3 (for high HCHO levels). The correlation is very good between TROPOMI and FTIR data (R=0.88 for 3 h mean coincidences; R=0.91 for monthly means coincidences). Using about 17 months of data (from May 2018 to September 2019), we show that the TROPOMI seasonal variability is in very good agreement at all of the FTIR sites. The FTIR network demonstrates the very good quality of the TROPOMI HCHO products, which is well within the pre-launch requirements for both accuracy and precision. This paper makes suggestions for the refinement of the TROPOMI random uncertainty budget and TROPOMI quality assurance values for a better filtering of the remaining outliers.

Verifying anthropogenic carbon dioxide (CO2) emissions globally is essential to inform about the progress of institutional efforts to mitigate anthropogenic climate forcing. To monitor localized emission sources, spectroscopic satellite sensors have been proposed that operate on the CO2 absorption bands in the shortwave-infrared (SWIR) spectral range with ground resolution as fine as a few tens of meters to about a hundred meters. When designing such sensors, fine ground resolution requires a trade-off towards coarse spectral resolution in order to achieve sufficient noise performance. Since fine ground resolution also implies limited ground coverage, such sensors are envisioned to fly in fleets of satellites, requiring low-cost and simple design, e.g., by restricting the spectrometer to a single spectral band. Here, we use measurements of the Greenhouse Gases Observing Satellite (GOSAT) to evaluate the spectral resolution and spectral band selection of a prospective satellite sensor with fine ground resolution. To this end, we degrade GOSAT SWIR spectra of the CO2 bands at 1.6 (SWIR-1) and 2.0 µm (SWIR-2) to coarse spectral resolution, without a further addition of noise, and we evaluate single-band retrievals of the column-averaged dry-air mole fractions of CO2 (XCO2) by comparison to ground truth provided by the Total Carbon Column Observing Network (TCCON) and by comparison to global “native” GOSAT retrievals with native spectral resolution and spectral band selection. Coarsening spectral resolution from GOSAT's native resolving power of >20 000 to the range of 700 to a few thousand makes the scatter of differences between the SWIR-1 and SWIR-2 retrievals and TCCON increase moderately. For resolving powers of 1200 (SWIR-1) and 1600 (SWIR-2), the scatter increases from 2.4 (native) to 3.0 ppm for SWIR-1 and 3.3 ppm for SWIR-2. Coarser spectral resolution yields only marginally worse performance than the native GOSAT configuration in terms of station-to-station variability and geophysical parameter correlations for the GOSAT–TCCON differences. Comparing the SWIR-1 and SWIR-2 configurations to native GOSAT retrievals on the global scale, however, reveals that the coarse-resolution SWIR-1 and SWIR-2 configurations suffer from some spurious correlations with geophysical parameters that characterize the light-scattering properties of the scene such as particle amount, size, height and surface albedo. Overall, the SWIR-1 and SWIR-2 configurations with resolving powers of 1200 and 1600 show promising performance for future sensor design in terms of random error sources while residual errors induced by light scattering along the light path need to be investigated further. Due to the stronger CO2 absorption bands in SWIR-2 than in SWIR-1, the former has the advantage that measurement noise propagates less into the retrieved XCO2 and that some retrieval information on particle scattering properties is accessible.

Observations of carbon monoxide (CO) from the Measurements Of Pollution In The Troposphere (MOPITT) instrument aboard the Terra spacecraft were expected to have an accuracy of 10 % prior to the launch in 1999. Here we evaluate MOPITT Version 7 joint (V7J) thermal-infrared and near-infrared (TIR–NIR) retrieval accuracy and precision and suggest ways to further improve the accuracy of the observations. We take five steps involving filtering or bias corrections to reduce scatter and bias in the data relative to other MOPITT soundings and ground-based measurements. (1) We apply a preliminary filtering scheme in which measurements over snow and ice are removed. (2) We find a systematic pairwise bias among the four MOPITT along-track detectors (pixels) on the order of 3–4 ppb with a small temporal trend, which we remove on a global scale using a temporally trended bias correction. (3) Using a small-region approximation (SRA), a new filtering scheme is developed and applied based on additional quality indicators such as the signal-to-noise ratio (SNR). After applying these new filters, the root-mean-squared error computed using the local median from the SRA over 16 years of global observations decreases from 3.84 to 2.55 ppb. (4) We also use the SRA to find variability in MOPITT retrieval anomalies that relates to retrieval parameters. We apply a bias correction to one parameter from this analysis. (5) After applying the previous bias corrections and filtering, we compare the MOPITT results with the GGG2014 ground-based Total Carbon Column Observing Network (TCCON) observations to obtain an overall global bias correction. These comparisons show that MOPITT V7J is biased high by about 6 %–8 %, which is similar to past studies using independent validation datasets on V6J. When using TCCON spectrometric column retrievals without the standard airmass correction or scaling to aircraft (WMO scale), the ground- and satellite-based observations overall agree to better than 0.5 %. GEOS-Chem data assimilations are used to estimate the influence of filtering and scaling to TCCON on global CO and tend to pull concentrations away from the prior fluxes and closer to the truth. We conclude with suggestions for further improving the MOPITT data products.

Providing timely information on urban greenhouse gas (GHG) emissions and their trends to stakeholders relies on reliable measurements of atmospheric concentrations and the understanding of how local emissions and atmospheric transport influence these observations. Portable Fourier transform infrared (FTIR) spectrometers were deployed at five stations in the Paris metropolitan area to provide column-averaged concentrations of CO2 (XCO2) during a field campaign in spring of 2015, as part of the Collaborative Carbon Column Observing Network (COCCON). Here, we describe and analyze the variations of XCO2 observed at different sites and how they changed over time. We find that observations upwind and downwind of the city centre differ significantly in their XCO2 concentrations, while the overall variability of the daily cycle is similar, i.e. increasing during night-time with a strong decrease (typically 2–3 ppm) during the afternoon. An atmospheric transport model framework (CHIMERE-CAMS) was used to simulate XCO2 and predict the same behaviour seen in the observations, which supports key findings, e.g. that even in a densely populated region like Paris (over 12 million people), biospheric uptake of CO2 can be of major influence on daily XCO2 variations. Despite a general offset between modelled and observed XCO2, the model correctly predicts the impact of the meteorological parameters (e.g. wind direction and speed) on the concentration gradients between different stations. When analyzing local gradients of XCO2 for upwind and downwind station pairs, those local gradients are found to be less sensitive to changes in XCO2 boundary conditions and biogenic fluxes within the domain and we find the model–data agreement further improves. Our modelling framework indicates that the local XCO2 gradient between the stations is dominated by the fossil fuel CO2 signal of the Paris metropolitan area. This further highlights the potential usefulness of XCO2 observations to help optimize future urban GHG emission estimates.

Among the more than 20 ground-based FTIR (Fourier transform infrared) stations currently operating around the globe, only a few have provided formaldehyde (HCHO) total column time series until now. Although several independent studies have shown that the FTIR measurements can provide formaldehyde total columns with good precision, the spatial coverage has not been optimal for providing good diagnostics for satellite or model validation. Furthermore, these past studies used different retrieval settings, and biases as large as 50 % can be observed in the HCHO total columns depending on these retrieval choices, which is also a weakness for validation studies combining data from different ground-based stations.For the present work, the HCHO retrieval settings have been optimized based on experience gained from past studies and have been applied consistently at the 21 participating stations. Most of them are either part of the Network for the Detection of Atmospheric Composition Change (NDACC) or under consideration for membership. We provide the harmonized settings and a characterization of the HCHO FTIR products. Depending on the station, the total systematic and random uncertainties of an individual HCHO total column measurement lie between 12 % and 27 % and between 1 and 11×1014 molec cm−2, respectively. The median values among all stations are 13 % and 2.9×1014 molec cm−2 for the total systematic and random uncertainties.This unprecedented harmonized formaldehyde data set from 21 ground-based FTIR stations is presented and its comparison with a global chemistry transport model shows consistency in absolute values as well as in seasonal cycles. The network covers very different concentration levels of formaldehyde, from very clean levels at the limit of detection (few 1013 molec cm−2) to highly polluted levels (7×1016 molec cm−2). Because the measurements can be made at any time during daylight, the diurnal cycle can be observed and is found to be significant at many stations. These HCHO time series, some of them starting in the 1990s, are crucial for past and present satellite validation and will be extended in the coming years for the next generation of satellite missions.

In this study we present the retrieval of the column-averaged dry air mole fraction of carbon dioxide (XCO2) from the Orbiting Carbon Observatory-2 (OCO-2) satellite observations using the RemoTeC algorithm, previously successfully applied to retrieval of greenhouse gas concentration from the Greenhouse Gases Observing Satellite (GOSAT). The XCO2 product has been validated with collocated ground-based measurements from the Total Carbon Column Observing Network (TCCON) for almost 2 years of OCO-2 data from September 2014 to July 2016. We found that fitting an additive radiometric offset in all three spectral bands of OCO-2 significantly improved the retrieval. Based on a small correlation of the XCO2 error over land with goodness of fit, we applied an a posteriori bias correction to our OCO-2 retrievals. In overpass averaged results, XCO2 retrievals have an SD of ∼ 1.30 ppm and a station-to-station variability of ∼ 0.40 ppm among collocated TCCON sites. The seasonal relative accuracy (SRA) has a value of 0.52 ppm. The validation shows relatively larger difference with TCCON over high-latitude areas and some specific regions like Japan.

We propose an approach to estimate urban emission ratios that takes advantage of the enhanced local urban signal in the atmosphere at low wind speed. We apply it to estimate monthly ratios between CO2, CO and some VOCs from several atmospheric concentration measurement datasets acquired in the centre of Paris between 2010 and 2014. We find that this approach is not very sensitive to the regional background level definition and that, in the case of Paris, it samples all days (weekdays and weekends) and all hours of the day evenly. A large seasonal variability of the ΔCO ∕ ΔCO2 ratio in Paris is shown, with a difference of around 60 % between the extreme values and a strong anti-correlation (r2 = 0.75) with atmospheric temperature. The comparison of the ratios obtained for two short measurement campaigns conducted in two different districts and two different periods (autumn and winter) shows differences ranging from −120 to +63 %. A comparison with a highly resolved regional emission inventory suggests some spatial variations of the ratio within the city.

This paper studies the seasonal variation of surface and column CO at three different sites (Paris, Jungfraujoch and Wollongong), with an emphasis on establishing a link between the CO vertical distribution and the nature of CO emission sources. We find the first evidence of a time lag between surface and free tropospheric CO seasonal variations in the Northern Hemisphere. The CO seasonal variability obtained from the total columns and free tropospheric partial columns shows a maximum around March–April and a minimum around September–October in the Northern Hemisphere (Paris and Jungfraujoch). In the Southern Hemisphere (Wollongong) this seasonal variability is shifted by about 6 months. Satellite observations by the IASI–MetOp (Infrared Atmospheric Sounding Interferometer) and MOPITT (Measurements Of Pollution In The Troposphere) instruments confirm this seasonality. Ground-based FTIR (Fourier transform infrared) measurements provide useful complementary information due to good sensitivity in the boundary layer. In situ surface measurements of CO volume mixing ratios at the Paris and Jungfraujoch sites reveal a time lag of the near-surface seasonal variability of about 2 months with respect to the total column variability at the same sites. The chemical transport model GEOS-Chem (Goddard Earth Observing System chemical transport model) is employed to interpret our observations. GEOS-Chem sensitivity runs identify the emission sources influencing the seasonal variation of CO. At both Paris and Jungfraujoch, the surface seasonality is mainly driven by anthropogenic emissions, while the total column seasonality is also controlled by air masses transported from distant sources. At Wollongong, where the CO seasonality is mainly affected by biomass burning, no time shift is observed between surface measurements and total column data.

The SPIRALE and SWIR balloon-borne instruments were launched in the Arctic polar region (near Kiruna, Sweden, 67.9° N–21.1° E) during summer on 7 and 24 August 2009 and on 14 August 2009, respectively. The SPIRALE instrument performed in situ measurements of several trace gases including CO and O3 at altitudes between 9 and 34 km, with very high vertical resolution (∼ 5 m). The SWIR-balloon instrument measured total and partial column of several species including CO. The CO stratospheric profile from SPIRALE for 7 August 2009 shows some specific structures with large concentrations in the low levels (potential temperatures between 320 and 380 K, i.e. 10–14 km height). These structures are not present in the CO vertical profile of SPIRALE for 24 August 2009, for which the volume mixing ratios are typical from polar latitudes (∼ 30 ppb). CO total columns retrieved from the IASI-MetOp satellite sounder for the three dates of flights are used to understand this CO variability. SPIRALE and SWIR CO partial columns between 9 and 34 km are compared, allowing us to confirm that the enhancement of CO is localised in the stratosphere. The measurements are also investigated in terms of CO:O3 correlations and using several modelling approaches (trajectory calculations, potential vorticity fields, results of chemistry transport model) in order to characterize the origin of the air masses sampled. The emission sources are qualified in terms of source type (fires, urban pollution) using NH3 and CO measurements from IASI-MetOp and fires detection from MODIS on board the TERRA/AQUA satellite. The results give strong evidence that the unusual abundance of CO on 7 August is due to surface pollution plumes from East Asia and North America transporting to the upper troposphere and then entering the lower stratosphere by isentropic advection. This study strengthens evidence that the composition of low polar stratosphere in summer may be affected by anthropogenic surface emissions through long-range transport.

The first spectral measurement of Earth's emitted radiation to space in the wideband range from 100 to 1400 cm−1 with 0.5 cm−1 spectral resolution is presented. The measurement was performed from a stratospheric balloon in tropical region using a Fourier transform spectrometer, during a field campaign held in Brazil in June 2005. The instrument, which has uncooled components including the detector module, is a prototype developed as part of the study for the REFIR (Radiation Explorer in the Far InfraRed) space mission. This paper shows the results of the field campaign with particular attention to the measurement capabilities of the prototype. The results are compared with measurements taken by IASI-balloon (Infrared Atmospheric Sounding Interferometer – Balloon version), aboard the same platform, and with forward model estimations. The infrared signature of clouds is observed in the measurements.

On 21–22 August 2001, NO, NO2 and HNO3 mixing ratio profiles were measured at high latitudes during sunset and sunrise using the Limb Profile Monitor of the Atmosphere (LPMA) and the DOAS experiments under stratospheric balloon. Photochemical simulations using the chemistry module of the Reprobus Chemistry Transport Model (CTM) that are constrained by ozone and total NOy balloon observations reproduce well the partitioning of NOx and NOy when model results are calculated at the exact time and location of the measurement for each tangent altitude. Taking the recently recommended reaction rate coefficients for the NOy partitioning (JPL-2003) and using realistic aerosol surface area in order to initialise the model leads to an agreement between calculations and measurements better than 10% all over the covered altitude range.

Gully erosion can be combatted in severely affected regions like sub-Saharan Africa using various low-cost interventions that are accessible to affected farmers. For successful implementation, however, biophysical evidence of intervention effectiveness needs to be validated against the interests and priorities of local communities. Working with farmers in a watershed in southern Ethiopia, we investigated (a) the effectiveness of low-cost gully rehabilitation measures to reduce soil loss and upward expansion of gully heads; (b) how farmers and communities view gully interventions; and (c) whether involving farmers in on-farm field trials to demonstrate gully interventions improves uptake, knowledge, and perceptions of their capacity to act. On-farm field experiments, key-informant interviews, focus group discussions, and household surveys were used to collect and analyse data. Three gully treatments were explored, all with riprap, one with grass planting, and one with grass planting and check-dam integration. Over a period of 26 months, these low-cost practices ceased measurable gully head expansion, whereas untreated gullies had a mean upward expansion of 671 cm, resulting in a calculated soil loss of 11.0 t. Farmers had a positive view of all gully rehabilitation measures explored. Ongoing rehabilitation activities and on-farm trials influenced the knowledge and understanding of similar gully treatments among survey respondents. On-farm experiments and field day demonstrations empowered farmers to act, addressing pessimism from some respondents about their capacity to do so.

Sandhoff disease, a lysosomal storage disorder, is caused by pathogenic variants in the HEXB gene, resulting in the loss of β-hexosaminidase activity and accumulation of sphingolipids including GM2 ganglioside. This accumulation occurs primarily in neurons, and leads to progressive neurodegeneration through a largely unknown process. Lysosomal storage diseases often exhibit dysfunctional mTOR signaling, a pathway crucial for proper neuronal development and function. In this study, Sandhoff disease model mice exhibited reduced mTOR signaling in the brain. To test if restoring mTOR signaling could improve the disease phenotype, we genetically reduced expression of the mTOR inhibitor Tsc2 in these mice. Sandhoff disease mice with reactivated mTOR signaling displayed increased survival rates and motor function, especially in females, increased dendritic-spine density, and reduced neurodegeneration. Tsc2 reduction also partially rescued aberrant synaptic function–related gene expression. These findings imply that enhancing mTOR signaling could be a potential therapeutic strategy for lysosomal-based neurodegenerative diseases.

Abstract Glacier speedups occur on daily to centennial timescales. While basal water and subglacial drainage configuration are thought to drive glacier speedups across these timescales, it remains unclear whether this forcing always occurs through the same mechanisms. Here, we explore whether the enthalpy model of glacier surging can explain speedups over a broader range of timescales if modified to account for seasonality in surface melt and continuous water supply to the glacier bed. We simulate velocity oscillations that range from seasonal to >100 years. Our model results more closely resemble observations of surges than previous model versions because ice flow variability at seasonal and multi‐year timescales is reproduced simultaneously through hydrological forcing. Under favorable conditions, seasonal water delivery to the bed gradually accumulates in a poorly‐connected basal drainage system, priming the glacier to surge. Surges themselves are marked by high water fluxes and enthalpy drainage from the glacier base.

Devising effective national-level climate action plans requires a more detailed understanding of the regional distribution of sources and sinks of greenhouse gases. Due to insufficient observations and modelling capabilities, India's current carbon source–sink estimates are uncertain. This study uses a high-resolution Lagrangian transport model to examine the potential of available CO2 observations over India for inverse estimation of regional carbon fluxes. We use four different sites in India that vary in the measurement technique, frequency and spatial representation. These observations exhibit substantial seasonal (7.5 to 9.2 ppm) and intra-seasonal (2 to 12 ppm) variability. Our modelling framework, a high-resolution Weather Research and Forecasting Model combined with the Stochastic Time-Inverted Lagrangian Transport model (WRF–STILT), performs better in simulating seasonal (R2=0.50 to 0.96) and diurnal (R2=0.96) variability (for the Mohali station) of observed CO2 than the current-generation global models (CarboScope, CarbonTracker and ECMWF EGG4). The seasonal CO2 concentration variability in Mohali, associated with crop residue burning, is largely underestimated by the models. WRF–STILT captures the seasonal biospheric variability over Nainital better than the global models but underestimates the strength of the CO2 uptake by crops. The choice of emission inventory in the modelling framework alone leads to significant biases in simulations (5 to 10 ppm), endorsing the need for accounting for emission fluxes, especially for non-background sites. Our study highlights the possibility of using the CO2 observations from these Indian stations for deducing carbon flux information at regional (Nainital) and suburban to urban (Mohali, Shadnagar and Nagpur) scales with the help of a high-resolution model. On accounting for observed variability in CO2, the global carbon data assimilation system can benefit from the measurements from the Indian subcontinent.

Abstract Background Academic procrastination is widespread among college students. Procrastination is strongly negatively correlated with psychological well-being, thus early interventions are needed. Internet- and mobile-based cognitive behavioral therapy (iCBT) could provide a low-threshold treatment option. Human guidance seems to be a decisive mechanism of change in iCBT. Persuasive design optimization of iCBT and guidance by a digital coach might represent a resource-saving alternative. The study evaluated the non-inferiority of a digital coach in comparison to human guidance with regard to the primary outcome procrastination. Methods The iCBT StudiCare procrastination was optimized by principles of the Persuasive System Design (PSD). A total of 233 college students were randomly assigned to either StudiCare procrastination guided by a digital coach (intervention group, IG) or by a human eCoach (control group, CG). All participants were assessed at baseline, 4-, 8- and 12-weeks post-randomization. Symptom change and between-group differences were assessed with latent growth curve models and supported by effect size levels. The non-inferiority margin was set at Cohen’s d = − 0.3. Results The primary outcome procrastination measured by the Irrational Procrastination scale (IPS) significantly decreased across groups (γ = − 0.79, p

Наукова стаття присвячена дослідженню особливостей використання захисником спеціальних знань у кримінальному провадженні. У ній зауважується, що підвищення ефективності захисту напряму залежить від прийняття захисником правильних тактичних рішень в межах криміналь­ного провадження, тобто побудови тієї лінії захисту, яка принесе бажаний результат. Вибір таких тактичних рішень залежить, зокрема, від обсягу володіння захисником спеціальними знаннями або його ерудованості щодо їх застосування іншими уповноваженими учасниками. Метою статті є визначення особливостей використання захисником спеціальних знань у кри­мінальному провадженні. У статті проведено лінгвістичне та наукове тлумачення поняття «спеціальні знання». Автора­ми зауважено на тому, що під час формування змісту вказаного поняття потрібно враховувати за­для якої мети вони застосовуються (тобто не лише для розслідування, розкриття чи попередження кримінальних правопорушень, а і захисту прав, свобод і законних інтересів підозрюваного, обви­нуваченого та інших суб’єктів, передбачених ч. 1 ст. 45 Кримінального процесуального кодексу України). У роботі звернена увага і на форми застосування захисником спеціальних знань у кри­мінальному провадженні. Наголошено, що сприяє покращенню ситуації розслідування обґрунту­вання позиції захисту, яка спирається на думку спеціаліста або висновок експерта з доведенням такого обґрунтування стороні обвинувачення. Автор дійшов висновку, що особливість використання захисником спеціальних знань у кримі­нальному провадженні виявляється в тому, що він: може як безпосередньо їх застосовувати (са­мостійно), проте обмежено в порівнянні з органами досудового розслідування, так і опосередко­вано (через слідчого, дізнавача, прокурора, слідчого суддю, суд залучити спеціаліста чи судового експерта (або затребувати їх висновки)); використовує їх, керуючись, зазвичай, загальновідомим конституційним принципом «дозволено все, що прямо не заборонено законом», а уповноважені особи органів досудового розслідування - лише відповідно до конкретної норми, яка передбачена у правовому акті різного ієрархічного рівня (за юридичною силою).

In Africa the population of refugees is increasing, resulting in ‘accidental urban centres’ often in remote arid landscapes. Firewood and charcoal, the main sources of cooking energy on the continent, are in short supply in refugee settings. This research is based on a series of interrelated studies conducted among Kalobeyei refugee and host communities in Northwestern Kenya using mixed methods from social and natural sciences. Firewood is inadequate, and 95% of households exchanged or sold food to buy cooking fuel. Energy poverty leaves women feeling disempowered and frustrated and causes conflicts between the refugee and host communities. Charcoal briquettes produced and used by the women emerged as a viable cooking fuel. Women stated that marketing challenges and difficulties in sourcing the raw materials may hinder growth of briquettes as a business. Possible solutions include using biomass from the invasive Prosopis juliflora (currently used for charcoal), establishing “briquette kiosks”, and integrating briquettes into development plans. In refugee contexts, approaches to food insecurity should integrate cooking energy and give women an opportunity to voice their needs and aspirations.

GM1 gangliosidosis is a neurodegenerative disorder caused by mutations in the GLB1 gene, which encodes lysosomal β-galactosidase. The enzyme deficiency blocks GM1 ganglioside catabolism, leading to accumulation of GM1 ganglioside and asialo-GM1 ganglioside (GA1 glycolipid) in brain. This disease can present in varying degrees of severity, with the level of residual β-galactosidase activity primarily determining the clinical course. Glb1 null mouse models, which completely lack β-galactosidase expression, exhibit a less severe form of the disease than expected from the comparable deficiency in humans, suggesting a potential species difference in the GM1 ganglioside degradation pathway. We hypothesized this difference may involve the sialidase NEU3, which acts on GM1 ganglioside to produce GA1 glycolipid. To test this hypothesis, we generated Glb1/Neu3 double KO (DKO) mice. These mice had a significantly shorter lifespan, increased neurodegeneration, and more severe ataxia than Glb1 KO mice. Glb1/Neu3 DKO mouse brains exhibited an increased GM1 ganglioside to GA1 glycolipid ratio compared with Glb1 KO mice, indicating that NEU3 mediated GM1 ganglioside to GA1 glycolipid conversion in Glb1 KO mice. The expression of genes associated with neuroinflammation and glial responses were enhanced in Glb1/Neu3 DKO mice compared with Glb1 KO mice. Mouse NEU3 more efficiently converted GM1 ganglioside to GA1 glycolipid than human NEU3 did. Our findings highlight NEU3’s role in ameliorating the consequences of Glb1 deletion in mice, provide insights into NEU3’s differential effects between mice and humans in GM1 gangliosidosis, and offer a potential therapeutic approach for reducing toxic GM1 ganglioside accumulation in GM1 gangliosidosis patients.

BACKGROUND AND OBJECTIVES: The amount of solid waste produced and its impact on communities and the environment are becoming a global concern. This study aims to assess the amount, composition, and prediction models of solid waste generation in the study area.METHODS: Solid waste data were collected from both residential and non-residential areas using stratified and systematic sampling approaches. Interviews and field measurements were used to obtain socioeconomic and solid waste data from 90 households and 69 samples from non-residential areas.FINDINGS: The research area's mean household solid waste generation rate is 0.39kilograms per capita per day. Organic waste accounted for the majority of the waste generated in the study area (71.28 percent), followed by other waste (9.77 percent), paper (6.71 percent), and plastic waste (6.41 percent). The solid waste generation rate demonstrated a positive relationship (p 0.05). Based on a high regression coefficient determination value (0.72), low mean absolute error (0.094), sum square error (1.28), and standard error of the estimate (0.908), Model 4 was chosen as the best-fit model among the proposed models.CONCLUSION: The developed models met multiple linear regression assumptions and could be used to estimate the rate of household solid waste generation. This study generated large amounts of organic waste present in municipal solid waste sources that can contaminate the environment and have an impact on human health while also having a massive energy recovery capability.

Based on advanced spectroscopic databases, line-by-line and layer-by-layer radiative transfer codes numerically solve the radiative transfer equation with very high accuracy. Taking advantage of its pre-calculated optical depth lookup table, the fast and accurate radiative transfer model Automatized Atmospheric Absorption Atlas OPerational (4A/OP) calculates the transmission and radiance spectra for a user-defined layered atmospheric model. Here, we present a module called 4A-Flux, which is developed and implemented into 4A/OP in order to include the calculation of the clear-sky longwave radiative flux profiles and heating rate profiles at a very high spectral resolution. Calculations are performed under the assumption of local thermodynamic equilibrium, a plane-parallel atmosphere, and specular reflection on the surface. The computation takes advantage of pre-tabulated exponential integral functions that are used instead of a classic angular quadrature. Furthermore, the sub-layer variation of the Planck function is implemented to better represent the emission of layers with a high optical depth. Thanks to the implementation of 4A-Flux, 4A/OP models have participated in the Radiative Forcing Model Intercomparison Project (RFMIP-IRF) along with other state-of-the-art radiative transfer models. 4A/OP hemispheric flux profiles are compared to other models over the 1800 representative atmospheric situations of RFMIP, yielding an outgoing longwave radiation (OLR) mean difference between 4A/OP and other models of −0.148 W m−2 and a standard deviation of 0.218 W m−2, showing a good agreement between 4A/OP and other models. 4A/OP is applied to the Thermodynamic Initial Guess Retrieval (TIGR) atmospheric database to analyze the response of the OLR and vertical heating rate to several perturbations of temperature or gas concentration. This work shows that 4A/OP with 4A-Flux module can successfully be used to simulate accurate flux and heating rate profiles and provide useful sensitivity studies including sensitivities to minor trace gases such as HFC134a, HCFC22, and CFC113. We also highlight the interest for the modeling community to extend intercomparison between models to comparisons between spectroscopic databases and modeling to improve the confidence in model simulations.

Abstract This study evaluates diabetes self-management mobile health applications available from European app stores with respect to quality, concordance with recommended self-management tasks and implementation of persuasive system design principles. The European Play Store and Apple App Store were systematically searched and relevant apps were tested. Two raters independently assessed app quality using the Mobile Application Rating Scale and conducted a content analysis of provided persuasive system design principles and self-management tasks. A total of 2,269 mobile health applications were identified and 120 could be included in the evaluation. The overall quality was rated as moderate M = 3.20 (SD = 0.39, min = 2.31, max = 4.62), with shortcomings in the subcategories of engagement (M = 2.80, SD = 0.67) and information quality (M = 2.26, SD = 0.48). Scientific evidence is available for 8% of the apps. The reviewed apps implemented a median of three persuasive system design principles (range 0–15) and targeted a median of 4.5 (range 1–8) self-management tasks, however, with a lack of information about psychosocial coping strategies. Most available diabetes self-management apps lack a scientific evidence base. Persuasive system design features are underrepresented and may form a promising tool to improve app quality. Furthermore, the interaction of physical and behavioral health should be improved in existing diabetes self-management mobile health applications.

Abstract In the southwest Japan forearc, slab‐fluids produced from subducted materials migrate to crustal levels and appear as deep‐seated brine. We have analyzed for the first‐time stable strontium isotopes in non‐volcanic spring water with high salinity, referred to as Arima‐type saline water that likely originate from slab‐fluid that upwelled along major faults. The stable strontium isotope compositions of the saline water are isotopically light (δ88/86Sr = 0.122–0.157‰) and different from those of local bedrock and near‐surface water. The light strontium‐enriched and radiogenic signature of the saline water reflects the primary characteristic of slab‐fluids without an isotopic overprint in the crust. The Arima‐type brines show signatures of slab‐fluids at forearc depth, which is different from the slab‐fluids at subarc depth estimated from arc lavas. The characteristic features of the Arima‐type brines are explained by a larger contribution of subducted sediments and strontium isotope fractionation during fluids generation at shallower depth.

The effect of casting method on the magnetic properties of Sm(Co0.63Fe0.28Cu0.07Zr0.02)7.6 magnet has been investigated. The results show that the plate-mold ingot (PM) and strip-casting flake (SC) have the same phase composition. Compared with PM, the SC has a smaller grain size and larger fine-grain region due to its larger degree of undercooling. The magnetic properties of the PM magnet are Br = 11.63 kG, Hcj = 25.65 kOe, (BH)max = 30.97 MGOe, while the magnetic properties of SC magnet are Br = 11.45 kG, Hcj = 24.00 kOe, (BH)max = 30.03 MGOe. The reason for the low remanence of the SC magnet is revealed by the XRD and EBSD results. It shows that the PM magnet has a better easy-axis orientation than the SC magnet, due to the existence of fine grains in SC that are unfavorable for the alignment under a magnetic field.

Here the authors show that hyporheic flow, bed morphology, and bed stability are intimately related, and that this relationship is expressed as distinct locked and segregated states of bedform dynamics, which carries implications for river system behavior in general and the storage of carbon, nutrients, and contaminants in particular.

Emissions of greenhouse gases (GHGs) from the Indian subcontinent have increased during the last 20 years along with rapid economic growth; however, there remains a paucity of GHG measurements for policy-relevant research. In northern India and Bangladesh, agricultural activities are considered to play an important role in GHG concentrations in the atmosphere. We performed weekly air sampling at Nainital (NTL) in northern India and Comilla (CLA) in Bangladesh from 2006 and 2012, respectively. Air samples were analyzed for dry-air gas mole fractions of CO2, CH4, CO, H2, N2O, and SF6 and carbon and oxygen isotopic ratios of CO2 (δ13C-CO2 and δ18O-CO2). Regional characteristics of these components over the Indo-Gangetic Plain are discussed compared to data from other Indian sites and Mauna Loa, Hawaii (MLO), which is representative of marine background air. We found that the CO2 mole fraction at CLA had two seasonal minima in February–March and September, corresponding to crop cultivation activities that depend on regional climatic conditions. Although NTL had only one clear minimum in September, the carbon isotopic signature suggested that photosynthetic CO2 absorption by crops cultivated in each season contributes differently to lower CO2 mole fractions at both sites. The CH4 mole fraction of NTL and CLA in August–October showed high values (i.e., sometimes over 4000 ppb at CLA), mainly due to the influence of CH4 emissions from the paddy fields. High CH4 mole fractions sustained over months at CLA were a characteristic feature on the Indo-Gangetic Plain, which were affected by both the local emission and air mass transport. The CO mole fractions at NTL were also high and showed peaks in May and October, while CLA had much higher peaks in October–March due to the influence of human activities such as emissions from biomass burning and brick production. The N2O mole fractions at NTL and CLA increased in June–August and November–February, which coincided with the application of nitrogen fertilizer and the burning of biomass such as the harvest residues and dung for domestic cooking. Based on H2 seasonal variation at both sites, it appeared that the emissions in this region were related to biomass burning in addition to production from the reaction of OH and CH4. The SF6 mole fraction was similar to that at MLO, suggesting that there were few anthropogenic SF6 emission sources in the district. The variability of the CO2 growth rate at NTL was different from the variability in the CO2 growth rate at MLO, which is more closely linked to the El Niño–Southern Oscillation (ENSO). In addition, the growth rates of the CH4 and SF6 mole fractions at NTL showed an anticorrelation with those at MLO, indicating that the frequency of southerly air masses strongly influenced these mole fractions. These findings showed that rather large regional climatic conditions considerably controlled interannual variations in GHGs, δ13C-CO2, and δ18O-CO2 through changes in precipitation and air mass.

Abstract Background How cigarette smoke (CS) and chronic obstructive pulmonary disease (COPD) affect severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection and severity is controversial. We investigated the effects of COPD and CS on the expression of SARS-CoV-2 entry receptor ACE2 in vivo in COPD patients and controls and in CS-exposed mice, and the effects of CS on SARS-CoV-2 infection in human bronchial epithelial cells in vitro. Methods We quantified: (1) pulmonary ACE2 protein levels by immunostaining and ELISA, and both ACE2 and/or TMPRSS2 mRNA levels by RT-qPCR in two independent human cohorts; and (2) pulmonary ACE2 protein levels by immunostaining and ELISA in C57BL/6 WT mice exposed to air or CS for up to 6 months. The effects of CS exposure on SARS-CoV-2 infection were evaluated after in vitro infection of Calu-3 cells and differentiated human bronchial epithelial cells (HBECs), respectively. Results ACE2 protein and mRNA levels were decreased in peripheral airways from COPD patients versus controls but similar in central airways. Mice exposed to CS had decreased ACE2 protein levels in their bronchial and alveolar epithelia versus air-exposed mice. CS treatment decreased viral replication in Calu-3 cells, as determined by immunofluorescence staining for replicative double-stranded RNA (dsRNA) and western blot for viral N protein. Acute CS exposure decreased in vitro SARS-CoV-2 replication in HBECs, as determined by plaque assay and RT-qPCR. Conclusions ACE2 levels were decreased in both bronchial and alveolar epithelial cells from COPD patients versus controls, and from CS-exposed versus air-exposed mice. CS-pre-exposure potently inhibited SARS-CoV-2 replication in vitro. These findings urge to investigate further the controversial effects of CS and COPD on SARS-CoV-2 infection.

Measurement error arising from vibration interference is recognized as the primary obstacle limiting the accuracy and stability of laser interference absolute gravimeters. The present work addresses this issue by proposing a global search optimization algorithm that determines the optimal absolute value of gravity based on the measured time–displacement coordinates of a falling body and the signal obtained from the passive vibration isolation system of the inertial reference corner cube in a laser interference absolute gravimeter. Results of numerical calculations conducted under vibration interference conditions with added white noise resulting in a signal-to-noise ratio of 40 dB demonstrate the following. The accuracy and standard deviation of the gravimeter obtained using the proposed algorithm are −0.04 µGal (1µGal=1×10-8 m s−2) and 0.24 µGal, respectively, while those values obtained by the standard least-squares solution are 10.19 and 154.11 µGal, respectively. The test results indicate that the average response of the reference value of acceleration due to gravity superimposed by a disturbance of 1.00 µGal is 1.01 µGal using the proposed algorithm and 0.87 µGal using the standard least-squares solution.