Abstract. Long-term greenhouse gas (GHG) measurements are essential for understanding the carbon cycle, detecting trends in atmospheric composition, and assessing the efficiency of climate change mitigation strategies. However, observational gaps over large geographic areas such as the Eastern Mediterranean and Middle East (EMME), a well-known regional GHG hotspot, are likely to increase uncertainties in estimations of their sources and sinks. Here, we describe a new Total Carbon Column Observing Network (TCCON) observatory for solar absorption spectroscopy measurements that has been operating in Nicosia, Cyprus, since September 2019. The site helps bridge a regional observational gap in the EMME, a strategic location at the crossroads of air masses from Europe, Asia, and Africa. Using near-infrared (NIR, InGaAs detector) solar absorption spectra, TCCON-Nicosia measures total column average dry-air mole fractions (Xgas) of key trace gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), carbon monoxide (CO), hydrogen fluoride (HF), water vapor (H2O), and semi-heavy water (HDO). These continuous observations, spanning more than 4 years, are presented along with a description of the quality control procedures, compliant with the TCCON standards, to ensure total column atmospheric data with minimal errors. In 2023, observations were extended into the mid-infrared (MIR) spectral region with the addition of a liquid-nitrogen-cooled InSb (LN2-InSb) detector enabling the retrieval of additional trace gases such as formaldehyde (HCHO), carbonyl sulfide (OCS), nitrogen monoxide (NO), nitrogen dioxide (NO2), and ethane (C2H6), herewith further contributing to the global Network for the Detection of Atmospheric Composition Change (NDACC). To tie the TCCON Nicosia with the WMO reference scale, an AirCore (AC) campaign conducted in June 2020 over Cyprus provided vertical in situ profiles, which were converted into total column quantities (AC.Xgas) and compared to TCCON observations (Xgas). The TCCON/in situ comparison showed agreement well within their respective uncertainty budget.

Abstract. We perform a global inverse modelling analysis to quantify biomass burning emissions of carbon monoxide (CO) from the extreme wildfires in Canada between May and September 2023. Using the GEOS-Chem model, we assimilated observations at 3 d temporal and 2° × 2.5° horizontal resolution from the Tropospheric Monitoring Instrument (TROPOMI) separately and then jointly with Total Carbon Column Observing Network (TCCON) measurements. We also evaluated prior emissions from the Quick Fire Emissions Dataset (QFED), Blended Global Biomass Burning Emissions Product eXtended (GBBEPx), Global Fire Assimilation System (GFAS), and Canadian Forest Fire Emissions Prediction System (CFFEPS). The assimilation of TROPOMI-only measurements estimated posterior North America emissions for QFED, GBBEPx, GFAS, and CFFEPS of 110.4 ± 20, 112.8 ± 20, 127.2 ± 17, and 125.6 ± 18 Tg CO compared to prior estimates of 37.1, 42.7, 91.0, and 90.2 Tg CO, respectively. The joint assimilation of TROPOMI+TCCON reduced the posterior 1σ uncertainty on the North American emission estimates by up to about 30 %, while showing only a modest impact (<5 %) on the mean estimate of the inferred emissions. An evaluation against independent measurements reveals that adding TCCON data increases the correlations and slightly lowers the biases and standard deviations. Additionally, including an experimental TCCON product at East Trout Lake with higher surface sensitivity, we find better agreement of the assimilation results with nearby in situ tall tower and aircraft measurements. This highlights the potential importance of vertical sensitivity in these experimental data for constraining local surface emissions. Our results demonstrate the complementarity of the greater temporal coverage provided by TCCON with the spatial coverage of TROPOMI when these data are jointly assimilated.

Abstract. Accurately measuring greenhouse gas concentrations to identify regional sources and sinks is essential for effectively monitoring and mitigating their impact on the Earth's changing climate. In this article we present the scientific data products of XCO2 and XCH4, retrieved with RemoTeC, from the Greenhouse Gases Observing Satellite-2 (GOSAT-2), which span a time range of 5 years. GOSAT-2 has the capability to measure total columns of CO2 and CH4 to the necessary requirements set by the Global Climate Observing System (GCOS), who define said requirements as accuracy<10 ppb and <0.5 ppm for XCH4 and XCO2 respectively, and stability of <3ppbyr-1 and <0.5ppmyr-1 for XCH4 and XCO2 respectively. Central to the quality of the XCO2 and XCH4 datasets is the post-retrieval quality flagging step. Previous versions of RemoTeC products have relied on threshold filtering, flagging data using boundary conditions from a list of retrieval parameters. We present a novel quality filtering approach utilising a machine learning technique known as Random Forest Classifier (RFC) models. This method is developed under the European Space Agency's (ESA) Climate Change Initiative+ (CCI+) program and applied to data from GOSAT-2. Data from the Total Carbon Column Observing Network (TCCON) are employed to train the RFC models, where retrievals are categorized as good or bad quality based on the bias between GOSAT-2 and TCCON measurements. TCCON is a global network of Fourier transform spectrometers that measure telluric absorption spectra at infrared wavelengths. It serves as the scientific community's standard for validating satellite-derived XCO2 and XCH4 data. Our results demonstrate that the machine learning-based quality filtering achieves a significant improvement, with data yield increasing by up to 85 % and RMSE improving by up to 30 %, compared to traditional threshold-based filtering. Furthermore, inter-comparison with the TROPOspheric Monitoring Instrument (TROPOMI) indicates that the quality filtering RFC models generalise well to the full dataset, as the expected behaviour is reproduced on a global scale. Low systematic biases are essential for extracting meaningful fluxes from satellite data products. Through TCCON validation we find that all data products are within the breakthrough bias requirements set, with RMSE for XCH4 < 15 ppb and XCO2 < 2 ppm. We derive station-to-station biases of 4.2 ppb and 0.5 ppm for XCH4 and XCO2 respectively, and linear drift of 0.6 ppb yr−1 and 0.2 ppm yr−1 for XCH4 and XCO2 respectively. For XCH4, GOSAT-2 and TROPOMI are highly correlated with standard deviations less than 18 ppb and globally averaged biases close to 0 ppb. The inter-satellite bias between GOSAT and GOSAT-2 is significant, with an average global bias of −15 ppb. This is comparable to that seen between GOSAT and TROPOMI, consistent with our findings that GOSAT-2 and TROPOMI are in close agreement.

Accurate quantification of carbon dioxide (CO2) sources and sinks is becoming a key aspect in recent carbon flux research; yet our understanding of satellite performance on regional scales remains insufficient. In this work, the column-averaged dry-air mole fraction of CO2 retrieved from OCO-2 v11.1r and GOSAT v03.05 was evaluated against CarbonTracker (CT) using data from March 2022 to August 2023. Also, the satellite data were validated against those from the Total Carbon Column Observing Network (TCCON) for March 2022 to February 2024. Comparison with CT revealed that both satellites had a general negative bias over land and the best performance in spring. In Southern Hemisphere land regions, the satellites captured monthly variability reliably, with OCO-2 obtaining the most accurate monthly concentrations. In Northern Hemisphere land regions, CT demonstrated the best performance, although both satellites accurately quantified monthly variations in some regions. In tropical land regions, none of the satellites showed superior performance. OCO-2 data showed bias features in sub-regional areas such as East and South Asia. For ocean regions, the bias was the largest in spring. Phase offset, slight underestimation of concentrations, and seasonal biases were found over several ocean regions in OCO-2 time series, whereas GOSAT was unable to provide reasonable results. When comparing TCCON with OCO-2 and GOSAT data, we found systematic errors of −0.12 and −0.56 ppm and root mean square errors of 1.08 and 1.70 ppm, respectively, mainly contributed by topographic variation and aerosol load. The errors were the smallest in spring and larger in summer and winter. Both CT- and TCCON-based analyses indicated that current satellite products may have better performance in desert surfaces. Clouds, aerosols, and surface pressure still challenged OCO-2 retrieval, while the bias-correction process can be emphasized for GOSAT.

Abstract Methane (CH 4 ) and carbon monoxide (CO) are gases with important climate impacts as direct and indirect greenhouse gases, respectively. Methane has a warming potential 28 times that of carbon dioxide on a 100‐year timescale, and carbon monoxide is a precursor to ozone in the troposphere. Modeling trace gas concentrations in the Arctic atmosphere can be challenging due to Arctic conditions and sensitivity to long‐range transport, and comparing model outputs to remote sensing measurements is essential for ensuring that models are performing well. Ground‐based Arctic measurements are spatially sparse, so it is important to make use of all such available data sets. In this study, we assess eight atmospheric models, comparing their simulations of atmospheric CO and CH 4 column‐averaged dry‐air mole fractions for 2014 and 2015 with ground‐based retrievals of these species at three Arctic stations in the Total Carbon Column Observing Network (TCCON). The multi‐model mean had mean biases (± one standard deviation of the mean) of −5.4% ± 8% at Eureka, Canada, −6.5% ± 8% at Ny‐Ålesund, Norway, and −11% ± 7% at Sodankylä, Finland for CO, and mean biases of −0.25% ± 0.5% at Eureka, −0.90% ± 0.5% at Ny‐Ålesund, and −1.0% ± 0.5% at Sodankylä for CH 4 . Individual model mean biases range from −33% to +35% for CO and −2.5% to +1.9% for CH 4 . These results indicate that models could benefit from improvements targeting simulations of Arctic CO.

Spatiotemporal variations of atmospheric XCH4 in China based on multiple spatially continuous satellite-derived products.

Satellite-based monitoring of atmospheric column-averaged dry-air mole fraction (XCH4) is essential for quantifying methane (CH4) emissions, yet uncharacterized spatially varying biases in XCH4 observations can cause misattribution in flux estimates. This study assesses the potential of the upcoming TanSat-2 satellite mission to estimate China’s CH4 emission using a series of Observing System Simulation Experiments (OSSEs) based on an Ensemble Kalman Filter (EnKF) inversion framework coupled with GEOS-Chem on a 0.5° × 0.625° grid, alongside an evaluation of current TROPOMI-based products against Total Carbon Column Observing Network (TCCON) observations. Assuming a target precision of 8 ppb, TanSat-2 could achieve an annual national emission estimate accuracy of 2.9% ± 4.2%, reducing prior uncertainty by 84%, with regional deviations below 5.0% across Northeast, Central, East, and Southwest China. In contrast, limited coverage in South China due to persistent cloud cover leads to a 26.1% discrepancy—also evident in pseudo TROPOMI OSSEs—highlighting the need for complementary ground-based monitoring strategies. Sensitivity analyses show that satellite retrieval biases strongly affect inversion robustness, reducing the accuracy in China’s total emission estimates by 5.8% for every 1 ppb increase in bias level across scenarios, particularly in Northeast, Central and East China. We recommend expanding ground-based XCH4 observations in these regions to support the correction of satellite-derived biases and improve the reliability of satellite-constrained inversion results.

Abstract The Orbiting Carbon Observatory 2 (OCO‐2) is NASA's first Earth observation satellite mission dedicated to studying the sources and sinks of carbon dioxide (CO2) on a global scale. The observations of reflected sunlight are inverted in a retrieval algorithm to produce estimates of the dry air mole‐fractions of CO2 (XCO2). The OCO‐2 Level 2 data release, version 11.1 (v11.1) retrievals from the Atmospheric Carbon Observations from Space (ACOS) algorithm, includes significant improvements in the XCO2 data product compared to older OCO‐2 data versions. This work compares the v11.1 XCO2 from OCO‐2 against XCO2 estimates collected from a global ground‐based network known as the Total Carbon Column Observing Network (TCCON), OCO‐2's primary validation source. The OCO‐2 project provides a version of the Level 2 data product, called “lite” files that include calibrated and bias‐corrected XCO2 values, accessible together with all OCO‐2 data products through the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). This work shows that OCO‐2 XCO2 observations made between September 2014 and December 2023, after quality filtering and the application of an averaging kernel correction, agree well with coincident TCCON data for all OCO‐2 observational modes of land (nadir, glint, target) and ocean (glint). The aggregated, bias‐corrected, and quality‐filtered absolute average bias values are less than or equal to 0.20 parts per million (ppm) globally for all OCO‐2 observation modes, where the biases do not indicate a statistically significant time dependence. The land nadir/glint mode has the lowest bias value of −0.03 ± 0.85 ppm.

Atmospheric methane (CH4) concentrations have increased to 2.5 times their pre-industrial levels, with a marked acceleration in recent decades. CH4 is responsible for approximately 30% of the global temperature rise since the Industrial Revolution. This growing concentration contributes to environmental degradation, including ocean acidification, accelerated climate change, and a rise in natural disasters. The column-averaged dry-air mole fraction of methane (XCH4) is a crucial indicator for assessing atmospheric CH4 levels. In this study, the Sentinel-5P TROPOMI instrument was employed to monitor, map, and estimate CH4 concentrations on both regional and global scales. However, TROPOMI data exhibits limitations such as spatial gaps and relatively coarse resolution, particularly at regional scales or over small areas. To mitigate these limitations, a novel Convolutional Neural Network Autoencoder (CNN-AE) model was developed. Validation was performed using the Total Carbon Column Observing Network (TCCON), providing a benchmark for evaluating the accuracy of various interpolation and prediction models. The CNN-AE model demonstrated the highest accuracy in regional-scale analysis, achieving a Mean Absolute Error (MAE) of 28.48 ppb and a Root Mean Square Error (RMSE) of 30.07 ppb. This was followed by the Random Forest (RF) regressor (MAE: 29.07 ppb; RMSE: 36.89 ppb), GridData Nearest Neighbor Interpolator (NNI) (MAE: 30.06 ppb; RMSE: 32.14 ppb), and the Radial Basis Function (RBF) Interpolator (MAE: 80.23 ppb; RMSE: 90.54 ppb). On a global scale, the CNN-AE again outperformed other methods, yielding the lowest MAE and RMSE (19.78 and 24.7 ppb, respectively), followed by RF (21.46 and 27.23 ppb), GridData NNI (25.3 and 32.62 ppb), and RBF (43.08 and 54.93 ppb).

The urgent challenge of climate change, driven by greenhouse gas (GHG) emissions, highlights the need for reliable monitoring to inform effective mitigation strategies. However, the lack of monitoring stations in India for direct GHG measurements poses a challenge. This study explores the potential of satellite data for monitoring CO2 and CH4 concentrations in Indian megacities, specifically for Mumbai and Delhi. We assessed the reliability of Orbiting Carbon Observatory-2 (OCO-2) satellite data for CO2 and Sentinel-5 Precursor data for CH4 by comparing them with ground-based Total Carbon Column Observing Network (TCCON) measurements. Additionally, we applied the Seasonal Autoregressive Integrated Moving Average (SARIMA) model for CO₂ forecasting in Mumbai, achieving high accuracy (mean absolute error = 2.1 ppm, root mean square error = 2.72 ppm). While data limitations restrict CO2 analysis for Delhi, the findings for Mumbai significantly contribute to understanding urban CO2 dynamics. The SARIMA model also shows promise for CH4 forecasting with MAE of 8.3 ppb (Mumbai) and 7.78 ppb (Delhi) and RMSE of 10.37 ppb (Mumbai) and 11.92 ppb (Delhi). These findings underscore the utility of satellite data and forecasting models in monitoring urban GHG emissions. The observed rise in CO2 and CH4 concentrations highlights the urgency of implementing serious actions to address climate change.

Abstract Vertical profiles of trace gases are crucial for validating satellite observations, model inversions, and estimating regional sources, sinks, and transport. This study presents the first two vertically measured CO2 profiles from the ground to 25 km in the economically developed regions of southeast China using a balloon‐borne AirCore system. From higher to lower altitudes, the two CO2 profiles revealed distinct characteristics, showing an initial increase, followed by a decrease, and then another increase with fluctuations up to 13.1 ppm. Notable peaks were observed in the stratosphere, contrasting with findings from northwestern China, likely due to strong aircraft emissions in economically active southern regions. Additionally, air mass transport from urban areas elevated upper‐atmosphere CO2 levels. After 2‐hr storage, the CO2 profile achieved a vertical resolution of 260 m at 10 km with the segmented design and narrow tube diameter enhancing high‐altitude resolution. Although CarbonTracker simulations and Total Carbon Column Observing Network (TCCON) prior profiles matched the overall trends, significant differences in concentrations and key altitude fluctuations highlight limited prior profile information and uncertainties in remote sensing inversions. Observations from OCO‐3, TCCON, and GOSAT‐2 likely overestimate column‐averaged CO2 concentration by 1–7 ppm due to limitations in spatial resolution, prior information, and inherent method constraints. The CO2 profiles in the same regions remain variable, influenced by terrain, meteorology, and sampling season. Given the scarcity of CO2 profile data, existing global data sets may lack regional representativeness, emphasizing the need for higher spatial and temporal resolution monitoring to accurately capture global CO2 dynamics.

The 2020 wildfire season in the Western U.S. was historic in its intensity and impact on the land and atmosphere. This study aims to characterize satellite retrievals of carbon monoxide (CO), a tracer of combustion and signature of those fires, from two key satellite instruments: the Cross-track Infrared Sounder (CrIS) and the Tropospheric Monitoring Instrument (TROPOMI). We evaluate them during this event and assess their synergies. These two retrievals are matched temporally, as the host satellites are in tandem orbit and spatially by aggregating TROPOMI to the CrIS resolution. Both instruments show that the Western U.S. displayed significantly higher daily average CO columns compared to the Central and Eastern U.S. during the wildfires. TROPOMI showed up to a factor of two larger daily averages than CrIS during the most intense fire period, likely due to differences in the vertical sensitivity of the two instruments and representative of near-surface CO abundance near the fires. On the other hand, there was excellent agreement between the instruments in downwind free tropospheric plumes (scatter plot slopes of 0.96–0.99), consistent with their vertical sensitivities and indicative of mostly lofted smoke. Temporally, TROPOMI CO column peaks were delayed relative to the Fire Radiative Power (FRP), and CrIS peaks were delayed with respect to TROPOMI, particularly during the intense initial weeks of September, suggesting boundary layer buildup and ventilation. Satellite retrievals were evaluated using ground-based CO column estimates from the Network for the Detection of Atmospheric Composition Change (NDACC) and the Total Carbon Column Observing Network (TCCON), showing Normalized Mean Errors (NMEs) for CrIS and TROPOMI below 32% and 24%, respectively, when compared to all stations studied. While Normalized Mean Bias (NMB) was typically low (absolute value below 15%), there were larger negative biases at Pasadena, likely associated with sharp spatial gradients due to topography and proximity to a large city, which is consistent with previous research. In situ CO profiles from AirCore showed an elevated smoke plume for 15 September 2020, highlighted consistency between TROPOMI and CrIS CO columns for lofted plumes. This study demonstrates that both CrIS and TROPOMI provide complementary information on CO distribution. CrIS’s sensitivity in the middle and lower free troposphere, coupled with TROPOMI’s effectiveness at capturing total columns, offers a more comprehensive view of CO distribution during the wildfires than either retrieval alone. By combining data from both satellites as a ratio, more detailed information about the vertical location of the plumes can potentially be extracted. This approach can enhance air quality models, improve vertical estimation accuracy, and establish a new method for assessing lower tropospheric CO concentrations during significant wildfire events.

Abstract. The Harwell observatory, located in Oxfordshire, UK (51.571° N, 1.315° W), now part of the Total Carbon Column Observing Network (TCCON), has been performing ground-based remote sensing of averaged dry columns of atmospheric greenhouse gases since September 2020. Measurements are performed through near-infrared and shortwave infrared high-resolution spectroscopy of the atmosphere's transmission in direct sun viewing geometry, following the TCCON methodology. We report on the development, the measurements, and the performance of the observing system installed at Harwell. The hardware and software are described and characterized, as well as the outputted data quality, based on the 4-year data record collected so far. The Harwell site is demonstrated to produce data of high quality, well in line with the requirements for the TCCON infrastructure. The dataset is available at https://doi.org/10.14291/tccon.ggg2020.harwell01.R0 (Weidmann et al., 2023).

Abstract NASA's Orbiting Carbon Observatory‐2 (OCO‐2) has the goal of accurately estimating column‐averaged dry‐air mole fractions of carbon dioxide (). In order to fit the measured radiances, many parameters besides are included in the optimal estimation state vector, including atmospheric water vapor and temperature. The current operational retrieval algorithm (v11) solves for a multiplicative scaling factor on an a priori water vapor profile and an additive offset on an a priori temperature profile. However, simulations have indicated that water vapor and temperature each have 1.5–3 degrees of freedom in the vertical column. This means that the retrieval is limited in its ability to fit the true profiles of temperature and water vapor. Here, we use singular value decomposition to determine the three most explanatory profile “shapes” of water vapor and temperature error, then retrieve a single scaling factor applied to each shape. We assess retrieval errors by comparing to the Total Carbon Column Observing Network (TCCON) and multiple atmospheric inverse models. We find that after applying quality filtering using Data Ordering Genetic Optimization and a custom bias correction, the scatter of the error versus TCCON is reduced from 1.02 to 1.01 ppm (2.3% reduction in variance) for land glint observations, 1.04 to 0.96 ppm (14.5% reduction in variance) for land nadir observations, and 0.68 to 0.66 ppm (4.7% reduction in variance) for ocean glint observations. We also see a small improvement in the agreement between OCO‐2 and models over oceans and the Amazon.

La sociedad está inmersa en una era digital de constante evolución y confronta problemáticas complejas que, para concretar una solución, exigen reflexión y elaboración de propuestas a partir de la integración de diferentes disciplinas. Utilizando un enfoque cualitativo basado en entrevistas semiestructuradas y análisis temático, se identificaron factores clave que vinculan la ITD y la TC con la práctica docente y el diseño curricular, proponiendo un modelo interdisciplinario que potencia la resolución de problemas empresariales y sociales, este artículo analiza la brecha identificada en la comprensión de cómo estos elementos se integran en el proceso formativo del programa la Maestría en Informática Administrativa del posgrado en ciencias de la administración de la Universidad Nacional Autónoma de México (UNAM). Los hallazgos de esta investigación contribuyen a identificar la sinergia entre la ITD y la TC en la educación a nivel posgrado proponiendo un modelo que incorpora el desarrollo cultural, educativo y social.

Accurate retrieval of column-averaged dry-air mole fraction of methane (XCH4) in the atmosphere is important for greenhouse gas emission management. Traditional XCH4 retrieval methods are complex, while machine learning can be used to model nonlinear relationships by analyzing large datasets, providing an efficient alternative. This study proposes an XGBoost algorithm-based retrieval method to improve the efficiency of atmospheric XCH4 retrieval. First, the key wavelengths affecting XCH4 retrieval were determined using a radiative transfer model. The TROPOspheric Monitoring Instrument (TROPOMI) L1B satellite data, L2 XCH4 products, and auxiliary data were matched to construct the dataset. The dataset constructed was used to train the XGBoost model and obtain the TRO_XGB_XCH4 model. Finally, the accuracy of the proposed model was evaluated using various parameter values and validated against XCH4 products and Total Carbon Column Observing Network (TCCON) ground-based observations. The results showed that the proposed TRO_XGB_XCH4 model had a tenfold cross-validation accuracy R of 0.978, a ground-based validation R of 0.749, and a temporal extension accuracy R of 0.863. Therefore, the accuracy of the TRO_XGB_XCH4 retrieval model is comparable to that of the official TROPOMI L2 product.

Abstract. Satellite-based observations of carbon dioxide (CO2) are sensitive to all processes that affect the propagation of radiation in the atmosphere, including scattering and absorption by atmospheric aerosols. Therefore, accurate retrievals of column-averaged CO2 (XCO2) benefit from detailed information on the aerosol conditions. This is particularly relevant for future missions focusing on observing anthropogenic CO2 emissions, such as the Copernicus Anthropogenic CO2 Monitoring mission (CO2M). To fully prepare for CO2M observations, it is informative to investigate existing observations in addition to other approaches. Our focus here is on observations from the NASA Orbiting Carbon Observatory-2 (OCO-2) mission. In the operational full-physics XCO2 retrieval used to generate OCO-2 level 2 products, the aerosol properties are known to have high uncertainty, but their main objective is to facilitate CO2 retrievals. We evaluate the OCO-2 product from the point of view of aerosols by comparing the OCO-2-retrieved aerosol properties to collocated Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua Dark Target aerosol products. We find that there is a systematic difference between the aerosol optical depth (AOD, τ) values retrieved by the two instruments such that τOCO-2∼0.4τMODIS. A similar difference is found when comparing OCO-2 with the Aerosol Robotic Network (AERONET). This results in 16.5 % of cases being misclassified as low AOD (good quality) by the OCO-2 quality filtering. We also find a dependence of the XCO2 on the AOD difference, indicating an aerosol-induced effect in the XCO2 retrieval. Furthermore, comparing with the Total Carbon Column Observing Network (TCCON), we find a small AOD-dependent bias in XCO2. In addition, we find a weak but statistically significant correlation between MODIS AOD and XCO2, which can be partly explained by natural covariance and co-emission of aerosols and CO2. Due to the co-emission, using an AOD threshold in the quality filtering leads to a sampling bias, where high XCO2 values are more often removed. To mitigate the effect of this on the anthropogenic CO2 emission monitoring, we investigate the effect of the AOD threshold on the fraction of acceptable XCO2 data. We find that relaxing the MODIS AOD threshold from 0.2 to 0.5, which is the goal for the CO2M, increases the fraction of acceptable data by 14 percentage points globally and by 31 percentage points for urban areas.

As carbon dioxide (CO2) concentrations continue to rise, climate change, characterized by global warming, presents a significant challenge to global sustainable development. Currently, most global shortwave infrared CO2 retrievals rely on fully physical retrieval algorithms, for which complex calculations are necessary. This paper proposes a method to predict the concentration of column-averaged CO2 (XCO2) from shortwave infrared hyperspectral satellite data, using machine learning to avoid the iterative computations of the physical method. The training dataset is constructed using the Orbiting Carbon Observatory-2 (OCO-2) spectral data, XCO2 retrievals from OCO-2, surface albedo data, and aerosol optical depth (AOD) measurements for 2019. This study employed a variety of machine learning algorithms, including Random Forest, XGBoost, and LightGBM, for the analysis. The results showed that Random Forest outperforms the other models, achieving a correlation of 0.933 with satellite products, a mean absolute error (MAE) of 0.713 ppm, and a root mean square error (RMSE) of 1.147 ppm. This model was then applied to retrieve CO2 column concentrations for 2020. The results showed a correlation of 0.760 with Total Carbon Column Observing Network (TCCON) measurements, which is higher than the correlation of 0.739 with satellite product data, verifying the effectiveness of the retrieval method.

A good quantitative knowledge of regional sources and sinks of atmospheric carbon dioxide (CO2) is essential for understanding the global carbon cycle. It is also a key prerequisite for elaborating cost-effective national strategies to achieve the goals of the Paris Agreement. However, available estimates of CO2 fluxes for many regions of the world remain uncertain, despite significant recent progress in the remote sensing of terrestrial vegetation and atmospheric CO2. In this study, we investigate the feasibility of inferring reliable regional estimates of the net ecosystem exchange (NEE) using column-averaged dry-air mole fractions of CO2 (XCO2) retrieved from Orbiting Carbon Observatory-2 (OCO-2) observations as constraints on parameters of the widely used Vegetation Photosynthesis and Respiration model (VPRM), which predicts ecosystem fluxes based on vegetation indices derived from multispectral satellite imagery. We developed a regional-scale inverse modeling system that applies a Bayesian variational optimization algorithm to optimize parameters of VPRM coupled to the CHIMERE chemistry transport model and which involves a preliminary transformation of the input XCO2 data that reduces the impact of the CHIMERE boundary conditions on inversion results. We investigated the potential of our inversion system by applying it to a European region (that includes, in particular, the EU countries and the UK) for the warm season (May–September) of 2021. The inversion of the OCO-2 observations resulted in a major (more than threefold) reduction of the prior uncertainty in the regional NEE estimate. The posterior NEE estimate agrees with independent estimates provided by the CarbonTracker Europe High-Resolution (CTE-HR) system and the ensemble of the v10 OCO-2 model intercomparison (MIP) global inversions. We also found that the inversion improves the agreement of our simulations of XCO2 with retrievals from the Total Carbon Column Observing Network (TCCON). Our sensitivity test experiments using synthetic XCO2 data indicate that the posterior NEE estimate would remain reliable even if the actual regional CO2 fluxes drastically differed from their prior values. Furthermore, the posterior NEE estimate is found to be robust to strong biases and random uncertainties in the CHIMERE boundary conditions. Overall, this study suggests that our approach offers a reliable and relatively simple way to derive robust estimates of CO2 ecosystem fluxes from satellite XCO2 observations while enhancing the applicability of VPRM in regions where eddy covariance measurements of CO2 fluxes are scarce.

Long-term (2000–2020) global 0.05° continuous atmospheric carbon dioxide mapping combining OCO-2 observations and model simulations