Air Mole Fractions Of CO2 Research Articles

Mapping high-resolution XCO2 concentrations in China from 2015 to 2020 based on spatiotemporal ensemble learning model

High-resolution column-averaged dry air mole fraction of CO2 (XCO2) data is crucial for understanding the spatiotemporal patterns of XCO2 and for mitigating carbon emissions. Due to the limited scanning range of sensors and strict inversion conditions, satellite-retrieved XCO2 data are often significantly incomplete. Machine learning models are widely used to fill these gaps in satellite XCO2 data. However, the limitations of individual machine learning models and the complexity of the spatial distribution of XCO₂ mean that the accuracy of XCO2 predictions still needs improvement. In this study, a new spatiotemporal stacked ensemble learning model (STEL) was developed by combining random forest (RF), extremely randomized trees (ERT), extreme gradient boosting (XGBoost), optical gradient boosting (LightGBM), and categorical boosting (CatBoost) using the stacking ensemble learning methodology. Considering the spatiotemporal heterogeneity of XCO2, a novel spatiotemporal weighting feature was constructed as part of the model's input parameters. Finally, the XCO2 observed by Orbiting Carbon Observatory 2 (OCO-2) was reconstructed using STEL, and a monthly mean XCO2 dataset covering China from 2015 to 2020 was generated at a spatial resolution of 0.1°. The results show that STEL exhibits superior performance and generalization capabilities compared to individual machine-learning models. R2 RMSE and MAPE were 0.9624, 1.0023 ppm, and 0.1583 % on the test set, and 0.8970, 1.4213 ppm, and 0.2475 % for R2, RMSE, and MAPE in ground validation, respectively. In 10-fold cross-validation, STEL's RMSE was reduced by 9.52 % compared to the best-performing single model (RF). The spatiotemporal trend of CO2 in China from 2015 to 2020 was analyzed using STEL XCO2 data. The results indicate that this dataset accurately reflects the spatiotemporal heterogeneity of XCO2 distribution at a fine scale. Overall, XCO2 exhibited a spatiotemporal pattern of “high in the east and low in the west” and “high in spring and low in summer.” Except in summer, high XCO₂ values were mainly distributed in the North China Plain. XCO2 trends and hotspots showed considerable spatial variation. The Pearl River Delta and Yangtze River Delta urban agglomerations have the fastest XCO2 growth rates, and the distribution of XCO2 hotspots is consistent with the distribution of population and economic centers. In the sparsely populated northwest of China, XCO2 is growing rapidly due to increased thermal power generation and coal mining. XCO2 hotspots in Northwest China are mainly located in Xinjiang, Ningxia, and Inner Mongolia. The methodology and data presented are useful for further research on carbon emissions, carbon sinks, and climate change.

Comparisons of Greenhouse Gas Observation Satellite Performances Over Seoul Using a Portable Ground‐Based Spectrometer

AbstractSatellites provide global coverage for monitoring atmospheric greenhouse gases, crucial for understanding global climate dynamics. However, their temporal and spatial resolutions fall short in detecting urban‐scale variations. To enhance satellite reliability over urban areas, this study presents the first comprehensive analysis of long‐term observations of column‐averaged dry air mole fractions of CO2, CH4, and CO (XCO2, XCH4, XCO) using two ground‐based fourier transform infrared spectrometers, EM27/SUNs, in a megacity. With over 2 years of observations, our study shows that EM27/SUN measurements can effectively capture the daily and seasonal variability of XCO2, XCH4, and XCO over Seoul, a megacity with complex topography and various emission sources. In addition, we use the advantage of having multiple greenhouse gas satellites targeting Seoul to compare with the EM27/SUNs. Our study highlights the importance of EM27/SUN observations in Seoul to identify the need for improvements in satellites to monitor greenhouse gas behaviors and emissions in urban areas.

Improving urban CO2 spatial distribution modelling using multi-source data

Urban areas contribute approximately 70% of the total anthropogenic CO2 emissions, making them the primary focus of global carbon monitoring. However, accurate modelling of urban meteorology is challenging because of complex artificial landscapes. Reducing errors in urban meteorological simulation and improving the accuracy of modelling the spatial distribution of urban CO2 is of great significance for the “top-down” CO2 emissions estimation in urban areas. In this study, local urban datasets were constructed based on multiple data sources to overcome the limitation of insufficient information from a single data source. In the development of urban datasets, grid-by-grid data processing is realized, and the evaluation results show that the developed urban datasets are greatly improved compared with the default. The developed urban datasets were applied to WRF-Chem with 1-km spatial resolution, and the simulated column-averaged dry air mole fraction of CO2 (XCO2) was verified with the EM27/SUN observed data considering the slant observation path. The results show that urban datasets strongly influence the spatial distribution modelling of XCO2, depending on the state of the atmosphere near the surface, especially wind velocity. Accurate local urban datasets can effectively reduce the bias in urban XCO2 spatial distribution modelling and improve the capability of the atmospheric CO2 transport model in urban carbon emission estimation.

A nonlinear data-driven approach to bias correction of XCO2 for NASA's OCO-2 ACOS version 10

Abstract. Measurements of column-averaged dry air mole fraction of CO2 (termed XCO2) from the Orbiting Carbon Observatory-2 (OCO-2) contain systematic errors and regional-scale biases, often induced by forward model error or nonlinearity in the retrieval. Operationally, these biases are corrected for by a multiple linear regression model fit to co-retrieved variables that are highly correlated with XCO2 error. The operational bias correction is fit in tandem with a hand-tuned quality filter which limits error variance and reduces the regime of interaction between state variables and error to one that is largely linear. While the operational correction and filter are successful in reducing biases in retrievals, they do not allow for throughput or correction of data in which biases become nonlinear in predictors or features. In this paper, we demonstrate a clear improvement in the reduction in error variance over the operational correction by using a set of nonlinear machine learning models, one for land and one for ocean soundings. We further illustrate how the operational quality filter can be relaxed when used in conjunction with a nonlinear bias correction, which allows for an increase in sounding throughput by 14 % while maintaining the residual error in the operational correction. The method can readily be applied to future Atmospheric CO2 Observations from Space (ACOS) algorithm updates, to OCO-2's companion instrument OCO-3, and to other retrieved atmospheric state variables of interest.

Spatial and temporal variations in sea surface pCO2 and air-sea flux of CO2 in the Bering Sea revealed by satellite-based data during 2003–2019

The understanding of long-time-series variations in air-sea CO2 flux in the Bering Sea is critical, as it is the passage area from the North Pacific Ocean water to the Arctic. Here, a data-driven remote sensing retrieval method is constructed based on a large amount of underway partial pressure of CO2 (pCO2) data in the Bering Sea. After several experiments, a Gaussian process regression model with input parameters of sea surface temperature, sea surface height, mixed-layer depth, chlorophyll a concentration, dry air mole fractions of CO2, and bathymetry was selected. After validation with independent data, the root mean square error of pCO2 was< 24 μatm (R2 = 0.94) with satisfactory performance. Then, we reconstructed the sea surface pCO2 in the Bering Sea from 2003 to 2019 and estimated the corresponding air-sea CO2 fluxes. Significant seasonal variations were identified, with higher sea surface pCO2 in winter/spring than in summer/autumn in both the basin and shelf area. Semiquantitative analysis reveals that the Bering Sea is a non-temperature-dominated area with a mean temperature effect on pCO2 of 12.7 μatm and a mean non-temperature effect of −51.8 μatm. From 2003 to 2019, atmospheric pCO2 increased at a rate of 2.1 μatm yr−1, while sea surface pCO2 in the basin increased rapidly (2.8 μatm yr−1); thus, the CO2 emissions from the basin increased. However, the carbon sink in the continental shelf still continuously increased. The whole Bering Sea exhibited an increasing carbon sink with the area integral of air-sea CO2 fluxes increasing from 6 to 19 TgC over 17 years. Meanwhile, the seasonal amplitudes in pCO2 in the shelf area also increased, approaching 14 μatm per decade. The reaction of the continuously added CO2 in continental seawater reduced the ocean CO2 system capacity. This is the first study to present long-time-series satellite data with high resolution in the Bering Sea, which is beneficial for studying the changes in ocean ecosystems and carbon sink capacity.

Lagrangian inversion of anthropogenic CO2 emissions from Beijing using differential column measurements

We estimated CO2 emissions from the Beijing region of China using differential ground-based observations of the column-averaged dry air mole fractions of CO2 (; urban minus upwind background column observations) in winter (1 November 2020–1 March 2021). Beijing is one of the most world’s populated cities and the CO2 emissions from this region contain large uncertainties in different bottom-up anthropogenic inventories. Differential measurements are potentially able to capture urban signals over Beijing (3.46 ± 2.35 ppm). The simulated XCO2 enhancements were calculated () based on three emission inventories (the Open-source Data Inventory for Anthropogenic CO2 (ODIAC), Multiresolution Emission Inventory for China (MEIC) and Emissions Database for Global Atmospheric Research (EDGAR) datasets) for Beijing. The values based on the ODIAC dataset were much higher than the observations, whereas the values from the EDGAR dataset were much lower and the MEIC dataset was more consistent. We performed a Lagrangian inversion framework based on Bayesian theory. The average and uncertainty of a priori estimates (12.18 ± 8.0, 7.09 ± 7.5 and 3.53 ± 11.4 μmol (m2 s)−1) were optimized to the posterior emissions (9.44 ± 5.7, 7.13 ± 4.9 and 7.15 ± 5.7 μmol (m2 s)−1), suggesting that the three posterior estimates tended to converge to become more consistent, transport errors (especially the horizontal transport errors) and the spatially uneven corrections in Beijing were the main reason for the differences between the posterior estimates. Sensitivity tests suggested that the prescribed spatial and temporal structures affected up to about 12.9%, 4.9% and 20.8%, respectively, of the three inventories.

Influences of aerosols and thin cirrus clouds on GOSAT XCO2 and XCH4 using Total Carbon Column Observing Network, sky radiometer, and lidar data

ABSTRACT The column-averaged, dry air mole fractions of CO2 and CH4 (XCO2 and XCH4, respectively) were retrieved from short-wavelength infrared (SWIR) spectra observed by the Greenhouse gases Observing SATellite (GOSAT). Continuous measurements of SWIR spectra have been made via GOSAT since 2009, but there has been insufficient investigation of the effects of cirrus clouds and aerosols on the observations. In this work, we investigated the influences of aerosols and cirrus clouds on the differences between GOSAT observations and Total Carbon Column Observing Network (TCCON) data for XCO2 and XCH4 (ΔXCO2 and ΔXCH4) at three sites: Tsukuba and Saga in Japan, and Lauder in New Zealand. We used aerosol optical thickness (AOT), Angstrom exponents (AEs), and single scattering albedo (SSA), all obtained from sky radiometer observations, as well as vertical profiles of aerosols and thin cirrus clouds from lidar observations. Matchups were performed within ±0.1° latitude/longitude rectangular areas of each TCCON site, and within 30 min of the GOSAT overpass time. The results show a negative slope between ΔXCO2 and AOT at 500 nm determined from sky radiometer data at Tsukuba and Saga. The GOSAT XCO2 values tended to be lowered in the presence of cirrus clouds and dense boundary-layer aerosols. Moreover, a significant negative ΔXCO2 was observed at times of large AOTs that resulted from dust-like events. At Lauder, ΔXCO2 was negatively correlated with the AOT at 500 nm, although the AOT at this site was generally small. The mean ± standard deviation for ΔXCO2 and ΔXCH4 at Lauder are −0.80 ± 1.83 (ppm) and −5.27 ± 10.79 (ppb) with correlation coefficients r between GOSAT and TCCON of 0.94 and 0.83, respectively. Both ΔXCO2 and ΔXCH4 were significantly and negatively correlated with the AOT during Sep-Oct-Nov. In addition, stratospheric aerosols caused large negative biases of ΔXCO2 and ΔXCH4 at Lauder despite the small stratospheric aerosol optical depth at that site.

On the Detection of COVID-Driven Changes in Atmospheric Carbon Dioxide.

We assess the detectability of COVID‐like emissions reductions in global atmospheric CO2 concentrations using a suite of large ensembles conducted with an Earth system model. We find a unique fingerprint of COVID in the simulated growth rate of CO2 sampled at the locations of surface measurement sites. Negative anomalies in growth rates persist from January 2020 through December 2021, reaching a maximum in February 2021. However, this fingerprint is not formally detectable unless we force the model with unrealistically large emissions reductions (2 or 4 times the observed reductions). Internal variability and carbon‐concentration feedbacks obscure the detectability of short‐term emission reductions in atmospheric CO2. COVID‐driven changes in the simulated, column‐averaged dry air mole fractions of CO2 are eclipsed by large internal variability. Carbon‐concentration feedbacks begin to operate almost immediately after the emissions reduction; these feedbacks reduce the emissions‐driven signal in the atmosphere carbon reservoir and further confound signal detection.

Spatial distributions of <i>X</i><sub>CO<sub>2</sub></sub> seasonal cycle amplitude and phase over northern high-latitude regions

Abstract. Satellite-based observations of atmospheric carbon dioxide (CO2) provide measurements in remote regions, such as the biologically sensitive but undersampled northern high latitudes, and are progressing toward true global data coverage. Recent improvements in satellite retrievals of total column-averaged dry air mole fractions of CO2 (XCO2) from the NASA Orbiting Carbon Observatory 2 (OCO-2) have allowed for unprecedented data coverage of northern high-latitude regions, while maintaining acceptable accuracy and consistency relative to ground-based observations, and finally providing sufficient data in spring and autumn for analysis of satellite-observed XCO2 seasonal cycles across a majority of terrestrial northern high-latitude regions. Here, we present an analysis of XCO2 seasonal cycles calculated from OCO-2 data for temperate, boreal, and tundra regions, subdivided into 5∘ latitude by 20∘ longitude zones. We quantify the seasonal cycle amplitudes (SCAs) and the annual half drawdown day (HDD). OCO-2 SCAs are in good agreement with ground-based observations at five high-latitude sites, and OCO-2 SCAs show very close agreement with SCAs calculated for model estimates of XCO2 from the Copernicus Atmosphere Monitoring Services (CAMS) global inversion-optimized greenhouse gas flux model v19r1 and the CarbonTracker2019 model (CT2019B). Model estimates of XCO2 from the GEOS-Chem CO2 simulation version 12.7.2 with underlying biospheric fluxes from CarbonTracker2019 (GC-CT2019) yield SCAs of larger magnitude and spread over a larger range than those from CAMS, CT2019B, or OCO-2; however, GC-CT2019 SCAs still exhibit a very similar spatial distribution across northern high-latitude regions to that from CAMS, CT2019B, and OCO-2. Zones in the Asian boreal forest were found to have exceptionally large SCA and early HDD, and both OCO-2 data and model estimates yield a distinct longitudinal gradient of increasing SCA from west to east across the Eurasian continent. In northern high-latitude regions, spanning latitudes from 47 to 72∘ N, longitudinal gradients in both SCA and HDD are at least as pronounced as latitudinal gradients, suggesting a role for global atmospheric transport patterns in defining spatial distributions of XCO2 seasonality across these regions. GEOS-Chem surface contact tracers show that the largest XCO2 SCAs occur in areas with the greatest contact with land surfaces, integrated over 15–30 d. The correlation of XCO2 SCA with these land surface contact tracers is stronger than the correlation of XCO2 SCA with the SCA of CO2 fluxes or the total annual CO2 flux within each 5∘ latitude by 20∘ longitude zone. This indicates that accumulation of terrestrial CO2 flux during atmospheric transport is a major driver of regional variations in XCO2 SCA.

GFIT3: a full physics retrieval algorithm for remote sensing of greenhouse gases in the presence of aerosols

Abstract. Remote sensing of greenhouse gases (GHGs) in cities, where high GHG emissions are typically associated with heavy aerosol loading, is challenging due to retrieval uncertainties caused by the imperfect characterization of scattering by aerosols. We investigate this problem by developing GFIT3, a full physics algorithm to retrieve GHGs (CO2 and CH4) by accounting for aerosol scattering effects in polluted urban atmospheres. In particular, the algorithm includes coarse- (including sea salt and dust) and fine- (including organic carbon, black carbon, and sulfate) mode aerosols in the radiative transfer model. The performance of GFIT3 is assessed using high-spectral-resolution observations over the Los Angeles (LA) megacity made by the California Laboratory for Atmospheric Remote Sensing Fourier transform spectrometer (CLARS-FTS). CLARS-FTS is located on Mt. Wilson, California, at 1.67 km a.s.l. overlooking the LA Basin, and it makes observations of reflected sunlight in the near-infrared spectral range. The first set of evaluations are performed by conducting retrieval experiments using synthetic spectra. We find that errors in the retrievals of column-averaged dry air mole fractions of CO2 (XCO2) and CH4 (XCH4) due to uncertainties in the aerosol optical properties and atmospheric a priori profiles are less than 1 % on average. This indicates that atmospheric scattering does not induce a large bias in the retrievals when the aerosols are properly characterized. The methodology is then further evaluated by comparing GHG retrievals using GFIT3 with those obtained from the CLARS-GFIT algorithm (used for currently operational CLARS retrievals) that does not account for aerosol scattering. We find a significant correlation between retrieval bias and aerosol optical depth (AOD). A comparison of GFIT3 AOD retrievals with collocated ground-based observations from AErosol RObotic NETwork (AERONET) shows that the developed algorithm produces very accurate results, with biases in AOD estimates of about 0.02. Finally, we assess the uncertainty in the widely used tracer–tracer ratio method to obtain CH4 emissions based on CO2 emissions and find that using the CH4/CO2 ratio effectively cancels out biases due to aerosol scattering. Overall, this study of applying GFIT3 to CLARS-FTS observations improves our understanding of the impact of aerosol scattering on the remote sensing of GHGs in polluted urban atmospheric environments. GHG retrievals from CLARS-FTS are potentially complementary to existing ground-based and spaceborne observations to monitor anthropogenic GHG fluxes in megacities.

CO2 Flux over the Contiguous United States in 2016 Inverted by WRF-Chem/DART from OCO-2 XCO2 Retrievals

High spatial resolution carbon dioxide (CO2) flux inversion systems are needed to support the global stocktake required by the Paris Agreement and to complement the bottom-up emission inventories. Based on the work of Zhang, a regional CO2 flux inversion system capable of assimilating the column-averaged dry air mole fractions of CO2 (XCO2) retrieved from Orbiting Carbon Observatory-2 (OCO-2) observations had been developed. To evaluate the system, under the constraints of the initial state and boundary conditions extracted from the CarbonTracker 2017 product (CT2017), the annual CO2 flux over the contiguous United States in 2016 was inverted (1.08 Pg C yr−1) and compared with the corresponding posterior CO2 fluxes extracted from OCO-2 model intercomparison project (OCO-2 MIP) (mean: 0.76 Pg C yr−1, standard deviation: 0.29 Pg C yr−1, 9 models in total) and CT2017 (1.19 Pg C yr−1). The uncertainty of the inverted CO2 flux was reduced by 14.71% compared to the prior flux. The annual mean XCO2 estimated by the inversion system was 403.67 ppm, which was 0.11 ppm smaller than the result (403.78 ppm) simulated by a parallel experiment without assimilating the OCO-2 retrievals and closer to the result of CT2017 (403.29 ppm). Independent CO2 flux and concentration measurements from towers, aircraft, and Total Carbon Column Observing Network (TCCON) were used to evaluate the results. Mean bias error (MBE) between the inverted CO2 flux and flux measurements was 0.73 g C m−2 d−1, was reduced by 22.34% and 28.43% compared to those of the prior flux and CT2017, respectively. MBEs between the CO2 concentrations estimated by the inversion system and concentration measurements from TCCON, towers, and aircraft were reduced by 52.78%, 96.45%, and 75%, respectively, compared to those of the parallel experiment. The experiment proved that CO2 emission hotspots indicated by the inverted annual CO2 flux with a relatively high spatial resolution of 50 km consisted well with the locations of most major metropolitan/urban areas in the contiguous United States, which demonstrated the potential of combing satellite observations with high spatial resolution CO2 flux inversion system in supporting the global stocktake.

XCO<sub>2</sub> estimates from the OCO-2 measurements using a neural network approach

Abstract. The Orbiting Carbon Observatory (OCO-2) instrument measures high-resolution spectra of the sun's radiance reflected at the earth's surface or scattered in the atmosphere. These spectra are used to estimate the column-averaged dry air mole fraction of CO2 (XCO2) and the surface pressure. The official retrieval algorithm (NASA's Atmospheric CO2 Observations from Space retrievals, ACOS) is a full-physics algorithm and has been extensively evaluated. Here we propose an alternative approach based on an artificial neural network (NN) technique. For training and evaluation, we use as reference estimates (i) the surface pressures from a numerical weather model and (ii) the XCO2 derived from an atmospheric transport simulation constrained by surface air-sample measurements of CO2. The NN is trained here using real measurements acquired in nadir mode on cloud-free scenes during even-numbered months and is then evaluated against similar observations during odd-numbered months. The evaluation indicates that the NN retrieves the surface pressure with a root-mean-square error better than 3 hPa and XCO2 with a 1σ precision of 0.8 ppm. The statistics indicate that the NN trained with a representative set of data allows excellent accuracy that is slightly better than that of the full-physics algorithm. An evaluation against reference spectrophotometer XCO2 retrievals indicates similar accuracy for the NN and ACOS estimates, with a skill that varies among the various stations. The NN–model differences show spatiotemporal structures that indicate a potential for improving our knowledge of CO2 fluxes. We finally discuss the pros and cons of using this NN approach for the processing of the data from OCO-2 or other space missions.

Quality controls, bias, and seasonality of CO<sub>2</sub> columns in the boreal forest with Orbiting Carbon Observatory-2, Total Carbon Column Observing Network, and EM27/SUN measurements

Abstract. Seasonal CO2 exchange in the boreal forest plays an important role in the global carbon budget and in driving interannual variability in seasonal cycles of atmospheric CO2. Satellite-based observations from polar orbiting satellites like the Orbiting Carbon Observatory-2 (OCO-2) offer an opportunity to characterize boreal forest seasonal cycles across longitudes with a spatially and temporally rich data set, but data quality controls and biases still require vetting at high latitudes. With the objective of improving data availability at northern, terrestrial high latitudes, this study evaluates quality control methods and biases of OCO-2 retrievals of atmospheric column-averaged dry air mole fractions of CO2 (XCO2) in boreal forest regions. In addition to the standard quality control (QC) filters recommended for the Atmospheric Carbon Observations from Space (ACOS) B8 (B8 QC) and ACOS B9 (B9 QC) OCO-2 retrievals, a third set of quality control filters were specifically tailored to boreal forest observations (boreal QC) with the goal of increasing data availability at high latitudes without sacrificing data quality. Ground-based reference measurements of XCO2 include observations from two sites in the Total Carbon Column Observing Network (TCCON) at East Trout Lake, Saskatchewan, Canada, and Sodankylä, Finland. OCO-2 retrievals were also compared to ground-based observations from two Bruker EM27/SUN Fourier transform infrared spectrometers (FTSs) at Fairbanks, Alaska, USA. The EM27/SUN spectrometers that were deployed in Fairbanks were carefully monitored for instrument performance and were bias corrected to TCCON using observations at the Caltech TCCON site. The B9 QC were found to pass approximately twice as many OCO-2 retrievals over land north of 50∘ N than the B8 QC, and the boreal QC were found to pass approximately twice as many retrievals in May, August, and September as the B9 QC. While boreal QC results in a substantial increase in passable retrievals, this is accompanied by increases in the standard deviations in biases at boreal forest sites from ∼1.4 parts per million (ppm) with B9 QC to ∼1.6 ppm with boreal QC. Total average biases for coincident OCO-2 retrievals at the three sites considered did not consistently increase or decrease with different QC methods, and instead, responses to changes in QC varied according to site and satellite viewing geometries. Regardless of the quality control method used, seasonal variability in biases was observed, and this variability was more pronounced at Sodankylä and East Trout Lake than at Fairbanks. Long-term coincident observations from TCCON, EM27/SUN, and satellites from multiple locations would be necessary to determine whether the reduced seasonal variability in bias at Fairbanks is due to geography or instrumentation. Monthly average biases generally varied between −1 and +1 ppm at the three sites considered, with more negative biases in spring (March, April, and May – MAM) and autumn (September and October – SO) but more positive biases in the summer months (June, July, and August – JJA). Monthly standard deviations in biases ranged from approximately 1.0 to 2.0 ppm and did not exhibit strong seasonal dependence, apart from exceptionally high standard deviation observed with all three QC methods at Sodankylä in June. There was no evidence found to suggest that seasonal variability in bias is a direct result of air mass dependence in ground-based retrievals or of proximity bias from coincidence criteria, but there were a number of retrieval parameters used as quality control filters that exhibit seasonality and could contribute to seasonal dependence in OCO-2 bias. Furthermore, it was found that OCO-2 retrievals of XCO2 without the standard OCO-2 bias correction exhibit almost no perceptible seasonal dependence in average monthly bias at these boreal forest sites, suggesting that seasonal variability in bias is introduced by the bias correction. Overall, we found that modified quality controls can allow for significant increases in passable OCO-2 retrievals with only marginal compromises in data quality, but seasonal dependence in biases still warrants further exploration.

Specific patterns of XCO2 observed by GOSAT during 2009–2016 and assessed with model simulations over China

Spatiotemporal patterns of column-averaged dry air mole fraction of CO2 (XCO2) have not been well characterized on a regional scale due to limitations in data availability and precision. This paper addresses these issues by examining such patterns in China using the long-term mapping XCO2 dataset (2009–2016) derived from the Greenhouse gases Observing SATellite (GOSAT). XCO2 simulations are also constructed using the high-resolution nested-grid GEOS-Chem model. The following results are found: Firstly, the correlation coefficient between the anthropogenic emissions and XCO2 spatial distribution is nearly zero in summer but up to 0.32 in autumn. Secondly, on average, XCO2 increases by 2.08 ppm every year from 2010 to 2015, with a sharp increase of 2.6 ppm in 2013. Lastly, in the analysis of three typical regions, the GOSAT XCO2 time series is in better agreement with the GEOS-Chem simulation of XCO2 in the Taklimakan Desert region (the least difference with bias 0.65±0.78 ppm), compared with the northern urban agglomeration region (−1.3±1.2 ppm) and the northeastern forest region (−1.4±1.4 ppm). The results are likely attributable to uncertainty in both the satellite-retrieved XCO2 data and the model simulation data.

Diagnosing spatial error structures in CO<sub>2</sub> mole fractions and XCO<sub>2</sub> column mole fractions from atmospheric transport

Abstract. Atmospheric inversions inform us about the magnitude and variations of greenhouse gas (GHG) sources and sinks from global to local scales. Deployment of observing systems such as spaceborne sensors and ground-based instruments distributed around the globe has started to offer an unprecedented amount of information to estimate surface exchanges of GHG at finer spatial and temporal scales. However, all inversion methods still rely on imperfect atmospheric transport models whose error structures directly affect the inverse estimates of GHG fluxes. The impact of spatial error structures on the retrieved fluxes increase concurrently with the density of the available measurements. In this study, we diagnose the spatial structures due to transport model errors affecting modeled in situ carbon dioxide (CO2) mole fractions and total-column dry air mole fractions of CO2 (XCO2). We implement a cost-effective filtering technique recently developed in the meteorological data assimilation community to describe spatial error structures using a small-size ensemble. This technique can enable ensemble-based error analysis for multiyear inversions of sources and sinks. The removal of noisy structures due to sampling errors in our small-size ensembles is evaluated by comparison with larger-size ensembles. A second filtering approach for error covariances is proposed (Wiener filter), producing similar results over the 1-month simulation period compared to a Schur filter. Based on a comparison to a reference 25-member calibrated ensemble, we demonstrate that error variances and spatial error correlation structures are recoverable from small-size ensembles of about 8 to 10 members, improving the representation of transport errors in mesoscale inversions of CO2 fluxes. Moreover, error variances of in situ near-surface and free-tropospheric CO2 mole fractions differ significantly from total-column XCO2 error variances. We conclude that error variances for remote-sensing observations need to be quantified independently of in situ CO2 mole fractions due to the complexity of spatial error structures at different altitudes. However, we show the potential use of meteorological error structures such as the mean horizontal wind speed, directly available from ensemble prediction systems, to approximate spatial error correlations of in situ CO2 mole fractions, with similarities in seasonal variations and characteristic error length scales.

A Comparative Analysis of Anthropogenic CO2 Emissions at City Level Using OCO‐2 Observations: A Global Perspective

AbstractSatellite observations of anthropogenic carbon dioxide (CO2) emissions within urban settings offer unique potential to understand carbon sources and sinks and evaluate carbon mitigation strategies. Despite availability of column‐averaged dry air mole fraction of CO2 ( ) from Orbiting Carbon Observatory‐2 (OCO‐2), temporal variations of and their drivers in cities remain poorly understood due to inconsistent definitions of urban extent, diverse urban forms, and unresolved impacts of urban vegetation on carbon fluxes. To this end, this study revealed that OCO‐2 measurements from 2014 to 2018 exhibited statistically significant seasonal and trend components for each city. A correlation analysis suggested a weak association between trends and fossil fuel CO2 emissions ( ) trends but a close relationship between yearly average and trends. Vegetation abundance exhibited a negative relationship with the seasonality, though it only explained 21% of the variance. No statistically significant relationship between urban morphological factors (areal extent, complexity, and compactness) and temporal components was observed. However, urban morphological factors had a close relationship with the total amount of aggregated over the study period. Thus, it was speculated that urban morphological factors exerted their influence on through fossil fuel consumption. When only cities of high normalized difference vegetation index seasonality were used, statistically significant correlation coefficients between urban morphological factors and winter/summer averaged measurements were found. The variations of these correlation coefficients between leaf‐on and leaf‐off seasons stress the important role that urban trees play in mitigating carbon emissions in cities.

An Assessment of Anthropogenic CO₂ Emissions by Satellite-Based Observations in China.

Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas and its concentration in atmosphere has been increasing rapidly due to the increase of anthropogenic CO2 emissions. Quantifying anthropogenic CO2 emissions is essential to evaluate the measures for mitigating climate change. Satellite-based measurements of greenhouse gases greatly advance the way of monitoring atmospheric CO2 concentration. In this study, we propose an approach for estimating anthropogenic CO2 emissions by an artificial neural network using column-average dry air mole fraction of CO2 (XCO2) derived from observations of Greenhouse gases Observing SATellite (GOSAT) in China. First, we use annual XCO2 anomalies (dXCO2) derived from XCO2 and anthropogenic emission data during 2010–2014 as the training dataset to build a General Regression Neural Network (GRNN) model. Second, applying the built model to annual dXCO2 in 2015, we estimate the corresponding emission and verify them using ODIAC emission. As a results, the estimated emissions significantly demonstrate positive correlation with that of ODIAC CO2 emissions especially in the areas with high anthropogenic CO2 emissions. Our results indicate that XCO2 data from satellite observations can be applied in estimating anthropogenic CO2 emissions at regional scale by the machine learning. This developed method can estimate carbon emission inventory in a data-driven way. In particular, it is expected that the estimation accuracy can be further improved when combined with other data sources, related CO2 uptake and emissions, from satellite observations.

Correcting atmospheric CO<sub>2</sub> and CH<sub>4</sub> mole fractions obtained with Picarro analyzers for sensitivity of cavity pressure to water vapor

Abstract. Measurements of dry air mole fractions of atmospheric greenhouse gases are used in inverse models of atmospheric tracer transport to quantify their sources and sinks. The measurements have to be calibrated to a common scale to avoid bias in the inferred fluxes. For this purpose, the World Meteorological Organization (WMO) has set requirements for the interlaboratory compatibility of atmospheric greenhouse gas (GHG) measurements. A widely used series of devices for these measurements are the GHG analyzers manufactured by Picarro, Inc. These are often operated in humid air, and the effects of water vapor are corrected for in post-processing. Here, we report on rarely detected and previously unexplained biases of the water correction method for CO2 and CH4 in the literature. They are largest at water vapor mole fractions below 0.5 % H2O, which were undersampled in previous studies, and can therefore affect measurements obtained in humid air. Setups that dry sample air using Nafion membranes may be affected as well if there are differences in residual water vapor levels between sample and calibration air. The biases are caused by a sensitivity of the pressure in the measurement cavity to water vapor. We correct these biases by modifying the water correction method from the literature. Our method relies on experiments that maintain stable water vapor levels to allow equilibration of cavity pressure. In our experiments with the commonly used droplet method, this requirement was not fulfilled. Correcting CO2 measurements proved challenging, presumably because of our humidification method. Open questions pertain to differences among analyzers and variability over time. In our experiments, the biases amounted to considerable fractions of the WMO interlaboratory compatibility goals. Since measurements of dry air mole fractions of CO2 and CH4 are also subject to other uncertainties, correcting the cavity pressure-related biases helps keep the overall accuracy of measurements obtained with Picarro GHG analyzers in humid and potentially in Nafion-dried air within the WMO goals.

Regional uncertainty of GOSAT XCO<sub>2</sub> retrievals in China: quantification and attribution

Abstract. The regional uncertainty of the column-averaged dry air mole fraction of CO2 (XCO2) retrieved using different algorithms from the Greenhouse gases Observing SATellite (GOSAT) and its attribution are still not well understood. This paper investigates the regional performance of XCO2 within a latitude band of 37–42° N segmented into 8 cells in a grid of 5° from west to east (80–120° E) in China, where typical land surface types and geographic conditions exist. The former includes desert, grassland and built-up areas mixed with cropland; and the latter includes anthropogenic emissions that change from small to large from west to east, including those from the megacity of Beijing. For these specific cells, we evaluate the regional uncertainty of GOSAT XCO2 retrievals by quantifying and attributing the consistency of XCO2 retrievals from four algorithms (ACOS, NIES, OCFP and SRFP) by intercomparison. These retrievals are then specifically compared with simulated XCO2 from the high-resolution nested model in East Asia of the Goddard Earth Observing System 3-D chemical transport model (GEOS-Chem). We also introduce the anthropogenic CO2 emissions data generated from the investigation of surface emitting point sources that was conducted by the Ministry of Environmental Protection of China to GEOS-Chem simulations of XCO2 over the Chinese mainland. The results indicate that (1) regionally, the four algorithms demonstrate smaller absolute biases of 0.7–1.1 ppm in eastern cells, which are covered by built-up areas mixed with cropland with intensive anthropogenic emissions, than those in the western desert cells (1.0–1.6 ppm) with a high-brightness surface from the pairwise comparison results of XCO2 retrievals. (2) Compared with XCO2 simulated by GEOS-Chem (GEOS-XCO2), the XCO2 values from ACOS and SRFP have better agreement, while values from OCFP are the least consistent with GEOS-XCO2. (3) Viewing attributions of XCO2 in the spatio-temporal pattern, ACOS and SRFP demonstrate similar patterns, while OCFP is largely different from the others. In conclusion, the discrepancy in the four algorithms is the smallest in eastern cells in the study area, where the megacity of Beijing is located and where there are strong anthropogenic CO2 emissions, which implies that XCO2 from satellite observations could be reliably applied in the assessment of atmospheric CO2 enhancements induced by anthropogenic CO2 emissions. The large inconsistency among the four algorithms presented in western deserts which displays a high albedo and dust aerosols, moreover, demonstrates that further improvement is still necessary in such regions, even though many algorithms have endeavored to minimize the effects of aerosols scattering and surface albedo.

Investigating the performance of a greenhouse gas observatory in Hefei, China

Abstract. A ground-based high-resolution Fourier transform spectrometer (FTS) station has been established in Hefei, China to remotely measure CO2, CO and other greenhouse gases based on near-infrared solar absorption spectra. Total column measurements of atmospheric CO2 and CO were successfully obtained from July 2014 to April 2016. The spectra collected with an InSb detector in the first year were compared with those collected by an InGaAs detector from July 2015, demonstrating that InGaAs spectra have better signal-to-noise ratios and rms of spectral fitting residuals relative to InSb spectra. Consequently, the measurement precision of the retrieved XCO2 and XCO for InGaAs spectra is superior to InSb spectra, with about 0.04 and 0.09 % for XCO2, and 1.07 and 2.00 % for XCO within clear-sky days respectively. Daily and monthly averages of column-averaged dry air mole fraction of CO2 show a clear seasonal cycle, while the daily and monthly averages of XCO displayed no seasonal variation. Also, we analysed the relationship of the anomalies of XCO and XCO2, found that the correlations are only observable for individual days, and the data under different prevailing wind conditions during the observations displayed weak correlation. The observations based on the high-resolution FTS were also compared with the temporally coinciding measurements taken with a low-resolution solar FTS instrument, the EM27/SUN. Ratioing the daily averaged XCO2 of EM27 and FTS gives an overall calibration factor of 0.996 ± 0.001. We also compared ground-based observations from the Tsukuba TCCON station with our observations, the results showing that the variation in phase and seasonal amplitude of XCO2 are similar to our results, but the variation of XCO in Tsukuba is quite different from our data in Hefei. To further evaluate our retrieved data, we made use of satellite measurements. The direct comparison of our observations with the Greenhouse Gases Observing Satellite (GOSAT) data shows good agreement of daily median XCO2, with a bias of −0.52 ppm and standard deviation of 1.63 ppm. The correlation coefficient (R2) is 0.79 for daily median XCO2 between our FTS and GOSAT observations. Daily median Orbiting Carbon Observatory 2 (OCO-2) data produce a positive bias of 0.81 ppm and standard deviation of 1.73 ppm relative to our ground-based data. Our daily median XCO2 also show strong correlation with OCO-2 data, with correlation coefficient (R2) of 0.83. Although there were a limited number of data during the observations due to instrument downtime and adverse weather, the results confirm the suitability of the observatory for ground-based long-term measurements of greenhouse gases with high precision and accuracy, and fulfil the requirements of the Total Carbon Column Observing Network (TCCON).