Inversion System Research Articles

Bayesian ensemble machine learning-assisted deterministic and stochastic groundwater DNAPL source inversion with a homotopy-based progressive search mechanism

A hyperheuristic homotopy algorithm (HH-HA) and a homotopy-based swarm intelligence algorithm for parallel stochastic search are proposed to improve the search ergodicity and stability of deterministic and stochastic inversion approaches, respectively, for the identification of dense nonaqueous phase liquid (DNAPL) source characteristics and contaminant transport parameters in groundwater. Meanwhile, a Bayesian ensemble machine learning (BEML) method for numerical simulation surrogate modeling is proposed to enhance the learning and generalization capability, while significantly reducing the computational cost of repetitive CPU-demanding likelihood evaluations in inversion iterations. Two cases are presented for the verification and application of the proposed inverse modeling methods. The results show that the BEML surrogate model is powerful in approximating the numerical model outputs, reducing the mean relative errors of the contaminant concentration and pressure head predictions to 1.18% and 1.29%, respectively. The homotopy-based progressive search mechanism is highly effective in achieving more accurate identification values for deterministic inversion and a more reliable posterior distribution for stochastic inversion. The HH-HA shows stable performance, approaching the global optimum in wide areas and reducing the mean relative error of identification from 11.95% to 4.11%. The homotopy-based parallel stochastic search lines adequately cover the search space tracking the change of homotopy parameter, and the mean relative error of point estimation is reduced to 2.03%. Though the deterministic inversion approach can provide intuitive identification values, the reliability of the inversion outputs cannot be quantified. In contrast, the proposed stochastic inversion system is of great significance for providing decision makers with comprehensive reference information.

Structural and tectonic assessment of the western Huincul High, Neuquén Basin (Argentina)—The role of structural inheritance and mechanical stratigraphy in inversion systems

AbstractThe Neuquén Basin is a major Mesozoic sedimentary depocentre located in the retroarc foreland of the Argentinian Andes. The basin hosts world renowned inversion systems that have been the target of georesource exploration for the last three decades. The Huincul High is a structurally and economically prominent ca. 270 km long, E–W trending feature that formed by the accretion of exotic Palaeozoic terranes, influencing subsequent Mesozoic deformation in the basin. Exploration in the Huincul High has been mainly focused on the shallow part of the inversion structures leaving a limited understanding of the deep structural architecture and early tectonic evolution, particularly in the western reaches of the high. This research reveals that Late Triassic extensional faulting was followed by widespread thermal subsidence in the Early Jurassic, as shown by the occurrence of an extensive ca. 60 km long, ca. 20 km wide, NE–SW‐trending, central depocentre. In the Early Jurassic, as contraction ensued across this regional sag basin, atypical inversion geometries were developed. These exhibited prominent thickening in the hanging‐wall and, strikingly, in the footwall of reactivated faults. The style of inversion was also markedly influenced by the mechanically weak stratigraphy of the thick, Lower Jurassic, Los Molles formation that promoted broad inversion folding, inhibited shortcut fault creation, and decoupled post inversion deformation from earlier faulting. Quantitative fault analysis suggests that the reactivated faults originated during the Late Triassic extensional phase as separate ca. 10 km long fault segments. The analysis also indicates that segmentation of extensional faults, as well as their orientation to the later contractional vector (SH), spatially dictated style and magnitude of inversion. This research highlights the critical role played by structural inheritance and mechanical stratigraphy in the development of inversion in the Neuquén Basin, which might be of relevance for characterising inversion systems elsewhere. This research also proposes an evolutionary model for the western reaches of the Huincul High that suggests crustal weakening and thermal sag in the Early Jurassic. Moreover, the model highlights a previously unknown late Early Cretaceous transtensional phase that overprints the main Early Jurassic–Early Cretaceous inversion.

Evaluating the consistency between OCO-2 and OCO-3 XCO2 estimates derived from the NASA ACOS version 10 retrieval algorithm

Abstract. The version 10 (v10) Atmospheric Carbon Observations from Space (ACOS) Level 2 full-physics (L2FP) retrieval algorithm has been applied to multiyear records of observations from NASA's Orbiting Carbon Observatory 2 and 3 sensors (OCO-2 and OCO-3, respectively) to provide estimates of the carbon dioxide (CO2) column-averaged dry-air mole fraction (XCO2). In this study, a number of improvements to the ACOS v10 L2FP algorithm are described. The post-processing quality filtering and bias correction of the XCO2 estimates against multiple truth proxies are also discussed. The OCO v10 data volumes and XCO2 estimates from the two sensors for the time period of August 2019 through February 2022 are compared, highlighting differences in spatiotemporal sampling but demonstrating broad agreement between the two sensors where they overlap in time and space. A number of evaluation sources applied to both sensors suggest they are broadly similar in data and error characteristics. Mean OCO-3 differences relative to collocated OCO-2 data are approximately 0.2 and −0.3 ppm for land and ocean observations, respectively. Comparison of XCO2 estimates to collocated Total Carbon Column Observing Network (TCCON) measurements shows root mean squared errors (RMSEs) of approximately 0.8 and 0.9 ppm for OCO-2 and OCO-3, respectively. An evaluation against XCO2 fields derived from atmospheric inversion systems that assimilated only near-surface CO2 observations, i.e., did not assimilate satellite CO2 measurements, yielded RMSEs of 1.0 and 1.1 ppm for OCO-2 and OCO-3, respectively. Evaluation of uncertainties in XCO2 over small areas, as well as XCO2 biases across land–ocean crossings, also indicates similar behavior in the error characteristics of both sensors. Taken together, these results demonstrate a broad consistency of OCO-2 and OCO-3 XCO2 measurements, suggesting they may be used together for scientific analyses.

The carbon sink in China as seen from GOSAT with a regional inversion system based on the Community Multi-scale Air Quality (CMAQ) and ensemble Kalman smoother (EnKS)

Abstract. Top-down inversions of China's terrestrial carbon sink are known to be uncertain because of errors related to the relatively coarse resolution of global transport models and the sparseness of in situ observations. Taking advantage of regional chemistry transport models for mesoscale simulation and spaceborne sensors for spatial coverage, the Greenhouse Gases Observing Satellite (GOSAT) retrievals of column-mean dry mole fraction of carbon dioxide (XCO2) were introduced in the Models-3 (a flexible software framework) Community Multi-scale Air Quality (CMAQ) and ensemble Kalman smoother (EnKS)-based regional inversion system to constrain China's biosphere sink at a spatiotemporal resolution of 64 km and 1 h. In general, the annual, monthly, and daily variation in biosphere flux was reliably delivered, attributable to the novel flux forecast model, reasonable CMAQ background simulation, well-designed observational operator, and Joint Data Assimilation Scheme (JDAS) of CO2 concentrations and natural fluxes. The size of the assimilated biosphere sink in China was −0.47 Pg C yr−1, which was comparable with most global estimates (i.e., −0.27 to −0.68 Pg C yr−1). Furthermore, the seasonal patterns were recalibrated well, with a growing season that shifted earlier in the year over central and south China. Moreover, the provincial-scale biosphere flux was re-estimated, and the difference between the a posteriori and a priori flux ranged from −7.03 Tg C yr−1 in Heilongjiang to 2.95 Tg C yr−1 in Shandong. Additionally, better performance of the a posteriori flux in contrast to the a priori flux was statistically detectable when the simulation was fitted to independent observations, indicating sufficient to robustly constrained state variables and improved fluxes estimation. This study serves as a basis for future fine-scale top-down carbon assimilation.

Construction and Application of a Regional Kilometer-Scale Carbon Source and Sink Assimilation Inversion System (CCMVS-R)

CO2 is one of the most important greenhouse gases (GHGs) in the earth’s atmosphere. Since the industrial era, anthropogenic activities have emitted excessive quantities of GHGs into the atmosphere, resulting in climate warming since the 1950s and leading to an increased frequency of extreme weather and climate events. In 2020, China committed to striving for carbon neutrality by 2060. This commitment and China’s consequent actions will result in significant changes in global and regional anthropogenic carbon emissions and therefore require timely, comprehensive, and objective monitoring and verification support (MVS) systems. The MVS approach relies on the top-down assimilation and inversion of atmospheric CO2 concentrations, as recommended by the Intergovernmental Panel on Climate Change (IPCC) Inventory Guidelines in 2019. However, the regional high-resolution assimilation and inversion method is still in its initial stage of development. Here, we have constructed an inverse system for carbon sources and sinks at the kilometer level by coupling proper orthogonal decomposition (POD) with four-dimensional variational (4DVar) data assimilation based on the weather research and forecasting–greenhouse gas (WRF-GHG) model. Our China Carbon Monitoring and Verification Support at the Regional level (CCMVS-R) system can continuously assimilate information on atmospheric CO2 and other related information and realize the inversion of regional and local anthropogenic carbon emissions and natural terrestrial ecosystem carbon exchange. Atmospheric CO2 data were collected from six ground-based monitoring sites in Shanxi Province, China to verify the inversion effect of regional anthropogenic carbon emissions by setting ideal and real experiments using a two-layer nesting method (at 27 and 9 km). The uncertainty of the simulated atmospheric CO2 decreased significantly, with a root-mean-square error of CO2 concentration values between the ideal value and the simulated after assimilation was close to 0. The total anthropogenic carbon emissions in Shanxi Province in 2019 from the assimilated inversions were approximately 28.6% (17%–38%) higher than the mean of five emission inventories using the bottom-up method, showing that the top-down CCMVS-R system can obtain more comprehensive information on anthropogenic carbon emissions.

Unbalanced emission reductions of different species and sectors in China during COVID-19 lockdown derived by multi-species surface observation assimilation

Abstract. The unprecedented lockdown of human activities during the COVID-19 pandemic has significantly influenced social life in China. However, understanding the impact of this unique event on the emissions of different species is still insufficient, prohibiting the proper assessment of the environmental impacts of COVID-19 restrictions. Here we developed a multi-air-pollutant inversion system to simultaneously estimate the emissions of NOx, SO2, CO, PM2.5 and PM10 in China during COVID-19 restrictions with high temporal (daily) and horizontal (15 km) resolutions. Subsequently, contributions of emission changes versus meteorological variations during the COVID-19 lockdown were separated and quantified. The results demonstrated that the inversion system effectively reproduced the actual emission variations in multi-air pollutants in China during different periods of COVID-19 lockdown, which indicate that the lockdown is largely a nationwide road traffic control measure with NOx emissions decreasing substantially by ∼40 %. However, emissions of other air pollutants were found to only decrease by ∼10 % because power generation and heavy industrial processes were not halted during lockdown, and residential activities may actually have increased due to the stay-at-home orders. Consequently, although obvious reductions of PM2.5 concentrations occurred over the North China Plain (NCP) during the lockdown period, the emission change only accounted for 8.6 % of PM2.5 reductions and even led to substantial increases in O3. The meteorological variation instead dominated the changes in PM2.5 concentrations over the NCP, which contributed 90 % of the PM2.5 reductions over most parts of the NCP region. Meanwhile, our results suggest that the local stagnant meteorological conditions, together with inefficient reductions of PM2.5 emissions, were the main drivers of the unexpected PM2.5 pollution in Beijing during the lockdown period. These results highlighted that traffic control as a separate pollution control measure has limited effects on the coordinated control of O3 and PM2.5 concentrations under current complex air pollution conditions in China. More comprehensive and balanced regulations for multiple precursors from different sectors are required to address O3 and PM2.5 pollution in China.

Do State‐Of‐The‐Art Atmospheric CO2 Inverse Models Capture Drought Impacts on the European Land Carbon Uptake?

AbstractThe European land carbon uptake has been heavily impacted by several recent severe droughts, yet quantitative estimates of carbon uptake anomalies are uncertain. Atmospheric CO2 inverse models (AIMs) provide observation‐based estimates of the large‐scale carbon flux dynamics, but how well they capture drought impacts on the terrestrial carbon uptake is poorly known. Here we assessed the capacity of state‐of‐the‐art AIMs in monitoring drought impacts on the European carbon uptake over 2001–2015 using observations of environmental variability and vegetation function and made comparisons with bottom‐up estimates of carbon uptake anomalies. We found that global inversions with only limited surface CO2 observations give divergent estimates of drought impacts. Regional inversions assimilating denser CO2 observations over Europe demonstrated some improved consistency, with all inversions capturing a reduction in carbon uptake during the 2012 drought. However, they failed to capture the reduction caused by the 2015 drought. Finally, we found that a set of inversions that assimilated satellite XCO2 or assimilated environmental variables plus surface CO2 observations better captured carbon uptake anomalies induced by both the 2012 and 2015 droughts. In addition, the recent Orbiting Carbon Observatory—2 XCO2 inversions showed good potential in capturing drought impacts, with better performances for larger‐scale droughts like the 2018 drought. These results suggest that surface CO2 observations may still be too sparse to fully capture the impact of drought on the carbon cycle at subcontinental scales over Europe, and satellite XCO2 and ancillary environmental data can be used to improve observational constraints in atmospheric inversion systems.

Inversion of multimodal dispersion curves from distributed acoustic sensing measurements for subsurface imaging: A field case of Garner Valley, California

Utilizing the surface-wave (Rayleigh-wave) dispersion properties to estimate subsurface S-wave velocity structure has become a popular method due to simple data collection and analysis procedure during the past decades. In particular, the promotion of distributed acoustic sensing (DAS) technique enhances its popularity due to the low-cost of ultra-dense observations. Moreover, under the same exploration requirement, the cost of the DAS data collection is much lower than that of the traditional acquisition implemented by the nodal seismographs. Therefore, it is necessary to expand the DAS application. Here, the multimodal surface-wave dispersion curves are successfully extracted from the phase-weighted stack of cross-correlation functions (CCFs) of the DAS data collected at Garner Valley Downhole Array field site, California by the proposed cylindrical-wave phase-shift. Then, the dispersion curves are inverted by the inversion workflow (expected to avoid the mode-misidentification) proposed by Yan et al. (2022a), consisting of staging strategy and pattern search with embedded Kuhn-Munkres (PSEKM) algorithm. Compared with other inversion schemes, the novel inversion method allows the presence of the observed values without prior mode-order definition in the inversion system, which omits the tedious and risky manual mode identification, and the mode-orders would be predicted dynamically during the inversion. The dispersion fittings for certain survey points indicate that the mode losses and aliasings appear to be widespread for the surface waves recorded on the free-surface. Finally, the S-wave velocity tomogram consistent with the geological materials of survey area is revealed. Our study demonstrates the feasibility of extracting the DAS-derived multimodal dispersion information and consequently obtaining a physical-reasonable subsurface S-wave velocity model.

Emission factors of ozone-depleting chloromethanes during production processes based on field measurements surrounding a typical chloromethane plant in China

Chloromethanes, including mono, di, tri, and tetrachloromethane, can contribute to stratospheric ozone depletion and hinder ozone layer recovery. Leakage from industrial chloromethane production plants is a major anthropogenic source of chloromethanes; however, accurate estimations of the emission factors are limited. In this study, a plant-scale inversion system was established using field measurements, dispersion simulation, and inversion algorithms to quantify the emission factors of chloromethanes from chloromethane production plants. The results show that, with the exception of the emission factors (kg/kg respective chloromethane produced) of trichloromethane (0.02 ± 0.006), those of dichloromethane (0.04 ± 0.02) and tetrachloromethane (0.03 ± 0.02) were comparable to those provided by the Intergovernmental Panel on Climate Change (0.04 kg/kg fluorinated gas produced). Compared with previous records of emission factors of chloromethanes, this study provided the latest and, to the best of our knowledge, the first field-measurement based estimates of emission factors. Subsequently, using these latest estimates, the emissions of monochloromethane (56 ± 21 Gg/yr), dichloromethane (43 ± 18 Gg/yr), trichloromethane (9.6 ± 3.9 Gg/yr), and tetrachloromethane (2.2 ± 1.6 Gg/yr) from chloromethane production plants in China from 2020 were estimated. Considering the detrimental effects of chloromethanes for ozone layer recovery, the method developed in this study can provide valuable insights for studying emission factors of chloromethanes from other sources and other ozone-depleting substances to accurately assess their impact on the ozone layer.

Rapid decline of carbon monoxide emissions in the Fenwei Plain in China during the three-year Action Plan on defending the blue sky

The Fenwei Plain is one of China's most polluted regions, with poor atmospheric dispersion conditions and an outdated energy structure. After implementing multiple policies in recent years, significant reductions in air pollutant concentrations were observed. In this study, based on the Lagrangian-Bayesian inversion framework FLEXINVERT, we constructed a variable resolution inversion system focusing on the Fenwei Plain and inferred the carbon monoxide (CO) emissions using in-situ atmospheric CO observations from April 2014 to March 2020. We analyzed the spatiotemporal variations of the CO emissions and discussed their causes, especially the effect of the “Three-year Action Plan on Defending the Blue Sky” (TAPDBS). Before the policy, CO emissions temporarily increased, and the overall decrease in CO emissions per unit of Gross Domestic Product (GDP) slowed down. When the policy was implemented, CO emission fluxes declined sharply, with an average drop of 28%, accompanied by an even higher 37% decrease of CO emission per GDP. The reasons for the decline in CO emissions in Shanxi, Shaanxi and Henan are diverse. The decrease in energy intensity is the reason for CO emission reduction in Shannxi and Henan province but not in Shanxi province. This research fills the gap in emission information in recent years and confirms that TAPDBS has brought a breakthrough in both economic development and air quality protection in the Fenwei Plain.

Record-high CO2 emissions from boreal fires in 2021.

Extreme wildfires are becoming more common and increasingly affecting Earth's climate. Wildfires in boreal forests have attracted much less attention than those in tropical forests, although boreal forests are one of the most extensive biomes on Earth and are experiencing the fastest warming. We used a satellite-based atmospheric inversion system to monitor fire emissions in boreal forests. Wildfires are rapidly expanding into boreal forests with emerging warmer and drier fire seasons. Boreal fires, typically accounting for 10% of global fire carbon dioxide emissions, contributed 23% (0.48 billion metric tons of carbon) in 2021, by far the highest fraction since 2000. 2021 was an abnormal year because North American and Eurasian boreal forests synchronously experienced their greatest water deficit. Increasing numbers of extreme boreal fires and stronger climate-fire feedbacks challenge climate mitigation efforts.

Why do inverse models disagree? A case study with two European CO2 inversions

Abstract. We present an analysis of atmospheric transport impact on estimating CO2 fluxes using two atmospheric inversion systems (CarboScope-Regional (CSR) and Lund University Modular Inversion Algorithm (LUMIA)) over Europe in 2018. The main focus of this study is to quantify the dominant drivers of spread amid CO2 estimates derived from atmospheric tracer inversions. The Lagrangian transport models STILT (Stochastic Time-Inverted Lagrangian Transport) and FLEXPART (FLEXible PARTicle) were used to assess the impact of mesoscale transport. The impact of lateral boundary conditions for CO2 was assessed by using two different estimates from the global inversion systems CarboScope (TM3) and TM5-4DVAR. CO2 estimates calculated with an ensemble of eight inversions differing in the regional and global transport models, as well as the inversion systems, show a relatively large spread for the annual fluxes, ranging between −0.72 and 0.20 PgC yr−1, which is larger than the a priori uncertainty of 0.47 PgC yr−1. The discrepancies in annual budget are primarily caused by differences in the mesoscale transport model (0.51 PgC yr−1), in comparison with 0.23 and 0.10 PgC yr−1 that resulted from the far-field contributions and the inversion systems, respectively. Additionally, varying the mesoscale transport caused large discrepancies in spatial and temporal patterns, while changing the lateral boundary conditions led to more homogeneous spatial and temporal impact. We further investigated the origin of the discrepancies between transport models. The meteorological forcing parameters (forecasts versus reanalysis obtained from ECMWF data products) used to drive the transport models are responsible for a small part of the differences in CO2 estimates, but the largest impact seems to come from the transport model schemes. Although a good convergence in the differences between the inversion systems was achieved by applying a strict protocol of using identical prior fluxes and atmospheric datasets, there was a non-negligible impact arising from applying a different inversion system. Specifically, the choice of prior error structure accounted for a large part of system-to-system differences.

Inversion of spatio-temporal distribution heat flux and reconstruction of transient temperature field of three-layered skin tissue during hyperthermia

Hyperthermia (for example, high-intensity focused ultrasound, laser, radio-frequency) of cancerous cells from in vitro to in vivo requires accurately obtaining the heat distribution induced by external heating into the three-layered skin tissue. Obtaining the boundary heat flux into the three-layered skin tissue is a necessary condition to realize the measurement of tissue heat distribution. Considering the complexity of multiple boundary heat fluxes in spatio-temporal distribution, this study proposes an inversion scheme to predict the spatio-temporal distribution of multiple boundary heat fluxes into the three-layered skin tissue. In the inversion scheme, a multivariable prediction model is established to solve the spatio-temporal coupling problem between the inversed boundary heat flux and measurement temperature information. Furthermore, based on the dependence between the predicted temperature and inversed boundary heat flux, the inversion system is constructed to realize the simultaneous optimization inversion of multiple boundary heat fluxes in spatio-temporal distribution. To examine the feasibility and effectiveness of inversion scheme, numerical experiments are carried out to discuss the influence of future time steps and measurement errors on the inversion results of boundary heat flux. In addition, the transient temperature field of three-layered skin tissue is reconstructed by inversed boundary heat flux, which could provide an economical, effective, and non-invasive solution for the measurement of thermal field of three-layered skin tissue during hyperthermia.

4DEnVar-based inversion system for ammonia emission estimation in China through assimilating IASI ammonia retrievals

Atmospheric ammonia has been hazardous to the environment and human health for decades. Current inventories are usually constructed in a bottom-up manner and subject to uncertainties and incapable of reproducing the spatiotemporal characteristics of ammonia emission. Satellite measurements, for example, Infrared Atmospheric Sounder Interferometer (IASI) and Cross-Track Infrared Sounder, which provide global coverage of ammonia distribution, have gained popularity in ammonia emission estimation through data assimilation methods. However, satellite-based emission inversion studies on China are limited. In this study, we propose a four-dimensional ensemble variational-based ammonia emission inversion system to optimize ammonia emissions in China. It was developed by assimilating the IASI ammonia retrievals onboard Meteorological Operational satellite A and B into a chemical transport model Goddard Earth Observing System Chemical model (GEOS-Chem). Monthly inversion experiments were conducted in April, July, and October 2016 to test the performance. The inversion result indicated that the prior inventory from the MEIC model captured ammonia spreads in general; however, it heterogeneously underrated the emission intensity. The increments obtained in the assimilation were as high as 50% in North, East, and Northwest China. The posterior emission inventory presented a regional emission flux consistent with relevant studies. Driven by the optimized source estimate, GEOS-Chem provides superior results than using the prior in the evaluation of the assimilated IASI retrievals and the surface ammonia concentration measured by the ground-based Ammonia Monitoring Network in China.

Constraint of satellite CO2 retrieval on the global carbon cycle from a Chinese atmospheric inversion system

Constraint of satellite CO2 retrieval on the global carbon cycle from a Chinese atmospheric inversion system

Evaluating Northern Hemisphere Growing Season Net Carbon Flux in Climate Models Using Aircraft Observations

AbstractUnderstanding terrestrial ecosystems and their response to anthropogenic climate change requires quantification of land‐atmosphere carbon exchange. However, top‐down and bottom‐up estimates of large‐scale land‐atmosphere fluxes, including the northern extratropical growing season net flux (GSNF), show significant discrepancies. We developed a data‐driven metric for the GSNF using atmospheric carbon dioxide concentration observations collected during the High‐Performance Instrumented Airborne Platform for Environmental Research Pole‐to‐Pole Observations and Atmospheric Tomography Mission flight campaigns. This aircraft‐derived metric is bias‐corrected using three independent atmospheric inversion systems. We estimate the northern extratropical GSNF to be 5.7 ± 0.3 Pg C and use it to evaluate net biosphere productivity from the Coupled Model Intercomparison Project phase 5 and 6 (CMIP5 and CMIP6) models. While the model‐to‐model spread in the GSNF has decreased in the CMIP6 models relative to that of the CMIP5 models, there is still disagreement on the magnitude and timing of seasonal carbon uptake with most models underestimating the GSNF and overestimating the length of the growing season relative to the observations. We also use an emergent constraint approach to estimate annual northern extratropical gross primary productivity to be 56 ± 17 Pg C, heterotrophic respiration to be 25 ± 13 Pg C, and net primary productivity to be 28 ± 12 Pg C. The flux inferred from these aircraft observations provides an additional constraint on large‐scale gross fluxes in prognostic Earth system models that may ultimately improve our ability to accurately predict carbon‐climate feedbacks.

Theoretical assessment of the ability of the MicroCarb satellite city-scan observing mode to estimate urban CO2 emissions

Abstract. We assess the theoretical capability of the upcoming France–UK MicroCarb satellite, which has a city-scan observing mode, to determine integrated urban emissions of carbon dioxide (CO2). To achieve this we report results from a series of closed-loop numerical experiments that use an atmospheric transport model with anthropogenic and biogenic fluxes to determine the corresponding changes in atmospheric CO2 column, accounting for changes in measurement coverage due to cloud loading. We use a maximum a posteriori inverse method to infer the CO2 fluxes based on the measurements and the a priori information. Using an urban CO2 inversion system, we explore the relative performance of alternative two-sweep and three-sweep city observing strategies to quantify CO2 emissions over the cities of Paris and London in different months when biospheric fluxes vary in magnitude. We find that both the two-sweep and three-sweep observing modes are able to reduce a priori flux errors by 20 %–40 % over Paris and London. The three-sweep observing strategy, which generally outperforms the two-sweep mode by virtue of its wider scan area that typically yields more cloud-free observations, can retrieve the total emissions of the truth within 7 % over Paris and 21 % over London. The performance of the limited-domain city-mode observing strategies is sensitive to cloud coverage and particularly sensitive to the prevailing wind direction. We also find that seasonal photosynthetic uptake of CO2 by the urban biosphere weakens atmospheric CO2 gradients across both cities, thereby reducing the sensitivity of urban CO2 enhancements and subsequently compromising the ability of MicroCarb to reduce bias in estimating urban CO2 emissions. This suggests that additional trace gases co-emitted with anthropogenic CO2 emissions, but unaffected by the land biosphere, are needed to quantify sub-city scale CO2 emissions during months when the urban biosphere is particularly active.

Comparing Sentinel-5P TROPOMI NO2 column observations with the CAMS regional air quality ensemble

Abstract. The Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) instrument, launched in October 2017, provides unique observations of atmospheric trace gases at a high resolution of about 5 km, with near-daily global coverage, resolving individual sources like thermal powerplants, industrial complexes, fires, medium-scale towns, roads, and shipping routes. Even though Sentinel-5P (S5P) is a global mission, these datasets are especially well suited to test high-resolution regional-scale air quality (AQ) models and provide valuable input for emission inversion systems. In Europe, the Copernicus Atmosphere Monitoring Service (CAMS) has implemented an operational regional AQ forecasting capability based on an ensemble of several European models, available at a resolution of 0.1∘ × 0.1∘. In this paper, we present comparisons between TROPOMI observations of nitrogen dioxide (NO2) and the CAMS AQ forecasts and analyses of NO2. We discuss the different ways of making these comparisons and present quantitative results in the form of maps for individual days, summer and winter months, and a time series for European subregions and cities between May 2018 and March 2021. The CAMS regional products generally capture the fine-scale daily and averaged features observed by TROPOMI in much detail. In summer, the comparison shows a close agreement between TROPOMI and the CAMS ensemble NO2 tropospheric columns with a relative difference of up to 15 % for most European cities. In winter, however, we find a significant discrepancy in the column amounts over much of Europe, with relative differences up to 50 %. The possible causes for these differences are discussed, focusing on the possible impact of retrieval and modeling errors. Apart from comparisons with the CAMS ensemble, we also present results for comparisons with the individual CAMS models for selected months. Furthermore, we demonstrate the importance of the free tropospheric contribution to the estimation of the tropospheric column and thus include profile information from the CAMS configuration of the ECMWF's (European Centre for Medium-Range Weather Forecasts) global integrated model above 3 km altitude in the comparisons. We also show that replacing the global 1∘ × 1∘ a priori information in the retrieval by the regional 0.1∘ × 0.1∘ resolution profiles of CAMS leads to significant changes in the TROPOMI-retrieved tropospheric column, with typical increases at the emission hotspots up to 30 % and smaller increases or decreases elsewhere. As a spinoff, we present a new TROPOMI NO2 level 2 (L2) data product for Europe, based on the replacement of the original TM5-MP generated global a priori profile by the regional CAMS ensemble profile. This European NO2 product is compared with ground-based remote sensing measurements of six Pandora instruments of the Pandonia Global Network and nine Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) instruments. As compared to the standard S5P tropospheric NO2 column data, the overall bias of the new product for all except two stations is 5 % to 12 % smaller, owing to a reduction in the multiplicative bias. Compared to the CAMS tropospheric NO2 columns, dispersion and correlation parameters with respect to the standard data are, however, superior.

Anthropogenic NOx Emission Estimations over East China for 2015 and 2019 Using OMI Satellite Observations and the New Inverse Modeling System CIF-CHIMERE

The Chinese government introduced regulations to control emissions and reduce the level of NOx pollutants for the first time with the 12th Five-Year Plan in 2011. Since then, the changes in NOx emissions have been assessed using various approaches to evaluate the impact of the regulations. Complementary to the previous studies, this study estimates anthropogenic NOx emissions in 2015 and 2019 over Eastern China using as a reference the Hemispheric Transport of Air Pollution (HTAP) v2.2 emission inventory for 2010 and the new variational inversion system the Community Inversion Framework (CIF) interfaced with the CHIMERE regional chemistry transport model and OMI satellite observations. We also compared the estimated NOx emissions with the independent Multi-resolution Emission Inventory for China (MEIC) v1.3, from 2015. The inversions show a slight global decrease in NOx emissions (in 2015 and 2019 compared to 2010), mainly limited to the most urbanized and industrialized locations. In the locations such as Baotou, Pearl River Delta, and Wuhan, the estimations in 2015 compared to 2010 are consistent with the target reduction (10%) of the 12th Five-Year Plan. Comparisons between our emission estimates and MEIC emissions in 2015 suggest that our estimates likely underestimate the emission reductions between 2010 and 2015 in the most polluted locations of Eastern China. However, our estimates suggest that the MEIC inventory overestimates emissions in regions where MEIC indicates an increase of the emissions compared to 2010.

An inversion model based on GEOS-Chem for estimating global and China's terrestrial carbon fluxes in 2019

The 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories added the method of assimilating atmospheric CO2 concentrations to invert carbon sources and sinks; however, many global carbon inversion models are not publicly available. In addition, our regional assimilation inversion system, CCMVS-R (China Carbon Monitoring, Verification and Supporting for Regional), needs a global carbon inversion model with higher assimilation efficiency to provide boundary conditions. Here, an inversion model based on the global atmospheric chemistry model GEOS-Chem and a more accurate and easier-to-implement ensemble square root Kalman filter (EnSRF) algorithm is constructed and used to infer global and China's carbon fluxes in 2019. Atmospheric CO2 concentrations from ObsPack sites and five additional CO2 observational sites from China's Greenhouse Gas Observation Network (CGHGNET) were used for data assimilation to improve the estimate. The inverted annual global terrestrial and oceanic carbon uptake is 2.12 and 2.53 Pg C per year, respectively, accounting for 21.1% and 25.1% of global fossil fuel CO2 emissions. The remaining 5.41 Pg C per year in the atmosphere is consistent with the global atmospheric CO2 growth rates of 5.44 Pg C per year reported by the National Oceanic and Atmospheric Administration (NOAA), showing that the inversion model can provide a reasonable estimate of global-scale natural carbon sinks. The inverted terrestrial carbon sink of China is 0.37 Pg C per year, accounting for approximately 13% of China's fossil CO2 emissions.