Terrestrial carbon sinks play a crucial role in regulating atmospheric CO2, yet their regional quantification remains highly uncertain. This study investigates how the uncertainty of fossil fuel emission inventories affects terrestrial carbon sink estimates in East Asia. Using inversion system and six emission inventories, we demonstrate that East Asia's carbon sink estimates for 2018-2023 vary dramatically from 0.039 PgC yr-1 (using MEIC) to 0.46 PgC yr-1 (using EDGAR)-a 1079% difference. China contributes 69% to this regional uncertainty, substantially outweighing contributions from Japan (16%), North and South Korea (15%), and Mongolia (0.46%). We identify two major sources of uncertainty: incomplete industrial processes and product use (IPPU) emission accounting and fossil fuel combustion emission estimates differences. After implementing IPPU corrections, the consistency among carbon sink estimates based on different inventories improved significantly, with the coefficient of variation decreasing from 70% to 17%. Concurrently, East Asia's estimated contribution to the global terrestrial carbon sink increased substantially from 9.1% to 18%, while its carbon offsetting capacity (ratio of sink to emission) rose from 5.7% to 11%. However, a notable difference of 7.0% (-0.15 PgC yr-1) in East Asia's contribution to the global terrestrial carbon sink persists between MEIC-based estimates (13%) and other inventory-based estimates (average 20%) even after IPPU corrections, indicating underlying differences in fossil fuel combustion emission accounting. Our findings highlight the critical importance of addressing both IPPU accounting completeness and fossil fuel combustion emission estimate discrepancies to improve the accuracy of East Asia's carbon sink quantification and its contribution to the global carbon budget, with significant implications for regional carbon mitigation pathway design.

Atmospheric oxidizing capacity (AOC) critically drives the formation of secondary air pollutants, which impose great health and ecosystem risks in spring. Yet many studies have investigated annual, winter, or summer AOC in China, leaving the evolution of springtime AOC and its underlying drivers unclear. Here we combine ground-based and satellite observations to analyze AOC dynamics in China from 2014 to 2019 and quantify its underlying drivers using high-resolution anthropogenic emissions inferred from nationwide in-situ observations. We identify a pronounced increase in springtime AOC after 2017, along with a marked seasonal shift in emissions from winter to spring. Numerical simulations show that neither meteorology nor emission inventory variations can explain this enhancement, whereas incorporating the seasonal emission shift explains approximately 74% of the observed national AOC increase. This shift is directly attributable to China's winter focused clean air policies implemented since late 2016, which rigidly curtailed industrial production from November 15 to March 15 and triggered a sharp spring rebound. Our findings underscore the urgent need to refine seasonal allocation in emission inventories and suggest that future air quality management should account for the seasonal disparities in atmospheric oxidation and photochemical pollution.

2024 is the hottest year on record, accompanied by extreme precipitation, droughts and fires. The global atmospheric CO2 growth rate in 2024 reached a historic high of 3.73 ppm yr-1, significantly surpassing the previous record set during the 2015/16 El Niño event. Here, we investigate the causes and underlying mechanisms of this record-high growth rate by combining satellite-based atmospheric inversions and estimates of gross primary production and fire emissions. We find that the record-high CO2 growth rate is due to large reductions in the land CO2 sink. This is dominated by a dramatic increase in total ecosystem respiration, which occurred primarily in grass and shrub lands, owing to compound hot-wet climatic conditions in 2024. Given the projected increase in the frequency and intensity of compound pluvial-hot extremes under warming, changes in ecosystem respiration will become more drastic and cause positive feedback to climate warming.

Accurate accounting of regional methane (CH4) emissions and removals is essential for tracking national climate mitigation progress and assessing potential carbon-climate feedbacks. China is the world's largest CH4 emitter, yet comprehensive sub-national estimates remain limited, constraining the development of effective mitigation strategies. This study analyzes CH4 emissions across China from 2000 to 2019 using atmospheric inversion ensembles (top-down approach, TD) and process-based model estimates (bottom-up approach, BU). The datasets for both approaches were contributed by international research teams coordinated through the Global Carbon Project. The spatial distribution of various CH4 fluxes exhibits high spatial heterogeneity. Approximately 60% of national CH4 emissions come from three of the nine sub-national regions (North China, Southeast China, and Southwest China), which together account for <30% of China's land area. These emissions are dominated by the energy and agricultural sectors. Natural sources contribute 9%-16% of the total CH4 budget, but they have the largest relative uncertainties, reaching approximately 150%-170% of their estimated magnitudes. This high uncertainty partly reflects the limited number of studies on natural CH4 sources. The increase in anthropogenic emissions is the primary driver of the changes between the two decades, with increases of 10.4 [2.7-16.9] Tg CH4 a-1 (BU) and 6.1 [-2.6-10.7] Tg CH4 a-1 (TD). The largest increases occur in North China, Southwest China, Southeast China, and Northeast China. This study highlights the need for improved regional monitoring of CH4 emissions and sinks in China to support integrated and spatially targeted mitigation strategies.

During the July-September (JAS) of 2022, a record-breaking heatwave-drought (DH2022) hit southern China, especially in the middle and lower reaches of the Yangtze River basin (MLYR). It caused an unprecedented decline in vegetation photosynthesis, however, its impact on the regional carbon budget remains unclear. Here, we assessed the response of regional terrestrial carbon fluxes to DH2022 using the Global Carbon Assimilation System (GCAS v2) by assimilating OCO-2 XCO2 retrievals. Our results indicate that, relative to 2015-2021, the MLYR region experienced a 45.8 TgC reduction in land sink during JAS, consistent with the TRENDYv13 simulations. Combining our inverse results with satellite proxies for GPP, we find that an unusually wet spring in 2022 boosted vegetation growth in the MLYR, increasing gross primary productivity (GPP) by 46.1 TgC and strengthening the land sink by 24.0 TgC, thereby substantially offsetting the carbon sink reductions observed during JAS. Outside the MLYR region in southern China, annual land sink increased by 49.9 TgC in remaining areas (RAS), also greatly mitigating the impact of the DH2022 on the regional carbon balance. Overall, the annual land sink in MLYR decreased by only 7.1 TgC, whereas in southern China, it increased by 42.8 TgC. During JAS, the decreased land sink in MLYR was primarily driven by a decline in GPP in forests and grass/shrub, coupled with an increase in total ecosystem respiration in croplands. Our study provides a comprehensive assessment of land carbon dynamics in southern China under the influence of DH2022, enhancing our understanding of the impacts of climate extremes on the regional carbon cycle.

We previously analyzed a saccular abdominal aortic aneurysm model and elucidated that saccular abdominal aortic aneurysms manifest abnormal hemodynamic factors from an early stage and that stenting improves these factors. In this study, we analyzed a model of a saccular abdominal aortic aneurysm implanted with stents of different diameters and lengths to determine the optimal stent size for improving hemodynamic factors. The stent diameter was set in 3 patterns as follows: 24 mm, the same as the aortic diameter; 26.4 mm, a 10% increase; and 28.8 mm, a +20% increase. The stent length was extended 10 mm vertically from the 28-mm length of the aneurysmal portion to 50 and 70 mm in 2 patterns, for a total of 6 types. The analysis revealed that all hemodynamic factors improved for all sizes compared with those prior to stenting. Streamlines entering the aneurysm were reduced with larger stent diameters; however, no difference in length was observed. Moreover, no clear differences in the mean flow velocity within the aneurysm, maximum shear stress, or pressure loss coefficient between the models were observed. An implanted stent was sufficient in terms of efficacy if it adhered to the aortic wall and covered the saccular aneurysm area. (This is a secondary publication from J Jpn Coll Angiol 2024; 64: 69-78.).

Marine isoprene emissions (MIEs), primarily from biogenic sources influence oceanic/coastal atmospheric chemistry but receive less attention than terrestrial emissions. To date, their contributions to ozone (O3) pollution in East Asia remain poorly quantified. Here, we investigate the contribution of MIEs to near-surface O3 in East Asia in 2017 using a source-tagging method integrated into a regional chemical transport model. The results indicate that MIEs increase O3 by up to 4.6 ppb over coastal seas and 1-2 ppb inland. The contribution is highest in summer (up to 10 ppb), with notable influences in spring and autumn in southern coastal regions. For 12 coastal cities, MIEs contributed an average of 0.5-2.9 ppb to surface O3 (accounting for 1.2%-4.9% of total) during January, April, July, and October, peaking at South Korea's Kanghwa/Cheju (up to 9%, 2.8-2.9 ppb). Diurnal peaks occur in the afternoon under strong solar radiation. Specifically, MIEs increase O3 via two pathways: a direct pathway (peak 3.9 ppb near marine sources) driven by local rapid photochemical reactions, and an indirect pathway (similar to 1.5 ppb inland) relying on long-range transport of oxidation products. Furthermore, MIEs enhance radical-mediated O3 production and suppress the net non-loss reaction between O3 and NO through NO depletion, ultimately leading to a net increase in O3 concentrations. These findings highlight the nonlocal contribution of MIEs and emphasize the need to incorporate marine isoprene sources in regional O3 assessments.

The response of net forest carbon uptake to warm extremes remains elusive. The year 2023 was at the time “the hottest year on record” globally, with Canada’s forests experiencing warm anomalies of above 2 °C and unprecedented drought and wildfires, providing a unique case to examine the response of boreal forest net carbon uptake to climate extremes. Here we combine satellite-based atmospheric CO2 flux inversions, and ground in-situ observations of CO2 fluxes and concentrations to investigate Canada’s forest net carbon uptake and its underlying mechanisms in 2023. We find that compared to 2015–2022, the Canada’s forest net carbon uptake was enhanced by 0.28 ± 0.23 PgC, offsetting 38–48% of Canadian wildfire emissions in 2023. This enhanced net uptake was dominated by large ecosystem respiration reductions, mainly attributable to severe root-zone soil moisture deficits and the unimodal temperature response of respiration. However, most dynamic global vegetation models failed to simulate the respiration reductions and the responses to hydrothermal conditions well. This study improves our understanding of boreal forest net carbon uptake in response to climate extremes and highlights an urgent need to improve vegetation models under global warming.

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesise datasets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (E-FOS) are based on energy and cement production data. Emissions from land-use change (E-LUC) are estimated by bookkeeping models based on land-use data. The global atmospheric CO2 growth rate (G(ATM)) is computed from changes in concentration measured at surface stations. The global net uptake of CO2 by the ocean (S-OCEAN) is estimated with global ocean biogeochemistry models and observation-based fCO(2)-products. The global net uptake of CO2 by the land (S-LAND) is estimated with dynamic global vegetation models. Additional lines of evidence are provided by atmospheric inversions, atmospheric oxygen measurements, ocean interior observation-based estimates, and Earth System Models. This year, we introduced corrections on the E-LUC, S-OCEAN and S-LAND estimates. The sum of all sources and sinks results in the carbon budget imbalance (B-IM), a measure of imperfect data and incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as +/- 1 sigma. For the year 2024, E-FOS increased by 1.1 % relative to 2023, with fossil emissions at 10.3 +/- 0.5 GtC yr(-1) (including the cement carbonation sink, 0.2 GtC yr(-1)), E-LUC was 1.3 +/- 0.7 GtC yr(-1), for total anthropogenic CO2 emissions of 11.6 +/- 0.9 GtC yr(-1) (42.4 +/- 3.2 GtCO(2) yr(-1)). Also, for 2024, G(ATM) was 7.9 +/- 0.2 GtC yr(-1) (3.73 +/- 0.1 ppm yr(-1)), 2.2 GtC above the 2023 growth rate. S-OCEAN was 3.4 +/- 0.4 GtC yr(-1) and S-LAND was 1.9 +/- 1.1 GtC yr(-1), leaving a large negative B-IM (-1.7 GtC yr(-1)), suggesting that the total sink or G(ATM) is strongly overestimated in 2024. The global atmospheric CO2 concentration averaged over 2024 reached 422.8 +/- 0.1 ppm. Preliminary data for 2025 suggest an increase in E-FOS relative to 2024 of +1.0 % (0.2 % to 1.7 %) globally, and atmospheric CO2 concentration increasing by 2.1 ppm reaching 425.6 ppm, 53 % above the pre-industrial level (around 278 ppm in 1750). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959-2024, with a near-zero overall budget imbalance, although discrepancies of up to around 1 GtC yr(-1) persist for the representation of annual to decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows: (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the mean ocean sink.

Abstract Accurate quantification of interannual variations in China's fossil fuel CO2 (FFCO2) emissions is critical for climate policy evaluation. However, bottom‐up inventories exhibit growing discrepancies. Here, we estimate China's 2015–2023 FFCO2 emissions using the Regional multi‐Air Pollutant Assimilation System, integrating in situ observations of co‐emitted NO2 and grid‐specific CO2‐to‐NOx ratios. National FFCO2 emissions averaged 11.5 ± 1.5 PgCO2 yr−1, with over 45% from North and East China. Emission intensity exhibited a southeast‐to‐northwest increasing gradient, aligning with economic development patterns. Interannually, emissions rose notably during 2015–2017, driven by North and Central China, then stabilized during 2018–2023 amid policy interventions and COVID‐19 disruptions, contrasting with the sustained growth reported in mainstream bottom‐up inventories. Total annual growth averaged 189.6 TgCO2 yr−1 (1.6%), while emission intensity fell 5.6% annually, with Southwest China experiencing a decline in both metrics. This observation‐constrained data set clarifies spatiotemporal dynamics and policy impacts, supporting targeted strategies for China's carbon goals.

African tropical moist forests are critical regulators of global carbon cycling, yet their contributions to national carbon budgets remain poorly constrained, impairing effective policy design. Here, we unravel carbon removals and emissions for 18 African tropical countries using multiple approaches over a data-rich period (2015–2019). Across countries, carbon removals (−286 ± 68.4 TgC yr−1) nearly offset land-use change emissions (343.4 ± 100.3 TgC yr−1), with consistent net fluxes between bottom-up and top-down approaches (57.4 ± 121.4 TgC yr−1 versus 99.1 ± 164.5 TgC yr−1). Results vary between countries, with highest removals in intact forests in the Democratic Republic of the Congo (−81.7 ± 64.2 TgC yr−1), lowest land-use change emissions in Gabon (2.6 ± 7.1 TgC yr−1), and highest fossil fuel emissions in Nigeria (31.2 ± 1.6 TgC yr−1). African tropical countries show high carbon removal rates and land-use change emissions but low fossil fuel emissions, contrasting with industrialized countries. (Inter)nationally endorsed conservation policies can reverse national carbon budgets from net sources to sinks. Carbon removals by intact African tropical moist forests nearly offset land-use emissions, underscoring the importance of environmental policies to protect this critical carbon sink, based on synthesized field data, remote-sensing products, and carbon-flux modelling.

Understanding ozone (O3) formation in the Beibu Gulf is critical for regional air quality management. This study integrated the WRF-CMAQ modeling system, an observation-based model (OBM), and multi-source data to investigate a severe springtime O3 episode in 2025. The episode evolved through four stages: accumulation and northward transport, attenuation, rebound, and final dissipation. Spatially, O3 patterns shifted from east-high/ west-low distribution to west-high/east-low. This shift was driven by a northerly-to-southerly wind transition, high temperature, low humidity, and enhanced solar radiation. CMAQ simulations show that northerly winds carried O3 precursors offshore, driving marine photochemistry to form a maritime "O3 pool". A subsequent wind shift recirculated these pollutants onshore, contributing up to 50.8% of the O3 in the coastal city of Beihai. OBM analysis confirms that during the rebound stage, local photochemical production accounted for 77.0% of O3 in the inland city of Nanning, with O3 formation rates (50-60 mu g m- 3h- 1) exceeding those in Beihai. Overall, an episode is sustained by the synergy between O3 transport from the maritime "O3 pool" and localized photochemical production. Furthermore, the analysis shows that urban and nearshore areas lean toward a VOC-limited regime, while actual NOx emissions in Qinzhou, Chongzuo, and Fangchenggang significantly exceed inventory estimates. The upcoming Pinglu Canal operation will generate substantial shipping NOx emissions. These findings highlight the need for strict maritime NOx controls to mitigate future O3 pollution in this region.

Abstract. Accurate estimation of coal mine methane (CMM) emissions in Shanxi Province, China's leading coal production hub, is essential for mitigating China's anthropogenic methane emissions. Hyperspectral remote sensing is an emerging method for real-time methane monitoring with significant potential for optimizing CMM emission factors. However, limited satellite revisit frequencies can introduce biases in CMM emission estimates. To address these issues, we developed a Hierarchical Bayesian Inversion Algorithm utilizing time-series observations from seven hyperspectral satellites in Shanxi (2019–2023), comprising 215 methane plumes from 26 coal mines, to estimate annual CMM emission rates with limited satellite revisit frequency. Subsequently, we integrated multi-source satellite observations with inventory data to estimate CMM emissions in Shanxi province. Our analysis yields a CMM emission factor of (7.9 ± 1.4)×10-3 Tg/Mt for Shanxi, with CMM emissions reaching 11 ± 2 Tg/yr in 2023. We demonstrate that CMM emissions follow a right-skewed distribution in Shanxi Province, where low-frequency extreme methane emission events (≥10000 kg/h) constitute approximately 25 % of all time-series observations. Additionally, our results reveal that capacity reduction policies initially decreased CMM emissions, but subsequent production recovery led to emission increases, with asymmetric responses to coal price fluctuations. Our findings establish a novel strategy for CMM accounting from hyperspectral satellite observations.

Abstract. Lightning is a primary driver of severe convective hazards and wildfire ignitions, yet long-term, high-resolution gridded records have remained scarce due to the limited temporal coverage of ground-based networks and the sampling constraints of satellite observations. Here, we presented a new global 0.25° × 0.25° monthly land lightning stroke-density dataset spanning 1979–2025. To ensure robustness, we developed a ridge regression stacking ensemble that integrated four complementary machine learning architectures: eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), and Deep Neural Network (DNN). The ensemble achieved superior performance over each single model (test R² = 0.6895, RMSE = 0.0108, MAE = 0.0030), indicating that model blending effectively enhanced predictive stability. Individual validations confirmed high spatial fidelity, as the ensemble successfully reproduced the observed large-scale spatial distribution and major tropical–subtropical continental lightning hotspots. Independent comparisons with the LIS/OTD gridded lightning climatology (±38°) further demonstrated strong spatiotemporal consistency, particularly in reproducing interannual variability. Our analysis revealed pronounced regional heterogeneity in multi-decadal trends: significant decreases were concentrated across several tropical convective centers, while localized increases emerged in specific mid-latitude regions. Attribution based on SHapley Additive exPlanations (SHAP) elucidated that these patterns were primarily governed by the coupling of thermodynamic instability (CAPE × TP), moisture availability, and ice-phase hydrometeor conditions. This dataset provided a physically constrained and spatially detailed basis for studying long-term lightning dynamics, offering practical inputs for natural-ignition modeling, lightning-produced NOx estimation, and the evaluation of lightning parameterizations in climate and Earth system models. The datasets of the 1979–2025 Global Land Lightning Density Reconstruction Version 1 (GLLDR v1) are publicly available at the Zenodo via the following DOI: https://doi.org/10.5281/zenodo.19722380 (Zheng et al., 2026a).

Abstract Accurately estimating the terrestrial carbon sink is crucial for understanding the global carbon cycle. Here, we examine how different parameterizations of the key leaf photosynthetic capacity parameter, namely the maximum Rubisco carboxylation rate normalized to 25°C (Vcmax25), influence terrestrial carbon flux estimates within an atmospheric inversion system. We demonstrate that using a spatially heterogeneous and seasonally varying Vcmax25 data set derived from satellite solar‐induced fluorescence (SIF) and leaf chlorophyll content (LCC) yields more realistic spatial patterns of net ecosystem exchange (NEE) compared to the conventional plant functional type (PFT)‐fixed approach. This improvement subsequently enhances both prior and posterior CO2 simulations constrained by GOSAT or OCO‐2 vertically averaged CO2 (XCO2) retrievals, as validated against independent Observation Package (ObsPack) surface flask and aircraft measurements, as well as Total Carbon Column Observing Network (TCCON) retrievals. Our results highlight that accurate photosynthesis parameterization is fundamental to advancing top‐down estimates of the terrestrial carbon sink.

Black carbon (BC) is an important climate forcing agent, yet its direct radiative forcing (DRF) remains highly uncertain at the global scale, largely due to simplified representations of particle morphology and chemical mixing state in numerical models. Despite advances in particle-scale studies, global assessments still commonly assume fully internal mixing. Here, we present an implementable modelling framework that characterises particle-scale chemical heterogeneity using the mixing state index (χ) and coating volume ratio (VR). Particle-resolved simulations are employed to quantify the effects of χ and VR on BC optical properties. Machine learning is then used to map this particle-scale information onto variables accessible in Earth system models, enabling the estimation of BC radiative forcing under more realistic mixing state conditions. This framework provides a practical pathway to improve global assessments of BC radiative effects.

The response of net forest carbon uptake to warm extremes remains elusive. The year 2023 was at the time ‘the hottest year on record’ globally, with Canada’s forests experiencing warm anomalies of above 2 °C and unprecedented drought and wildfires, providing a unique case to examine the response of boreal forest net carbon uptake to climate extremes. Here we combine satellite-based atmospheric CO2 flux inversions with ground-based in situ observations of CO2 fluxes and concentrations to investigate Canada’s forest net carbon uptake and its underlying mechanisms in 2023. We find that, compared with 2015–2022, Canada’s forest net carbon uptake was enhanced by 0.28 ± 0.23 PgC, offsetting 38–48

Abstract Air quality forecasts are essential to support decision‐making in urban agglomerations, where millions of people are exposed to high levels of pollution. However, these forecasts are often limited by uncertainties in anthropogenic emissions, especially in large urban agglomerations where no dedicated operational system exists. In this study, we present an observation‐based emission scaling approach aimed at improving operational forecasts. This method derives scaling factors (SF) from the ratio of observed‐to‐modeled concentrations over the previous week and applies them to anthropogenic emissions in the forecast model, assuming that in large urban agglomerations, biases between observed and modeled concentrations are primarily driven by uncertainties in anthropogenic emission inventories. The method also derives SF for anthropogenic volatile organic compound emissions based on modeled daytime O3 biases under the assumption of a NOx‐saturated regime. We implement this method in a chemistry‐transport model using a global anthropogenic emission inventory and apply it to São Paulo for two distinct periods (February–April 2023 and July–September 2024). The results show that forecasts of CO, NO2, O3 and SO2 concentrations are significantly improved within a few weeks. For PM2.5 and PM10, improvements are more limited by the influence of secondary aerosol formation and by pollution transport from outside of the agglomeration. Overall, our results demonstrate that observation‐based emission scaling provides an efficient and transferable methodological approach for improving operational air quality forecasts in urban agglomerations without requiring model‐specific developments.

Abstract. Accurately quantifying anthropogenic CO2 emissions is essential for evaluating carbon budget and mitigation strategies. However, traditional "bottom-up" emission inventories suffer from substantial uncertainties and update time lags, urgently requiring top-down constraints from atmospheric observations while accounting for confounding terrestrial biogenic interferences. In this study, we extended RegGCAS, a regional carbon assimilation system based on the WRF-CMAQ atmospheric chemical transport model and the Ensemble Kalman Filter algorithm. By assimilating column-averaged dry-air CO2 mole fractions (XCO2) from OCO-2/3 satellite observations, we inverted anthropogenic CO2 emissions over mainland China during winter 2022–2023. The results revealed that the total national anthropogenic CO2 emissions amounted to 2808.3 ± 157.0 Tg, 16.1 % higher than the MEIC inventory. For key emission regions, emissions increased by 13.1 % in the Beijing-Tianjin-Hebei region, whereas they decreased by 10.4 % in the Yangtze River Delta. The system captured distinct urban-suburban emission adjustment differences in key regions, with reductions in city centers and increases in surrounding areas. It also reflected short-term emission fluctuations related to anthropogenic activity changes, such as the Spring Festival work stoppages. Evaluation demonstrates that assimilation effectively reduces prior emission errors by 68.0 %. Validation shows that posterior simulation RMSE decrease by 5.8 % against the assimilated OCO-2/3 XCO2, and by 15.3 %, 7.7 %, and 25.2 % against independent TCCON, ObsPack, and urban site observations, respectively, confirming the enhanced accuracy of the posterior emission estimates. This study provides a reliable inversion framework for tracking regional carbon dynamics and refining bottom-up emission inventories.