Anthropogenic CO<sub>2</sub> Emissions in China Constrained by OCO-2/3 XCO<sub>2</sub> Observations

Qiu, Shenpeng; Feng, Shuzhuang; Jiang, Fei; Wei, Chong; Shi, Yusheng; Jia, Mengwei; Zhuang, Honglin; Wang, Hengmao; Zhang, Yongguang; Ju, Weimin

doi:10.5194/egusphere-2026-3241

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https://doi.org/10.5194/egusphere-2026-3241

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13 Jul 2026

| 13 Jul 2026

Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Anthropogenic CO₂ Emissions in China Constrained by OCO-2/3 XCO₂ Observations

Shenpeng Qiu, Shuzhuang Feng, Fei Jiang, Chong Wei, Yusheng Shi, Mengwei Jia, Honglin Zhuang, Hengmao Wang, Yongguang Zhang, and Weimin Ju

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.

Received: 09 Jun 2026 – Discussion started: 13 Jul 2026

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Shenpeng Qiu, Shuzhuang Feng, Fei Jiang, Chong Wei, Yusheng Shi, Mengwei Jia, Honglin Zhuang, Hengmao Wang, Yongguang Zhang, and Weimin Ju

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RC1:
'Comment on egusphere-2026-3241', Anonymous Referee #1, 07 Sep 2026 reply
This manuscript extends RegGCAS to assimilate OCO-2/3 XCO₂ observations and invert anthropogenic CO₂ emissions over mainland China during winter 2022–2023. National posterior emissions are reported to be 16.1% higher than the 2019 MEIC prior, with opposite adjustments in BTH and YRD. Results are evaluated through an OSSE, independent observations (TCCON, ObsPack, urban site), and sensitivity experiments. The study addresses an important topic and is worthy of publication after addressing the comments below.
General comments
The study uses the MEIC v1.4 2019 inventory as the prior for a winter 2022–2023 inversion and concludes that MEIC systematically underestimates winter emissions by 16.1%. However, China's fossil CO₂ emissions rose by ~10% from 2019 to 2022 and ~14% to 2023 (Friedlingstein et al., 2026), with even larger increases in coal, which dominates winter heating. A substantial fraction of the 16.1% difference may reflect real emission growth rather than a structural inventory bias. Please clarify this point.

Individual OCO-2/3 sounding retrieval errors are not independent. Baker et al. (2022) showed that along-track OCO-2 errors exhibit spatial correlation scales of ~10 km and ~20 km. This study uses per-sounding XCO₂ uncertainties as the observation error. Assimilating multiple correlated soundings within a 27 km grid cell may overestimate the effective observational constraint and lead to overfitting. Please clarify whether the data have been aggregated along-track (e.g., 10-second averages) or whether error correlations have been accounted for.

The OSSE uses a spatially uniform 30% reduction of MEIC as the prior, but one of the key findings “downward adjustment in city centers and upward adjustment in suburbs” is a spatially resolved bias that this design cannot test. In fact, Fig. 3 shows that the OSSE inversion derives correction factors with substantial spatial variations, despite a uniform 30% difference between “true” and prior. This suggests that results for small regions (e.g., from a city) should be interpreted with caution. I’d suggest that the authors have some discussion this limitation.

Specific comments
Line 36–39 and Line 584–586: The improvement against assimilated OCO-2/3 observations is an internal consistency diagnostic, not an independent validation. The Methods section correctly distinguishes between the two, but the Abstract and Conclusions list the OCO-2/3 fit alongside TCCON, ObsPack, and the urban site as if they were equivalent validations. Please correct this.
Line 104–105: The text states that winter biogenic fluxes in the YRD account for only 0.2% of anthropogenic emissions, yet the BIO_CASA sensitivity experiment (Table 2) shows an 11.7% change in posterior anthropogenic emissions when switching the biogenic flux product. Please explain why biogenic fluxes that are small in absolute magnitude have such a large impact on the inversion.
Line 256–278: Please describe the spatiotemporal collocation methods used to compare model output with TCCON, ObsPack, and the ZJ urban site.
Line 379-381: I do not agree that the two methods are consistent in BTH based on Fig. 2. Reporting regional totals and also time series may help a reader. In addition, how interference of biogenic fluxes affects the inference on anthropogenic emissions. This is a key question that is not adequately discussed.
Line 382–394: Posterior emissions show a marked drop during the Spring Festival (Fig. 1f), while the prior has only monthly-mean temporal resolution. Could part of this signal arise from meteorological variations during the holiday period, rather than from emission changes alone?
Line 609–610: The statement "the inherent structural biases identified within the bottom-up inventories are likely applicable throughout the year" is not sufficiently supported by winter-only results. Please soften this claim.
Fig. 4g: Just a comment: I appreciate that the authors evaluate the posterior simulation against the ZJ site located within the source region. This is valuable.
Technical corrections
Line 36–37: decrease -> decreases
Line 244: "(Taylor et al., 2023) reported" -> "Taylor et al. (2023) reported".
Line 314: Which months are included as winter?
Line 342: "overestimated" -> "overestimation".
Line 364: A little bit of ambiguity on “the proxy-based method”, as you have just mentioned “proxy variables” in the last paragraph. Better to describe the method with clearer language (maybe “anthropogenic CO2 emissions from the NO2-proxy method”).
Line 447: "Fig. 4h-i" is referenced, but the Fig. 4 caption only describes panels (a)–(g). Please check and correct the panel labels.
Line 812: "GenevaWMO-No. 1368". Change to "Geneva, WMO-No. 1368".

References
Baker, D. F., Bösch, H., Doney, S. C., O'Brien, D., and Schimel, D. S.: Carbon source/sink information provided by column CO₂ measurements from the Orbiting Carbon Observatory, Atmos. Chem. Phys., 10, 4145–4165, https://doi.org/10.5194/acp-10-4145-2010, 2010.
Friedlingstein, P., et al.: Global Carbon Budget 2025, Earth Syst. Sci. Data, 18, 3211–3288, https://doi.org/10.5194/essd-18-3211-2026, 2026.

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Citation: https://doi.org/10.5194/egusphere-2026-3241-RC1

Shenpeng Qiu, Shuzhuang Feng, Fei Jiang, Chong Wei, Yusheng Shi, Mengwei Jia, Honglin Zhuang, Hengmao Wang, Yongguang Zhang, and Weimin Ju

Data sets

Anthropogenic CO2 Emissions in China Constrained by OCO-2/3 XCO2 Observations Shenpeng Qiu and Shuzhuang Feng https://doi.org/10.5281/zenodo.20605680

Shenpeng Qiu, Shuzhuang Feng, Fei Jiang, Chong Wei, Yusheng Shi, Mengwei Jia, Honglin Zhuang, Hengmao Wang, Yongguang Zhang, and Weimin Ju

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Short summary

Satellite measurements and a model were used to estimate China's CO₂ emissions in winter 2022–2023. National total was 16 % higher than previous estimates. The Beijing-Tianjin-Hebei region showed higher emissions due to winter heating, while the Yangtze River Delta showed lower emissions. Emission drops during the Spring Festival holiday were also captured. This work offers a reliable, independent way to monitor CO₂ emissions and support climate policies.


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