the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Feb 2026
Spatiotemporal dynamics of atmospheric CO2 across China revealed by long-term, high-resolution satellite-derived data
Abstract. Understanding the spatiotemporal dynamics of atmospheric carbon dioxide (CO2) is fundamental for advancing climate change research and designing effective mitigation strategies. Yet current analyses are constrained by two key limitations: sparse observations that hinder intra-urban assessment and relatively short monitoring periods that limit long-term consistency. To overcome these challenges, we developed a long-term atmospheric CO2 hindcast modeling framework that generates daily 1-km column-averaged dry-air mole fraction of CO2 (XCO2) across China for 2000–2020. The framework adapts the proven PM2.5 hindcast approach to CO2 estimation by training an Extremely Randomized Trees model on the residuals between OCO-2 observations and CarbonTracker simulations. The model integrates a comprehensive set of physically interpretable predictors—including MAIAC aerosol optical depth, NO2, peroxyacetyl nitrate, meteorological variables, and land-use indicators—linking CO2 variability to co-emitted tracers and boundary-layer processes. Rigorous evaluation demonstrated high reliability (cross-validation R2 = 0.94–0.97, RMSE = 0.82–1.29 ppm; independent validation R2 = 0.82–0.97). The resulting long-term, high-resolution dataset reveals distinct carbon hotspots and their evolution: the North China Plain remained persistently elevated with rapid increases during 2000–2010, while southern China exhibited accelerated growth after 2010. Enhancement analyses identified consistent intra-regional hotspots in southeastern Beijing-Tianjin-Hebei and northern Zhejiang, with emissions declining after 2012 and rebounding after 2018. During the Wuhan COVID-19 lockdown, urban cores showed sharper reductions than suburban areas. The proposed XCO2 hindcast modeling framework and the resulting dataset provide a valuable foundation for advancing carbon-neutrality assessments and guiding climate policy across multiple spatial scales.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-5647', Anonymous Referee #2, 11 Mar 2026
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RC2: 'Comment on egusphere-2025-5647', Anonymous Referee #3, 01 Jul 2026
This manuscript develops a long-term (2000–2020), daily 1-km XCO2 dataset over China using a machine-learning framework trained with OCO-2 observations and CarbonTracker data. The study addresses an important topic because long-term, high-resolution atmospheric CO2 products remain limited, particularly prior to the launch of OCO-2. The modeling framework is well designed, and the validation results demonstrate good predictive performance using both cross-validation and independent observations. The enhancement analysis also produces interesting spatial and temporal patterns over major urban agglomerations.
Overall, I think this is a solid contribution that is suitable for ACP after moderate revision. My main concern is the interpretation of the enhancement results, while several other issues related to clarity and presentation should also be addressed.
Major comment
My primary concern is the interpretation of XCO2 enhancement.
Throughout the manuscript, the enhancement maps are frequently interpreted as "carbon emissions" or "changes in carbon emissions." Although the enhancement patterns clearly reveal anthropogenic influences, the enhancement itself remains an atmospheric concentration signal that is influenced not only by emissions but also by atmospheric transport, boundary-layer dynamics, biospheric exchange, and the definition of the background. Therefore, I suggest that the authors carefully review the manuscript and moderate statements that directly equate enhancement with emissions unless additional atmospheric transport or inverse modeling is incorporated. This issue affects not only the terminology but also some of the causal and policy interpretations in the Abstract, Sections 2.4.2 and 3.3, the Discussion, and the Conclusions.
In addition, the distinction between atmospheric XCO2, XCO2 enhancement, and carbon emissions should be maintained consistently throughout the manuscript. In several places these concepts appear interchangeable, which may confuse readers.
Minor comments
1. The caption of Figure 1 is unusually long. Some details, particularly the precise positions of the color bars, are not necessary for interpreting the figure. The caption could be shortened while retaining the definitions of panels (a)–(h), the validation datasets, and the principal information represented by each panel.
2. Caption of Fig. 2 caption beginning around line 270 is grammatically incomplete: “Figure 2. of each predictor on XCO2 levels quantified using the SHAP method…”
Please also insert a space in “Fig.2” in Section 3.1.3.
3. The caption of Fig. 3 lists panels (a), (b), and (c), but the panel identifier “(d)” is missing before the GOSAT interpolation. The sentence structure is also difficult to follow because the daily, monthly, and annual columns are described in reverse order. I suggest revising it to clearly identify all four rows or products and the three temporal aggregations.
4. The title of Section 3.3.2 contains duplicated wording: carbon emission.
In addition, the paragraph following Fig. 5 states, “As shown in Fig. 5d,” when discussing spatial changes during the Wuhan lockdown. According to the Figure 5 caption, panel (d) presents the multiyear mean enhancement in the YRD, whereas the Wuhan percentage changes are presented in panel (a). This reference should therefore be checked and likely changed to Figure 5a.
5. Specific grammar and wording corrections
The manuscript would benefit from careful language editing. Examples include:
1) Lines 119–122: The sentence beginning “We obtained annual land cover classification data…” joins several independent clauses with commas. It should be divided into separate sentences or restructured with parallel verbs.
2) Line 223 should be revised to “with more than 65% of grid cells containing only one observation.”
3) Lines 309–310: “These of accuracy and spatiotemporal patterns underscore…” is incomplete. It may have been intended to read: “These comparisons of accuracy and spatiotemporal patterns underscore…”
4) Line 466: Insert a space before the citation in “previous studies(Lu et al., 2025).”
5) Lines 503–505: The sentence here is missing a noun after “Wuhan-specific.” It should be revised to “consistent with previous Wuhan-specific studies.” There is also an extra closing parenthesis in “40–60%)).”
6) Line 526 should be revised to “applying it to hindcast XCO2 for earlier years”.
6. Undefined dataset abbreviations
Most regional and methodological abbreviations are appropriately defined. However, several dataset or reanalysis abbreviations appear without their full names, particularly MERRA2-GMI and EAC4 in Section 2.1.2. ERA and ERA5 are also introduced only as product names, and CAMS is used later without first providing its complete name. Please define these terms at their first occurrence and then use the abbreviations consistently.
Citation: https://doi.org/10.5194/egusphere-2025-5647-RC2
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- 1
"Spatiotemporal Dynamics of Atmospheric CO2 across China Revealed by Long-Term, High-Resolution Satellite Data"
General Comments
This manuscript presents a long-term, high-resolution satellite-derived XCO2 dataset over China using a machine learning approach. While the topic is timely and the dataset potentially valuable, the manuscript in its current form suffers from several fundamental methodological concerns that undermine confidence in the validity and interpretability of the results. Key issues include: (1) an inadequately described and physically questionable methodology (2) an overstated and poorly validated claim that the model improves upon existing products; (3) a misuse of the XCO2 enhancement framework, which cannot be straightforwardly interpreted as a proxy for CO2 emissions without accounting for wind speed and other confounding factors; and (4) several factual errors, inconsistent terminology, and unclear or internally inconsistent figure descriptions. Taken together, these issues represent substantial weaknesses that require major revision before the manuscript can be considered suitable for publication. Given the number and severity of the concerns raised below, the manuscript is not recommended for publication in its current form.
Specific Comments
Lines 37–40:
The argument that atmospheric CO2 data can be used to assess mitigation effectiveness and inform sustainable development is stated too broadly and lacks specificity. Connecting column-averaged XCO2 retrievals to surface emissions is scientifically challenging due to CO2's long atmospheric lifetime, its associated large and variable background signal, a low signal-to-noise ratio relative to emission-driven enhancements, and the difficulty of separating anthropogenic signals from biospheric fluxes. The authors should provide a more rigorous and nuanced discussion of how their dataset could be used for these purposes—acknowledging these limitations.
Line 50:
The manuscript states that OCO-2 has a "daily revisit capability." This is incorrect. According to the OCO-2 mission documentation, the satellite operates on a 16-day repeat cycle. This should be corrected, and the relevant citation should be verified accordingly.
Lines 99–101:
The citation of Crisp et al. (2017) as a reference for the CarbonTracker (CT) XCO2 product is inappropriate. Crisp et al. (2017) describes the algorithm theoretical basis for OCO-2 Level 2 retrievals, not the CarbonTracker data assimilation system. The authors should replace this with an appropriate reference for CarbonTracker CT2022 (e.g., Peters et al. or the relevant NOAA/GML documentation).
Lines 101–104:
The description of how the coarse-resolution CarbonTracker data (3° × 2°) are resampled to a 0.01° grid is insufficient. Also, the authors should clarify how the final XCO2 estimate is reconstructed—specifically, whether the predicted residual is added back to the CT XCO2 value.
Lines 115–117:
The use of daily-mean meteorological and air pollution data to predict XCO2 at the satellite overpass time (approximately 13:30 local time) requires physical justification. Atmospheric properties such as planetary boundary layer height, temperature, humidity, and trace gas concentrations can vary substantially throughout the day. Using daily-mean values rather than values contemporaneous with the satellite overpass may introduce systematic biases. The authors should either provide a physical justification for this approach or conduct a sensitivity analysis to evaluate how the temporal sampling of input predictors affects model performance for the target overpass time.
Lines 163–167:
The leave-one-year-out cross-validation strategy described here appears to withhold one year from within the 2015–2020 period for evaluation. However, the authors claim this approach allows assessment of model performance for the pre-2015 period. This logic is not convincing: withholding a year from the middle of the training period does not simulate the extrapolation challenge posed by hindcasting to years before the OCO-2 era, which may involve structural differences in the predictor-XCO2 relationships. The authors should clarify the validation approach and, if the goal is to assess pre-2015 performance, adopt a more appropriate out-of-sample evaluation strategy (e.g., training exclusively on post-2015 data and evaluating against ground-based observations in pre-2015 years).
Lines 183–185:
The use of a 10:30–16:30 time window to average ground-based observations for comparison with OCO-2 retrievals (overpass time ~13:30) is overly broad and requires justification. Atmospheric CO2 concentrations at surface sites exhibit pronounced diurnal variability driven by boundary layer dynamics and biospheric fluxes, meaning that measurements taken in the early morning or late afternoon may differ substantially from those at solar noon. Averaging over a six-hour window centered loosely on the overpass time may introduce significant biases in the validation. The authors should either narrow the averaging window (e.g., ±1–2 hours around the overpass time) or provide a sensitivity analysis demonstrating that this choice does not materially affect the validation statistics.
Lines 196–199:
The XCO2 enhancement method, while widely used in exploratory analysis, cannot be directly interpreted as an indicator of surface CO2 emissions without accounting for atmospheric transport, particularly wind speed and direction. An XCO2 enhancement above a background value reflects a combination of upstream emissions, atmospheric dilution (governed by wind speed), boundary layer height, and biospheric signals. High XCO2 enhancements under calm wind conditions may not correspond to higher emissions than lower enhancements under strong winds. The authors should either (1) incorporate a wind-speed correction or apply a more physically rigorous emission estimation framework, or (2) explicitly characterize the temporal variability of wind conditions over the study regions and discuss the extent to which this limits the interpretation of enhancements as emission proxies.
Figure 1g:
A visible data gap appears in the estimated XCO2 time series for approximately 2002–2003. Is it attributable to missing input data (e.g., predictor variables), or a deliberate data exclusion decision? This should be addressed explicitly in the text.
Section 3.1.3:
This section discusses feature importance but focuses almost exclusively on MAIAC AOD, neglecting several other highly ranked predictors. Notably, total column water vapour emerges as the most important predictor in both the YRD and PRD regions, yet this is not discussed. The authors should provide a physical explanation for this result—is this relationship physically meaningful or potentially spurious? Similarly, Day of Year appears to be among the most important features across regions, raising the question of whether the model's representation of XCO2 seasonality is driven primarily by this temporal index rather than by physically meaningful predictors. The implications of this for spatial generalization and for hindcast periods should be discussed.
Lines 270–272:
There is a typographical error in the figure caption: "Figure 2. of each …" should be corrected.
Lines 276–280:
The comparison of R² and RMSE values across different models to argue that the present model outperforms previous studies is methodologically inappropriate. Model skill metrics are highly sensitive to the spatial domain, temporal coverage, and resolution of the evaluation dataset, as well as the choice of validation sites and periods. Without a controlled, identical evaluation framework applied to all compared models, such inter-model comparisons cannot support strong claims of superiority. The authors should reframe this comparison more cautiously, noting that direct performance comparisons are not possible across studies with differing spatiotemporal coverage and evaluation protocols.
Lines 294–296:
The claim that previous machine-learning studies producing daily 1-km XCO2 estimates have been limited to post-2015 data is made without citation. Please add appropriate references to support this statement.
Lines 303–305 and 309–310:
The claim that the high-resolution product captures intra-urban XCO2 variations "with greater accuracy" than CT is not substantiated. To support this claim, the authors must provide a direct comparison of CT XCO2 and the model-estimated XCO2 against independent validation data (i.e., data not used in training), with evaluation metrics reported for both. Without this, the claim that the machine learning model improves upon CT at fine spatial scales remains unverified.
Figure 3a:
A sharp spatial gradient in XCO2 is visible around the Taklamakan Desert region, which appears physically implausible for a remote arid region with minimal anthropogenic activity. The authors should investigate whether this feature is present in the OCO-2 retrieval data or the CarbonTracker product, and if not, identify which input variable(s) are driving this artifact.
Lines 351–352:
The terms "CO2," "XCO2," and "mixing ratio/concentration/level" are used inconsistently throughout the manuscript. XCO2 is a column-averaged dry-air mole fraction expressed in parts per million (ppm), not a concentration in the physical chemistry sense (e.g., mol/m³). The authors should adopt consistent and scientifically precise terminology throughout the manuscript and avoid referring to ppm values as "concentrations."
Figures 5c and 5d:
The interpretation of XCO2 enhancements as indicators of urban CO2 emissions in these figures is problematic. If a single background XCO2 value is used for each region, the spatial patterns shown primarily reflect the climatological XCO2 gradient across the region, which integrates regional wind transport, biospheric fluxes, and the regional XCO2 gradient—not local emission differences between cities. For example, the larger enhancements observed in the southern portion of the BTH region do not necessarily imply greater emissions than those in Beijing; they may simply reflect more favorable transport or boundary layer conditions. The authors should clarify the background correction methodology and substantially revise the interpretation of these figures.
Lines 383–385:
The description of Figure 5d is inconsistent with its caption. The text refers to "a striking difference in the spatial patterns of XCO2 enhancements between the two years" and reports specific percentage reductions during the COVID-19 lockdown in Wuhan, but the figure caption states it shows the "20-year mean XCO2 enhancements for the YRD region." It is unclear which figure the text is actually referring to. The authors should ensure that all in-text figure references are accurate and that figure captions correctly describe the displayed content.