the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 31 Dec 2025
Near real-time inversion of high-resolution anthropogenic carbon emissions in the Pearl River Delta region based on the four-dimensional local ensemble transform Kalman filter
Abstract. For climate mitigation, it is necessary to address the dynamic updating and assessment of CO2 emissions at regional scales. This study developed a kilometer-scale carbon assimilation system (the Guangzhou Regional Atmospheric Composition and Environment Forecasting System–Greenhouse Gas–Data Assimilation, GRACES-GHG-DA) by coupling the weather research and forecasting–greenhouse gas (WRF-GHG) model with the four-dimensional local ensemble transform Kalman filter (4D-LETKF). GRACES-GHG-DA constructs a near-real-time 4-km anthropogenic emission inventory, constrained by simulated CO2 observation data from seven high-precision greenhouse gas monitoring stations in the Pearl River Delta (PRD) region, to analyze spatiotemporal emission distributions and their relationship with ambient CO2 concentrations. The results indicate that: (1) GRACES-GHG-DA accurately downscales CO2 concentrations from a resolution of 36 to 4 km, with the finer resolution better capturing meso- and micro-scale variations (hourly and monthly mean biases of −0.77 and −0.51 ppm, respectively). (2) In 2022, the inverted annual anthropogenic CO2 flux in core PRD areas exceeded 7500 g C m−2 a−1, contrasting with values below 1000 g C m−2 a−1 in peripheral regions. Compared to the inversion estimates, statistical inventories (EDGAR, ODIAC, GCP, and MEIC) underestimated total emissions by 14.71% on average. (3) Seasonal anthropogenic emissions were 24.03, 29.86, 30.61, and 27.26 Tg C for spring, summer, autumn, and winter, respectively, showing a unimodal diurnal pattern largely influenced by fossil-fuel electricity generation.(4) Anthropogenic emissions are not the dominant factor governing atmospheric CO2 concentrations in the PRD; vegetation carbon uptake/release, boundary layer evolution, and regional transport also play critical roles.
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Status: open (until 16 Feb 2026)
- CC1: 'Comment on egusphere-2025-6272', Nima Zafarmomen, 02 Jan 2026 reply
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RC1: 'Comment on egusphere-2025-6272', Anonymous Referee #1, 26 Jan 2026
reply
General assessment
Overall, this is an interesting and potentially valuable study. The authors couple the widely used WRF-GHG model with a four-dimensional local ensemble transform Kalman filter (4D-LETKF) to estimate anthropogenic CO₂ emissions in the Pearl River Delta (PRD) region for the year 2022, using in situ CO₂ observations from seven high-precision monitoring stations. The ability to derive top-down CO₂ emission estimates for a large and highly urbanized region such as the PRD is highly relevant for climate mitigation efforts and provides an important independent validation of bottom-up emission inventories.
The chosen methodological framework is, in principle, well suited for addressing this problem. WRF-GHG, coupled with VPRM, and embedded in a 4D-LETKF inversion framework, represents a state-of-the-art approach for regional carbon flux estimation. The results show systematic upward adjustments of emissions in the urban core, particularly around Shenzhen and Guangzhou, which lead to correspondingly higher simulated CO₂ concentrations. The inferred seasonal cycle, with higher emissions in summer and autumn and lowest emissions in spring, is plausible for the PRD region and can reasonably be linked to variations in energy demand, in particular electricity generation for cooling. The results are presented from multiple perspectives, including comparisons with several emission inventories and analyses of modeled CO₂ concentrations.
However, in my view the study has a major weakness: it lacks a rigorous evaluation of the CO₂ simulations and of the inversion system itself. Modeling atmospheric CO₂ at regional scales is inherently challenging, and regional inversions are particularly susceptible to systematic biases in emission estimates. These challenges are well known in the community, and considerable effort is currently made to address them. While the authors briefly acknowledge some of these issues in the Results section, no concrete evaluation or sensitivity analysis is carried out. As a result, it is difficult to assess the robustness of the inferred emission adjustments.
This concern can be summarized in three points:
- First, biospheric fluxes represent a major source of uncertainty. Photosynthetic uptake and ecosystem respiration are both large fluxes that partially cancel each other, and small errors in their representation can strongly affect inferred anthropogenic emissions. In a regional inversion framework, biospheric fluxes should ideally be jointly optimized, or at least rigorously evaluated. In the present study, however, no validation of the vegetation fluxes is provided. At a minimum, a sensitivity analysis of the emission estimates with respect to the net ecosystem exchange (NEE) would be necessary to assess the impact of biospheric flux uncertainties.
- Second, the treatment of background CO₂ concentrations is insufficiently constrained. While CarbonTracker products may exhibit small biases at large scales, their representation at regional scales is limited. In regional inversions, it is common practice to include an upwind background station and to simultaneously optimize background concentrations in order to avoid systematic biases in emission estimates arising from background errors. This aspect is not addressed in the current study.
- Third, the assimilation of nighttime CO₂ observations (if performed) raises additional concerns. Nighttime boundary layer heights are often overestimated in atmospheric models, which can lead to systematic concentration biases. An evaluation of model performance with respect to boundary layer dynamics, particularly at night, would therefore be essential to assess the reliability of the inferred emissions.
The systematic upward correction of a posteriori CO₂ concentrations found in the study could plausibly be attributed, at least in part, to one or a combination of these three factors. This issue requires a much more thorough investigation.
Also, the term “near real-time” in the title appears misleading, as the study presents a retrospective inversion for the year 2022 rather than an operational or low-latency system. While the framework may be suitable for near-real-time applications, this is not demonstrated in the current analysis.
Finally, several crucial methodological details are missing. Critical information about the observations (e.g., sampling heights, assimilated time periods, potential rejection thresholds) and about the inversion system itself (e.g., ensemble size, spatial correlation lengths, localization strategy or cut-off radii, and assumed observation error correlation structures) are not provided. These details are essential for evaluating and interpreting the results and must be included for the study to be reproducible and scientifically assessable.
Minor / Technical Comments
- S2L45–50: Consider citing ICON-ART applications for CO₂ simulations and inversions (e.g., Ponomarev et al., 2026). This reference is also relevant for S2L82–85, as it similarly addresses urban CO₂ emissions.
- S4L101: A brief description of the 4D-LETKF method, its key features, and references to foundational studies (e.g., Hunt et al., Ott et al. Etc...) would help contextualize the methodology for readers unfamiliar with it.
- S4L111: The phrase “inverted CO₂ concentrations” is misleading. Emissions are inferred by the inversion, not the concentrations themselves. A more appropriate term would be posterior or analyzed CO₂ concentrations.
- S4L118: Please add a reference for VPRM (e.g., Mahadevan et al., 2008).
- S7L179: Clarify the definition of xb . It is the ensemble mean, not the emissions; EDGAR provides the prior from which the ensemble is drawn. Also specify whether the stated uncertainty corresponds to 1σ or 2σ.
- S7L180: The correlation length (and structure) used in the inversion should be stated explicitly.
- S7L181: The description of Xb as a “matrix of each ensemble member” is unclear. Consider phrasing it as the ensemble perturbation matrix.
- S7L187: Provide details on the observation error covariance R, including the magnitudes and the assumed correlation structure. How are they determined? Are they dependent on the prior concentrations or meteorological conditions?
- S7 Sect. 2.3: Several methodological aspects remain unclear. For example, how and where was localization applied? Which observations were assimilated, including night-time measurements? What are the prior uncertainty correlation lengths and the observation error assumptions?
- S8L198–199: The formulation is imprecise. Xa represents ensemble perturbations, not a state ensemble. The posterior ensemble members are reconstructed from xˉa and Xa , not by simply adding xˉa to Xa .
- S8L208: The term “observation biases” is likely intended to describe residuals or innovations. Consider clarifying this.
- S9L230 ff.: Provide the sampling height of the in-situ CO₂ observations, which is critical for interpreting results.
- S10L263: Comparing posterior concentrations at in-situ stations with (prior) satellite XCO₂ is problematic. It’s not an apples-to-apples comparison due to fundamentally different vertical sensitivities and averaging kernels. Please clarify the rationale.
- S9 Sect. 3: So far, the inversion period has not been explicitly stated. Please do so.
- S9 Sect. 3: The analysis shows the inversion reproduces the assimilated observations better than the prior forward run (so what an inversion is supposed to do), but this is not a proper validation. Are there independent stations that could be used? Fit-to-obs validations, such as the chi-squared metrics, could help quantify fit quality.
- S10L267–268: Strongly improved agreements with assimilated observations may reflect overfitting. Presenting chi-squared or fit-to-observation statistics would help assess the robustness of the inversion.
- S12L291: The statement attributing discrepancies solely to underestimated EDGAR emissions may be oversimplified. Other factors, such as uncertainties in biospheric fluxes, background concentrations, or night-time boundary layer representation, should be tested and discussed (see my general comments).
- S13L304: Correct terminology: time series rather than distributions.
- S13 Fig. 6: The inversion reproduces the assimilated observations, but it is unclear how this compares to observational uncertainties. Possible overfitting should be discussed; again, independent validation or chi-squared metrics would be informative.
- S14L330: Especially the influence of misrepresented nocturnal PBL height could be large and contribute to systematic biases.
- S17 Fig. 9b: Increase the figure size. Inversion-EDGAR maps are particularly important, as they reflect actual emission innovations. Please indicate station locations on the maps.
- S17 Fig. 9b: Inversion-Edgar: Why do we see the pattern where upward adjustments are surrounded by downward adjustments?
- S17 and all other figures: Please use perceptually uniform colormaps.
- S19 Fig. 11 and S22 Fig. 13a: Displaying sectoral emission contributions (traffic, energy, industry) would help interpret seasonal and diurnal emission patterns. Their discussion would strengthen the interpretation of the results.
- S22L515: In addition, the winter monsoon transports air from inland regions that has already been influenced by emissions. It’s not only the higher CO2 concentrations of the air coming from higher latitudes.
- Results section (general): Some descriptions of individual numerical values are lengthy but add little value beyond the figures. Consider condensing the text and focusing on interpretation and key insights.
Citation: https://doi.org/10.5194/egusphere-2025-6272-RC1 - First, biospheric fluxes represent a major source of uncertainty. Photosynthetic uptake and ecosystem respiration are both large fluxes that partially cancel each other, and small errors in their representation can strongly affect inferred anthropogenic emissions. In a regional inversion framework, biospheric fluxes should ideally be jointly optimized, or at least rigorously evaluated. In the present study, however, no validation of the vegetation fluxes is provided. At a minimum, a sensitivity analysis of the emission estimates with respect to the net ecosystem exchange (NEE) would be necessary to assess the impact of biospheric flux uncertainties.
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This paper presents a sophisticated approach to carbon cycle science by developing the GRACES-GHG-DA system. It bridges the gap between mesoscale meteorological modeling (WRF-GHG) and advanced data assimilation (4D-LETKF) to achieve kilometer-scale, hourly updates for anthropogenic CO₂ emissions in one of the world's most complex urban clusters: the Pearl River Delta (PRD). The novelty of this research lies in its spatiotemporal granularity and the application of 4D-LETKF for near real-time inversion. While global and regional models often struggle with the "representation error" of urban environments, this study successfully downscales to a 4-km resolution, allowing for the identification of meso- and micro-scale variations that coarser models (36 km) overlook.
The use of 4D-LETKF represents a meaningful advancement over more commonly applied 3D EnKF or EnSRF approaches, especially in the assimilation of asynchronous hourly observations. However, the manuscript would benefit from a clearer articulation of its novelty relative to previous WRF-GHG-based regional inversion studies. While the technical improvements are evident from the results, explicitly highlighting how the near–real-time, hourly-updated anthropogenic emission inversion and the kilometer-scale resolution extend beyond prior work would strengthen the paper’s positioning.
The comparison between the top-down inversion and bottom-up inventories is one of the manuscript’s strengths. The spatial patterns of disagreement are well analyzed, particularly in the PRD urban core. However, the discussion could be deepened by offering more interpretation of why certain inventories, especially MEIC, show large underestimations in core cities. Even a qualitative sectoral explanation would help contextualize these differences and enhance the relevance for inventory developers and policymakers.
To strengthen the discussion on urban emission heterogeneity and the challenges of capturing mobile source contributions (specifically mentioned in Section 3.3 regarding traffic emissions), I strongly suggest that the authors cite the following paper:
Comprehensive spatiotemporal analysis of long-term mobile monitoring for traffic-related particles in a complex urban environment. > DOI: 10.1016/j.apr.2025.102870