the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Urban atmospheric CO2 plumes from space – Part 1: Atmospheric modeling of the urban boundary layer
Abstract. Interpreting atmospheric CO2 observations over cities from space requires transport models that accurately link concentration patterns to surface fluxes, making realistic urban boundary-layer representation critical. This study examines how urban physics parameterizations influence boundary-layer dynamics and near-surface CO2 mixing ratios over the Paris metropolitan area under winter and summer conditions. Using the Weather Research and Forecasting (WRF) model, four configurations are evaluated: no-urban representation (No_URB), a single-layer urban canopy model (SLUCM), and two multi-layer schemes (BEP: Building Effect Parameterization and BEM: Building Energy Model). Model outputs are assessed against surface energy flux observations, turbulence measurements, planetary boundary layer height (PBLH), and near-surface CO2 mixing ratios from dense urban and suburban monitoring networks, alongside wind, temperature and humidity. Urban physics exert strong control on wintertime boundary-layer structure and CO2 variability, with scheme differences driven primarily by sensible heat flux, friction velocity, and turbulent kinetic energy, producing large contrasts in PBLH and CO2 accumulation. In summer, PBLH diurnal patterns converge across schemes, with a characteristic plateau during active convection, and CO2 variability becomes dominated by convective mixing. BEM provides the most physically consistent representation across both seasons. Sensitivity tests with three planetary boundary layer schemes show that Mellor–Yamada–Janjic coupled with BEM best reproduces wintertime CO2, capturing realistic nighttime accumulation and daytime mixing, while Yonsei University and BouLac exhibit systematic biases. These results demonstrate that realistic urban physics combined with an appropriate turbulence scheme are essential for physically consistent urban CO2 simulations, particularly in winter.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2026-2109', Ivo Suter, 05 Aug 2026
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AC1: 'Reply on RC1', Alohotsy Rafalimanana, 12 Aug 2026
We thank the reviewer for the thorough and constructive evaluation of our manuscript. We greatly appreciate the positive assessment of the study and the recognition of its contribution to urban atmospheric transport modelling and to linking atmospheric measurements with emission inventories. The reviewer's comments have provided valuable guidance for improving the manuscript.
Our detailed responses to the reviewer's comments are provided in the attached response document. Each comment is addressed individually, with our proposed responses and the corresponding changes that will be considered in the revised manuscript.
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AC1: 'Reply on RC1', Alohotsy Rafalimanana, 12 Aug 2026
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RC2: 'Comment on egusphere-2026-2109', Anonymous Referee #2, 24 Aug 2026
Rafalimanana et al. (2026) investigate how urban physics parameterizations in the model (here WRF) affect the structure and dynamics of the boundary layer, as well as near-surface CO2 mixing ratios in Paris. The analysis was conducted separately for winter and summer, utilizing both observations and simulations of meteorological variables and surface CO2 mixing ratios. The study made use of four urban configurations in WRF: No_URB, SLUCM, BEP, and BEM, to assess their impact on the boundary layer and, consequently, on CO2 mixing ratios. The BEM urban configuration was coupled with other PBL schemes available in WRF—MYJ, YSU, and BouLac—to further assess their effects on near-surface CO2 concentrations throughout the diurnal cycle. The main conclusion of this study, I understand, is that an accurate representation of turbulent mixing, combined with urban energy exchanges, is critical for effectively modeling urban CO2 mixing ratios, which is often overlooked in inverse modeling efforts.
Overall, I find this study important, supported by dense and unique observations, particularly in the context of inverse modeling to derive urban emissions utilizing ground-based or satellite CO2 mixing ratio measurements. However, my primary criticism lies in the quality of the presentation, clarifying the robustness of the results. The manuscript suffers from a lack of conciseness, crispness, and clarity in presenting results and discussion, the inclusion of trivial details many times, and avoidable narratives that hinder the readability of the manuscript. At the same time, it excludes important quantitative outcomes and relies heavily on qualitative statements in many aspects. Given these issues, the novelty of this study in its current form is somewhat questionable. Hence, I recommend restructuring the manuscript so as not to lose the strength of its findings, including summarizing the data and methodology details in tables that could also help clarify the information for scientific readers. Specific comments are provided below:
Abstract. The overall impact of the content in the abstract is limited in the current form. It predominantly relies on qualitative statements that lack depth and useful detail. Authors are therefore recommended to incorporate quantitative information to support their claims. Also, most of these current assertions do not offer any novel insights to the scientific community at large, beyond specific regional details pertaining to Paris. I recommend authors to rework it to make findings that would be valuable to the broader scientific community beyond this particular locale.
Introduction. Overall, the introduction reads well and establishes the context of the study. However, it would be more meaningful if the manuscript may provide a broader global context, including the previous research based on WRF CO2 modeling to identify the current research gap. For instance, the citations in the introduction predominantly refer to European and US studies, though WRF modeling has been widely used and, importantly, contributed to other regions as well.
Also, please note that for more than 15 years, WRF has been used to simulate atmospheric CO2 using passive tracer options at fine-scale applications (e.g., Ahmadov et al., 2007; Pillai et al., 2010; Beck et al., 2011; Pillai et al., 2011); therefore, I suggest that the authors consider the citations mentioned above when first referring to WRF CO2 modeling with passive tracers, which reflect prior contributions fairly from elsewhere beyond some local contexts set in the current form.
Methodology, Models and Data.
This section seems unnecessarily long, spanning ~7 pages. It includes many trivial details and is often presented in a disorganized fashion. The authors are recommended to shorten this section by incorporating tables, charts, or similar elements to help readers easily locate and readily refer to information when needed. For example, a close look at how the sections 'WRF Model Configurations' and 'Model Input Data' are currently presented in the manuscript clearly shows that they could be made more concise with improved organization or presentation styles. The same is true for ‘Observational datasets’. Also, please retain only the most relevant details for ‘Evaluation strategy and statistical metrics’ so readers can follow the results and discussion, and move the rest of the details to the supplementary material. An example of trivial details for the readers (if it helps the authors to identify): “Additionally, model–observation comparisons are visualized using diurnal cycles, scatterplots, time series, vertical profiles, spatial transects, and distribution plots to illustrate temporal evolution, spatial variability, correlation, and overall variability of the studied variables.”
Currently, the manuscripts contain such trivial statements in many places.
Results (and Discussion)
This section is the most extensive part of the manuscript, making it difficult for readers to grasp the study's main findings due to a lack of concision. There are a total of 13 figures (excluding 2 figures from previous sections), spanning ~25 pages. The evaluation results of the WRF meteorology are presented in 5 sub-sections, while the CO2 evaluation —the important aspect of this study—is covered in 2 sub-sections. Although ACP does not impose restrictions on the number of figures or pages used to demonstrate the robustness of the results, this lengthy format, as used in the current manuscript, negatively affects this study's overall readability and effectiveness.
The Discussion (currently in the next section) is generally well-written but often lacks sufficient quantitative detail, aside from references to figures. At the same time, I don’t see enough elements in the current manuscript to fully convince readers of the quantitative impact of urban meteorology on CO2, as important take-home messages. Without these convictions, it is very difficult to justify the novelty of the current study. I strongly feel that the above aspect is crucial to the publication of this study in ACP without major revisions, given the concerns outlined above.
Conclusion.
Based on the suggested revisions, authors are encouraged to refine the conclusions in a very concise, robust, and quantitative manner.
Specific Comments
Abstract: pl include more quantitative statementsL08: Urban physics exert … : “Urban physics schemes exert
L33: Explain “small-scale” in this context.
L96: Please provide the basis of the simulation period?
L125: Parameterization and schemes used: please make a table for the conciseness.
L212: real-world boundary data -> pl avoid “real-world” phrasing.
U∗ & TKE: formula missing
Section 2.4 Evaluation strategy and statistical metrics: Much of this section can be made concise by reworking it (also including the table), which helps the readability of the article.
L230-231: ambiguous, please rephraseL276-278: repetition.
L349-350: Any reference to support the claims?
L384-386: season?
L391-392: Why?
L393: give obs. site information (table/others).
L404-406: boundary layer development has not yet been discussed. So this claim here is difficult to stand. Rephrase/move/give reference to relevant sections.
L414: Give a reference to Figure 5.
L453-454: The statistics do not give that information, but Figure 5 gives that distinct influence.
Duplication of explanations in the results section.
L524 & 529: Figure numbers wrong.
3.5: Table 6 reference is not given anywhere.
L662: “Across all sites”: Not at PAARBO
L776-777: Repetition
L828-829: U* identical at JUS during winter?
Section 4: Many repetitions, e.g., line nos. 923, 933.
L959-962: “Error in boundary layer propagates directly to the tracer concentration bias” is not a new addition for the scientific community.
References:
- Ahmadov, R., Gerbig, C., Kretschmer, R., Koerner, S., Neininger, B., Dolman, A. J., and Sarrat, C.: Mesoscale covariance of transport and CO2 fluxes: Evidence from observations and simulations using the WRF-VPRM coupled atmosphere-biosphere model, J. Geophys. Res.-Atmos., 112, D22107, https://doi.org/10.1029/2007JD008552, 2007.
- Beck, V., Koch, T., Kretschmer, R., Marshall, J., Ahmadov, R., Gerbig, C., Pillai, D., and Heimann, M.: The WRF Greenhouse Gas Model (WRF-GHG). Technical Report No. 25, Tech. rep., Max Planck Institute for Biogeochemistry, Jena, Germany, https://www.bgc-jena.mpg.de/bgc-systems/uploads/Wrf-ghg/Technical Reports 2011 Beck.pdf, 2011.
- Pillai, D., Gerbig, C., Marshall, J., Ahmadov, R., Kretschmer, R., Koch, T., and Karstens, U.: High resolution modeling of CO2 over Europe: implications for representation errors of satellite retrievals, Atmos. Chem. Phys., 10, 83–94, https://doi.org/10.5194/acp-10-83-2010, 2010
- Pillai, D., Gerbig, C., Ahmadov, R., Rödenbeck, C., Kretschmer, R., Koch, T., Thompson, R., Neininger, B., and Lavrié, J. V.: High-resolution simulations of atmospheric CO2 over complex terrain – representing the Ochsenkopf mountain tall tower, Atmos. Chem. Phys., 11, 7445–7464, https://doi.org/10.5194/acp-11-7445-2011, 2011
Citation: https://doi.org/10.5194/egusphere-2026-2109-RC2
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The manuscript presents the first part of an evaluation of modelling CO2 with WRF-Chem over the Paris area. The focus is on the impact of various parameterizations of boundary layer processes and urban physics. Specifically four urban canopy represenations and three PBL-schemes were evaluated against meteorological observations, surface flux measurements, CO2-monitoring and lidar measurements. The study concludes that the BEM urban scheme coupled with the MYJ PBL scheme matches the observations best, particularly during winter.
This article is a contribution for urban atmospheric transport modelling and a step to link measurements to emission inventories. The goal and design of the study are very clear. The observational dataset is impressive. The manuscript could benefit from some shortening by trying to avoid repeating information.
My major concern lies in that some of the conclusions should be supported by more evidence or worded more carefully. The model comparison should be supported by adding confidence intervals or significance testing.
Overall, I believe the manuscript is valuable and suitable for publication after revision.
Please find the comments in the attached file.