The scope of this paper is the monitoring of CO2 emissions of European cities from the atmospheric concentrations.  Indeed, the emission from the cities increases the atmospheric concentrations so that concentration measurements can be used to infer the emissions.  There are a number of challenges however linked to the emission-concentration relationship that depends on the variable atmospheric transport, the heterogeneity of the emission or the other (than the city emissions) fluxes that impact the CO2 concentrations.  The challenges are different among cities and this paper attempts to classify the European cities according to these challenges.  They have defined a number of indicators to quantify the various challenges and make a statistical analysis based on this criteria to identify the cities that are the most suitable for CO2 monitoring experiments.

The paper is interesting and can be of interest for the growing community that attempts to estimate city emissions either from surface network measurements of from remote sensing imagery.  One could certainly criticize the definition of the challenge indicators but the choice of the authors appear reasonable.

Note that some challenge apply mostly to remote sensing applications (such as the cloud cover) whereas other are more applicable to the definition of use of a surface network (such as the wind direction).  All indicators are made available so that one can make its own classification.

Minor comments to be considered by the authors:

Line 38 : Cities account for approximately : "account for" is not clear enough. Is it scope 1, scope 2 or scope 3 ?  Only scope 1 emissions could be measured from the atmospheric concentrations

Line 52 : (such as kgCO2/vehicle), : It would make more sense to have kkCO2/km

Line 83 : in Indianapolis the enhancement is only about 3 ppm.  Is it on average, or max ?

Line 148 : “about 50 out of 365 plumes per year could” .  Better to say that, out of the 365 days in a year, only 50 appear suitable to observe the CO2 plume from space"

Line 149 : Furthermore, the collected samples were higher…   Not clear.  What is higher ?  Emissions or CO2 plume ?

Line 153.  might be monitored from ».  "might be monitored" lacks detail. It depends on the accuracy requirement

Line 449 : make eddy covariance measurements ».  I did not understood that this paper analyzes the possibility to make such measurements

Line 458 : What emissions from airport are considered ?  Is it mostly that of the building or that of the plane take off ?  For those, the temporal profile of the emissions may be quite challenging

# Review of essd-2025-63: "Monitoring CO₂ in diverse European cities: Highlighting needs and challenges through characterisation"

## 1. General Comments

This paper presents a timely framework, the "CO₂ Monitoring Challenges

City Mapbooks" (CMC-CITYMAP), for characterizing and classifying

European cities based on the challenges they pose to urban CO₂

monitoring. The manuscript is well-structured, and the methodology is

generally well-explained. The authors use a systematic approach to

quantify four key challenges (background, biogenic, modelling, and

observational) with 18 metrics derived from public datasets, providing a

powerful tool for network design and strategic planning.

However, the manuscript's primary weakness is the lack of a clear,

reproducible justification for the weighting scheme used to combine the

18 metrics into the four challenge scores. The weights are central to

the analysis, yet their derivation is opaquely described as being

"assigned based on expert knowledge and consideration of our literature

review" without clear descriptions (line 386). This subjectivity

undermines the framework's quantitative and objective claims.

Addressing this major point is essential for publication. Additionally,

a more in-depth discussion of the study's limitations, particularly

regarding city boundary definitions and the selection of metrics, would

significantly enhance the scientific rigor, reproducibility, and impact

of this important work.

## 2. Detailed Comments

### **Weighting of Metrics (Major Point)**

The manuscript's most significant issue is the insufficient

justification for the weights assigned to each of the 18 metrics (Tables

1 and 2). The claim that they are based on "expert knowledge" is not

sufficient for a scientific paper aiming to establish a quantitative

framework. To address this, the authors should:

- **Provide a detailed rationale:** Include an appendix or

  supplementary material that explains the reasoning for each specific

  weight. For example, why is "Share of dominant wind" (30%)

  considered three times more important than "Share of wind \>2 m/s"

  (10%) for the background challenge?

- **Perform a sensitivity analysis:** Ideally, demonstrate or at least

  give examples of how the final challenge scores and city rankings

  change under different plausible weighting schemes. This would show

  the robustness of the conclusions.

- **Discuss alternative methods:** Discuss other, more objective

  methods for determining weights, even if not implemented (e.g.,

  Principal Component Analysis). If these are too complex, simpler,

  clearer methods should be considered, or at least suggested. The

  authors should justify why the current approach was chosen over

  these alternatives.

### **Methodology & Justification**

- **City Definition and Boundaries:** The authors correctly note that

  the OECD functional urban area definition can lead to significant

  issues, such as excluding major emission sources like the Zurich

  airport (lines 555-556). The discussion (Section 4) should be

  expanded to explore the quantitative impact of this. For instance,

  how much would the "modelling" or "background" challenge scores for

  Zurich change if the airport were included or excluded? It would

  also be beneficial to show a brief example of how the proposed

  "carbon cities" alternative would alter the metrics for one city.

- **Normalization:** While the 10-90 percentile range for min-max

  normalization is a reasonable choice to reduce the influence of

  outliers, the impact of this choice should be briefly stated. For

  example, how many cities fall outside this range for key metrics,

  and how does this affect their final scores?

### **Specific Challenges & Metrics**

- **"Application-Specific Observational Challenge":** This category

  feels heterogeneous, combining challenges for two different

  measurement systems: satellite (cloud cover) and radiocarbon

  (nuclear masking/bias). As the authors suggest (lines 614-616),

  these should be treated as separate challenges. I recommend

  splitting this into a "Satellite Observation Challenge" and a

  "Radiocarbon (¹⁴C) Challenge" to provide more targeted and

  actionable information.

- **Biogenic Challenge:** The metrics used are NEE-related and

  vegetation heterogeneity. However, the influence of urban-specific

  biogenic factors (e.g., irrigation, urban heat island effects on

  phenology) is not explicitly captured. The authors should be more

  explicit about these limitations in the main text when introducing

  the challenge.

- **Clarification Needed on Wind Speed:** The following statement

  needs clarification: "Higher wind speeds result in larger influence

  regions ("footprints"), which reduce the impact of strong local

  sources that could interfere with the goal of obtaining spatially

  representative observations. This leads to generally better

  agreement between modeled and observed values" (Lines 312--313). Is

  this influence related to the background region? If so, the sentence

  needs to be clearer. Subtracting a large, uncertain background from

  an observation can reduce the local signal, making it more

  uncertain. If the point is about the dilution of the local signal

  due to high winds, this is a separate issue from the background.

  These sentences need to be presented more clearly.

- **Missing Metrics:** Was the inclusion of other potentially relevant

  metrics considered? For example, for the "modelling challenge," were

  metrics for urban canyons or street-level aspect ratios considered?

  For the "background challenge," was population density in the upwind

  buffer zone considered as a proxy for diffuse emissions? A brief

  mention of why certain metrics were considered but ultimately

  excluded would be helpful.

### **Results & Discussion**

- **Results of Munich and Zurich (lines 452--453):** While a strong

  dominant wind direction can help estimate boundary conditions, it

  may also prevent the sampling of emission sources across a city

  unless there are enough sampling sites. The authors do not seem to

  consider this. It is also very challenging to model emissions for a

  region with a strong dominant wind direction, as modeled wind biases

  can lead to significant misestimations, e.g., due to the omission of

  sources.

- **Clarification Needed on Low Wind Speed:** The following sentence

  needs to be explained more clearly: "However, compared to the other

  cities, the wind speed is quite frequently below 2 m s⁻¹, which

  could be a challenge for collecting spatially representative up-wind

  observations." (Lines 453--455).

- **Discussion of Paris (lines 584-599):** The comparison of the

  paper's findings for Paris with results from Lian et al. (2023) is

  excellent and provides strong validation for the framework. This

  section could serve as a model for expanding the discussion of other

  cities or challenges.

- **Conclusion:** The conclusion rightly points out that the results

  are useful for identifying similarities between cities. However,

  there is little discussion about how to mitigate the identified

  challenges. Adding a discussion of mitigation strategies would

  strengthen the paper.

### **Presentation & Clarity**

- **Figure 3 (Challenge Scores):** This is an effective figure. A

  minor suggestion for panel (a) is to add the numerical score for

  each of the three pilot cities next to their bars to make comparison

  with Table 3 easier.

- **Table 1:** This table could be improved with some explanatory

  comments. For example, an asterisk could note that the weights

  within each category sum to 1. The meaning of some metrics is not

  immediately clear from the table, such as "Share of wind from

  dominant wind direction (limited to \>2 m s−1)". It is also not

  clear if a higher or lower share is better and why (after reading

  the paper more, it is better, but providing more information at the

  beginning would help the reader).

- **Metric Explanations:** Does Section 2.2 explain all the metrics in

  Table 1? The section seems to mix data source and metric

  descriptions, and some metrics appear to be missing a clear

  explanation.

- **Equations:**

  - **Equation 1:** The term `x_{i,j}` is not explained. The index

    `j` represents "characteristics," but the paper states, "These

    metrics represent specific characteristics of the city" (Line

    158), implying that characteristics are the metrics. This should

    be clarified, although it appears to be.

  - **Equation 3:** This equation needs more explanation. The term

    `x_i` (or `y_i`) appears to be a vector. How is it formed? For

    example, for the background challenge, does the vector `x` have

    5 elements? This is not clear from the text, although it seems

    so.

- **Typos and Grammar:** There are several typos (e.g., "with an

  emphasize on the surrounding" in Line 228) and long sentences that

  could be revised to improve readability.

In summary, this paper makes a valuable contribution to the field of

urban GHG monitoring. Addressing the major point regarding the

justification of the metric weights, along with the other detailed

comments, will elevate the manuscript to a more impactful publication.