the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Apr 2025
Technical note: Sensitivity of the CAMS regional air quality modelling system to anthropogenic emission temporal variability
Abstract. An accurate characterization of the temporal distribution in primary emissions is essential for air quality modeling. This study evaluates the impact of replacing the default temporal profiles in the Copernicus Atmosphere Monitoring Service (CAMS) European air quality multi-model ensemble with an updated dataset (CAMS-REG-TEMPO). The sensitivity of 11 regional models and the ensemble to these changes is assessed by comparing modeled and observed monthly, weekly, and diurnal cycles of nitrogen dioxide (NO2), ozone (O3), coarse particulate matter (PM10), and fine particulate matter (PM2.5) across Europe. NO2 shows the greatest improvement, with weekly cycle correlations increasing up to +0.17 due to better road transport emissions representation. PM10 correlations improve in winter (up to +0.13 weekly and +0.07 diurnal) due to refined residential wood combustion emissions. PM2.5 correlations remain largely unchanged, except for diurnal cycles, which improve in winter (+0.18) but slightly degrade in spring and summer (-0.02). O3 is the least affected, as correlations were already high with default profiles (0.9–0.95). For some species and timescales (e.g., NO2 diurnal cycles), results vary across models, highlighting the complex interactions between emission timing and atmospheric processes. CAMS-REG-TEMPO has little effect on annual RMSE and bias, aside from slight improvements in high PM10 concentrations. Overall, the findings support implementing CAMS-REG-TEMPO in the operational CAMS multi-model ensemble.
- Preprint
(8908 KB) - Metadata XML
-
Supplement
(2039 KB) - BibTeX
- EndNote
Status: final response (author comments only)
-
RC1: 'Comment on egusphere-2025-1287', Anonymous Referee #1, 30 Apr 2025
General comments
The manuscript presents a sensitivity analysis of the CAMS regional air quality modelling system to the temporal variability of anthropogenic emissions. This is a highly relevant and timely topic with valuable implications for the air quality modelling community. The study has potential to contribute significantly to the understanding of how emission timing affects model performance.
However, in its current form, the manuscript lacks sufficient depth in several key areas. The comparison of temporal profiles (monthly, weekly, hourly) would benefit from a more detailed, country-level analysis. For instance, do some countries show larger discrepancies across profiles? If so, are there data-related or methodological reasons behind these patterns? This level of detail would be particularly relevant, given that the model evaluation using observations is also conducted by country (the correlation coefficient). A clearer linkage between country-specific emission characteristics and model results would strengthen the overall interpretation.
Additionally, the discussion of results is often limited to descriptive statements, with vague terms such as “similar” or “slight improvement” used without supporting metrics. Quantifying such terms (e.g., using statistical measures or percentage differences) would improve the clarity, objectivity, and scientific robustness of the analysis. The manuscript would also benefit from a more critical reflection on the causes of observed differences, particularly where the choice of temporal profiles leads to notable changes in model performance.
In summary, while the manuscript addresses an important topic and presents promising results, more in-depth analysis, clearer quantification, and stronger interpretive discussion are needed to fully support the conclusions and maximize the manuscript contribution to the field.
Specific comments
Lines 100-102: Provide examples of the surrogate statistics considered, and specify the reference where the complete information can be found.
Line 116: Could the authors clarify what is meant by "degree days" in this context? Is this simply referring to temperature, or is it a more specific metric (e.g., heating degree)?
Lines 119–120: What type of data is used for energy and livestock emissions?
Line 121: What do you mean by "identical"? Which sectors have different hourly, sector-dependent profiles?
Lines 146-147: Did the authors classify the automatic traffic stations as urban or rural using the GHSL dataset?
Lines 202-204: How are the pollutant-specific thresholds defined? The authors should support these values with appropriate references.
Line 119: Why was the year 2018 selected for the simulations?
Line 223: Why was the 2017 inventory used? The authors should justify this choice and explain whether it has any implications for the objectives of the paper.
Line 244: What do you mean by “similar”? This should be quantified, for example, by using a correlation coefficient.
Lines 250-251: Do TNO and GENEMIS provide static profiles, or do these datasets not offer year-specific profiles based on meteorological data?
Lines 257-258: Why is there this discrepancy? What is the difference between the methodologies used to obtain these results?
Line 260: What are the winter months? In Europe, the winter months are December, January, and February, but this is not clearly stated in the document.
Line 268: Is the lower-intensity peak in June or July? Please verify.
Line 288-289: What type of information is used in the TNO profiles for the residential and commercial combustion sector?
Line 303: How large is the "significantly larger drop"? Please provide a quantification.
Line 308: What do you mean by 'lower extent'? This type of statement should be quantified or clarified for better precision. Please ensure this issue is addressed consistently throughout the document (e.g., nearly identical, almost identical, identical, similar)
Lines 303-330: The authors should try justify the results based on the datasets used for each European temporal profile. For example, lines 355-360, what type of data is used by TNO for road transport? What type of data is used by CAMS-REG-TEMP and TNO for off-road transport?
Lines 403-404: This statement is not clear.
Lines 411-412: What could be the cause of this issue? Could it be related to the VOC emission profiles?
Line 415-416: Which model is the exception? Is there any explanation for this?
Lines 420-421: What could be the reason for this? How does the monthly PM10 temporal profile used by CAMS-REG-TEMP differ from the others?
Figure 10: It is unclear why the authors chose to present NO2 weekly cycle concentrations specifically for JFM and JAS. Do these periods represent the highest and lowest values? If so, it would be helpful to clarify this in the figure or caption to guide the reader (the same comment for the other pollutants).
Figure 10: It would be helpful to also include the results from the CAMS European multi-model ensemble air quality modelling system (not only by model) for comparison (the same suggestion applies to the other pollutants). This would allow for a more complete assessment of model performance.
Lines 524-525: What could be the reason for this reduction in accuracy?
Lines 521-522: Information such as “8 out of 11” could be added for the remaining pollutants and temporal profiles. This type of detail would help support the authors' statements (e.g., “slight improvement”).
Section 3: The authors need to provide specific values; for example, what does “slight decrease” mean? Why did the authors obtain these particular results? Why were only O3 and PM2.5 results presented? Why are only two seasons represented for each pollutant? A more thorough explanation would enhance the clarity and robustness of the analysis.
Technical corrections
Line 79: The final punctuation mark is missing.
Table 1: The abbreviations should be defined. The authors can include this information at the bottom of the table.
Line 101: Replace "x" (the letter) with "×" (the multiplication symbol). Please ensure this formatting is consistent throughout the document.
Line 117: Please verify whether “TNO profiles” should be referred to as “TNO_MACC-III profiles”.
Line 131: The pollutant names should be defined, and the “10” and “2.5” in PM₁₀ and PM₂.₅ should be properly formatted as subscripts. Please ensure this formatting is consistent throughout the document.
Line 135: Please verify the sentence.
Line 202: I suggest replacing the word “aberrant” with “outliers,” for example.
Line 244: Include also the Fig. S1 (“Fig. 1 and Fig. S1”).
Figure 2: There is a strange black line in the NH₃ graphs. It seems that the GNFR lines are too close to each other. I suggest removing the black border from the stacked lines plots to improve readability (e.g., edgecolor='none').
Line 342: Should "GENEMIS-Menutetal2012" be replaced by "GENEMIS"? Please ensure consistency throughout the document. Please check also the Figure S3.
Figure 7: The sentence “Boxes highlighted in green/orange/grey indicate” should be revised -there are no orange boxes in the figure.
Figure 9: The unit for the correlation coefficient should be provided (i.e., (-)). Additionally, the "3" should be formatted as a superscript. It would also be helpful to add the month abbreviations on the x-axis, as shown in Figure 2. The same comment applies to the remaining figures.
Line 500: remove the parenthesis “(“.
Line 533: GEM-AQ or GEMAQ?
Citation: https://doi.org/10.5194/egusphere-2025-1287-RC1 -
RC2: 'Comment on egusphere-2025-1287', Anonymous Referee #2, 20 May 2025
The manuscript submitted by the authors presents a thorough and diligent effort comparing different temporal disaggregation methods for emissions against traditionally used profiles. The study is well-structured and addresses one of the current key challenges in air quality modeling: the temporal allocation of emission inventories and the sensitivity of Chemical Transport Models (CTMs) to such changes.
The work includes advancements that may be highly relevant for future modeling efforts. A clear improvement in simulated NO2 concentrations is observed due to the introduced refinements. However, some limitations remain, which pose ongoing challenges for the air quality modeling community in Europe. I believe the manuscript does not require major revisions. That said, I would have appreciated the inclusion of a set of recommendations or at least some hypotheses for the future application of the proposed temporal profiles. It is implied that the authors would recommend adopting this new temporal allocation, but this could be stated more explicitly.
One particular result is the spatial behavior of ozone, which shows deterioration in Western and Eastern Europe, but not in Central Europe. The authors attribute this to the inherent difficulties in modeling ozone and highlight once again the nonlinear relationship between O3 and its precursors. Still, there appears to be a discernible pattern that might benefit from further exploration. I suggest the authors propose a hypothesis to explain this phenomenon. Similarly, a brief discussion or hypothesis regarding the behavior of PM and the observed shift would add value. While it is helpful that the authors identify both improvements and deteriorations resulting from the redistribution of emissions, articulating hypotheses and providing recommendations for future modeling would strengthen the scientific impact of the work.
It would also be beneficial to briefly mention the speciation approach used for emissions. While I assume the speciation follows standard practice.
Another point worth a brief mention in the manuscript is the selection of the year 2018. While it is understandable that 2018 can be considered a representative and valid year for the analysis, a short explanation of why this particular year was chosen would add helpful context.
Regarding the selection of air quality monitoring stations, is there a reference or link to the specific stations used that could be consulted (list of stations)? The ozone curve seems to show a certain regional bias—perhaps linked to traffic patterns? I am not familiar with overall averaged curves.
Finally, the authors rightly highlight the uncertainties associated with NMVOC emissions, particularly given that a significant share comes from solvent use, and that this profile remains largely unchanged. As an additional comment (not necessarily for inclusion in the manuscript), I am curious whether the authors believe that the temporal redistribution in these emissions could have a more substantial impact on concentrations, and on which pollutants in particular. This could be an important consideration for future modeling work, especially as speciation and analysis of NMVOCs become increasingly relevant for air quality.
Minor issues identified:
Line 68: A verb appears to be missing—perhaps “show”?
Line 241: The text refers to “GENEMIS-Menuetal2012” and later “Menutetal”—this should be made consistent or corrected.
Line 458: It would be helpful to briefly clarify that the “Harmut cold spell” refers to a negative temperature anomaly, explicitly.
Citation: https://doi.org/10.5194/egusphere-2025-1287-RC2 -
AC1: 'Comment on egusphere-2025-1287', Marc Guevara, 25 Jul 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1287/egusphere-2025-1287-AC1-supplement.pdf
Data sets
CAMS-REG-TEMPOv3.2 Marc Guevara, Oriol Jorba, and Carlos Pérez García-Pando https://doi.org/10.5281/zenodo.15011343
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,110 | 135 | 21 | 1,266 | 37 | 19 | 39 |
- HTML: 1,110
- PDF: 135
- XML: 21
- Total: 1,266
- Supplement: 37
- BibTeX: 19
- EndNote: 39
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1