the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Air quality model assessment in city plumes of Europe and East Asia
Abstract. An air quality model ensemble is used to represent the current state-of-the-art in atmospheric modeling, composed of two global forecasts and two regional simulations. The model ensemble assessment focuses on both carbonaceous aerosols, i.e. black carbon (BC) and organic aerosol (OA), and five trace gases during two aircraft campaigns of the EMeRGe (Effect of Megacities on the Transport and Transformation of Pollutants on the Regional to Global Scales) project. These campaigns, designed with similar flight plans for Europe and Asia, along with identical instrumentation, provide a unique opportunity to evaluate air quality models with a specific focus on city plumes.
The observed concentration ranges for all pollutants are reproduced by the ensemble in the various environments sampled during the EMeRGe campaigns. The evaluation of the air quality model ensemble reveals differences between the two campaigns, with carbon monoxide (CO) better reproduced in East Asia, while other studied pollutants exhibit a better agreement in Europe. These differences may be associated to the modeling of biomass burning pollution during the EMeRGe Asian campaign. However, the modeled CO generally demonstrates good agreement with observations with a correlation coefficient (R) of ≈ 0.8. For formaldehyde (HCHO), nitrogen dioxide (NO2), ozone (O3) and BC the agreement is moderate (with R ranging from 0.5 to 0.7), while for OA and SO2 the agreement is weak (with R ranging from 0.2 to 0.3).
The modeled wind speed shows very good agreement (R ≈ 0.9). This supports the use of modeled pollutant transport to identify flight legs associated with pollution originating from major population centers targeted among different flight plans. City plumes are identified using a methodology based on numerical tracer experiments, where tracers are emitted from city centers. This approach robustly localizes the different city plumes in both time and space, even after traveling several hundred kilometers. Focusing on city plumes, the fractions of high concentration are overestimated for BC, OA, HCHO, and SO2, which degrades the performance of the ensemble.
This assessment of air quality models with collocated airborne measurements provides a clear insight into the existing limitations in modeling the composition of carbonaceous aerosols and trace gases, especially in city plumes.
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Status: open (until 04 May 2024)
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RC1: 'Comment on egusphere-2024-516', Anonymous Referee #1, 09 Apr 2024
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Deroubaix et al. present an evaluation of an air quality model ensemble using two aircraft campaigns focusing on carbonaceous aerosols and multiple trace gases. They find that each member of the ensemble reproduces carbon monoxide reasonably well, whereas the correlation between observation and the ensemble for organic aerosols is weak. Overall, the performed analysis is, however, superficial and focuses, at last in their discussion, on the Pearson correlation coefficient only. It includes many statements that are not supported by the analysis or are of a confusing manner. The abstract includes conclusions which are not discussed anywhere in the manuscript. The manuscript needs expanded revision to ensure a comprehensive analysis in order to meet the quality standards of ACP. Further, the manuscript needs major improvements in its language and presentation. Thus, I cannot support the publication of the manuscript in ACP.
Major comments
Why is the analysis only performed for each individual ensemble member but not for the ensemble mean? The strength of a multi-model ensemble is to provide an estimate of the forecast uncertainty. Therefore, I would expect that the ensemble spread is discussed and evaluated with respect to the observation intercomparison. In the current version of the manuscript, the evaluation only considers a basic model intercomparison.
Some of the trace gases analyzed have a strong diurnal cycle. Even though the authors discuss the impact of averaging the observations by 1, 3, and 10 minutes, the model output frequency provided for at least one model is 6 hours. I suspect that the low model output frequency (6 hours) and the performed interpolation has a much stronger effect on the evaluation. How do you justify that a 6 hour output is sufficient? Why not obtain model output at a higher frequency?
The authors tend to attribute differences between the models only to the different emissions used (e.g., line 417 or 435), even though the models differ in the gas phase chemical mechanism and the representation of aerosols. Further, these statements are pure guesses and are not supported by any in depth analysis. What are the differences in the emission inventories?
The motivation of the authors to use the selected two flight campaigns bases on the similarity of the flight plans. Later in the manuscript, however, the authors state that the flights differ significantly (different seasons, different time periods spend over the ocean, etc.). This is not consistent.
Why are you only focusing on wind speed? The wind direction is equally important in order to assess transport pattern. How well does each ensemble member perform with respect to wind direction?
Line 9: This statement is confusing. It sounds as if only due to the ensemble, differences in the observed concentrations are revealed.
Line 11: I cannot find any statement about biomass burning in the main manuscript, except a general statement in the introduction.
Figure 1 is unreadable.
Line 229 to 231: This statement is confusing. I would suggest to focus on the correlation coefficient, root-mean-square error, and the standard deviation to assess the performance of each ensemble member. Here, the use of Taylor diagrams would significantly improve the value and readability of the manuscript.
Citation: https://doi.org/10.5194/egusphere-2024-516-RC1
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