the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Oct 2025
WRF-Chem simulations of CO2 over Western Europe assessed by ground-based measurements
Abstract. The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), in its passive tracer option (WRF-GHG), was used to simulate CO2 concentrations over Western Europe during summer 2018. The model performance was evaluated against ground-based observations. Due to the large variety of anthropogenic emissions, we conducted five sensitivity tests using a combination of three different inventories (CAMS-REG-ANT, EDGAR, and TNO) and source-specific vertical emission profiles. Compared with observations from five Integrated Carbon Observation System (ICOS) atmospheric stations, the model captures diurnal CO2 variations at different heights. At the ICOS site in Karlsruhe, Germany, simulated near-surface CO2 mole fractions are highly sensitive to the choice of anthropogenic emission inventory, with discrepancies up to 14.99±31.98 ppm, due to large nearby emission sources. Furthermore, incorporating source-specific vertical profiles notably improves accuracy, increasing the correlation coefficient from 0.53 to 0.78 when using EDGAR. The column-averaged dry-air mole fractions of CO2 (XCO2) from the Total Column Carbon Observing Network (TCCON) are well simulated by WRF-GHG. However, an overestimation of approximately 1.2 ppm was found at the Paris site, likely due to uncertainties in anthropogenic emissions and boundary conditions. In addition, a negative bias was found in early June at most ICOS and TCCON sites, may be attributed to errors in simulated fluxes during the growing season. However, due to the lack of co-located flux observations, the exact cause remains uncertain. Overall, this study demonstrates the capability of WRF-GHG in simulating CO2 over Western Europe, while showing the need for improving model configuration.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-4537', Anonymous Referee #1, 01 Dec 2025
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RC2: 'Comment on egusphere-2025-4537', Anonymous Referee #2, 03 Dec 2025
General comments:
This manuscript simulates CO2 concentrations in Belgium and surrounding areas and conducts sensitivity tests using different emission inventories and in situ and remote-sensing CO2 observations. The topic is interesting and meaningful, but many statements and explanations in the manuscripts are not rigorous enough. I suggest more modifications and improvements before acceptance.
Special comments:
- The title includes Western Europe, but this study only focuses on Belgium and the surrounding areas. Is it reasonable to use Western Europe in the title?
- This paper also mentioned that the drought could increase CO2 I think it is necessary to include precipitation in the research period and compare it with the year before and after.
- I think Figures 1 and 2 can be combined.
- Lines 130-135: I suggest consistency in the parameters used in the equation and the text. For example, “Tscale” in Eq and “Tscale” in text. Maybe “Tscale” is more suitable. Also, other parameters such as Wscale, Pscale, Ts, Tmin, Tmax, …
- Lines 158-159, Here are two downscaling methods used. What are the differences between them?
- Table 1. I suggest adding some words in the column of the aberrative. Also, the CBW attitude is 0?
- Figures, the figures in the main text are not clear as the Figures in the Appendix.
- Figure 3. What are the sunrise and sunset times in these ICOS stations? The highest temperature occurred at 18:00 local time, and the PBL reached its maximum height between 15:00-17:00 (line 319). It seems unreasonable.
- Figure 5. I think it is better to keep the y-axis of CO2 concentrations the same across different emission inventories at the same height.
- Figure 6. It seems there is no contribution from biomass burning in these figures. Why are biogenic contributions nearly negative from 10:00- 23:00 at all sites? Why are biogenic contributions only positive around 10:00?
- Figure 7. It is better to give the slope and correlation coefficient in Figure 7c for the three TCCON sites.
- Figure 8. Although the STD and RMSE increase from S to P, MBE becomes large. Which parameters are more critical to evaluate the model's sensitivity?
- Line 378. What does SNAP mean in this paper?
- Figure 11. There is a large gap between SNAP 1 for CAMS and EDGAR. Figure 10d also showed more emission sources than Figure 1a. Why did this gap occur? Were the emissions included in other emission sectors in CAMS?
- Figure 12. It seems that in late July and August, the land system was also active as a carbon source (Figure 12c), but anthropogenic emissions nearly disappeared from Figure 12b. Usually, drought can increase temperatures and the electricity demand for air conditioning, hence the anthropogenic emissions could increase in this period.
- Lines 438-439, please add ° before N and E for the GPS location. Also, add this to the GPS location in Table A3. What does “acid fen” mean here for FR_LGt?
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- 1
Wang et al. simulated CO2 mole fractions over Western Europe in the summer of 2018 using the WRF-Chem model combined with three different CO2 emission inventories. The simulations were evaluated by comparing with ground-based in situ and column observations. They showed, by taking into account the sector-specific vertical profiles of emissions, the agreement between the simulations and the observations was significantly improved for sites near large emission sources.
The topic of this manuscript is important and relevant to the scope of Atmospheric Chemistry and Physics. In addition, the analysis method is appropriate, and the writing structure is well organized. However, it seems to lack novelty and contains few new scientific findings. I recommend clarifying what is novel and addressing the following concerns and questions.
Specific comments
L56: Please provide a clear description of whether the signal is positive or negative.
L72: Do the multi-source observational data refer to ICOS and TCCON data? If so, I do not think they were used for simulating CO2 mole fractions.
L242–247: Please add a discussion of how the overestimation in inland areas and the underestimation in coastal regions affected the mean bias error in wind speed.
L253: It would be easier to read if the expressions “between the observations and the simulations” and “between the simulations and the observations” were made consistent throughout this manuscript.
Figures 8 and 9: The differences between the WRF-GHG simulations and TCCON data in Figure 9 appear to be larger than those in Figure 8. Are the differences due to the fact that Figure 9 does not take into account smoothing using the column averaging kernel?
338–347: If the data period for Orleans was matched with that for Paris, would similar results be obtained? In other words, would the difference between Paris (an urban area) and Orleans (a suburban area) be reflected in the observed XCO2?
L378: Please add an explanation of SNAP.
L382–383: In Figure 10, it appears that there are also large emission sources near sites other than KIT (i.e., CBW and SAC). What degree of closeness does “near” represent?
L419: including -> includes
L431–434: The simulated XCO2 values in July and August 2018 were higher than the observed XCO2. What caused this overestimation by the model? In years other than 2018 when no drought occurred, will the simulated XCO2 values be lower than the observed values?
L454: analyze -> analyzing
L486–488: Please revise the sentence by adding a conjunction.