the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Oct 2024
A WRF-Chem study of the greenhouse gas column and in situ surface concentrations observed at Xianghe, China. Part 1: Methane (CH4)
Abstract. This study is the first of two companion papers which investigate the temporal variability of CO2, CH4 and additionally CO concentrations measured at the Xianghe observation site near Beijing in China using the Weather Research and Forecast model coupled with Chemistry (WRF-Chem), aiming to understand the contributions from different emission sectors and the influence of meteorological processes. Simulations of the in situ (PICARRO) and remote sensing (TCCON-affiliated) measurements are produced by the model’s greenhouse gas option, called WRF-GHG, from September 2018 until September 2019. The present study discusses the results for CH4. The model shows good performance, after correcting for biases in boundary conditions, achieving correlation coefficients up to 0.66 for near-surface concentrations and 0.65 for column-averaged data. The simulations use separate tracers for different source sectors and revealed that energy, residential heating, waste management and agriculture are the primary contributors to the CH4 concentrations, with the energy sector having a greater impact on column measurements than surface concentrations. Monthly variability is linked to both emission patterns and meteorological influences, with advection of either clean or polluted air masses from the North China Plain playing a significant role. The diurnal variation of the in situ concentrations due to planetary boundary layer dynamics is quite well captured by WRF-GHG. Despite capturing the key variability of the CH4 observations, the model displays a seasonal bias, likely originating from an incorrect seasonality in the emissions from agricultural and/or waste management activities. Our findings highlight the value of WRF-GHG to interpret both surface and column observations at Xianghe, offering source sector attribution and insights in the link with local and large-scale winds based on the simultaneously computed meteorological fields. However, they also highlight the need to improve the knowledge on the seasonal CH4 cycle in northern China to obtain more accurate emission data and boundary conditions for high-resolution modeling.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(11659 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-3228', Anonymous Referee #1, 23 Jan 2025
General Comments
This paper by Callewaert et al. presents an interesting case study of the application of the WRF-Chem model (with its GHG option) to investigate the influence of different emission sources on the atmospheric variability of CH4 observed at a site in China (Xianghe). In addition, by discussing the discrepancies between WRF-Chem(GHG) simulations and in-situ, ground and satellite total column CH4 observations, the authors suggest specific emission sectors (i.e. agriculture and waste management) for which the used inventories should be carefully checked.
The paper is well and clearly written and the topic presented is of interest to the ACP audience.
I recommend the publication, after that some points have been adequately discussed and considered, mostly related to the possibility of a better use of observational data (Sections 3.4.1, 3.4.3), the comparison of the TROPOMI satellite XCH4 field with those from WRF-Chem (Sections 3.3 and 3.5) and the discussion on the role of emissions from coal mines.
Specific comments
Page 3, line 85: In Yang et al. (2021) it is reported that the WMO X2007 calibration scale was used, did you switch to the "new" WMO CO2 X2019 scale?
Pag. 4, line 92: What do you mean by "a quality filter of 1.0 was applied"?
Table 2. Why did you decide to merge the very different emissions sectors of residential and waste? Does, also in light of your discussion on page 13, the separation of these sectors allow for refinements in the attribution of differences between simulation and observations?
Table 3 & Figure 2: Looking at the wide range of deviations (model - observations) reported in Figure2d, ranging from -1000 ppb to +1000 ppb, I have the feeling that the statistics reported in Table 3 give an overly "optimistic" view of the discrepancies with in-situ observations. I would suggest to also report the main quantiles of the model - observations deviations. However, looking at Fig. 2, it seems that the agreement would increase over longer time scales (i.e. day-to-day).
Pag. 13, line 259: What fugitive CH4 emissions are associated with coal transport?
Pag. 14, line 14: I think it's rather challenging to get hints about misrepresentation of emissions in WRF-CHEM by directly comparing CH4 fields with TROPOMI measurements as done in Figure 6. This approach misses any inaccuracies in atmospheric transport and chemistry. I would suggest that these points are highlighted in the discussion and that direct comparisons with TROPOMI are treated with more caution (also in Section 3.5).
Section 3.4.1: Reading the first few words of this section, I had the impression that the analysis was also based on FTIR data, but this was not actually the case. I think that showing the same analysis also for FTIR data, as reported in Figure 7 for WRF-CHEM, may provide more guidance in assessing the agreement between observations and simulation, as well as the role of synoptic transport in the column CH4 observations.
Page 18, line 387: How was the 45 ppb threshold for XCO defined?
Figure 9: I suggest adding a wind rose to Figure 9 for the in situ data. This would allow a direct assessment of local emissions against the observed data. To save space, plates (f) can be moved to the supplementary material.
Page 19, line 396: For wetlands there is also an apparent contribution from E during the night.
Section 3.5: Based on the comparison between annual TROPOMI and WRF-CHEM XCH4 fields (see my previous concern about this approach), this section concludes that the contribution from coal mines near Tangshan may be overestimated by the CAMS-GLOB-ANT emission fluxes. However, looking at Figure 10, there are other sectors (energy, industry) contributing to this emission hotspot. How can you say that only emissions from coal mines contribute to the higher XCH4 values of WRF-GHG? Furthermore, the deviations of TROPOMI vs. WRF-GHG were about the same order of magnitude as the biases reported in Section 2.2 for TROPOMI, so are they really significant?
Page 21, line 414: "This suggests ... sources". Are you referring to TROPOMI or WRF-CHEM?
Citation: https://doi.org/10.5194/egusphere-2024-3228-RC1 -
RC2: 'Comment on egusphere-2024-3228', Anonymous Referee #2, 31 Mar 2025
General Comments
This paper by Callewaert et al., in this manuscript titled “A WRF-Chem study of the greenhouse gas column and in situ surface concentrations observed at Xianghe, China. Part 1: Methane (CH4),” investigates the influence of different emission sources on the atmospheric variability of CH4 observed in Xianghe, China, using the WRF-Chem model with its GHG option. The study compares model simulations with in situ, ground-based, and satellite total column CH4 observations.
The paper is well-written and clearly structured, and the topic presented is of interest to the ACP audience.
I recommend its publication after addressing some points, particularly those listed in the specific comments below.
Specific comments
Page 5, Table 1: Was the cumulus parameterization activated also in the last nested grid (D03)? Since it is generally not recommended for high-resolution domains, clarification on this decision would be valuable.
Page 5, Line 113: How was the interpolation of the CAMS EGG4 global product performed for the model? It would be beneficial to include this information. Additionally, was any other boundary condition product tested besides the CAMS global reanalysis for greenhouse gases (EGG4)?
Page 9, Figure 2: Why do the differences between the model and observations exhibit distinct patterns when comparing XCH4 column values and in situ measurements? In September, October, and November 2018, the model underestimates XCH4 column concentrations, whereas the in situ values do not show the same underestimation. However, for the rest of the period, the two comparisons exhibit a similar pattern. Discussing this discrepancy would strengthen the analysis.
How well does the model perform in simulating meteorological fields? Including an assessment of statistical performance would be valuable.
Page 14, Line 312: The methodology for the simulations using the FLEXible PARTicle dispersion model is only mentioned in the Appendix. A brief explanation should be included in the main text for clarity.
Minor comments
Figure 6: It would be helpful to indicate the location of Xianghe in the plots.
Why are the table captions generally placed at the bottom? Consistency in formatting should be considered.
Adding background grid lines to all figures with concentration axes would facilitate comparisons between the model and observations.
Page 11, Figure 4: The color palette for the "Residential & Waste" and "Agriculture" sectors should be adjusted. The current choice makes it difficult to distinguish between them. More contrasting colors would improve readability, especially since these sectors have significant regional influence.
Citation: https://doi.org/10.5194/egusphere-2024-3228-RC2 - AC1: 'Comment on egusphere-2024-3228', Sieglinde Callewaert, 06 May 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-3228', Anonymous Referee #1, 23 Jan 2025
General Comments
This paper by Callewaert et al. presents an interesting case study of the application of the WRF-Chem model (with its GHG option) to investigate the influence of different emission sources on the atmospheric variability of CH4 observed at a site in China (Xianghe). In addition, by discussing the discrepancies between WRF-Chem(GHG) simulations and in-situ, ground and satellite total column CH4 observations, the authors suggest specific emission sectors (i.e. agriculture and waste management) for which the used inventories should be carefully checked.
The paper is well and clearly written and the topic presented is of interest to the ACP audience.
I recommend the publication, after that some points have been adequately discussed and considered, mostly related to the possibility of a better use of observational data (Sections 3.4.1, 3.4.3), the comparison of the TROPOMI satellite XCH4 field with those from WRF-Chem (Sections 3.3 and 3.5) and the discussion on the role of emissions from coal mines.
Specific comments
Page 3, line 85: In Yang et al. (2021) it is reported that the WMO X2007 calibration scale was used, did you switch to the "new" WMO CO2 X2019 scale?
Pag. 4, line 92: What do you mean by "a quality filter of 1.0 was applied"?
Table 2. Why did you decide to merge the very different emissions sectors of residential and waste? Does, also in light of your discussion on page 13, the separation of these sectors allow for refinements in the attribution of differences between simulation and observations?
Table 3 & Figure 2: Looking at the wide range of deviations (model - observations) reported in Figure2d, ranging from -1000 ppb to +1000 ppb, I have the feeling that the statistics reported in Table 3 give an overly "optimistic" view of the discrepancies with in-situ observations. I would suggest to also report the main quantiles of the model - observations deviations. However, looking at Fig. 2, it seems that the agreement would increase over longer time scales (i.e. day-to-day).
Pag. 13, line 259: What fugitive CH4 emissions are associated with coal transport?
Pag. 14, line 14: I think it's rather challenging to get hints about misrepresentation of emissions in WRF-CHEM by directly comparing CH4 fields with TROPOMI measurements as done in Figure 6. This approach misses any inaccuracies in atmospheric transport and chemistry. I would suggest that these points are highlighted in the discussion and that direct comparisons with TROPOMI are treated with more caution (also in Section 3.5).
Section 3.4.1: Reading the first few words of this section, I had the impression that the analysis was also based on FTIR data, but this was not actually the case. I think that showing the same analysis also for FTIR data, as reported in Figure 7 for WRF-CHEM, may provide more guidance in assessing the agreement between observations and simulation, as well as the role of synoptic transport in the column CH4 observations.
Page 18, line 387: How was the 45 ppb threshold for XCO defined?
Figure 9: I suggest adding a wind rose to Figure 9 for the in situ data. This would allow a direct assessment of local emissions against the observed data. To save space, plates (f) can be moved to the supplementary material.
Page 19, line 396: For wetlands there is also an apparent contribution from E during the night.
Section 3.5: Based on the comparison between annual TROPOMI and WRF-CHEM XCH4 fields (see my previous concern about this approach), this section concludes that the contribution from coal mines near Tangshan may be overestimated by the CAMS-GLOB-ANT emission fluxes. However, looking at Figure 10, there are other sectors (energy, industry) contributing to this emission hotspot. How can you say that only emissions from coal mines contribute to the higher XCH4 values of WRF-GHG? Furthermore, the deviations of TROPOMI vs. WRF-GHG were about the same order of magnitude as the biases reported in Section 2.2 for TROPOMI, so are they really significant?
Page 21, line 414: "This suggests ... sources". Are you referring to TROPOMI or WRF-CHEM?
Citation: https://doi.org/10.5194/egusphere-2024-3228-RC1 -
RC2: 'Comment on egusphere-2024-3228', Anonymous Referee #2, 31 Mar 2025
General Comments
This paper by Callewaert et al., in this manuscript titled “A WRF-Chem study of the greenhouse gas column and in situ surface concentrations observed at Xianghe, China. Part 1: Methane (CH4),” investigates the influence of different emission sources on the atmospheric variability of CH4 observed in Xianghe, China, using the WRF-Chem model with its GHG option. The study compares model simulations with in situ, ground-based, and satellite total column CH4 observations.
The paper is well-written and clearly structured, and the topic presented is of interest to the ACP audience.
I recommend its publication after addressing some points, particularly those listed in the specific comments below.
Specific comments
Page 5, Table 1: Was the cumulus parameterization activated also in the last nested grid (D03)? Since it is generally not recommended for high-resolution domains, clarification on this decision would be valuable.
Page 5, Line 113: How was the interpolation of the CAMS EGG4 global product performed for the model? It would be beneficial to include this information. Additionally, was any other boundary condition product tested besides the CAMS global reanalysis for greenhouse gases (EGG4)?
Page 9, Figure 2: Why do the differences between the model and observations exhibit distinct patterns when comparing XCH4 column values and in situ measurements? In September, October, and November 2018, the model underestimates XCH4 column concentrations, whereas the in situ values do not show the same underestimation. However, for the rest of the period, the two comparisons exhibit a similar pattern. Discussing this discrepancy would strengthen the analysis.
How well does the model perform in simulating meteorological fields? Including an assessment of statistical performance would be valuable.
Page 14, Line 312: The methodology for the simulations using the FLEXible PARTicle dispersion model is only mentioned in the Appendix. A brief explanation should be included in the main text for clarity.
Minor comments
Figure 6: It would be helpful to indicate the location of Xianghe in the plots.
Why are the table captions generally placed at the bottom? Consistency in formatting should be considered.
Adding background grid lines to all figures with concentration axes would facilitate comparisons between the model and observations.
Page 11, Figure 4: The color palette for the "Residential & Waste" and "Agriculture" sectors should be adjusted. The current choice makes it difficult to distinguish between them. More contrasting colors would improve readability, especially since these sectors have significant regional influence.
Citation: https://doi.org/10.5194/egusphere-2024-3228-RC2 - AC1: 'Comment on egusphere-2024-3228', Sieglinde Callewaert, 06 May 2025
Peer review completion
Journal article(s) based on this preprint
Data sets
WRF-Chem simulations of CO2, CH4 and CO around Xianghe, China Sieglinde Callewaert https://doi.org/10.18758/P34WJEW2
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Sieglinde Callewaert
Minqiang Zhou
Bavo Langerock
Pucai Wang
Ting Wang
Emmanuel Mahieu
Martine De Mazière
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(11659 KB) - Metadata XML