the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Aug 2023
Potential of using CO2 observations over India in regional carbon budget estimation by improving the modelling system
Abstract. Devising effective national-level climate action plans needs a more detailed understanding of the regional distribution of sources and sinks of greenhouse gases. Due to insufficient observations and modelling capabilities, India’s current carbon source-sink estimates are uncertain. This study uses a high-resolution transport model to examine the potential of CO2 observations over India for inverse estimation of regional carbon fluxes. We make use of four different sites in India that vary in measurement technique, frequency and spatial representation. These observations exhibit substantial seasonal (7.5 to 9.2 ppm) and intra-seasonal (2 to 12 ppm) variability. Our modelling approach, a high-resolution Weather Research and Forecasting Model combined with Stochastic Time Inverted Lagrangian Transport (WRF-STILT) model, performs better in simulating seasonal (R2 = 0.50 to 0.96) and diurnal (R2 = 0.96) variability of observed CO2 than the current generation global models analysed in the study. Representation of local flux variability like biomass burning in the model needs further refinement, depending on the site location. During the agricultural season, crop biospheric uptake in the Indo-Gangetic Plain region significantly modulates the CO2 variability in the northern Indian stations. Depending on the region and time of the year, the anthropogenic and biospheric emission components contribute differently to CO2 variability. The choice of emission inventory in the modelling framework alone leads to significant biases in simulations (5 to 10 ppm), endorsing the need for accounting emission fluxes, especially for non-background sites. By implementing a high-resolution model, our results emphasise that observations from Indian sites can be useful in deducing carbon flux information at regional (Nainital) and sub-urban to urban (Mohali, Shadnagar, Nagpur) scales. On accounting for observed variability, the global carbon data assimilation system can thus benefit from the measurements from the Indian subcontinent.
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RC1: 'Comment on egusphere-2023-1582', Sajeev Philip, 30 Sep 2023
In this manuscript, Thilakan et al. investigated the capability of a high-resolution Lagrangian chemical transport model to simulate spatiotemporal pattern of atmospheric CO2 mixing ratios in India. The primary goal of this effort was about to assess the potential of CO2 atmospheric mixing ratio measurement data in the regional and global inverse modeling systems to optimize CO2 fluxes.
Their approach involved the use of WRF-STILT modeling system to simulate CO2 concentrations at four sites in India. First, they described the CO2 mixing ratio variability in four different CO2 ground monitoring stations. Then they evaluated model simulations and three global model outputs against CO2 observations from these four sites. They found an outperformance of WRF-STILT compared to three global models, in representing the spatiotemporal variability of observed CO2. Finally, authors investigated the factors leading to model-observation differences. They conclude that regional and global data assimilation models can benefit from CO2 measurement data if the models are improved in certain aspects.
This is a timely effort examining the potential of modeling systems to simulate CO2 concentrations and infer fluxes driving those atmospheric mole fractions. CO2 flux estimation in India is in its developing stages, as more measurement data from monitoring stations are now available (still sparse). Assessing the potential of CO2 measurement data in the regional and global inverse modeling systems to accurately estimate CO2 fluxes is a great effort.
Therefore, this paper is within the scope of Atmospheric Chemistry and Physics (ACP) journal and investigates an important research question. The high-resolution model simulations were conducted, evaluated with observations, and model-data mismatch assessed. This manuscript can definitely be accepted to ACP, after addressing the below mentioned comments.
Major comments
- Assessing the potential of observations in data assimilation models: The primary goal in this manuscript (and the title of the manuscript) was about to investigate the potential of CO2 atmospheric mixing ratio measurements in the regional and global inverse modeling systems to optimize CO2 Unfortunately, authors could not demonstrate this aspect in a robust manner. Authors found that: 1) the STILT model simulations outperformed global reanalysis products, 2) the STILT model still require some improvements in representing emission fluxes accurately, 3) and the contribution of systematic RMSE for STILT simulations is only a certain % of the overall error for certain seasons/stations. However, it is unclear how the potential of in situ observations in inverse models was assessed.
- Applicability to Eulerian models and global model: The authors tried to investigate the potential of observations in the WRF-STILT model (see major comment #1). The last statement of the abstract: “…the global carbon data assimilation system can thus benefit…”. Authors should explain clearly how the results from this manuscript is applicable for regional/global Eulerian transport models (forward and inverse).
- All emissions to be added in the base simulations: The impact of biospheric fluxes and biomass burning emissions were assessed at some point in the manuscript. If authors had incorporated the biomass burning fluxes and an additional biosphere model (in addition to VPRM) to the base-model simulations, some sub-sections in Sect. 6 could have been avoided. Also, we all know that these emissions and local transport should be well represented in any kind of models. Authors should at least mention the novel aspects of this analysis conducted here (the impact assessments).
- Novelty aspect to be added to the introduction section: The novel aspect of this study should be added to the introduction section with detailed citations of the relevant papers.
- Need well-defined analysis approach: From the major comments #1 to #3, it is clear that a well-defined analysis approach should have been developed and described well in the start of Section 2 itself.
- Selection of sites and models: Better if authors mention clearly why certain monitoring stations (four) were selected. Just due to data availability? Also, better if they compared their model simulations to WRF-GHG model simulations (in addition to global products) from the same authors (Thilakan et al., 2022).
- Presentation of results through summary figures: The manuscript comes with 9 (main manuscript) + 14 (supporting document) + 15 (additional materials: https://zenodo.org/record/8143361) = 38 figures! Most of the sentences in results and discussions sections are going back and forth between different figures, which makes it difficult to read and understand. Authors should be able to come up with <10 simple summary figures in the main manuscript to represent their results. For example, the subplots of the figures 2, 3, 5, 6, and 7 can be represented in a single plot with observation bar + 9 bars. Other options can be explored.
- Need clarity in conclusions: A major drawback of this manuscript is the loose conclusions in each and every section in Sect. 5 and 6. Authors first describe some figures and then make some vague general statements at the end of the paragraphs (see minor comments for some examples).
- Presentation of results and discussions in the manuscript: Authors should consider revising/re-organizing the results (Sect. 5) and discussion (Sect. 6) sections to convey novel findings of this manuscript clearly. Also, abstract and conclusion sections need to be revised (see minor comments).
Minor comments
Line 3: Better to use: “high-resolution Lagrangian chemical transport model”
Line 6: Better use: “Our modeling framework”
Line 8: How about sub-seasonal?
Line 8: Diurnal only for one site? If so, better specify that.
Line 8: Be very specific: “current general global models”.
Line 9: Representation of all processes and emission categories needs refinement irrespective of the site location. I would say its an empty and loose sentence.
Lines 9:14: Abstract should be having take-home messages from your article. All these lines are general statements, without any quantitative info.
Line 14: Better rewrite this sentence for clarity: “By implementing a model, our results emphasize…”.
Line 16: Observed variability of …
Line 21: greenhouse gas
Line 29: Change: “suffer from”
Lines 32 and 35: Better to discuss about CO2 rather than carbon fluxes
Line 34: Restrict # of citations. Add those relevant to India.
Line 36: Ganesan et al. deals with methane. Better to discuss about CO2.
Lines 34-36: Better mention that these models require observations to constrain fluxes. That way, this sentence connects with the next sentence (Lines 36-38).
Line 39-40: Rewrite for clarity: “detect surface variations in addition to data gaps”.
Line 41: Are there any dedicated network? Or it’s just individual observation stations? Also, there could be some more papers describing recent in situ observations.
Line 48: Not sure these constraints are the reason not to use Indian observations in global models (data availability…).
Lines 48-50 and 55: There should be at least one paper (Sijikumar et al. 2023): https://doi.org/10.1016/j.atmosenv.2023.119868.
Lines 53-54: Better to avoid “associated with… transport and flux”.
Line 59: Not sure if Thompson et al. found convection responsible for the difficulty in simulating CO2 over tropics.
Line 62: Not just over India.
Lines 65-66: It’s not just about resolution. Modeling system should accurately represent all kinds of processes etc.
Lines 67-84: This paragraph should better fit in the material and methods section. Section 2 can start with the overall approach of this study. Here, in the introduction section, this paragraph can be described in 2-3 sentences.
Line 77: Correct this: “indented”
Line 115: Briefly mention how PBLH is calculated, in one or two sentences.
Line 116: Which approach? Approach to calculate PBLH?
Line 132: Add some more info: “which found it promising”.
Line 137: Which satellite product?
Lines 135-144: Three anthropogenic inventories were used. Why not different biosphere flux models?
Line 141: Already mentioned that “derives…at receptor locations using Eq. (4)” in lines 132 and 138.
Lines 143-146 and 162-166: Better to have a Table with the details of different model experiments and data/inventory sources.
Lines 155:158 and Eq. (5): Why not biomass burning fluxes? Other chemical sources of CO2 (may not be a significant fraction)?
Line 169: Why not refer to these four monitoring stations with names of the cities (e.g., Mohali) rather than short forms (e.g., MHL).
Lines 180-181: Better mention those three cities, not just Chandigarh.
Lines 232-235: Rewrite for clarity. First level of CT for all stations. Then explain the rest.
Section 4: Good metrics from Willmott, (1981) and Willmott et al. (2012)!
Line 266: Any evidence shown here?
Lines 269-270: Better assess diurnal aspects in a separate section along with a figure. All analysis and main figures should be based on daytime values.
Line 261: The seasonal aspects can be described in a separate para.
Lines 276-277: Better if you cite relevant publications to support this statement.
Lines 277-279: Any reason why the CO2 contribution from biomass burning was not included as part of the base simulations?
Figure 2: In the figure caption, describe what these color bars represent. Error bars?
Figure 2: The subplots of the figure 2 (this is applicable for all figure 3 and 5 to 7) can be represented in a single plot with observation bar + 9 bars. Or explore any other figure options to represent your results in a clearly.
Section 5.1: Not sure if these aspects were already mentioned in publications from research groups who conducted the CO2 measurements (e.g., Sreenivas et al 2016)?
Section 5.2 and 5.3: Hard to read. Going back and forth between different figures. Better to have some summary figures to demonstrate that STILT model outperforms global reanalysis products (obviously). Authors should consider revising/re-organizing the results (Sect. 5) and discussion (Sect. 6) sections to convey novel findings of this manuscript clearly.
Line 415: Revise: “Using the STILT model has improved the capabilities…”
Line 418: I doubt you are trying to improve data assimilation “approaches”.
Lines 435-439: Better if you show a figure with the biomass emissions from those two inventories (in addition to STILT simulations with those emissions).
Line 440: Not clear to me: “Along with mixing height issues…”?
Lines 440-442: We all know that misrepresentation of emission fluxes can lead to errors in the simulated concentrations. If authors had incorporated the biomass burning fluxes to the base simulations, this entire sub-section 6.1 could have been avoided (?) I wonder what new aspect is critically examined here. Also, not specified the resolution of the flux inventories. Not sure how you came to this conclusion: “…shows the role of high-resolution biomass burning fluxes…”.
Lines 466-468: Clearly mention: GPP in Fig. S14a and NDVI in Fig. S14b.
Lines 474-476: This second part of the sentence is not clear: “…and by using…”.
Section 6.3: So far, the authors assessed the impact of biomass burning emissions and biospheric flux model on the variability of CO2 concentration. Now, they discuss relative contribution of different components. There is no coherence. Biosphere component is already discussed in Section 6.2.
Lines 499-501: Very loose conclusions. We all know these aspects. I wonder what new aspect is critically examined here. This is applicable for these paragraphs in Section 6.
Section 6.6: Section 6 started with an aim to explore the shortcomings to be addressed for using Indian CO2 observations in inverse models. In sections 6.1 to 6.5 author noted the inherent issues with their modeling system. Now, in section 6.6, they try to assess the potential of in situ observations in inverse model. Structuring of different sections is not in a coherent manner. Sect. 6.6 should be a stand-alone section.
Lines 510-511: better not to start a new section of paragraph with some conclusions: “…from issues in representing…”.
Lines 513-514: The approach by the authors (to assess the potential of observation in models) is now somewhat clear. Seems like an important section/paragraph…
Line 515: Better start with describing MHL (top left in the figure).
Figure 8: Caption: describe in the order: Blue and black lines represent…dr and r.
Figure 8: Caption: Describe in the right way “(a) Mohali (b) Nainital (c) Shadnagar (d) Nagpur”
Lines 517-520: Without a figure in the main manuscript, it is difficult to understand.
Line 522: An empty/loose sentence. Better revise.
Line 523: Beter starts this sentence like this: “Figure 8a shows that the Mohali model-data mismatch is more systematic…”. Just a suggestion.
Line 533-534: Obviously coarse resolution model fails in representing fine-scale features as compared to high-resolution models (with a more realistic representation of local influences).
Section 6.6: Not sure if the second objective of the article (to assess the potential of observational data in inverse modeling) is investigated in detail.
Line 540: Revise: “…capture every fine-scale…”.
Lines 545-546: Better combine these two sentences.
Conclusion section: This section should be described in a coherent manner. Right now, it is just a collection of some select sentences from the results and discussion sections.
Line 555: Not just in inverse modeling. These aspects are important for any models, forward or in inverse modes.
Line 556-557: Not sure how authors proved it.
Line 563: Revise: “…improving our carbon estimates…”.
Citation: https://doi.org/10.5194/egusphere-2023-1582-RC1 - AC1: 'Reply on RC1', Vishnu Thilakan, 22 Nov 2023
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RC2: 'Comment on egusphere-2023-1582', Anonymous Referee #2, 08 Oct 2023
Title: Potential of using CO2 observations over India in regional carbon budget estimation by improving the modelling system by Thilakan et al.
The authors have conducted an analysis using a high-resolution transport model to assess the potential of CO2 observations over India for the inverse estimation of regional carbon fluxes. They have also evaluated the performance of the region-specific models by comparing them to surface-based observations and using statistical metrics. This contribution is undoubtedly of interest to the scientific community, although it necessitates careful interpretation in the presentation of results. Nonetheless, there are certain important scientific aspects that have not addressed properly and therefore have provided specific comments and suggestions that necessitate clarification in the revised manuscript. Therefore, I recommend that it undergoes Major revision, incorporating all the suggestions and comments provided.
Major Comments:
In the introduction, specifically on line 40, the authors emphasize the ongoing efforts on the surface-based CO2 observations over India but do not reference previously published studies that have reported on the diurnal and seasonal cycle variability in the Indian region. For instance, studies such as Chandra et al. (2016) and Jain et al. (2021) conducted at Gadanki, among others, have explored this aspect. It would be beneficial to include references to existing literature on ground-based CO2 studies in India.
Furthermore, there are ongoing modeling efforts in India aimed at representing CO2 variability and fluxes using high-resolution models. For instance, studies by Halder et al. (2021) and Siji Kumar et al. (2023), among others, have contributed to this field. It is essential to incorporate literature on these modeling efforts in order to enhance clarity and identify areas where the modeling framework may have gaps.
One major concern in the design of the model simulations is the omission of the role played by biomass burning and oceanic fluxes. Although their contributions are minimal, I believe that the authors should also take these fluxes into account when simulating CO2 concentrations at specific sites. For instance, Mohali, one of the sites, is heavily impacted by biomass/ crop residue burning. More details to be provided during the revision process.
In Section 5.1, authors made an attempt to address the seasonal cycle of CO2 observations and model simulations. I believe it would be more beneficial to focus on a discussion of the model's strengths and weaknesses in capturing seasonality, rather than solely presenting the comparison results. In some instances, the model appears to be significantly out of sync with the observations, and it would be valuable to provide explanations for these discrepancies.
Discussing the observations alone and later (section 5.2) section about the model comparison doesn’t make much information. If authors want to make robustness in the model simulations, better to provide them together for a better clarity. It would better to reorganize these sections and if needed make one section to discuss both observational and model discussions together.
In Figure 3, why did the authors choose to present three-hourly averages instead of reporting the hourly means of observations and model simulations, if the data for hourly means is available?
It would be shown and discussions in the main text along with the model’s credibility in reproducing the diurnal cycle. In fact, this is also true for other locations too depends on the data availability.
Please include a table in the main text that provides correlations (r2) between the model, including WRF and global reanalysis, and observations for all the stations. This will help emphasize the advantages of the regional high-resolution model
If the model simulations are capable of generating monthly values, it would be beneficial to include these values for assessing the relative contributions to CO2 variability for all four stations at a seasonal scale. This could offer insights into the seasonal characteristics of different components. Additionally, it would be advisable to incorporate the contribution of biomass burning along with other contributions.
In the conclusion section, it is important to address the limitations in replicating fine-scale CO2 variability at various locations using high-resolution models. Discuss whether these limitations stem from resolution issues or the potential for flux improvements to enhance agreement. These considerations are crucial for the development of regional carbon data assimilation system that can robustly estimate sources and sinks tailored to the Indian region.
Minor comments:
Line No 130: “The performance of the WRF model simulations over India was assessed by previous studies”. The intended subject of the reference in question is unclear. It could pertain to studies related to either CO2 or meteorological variables. However, it's worth noting that numerous studies exist concerning meteorology in India. To enhance clarity, please specify the subject when revising the manuscript.
In the methodology section, when using CO2 data from various instruments (PICARRO/LGR/LICOR), it would be beneficial to include information about the associated uncertainty and limitations.
Line No 232: It would be more appropriate to interpolate the respective vertical levels from the model outputs that closely match the altitude of the observations, rather than simply selecting the nearest level for validation against the ground-based observations.
Citation: https://doi.org/10.5194/egusphere-2023-1582-RC2 - AC1: 'Reply on RC1', Vishnu Thilakan, 22 Nov 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-1582', Sajeev Philip, 30 Sep 2023
In this manuscript, Thilakan et al. investigated the capability of a high-resolution Lagrangian chemical transport model to simulate spatiotemporal pattern of atmospheric CO2 mixing ratios in India. The primary goal of this effort was about to assess the potential of CO2 atmospheric mixing ratio measurement data in the regional and global inverse modeling systems to optimize CO2 fluxes.
Their approach involved the use of WRF-STILT modeling system to simulate CO2 concentrations at four sites in India. First, they described the CO2 mixing ratio variability in four different CO2 ground monitoring stations. Then they evaluated model simulations and three global model outputs against CO2 observations from these four sites. They found an outperformance of WRF-STILT compared to three global models, in representing the spatiotemporal variability of observed CO2. Finally, authors investigated the factors leading to model-observation differences. They conclude that regional and global data assimilation models can benefit from CO2 measurement data if the models are improved in certain aspects.
This is a timely effort examining the potential of modeling systems to simulate CO2 concentrations and infer fluxes driving those atmospheric mole fractions. CO2 flux estimation in India is in its developing stages, as more measurement data from monitoring stations are now available (still sparse). Assessing the potential of CO2 measurement data in the regional and global inverse modeling systems to accurately estimate CO2 fluxes is a great effort.
Therefore, this paper is within the scope of Atmospheric Chemistry and Physics (ACP) journal and investigates an important research question. The high-resolution model simulations were conducted, evaluated with observations, and model-data mismatch assessed. This manuscript can definitely be accepted to ACP, after addressing the below mentioned comments.
Major comments
- Assessing the potential of observations in data assimilation models: The primary goal in this manuscript (and the title of the manuscript) was about to investigate the potential of CO2 atmospheric mixing ratio measurements in the regional and global inverse modeling systems to optimize CO2 Unfortunately, authors could not demonstrate this aspect in a robust manner. Authors found that: 1) the STILT model simulations outperformed global reanalysis products, 2) the STILT model still require some improvements in representing emission fluxes accurately, 3) and the contribution of systematic RMSE for STILT simulations is only a certain % of the overall error for certain seasons/stations. However, it is unclear how the potential of in situ observations in inverse models was assessed.
- Applicability to Eulerian models and global model: The authors tried to investigate the potential of observations in the WRF-STILT model (see major comment #1). The last statement of the abstract: “…the global carbon data assimilation system can thus benefit…”. Authors should explain clearly how the results from this manuscript is applicable for regional/global Eulerian transport models (forward and inverse).
- All emissions to be added in the base simulations: The impact of biospheric fluxes and biomass burning emissions were assessed at some point in the manuscript. If authors had incorporated the biomass burning fluxes and an additional biosphere model (in addition to VPRM) to the base-model simulations, some sub-sections in Sect. 6 could have been avoided. Also, we all know that these emissions and local transport should be well represented in any kind of models. Authors should at least mention the novel aspects of this analysis conducted here (the impact assessments).
- Novelty aspect to be added to the introduction section: The novel aspect of this study should be added to the introduction section with detailed citations of the relevant papers.
- Need well-defined analysis approach: From the major comments #1 to #3, it is clear that a well-defined analysis approach should have been developed and described well in the start of Section 2 itself.
- Selection of sites and models: Better if authors mention clearly why certain monitoring stations (four) were selected. Just due to data availability? Also, better if they compared their model simulations to WRF-GHG model simulations (in addition to global products) from the same authors (Thilakan et al., 2022).
- Presentation of results through summary figures: The manuscript comes with 9 (main manuscript) + 14 (supporting document) + 15 (additional materials: https://zenodo.org/record/8143361) = 38 figures! Most of the sentences in results and discussions sections are going back and forth between different figures, which makes it difficult to read and understand. Authors should be able to come up with <10 simple summary figures in the main manuscript to represent their results. For example, the subplots of the figures 2, 3, 5, 6, and 7 can be represented in a single plot with observation bar + 9 bars. Other options can be explored.
- Need clarity in conclusions: A major drawback of this manuscript is the loose conclusions in each and every section in Sect. 5 and 6. Authors first describe some figures and then make some vague general statements at the end of the paragraphs (see minor comments for some examples).
- Presentation of results and discussions in the manuscript: Authors should consider revising/re-organizing the results (Sect. 5) and discussion (Sect. 6) sections to convey novel findings of this manuscript clearly. Also, abstract and conclusion sections need to be revised (see minor comments).
Minor comments
Line 3: Better to use: “high-resolution Lagrangian chemical transport model”
Line 6: Better use: “Our modeling framework”
Line 8: How about sub-seasonal?
Line 8: Diurnal only for one site? If so, better specify that.
Line 8: Be very specific: “current general global models”.
Line 9: Representation of all processes and emission categories needs refinement irrespective of the site location. I would say its an empty and loose sentence.
Lines 9:14: Abstract should be having take-home messages from your article. All these lines are general statements, without any quantitative info.
Line 14: Better rewrite this sentence for clarity: “By implementing a model, our results emphasize…”.
Line 16: Observed variability of …
Line 21: greenhouse gas
Line 29: Change: “suffer from”
Lines 32 and 35: Better to discuss about CO2 rather than carbon fluxes
Line 34: Restrict # of citations. Add those relevant to India.
Line 36: Ganesan et al. deals with methane. Better to discuss about CO2.
Lines 34-36: Better mention that these models require observations to constrain fluxes. That way, this sentence connects with the next sentence (Lines 36-38).
Line 39-40: Rewrite for clarity: “detect surface variations in addition to data gaps”.
Line 41: Are there any dedicated network? Or it’s just individual observation stations? Also, there could be some more papers describing recent in situ observations.
Line 48: Not sure these constraints are the reason not to use Indian observations in global models (data availability…).
Lines 48-50 and 55: There should be at least one paper (Sijikumar et al. 2023): https://doi.org/10.1016/j.atmosenv.2023.119868.
Lines 53-54: Better to avoid “associated with… transport and flux”.
Line 59: Not sure if Thompson et al. found convection responsible for the difficulty in simulating CO2 over tropics.
Line 62: Not just over India.
Lines 65-66: It’s not just about resolution. Modeling system should accurately represent all kinds of processes etc.
Lines 67-84: This paragraph should better fit in the material and methods section. Section 2 can start with the overall approach of this study. Here, in the introduction section, this paragraph can be described in 2-3 sentences.
Line 77: Correct this: “indented”
Line 115: Briefly mention how PBLH is calculated, in one or two sentences.
Line 116: Which approach? Approach to calculate PBLH?
Line 132: Add some more info: “which found it promising”.
Line 137: Which satellite product?
Lines 135-144: Three anthropogenic inventories were used. Why not different biosphere flux models?
Line 141: Already mentioned that “derives…at receptor locations using Eq. (4)” in lines 132 and 138.
Lines 143-146 and 162-166: Better to have a Table with the details of different model experiments and data/inventory sources.
Lines 155:158 and Eq. (5): Why not biomass burning fluxes? Other chemical sources of CO2 (may not be a significant fraction)?
Line 169: Why not refer to these four monitoring stations with names of the cities (e.g., Mohali) rather than short forms (e.g., MHL).
Lines 180-181: Better mention those three cities, not just Chandigarh.
Lines 232-235: Rewrite for clarity. First level of CT for all stations. Then explain the rest.
Section 4: Good metrics from Willmott, (1981) and Willmott et al. (2012)!
Line 266: Any evidence shown here?
Lines 269-270: Better assess diurnal aspects in a separate section along with a figure. All analysis and main figures should be based on daytime values.
Line 261: The seasonal aspects can be described in a separate para.
Lines 276-277: Better if you cite relevant publications to support this statement.
Lines 277-279: Any reason why the CO2 contribution from biomass burning was not included as part of the base simulations?
Figure 2: In the figure caption, describe what these color bars represent. Error bars?
Figure 2: The subplots of the figure 2 (this is applicable for all figure 3 and 5 to 7) can be represented in a single plot with observation bar + 9 bars. Or explore any other figure options to represent your results in a clearly.
Section 5.1: Not sure if these aspects were already mentioned in publications from research groups who conducted the CO2 measurements (e.g., Sreenivas et al 2016)?
Section 5.2 and 5.3: Hard to read. Going back and forth between different figures. Better to have some summary figures to demonstrate that STILT model outperforms global reanalysis products (obviously). Authors should consider revising/re-organizing the results (Sect. 5) and discussion (Sect. 6) sections to convey novel findings of this manuscript clearly.
Line 415: Revise: “Using the STILT model has improved the capabilities…”
Line 418: I doubt you are trying to improve data assimilation “approaches”.
Lines 435-439: Better if you show a figure with the biomass emissions from those two inventories (in addition to STILT simulations with those emissions).
Line 440: Not clear to me: “Along with mixing height issues…”?
Lines 440-442: We all know that misrepresentation of emission fluxes can lead to errors in the simulated concentrations. If authors had incorporated the biomass burning fluxes to the base simulations, this entire sub-section 6.1 could have been avoided (?) I wonder what new aspect is critically examined here. Also, not specified the resolution of the flux inventories. Not sure how you came to this conclusion: “…shows the role of high-resolution biomass burning fluxes…”.
Lines 466-468: Clearly mention: GPP in Fig. S14a and NDVI in Fig. S14b.
Lines 474-476: This second part of the sentence is not clear: “…and by using…”.
Section 6.3: So far, the authors assessed the impact of biomass burning emissions and biospheric flux model on the variability of CO2 concentration. Now, they discuss relative contribution of different components. There is no coherence. Biosphere component is already discussed in Section 6.2.
Lines 499-501: Very loose conclusions. We all know these aspects. I wonder what new aspect is critically examined here. This is applicable for these paragraphs in Section 6.
Section 6.6: Section 6 started with an aim to explore the shortcomings to be addressed for using Indian CO2 observations in inverse models. In sections 6.1 to 6.5 author noted the inherent issues with their modeling system. Now, in section 6.6, they try to assess the potential of in situ observations in inverse model. Structuring of different sections is not in a coherent manner. Sect. 6.6 should be a stand-alone section.
Lines 510-511: better not to start a new section of paragraph with some conclusions: “…from issues in representing…”.
Lines 513-514: The approach by the authors (to assess the potential of observation in models) is now somewhat clear. Seems like an important section/paragraph…
Line 515: Better start with describing MHL (top left in the figure).
Figure 8: Caption: describe in the order: Blue and black lines represent…dr and r.
Figure 8: Caption: Describe in the right way “(a) Mohali (b) Nainital (c) Shadnagar (d) Nagpur”
Lines 517-520: Without a figure in the main manuscript, it is difficult to understand.
Line 522: An empty/loose sentence. Better revise.
Line 523: Beter starts this sentence like this: “Figure 8a shows that the Mohali model-data mismatch is more systematic…”. Just a suggestion.
Line 533-534: Obviously coarse resolution model fails in representing fine-scale features as compared to high-resolution models (with a more realistic representation of local influences).
Section 6.6: Not sure if the second objective of the article (to assess the potential of observational data in inverse modeling) is investigated in detail.
Line 540: Revise: “…capture every fine-scale…”.
Lines 545-546: Better combine these two sentences.
Conclusion section: This section should be described in a coherent manner. Right now, it is just a collection of some select sentences from the results and discussion sections.
Line 555: Not just in inverse modeling. These aspects are important for any models, forward or in inverse modes.
Line 556-557: Not sure how authors proved it.
Line 563: Revise: “…improving our carbon estimates…”.
Citation: https://doi.org/10.5194/egusphere-2023-1582-RC1 - AC1: 'Reply on RC1', Vishnu Thilakan, 22 Nov 2023
-
RC2: 'Comment on egusphere-2023-1582', Anonymous Referee #2, 08 Oct 2023
Title: Potential of using CO2 observations over India in regional carbon budget estimation by improving the modelling system by Thilakan et al.
The authors have conducted an analysis using a high-resolution transport model to assess the potential of CO2 observations over India for the inverse estimation of regional carbon fluxes. They have also evaluated the performance of the region-specific models by comparing them to surface-based observations and using statistical metrics. This contribution is undoubtedly of interest to the scientific community, although it necessitates careful interpretation in the presentation of results. Nonetheless, there are certain important scientific aspects that have not addressed properly and therefore have provided specific comments and suggestions that necessitate clarification in the revised manuscript. Therefore, I recommend that it undergoes Major revision, incorporating all the suggestions and comments provided.
Major Comments:
In the introduction, specifically on line 40, the authors emphasize the ongoing efforts on the surface-based CO2 observations over India but do not reference previously published studies that have reported on the diurnal and seasonal cycle variability in the Indian region. For instance, studies such as Chandra et al. (2016) and Jain et al. (2021) conducted at Gadanki, among others, have explored this aspect. It would be beneficial to include references to existing literature on ground-based CO2 studies in India.
Furthermore, there are ongoing modeling efforts in India aimed at representing CO2 variability and fluxes using high-resolution models. For instance, studies by Halder et al. (2021) and Siji Kumar et al. (2023), among others, have contributed to this field. It is essential to incorporate literature on these modeling efforts in order to enhance clarity and identify areas where the modeling framework may have gaps.
One major concern in the design of the model simulations is the omission of the role played by biomass burning and oceanic fluxes. Although their contributions are minimal, I believe that the authors should also take these fluxes into account when simulating CO2 concentrations at specific sites. For instance, Mohali, one of the sites, is heavily impacted by biomass/ crop residue burning. More details to be provided during the revision process.
In Section 5.1, authors made an attempt to address the seasonal cycle of CO2 observations and model simulations. I believe it would be more beneficial to focus on a discussion of the model's strengths and weaknesses in capturing seasonality, rather than solely presenting the comparison results. In some instances, the model appears to be significantly out of sync with the observations, and it would be valuable to provide explanations for these discrepancies.
Discussing the observations alone and later (section 5.2) section about the model comparison doesn’t make much information. If authors want to make robustness in the model simulations, better to provide them together for a better clarity. It would better to reorganize these sections and if needed make one section to discuss both observational and model discussions together.
In Figure 3, why did the authors choose to present three-hourly averages instead of reporting the hourly means of observations and model simulations, if the data for hourly means is available?
It would be shown and discussions in the main text along with the model’s credibility in reproducing the diurnal cycle. In fact, this is also true for other locations too depends on the data availability.
Please include a table in the main text that provides correlations (r2) between the model, including WRF and global reanalysis, and observations for all the stations. This will help emphasize the advantages of the regional high-resolution model
If the model simulations are capable of generating monthly values, it would be beneficial to include these values for assessing the relative contributions to CO2 variability for all four stations at a seasonal scale. This could offer insights into the seasonal characteristics of different components. Additionally, it would be advisable to incorporate the contribution of biomass burning along with other contributions.
In the conclusion section, it is important to address the limitations in replicating fine-scale CO2 variability at various locations using high-resolution models. Discuss whether these limitations stem from resolution issues or the potential for flux improvements to enhance agreement. These considerations are crucial for the development of regional carbon data assimilation system that can robustly estimate sources and sinks tailored to the Indian region.
Minor comments:
Line No 130: “The performance of the WRF model simulations over India was assessed by previous studies”. The intended subject of the reference in question is unclear. It could pertain to studies related to either CO2 or meteorological variables. However, it's worth noting that numerous studies exist concerning meteorology in India. To enhance clarity, please specify the subject when revising the manuscript.
In the methodology section, when using CO2 data from various instruments (PICARRO/LGR/LICOR), it would be beneficial to include information about the associated uncertainty and limitations.
Line No 232: It would be more appropriate to interpolate the respective vertical levels from the model outputs that closely match the altitude of the observations, rather than simply selecting the nearest level for validation against the ground-based observations.
Citation: https://doi.org/10.5194/egusphere-2023-1582-RC2 - AC1: 'Reply on RC1', Vishnu Thilakan, 22 Nov 2023
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Vishnu Thilakan
Jithin Sukumaran
Christoph Gerbig
Haseeb Hakkim
Vinayak Sinha
Yukio Terao
Manish Naja
Monish Vijay Deshpande
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