the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 May 2023
3D assimilation and radiative impact assessment of aerosol black carbon over the Indian region using aircraft, balloon, ground-based, and multi-satellite observations
Abstract. A three-dimensional (spatial and vertical) gridded data set of black carbon (BC) aerosols has been developed for the first time over the Indian mainland using data from a dense ground-based network, aircraft- and balloon-based measurements from multiple campaigns, and multi-satellite observations, following statistical assimilation techniques. The assimilated data reveals that the satellite products tend to underestimate (overestimate) the aerosol absorption at lower (higher) altitudes with possible climate implications. The regional maps of atmospheric heating due to BC, derived using this dataset, well-captures the elevated aerosol heating layers over the Indian region and the spatial high over the Indo Gangetic Plains. It is shown that over most of the Indian region, the incorporation of realistic profiles of aerosol absorption/extinction coefficients and SSA into the radiative transfer calculations leads to enhanced high-altitude warming. This will have larger implications for atmospheric stability than what would be predicted using satellite observations alone and could strongly influence the upper tropospheric and lower stratospheric processes, including increased vertical transport of BC to higher altitudes. The 3D assimilated BC data set will be helpful in reducing the uncertainty in aerosol radiative effects in climate model simulations over the Indian region.
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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
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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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Supplement
(627 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-499', Anonymous Referee #1, 12 Jun 2023
The manuscript titled ‘3D assimilation and radiative impact assessment of aerosol black carbon over the Indian region using aircraft, balloon, ground-based, and multi-satellite observations’ by Kala et al. presents the three-dimensional gridded data set of black carbon (BC) aerosols over the Indian mainland utilizing data from the ground-based network, aircraft- and balloon-based measurements from multiple campaigns, and multi-satellite observations, followed by statistical assimilation techniques. It is a well-written work with excellent scientific merit, and I do not hesitate to recommend this manuscript for publication. I have a few minor suggestions below; those can be incorporated during the revision.
Specific comments:
Line 27-111: The introduction looks a bit wordy. Therefore, I recommend that the authors be concise in the introduction without losing the theme of this work.
Line 16-18: Consider rewriting this sentence
Line 20-23: Rephrase this sentence.
Line 32: I would suggest the authors add a few more references for the transport of BC aerosols to the high-altitude Himalayas in this context. E.g., Kompalli et al., 2016; Negi et al., 2019; Arun et al., 2019; Roseline et al., 2021; Gogoi et al., 2021; Arun et al., 2021; etc. are the recent ones and authors can find more in the literature.
Line 41: faraway?
Line 104-111: I would suggest removing these explanations here in the introduction since each section are well defined subsequently
Line 116-118: How are these seasons classified? Explain the criteria behind this.
Line 124-127: Are the Aethalometer datasets pressure corrected for the high-altitude ground-based network stations? Explain these details also in the revised version.
Line 143: Explain the uncertainties about the Absorption AOD from OMI used in this study. Also, there are a number of caveats that concern the validity of the results in this study. I would recommend that the authors also add details about this in the discussions.
Line155: How the BC AOD is estimated using OPAC model? Please elaborate more on this. Explain the constrained variables and uncertainties in doing so.
Line 174: Consider rewriting this sentence.
Line 176, Figure-1: I recommend the authors to also show variabilities in the elevations of the study domain in the same map. There are different resources that provide digital elevation data.
Line 262: I suggest the authors to add some information regarding the role of crop residue burning during October-November in northern India.
Line 389, Figure-8: I would recommend the authors to show the R2 values in these plots.
Line 418: Explain the statistical significance of the derived Heating rate values and their uncertainties.
Line 432: I recommend the authors add some literature related to aerosol-cloud interaction and the role of BC in it.
Line 475: Why authors offer BC absorption coefficient profiles at a horizontal resolution of 1˚×1˚ and a vertical resolution of 0.5 km. What is the reason behind choosing this particular resolution? Is it possible to have a much higher-resolution dataset in this study?
Citation: https://doi.org/10.5194/egusphere-2023-499-RC1 - AC1: 'Reply on RC1', Kala Nair. K, 07 Aug 2023
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RC2: 'Comment on egusphere-2023-499', Anonymous Referee #2, 19 Jun 2023
This paper attempts to generate 3D composite grid data of BC from data obtained from various observation platforms and estimate the radiative effects of BC considering the vertical profiles. This study potentially provides materials with some implications to improve our understanding of BC over India, which can lead to a solid contribution to ACP. I am afraid, however, that there is critical issues need to be addressed before this paper can be considered for publication.
Major comments:
#1
In the earth sciences, assimilation generally refers to the process of integrating different types of observational data into a numerical model in order to improve the accuracy and reliability of model predictions and simulations. Even though the authors are using the mathematical methods used for assimilation, in this study they just combined the various observations together to create composite data. Using the word assimilation in the title and texts is misleading to the reader (and I misunderstood it too). I suggest using a different word.#2
It is difficult to understand the process of creating composite data from the description in the text and Figure 3. First, each of the data used should be described, and then the process shown in Fig.3 should be described carefully and in sequence.#3
The authors are using the assimilation method in the wrong way. Figure 3 shows that common data (ARFINET BC AAOD, CALIPSO profiles) is used to generate both k_obs and k_bg. In other words, k_obs and k_bg are not independent. This does not satisfy the preconditions for maximum likelihood estimation, which is the basis of the variational method. In this case, the covariance between k_obs and k_bg must be taken into account.Specific comments:
#1
Line 141: It is very difficult to determine the absorption of dust alone from satellite observations. Please discuss the uncertainty of that and the uncertainty in the BC AAOD obtained.
#2
Line 223: I am concerned about the very simple method of calculating background error covariance. Because the matrix A is only a deviation from the climatic value not including information of uncertainty of k_bs. Did you not try the method of calculating from the uncertainty of the data sets used to generate K_bg? I also concerned that the simple multiplication of the deviations (i.e., equation (3)) can properly estimated the off-diagonal components (i.e., the covariance in the spatial direction) of matrix B. Have you examined the structure of the covariance closely? This also relates to the advantage of the 3D-Var as pointed out in line 367-369.#3
Line 360: The values of Delta_k in Figure 7 show very fine-scale variation, particularly in MAM. What is the cause of this? As a result, we also see fine-scale spatial variation in k_asm compared with Assimilated BC AAOD (Figure 4). This fine-scale variation is a realistic result?#4
Line 367-369: In the method of obtaining from deviations as in equation (3), apparent correlations may appear, especially between grids separated by a distance. Have you examined about this?#5
Line 387-394: It is obvious that K_asm is more consistent with K_obs than K_bg; the discussion using Fig.8 makes no sense. If authors want to verify Kasm, authors should do so with independent data.#6
Line 413-425: Very interesting point. If there are any previous studies that make similar points, please include them.#7
LIne437-450: Were you able to find any traces of self-lofting in this data set?#8
Line426-470: Although related to the need for an accurate vertical profile of BC, it is mostly redundant as it is mostly a description of previous studies. The description should be more concise in conjunction with the results of this study.Citation: https://doi.org/10.5194/egusphere-2023-499-RC2 - AC2: 'Reply on RC2', Kala Nair. K, 07 Aug 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-499', Anonymous Referee #1, 12 Jun 2023
The manuscript titled ‘3D assimilation and radiative impact assessment of aerosol black carbon over the Indian region using aircraft, balloon, ground-based, and multi-satellite observations’ by Kala et al. presents the three-dimensional gridded data set of black carbon (BC) aerosols over the Indian mainland utilizing data from the ground-based network, aircraft- and balloon-based measurements from multiple campaigns, and multi-satellite observations, followed by statistical assimilation techniques. It is a well-written work with excellent scientific merit, and I do not hesitate to recommend this manuscript for publication. I have a few minor suggestions below; those can be incorporated during the revision.
Specific comments:
Line 27-111: The introduction looks a bit wordy. Therefore, I recommend that the authors be concise in the introduction without losing the theme of this work.
Line 16-18: Consider rewriting this sentence
Line 20-23: Rephrase this sentence.
Line 32: I would suggest the authors add a few more references for the transport of BC aerosols to the high-altitude Himalayas in this context. E.g., Kompalli et al., 2016; Negi et al., 2019; Arun et al., 2019; Roseline et al., 2021; Gogoi et al., 2021; Arun et al., 2021; etc. are the recent ones and authors can find more in the literature.
Line 41: faraway?
Line 104-111: I would suggest removing these explanations here in the introduction since each section are well defined subsequently
Line 116-118: How are these seasons classified? Explain the criteria behind this.
Line 124-127: Are the Aethalometer datasets pressure corrected for the high-altitude ground-based network stations? Explain these details also in the revised version.
Line 143: Explain the uncertainties about the Absorption AOD from OMI used in this study. Also, there are a number of caveats that concern the validity of the results in this study. I would recommend that the authors also add details about this in the discussions.
Line155: How the BC AOD is estimated using OPAC model? Please elaborate more on this. Explain the constrained variables and uncertainties in doing so.
Line 174: Consider rewriting this sentence.
Line 176, Figure-1: I recommend the authors to also show variabilities in the elevations of the study domain in the same map. There are different resources that provide digital elevation data.
Line 262: I suggest the authors to add some information regarding the role of crop residue burning during October-November in northern India.
Line 389, Figure-8: I would recommend the authors to show the R2 values in these plots.
Line 418: Explain the statistical significance of the derived Heating rate values and their uncertainties.
Line 432: I recommend the authors add some literature related to aerosol-cloud interaction and the role of BC in it.
Line 475: Why authors offer BC absorption coefficient profiles at a horizontal resolution of 1˚×1˚ and a vertical resolution of 0.5 km. What is the reason behind choosing this particular resolution? Is it possible to have a much higher-resolution dataset in this study?
Citation: https://doi.org/10.5194/egusphere-2023-499-RC1 - AC1: 'Reply on RC1', Kala Nair. K, 07 Aug 2023
-
RC2: 'Comment on egusphere-2023-499', Anonymous Referee #2, 19 Jun 2023
This paper attempts to generate 3D composite grid data of BC from data obtained from various observation platforms and estimate the radiative effects of BC considering the vertical profiles. This study potentially provides materials with some implications to improve our understanding of BC over India, which can lead to a solid contribution to ACP. I am afraid, however, that there is critical issues need to be addressed before this paper can be considered for publication.
Major comments:
#1
In the earth sciences, assimilation generally refers to the process of integrating different types of observational data into a numerical model in order to improve the accuracy and reliability of model predictions and simulations. Even though the authors are using the mathematical methods used for assimilation, in this study they just combined the various observations together to create composite data. Using the word assimilation in the title and texts is misleading to the reader (and I misunderstood it too). I suggest using a different word.#2
It is difficult to understand the process of creating composite data from the description in the text and Figure 3. First, each of the data used should be described, and then the process shown in Fig.3 should be described carefully and in sequence.#3
The authors are using the assimilation method in the wrong way. Figure 3 shows that common data (ARFINET BC AAOD, CALIPSO profiles) is used to generate both k_obs and k_bg. In other words, k_obs and k_bg are not independent. This does not satisfy the preconditions for maximum likelihood estimation, which is the basis of the variational method. In this case, the covariance between k_obs and k_bg must be taken into account.Specific comments:
#1
Line 141: It is very difficult to determine the absorption of dust alone from satellite observations. Please discuss the uncertainty of that and the uncertainty in the BC AAOD obtained.
#2
Line 223: I am concerned about the very simple method of calculating background error covariance. Because the matrix A is only a deviation from the climatic value not including information of uncertainty of k_bs. Did you not try the method of calculating from the uncertainty of the data sets used to generate K_bg? I also concerned that the simple multiplication of the deviations (i.e., equation (3)) can properly estimated the off-diagonal components (i.e., the covariance in the spatial direction) of matrix B. Have you examined the structure of the covariance closely? This also relates to the advantage of the 3D-Var as pointed out in line 367-369.#3
Line 360: The values of Delta_k in Figure 7 show very fine-scale variation, particularly in MAM. What is the cause of this? As a result, we also see fine-scale spatial variation in k_asm compared with Assimilated BC AAOD (Figure 4). This fine-scale variation is a realistic result?#4
Line 367-369: In the method of obtaining from deviations as in equation (3), apparent correlations may appear, especially between grids separated by a distance. Have you examined about this?#5
Line 387-394: It is obvious that K_asm is more consistent with K_obs than K_bg; the discussion using Fig.8 makes no sense. If authors want to verify Kasm, authors should do so with independent data.#6
Line 413-425: Very interesting point. If there are any previous studies that make similar points, please include them.#7
LIne437-450: Were you able to find any traces of self-lofting in this data set?#8
Line426-470: Although related to the need for an accurate vertical profile of BC, it is mostly redundant as it is mostly a description of previous studies. The description should be more concise in conjunction with the results of this study.Citation: https://doi.org/10.5194/egusphere-2023-499-RC2 - AC2: 'Reply on RC2', Kala Nair. K, 07 Aug 2023
Peer review completion
Journal article(s) based on this preprint
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| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 336 | 130 | 17 | 483 | 34 | 9 | 10 |
- HTML: 336
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Viewed (geographical distribution)
| Country | # | Views | % |
|---|---|---|---|
| India | 1 | 144 | 30 |
| United States of America | 2 | 133 | 27 |
| Germany | 3 | 47 | 9 |
| China | 4 | 31 | 6 |
| France | 5 | 12 | 2 |
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| PDF: | 0 |
| XML: | 0 |
- 1
- 144
Nair Krishnan Kala
Narayana Anand
Mohanan R. Manoj
Srinivasan Prasanth
Harshavardhana S. Pathak
Thara Prabhakaran
Pramod D. Safai
Krishnaswamy K. Moorthy
Sreedharan K. Satheesh
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(3177 KB) - Metadata XML
-
Supplement
(627 KB) - BibTeX
- EndNote
- Final revised paper