the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Jun 2023
Spring and summertime aerosol optical depth variability over Arctic cryosphere from space-borne observations and model simulation
Abstract. The Arctic is a unique part of the Earth system that is currently undergoing a warming phase called the Arctic Amplification (AA). Changes in aerosol abundance and composition are under the influence of the AA and may be, in turn, important drivers to the AA. However, their ground and space observations are particularly difficult and spatio-temporally sparse in this region, limiting the knowledge and ability to model their variability. In this study, we have used the total aerosol optical depth (AOD) determined by the AEROSNOW algorithm using data from the AATSR satellite instrument over snow- and ice-covered regions of the Arctic. This data is then used to evaluate the global GEOS-Chem 3D chemical transport model for the period 2003–2011. Thus, the main drivers of monthly and seasonal variations in spaceborne AOD were determined by using the GEOS-Chem model-simulated aerosol components. By comparing these two AOD datasets, we examined the spring and summer AOD over Arctic snow and ice for the period of space-borne observations. The space-borne and modelled AOD show consistent spatio-temporal distributions in both seasons, with a pronounced chemical speciation in GEOS-Chem. This behaviour is attributed to the different seasonal sources of AOD. In spring, Arctic aerosols originate from long-range pollution transport from low and mid-latitudes as well as from local sources, whereas in summer natural local sources within the Arctic Circle (here defined as > 60° N) dominate. Arctic AOD is generally highest in spring and lowest in summer due to wet scavenging. In addition, carbonaceous aerosols (black carbon, BC, and organic carbon, OC) are an increasingly important contributor to total AOD over Arctic sea ice in summer due to the expected increase in boreal forest fires. The relative contribution of sulfate to total AOD over Arctic sea ice decreases while that of carbonaceous aerosols increases during the spring-summer transition. This suggests that boreal wildfires are penetrating more deeply into Arctic sea ice at higher latitudes during this study period. GEOS-Chem showed a systematically smaller AOD value compared to AEROSNOW over the Arctic sea ice region in summer. The promising results of AEROSNOW could also serve as the baseline for the evaluation and improvement of aerosol forecasts for various chemical transport models, especially over Arctic sea ice.
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RC1: 'Comment on egusphere-2023-730', Anonymous Referee #3, 23 Jun 2023
EGUSPHERE-2023-730
Spring and summertime aerosol optical depth variability over Arctic cryosphere from space-borne observations and model simulation
Swain et al.
Summary
This work builds on AERONET data and a satellite monthly AOD product for the Arctic (AEROSNOW) described in a separate paper, to evaluate the performance of GEOS-Chem, and describe the seasonality of AOD per species and mode for 4 stations in the Arctic and for the sea-ice regions. The main conclusions are that GEOS-Chem reproduces adequately the known features of Arctic aerosols (haze, intrusions of biomass burning plumes…). Some suggestions are proposed to explain the observed discrepancies between GEOS-Chem and AERONET/AEROSNOW.
General comments
The idea of this work is of interest for the community, as a better knowledge and understanding of Arctic aerosols is a critical topic. The manuscript is generally well written, and most of the figures are easy to read.
My biggest concern is that the study relies on a methodology for satellite AOD presented in a companion paper that is under review in AMT: Swain et al., 2023a. Spring and summertime aerosol optical depth retrieval over the Arctic cryosphere by using satellite observations. (same authors, almost same title, several common figures).
This is problematic for 2 reasons:- I cannot review the other paper and assess its validity, so the AEROSNOW product is not officially validated until this is published, and therefore not really fit for use here
- AEROSNOW is presented here as one of the main value-added of the paper, but it is already going to be published in the companion paper, including with similar/same plots in both manuscripts. All of the AERONET and AEROSNOW-based analyses and results shown here are already presented in the other paper. Therefore, the only visible difference is the GEOS-Chem analysis. At the very least, the scope of the paper should be changed, explicitly stating that the new results are only from the GEOS-Chem modeling and focusing on this part.
As a result, this paper is mostly a model evaluation of Arctic AOD simulated by GEOS-Chem, albeit with a new data set for evaluation, showing that the model behaves as expected compared to what was already known in the literature. Therefore the conclusions do not really bring new knowledge. In particular the seasonality and composition of Arctic aerosols, including over sea ice, which is the focus here, is already described in recent literature such as Sand et al., 2017; Schmale et al., 2022; Moschos et al., 2022. In particular, the AEROCOM models evaluated for the Arctic in Sand et al., 2017 include GEOS-Chem.
The novelty of this work is therefore not clear to me. In particular, I expect for example that analyses of AOD based on ensembles, as provided by CMIP or AEROCOM would be as relevant as using only one model, at coarse resolution (4°x5°) as done here. The same can be said about reanalyses such as CAMS or MERRA2 which also provide AOD by species/mode at higher spatial and time resolution.
Also, the MACv2 aerosol climatology (Kinne 2019) proposes the same information as presented with GEOS-Chem here (monthly AOD by size/species), but based on an ensemble, at 1° resolution, and corrected using AERONET and MAN. Again, I expect that MACv2 does as good a job as the GEOS-Chem simulations presented here. This product (and/or reanalysis/ensembles) should at least be mentioned, and if possible compared to GEOS-Chem in order to make the paper relevant and turn into a multi-model evaluation.
In conclusion, although the idea of this work is of interest for the community, I think the novelty and conclusions that can be drawn from it are too limited, and I do not recommend publication in ACP, unless the focus of the paper is changed, with a deeper evaluation and analysis of the information provided by GEOS-Chem to extract novel knowledge on aerosols in sea-ice regions. An idea could be to leverage more the information on the vertical provided by the model.
I also include specific comments below that could help improve the manuscript.
Specific comments
Abstract
L11 - “with a pronounced chemical speciation in GEOS-Chem”: what do you mean?
L20 - seeing as AEROSNOW is built upon AATSR data, which is discontinued since 2012, I do not understand how you would use that for evaluation of aerosol forecast in more recent years. Also, forecast applications are not mentioned elsewhere in the manuscript.
Introduction
The reference [Willis et al., 2018] is used many times throughout the introduction. Although I agree this review paper fits very well here, I suggest adding complementary, more specific references. I give a few examples below of suggestions for additional/more specific papers, please try to diversify your references.
L36 - Schmale et al., 2021; Pernov et al., 2022
L39 - Li et al., 2022
L42 - Skiles et al., 2018
L49 - Meinander et al., 2022 (dust); Lapere et al., 2023 (sea salt); Eck et al., 2009; Marelle et al., 2015 (biomass burning)
L49 - seeing as you focus on sea-ice covered region in the rest of the paper, you should mention blowing snow sources of sea salt. Even though sea salt does not contribute a lot to AOD, it is still important to mention.
L51 - “… our understanding of the effect of aerosols on the Arctic climate…”
L53 - please use the appropriate reference for the MOSAiC campaign instead of the website address (Shupe et al., 2022 - https://doi.org/10.1525/elementa.2021.00060)
L57 - this sentence is not clear, what do you mean? please rephrase
L65 - unclear what you mean, please rephrase
Please remain neutral in your wording: L71 “successful”, L82 “very well explained”, L83 “well explained”, etc…
L83-L84 - “The former …”, “The latter …” - unclear what this refers to, please rephrase.
L84-85 - what is an “optimal selection of AOD components”?
L86 - please include a reference for the mid-latitude product you say is available.
L87-93 - repletion of previous points - I suggest moving this part before L50 and merging with paragraph L45
L103 - the AEROSNOW AOD data was evidently already generated in Swain et al. (2023a), so this sentence does not belong in this paper
2 - Data sets and data processing
Generally speaking, I feel like this section could be shorter, with the more detailed parts moved to an Appendix or supplementary.
L114 - why/how? please explain
Figure 1 - the same figure appears in Swain et al., 2023a - please mention it in the caption.
L152-154 - this is not relevant to explain here, please remove.
L155-158 - I suggest moving this text into a table instead. This is a bit wordy.
Section 2.2.1 - this section has too much detail. I suggest moving most of it to an Appendix or supplementary material, and keep only keep information on the emission inventories directly into section 2.2
3 - Results: Evaluation of AODs from AERONET, AEROSNOW, and GEOS-Chem
Figure 2 - AEROSNOW and AERONET data are already presented in the exact same way in Figure 5 of Swain et al., 2023a. Please mention it in the caption.
L218 - R is not defined
L218/219 - I would be more nuanced: the correlation coefficients are only computed over a handful of points and for monthly averages. Therefore it is not surprising to obtain this kind of correlation. Also, a confidence interval/level of significance of these correlations would be appreciated given the limited number of data points.
L219 - “comparatively”
L227 “a detailed description is given in Part-I of this work”. Is Part-I Swain et al., 2023a?
L233 - R is defined here for the first time but already used in L218
Figure A1 - this exact figure is already in Swain et al., 2023a as Figure A1.
L240 - this is a bit of a stretch in my opinion. The speciation given by GEOS-Chem is not validated in this paper. As a result, you could get the right total AOD for the wrong reasons via error compensation across species. It is not possible to conclude that you have the right emissions/optical properties, only the right AOD. I would be more careful and nuanced about this type of conclusions.
L241 - same comment, plenty of other reasons could explain the discrepancies, other than spatial resolution (biases in meteorology e.g.). Besides, it is unclear how you do the comparisons: do you compare only for times/days where you have data in all datasets, or do you just take all the available data for all. This can also strongly affect the results since there are probably many missing data points in observations (night-time, non-clear sky…) whereas GC provides “continuous” data. Ideally you should compare only over times with data available in all data sets, otherwise you get a sampling bias.
Figure 4 - the color code is misleading with same colors for fine/coarse and BC/Sulfate. Please correct.
L252 - I do not understand what 11% and 7% refer to. Please clarify this.
L255-256 - or to problems of emissions/optical properties/meteorology - cf my previous comment on that.
L265 - “we suspect” - could you do dedicated simulations with/without forest fire emissions to prove that?
L266-267 - I agree this is the most likely cause, but there is no evidence here. Please rephrase.
Figure A5 - I am used to seeing AOD as an integrated value over the vertical, i.e. a 2D, not 3D field. What does a vertical profile of AOD mean? This should be explained in the data section to help the reader.
4 - Arctic AOD climatology and boreal forest fires
L296 - are these secondary aerosols really important for AOD? What about sea salt re-emissions from blowing snow processes? Could that account for the missing AOD?
L319 - based on what? the emission inventory in GC? General observations from the literature?
5 - Conclusions
In general the conclusions do not bring any new knowledge about aerosols in the Arctic, they only confirm that GC behaves reasonably, in accordance with what is already known.
L332-335 - again, I think it is very important to note that points (i) and (ii) mentioned in this sentence are already presented in Swain et al., 2023a and are not new to the present work. Only (iii) is a novelty.
L336-345 - several sentences are very similar to the conclusions in Swain et al., 2023a, sometimes word for word.
L361 - “(cite some articles here)”. Please review more carefully your manuscript before submission.
L370 - I believe this is contradictory with your assessment that emissions are correct earlier (L240)
References
Sand, M. et al. (2017) Aerosols at the poles: an AeroCom Phase II multi-model evaluation. Atmos Chem Phys. 17, 12197-12218. doi: 10.5194/acp-17-12197-2017
Schmale, J., et al. (2021) Aerosols in current and future Arctic climate. Nat. Clim. Chang. 11, 95–105. doi: 10.1038/s41558-020-00969-5
Schmale, J. et al. (2022) Pan-Arctic seasonal cycles and long-term trends of aerosol properties from 10 observatories. Atmos Chem Phys. 22, 3067-3096. doi:10.5194/acp-22-3067-2022
Moschos, V. et al. (2022) Elucidating the present-day chemical composition, seasonality and source regions of climate-relevant aerosols across the Arctic land surface. Env Res Lett. 17, 034032. doi: 10.1088/1748-9326/ac444b
Pernov, J.B., et al. (2022) Increased aerosol concentrations in the High Arctic attributable to changing atmospheric transport patterns. npj Clim Atmos Sci 5, 62. doi: 10.1038/s41612-022-00286-y
Li, J., et al. (2022) Scattering and absorbing aerosols in the climate system. Nat Rev Earth Environ 3, 363–379. doi: 10.1038/s43017-022-00296-7
Skiles, S. M., et al (2018). Radiative forcing by light-absorbing particles in snow. Nature Climate Change, 8 (11), 964–971. doi: 10.1038/s41558-018-0296-5
Meinander, O., et al. (2022) Newly identified climatically and environmentally significant high-latitude dust sources, Atmos. Chem. Phys., 22, 11889–11930, doi: 10.5194/acp-22-11889-2022.
Lapere, R., et al. (2023). The representation of sea salt aerosols and their role in polar climate within CMIP6. Journal of Geophysical Research: Atmospheres, 128, e2022JD038235. doi: 10.1029/2022JD038235
Eck, T. F., et al. (2009), Optical properties of boreal region biomass burning aerosols in central Alaska and seasonal variation of aerosol optical depth at an Arctic coastal site, J. Geophys. Res., 114, D11201, doi:10.1029/2008JD010870.
Marelle, L., et al. (2015) Transport of anthropogenic and biomass burning aerosols from Europe to the Arctic during spring 2008, Atmos. Chem. Phys., 15, 3831–3850, doi:10.5194/acp-15-3831-2015.
Shupe M. et al., (2022). Overview of the MOSAiC expedition: Atmosphere. Elementa: Science of the Anthropocene. 10 (1): 00060. doi: 10.1525/elementa.2021.00060
Kinne S. (2019) The MACv2 aerosol climatology, Tellus B: Chemical and Physical Meteorology, 71:1, 1-21, DOI: 10.1080/16000889.2019.1623639
Citation: https://doi.org/10.5194/egusphere-2023-730-RC1 -
AC1: 'Reply on RC1', Basudev Swain, 06 Oct 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-730/egusphere-2023-730-AC1-supplement.pdf
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RC2: 'Comment on egusphere-2023-730', Anonymous Referee #2, 12 Aug 2023
In this study, the authors have used the total aerosol optical depth (AOD)
determined by the AEROSNOW algorithm and data from the AATSR satellite instrument over snow- and ice-covered regions
of the Arctic. The dataset is used to evaluate the global GEOS-Chem 3D chemical transport model for the period 2003-2011.The retrievals over bright surfaces are associated with large uncertainties because the main contribution to the signal comes from the surface and not from atmosphere, which is optically thin in Arctic in majority cases. Therefore, I appreciate the work performed by the authors in the evaluation and intercomparison of the retrieved AOT with the global GEOS-Chem 3D chemical transport model for the extended perid of time.
I advice that the authors improve the paper. The paper can be reconsidered after major revision. Some comments are given below:
1. What is the definition of Q_ext in Eq.(2)? Is it /? is the average extinction cross section of particles, is the average projected area of the particles.
2. I would advice to change the title of this paper. The title is similar to the title of the paper under review located at https://amt.copernicus.org/preprints/amt-2023-65/amt-2023-65.pdf. I do not think that it is a good idea to have several identical figures in both papers. The identical figures must be removed from this paper.
Citation: https://doi.org/10.5194/egusphere-2023-730-RC2 -
AC2: 'Reply on RC2', Basudev Swain, 06 Oct 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-730/egusphere-2023-730-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Basudev Swain, 06 Oct 2023
Status: closed
-
RC1: 'Comment on egusphere-2023-730', Anonymous Referee #3, 23 Jun 2023
EGUSPHERE-2023-730
Spring and summertime aerosol optical depth variability over Arctic cryosphere from space-borne observations and model simulation
Swain et al.
Summary
This work builds on AERONET data and a satellite monthly AOD product for the Arctic (AEROSNOW) described in a separate paper, to evaluate the performance of GEOS-Chem, and describe the seasonality of AOD per species and mode for 4 stations in the Arctic and for the sea-ice regions. The main conclusions are that GEOS-Chem reproduces adequately the known features of Arctic aerosols (haze, intrusions of biomass burning plumes…). Some suggestions are proposed to explain the observed discrepancies between GEOS-Chem and AERONET/AEROSNOW.
General comments
The idea of this work is of interest for the community, as a better knowledge and understanding of Arctic aerosols is a critical topic. The manuscript is generally well written, and most of the figures are easy to read.
My biggest concern is that the study relies on a methodology for satellite AOD presented in a companion paper that is under review in AMT: Swain et al., 2023a. Spring and summertime aerosol optical depth retrieval over the Arctic cryosphere by using satellite observations. (same authors, almost same title, several common figures).
This is problematic for 2 reasons:- I cannot review the other paper and assess its validity, so the AEROSNOW product is not officially validated until this is published, and therefore not really fit for use here
- AEROSNOW is presented here as one of the main value-added of the paper, but it is already going to be published in the companion paper, including with similar/same plots in both manuscripts. All of the AERONET and AEROSNOW-based analyses and results shown here are already presented in the other paper. Therefore, the only visible difference is the GEOS-Chem analysis. At the very least, the scope of the paper should be changed, explicitly stating that the new results are only from the GEOS-Chem modeling and focusing on this part.
As a result, this paper is mostly a model evaluation of Arctic AOD simulated by GEOS-Chem, albeit with a new data set for evaluation, showing that the model behaves as expected compared to what was already known in the literature. Therefore the conclusions do not really bring new knowledge. In particular the seasonality and composition of Arctic aerosols, including over sea ice, which is the focus here, is already described in recent literature such as Sand et al., 2017; Schmale et al., 2022; Moschos et al., 2022. In particular, the AEROCOM models evaluated for the Arctic in Sand et al., 2017 include GEOS-Chem.
The novelty of this work is therefore not clear to me. In particular, I expect for example that analyses of AOD based on ensembles, as provided by CMIP or AEROCOM would be as relevant as using only one model, at coarse resolution (4°x5°) as done here. The same can be said about reanalyses such as CAMS or MERRA2 which also provide AOD by species/mode at higher spatial and time resolution.
Also, the MACv2 aerosol climatology (Kinne 2019) proposes the same information as presented with GEOS-Chem here (monthly AOD by size/species), but based on an ensemble, at 1° resolution, and corrected using AERONET and MAN. Again, I expect that MACv2 does as good a job as the GEOS-Chem simulations presented here. This product (and/or reanalysis/ensembles) should at least be mentioned, and if possible compared to GEOS-Chem in order to make the paper relevant and turn into a multi-model evaluation.
In conclusion, although the idea of this work is of interest for the community, I think the novelty and conclusions that can be drawn from it are too limited, and I do not recommend publication in ACP, unless the focus of the paper is changed, with a deeper evaluation and analysis of the information provided by GEOS-Chem to extract novel knowledge on aerosols in sea-ice regions. An idea could be to leverage more the information on the vertical provided by the model.
I also include specific comments below that could help improve the manuscript.
Specific comments
Abstract
L11 - “with a pronounced chemical speciation in GEOS-Chem”: what do you mean?
L20 - seeing as AEROSNOW is built upon AATSR data, which is discontinued since 2012, I do not understand how you would use that for evaluation of aerosol forecast in more recent years. Also, forecast applications are not mentioned elsewhere in the manuscript.
Introduction
The reference [Willis et al., 2018] is used many times throughout the introduction. Although I agree this review paper fits very well here, I suggest adding complementary, more specific references. I give a few examples below of suggestions for additional/more specific papers, please try to diversify your references.
L36 - Schmale et al., 2021; Pernov et al., 2022
L39 - Li et al., 2022
L42 - Skiles et al., 2018
L49 - Meinander et al., 2022 (dust); Lapere et al., 2023 (sea salt); Eck et al., 2009; Marelle et al., 2015 (biomass burning)
L49 - seeing as you focus on sea-ice covered region in the rest of the paper, you should mention blowing snow sources of sea salt. Even though sea salt does not contribute a lot to AOD, it is still important to mention.
L51 - “… our understanding of the effect of aerosols on the Arctic climate…”
L53 - please use the appropriate reference for the MOSAiC campaign instead of the website address (Shupe et al., 2022 - https://doi.org/10.1525/elementa.2021.00060)
L57 - this sentence is not clear, what do you mean? please rephrase
L65 - unclear what you mean, please rephrase
Please remain neutral in your wording: L71 “successful”, L82 “very well explained”, L83 “well explained”, etc…
L83-L84 - “The former …”, “The latter …” - unclear what this refers to, please rephrase.
L84-85 - what is an “optimal selection of AOD components”?
L86 - please include a reference for the mid-latitude product you say is available.
L87-93 - repletion of previous points - I suggest moving this part before L50 and merging with paragraph L45
L103 - the AEROSNOW AOD data was evidently already generated in Swain et al. (2023a), so this sentence does not belong in this paper
2 - Data sets and data processing
Generally speaking, I feel like this section could be shorter, with the more detailed parts moved to an Appendix or supplementary.
L114 - why/how? please explain
Figure 1 - the same figure appears in Swain et al., 2023a - please mention it in the caption.
L152-154 - this is not relevant to explain here, please remove.
L155-158 - I suggest moving this text into a table instead. This is a bit wordy.
Section 2.2.1 - this section has too much detail. I suggest moving most of it to an Appendix or supplementary material, and keep only keep information on the emission inventories directly into section 2.2
3 - Results: Evaluation of AODs from AERONET, AEROSNOW, and GEOS-Chem
Figure 2 - AEROSNOW and AERONET data are already presented in the exact same way in Figure 5 of Swain et al., 2023a. Please mention it in the caption.
L218 - R is not defined
L218/219 - I would be more nuanced: the correlation coefficients are only computed over a handful of points and for monthly averages. Therefore it is not surprising to obtain this kind of correlation. Also, a confidence interval/level of significance of these correlations would be appreciated given the limited number of data points.
L219 - “comparatively”
L227 “a detailed description is given in Part-I of this work”. Is Part-I Swain et al., 2023a?
L233 - R is defined here for the first time but already used in L218
Figure A1 - this exact figure is already in Swain et al., 2023a as Figure A1.
L240 - this is a bit of a stretch in my opinion. The speciation given by GEOS-Chem is not validated in this paper. As a result, you could get the right total AOD for the wrong reasons via error compensation across species. It is not possible to conclude that you have the right emissions/optical properties, only the right AOD. I would be more careful and nuanced about this type of conclusions.
L241 - same comment, plenty of other reasons could explain the discrepancies, other than spatial resolution (biases in meteorology e.g.). Besides, it is unclear how you do the comparisons: do you compare only for times/days where you have data in all datasets, or do you just take all the available data for all. This can also strongly affect the results since there are probably many missing data points in observations (night-time, non-clear sky…) whereas GC provides “continuous” data. Ideally you should compare only over times with data available in all data sets, otherwise you get a sampling bias.
Figure 4 - the color code is misleading with same colors for fine/coarse and BC/Sulfate. Please correct.
L252 - I do not understand what 11% and 7% refer to. Please clarify this.
L255-256 - or to problems of emissions/optical properties/meteorology - cf my previous comment on that.
L265 - “we suspect” - could you do dedicated simulations with/without forest fire emissions to prove that?
L266-267 - I agree this is the most likely cause, but there is no evidence here. Please rephrase.
Figure A5 - I am used to seeing AOD as an integrated value over the vertical, i.e. a 2D, not 3D field. What does a vertical profile of AOD mean? This should be explained in the data section to help the reader.
4 - Arctic AOD climatology and boreal forest fires
L296 - are these secondary aerosols really important for AOD? What about sea salt re-emissions from blowing snow processes? Could that account for the missing AOD?
L319 - based on what? the emission inventory in GC? General observations from the literature?
5 - Conclusions
In general the conclusions do not bring any new knowledge about aerosols in the Arctic, they only confirm that GC behaves reasonably, in accordance with what is already known.
L332-335 - again, I think it is very important to note that points (i) and (ii) mentioned in this sentence are already presented in Swain et al., 2023a and are not new to the present work. Only (iii) is a novelty.
L336-345 - several sentences are very similar to the conclusions in Swain et al., 2023a, sometimes word for word.
L361 - “(cite some articles here)”. Please review more carefully your manuscript before submission.
L370 - I believe this is contradictory with your assessment that emissions are correct earlier (L240)
References
Sand, M. et al. (2017) Aerosols at the poles: an AeroCom Phase II multi-model evaluation. Atmos Chem Phys. 17, 12197-12218. doi: 10.5194/acp-17-12197-2017
Schmale, J., et al. (2021) Aerosols in current and future Arctic climate. Nat. Clim. Chang. 11, 95–105. doi: 10.1038/s41558-020-00969-5
Schmale, J. et al. (2022) Pan-Arctic seasonal cycles and long-term trends of aerosol properties from 10 observatories. Atmos Chem Phys. 22, 3067-3096. doi:10.5194/acp-22-3067-2022
Moschos, V. et al. (2022) Elucidating the present-day chemical composition, seasonality and source regions of climate-relevant aerosols across the Arctic land surface. Env Res Lett. 17, 034032. doi: 10.1088/1748-9326/ac444b
Pernov, J.B., et al. (2022) Increased aerosol concentrations in the High Arctic attributable to changing atmospheric transport patterns. npj Clim Atmos Sci 5, 62. doi: 10.1038/s41612-022-00286-y
Li, J., et al. (2022) Scattering and absorbing aerosols in the climate system. Nat Rev Earth Environ 3, 363–379. doi: 10.1038/s43017-022-00296-7
Skiles, S. M., et al (2018). Radiative forcing by light-absorbing particles in snow. Nature Climate Change, 8 (11), 964–971. doi: 10.1038/s41558-018-0296-5
Meinander, O., et al. (2022) Newly identified climatically and environmentally significant high-latitude dust sources, Atmos. Chem. Phys., 22, 11889–11930, doi: 10.5194/acp-22-11889-2022.
Lapere, R., et al. (2023). The representation of sea salt aerosols and their role in polar climate within CMIP6. Journal of Geophysical Research: Atmospheres, 128, e2022JD038235. doi: 10.1029/2022JD038235
Eck, T. F., et al. (2009), Optical properties of boreal region biomass burning aerosols in central Alaska and seasonal variation of aerosol optical depth at an Arctic coastal site, J. Geophys. Res., 114, D11201, doi:10.1029/2008JD010870.
Marelle, L., et al. (2015) Transport of anthropogenic and biomass burning aerosols from Europe to the Arctic during spring 2008, Atmos. Chem. Phys., 15, 3831–3850, doi:10.5194/acp-15-3831-2015.
Shupe M. et al., (2022). Overview of the MOSAiC expedition: Atmosphere. Elementa: Science of the Anthropocene. 10 (1): 00060. doi: 10.1525/elementa.2021.00060
Kinne S. (2019) The MACv2 aerosol climatology, Tellus B: Chemical and Physical Meteorology, 71:1, 1-21, DOI: 10.1080/16000889.2019.1623639
Citation: https://doi.org/10.5194/egusphere-2023-730-RC1 -
AC1: 'Reply on RC1', Basudev Swain, 06 Oct 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-730/egusphere-2023-730-AC1-supplement.pdf
-
RC2: 'Comment on egusphere-2023-730', Anonymous Referee #2, 12 Aug 2023
In this study, the authors have used the total aerosol optical depth (AOD)
determined by the AEROSNOW algorithm and data from the AATSR satellite instrument over snow- and ice-covered regions
of the Arctic. The dataset is used to evaluate the global GEOS-Chem 3D chemical transport model for the period 2003-2011.The retrievals over bright surfaces are associated with large uncertainties because the main contribution to the signal comes from the surface and not from atmosphere, which is optically thin in Arctic in majority cases. Therefore, I appreciate the work performed by the authors in the evaluation and intercomparison of the retrieved AOT with the global GEOS-Chem 3D chemical transport model for the extended perid of time.
I advice that the authors improve the paper. The paper can be reconsidered after major revision. Some comments are given below:
1. What is the definition of Q_ext in Eq.(2)? Is it /? is the average extinction cross section of particles, is the average projected area of the particles.
2. I would advice to change the title of this paper. The title is similar to the title of the paper under review located at https://amt.copernicus.org/preprints/amt-2023-65/amt-2023-65.pdf. I do not think that it is a good idea to have several identical figures in both papers. The identical figures must be removed from this paper.
Citation: https://doi.org/10.5194/egusphere-2023-730-RC2 -
AC2: 'Reply on RC2', Basudev Swain, 06 Oct 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-730/egusphere-2023-730-AC2-supplement.pdf
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AC2: 'Reply on RC2', Basudev Swain, 06 Oct 2023
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