the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 16 May 2024
Large Spatiotemporal Variability in Aerosol Properties over Central Argentina during the CACTI Field Campaign
Abstract. Few field campaigns with extensive aerosol measurements have been conducted over continental areas in the southern hemisphere. To address this data gap and better understand the interactions of convective clouds and the surrounding environment, extensive in situ and remote sensing measurements were collected during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign conducted between October 2018 and April 2019 over the Sierras de Córdoba range of central Argentina. This study describes measurements of aerosol number, size, composition, mixing state, and cloud condensation nuclei (CCN) collected at the ground and from a research aircraft during seven weeks of the campaign. Large spatial and multi-day variations in aerosol number, size, composition, and CCN were observed due to transport from upwind sources controlled by mesoscale to synoptic-scale meteorological conditions. Large vertical wind shears, back trajectories, single particle measurements, and chemical transport model predictions indicate that different types of emissions and source regions, including biogenic emissions and biomass burning from the Amazon and anthropogenic emissions from Chile and eastern Argentina, contribute to aerosols observed during CACTI. Repeated aircraft measurements near the boundary layer top reveal strong spatial and temporal variations in CCN and demonstrate that understanding the complex co-variability of aerosol properties and clouds is critical to quantify the impact of aerosol-cloud interactions. In addition to quantifying aerosol properties in this data-sparse region, these measurements will be valuable to evaluate predictions over the mid latitudes of South America and improve parameterized aerosol processes in local, regional, and global models.
- Preprint
(8256 KB) - Metadata XML
-
Supplement
(3157 KB) - BibTeX
- EndNote
Status: closed
-
RC1: 'Comment on egusphere-2024-1349', Anonymous Referee #1, 22 May 2024
This manuscript provides a comprehensive description of data collected in Argentina during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in 2018 and 2019. The subject matter is appropriate for publication in ACP, and the manuscript is generally well written and fairly clear.
This manuscript is largely descriptive, lacking substantial findings that expand the field. However, this is a very undersampled region of the world, and it is appropriate to have a paper that relies primarily on displaying and categorizing the data. The key scientific finding is that the aerosol over the Sierras de Córdoba (SDC) mountain range is highly variable, and that co-variability of aerosols and clouds is especially important in evaluating aerosol-cloud interactions in this region. The aerosol is extremely complex and variable, and it's difficult to draw many broad conclusions based on the type of sampling that was done (a single ground-based site and relatively limited aircraft sampling).
While the text is mostly clear, I found the figures to be lacking. Specifically, the color scales and line color choices used in all figures are not consistent with Copernicus guidelines, which ask for authors to use colors that can be read by most people with color vision impairments. These requirements are detailed at https://www.atmospheric-chemistry-and-physics.net/submission.html#figurestables. All figures need to be reworked with appropriate color choices and/or symbols on lines. The figures that show the flight tracks on a map are especially hard to read; please make the flight track symbols wider so that the color scale can be seen against the background contour lines and wind vectors.
Additional comments:
Tables 1 and 2: What is "BEASD"? Is this what is described on Line 194?
Line 141: Excessive significant figures.
Line 179: What is the size range of the miniSPLAT? This is important in the context of the wide-ranging size distributions.
Fig. 2e. This panel and similar in other figures showing particle number concentration might best be shown on a logarithmic scale--the variability in the UHSAS is difficult to see on this linear scale.
Lines 253 and 254. How do you define when NPF occurs? Isn't is pretty synonymous with "days that produce large numbers of ultrafine particles"?
Line 262. You speculate that increasing PM1 concentrations suppress the formation and growth of UFP. Calculating and presenting condensation sink (or even surface area) would support this conjecture.
Line 265. Change to "Concentrations of CCN are a function . . . ."
Line 268. What are the slopes of these correlations? There should be close to a 1:1 relationship at the highest supersaturation, at least.
Line 294. Aren't there local emissions of rBC from motor vehicles, especially diesel?
Line 324. Are the differences in the concentrations of larger particles on the low- and high-UFP days statistically significant? It's hard to tell just from looking at the graph. How different are they?
Fig. 4 (and Fig.14). It is very difficult to intuit much from size distributions plotted on a logarithmic y-axis. Small features are expanded, and large features are suppressed. The human eye and mind try to integrate the area on the plot, but this doesn't make sense on a logarithmic plot. If plotted on a linear scale, the number size distribution will emphasize which particle contribute to number, and the volume plot will show the significance of the larger particles. I feel this would be clearer and more clearly show the importance of these ultrafine and fine size classes to number and mass, respectively.
Line 368. Do you have a reference for transport by the low level jet?
Paragraph beginning on line 378. The interquartile *range* is not varying as described in the text. The IQR is the difference between the 75th and 25th percentile. The magnitudes of the 75th and 25th percentiles are varying as described, not the IQR.
Line 397. Fires emit SO2 and about half the rate as CO? That's a *lot* of SO2! Please provide a reference.
Fig. 7d. There is a data point off scale.
Fig. 8. Replace "pyridinum" with "pyridinium" in the legend.
Fig. 9b (and 10b). It might be more useful to plot the number of samples on a log scale.
Fig. 13. You point out the range of CPC and CCN values from during legs 3 and 21 at the bottom of the profiles, but it took me a while to understand that these were from the AMF site. Please mention this in the figure caption.
Fig. 14. They grey line is very hard to see. (And linear y-axes, please.)
Line 685. What is meant by "low anthropogenic emission regions". Low altitude? Or low emissions?
Line 694. Schill et al. (Nature Geoscience, doi:10.1038/s41561-020-0586-1) found that about 1/4 of accumulation mode particles in remote areas had a signature of biomass burning. This seems relevant to the discussion here.
Line 718. Just as flow over the SDC moves PBL air into the FT, you may want to mention here that the trajectories suggest that you will see PBL air from upstream of the Andes lofted into the FT where you measured, helping explain some of the observed variability.
Line 812: "Around" misspelled as "arounc".
Fig. S10. If statistics support it, it would be more interesting to see the miniSPLAT particle types plotted averaged over altitude bins rather than plotted as a function of time, so we could see the difference between PBL and FT particle abundance. Alternately, plot altitude as a line graph on the right axis.
Citation: https://doi.org/10.5194/egusphere-2024-1349-RC1 -
RC2: 'Comment on egusphere-2024-1349', Anonymous Referee #2, 25 Jul 2024
General comments
The authors present the results of the CACTI campaign conducted in the Córdoba area (Argentina), in which a valuable amount of information on aerosols and meteorological variables was collected. The study area belongs to one of the typically “under-characterized” regions in relation to its air quality data, making the production of these data extremely useful for both local assessment and global science. However, since all of these data have been previously published (e.g., in “Deep Convection Initiation, Growth, and Environments in the Complex Terrain of Central Argentina during CACTI” by Feng et al 2022, published in a specific journal of the American Meteorological Society, RELAMPAGO-CACTI: High Impact Weather in Subtropical South America) their publication here does not add value to the manuscript. The value of this paper lies in the analysis of the spatio-temporal variations of aerosols and in the identification of the role of emission sources, suitable for publication in ACP with minor revisions, as detailed below.
Specific comments:
Interestingly, the evaluation presented with CAM-Chem of the episode that took place between November 11 and 13, where the transport of pollutants from the biomass burning in Amazonia to the monitoring site is detected due to the occurrence of a low-level jet, but, at the same time, this event is not distinguishable at the surface due to the recirculation on the western side of the plume. It would be interesting to understand if this is a specific circumstance of that day, or if it is related with the complex geography of the site. As expressed by the authors throughout the paper (e.g., in line 430) there were other circumstances where the site was influenced by biomass burning. What was the relationship in those cases between the pollutant levels at height and surface levels? Please expand on the assessment of the effect that low-level jets have on surface concentrations, which is a long-standing scientific question in the region.
The reason for choosing December 3 to show the spatial and temporal variability of aerosols is not clear. What happened on that day? In Figure S6 of the supplementary material, it is noted that for that day CO concentrations are particularly low, and in Figure S3 that it is a day with few clouds. Did these circumstances play a role? Since it is here that the large variation in aerosol properties is shown, more detailed explanation is needed to understand why the December 3 assessment is considered representative of the entire period (and location).
Technical corrections
- The authors present the evaluation of a sub-period of the CACTI campaign: the campaign took place between October 2018 and April 2019. On the other hand, in lines 153-154 they indicate that the G-1 aircraft collected data between 4 November and 8 December, to finally analyze data from 23 October to 16 December, as presented in Figure 2. Please clarify the rationale for evaluating a sub-period of the campaign, and clarify from where the data were taken at altitude after 8 December.
- In section 3.2 called “Sources of aerosols and trace gases observed on the ground”, in the first part the sources are evaluated with surface data (lines 354-391), but in the part where CAM-CHEM is used (lines 411-420) the data at altitude measured with the G-1 are evaluated. In section 3.2, called “Aircraft measurements, the vertical profiles are actually presented. It may be desirable to simply change the titles to be more in line with what is presented.
- Figure 2 is too small. It is where the surface results and those measured with aircraft are combined, and based on this figure some relevant conclusions are presented that could not be seen in this small representation:
1.1) The correlation between precipitation and low PM1 concentrations is not clearly seen.
1.2) What is stated in lines 258 and 259: “Since gas-phase aerosol precursors preferentially condense on larger particles, higher PM1 concentrations during period A suppress UFP formation and growth (Fig. S2g)” is not seen. For example, it appears that on October 28 there was rain and yet the CCN for supersaturations of 0.1% and 0.2% does not decay.
1.3) It also does not lead to visualize what is stated in lines 251-254 : “temporal variations in aerosol number distributions (Fig. 2d) and total aerosol number concentrations (Fig. 2e) reveal that new particle formation (NPF) events occur on many days producing large numbers of ultrafine particles (UFP, diameter < 50 nm)”.
- In Figure 9 the top headings are confusing: (1) the top of 9 (a) says “Particle concentrations” and refers to “particle number concentrations”; (2) the top of 9 (c) refers to the difference between ultrafine (> 3 nm) and fine (> 10 nm) condensation particle counters (CPCs). For some reason they put CPC with u subincident to those smaller than 3nm and CPC to those larger than 10 nm. Maybe putting 3um<CPC<10um would be enough.
- Line 423: confuses this evaluation with what is presented in Figure 2b, where for November 11 and 12 it is observed that there is practically no OM, coinciding with the fact that PM1 is very very low. However, line 523 indicates that 54% corresponds to OM. Perhaps this is an erroneous interpretation (line 423 refers to the average of the whole period?), please clarify.
- Figure 5 on the right shows a set of points in gray color that are not explained in the text, please clarify or modify the figure.
- Biogenic sources emit very little BC relative to total OM, but it is inaccurate to say that they do not emit BC (correct line 365).
- The authors evaluate the numerous observations to find clues that help to understand the process of aerosol formation and growth. For this purpose, they decide to divide the evaluation into periods called A, B and C. This decision is unclear because it masks the understanding of the impact of individual situations. Although the authors indicate that “rainy days divide the campaign into three periods” (line 741), this reviewer notes that in all three cases there were circumstances with and without rain. Please add a short paragraph clarifying the reasons why the selection of these 3 particular periods adds substantive information to the spatio-temporal assessment of the aerosols in the region.
Citation: https://doi.org/10.5194/egusphere-2024-1349-RC2 -
AC1: 'response to referee comments for egusphere-2024-1349', Jerome Fast, 23 Sep 2024
Referee #1
This manuscript provides a comprehensive description of data collected in Argentina during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in 2018 and 2019. The subject matter is appropriate for publication in ACP, and the manuscript is generally well written and fairly clear.
This manuscript is largely descriptive, lacking substantial findings that expand the field. However, this is a very undersampled region of the world, and it is appropriate to have a paper that relies primarily on displaying and categorizing the data. The key scientific finding is that the aerosol over the Sierras de Córdoba (SDC) mountain range is highly variable, and that co-variability of aerosols and clouds is especially important in evaluating aerosol-cloud interactions in this region. The aerosol is extremely complex and variable, and it's difficult to draw many broad conclusions based on the type of sampling that was done (a single ground-based site and relatively limited aircraft sampling).
While the text is mostly clear, I found the figures to be lacking. Specifically, the color scales and line color choices used in all figures are not consistent with Copernicus guidelines, which ask for authors to use colors that can be read by most people with color vision impairments. These requirements are detailed at https://www.atmospheric-chemistry-and-physics.net/submission.html#figurestables. All figures need to be reworked with appropriate color choices and/or symbols on lines. The figures that show the flight tracks on a map are especially hard to read; please make the flight track symbols wider so that the color scale can be seen against the background contour lines and wind vectors.
Response: All the figures have been changed to address color vision impairments.
Additional comments:
Tables 1 and 2: What is "BEASD"? Is this what is described on Line 194?
Response: The reviewer is correct to point out that BEASD should be defined in the text. BEASD is now defined in this sentence. We use that data product for aerosol size distribution analyses since it merges measurements from four instruments to better define the aerosol size distribution between ~15 nm to ~9 um.
Line 141: Excessive significant figures.
Response: Changed 55.81 to 55.8.
Line 179: What is the size range of the miniSPLAT? This is important in the context of the wide-ranging size distributions.
Response: The sentence has been altered to include information: “The miniSPLAT (Zelenyuk et al., 2015) instrument was deployed to measure the size and chemical composition of thousands of individual particles with diameters from 50 to 2000 nm. miniSPLAT detects 50% of 85 nm diameter particles and 100% of spherical particles in the size range 125 to 600 nm.”
Fig. 2e. This panel and similar in other figures showing particle number concentration might best be shown on a logarithmic scale--the variability in the UHSAS is difficult to see on this linear scale.
Response: Changed to a log scale as suggested to better visualize the variability in the UHSAS concentrations. The similarities in the CPCu, CPC, and SMPS total number concentrations make differences between those measurements difficult to see on both a linear and log scale.
Lines 253 and 254. How do you define when NPF occurs? Isn't is pretty synonymous with "days that produce large numbers of ultrafine particles"?
Response: NPF events are defined by sudden increases in concentrations of very small particles that gradually grow with time, and often appear as a “banana shape” such as those in Fig. 2d. The initial diameters of these particles are 1 nm or less that are not detectable by this SMPS instrument. Instead, we infer their occurrence by their initial growth past 10 nm. Yes, “NPF events” or “NPF days” are synonymous with “days that produce large numbers of ultrafine particles” and we use phrases interchangeably.
Line 262. You speculate that increasing PM1 concentrations suppress the formation and growth of UFP. Calculating and presenting condensation sink (or even surface area) would support this conjecture.
Response: A new figure in the supplement (Fig. S4) has been added to show the relationship between particle number concentrations and surface area for Period B where there are several NFP events. The highest number concentrations occur during periods with a minimum surface area. As these small particles grow to accumulation mode size, surface area increases. Conversely, particle concentrations drop during periods with relatively high surface area. Text has been added to explain the figure and how it relates to the condensational sink. The rest of the supplemental figures have been renumbered.
Line 265. Change to "Concentrations of CCN are a function . . . ."
Response: Changed as suggested.
Line 268. What are the slopes of these correlations? There should be close to a 1:1 relationship at the highest supersaturation, at least.
Response: Added a phrase in this sentence to include the slopes after the correlations. Indeed, the slope for the highest supersaturation is closest to the 1:1 line.
Line 294. Aren't there local emissions of rBC from motor vehicles, especially diesel?
Response: This sentence has been revised. The reviewer is correct, there are probably some local emissions but the overall emission rate is likely to be low. The text has been changed to “The peak rBC concentration at night is more difficult to explain since the local emissions of black carbon from vehicles and outdoor and wood burning in the vicinity of the AMF site are likely to be very low. There is one rural road adjacent to the AMF site, the closest town with a population of ~1000 is about 2 km to the northwest, towns with populations of ~10,000 are located at lower elevations 10 km or more to the east, and larger cities with populations of 50,000 or more are more than 30 km away.” In addition, predominate surface wind directions suggest that emissions from the road adjacent to the AMF site does not frequently impact the measurements.
Line 324. Are the differences in the concentrations of larger particles on the low- and high-UFP days statistically significant? It's hard to tell just from looking at the graph. How different are they?
Response: This sentence has been revised to provide to better quantify the difference as “Figure 3c also shows that concentrations of particles with diameters greater than 200 nm on high-UFP days are ~30% lower than on low-UFP days.”
Fig. 4 (and Fig.14). It is very difficult to intuit much from size distributions plotted on a logarithmic y-axis. Small features are expanded, and large features are suppressed. The human eye and mind try to integrate the area on the plot, but this doesn't make sense on a logarithmic plot. If plotted on a linear scale, the number size distribution will emphasize which particle contribute to number, and the volume plot will show the significance of the larger particles. I feel this would be clearer and more clearly show the importance of these ultrafine and fine size classes to number and mass, respectively.
Response: When plotting the y-axis as a linear scale, the differences in the number concentrations (and volume) become more exaggerated for small particles (< 100 nm) and the differences in number concentrations (and volume) collapse and are more difficult to see for larger particles (> 100 nm). Log-log plots of dN/dlogDp and dVdlogDp is the conventional way of depicting aerosol size distributions by the aerosol research community. For these two reasons we have chosen to keep these figures the same.
Line 368. Do you have a reference for transport by the low level jet?
Response: Another paper studying smoke transport by the low level jet from Amazonian wildfires is now cited.
Paragraph beginning on line 378. The interquartile *range* is not varying as described in the text. The IQR is the difference between the 75th and 25th percentile. The magnitudes of the 75th and 25th percentiles are varying as described, not the IQR.
Response: The paragraph has been modified in several places to address this issue.
Line 397. Fires emit SO2 and about half the rate as CO? That's a *lot* of SO2! Please provide a reference.
Response: We meant to say an order of magnitude 2 to 3 times lower (100 to 1000 times), as indicated by Fig. S5. This is an important catch by the reviewer. We have revised the text to correct this.
Fig. 7d. There is a data point off scale.
Response: Adjusted x-axis of plot.
Fig. 8. Replace "pyridinum" with "pyridinium" in the legend.
Response: Fixed spelling.
Fig. 9b (and 10b). It might be more useful to plot the number of samples on a log scale.
Response: Changed to log scale.
Fig. 13. You point out the range of CPC and CCN values from during legs 3 and 21 at the bottom of the profiles, but it took me a while to understand that these were from the AMF site. Please mention this in the figure caption.
Response: Figure caption now includes “Gray shading denotes average terrain elevation and the range of surface concentrations from the AMF site during the aircraft flight period are denoted on the bottom of the East of crest panels.”
Fig. 14. They grey line is very hard to see. (And linear y-axes, please.)
Response: A darker shade of gray is now used for those lines.
Line 685. What is meant by "low anthropogenic emission regions". Low altitude? Or low emissions?
Response: Agree this phrase is confusing. Changed sentence to “Some trajectories passing over southern Argentina, where the anthropogenic emission rates are relatively low, are transported …”
Line 694. Schill et al. (Nature Geoscience, doi:10.1038/s41561-020-0586-1) found that about 1/4 of accumulation mode particles in remote areas had a signature of biomass burning. This seems relevant to the discussion here.
Response: This is a great point, and we thank the reviewer for pointing this out. We added a sentence in the middle of this paragraph to put the present results into the context of that study.
Line 718. Just as flow over the SDC moves PBL air into the FT, you may want to mention here that the trajectories suggest that you will see PBL air from upstream of the Andes lofted into the FT where you measured, helping explain some of the observed variability.
Response: Added a sentence as suggested: “Thus, the combination of local lofting of aerosols over the Sierras de Córdoba range and upwind lofting over the Andes mountains likely contributes to variability in aerosol properties.”
Line 812: "Around" misspelled as "arounc".
Response: Fixed typo.
Fig. S10. If statistics support it, it would be more interesting to see the miniSPLAT particle types plotted averaged over altitude bins rather than plotted as a function of time, so we could see the difference between PBL and FT particle abundance. Alternately, plot altitude as a line graph on the right axis.
Response: We think the reviewer means Fig. S11. We chose to overlay the altitude of the aircraft sampling on the plot instead. We did this for Fig. 11 as well.
Referee #2
General comments
The authors present the results of the CACTI campaign conducted in the Córdoba area (Argentina), in which a valuable amount of information on aerosols and meteorological variables was collected. The study area belongs to one of the typically “under-characterized” regions in relation to its air quality data, making the production of these data extremely useful for both local assessment and global science. However, since all of these data have been previously published (e.g., in “Deep Convection Initiation, Growth, and Environments in the Complex Terrain of Central Argentina during CACTI” by Feng et al 2022, published in a specific journal of the American Meteorological Society, RELAMPAGO-CACTI: High Impact Weather in Subtropical South America) their publication here does not add value to the manuscript. The value of this paper lies in the analysis of the spatio-temporal variations of aerosols and in the identification of the role of emission sources, suitable for publication in ACP with minor revisions, as detailed below.
Specific comments:
Interestingly, the evaluation presented with CAM-Chem of the episode that took place between November 11 and 13, where the transport of pollutants from the biomass burning in Amazonia to the monitoring site is detected due to the occurrence of a low-level jet, but, at the same time, this event is not distinguishable at the surface due to the recirculation on the western side of the plume. It would be interesting to understand if this is a specific circumstance of that day, or if it is related with the complex geography of the site. As expressed by the authors throughout the paper (e.g., in line 430) there were other circumstances where the site was influenced by biomass burning. What was the relationship in those cases between the pollutant levels at height and surface levels? Please expand on the assessment of the effect that low-level jets have on surface concentrations, which is a long-standing scientific question in the region.
Response: The reviewer is correct that surface aerosols on Nov 11-13 were relatively low and did not exhibit a clear response to an influx of smoke from the north. This is one reason why we chose to examine CO since it is not affected by wet scavenging. Wet scavenging during this period likely reduced aerosol concentrations in the region as smoke was transported from the north. Aerosol concentrations aloft on the G-1 were also relatively low compared to other days, except above the cloud layers above ~3.2 km MSL. While the aerosol concentrations were low, miniSPLAT still detected biomass burning aerosols (Figure 8). Figure 2 also shows that aerosol concentrations were higher on November 10, prior to the rainy periods the next couple of days. While the low-level jet likely transported aerosols and affected surface concentrations on November 10, cold air becomes locked in place near the surface as a cold front moves in on November 11. The evaporative cooling and precipitation keep the near-surface air decoupled from the northerly flow aloft that persists into November 12.
The aircraft measurements and model predictions in Figures S6 and S7 indicate that smoke plumes transported by low level jets could have occurred on many days. We agree with the reviewer that examine such events is important; however, this would require additional analyses that would greatly add to the length of an already long paper that is more focused on providing an overview of the aerosol properties observed during CACTI. In addition to the wind shears associated with changing synoptic conditions (e.g., cold front on November 11), the SDC terrain likely has an impact on the low-level jet which would require careful interpretation of the wind profiles over the AMF site. And these terrain influences could vary from case to case. Instead, we believe such an analysis is best left to a subsequent paper that focuses solely on the northerly low-level jet, its variation with altitude, and coupling with the surface. A short discussion of this topic has been added to Section 4.
The reason for choosing December 3 to show the spatial and temporal variability of aerosols is not clear. What happened on that day? In Figure S6 of the supplementary material, it is noted that for that day CO concentrations are particularly low, and in Figure S3 that it is a day with few clouds. Did these circumstances play a role? Since it is here that the large variation in aerosol properties is shown, more detailed explanation is needed to understand why the December 3 assessment is considered representative of the entire period (and location).
Response: December 3 was chosen for several reasons. First, it was a day with partly cloudy conditions over the SDC and clear skies elsewhere. Therefore, the local aerosol properties are not affected by deep convection (vertical transport, wet scavenging) that would complicate the interpretation of the aerosol properties. Second, the vertical wind shear on this day is similar to other days. Third, surface aerosol concentrations during the afternoon varied between 2 and 3 ug/m3, so we did not pick an extreme day with very low or high concentrations. And finally, relatively high concentrations of ultrafine particles occurred both at the surface and aloft. We are not implying that December 3 is a representative day over the whole campaign, since the aircraft also flew on days with clear skies and days with partly cloudy conditions transitioning to deep convection. Instead, December 3 is likely more representative of partly cloudy days, and we use these conditions to describe spatial variability patterns and processes that do occur on other days.
We have added some text to the beginning of Section 3.4 to provide the reader with some rationale why we chose this day.
Technical corrections
- The authors present the evaluation of a sub-period of the CACTI campaign: the campaign took place between October 2018 and April 2019. On the other hand, in lines 153-154 they indicate that the G-1 aircraft collected data between 4 November and 8 December, to finally analyze data from 23 October to 16 December, as presented in Figure 2. Please clarify the rationale for evaluating a sub-period of the campaign, and clarify from where the data were taken at altitude after 8 December.
Response: There was no data collected aloft after December 8 as suggested by the last phrase of the comment. Figure 2 only shows data from the ground site. The arrow at the bottom of the figure is there just to indicate the aircraft sampling period for reference. We included a few days of surface measurements before and after the aircraft measuring period. We only analyzed a sub-period of the campaign so that we examine variability of aerosols both at the surface and aloft during the same time frame. We included a few extra days of surface measurements to illustrate the temporal variations in aerosol properties shortly before and after the aircraft measurement period. Analyzing the entire campaign surface measurements would introduce seasonal factors which we leave for subsequent studies given this study is already lengthy. We added a couple of sentences at the end of Section 2.1 to note that we did not assess the seasonal variability of the aerosol measurements.
- In section 3.2 called “Sources of aerosols and trace gases observed on the ground”, in the first part the sources are evaluated with surface data (lines 354-391), but in the part where CAM-CHEM is used (lines 411-420) the data at altitude measured with the G-1 are evaluated. In section 3.2, called “Aircraft measurements, the vertical profiles are actually presented. It may be desirable to simply change the titles to be more in line with what is presented.
Response: The reviewer is correct that both surface and aircraft measurements are presented in this section. We changed the section title to be “Sources of aerosols and trace gases” so that it is inclusive of surface measurements as well as measurement and modeling predictions aloft.
- Figure 2 is too small. It is where the surface results and those measured with aircraft are combined, and based on this figure some relevant conclusions are presented that could not be seen in this small representation:
Response: Figure 2 only contains surface measurements. The arrow at the bottom of the figure is there just to indicate the aircraft sampling period. We understand there is a lot of material presented in this figure. Response: Instead of breaking the figure up into a series of smaller figures, we kept all the panels so that a common x-axis time scale is used and the results from one panel can be compared to another panel. We removed the white space on the left and expanded the plots horizontally, so hopefully this better depicts the temporal variability of surface aerosol properties which is the primary objective of this figure.
- The correlation between precipitation and low PM1 concentrations is not clearly seen.
Response: Arrows have been added to connect the rainy periods and the low PM1 concentrations.
1.2) What is stated in lines 258 and 259: “Since gas-phase aerosol precursors preferentially condense on larger particles, higher PM1 concentrations during period A suppress UFP formation and growth (Fig. S2g)” is not seen. For example, it appears that on October 28 there was rain and yet the CCN for supersaturations of 0.1% and 0.2% does not decay.
Response: This sentence is discussing the overall behavior of aerosol populations by comparing Fig. S2f and S2g. The highest aerosol number concentrations (due to ultrafine particles) occur during period B (S2g) which also has the lowest accumulation mode aerosol number concentrations (S2f). The converse is true where periods A and C have higher accumulation mode aerosol number concentrations and also lower ultrafine aerosol number concentrations. Yes, October 8 has rain, but the PM1 mass (and also CCN) does not decrease. We have changed the text to be clearer about this point. As shown in Fig. S2a, the beginning and end of period A and B have the largest accumulated rain so that it is more likely aerosols are removed by precipitation. The other rain events have lower rain rates that would remove less aerosols, and the area of precipitation may be smaller so that the removal of aerosols in the vicinity of the AMF site may be less as well.
- It also does not lead to visualize what is stated in lines 251-254 : “temporal variations in aerosol number distributions (Fig. 2d) and total aerosol number concentrations (Fig. 2e) reveal that new particle formation (NPF) events occur on many days producing large numbers of ultrafine particles (UFP, diameter < 50 nm)”.
Response: Figure 2 has been modified to include dashed arrows that connect the NPF events in 2d with the peak particle number concentrations in Fig. 2e. Reviewer #1 suggested changing the y-axis in Fig. 2e to a log scale; therefore, the peaks in number concentrations are not as pronounced as with a linear scale. With the log scale, it is easier to see the NPF events also correspond to local minima in UHSAS accumulation model particle number concentrations for diameters > 100 nm.
- In Figure 9 the top headings are confusing: (1) the top of 9 (a) says “Particle concentrations” and refers to “particle number concentrations”; (2) the top of 9 (c) refers to the difference between ultrafine (> 3 nm) and fine (> 10 nm) condensation particle counters (CPCs). For some reason they put CPC with u subincident to those smaller than 3nm and CPC to those larger than 10 nm. Maybe putting 3um<CPC<10um would be enough.
Response: Changed title of Fig. 9c as suggested.
- Line 423: confuses this evaluation with what is presented in Figure 2b, where for November 11 and 12 it is observed that there is practically no OM, coinciding with the fact that PM1 is very very low. However, line 523 indicates that 54% corresponds to OM. Perhaps this is an erroneous interpretation (line 423 refers to the average of the whole period?), please clarify.
Response: The sentence regarding the average PM1 has been revised to indicate the numbers are an average over the 3-h aircraft flight period. The reviewer is correct to point out that PM1 concentrations are usually very low between November 11-13. However, the 3-h aircraft flight period corresponds to a short spike in PM1 concentrations during the afternoon of November 12 shortly after rain stopped. The increase in aerosol mass is likely due to a change in air mass as the precipitating cells moved out of the area.
- Figure 5 on the right shows a set of points in gray color that are not explained in the text, please clarify or modify the figure.
Response: The figure caption has been changed to note what the gray dots are. They are for periods when the wind directions are not northerly, northeasterly, or southerly (as indicated by the colored dots).
- Biogenic sources emit very little BC relative to total OM, but it is inaccurate to say that they do not emit BC (correct line 365).
Response: We are not aware of any studies showing BC is emitted from plants, even in small amounts. BC can be formed by combustion, but then it is considered a biomass burning source.
- The authors evaluate the numerous observations to find clues that help to understand the process of aerosol formation and growth. For this purpose, they decide to divide the evaluation into periods called A, B and C. This decision is unclear because it masks the understanding of the impact of individual situations. Although the authors indicate that “rainy days divide the campaign into three periods” (line 741), this reviewer notes that in all three cases there were circumstances with and without rain. Please add a short paragraph clarifying the reasons why the selection of these 3 particular periods adds substantive information to the spatio-temporal assessment of the aerosols in the region.
Response: The introductory material in Section 3.1 has been revised to provide some additional discussion on the rationale for periods A, B, and C. This divided the first paragraph into three paragraphs, with the second paragraph now providing additional discussion.
Citation: https://doi.org/10.5194/egusphere-2024-1349-AC1
Status: closed
-
RC1: 'Comment on egusphere-2024-1349', Anonymous Referee #1, 22 May 2024
This manuscript provides a comprehensive description of data collected in Argentina during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in 2018 and 2019. The subject matter is appropriate for publication in ACP, and the manuscript is generally well written and fairly clear.
This manuscript is largely descriptive, lacking substantial findings that expand the field. However, this is a very undersampled region of the world, and it is appropriate to have a paper that relies primarily on displaying and categorizing the data. The key scientific finding is that the aerosol over the Sierras de Córdoba (SDC) mountain range is highly variable, and that co-variability of aerosols and clouds is especially important in evaluating aerosol-cloud interactions in this region. The aerosol is extremely complex and variable, and it's difficult to draw many broad conclusions based on the type of sampling that was done (a single ground-based site and relatively limited aircraft sampling).
While the text is mostly clear, I found the figures to be lacking. Specifically, the color scales and line color choices used in all figures are not consistent with Copernicus guidelines, which ask for authors to use colors that can be read by most people with color vision impairments. These requirements are detailed at https://www.atmospheric-chemistry-and-physics.net/submission.html#figurestables. All figures need to be reworked with appropriate color choices and/or symbols on lines. The figures that show the flight tracks on a map are especially hard to read; please make the flight track symbols wider so that the color scale can be seen against the background contour lines and wind vectors.
Additional comments:
Tables 1 and 2: What is "BEASD"? Is this what is described on Line 194?
Line 141: Excessive significant figures.
Line 179: What is the size range of the miniSPLAT? This is important in the context of the wide-ranging size distributions.
Fig. 2e. This panel and similar in other figures showing particle number concentration might best be shown on a logarithmic scale--the variability in the UHSAS is difficult to see on this linear scale.
Lines 253 and 254. How do you define when NPF occurs? Isn't is pretty synonymous with "days that produce large numbers of ultrafine particles"?
Line 262. You speculate that increasing PM1 concentrations suppress the formation and growth of UFP. Calculating and presenting condensation sink (or even surface area) would support this conjecture.
Line 265. Change to "Concentrations of CCN are a function . . . ."
Line 268. What are the slopes of these correlations? There should be close to a 1:1 relationship at the highest supersaturation, at least.
Line 294. Aren't there local emissions of rBC from motor vehicles, especially diesel?
Line 324. Are the differences in the concentrations of larger particles on the low- and high-UFP days statistically significant? It's hard to tell just from looking at the graph. How different are they?
Fig. 4 (and Fig.14). It is very difficult to intuit much from size distributions plotted on a logarithmic y-axis. Small features are expanded, and large features are suppressed. The human eye and mind try to integrate the area on the plot, but this doesn't make sense on a logarithmic plot. If plotted on a linear scale, the number size distribution will emphasize which particle contribute to number, and the volume plot will show the significance of the larger particles. I feel this would be clearer and more clearly show the importance of these ultrafine and fine size classes to number and mass, respectively.
Line 368. Do you have a reference for transport by the low level jet?
Paragraph beginning on line 378. The interquartile *range* is not varying as described in the text. The IQR is the difference between the 75th and 25th percentile. The magnitudes of the 75th and 25th percentiles are varying as described, not the IQR.
Line 397. Fires emit SO2 and about half the rate as CO? That's a *lot* of SO2! Please provide a reference.
Fig. 7d. There is a data point off scale.
Fig. 8. Replace "pyridinum" with "pyridinium" in the legend.
Fig. 9b (and 10b). It might be more useful to plot the number of samples on a log scale.
Fig. 13. You point out the range of CPC and CCN values from during legs 3 and 21 at the bottom of the profiles, but it took me a while to understand that these were from the AMF site. Please mention this in the figure caption.
Fig. 14. They grey line is very hard to see. (And linear y-axes, please.)
Line 685. What is meant by "low anthropogenic emission regions". Low altitude? Or low emissions?
Line 694. Schill et al. (Nature Geoscience, doi:10.1038/s41561-020-0586-1) found that about 1/4 of accumulation mode particles in remote areas had a signature of biomass burning. This seems relevant to the discussion here.
Line 718. Just as flow over the SDC moves PBL air into the FT, you may want to mention here that the trajectories suggest that you will see PBL air from upstream of the Andes lofted into the FT where you measured, helping explain some of the observed variability.
Line 812: "Around" misspelled as "arounc".
Fig. S10. If statistics support it, it would be more interesting to see the miniSPLAT particle types plotted averaged over altitude bins rather than plotted as a function of time, so we could see the difference between PBL and FT particle abundance. Alternately, plot altitude as a line graph on the right axis.
Citation: https://doi.org/10.5194/egusphere-2024-1349-RC1 -
RC2: 'Comment on egusphere-2024-1349', Anonymous Referee #2, 25 Jul 2024
General comments
The authors present the results of the CACTI campaign conducted in the Córdoba area (Argentina), in which a valuable amount of information on aerosols and meteorological variables was collected. The study area belongs to one of the typically “under-characterized” regions in relation to its air quality data, making the production of these data extremely useful for both local assessment and global science. However, since all of these data have been previously published (e.g., in “Deep Convection Initiation, Growth, and Environments in the Complex Terrain of Central Argentina during CACTI” by Feng et al 2022, published in a specific journal of the American Meteorological Society, RELAMPAGO-CACTI: High Impact Weather in Subtropical South America) their publication here does not add value to the manuscript. The value of this paper lies in the analysis of the spatio-temporal variations of aerosols and in the identification of the role of emission sources, suitable for publication in ACP with minor revisions, as detailed below.
Specific comments:
Interestingly, the evaluation presented with CAM-Chem of the episode that took place between November 11 and 13, where the transport of pollutants from the biomass burning in Amazonia to the monitoring site is detected due to the occurrence of a low-level jet, but, at the same time, this event is not distinguishable at the surface due to the recirculation on the western side of the plume. It would be interesting to understand if this is a specific circumstance of that day, or if it is related with the complex geography of the site. As expressed by the authors throughout the paper (e.g., in line 430) there were other circumstances where the site was influenced by biomass burning. What was the relationship in those cases between the pollutant levels at height and surface levels? Please expand on the assessment of the effect that low-level jets have on surface concentrations, which is a long-standing scientific question in the region.
The reason for choosing December 3 to show the spatial and temporal variability of aerosols is not clear. What happened on that day? In Figure S6 of the supplementary material, it is noted that for that day CO concentrations are particularly low, and in Figure S3 that it is a day with few clouds. Did these circumstances play a role? Since it is here that the large variation in aerosol properties is shown, more detailed explanation is needed to understand why the December 3 assessment is considered representative of the entire period (and location).
Technical corrections
- The authors present the evaluation of a sub-period of the CACTI campaign: the campaign took place between October 2018 and April 2019. On the other hand, in lines 153-154 they indicate that the G-1 aircraft collected data between 4 November and 8 December, to finally analyze data from 23 October to 16 December, as presented in Figure 2. Please clarify the rationale for evaluating a sub-period of the campaign, and clarify from where the data were taken at altitude after 8 December.
- In section 3.2 called “Sources of aerosols and trace gases observed on the ground”, in the first part the sources are evaluated with surface data (lines 354-391), but in the part where CAM-CHEM is used (lines 411-420) the data at altitude measured with the G-1 are evaluated. In section 3.2, called “Aircraft measurements, the vertical profiles are actually presented. It may be desirable to simply change the titles to be more in line with what is presented.
- Figure 2 is too small. It is where the surface results and those measured with aircraft are combined, and based on this figure some relevant conclusions are presented that could not be seen in this small representation:
1.1) The correlation between precipitation and low PM1 concentrations is not clearly seen.
1.2) What is stated in lines 258 and 259: “Since gas-phase aerosol precursors preferentially condense on larger particles, higher PM1 concentrations during period A suppress UFP formation and growth (Fig. S2g)” is not seen. For example, it appears that on October 28 there was rain and yet the CCN for supersaturations of 0.1% and 0.2% does not decay.
1.3) It also does not lead to visualize what is stated in lines 251-254 : “temporal variations in aerosol number distributions (Fig. 2d) and total aerosol number concentrations (Fig. 2e) reveal that new particle formation (NPF) events occur on many days producing large numbers of ultrafine particles (UFP, diameter < 50 nm)”.
- In Figure 9 the top headings are confusing: (1) the top of 9 (a) says “Particle concentrations” and refers to “particle number concentrations”; (2) the top of 9 (c) refers to the difference between ultrafine (> 3 nm) and fine (> 10 nm) condensation particle counters (CPCs). For some reason they put CPC with u subincident to those smaller than 3nm and CPC to those larger than 10 nm. Maybe putting 3um<CPC<10um would be enough.
- Line 423: confuses this evaluation with what is presented in Figure 2b, where for November 11 and 12 it is observed that there is practically no OM, coinciding with the fact that PM1 is very very low. However, line 523 indicates that 54% corresponds to OM. Perhaps this is an erroneous interpretation (line 423 refers to the average of the whole period?), please clarify.
- Figure 5 on the right shows a set of points in gray color that are not explained in the text, please clarify or modify the figure.
- Biogenic sources emit very little BC relative to total OM, but it is inaccurate to say that they do not emit BC (correct line 365).
- The authors evaluate the numerous observations to find clues that help to understand the process of aerosol formation and growth. For this purpose, they decide to divide the evaluation into periods called A, B and C. This decision is unclear because it masks the understanding of the impact of individual situations. Although the authors indicate that “rainy days divide the campaign into three periods” (line 741), this reviewer notes that in all three cases there were circumstances with and without rain. Please add a short paragraph clarifying the reasons why the selection of these 3 particular periods adds substantive information to the spatio-temporal assessment of the aerosols in the region.
Citation: https://doi.org/10.5194/egusphere-2024-1349-RC2 -
AC1: 'response to referee comments for egusphere-2024-1349', Jerome Fast, 23 Sep 2024
Referee #1
This manuscript provides a comprehensive description of data collected in Argentina during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in 2018 and 2019. The subject matter is appropriate for publication in ACP, and the manuscript is generally well written and fairly clear.
This manuscript is largely descriptive, lacking substantial findings that expand the field. However, this is a very undersampled region of the world, and it is appropriate to have a paper that relies primarily on displaying and categorizing the data. The key scientific finding is that the aerosol over the Sierras de Córdoba (SDC) mountain range is highly variable, and that co-variability of aerosols and clouds is especially important in evaluating aerosol-cloud interactions in this region. The aerosol is extremely complex and variable, and it's difficult to draw many broad conclusions based on the type of sampling that was done (a single ground-based site and relatively limited aircraft sampling).
While the text is mostly clear, I found the figures to be lacking. Specifically, the color scales and line color choices used in all figures are not consistent with Copernicus guidelines, which ask for authors to use colors that can be read by most people with color vision impairments. These requirements are detailed at https://www.atmospheric-chemistry-and-physics.net/submission.html#figurestables. All figures need to be reworked with appropriate color choices and/or symbols on lines. The figures that show the flight tracks on a map are especially hard to read; please make the flight track symbols wider so that the color scale can be seen against the background contour lines and wind vectors.
Response: All the figures have been changed to address color vision impairments.
Additional comments:
Tables 1 and 2: What is "BEASD"? Is this what is described on Line 194?
Response: The reviewer is correct to point out that BEASD should be defined in the text. BEASD is now defined in this sentence. We use that data product for aerosol size distribution analyses since it merges measurements from four instruments to better define the aerosol size distribution between ~15 nm to ~9 um.
Line 141: Excessive significant figures.
Response: Changed 55.81 to 55.8.
Line 179: What is the size range of the miniSPLAT? This is important in the context of the wide-ranging size distributions.
Response: The sentence has been altered to include information: “The miniSPLAT (Zelenyuk et al., 2015) instrument was deployed to measure the size and chemical composition of thousands of individual particles with diameters from 50 to 2000 nm. miniSPLAT detects 50% of 85 nm diameter particles and 100% of spherical particles in the size range 125 to 600 nm.”
Fig. 2e. This panel and similar in other figures showing particle number concentration might best be shown on a logarithmic scale--the variability in the UHSAS is difficult to see on this linear scale.
Response: Changed to a log scale as suggested to better visualize the variability in the UHSAS concentrations. The similarities in the CPCu, CPC, and SMPS total number concentrations make differences between those measurements difficult to see on both a linear and log scale.
Lines 253 and 254. How do you define when NPF occurs? Isn't is pretty synonymous with "days that produce large numbers of ultrafine particles"?
Response: NPF events are defined by sudden increases in concentrations of very small particles that gradually grow with time, and often appear as a “banana shape” such as those in Fig. 2d. The initial diameters of these particles are 1 nm or less that are not detectable by this SMPS instrument. Instead, we infer their occurrence by their initial growth past 10 nm. Yes, “NPF events” or “NPF days” are synonymous with “days that produce large numbers of ultrafine particles” and we use phrases interchangeably.
Line 262. You speculate that increasing PM1 concentrations suppress the formation and growth of UFP. Calculating and presenting condensation sink (or even surface area) would support this conjecture.
Response: A new figure in the supplement (Fig. S4) has been added to show the relationship between particle number concentrations and surface area for Period B where there are several NFP events. The highest number concentrations occur during periods with a minimum surface area. As these small particles grow to accumulation mode size, surface area increases. Conversely, particle concentrations drop during periods with relatively high surface area. Text has been added to explain the figure and how it relates to the condensational sink. The rest of the supplemental figures have been renumbered.
Line 265. Change to "Concentrations of CCN are a function . . . ."
Response: Changed as suggested.
Line 268. What are the slopes of these correlations? There should be close to a 1:1 relationship at the highest supersaturation, at least.
Response: Added a phrase in this sentence to include the slopes after the correlations. Indeed, the slope for the highest supersaturation is closest to the 1:1 line.
Line 294. Aren't there local emissions of rBC from motor vehicles, especially diesel?
Response: This sentence has been revised. The reviewer is correct, there are probably some local emissions but the overall emission rate is likely to be low. The text has been changed to “The peak rBC concentration at night is more difficult to explain since the local emissions of black carbon from vehicles and outdoor and wood burning in the vicinity of the AMF site are likely to be very low. There is one rural road adjacent to the AMF site, the closest town with a population of ~1000 is about 2 km to the northwest, towns with populations of ~10,000 are located at lower elevations 10 km or more to the east, and larger cities with populations of 50,000 or more are more than 30 km away.” In addition, predominate surface wind directions suggest that emissions from the road adjacent to the AMF site does not frequently impact the measurements.
Line 324. Are the differences in the concentrations of larger particles on the low- and high-UFP days statistically significant? It's hard to tell just from looking at the graph. How different are they?
Response: This sentence has been revised to provide to better quantify the difference as “Figure 3c also shows that concentrations of particles with diameters greater than 200 nm on high-UFP days are ~30% lower than on low-UFP days.”
Fig. 4 (and Fig.14). It is very difficult to intuit much from size distributions plotted on a logarithmic y-axis. Small features are expanded, and large features are suppressed. The human eye and mind try to integrate the area on the plot, but this doesn't make sense on a logarithmic plot. If plotted on a linear scale, the number size distribution will emphasize which particle contribute to number, and the volume plot will show the significance of the larger particles. I feel this would be clearer and more clearly show the importance of these ultrafine and fine size classes to number and mass, respectively.
Response: When plotting the y-axis as a linear scale, the differences in the number concentrations (and volume) become more exaggerated for small particles (< 100 nm) and the differences in number concentrations (and volume) collapse and are more difficult to see for larger particles (> 100 nm). Log-log plots of dN/dlogDp and dVdlogDp is the conventional way of depicting aerosol size distributions by the aerosol research community. For these two reasons we have chosen to keep these figures the same.
Line 368. Do you have a reference for transport by the low level jet?
Response: Another paper studying smoke transport by the low level jet from Amazonian wildfires is now cited.
Paragraph beginning on line 378. The interquartile *range* is not varying as described in the text. The IQR is the difference between the 75th and 25th percentile. The magnitudes of the 75th and 25th percentiles are varying as described, not the IQR.
Response: The paragraph has been modified in several places to address this issue.
Line 397. Fires emit SO2 and about half the rate as CO? That's a *lot* of SO2! Please provide a reference.
Response: We meant to say an order of magnitude 2 to 3 times lower (100 to 1000 times), as indicated by Fig. S5. This is an important catch by the reviewer. We have revised the text to correct this.
Fig. 7d. There is a data point off scale.
Response: Adjusted x-axis of plot.
Fig. 8. Replace "pyridinum" with "pyridinium" in the legend.
Response: Fixed spelling.
Fig. 9b (and 10b). It might be more useful to plot the number of samples on a log scale.
Response: Changed to log scale.
Fig. 13. You point out the range of CPC and CCN values from during legs 3 and 21 at the bottom of the profiles, but it took me a while to understand that these were from the AMF site. Please mention this in the figure caption.
Response: Figure caption now includes “Gray shading denotes average terrain elevation and the range of surface concentrations from the AMF site during the aircraft flight period are denoted on the bottom of the East of crest panels.”
Fig. 14. They grey line is very hard to see. (And linear y-axes, please.)
Response: A darker shade of gray is now used for those lines.
Line 685. What is meant by "low anthropogenic emission regions". Low altitude? Or low emissions?
Response: Agree this phrase is confusing. Changed sentence to “Some trajectories passing over southern Argentina, where the anthropogenic emission rates are relatively low, are transported …”
Line 694. Schill et al. (Nature Geoscience, doi:10.1038/s41561-020-0586-1) found that about 1/4 of accumulation mode particles in remote areas had a signature of biomass burning. This seems relevant to the discussion here.
Response: This is a great point, and we thank the reviewer for pointing this out. We added a sentence in the middle of this paragraph to put the present results into the context of that study.
Line 718. Just as flow over the SDC moves PBL air into the FT, you may want to mention here that the trajectories suggest that you will see PBL air from upstream of the Andes lofted into the FT where you measured, helping explain some of the observed variability.
Response: Added a sentence as suggested: “Thus, the combination of local lofting of aerosols over the Sierras de Córdoba range and upwind lofting over the Andes mountains likely contributes to variability in aerosol properties.”
Line 812: "Around" misspelled as "arounc".
Response: Fixed typo.
Fig. S10. If statistics support it, it would be more interesting to see the miniSPLAT particle types plotted averaged over altitude bins rather than plotted as a function of time, so we could see the difference between PBL and FT particle abundance. Alternately, plot altitude as a line graph on the right axis.
Response: We think the reviewer means Fig. S11. We chose to overlay the altitude of the aircraft sampling on the plot instead. We did this for Fig. 11 as well.
Referee #2
General comments
The authors present the results of the CACTI campaign conducted in the Córdoba area (Argentina), in which a valuable amount of information on aerosols and meteorological variables was collected. The study area belongs to one of the typically “under-characterized” regions in relation to its air quality data, making the production of these data extremely useful for both local assessment and global science. However, since all of these data have been previously published (e.g., in “Deep Convection Initiation, Growth, and Environments in the Complex Terrain of Central Argentina during CACTI” by Feng et al 2022, published in a specific journal of the American Meteorological Society, RELAMPAGO-CACTI: High Impact Weather in Subtropical South America) their publication here does not add value to the manuscript. The value of this paper lies in the analysis of the spatio-temporal variations of aerosols and in the identification of the role of emission sources, suitable for publication in ACP with minor revisions, as detailed below.
Specific comments:
Interestingly, the evaluation presented with CAM-Chem of the episode that took place between November 11 and 13, where the transport of pollutants from the biomass burning in Amazonia to the monitoring site is detected due to the occurrence of a low-level jet, but, at the same time, this event is not distinguishable at the surface due to the recirculation on the western side of the plume. It would be interesting to understand if this is a specific circumstance of that day, or if it is related with the complex geography of the site. As expressed by the authors throughout the paper (e.g., in line 430) there were other circumstances where the site was influenced by biomass burning. What was the relationship in those cases between the pollutant levels at height and surface levels? Please expand on the assessment of the effect that low-level jets have on surface concentrations, which is a long-standing scientific question in the region.
Response: The reviewer is correct that surface aerosols on Nov 11-13 were relatively low and did not exhibit a clear response to an influx of smoke from the north. This is one reason why we chose to examine CO since it is not affected by wet scavenging. Wet scavenging during this period likely reduced aerosol concentrations in the region as smoke was transported from the north. Aerosol concentrations aloft on the G-1 were also relatively low compared to other days, except above the cloud layers above ~3.2 km MSL. While the aerosol concentrations were low, miniSPLAT still detected biomass burning aerosols (Figure 8). Figure 2 also shows that aerosol concentrations were higher on November 10, prior to the rainy periods the next couple of days. While the low-level jet likely transported aerosols and affected surface concentrations on November 10, cold air becomes locked in place near the surface as a cold front moves in on November 11. The evaporative cooling and precipitation keep the near-surface air decoupled from the northerly flow aloft that persists into November 12.
The aircraft measurements and model predictions in Figures S6 and S7 indicate that smoke plumes transported by low level jets could have occurred on many days. We agree with the reviewer that examine such events is important; however, this would require additional analyses that would greatly add to the length of an already long paper that is more focused on providing an overview of the aerosol properties observed during CACTI. In addition to the wind shears associated with changing synoptic conditions (e.g., cold front on November 11), the SDC terrain likely has an impact on the low-level jet which would require careful interpretation of the wind profiles over the AMF site. And these terrain influences could vary from case to case. Instead, we believe such an analysis is best left to a subsequent paper that focuses solely on the northerly low-level jet, its variation with altitude, and coupling with the surface. A short discussion of this topic has been added to Section 4.
The reason for choosing December 3 to show the spatial and temporal variability of aerosols is not clear. What happened on that day? In Figure S6 of the supplementary material, it is noted that for that day CO concentrations are particularly low, and in Figure S3 that it is a day with few clouds. Did these circumstances play a role? Since it is here that the large variation in aerosol properties is shown, more detailed explanation is needed to understand why the December 3 assessment is considered representative of the entire period (and location).
Response: December 3 was chosen for several reasons. First, it was a day with partly cloudy conditions over the SDC and clear skies elsewhere. Therefore, the local aerosol properties are not affected by deep convection (vertical transport, wet scavenging) that would complicate the interpretation of the aerosol properties. Second, the vertical wind shear on this day is similar to other days. Third, surface aerosol concentrations during the afternoon varied between 2 and 3 ug/m3, so we did not pick an extreme day with very low or high concentrations. And finally, relatively high concentrations of ultrafine particles occurred both at the surface and aloft. We are not implying that December 3 is a representative day over the whole campaign, since the aircraft also flew on days with clear skies and days with partly cloudy conditions transitioning to deep convection. Instead, December 3 is likely more representative of partly cloudy days, and we use these conditions to describe spatial variability patterns and processes that do occur on other days.
We have added some text to the beginning of Section 3.4 to provide the reader with some rationale why we chose this day.
Technical corrections
- The authors present the evaluation of a sub-period of the CACTI campaign: the campaign took place between October 2018 and April 2019. On the other hand, in lines 153-154 they indicate that the G-1 aircraft collected data between 4 November and 8 December, to finally analyze data from 23 October to 16 December, as presented in Figure 2. Please clarify the rationale for evaluating a sub-period of the campaign, and clarify from where the data were taken at altitude after 8 December.
Response: There was no data collected aloft after December 8 as suggested by the last phrase of the comment. Figure 2 only shows data from the ground site. The arrow at the bottom of the figure is there just to indicate the aircraft sampling period for reference. We included a few days of surface measurements before and after the aircraft measuring period. We only analyzed a sub-period of the campaign so that we examine variability of aerosols both at the surface and aloft during the same time frame. We included a few extra days of surface measurements to illustrate the temporal variations in aerosol properties shortly before and after the aircraft measurement period. Analyzing the entire campaign surface measurements would introduce seasonal factors which we leave for subsequent studies given this study is already lengthy. We added a couple of sentences at the end of Section 2.1 to note that we did not assess the seasonal variability of the aerosol measurements.
- In section 3.2 called “Sources of aerosols and trace gases observed on the ground”, in the first part the sources are evaluated with surface data (lines 354-391), but in the part where CAM-CHEM is used (lines 411-420) the data at altitude measured with the G-1 are evaluated. In section 3.2, called “Aircraft measurements, the vertical profiles are actually presented. It may be desirable to simply change the titles to be more in line with what is presented.
Response: The reviewer is correct that both surface and aircraft measurements are presented in this section. We changed the section title to be “Sources of aerosols and trace gases” so that it is inclusive of surface measurements as well as measurement and modeling predictions aloft.
- Figure 2 is too small. It is where the surface results and those measured with aircraft are combined, and based on this figure some relevant conclusions are presented that could not be seen in this small representation:
Response: Figure 2 only contains surface measurements. The arrow at the bottom of the figure is there just to indicate the aircraft sampling period. We understand there is a lot of material presented in this figure. Response: Instead of breaking the figure up into a series of smaller figures, we kept all the panels so that a common x-axis time scale is used and the results from one panel can be compared to another panel. We removed the white space on the left and expanded the plots horizontally, so hopefully this better depicts the temporal variability of surface aerosol properties which is the primary objective of this figure.
- The correlation between precipitation and low PM1 concentrations is not clearly seen.
Response: Arrows have been added to connect the rainy periods and the low PM1 concentrations.
1.2) What is stated in lines 258 and 259: “Since gas-phase aerosol precursors preferentially condense on larger particles, higher PM1 concentrations during period A suppress UFP formation and growth (Fig. S2g)” is not seen. For example, it appears that on October 28 there was rain and yet the CCN for supersaturations of 0.1% and 0.2% does not decay.
Response: This sentence is discussing the overall behavior of aerosol populations by comparing Fig. S2f and S2g. The highest aerosol number concentrations (due to ultrafine particles) occur during period B (S2g) which also has the lowest accumulation mode aerosol number concentrations (S2f). The converse is true where periods A and C have higher accumulation mode aerosol number concentrations and also lower ultrafine aerosol number concentrations. Yes, October 8 has rain, but the PM1 mass (and also CCN) does not decrease. We have changed the text to be clearer about this point. As shown in Fig. S2a, the beginning and end of period A and B have the largest accumulated rain so that it is more likely aerosols are removed by precipitation. The other rain events have lower rain rates that would remove less aerosols, and the area of precipitation may be smaller so that the removal of aerosols in the vicinity of the AMF site may be less as well.
- It also does not lead to visualize what is stated in lines 251-254 : “temporal variations in aerosol number distributions (Fig. 2d) and total aerosol number concentrations (Fig. 2e) reveal that new particle formation (NPF) events occur on many days producing large numbers of ultrafine particles (UFP, diameter < 50 nm)”.
Response: Figure 2 has been modified to include dashed arrows that connect the NPF events in 2d with the peak particle number concentrations in Fig. 2e. Reviewer #1 suggested changing the y-axis in Fig. 2e to a log scale; therefore, the peaks in number concentrations are not as pronounced as with a linear scale. With the log scale, it is easier to see the NPF events also correspond to local minima in UHSAS accumulation model particle number concentrations for diameters > 100 nm.
- In Figure 9 the top headings are confusing: (1) the top of 9 (a) says “Particle concentrations” and refers to “particle number concentrations”; (2) the top of 9 (c) refers to the difference between ultrafine (> 3 nm) and fine (> 10 nm) condensation particle counters (CPCs). For some reason they put CPC with u subincident to those smaller than 3nm and CPC to those larger than 10 nm. Maybe putting 3um<CPC<10um would be enough.
Response: Changed title of Fig. 9c as suggested.
- Line 423: confuses this evaluation with what is presented in Figure 2b, where for November 11 and 12 it is observed that there is practically no OM, coinciding with the fact that PM1 is very very low. However, line 523 indicates that 54% corresponds to OM. Perhaps this is an erroneous interpretation (line 423 refers to the average of the whole period?), please clarify.
Response: The sentence regarding the average PM1 has been revised to indicate the numbers are an average over the 3-h aircraft flight period. The reviewer is correct to point out that PM1 concentrations are usually very low between November 11-13. However, the 3-h aircraft flight period corresponds to a short spike in PM1 concentrations during the afternoon of November 12 shortly after rain stopped. The increase in aerosol mass is likely due to a change in air mass as the precipitating cells moved out of the area.
- Figure 5 on the right shows a set of points in gray color that are not explained in the text, please clarify or modify the figure.
Response: The figure caption has been changed to note what the gray dots are. They are for periods when the wind directions are not northerly, northeasterly, or southerly (as indicated by the colored dots).
- Biogenic sources emit very little BC relative to total OM, but it is inaccurate to say that they do not emit BC (correct line 365).
Response: We are not aware of any studies showing BC is emitted from plants, even in small amounts. BC can be formed by combustion, but then it is considered a biomass burning source.
- The authors evaluate the numerous observations to find clues that help to understand the process of aerosol formation and growth. For this purpose, they decide to divide the evaluation into periods called A, B and C. This decision is unclear because it masks the understanding of the impact of individual situations. Although the authors indicate that “rainy days divide the campaign into three periods” (line 741), this reviewer notes that in all three cases there were circumstances with and without rain. Please add a short paragraph clarifying the reasons why the selection of these 3 particular periods adds substantive information to the spatio-temporal assessment of the aerosols in the region.
Response: The introductory material in Section 3.1 has been revised to provide some additional discussion on the rationale for periods A, B, and C. This divided the first paragraph into three paragraphs, with the second paragraph now providing additional discussion.
Citation: https://doi.org/10.5194/egusphere-2024-1349-AC1
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 387 | 106 | 24 | 517 | 52 | 21 | 18 |
- HTML: 387
- PDF: 106
- XML: 24
- Total: 517
- Supplement: 52
- BibTeX: 21
- EndNote: 18
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1