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.
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RC1: 'Comment on egusphere-2024-1349', Anonymous Referee #1, 22 May 2024
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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
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