the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 01 Oct 2024
Measurement report: Characterization of Aerosol Hygroscopicity over Southeast Asia during the NASA CAMP2Ex Campaign
Abstract. This study characterizes the spatial and vertical nature of aerosol hygroscopicity in Southeast Asia and relates it to aerosol composition and sources. Aerosol hygroscopicity via the light scattering hygroscopic growth factor, f(RH), is calculated from the amplification of PM5 aerosol (Dp < 5 μm) scattering measurements from < 40 % to 82 % relative humidity during the Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex) between August to October 2019 over the northwest tropical Pacific. Median f(RH) is relatively low (1.26 with lower to upper quartiles of 1.05 to 1.43) like polluted environments, due to the dominance of the mixture of organic carbon and elemental carbon. The f(RH) is lowest due to smoke from the Maritime Continent (MC) during its peak biomass burning season, coincident with high carbon monoxide concentrations (> 0.25 ppm) and pronounced levels of accumulation mode particles and organic mass fractions. The highest f(RH) values are linked to coarser particles from the West Pacific and aged biomass burning particles in the region farthest away from the MC, where f(RH) values are lower than typical polluted marine environments. Convective transport and associated cloud processing in these regions decrease and increase hygroscopicity aloft in cases with transported air masses exhibiting increased organic and sulfate mass fractions, respectively. An evaluation of a global chemical transport model (CAM-chem) for cases of vertical transport showed the underrepresentation of organics resulting in overestimated modeled aerosol hygroscopicity. These findings on aerosol hygroscopicity can help to improve aerosol representation in models and the understanding of cloud formation.
- Preprint
(2777 KB) - Metadata XML
-
Supplement
(390 KB) - BibTeX
- EndNote
Status: final response (author comments only)
-
RC1: 'Comment on egusphere-2024-2604', Anonymous Referee #1, 01 Nov 2024
Review of “Measurement Report: Characterization of Aerosol Hygroscopicity over Southeast Asia during the NASA CAMP2Ex Campaign” by Lorenzo et al.
This report details a study of the spatial distribution of aerosol hygroscopicity in Southeast Asia during NASA’s “CAMP2Ex” mission, a monsoon impacted region / period. Hygroscopicity if discussed in the context of the overall aerosol composition and likely sources, e.g. regional biomass burning. The results are then evaluated using a chemical transport model (CAM-chem) to show that, compared to the data, there is an underrepresentation of organics resulting in overestimated modeled aerosol hygroscopicity.
Overall, this is a solid “Measurement Report” with important information that should appear in a peer-reviewed publication as well as relevant scientific results. Pending minor revisions, it can be published in ACP.
Main Paper
Introduction –
- The introduction is well written with an extensive treatment of kappa. I believe this section would benefit from relevant values of kappa for the aerosol types being discussed (i.e., marine, organic, EC are mentioned and ranked comparatively, can you add and reference their kappa values? – e.g. those found in the supplement?).
- The paragraph starting at line 126 needs to be rewritten. “In the CAMP2Ex region, biomass burning aerosol hygroscopicity is over-estimated by global atmosphere models simulating the CAMP2Ex campaign…”
- Note that the superscript 2 is sometimes used and sometimes not used (please be consistent).
- The phrasing here seems to indicate the Collow et al. and Edwards et al. references have already done what this paper claims to do (next paragraph “To our knowledge, this is the first time this dataset has been explored extensively to characterize aerosol hygroscopicity properties in the region.” – please clarify what of the points i – iv has been done and what is novel about this report? Specifically, how are i and ii different from what is done in the above references?
- At line 139 the word “opportune” seems unnecessary, please remove.
Methods –
- At line 162 and elsewhere “submicron” should be “submicrometer”
- At 2.3.1 Trajectory Analysis – Can you include some detail on the trajectory results? Direct reference to Hilario is fine but some detail on how the trajectories are obtained should be contained in the paper; this is probably more a description of HYSPLIT than anything but, as this is directly impactful on this report, please make the reader aware of the method (i.e., as is done for CAM-chem).
Results and Discussion
- Throughout this section (and through the Conclusions) subjective terms such as relatively low, narrow range, etc. are used. Please rewrite this as quantities and remove subjective terminology (as an example, f(RH) being relatively low needs to be compared to something for context, better instead to simply reported the observed values).
One final statement is that this paper is on the long side and contains strange formatting. The paper would benefit from an extensive read through in an attempt to shorten; this is nothing especially troubling but, at 33 pages with 17 figures, the authors could consider if anything can be eliminated / combined (e.g. the vertical profile figures or combining case study figures)? I suspect the formatting is a result of some figure formats and will probably be corrected in final edits. None the less, there exist large gaps and occasional blank lines that should be corrected.
Citation: https://doi.org/10.5194/egusphere-2024-2604-RC1 -
RC2: 'Comment on egusphere-2024-2604', Anonymous Referee #2, 01 Dec 2024
This study uses aircraft-based measurements of the aerosol light scattering humidity enhancement factor f(RH) made during the August-October 2019 NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex field campaign over SE Asia. The CAMP2Ex was designed to understand the role of aerosol particles in cloud formation and in regulating solar radiation during the southwest monsoon. The goals of this proposed study are to (i) characterize the spatial distribution of aerosol hygroscopicity in Southeast Asia during the CAMP2Ex airborne mission, (ii) relate aerosol hygroscopicity and composition, (iii) identify emission events that impact aerosol hygroscopic growth, and (iv) evaluate a global chemical transport model for aerosol vertical transport
The authors provide an excellent discussion of background, including the importance of aerosol hygroscopicity measurements. The measurements and methods are well-described, although I have a few suggestions for additions (See below). The authors use particle light scattering at 2 RH values (< 40% and ~82%) to calculate the γ fit parameter (Eq. 3 of Titos et al., 2021 and many other papers) of the f(RH) curve , which is then used to calculate f(RH) as the ratio of scattering at RH=80% divided by that at RH=20%. The f(RH) was then used to calculate the optical kappa factor κopt, using Eq. 5 of Brock et al (2016). For comparisons with CAM-modeled κchem and with κchem derived from collocated aerosol chemical speciation measurements, the authors used the relationship κchem =0.56 / κopt (Brock et al., 2016). Median f(RH) during the field campaign was lower than those from other studies and the κchem derived from measurement-based f(RH) was lower than that predicted by the models by a factor of ~2 (Fig. 14). Median f(RH) <1 was reported for the “organic-dominated” aerosol type (Fig. 5). The authors also calculate intensive AOPs such as SSA, SAE, AAE for cases when scattering coefficient and absorption coefficient exceeds 2Mm-1. This lower threshold for scattering coefficient is very low (many of the papers based on NOAA FAN data use 5Mm-1 as a lower threshold for scattering coefficient).
The paper is well-written and the collocated aerosol chemical speciation and CO measurements, along with HYSPLIT back-trajectories, facilitate a thorough investigation of source region and aerosol composition influences on measured f(RH) during the campaign. I recommend that it be published after consideration of the suggestions below, particularly those related to the apparent under-estimation of f(RH).
Suggestions:
(1) Possible role of measurement/analysis methods in the low f(RH) values: It is conceivable that experimental conditions and the way in which f(RH) was calculated could have played a role in f(RH) values that were lower than other studies and the f(RH)<1 values. Below I briefly elaborate on 4 sources that could potentially lead to inaccurate f(RH) values. At the very least, these should be discussed in the paper.
- The authors did a nice job of studying possible reasons for f(RH) lower than reported in other field campaigns, including the relationships between low f(RH) and high OA fractions. However, the way in which f(RH) was calculated (based on only two RH values to calculate γ, as I summarized above) could also have contributed to the low f(RH). Most studies (Burgos, et al. 2019 and others) use the full humidogram scans for the γ fit, which is better because the slope of wet/dry scattering curve isn’t constant and often increases near RH =>80%. I realize that a full humidogram scan is not feasible for the aircraft-based measurements but believe that the authors should at least consider this possible error source via a simple simulation or other means.
- The authors briefly described how the RH probes were calibrated but a single-point calibration isn’t always adequate for the Vaisala probes. Using a fit based on 2-3 saturated salts (including one at low RH to get the intercept of the calibration curve) can reduce the RH uncertainty by ~2-3% relative to a single point calibration, based on my experience. The authors also did not say whether the TSI nephelometer internal RH sensor was used for the wet scattering RH value or whether a Vaisala RH probe placed near the exhaust was used, along with assumption of constant dewpoint. The latter method is often preferred due to the large RH errors (up to ~10%) in the Honeywell RH sensors placed in TSI nephelometers. Part of this error can arise due to the sensor itself while part is due to the location of the sensor inside the nephelometer. The authors should explicitly state which RH sensor (nephelometer internal sensor or other) was used and how the sensor was calibrated (i.e. with sensor taken out of the nephelometer or left in its normal sampling location) and consider this as a possible source of systematic error in f(RH). For example, an RH sensor reading that is even a few percent too high can lead to underestimation of f(RH).
- Comparisons of f(RH) with those reported in other studies (ex: Titos, et al., 2021) must consider the RH values used, since f(RH) is dependent on RH. Burgos et al., (2019), Titos et al (2021), and most other studies used RH=85% and 40% to calculate f(RH) while the current study uses RH=80% and RH=20% to compute f(RH). As such, the f(RH) values from the two studies are not directly comparable.
- The authors did not say whether both nephelometers were calibrated (zero and span checks) before and after the field campaign (my guess is that they were) and whether the scattering measured by both nephelometers at low RH was compared before the campaign.
As a side note, I am surprised that this field campaign did not utilize f(RH) measurements at a ground site (along with dry particle scattering and absorption) to compare with those measured on the aircraft, especially given the difficulties in making such measurements. Comparisons of aircraft and ground-based f(RH) measurements would have provided valuable information as to the possible experimental role in the low f(RH) values.
(2) Cazorla (2013) Aerosol Classification Method: I am not convinced that the Cazorla (2013) aerosol type classifications add much to the paper, unless the objective is to show that they don’t work very well for the region and the time period studied. The paper is rather long and contains many figures (17) and could be shortened by eliminating this discussion, especially since aerosol chemical speciation and CO measurements are available.
(3) AAE values less than one (as reported in many cases of this study) sometimes result from PSAP filter loading nonlinearity effects, especially in regions with high OA fractions. They can also result from low signal-to-noise during low aerosol loading conditions. The authors used a lower threshold of 2 Mm-1 for absorption coefficient data used in the study.
(4) Median values of κchem derived from measurement-based f(RH) were lower than those predicted by the models by a factor of ~2-3 (Fig. 14) and lower than median κchem calculated from collocated AMS and SP2 measurements by a factor of ~2 . The authors used the relationship κchem =0.56 / κopt (Brock et al., 2016) but this relationship was derived from measurements in the SE US during a period (Summer 2013) where biogenic SOA was the dominant aerosol mass component and without much biomass burning influence. It is possible that the numerical constant of proportionality used by Brock may not work well in SE Asia during the CAMP2Ex field campaign but the authors did not consider this as a potential source for the large discrepancy (Fig. 14).
Minor Suggestions
(5) Fig 1(a) is confusing to me. It shows color coded f(RH) values overlaid on a lat/long plot with source regions labeled. However, I am not sure what I am looking at. Do each of the color coded datapoints correspond to the location of the measured f(RH) along the flight path or the “location” of the source region, based on the HYSPLIT trajectories and the methods outlined in Sect. 2.3.1? Maybe I am missing something obvious but a bit more clarification in the figure caption would be helpful.
(6) The authors make the claim (bottom of P. 14) that ”the highest median f(RH) values are from the WP and EA air masses (Fig. 2a). Both have lower organic mass fractions (0.29 and 0.30, respectively), but have distinctly larger aerosol size profiles based on their SAE values (Fig. 3a) ". The plot in Fig 3(a) supports this assertion for the WP air mass (whose SAE percentiles all are clearly lower than those of other source regions) but not for the EA air mass, whose SAE is not much different than those for the MC, PSEA, and Other source regions. Values of SAE are close to 2 for each of these source regions (Fig.3a) and indicates primarily accumulation-mode aerosols. Since the SAE is more sensitive to course and fine mode fractions and less sensitive to small differences in accumulation mode particle size (except for monomodal aerosols), it is difficult to make the claim that EA air masses have “distinctly larger aerosol size profiles” than MC, PSEA, and Other regions. This is especially true for lower values of σsp (Authors use σsp > 2 Mm-1 as threshold for SAE calculations), due to larger SAE uncertainties.
(7) Table 1 lists the measurements of cloud particles but there was no discussion of CCN or cloud particles in the manuscript. I recommend deleting the final two rows in the table, listing the cloud particle measurements
Citation: https://doi.org/10.5194/egusphere-2024-2604-RC2
Data sets
CAM-chem Model Outputs used in "Measurement Report: Characterization of Aerosol Hygroscopicity over Southeast Asia during the NASA CAMP2Ex Campaign" Simone Tilmes and Jun Zhang https://doi.org/10.6084/m9.figshare.26755936.v2
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 191 | 42 | 104 | 337 | 18 | 4 | 8 |
- HTML: 191
- PDF: 42
- XML: 104
- Total: 337
- Supplement: 18
- BibTeX: 4
- EndNote: 8
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1