Long-range transported continental aerosol in the Eastern North Atlantic: three multiday event regimes influence cloud condensation nuclei
- 1Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA
- 2Environment and Climate Science Department, Brookhaven National Laboratory, Upton, NY, USA
- 3NASA Langley Research Centre, Hampton, VA
- 4Center for Aerosol Science and Engineering, Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
- 5Department of Atmospheric Sciences, University of Washington, Seattle, USA
- 6Atmospheric Measurement and Data Sciences, Pacific Northwest National Laboratory, Richland, WA, USA
- 7School of Meteorology, University of Oklahoma, OK, USA
- 8Group of Climate, Meteorology and Global Change (CMMG), University of Azores, Portugal
- anow at: NASA Langley Research Centre, Hampton, VA
Abstract. The Eastern North Atlantic (ENA) is a region dominated by pristine marine environment and subtropical marine boundary layer clouds. Under unperturbed atmospheric conditions, the regional aerosol regime at ENA varies seasonally due to different seasonal surface-ocean biogenic emissions, removal processes, and meteorological regimes. However, during periods when the marine boundary layer aerosol at ENA is impacted by particles transported from continental sources, aerosol properties within the marine boundary layer change significantly, affecting the concentration of cloud condensation nuclei. Here, we investigate the impact of long-range transported continental aerosol on the regional aerosol regime at ENA using data collected at the U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) User Facility on Graciosa Island in 2017 during the Aerosol and Cloud Experiments (ACE-ENA) campaign. We develop an algorithm that integrates number concentrations of particles with optical particle dry diameter (Dp) between 100 and 1000 nm, single scattering albedo, and black carbon concentration to identify multiday events (with duration > 24 consecutive hours) of long-range continental aerosol transport at ENA. In 2017, we detected nine multiday events of long-range transported particles that correspond to ~7.5 % of the year. For each event, we perform HYSPLIT 10-day backward trajectories analysis, and we evaluate CALIPSO aerosol products to assess respectively origins and compositions of aerosol particles arriving at ENA. Subsequently, we group the events into three categories 1) mixture of dust and marine aerosols from North Africa, 2) mixture of marine and polluted continental aerosols from industrialized areas, and 3) biomass burning aerosol from North America and Canada, and we evaluate their influence on aerosol population and cloud condensation nuclei in terms of potential activation fraction and concentrations at supersaturation of 0.1 % and 0.2 %. The arrival of dust and marine aerosol mixture plumes at ENA in the winter caused significant increases in Ntot. Simultaneously, the particle size modes and CCN potential activation fraction remained almost unvaried, while cloud condensation nuclei concentrations increased proportionally to Ntot. Events dominated by mixture of marine and polluted continental aerosols in spring, fall, and winter led to statistically significant increase in Ntot, shift towards larger particular sizes, higher CCN potential activation fractions, and cloud condensation nuclei concentrations > 170 % and up to 240 % higher than during baseline regime. Finally, the transported aerosol plumes characterized by elevated concentration of biomass burning aerosol from continental wildfires detected in the summertime did not statistically contribute to increase aerosol particle concentrations at ENA. However, particles diameters were larger than under baseline conditions and CCN potential activation fractions was > 75 % higher. Consequentially, cloud concentration nuclei concentrations increased ~115 % during the period affected by the events. Our results suggest that, through the year, multiday events of long-range continental aerosol transport periodically affect ENA and represent a significant source of CCN in the marine boundary layer. Based on our analysis, in 2017, the multiday aerosol plume transport events at ENA caused a total NCCN increase at SS 0.1 % of ~22 % (23 % at SS 0.2 %) being 6.6 % (6.5 % at SS 0.2 %), 8 % (8.2 % at SS 0.2 %), and 7.4 % (7.3 % at SS 0.2 %) respectively the contribution attributable to plumes dominated by mixture of dust and marine aerosols, mixture of marine and polluted continental aerosols, and biomass burning aerosols. Changes in baseline Ntot and particle size modes during the events might be used as a proxy to estimate the contribution to NCCN.
Francesca Gallo et al.
Francesca Gallo et al.
Francesca Gallo et al.
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This paper presents novel research with a developed algorithm that utilizes several aerosol properties using thresholds from previous studies to identify and classify multi day aerosol plume transports in the Eastern North Atlantic (ENA). The authors perform statistical analysis to determine if differences in aerosol properties during regional aerosol baseline conditions and plume transport events are statistically significant. They go a step further by using HYSPLIT to determine their origin and CALIPSO to determine their type. Finally, the authors present 3 case studies corresponding to each of the 3 classification schemes. Overall, this is a good quality study with clear motivation, methodology, and discussion of results that is strongly supported by past literature. It could provide a useful constraint for climate models. It is certainly of interest for publication, although without a stronger comparison to literature to make the significance of the findings more clear, it may fit better as a Measurement Report.
The main weakness with the paper is that it seems to be attempting source attribution without chemical measurements, relying solely on back trajectories. Certainly this has been done before, but the authors would need to carefully review the success of those attempts in order to provide appropriate context for this work. However, I do wonder why this is done here, given that AOS includes ACSM measurements. Is there some problem with those that prevents their inclusion? If ACSM only available for part of the time, could that be used to strengthen the conclusions of this work by showing similarities for part of time?
The work discusses the algorithm for classification and how it is applied, but never actually provides the algorithm. There is a discussion of “multiday transport” criteria, but I am more interested in the differences of the 3 categories identified in abstract. Or is this just a subjective classification of 9 events based on Table 2? Table 3 provides the average characteristics of each, but if the separation is based on backtrajectories then what are the specific criteria for those or are they clustered or something? Sorry if I missed it, but I assume it is not based on CALIPSO as Table 2 might indicate. Also, the CALIPSO mixtures show more complexity than the three categories in Table 3 and the abstract. Or does that result refer to just 3 case studies rather than 3 categories of the 9 events (abstract: “group the events into 3 categories”)?
Given the diversity of the origins of these events, why is it appropriate to summarize the results of all of them together? (p.2 line 5) It would seem that averaging such events dampens the differences between them rather than showing how they contribute to variability.
Also the authors cite Wang et al. 2021, but I think a more quantitative and specific comparison to that work is needed to clearly show how this work improves/extends their results.
Table 3 Which events are summarized in each category? Need to specify here or in Table 2. I think Table 3 would also benefit from some punctuation. It looks more like a ppt slide than an archival table.
Many places – significant increases of WHAT with respect to WHAT? (The latter is often missing.)
p.13 l.28 Why was Ntot higher for marine and dust? Seems like marine should be same or lower than baseline and dust would have low N (high M), so please explain.
How was baseline defined?
Averaging aerosol properties for 6 hour periods results in a coarse time resolution. A recent study by Dedrick et al. 2022 using ARM instrument aerosol properties to define marine and non-marine periods in the Southeast Atlantic shows moderate variability with 2 hour averaging periods. Please state the reason for 6-hr averaging.
How high is the variability of your aerosol properties using 6 hour averaging periods?
There is a lot of comparison/citation to Mace Head, but is that really the most appropriate comparison for ENA? Please consider a more broad consideration of the literature for some discussions, and/or please justify why Mace Head is same.
What is a phytoplankton-derived aerosol? Do you mean sulfate from DMS?
Does it result in a different amount of multi day aerosol plume transport events?
Why do certain aerosol properties use mean or median to define thresholds?
Entrainment is mentioned several times in this paper. Have you looked into separating aerosol property data using proxies for entrainment rate such as delta-T at top of MBL?
Section 3.1.1 and Section 3.1.2
The paper discusses the removal of large particles by precipitation several times. What happens when you separate the aerosol properties that follow precipitation events?
The paper also discusses the effect of wind speed on large Ac mode several times. How well do wind speed and parameters of the large Ac mode such as mean diameter and number concentration correlation at the ENA?
The paper introduces HSD to define statistically significant changes on baseline aerosol number concentrations, aerosol size modes, and CCN potential activation fraction. However, scattered throughout the paper in sections before that significance is also used interchangeably to describe differences in seasonal statistics and baseline conditions. I recommend a different word or plainly writing out the quantitative differences to avoid confusion.
In this paper, the authors use activated CCN fraction and N_tot to speculate whether aerosol composition or increased aerosol concentration affect CCN at the ENA. Have collocated cloud properties (by either ARM ground or NASA satellite retrievals) such as cloud effective radius been analyzed for these case studies? It would be convincing to see if there is a statistically significant difference in cloud properties versus baseline conditions due to rapid cloud adjustments.
There are a significant number of typos. Some are noted below. Please reread and check for these.
p.6 line 8 “era”
p.5 line 21 Saliba et al. was not at Mace Head
p.1 line 34 “mixture of dust and marine aerosols from North Af” – is the marine from N.A. too or is that just the dust?
p.2 line 1 cloud concentration nuclei concentrations
Overall, please stay consistent with usage At mode and Ac mode versus fully writing out Accumulation Mode and Aitken mode.
Section 2.1 “is given” should be “are given”
Section 3.1.1 Please define what months belong to which seasons earlier in the paper as you discuss summer mean values before doing so.
Section 3.1.1 “The influence of local aerosol sources on Ac mode aerosols measurements at ENA is minimal” can be written more concisely as “There is minimal influence of local aerosol sources on Ac mode aerosol measurements at ENA”
Section 3.2 Line 40 Can remove “specific” in front of case studies to reduce redundancy
Section 3.2.3 Line 13 Missing space before “Here we”
Section 3.2.3 Line 14 Missing space after “September 09th”. “September 09th” should also be “September 9th”. “During the period in analysis,” can be written more concisely as “During this period”.
Section 3.2.3 Line 23 Can remove “under” to be more concise.
Section 3.2.3 Line 32 This sentence is worded confusingly.
Section 3.2.3 Line 37 Add comparison values in parenthesis to your percentage increases.
Section 3.2.3 Line 37 Can more concisely word this as “aged wildfire aerosols dominate the accumulation mode and act better as CCN”
Section 4 Line 23-24 add “a” before “mixture” and remove “,” after “March 2017”
Section 4 Line 25 add “a” before mixture
Section 4 Line 26 “form” should be “from”
Section 4 Line 27 “the aerosol plumes composition” can be written as “aerosol plume composition”
Section 4 Line 29 “causeed” should be “caused”
Section 4 Line 30 “ Mixture of marine and polluted continental aerosol plumes showed high CCN concentrations attributable to both high Ntot, and predominance of large particles (Dp > 100 nm) of sufficient size to readily serve as CCN.” can be written in active voice and more concisely as “High CCN concentrations are attributed to both high Ntot and dominance of particles large enough to act as CCN (Dp > 100 nm) from mixed marine and polluted continental aerosol plumes.”
Section 4 Line 35 Move “,in 2017,” to the end of the sentence.