In situ measurements were made during two research campaigns: MARCUS and CAPRICORN2. The voyage tracks of all the campaigns are shown in Fig. 1.

2.1 Measurement campaigns

The MARCUS (Measurements of Aerosols, Radiation and CloUds over the Southern Oceans) campaign occurred between October 2017 and March 2018 aboard RSV Aurora Australis during its summer season, resupplying Australia's Antarctic research stations from its port in Hobart, Australia. In this campaign, the United States Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program Mobile Facility 2 (AMF2) Aerosol Observing System (AOS) (https://www.arm.gov/capabilities/instruments/aos, last access: 18 June 2021) was deployed aboard the Aurora Australis to collect data in sea-ice locations inaccessible to platforms without ice-breaking capability. Because the campaign was supplementary to the resupply operations of the Aurora Australis, the Mobile Facility could only be mounted on the monkey island, slightly to the fore of the smoke stack. The chosen location on the ship, while ideal for some measurements, was positioned directly adjacent to the ship's exhaust (see Fig. A1), which, when combined with the operations of the ship during the campaign, resulted in almost 90 % of data being contaminated with the platform's own exhaust, limiting the usable data (detailed in Sect. 2.3 below).

Fortunately, simultaneous measurements in a similar geographic region occurred as part of the CAPRICORN2 (Clouds, Aerosols, Precipitation, Radiation, and atmospheric Composition Over the southeRn oceaN) campaign aboard the RV Investigator during January–February of 2018. While these measurements were also affected by RV Investigator's own exhaust, this was limited to less than 15 % of data due to the ship operations predominantly requiring orientation into the wind and the location of the air sampling inlet being as far fore on the ship as possible, well away from the exhaust (Fig. A1). Aerosol measurements during this campaign were made in the aerosol laboratory directly below the air sampling inlet on the RV Investigator and form part of the permanent observations aboard the vessel. Both platforms host standard meteorological stations, deployed in duplicate, which run as part of the respective ongoing underway systems and whose data are known as “underway data”, and are used as part of the analyses in this paper. Underway data, together with their associated metadata, are publicly available (Marine National Facility, 2018; Symons, 2019a, b, c, d).

Figure 1The voyages tracks from MARCUS (various orange tracks) and CAPRICORN2 (red track) overlaid on MODIS Aqua chl a concentrations averaged over the measurement period from November 2017 to March 2018. Grey markers show voyage locations where exhaust-free data were obtained.

Despite the significant removal of data due to exhaust in the MARCUS campaign, the utilisation of the CAPRICORN2 campaign data, which occurred at the same time in the same regional area, meant that division of the data into 5^∘ latitudinal bins resulted in sample numbers high enough to calculate robust statistics in each bin, while having reasonable latitudinal resolution. In Fig. 2, violin plots show measurements' distribution in each latitudinal bin for each of CCN_0.2, CCN_0.5 and CN₁₀ (full statistics for each bin are presented in Table A1, and latitudinal gradients for each voyage for CCN_0.5 and CN₁₀ are presented in Figs. A3 and A5, respectively). The highest concentrations (means of 169, 322 and 681 cm⁻³ for CCN_0.2, CCN_0.5 and CN₁₀, respectively) for all parameters are unsurprisingly observed in the northernmost bin, 40–45^∘ S, which is closest to the coast of Tasmania, Australia, resulting in increased continental and anthropogenic influence. Moving south, CN₁₀ concentrations appear to be stable (300–400 cm⁻³) from 45^∘ S to 65^∘ S. This is not the case with CCN at both supersaturations, which show slightly elevated concentrations in the 45–50^∘ S bin (131 and 197 cm⁻³ for CCN_0.2 and CCN_0.5, respectively), after which it becomes reasonably constant from 50 to 65^∘ S (∼ 100 and 150 cm⁻³, respectively).

Figure 2Latitudinal distributions of CCN (at (a) 0.2 % and (b) 0.5 % supersaturation) and CN₁₀ (c) from collated data from both MARCUS and CAPRICORN2 campaigns shown in blue. Data are binned into 5^∘ latitudinal bins and plotted as violin plots with medians (circles) and 25th and 75th percentiles (grey box) shown. Data from November 2017 to March 2018 from Macquarie Island ($\mathrm{54}{}^{\circ }{\mathrm{30}}^{\prime }$ S, $\mathrm{158}{}^{\circ }{\mathrm{57}}^{\prime }$ E; orange) and Cape Grim ($\mathrm{40}{}^{\circ }{\mathrm{39}}^{\prime }$ S, $\mathrm{144}{}^{\circ }{\mathrm{44}}^{\prime }$ E; left, green is baseline (Rn < 100 mBq, wind directions between 190 and 280^∘); red is all data) at their respective latitudinal bins. Subplots above each plot show the number of hourly data points in each bin. Note the y-axis scales are custom for each dataset.

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Measurements taken from nearby land-based research stations at Macquarie Island ($\mathrm{54}{}^{\circ }{\mathrm{30}}^{\prime }$ S, $\mathrm{158}{}^{\circ }{\mathrm{57}}^{\prime }$ E; Humphries, 2020) and Cape Grim ($\mathrm{40}{}^{\circ }{\mathrm{39}}^{\prime }$ S, $\mathrm{144}{}^{\circ }{\mathrm{44}}^{\prime }$ E; Gras and Keywood, 2017) were utilised for comparison with the ship-based measurements considered here. Macquarie Island is one of the research stations visited as part of the seasonal resupply operations undertaken by the Aurora Australis during MARCUS (voyage 4 of the 2017/18 ship schedule), and consequently, data from MARCUS and from the land station are directly comparable. In addition, the upwind fetch of Macquarie Island is dominated solely by the Southern Ocean, so other voyage data at these latitudes, which happen to all be within the upwind fetch of Macquarie Island, are also comparable. The Cape Grim station is classified as a global station in the Global Atmospheric Watch station, being representative of a globally significant region, so although the location is a reasonable distance from the ship measurements, we use the data here with confidence. In Fig. 2, the median values from November 2017 to March 2018 (chosen to coincide with the MARCUS campaign period) are presented from both stations for each available parameter and overlaid on the latitudinal gradients in their respective latitudinal bins. Two datasets are presented for Cape Grim: all data (red) and baseline data (green). Baseline refers to periods that represent a clean marine background with fetches from the Southern Ocean with little to no continental influence (Gras and Keywood, 2017).

For both CCN and CN data, values measured at Macquarie Island agree well with those measured aboard the ships and are likely to be a good representation of Southern Ocean mid-latitudes. Cape Grim values are markedly higher for CN₁₀ and non-baseline selected CCN data and generally agree well with ship data in the respective latitudinal bin. Cape Grim baseline data appear to be lower than ship-based measurements in their respective latitudinal bin, which may be explained by ship data not being filtered for baseline criteria and are consequently likely to include some level of continental/anthropogenic influence. Non-baseline CN₁₀ data from Cape Grim are higher than those measured on the ship, and this is likely because of the influence of fine-mode aerosol emissions from the metropolitan region of Melbourne, as well as emissions from Tasmania, both of which can influence Cape Grim measurements in non-baseline conditions. Curiously, ship-based CCN data are similar to non-baseline Cape Grim data and significantly higher than baseline data (which is actually similar to the higher latitude bins), which suggests ship-based measurements were influenced significantly by continental sources while measuring at these latitudes, a result confirmed by trajectory analyses presented later in the paper.

Most striking in the latitudinal distribution is the statistically significant increase in all aerosol parameters in the southernmost bin along the Antarctic coastline (p<0.001 compared to 60–65^∘ S bin). While most pronounced in CCN_0.5 (mean concentrations increase by 50 % compared to mid-latitudes with 30 % increases for both CCN_0.2 and CN₁₀), corresponding changes are also apparent in the $\mathrm{CCN}/\mathrm{CN}$ ratio, wind speed and more significantly in precipitation, as shown in Fig. A7. It is likely that the larger increase in CCN_0.5 relative to CCN_0.2 in this bin is a result of the changing size distributions and aerosol composition when moving between air masses. To explore this apparent change in composition further, we examined more closely the CAPRICORN2 data which provided both real-time and filter-based aerosol composition data. Latitudinal aerosol composition data from this voyage are shown in Fig. 3 alongside binned wind speed and precipitation data.

While the increase in this southernmost bin was not significant in the MARCUS CCN_0.2 data, CAPRICORN2 data (Figs. 3, A3, A4, A5, A6) show significant increases in CCN at all supersaturations, in the $\mathrm{CCN}/\mathrm{CN}$ ratios, as well as changes in the dominant species contributing to the aerosol composition (Fig. 3). Andreae (2009) found that the ${\mathrm{CCN}}_{\mathrm{0.4}}/\mathrm{CN}$ ratios from a wide range of environments averaged 0.4, agreeing well with mid-latitude data from this study but highlighting the unique nature of the polar populations. Aerosol composition further south is dominated by sulfur-based particles, consistent with the established literature (e.g. Fossum et al., 2018; Schmale et al., 2019), whose relative contribution to aerosol mass (as measured by the ToF-ACSM) was around 60 %–70 % at lower latitudes but increased to its maximum in the southernmost bin, reaching over 80 %. We note here that while trends from the filters are similar to those observed on from the ToF-ACSM, the differences observed are likely the result of different sampling techniques: the ToF-ACSM measures non-refractory aerosol composition, while the filters are analysed for soluble ions. The increases in CCN ratio could be driven by a stronger source of sulfur precursors (sulfate and MSA derived from DMS) emitted from enhanced phytoplankton near the Antarctic continent (Fig. 1) but are likely to also be driven by a significant drop in precipitation which would preferentially scavenge CCN compared to other aerosols. Chloride, which is used as a proxy for sea spray aerosol, is observed to be dominant at lower latitudes (and varies proportionately to wind speed; Fig. A9) but reaches its minimum in the high-latitude bin. This significant reduction in the high-latitude bin is consistent with the combined effect of decreased wind speeds and the occurrence of sea ice covering the ocean surface, resulting in a substantially lower source strength, which outweighs the reduced precipitation sink. Interestingly, comparison of the distributions of CCN with the sulfur and chloride composition measurements suggests that while sea-salt aerosol contributes an important baseline to CCN numbers, the variability, and in particular the vast population of CCN at high latitudes, is driven by sulfur-based aerosols, similar to what has been reported by Vallina et al. (2006) and others. Recent work by Fossum et al. (2020) suggests an inverse relationship between sea-salt aerosol concentrations and sulfate CCN activation, which could also help to explain this change in the southernmost bin.

Figure 3Latitudinal distributions for parameters measured during CAPRICORN2 highlighting the aerosol composition and source and sink mechanisms. The $\mathrm{CCN}/\mathrm{CN}$₁₀ ratios for 0.2 % and 0.5 % supersaturation are shown in (a) and (b), respectively; the major aerosol chemical components from the ToF-ACSM in (c); and the total soluble ions from PM₁ filters in (d). Panel (e) shows the wind speed measured onboard the vessel, and (f) shows the total precipitation calculated using ERA5 reanalysis data along the backward trajectory for each measurement. Note that in (c), the sulfate is split to the left axis to enable better visibility of trends of other components.

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To further understand the source regions of the observed latitudinal changes, we calculated the trajectories for each hour of exhaust-free data during the five voyages. In Fig. 4, these trajectories, split into the six latitudinal bins based on each trajectory's end location, are shown as density plots. As expected, the northernmost bin shows influence primarily from the marine boundary layer upwind of the measurements and significant influence from both Tasmania and the more heavily populated areas of south-eastern Australia. This trajectory footprint is consistent with CN₁₀ ship values in this bin being higher than baseline values at Cape Grim (Fig. 2), where these continental and anthropogenic influences are excluded. Note that non-baseline values from Cape Grim are higher than those from the ship-measurements in this bin, despite similar source regions. This is likely to be explained simply by the closer proximity of Cape Grim to these anthropogenic sources than the ship measurements. For bins from 45 to 60^∘ S, all fetches reside within the Southern Ocean's marine boundary layer upwind of the measurements, with only a small subset of trajectories arriving from the free troposphere. Unlike other bins, the southernmost bin has fetches that consist primarily of coastal and Antarctic continental latitudes, with minimal marine influence. The altitude plot shows the boundary layer influence is significantly reduced, and instead, fetches are distributed across a range of heights in the troposphere. Interestingly, the 60 to 65^∘ S bin is a mix of marine boundary layer and free tropospheric fetches and is likely a result of the atmospheric polar front varying at latitudes covered by this bin.

The trajectory analysis shows the air-mass histories in the region are consistent with more detailed trajectory studies undertaken previously (Humphries et al., 2016; Alroe et al., 2020). In particular, measurements from the mid-latitudes of the Southern Ocean are found to have air mass histories confined primarily to the marine boundary layer, whereas the closer the approach to the East Antarctic continent, the greater the influence from the free troposphere of the polar cell. These large-scale air-flow differences are likely the leading cause of the differences between the mid- and high-latitude aerosol properties measured in this study. Given the remoteness of the region and the limited number of aerosols sources, typically phytoplankton emissions and sea spray, it is likely that the aerosol sources are consistent across all latitudes. However, differences observed in the atmosphere are driven by two factors: (1) air-mass fetches alter the efficiency and strength of the sea-salt source and (2) sources of secondary aerosols are the same, but because of the differences of air-mass transport, the properties of the aerosol populations differ. For example, if high-latitude transport pathways take the bulk of phytoplankton emissions from the sea-ice region into the Antarctic free troposphere before being brought back to the surface (as proposed by Humphries et al., 2016), the increased precursor concentrations may result in more aerosol nucleation and growth in the free troposphere, resulting in the enhanced CCN concentrations observed in the high-latitude bin. In addition, free tropospheric aerosols would be less exposed to the high surface area of sea-salt aerosols that typically dominate the aerosol mass in the marine boundary layer and so would be less likely to be scavenged, resulting in the higher CCN number concentrations.

Figure 4Trajectory frequency plots showing the spatial footprint of measurements in each latitudinal bin in each dimension. Five-day backward trajectories were calculated for each hour of valid, exhaust-free data, and then the number of times trajectories passed through 0.5^∘ bins was summed before smoothing, resulting in frequency plots. Map plots are shown in the left column, with the starting locations of each of the trajectories used for each binned plot shown in black. The right column shows the frequency plots as a function of time and altitude, giving an indication of the dominant atmospheric layers important in each region.

These data suggest that there are three distinct latitudinal regions that govern the Southern Ocean aerosol populations in the summer season: the northern sector north of 45^∘ S; the mid-latitude sector (45–65^∘ S); and the high-latitude coastal region of Antarctica (65–70^∘ S).

Unsurprisingly, the northern sector exhibits the most anthropogenic and continental influence, resulting in aerosol concentrations approximately twice that of those in the open Southern Ocean for both CN₁₀ and CCN_0.5. For CCN_0.2, this distinction is not so clear, suggesting that the same source as mid-latitudes is driving CCN concentrations at this supersaturation (presumably sea salt). This would suggest that aerosols arising from anthropogenic and continental sources are less hygroscopic than sea salt, which is consistent with what is expected from the literature (e.g. Swietlicki et al., 2008).

The mid-latitude observations are consistent throughout a large range of latitudes, being dominated by sea-salt and sulfur-based aerosols (Fig. A9). Given the lack of any land masses in this region, the primary aerosol sources are driven by wind-produced sea salt and secondary aerosol formation, typically resulting from both local and long-range transport of aerosol precursors emitted from biological sources, chiefly DMS from phytoplankton. The dependence of sea-salt aerosol concentrations on wind speed and precipitation is striking, being directly and inversely proportional, respectively. This relationship breaks down in the high-latitude bins, where sea-ice cover impacts the wind mechanism of sea-salt aerosol production.

While observations in both the northern and mid-latitudes have been noted previously in the literature, the high-latitude observations are novel. This is in part driven by how remote the region is and how infrequently it has been sampled in the past (e.g. high-latitude Southern Ocean measurements off East Antarctica are reported only by Humphries et al., 2016; Alroe et al., 2020; Simmons et al., 2021; Schmale et al., 2019). For example, the recent SOCRATES aircraft campaign (McFarquhar et al., 2021) involved measurements made on flights originating from Hobart. However, because of the limited range of the aircraft, measurements could only be made to 62^∘ S, so that the significant change in aerosol populations at higher latitudes could not be observed.

Measurements have been made in other parts of the Antarctic sea ice (e.g. Davison et al., 1996; Fossum et al., 2018; Schmale et al., 2019). Typically, these sectors do not show the step changes observed in the East Antarctic sea-ice measurements and instead are reasonably well represented by continental measurements (e.g. Asmi et al., 2010; Hansen et al., 2009; Hara et al., 2011; Ito, 1993; Järvinen et al., 2013; Koponen et al., 2003; Pant et al., 2011; Samson et al., 1990; Virkkula et al., 2009; Weller et al., 2011; Hara et al., 2020). During a campaign around the Antarctic Peninsula in summer 2015, Fossum et al. (2018) observed two distinct air masses: those coming from continental Antarctica and those from the marine region north of the polar front. They found that, despite the differing composition of the two air masses which reflected observations described in this paper (i.e. a decrease in sea salt in air masses from the south), CCN concentrations at realistic supersaturations for this region (0.3 %) remained relatively constant at around 200 cm⁻³. Schmale et al. (2019) report CCN concentrations determined while circumnavigating Antarctica between December 2016 and March 2017, with leg 2 of the voyage passing closest to the Antarctic continent (mainly West Antarctica). Median CCN concentration at 0.02 % supersaturation during leg 2 was 111 cm⁻³, similar to the concentrations measured south of 65^∘ S in this study, and the fraction of CN acting as CCN was highest near the Antarctic continent, also observed in our study. Schmale et al. (2019) suggested this could be due to differences in aerosol chemical composition, with a combination of many cloud processing cycles and greater supply of DMS oxidation products resulting in particles large enough to act as CCN.

Contrary to the West Antarctic region, the region of the East Antarctic coast included in this study is not well represented by measurements on the Antarctic continent itself, a phenomenon driven by well defined air-mass transport, which isolates the continent from the sea-ice region (Humphries et al., 2016). This result was true for springtime measurements, and further work by the same authors (currently unpublished) suggests that the meteorology that leads to this phenomenon may break down both during summertime and around the Antarctic Peninsula. Since the majority of measurements in the region occur in summer and at either Antarctic stations or at lower latitudes, the East Antarctic coastal region remains one of the more poorly represented regions of the world.

The change in aerosol properties at this high-latitude bin is consistent with the crossing of the Antarctic atmospheric polar front, as first described by Humphries et al. (2016) and observed by both Alroe et al. (2020) and Simmons et al. (2021). While values observed in this paper are in line with those previously observed across the polar front (Alroe et al., 2020; Simmons et al., 2021), this dataset adds confidence that the change observed in these previous studies is an enduring phenomenon across a wider range of East Antarctic longitudes and seasons. While the definition of the Antarctic polar cell is traditionally understood in terms of climatological averages, it is evident that a very real-time boundary exists that can be seen in the atmospheric composition observations, which is not necessarily observed in the meteorological variables from which it is typically defined (i.e. a sharp change in wind direction and air temperature). Because of this, and to create a distinction from the traditionally defined meteorological front, we introduce a new term to define it here as the Atmospheric Compositional Front of Antarctica (ACFA), which represents the northern boundary of the region that extends south to approximately the Antarctic coastline – a region we term the Antarctic Sea Ice Atmospheric Compositional Zone (ASIACZ). It is important to note that while the aerosol properties in the ASIACZ are not captured by surface measurements on the Antarctic continent, nor those in the Southern Ocean mid-latitudes, trajectory studies suggest that air masses from this region travel both north and south, typically above the boundary layer (Humphries et al., 2016), making this an important region of exporting aerosols and precursors from a highly biologically productive region to other regions. This could help reconcile the predicted missing aerosol source in the wider region.

The ACFA is known to vary in time and space and can be advected by the synoptic-scale meteorology. This is evident from the individual voyage latitudinal plots (Figs. A3 and A5), where the increases in the southernmost bin latitudes differ depending on the voyage and location. This movement can even occur within a single voyage, as evidenced clearly from the CAPRICORN2 voyage data (Figs. A3 and A8), where the increase in CCN occurred at approximately 64^∘ S during one crossing at 150^∘ E and 62.5^∘ S during the 140^∘ E transect. During this voyage, we also tried to intentionally cross the front while sampling south along the 132^∘ E meridian. However we were unable to locate the front, even when travelling further south (> 65^∘ S) of the ACFA's latitude just days before.

By investigating latitudinal gradients across the parts of the Southern Ocean, this work raises some important objectives for future work. A significant motivation for this work is to better inform and reconcile the radiation biases arising from poor representation of clouds in climate and earth system models. Hence, relating these observations to recent cloud observations is important, and this work is well underway. Mace et al. (2021) analysed MARCUS and CAPRICORN2 data and found gradients in cloud droplet number concentrations in reasonable agreement with the gradients in CCN concentrations identified here. Interestingly, Mace et al. (2021) observed a bimodal distribution in cloud properties poleward of 62.5^∘ S. While one mode displayed properties of marine clouds from farther north, the second showed relatively high cloud droplet numbers and low effective radii. The bimodality was inferred to be associated with changes in air mass properties (such as CN, CCN and aerosol chemistry), identified in previous work (Humphries et al., 2016), in case study events described in Mace et al. (2021) and in further analysis of CAPRICORN 2 data currently underway. In particular, these changes in air mass properties included those identified systematically in this work (i.e. high CCN and $\mathrm{CCN}/\mathrm{CN}$ and high sulfate and MSA indicative of biogenic aerosol sources) in the southern sector. The potential sensitivity of the cloud properties to these biogenic aerosol sources suggests a strong feedback with biological and photochemical activity in the region, an issue that warrants further and extensive investigation.

Further studies should also address the transition across the ACFA. A more detailed assessment of the datasets used in this paper that focuses on the transition and the chemical and physical changes that occur in gases, aerosols and clouds in this region is currently underway and will be described in a future publication. The comprehensive, continuous measurements aboard the RV Investigator also provide a perfect opportunity for understanding transition as the vessel frequently undertakes voyages into this region – albeit primarily limited to the summertime.

These conclusions are all based on the sector of the Southern Ocean between Australia and the East Antarctic and are only valid for late spring and summer measurements. It is possible that different patterns may be observed in other sectors of the Southern Ocean that are influenced by disparate continental influences, such as those around Africa and South America. Hence, it is important that future observation campaigns investigate these regions. While important circumpolar measurements such as those undertaken by Schmale et al. (2019) give an insight into this variability, campaign measurements are limited in their duration and are generally undertaken during the summertime (e.g. Humphries et al., 2016; Fossum et al., 2018). To avoid biases that may arise by applying these conclusions to other seasons, long-term measurements, such as those undertaken at Cape Grim and at Cape Point, South Africa (Labuschagne et al., 2018), and by the RV Investigator are needed across other longitudes of the Southern Ocean. These include remote sites such as Macquarie Island (measurements included here were limited to just 2 years) and research platforms that frequent the ASIACZ, such as the soon-to-be commissioned RSV Nuyina. Long-term year-round atmospheric aerosol datasets reveal important seasonal and annual variability and the processes that contribute to this variability. Hence, these data will be critical for ensuring reduced biases in modelling efforts.

Datasets utilised in this study are all publicly available from the CSIRO Data Access Portal, the Australian Antarctic Data Centre, the Atmospheric Radiation Measurement Data Discovery and the Copernicus Climate Data Store. Citations including DOIs or URLs for individual datasets are provided at relevant locations within the paper. In particular, Humphries (2020) (https://doi.org/10.25919/ezp0-em87) and Humphries et al. (2021a) (https://doi.org/10.25919/2h1c-t753) are available from the CSIRO Data Access Portal.

RSH undertook the CAPRICORN2 measurements, data quality control, analysis and interpretation. RSH and MDK wrote the manuscript. SG and RH undertook the trajectory analyses. RSH, IMM, JPW, PS, ST, JH and MDK were all critical in producing measurements from CAPRICORN2. CF and GRK were PIs of the MARCUS CCN data. MDK, JPW and JH are responsible for Cape Grim data, while RSH, MDK, JPW and JH are responsible for Macquarie Island data. AP, SPA and GM were chief scientists enabling all the measurements undertaken during CAPRICORN2, Macquarie Island (MICRE) campaigns and MARCUS, respectively. GM contributed to the analysis and interpretation of data. All authors contributed to the writing and review of the manuscript.

The authors declare that they have no conflict of interest.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This research has been supported by the Biological and Environmental Research (grant no. DE-SC0018995), the National Aeronautics and Space Administration (grant no. 80NSSC19K1251) and the Department of Energy, Labor and Economic Growth (grant nos. DE-SC0018626 and SC0021159).

This paper was edited by Sachin S. Gunthe and reviewed by two anonymous referees.