African aerosol particles, traveling thousands of kilometers before reaching the Americas and the Caribbean, directly scatter and absorb solar
radiation and indirectly impact climate by serving as cloud condensation nuclei (CCN) or ice-nucleating particles (INPs) that form clouds. These
particles can also affect the water budget by altering precipitation patterns that subsequently affect ecosystems. As part of the NSF-funded
Luquillo Critical Zone Observatory, field campaigns were conducted during the summers of 2013 (23
Great strides have been made in our understanding of aerosol–cloud–radiation interactions; however, substantial uncertainty remains (Mhyre et al., 2013; Li et al., 2019), particularly in our understanding of the role of mineral dust during such interactions. Dust has a direct impact on climate since it is considered one of the most important light-absorbing particles in the atmosphere at shorter solar wavelengths (Sokolik and Toon, 1999). Recent studies show that by its radiation absorbing potential, dust can increase air temperature and reduce sea surface temperature at low altitudes (Sun and Zhao, 2020). Dust is a good ice-nucleating particle (DeMott et al., 2003), but its activity as cloud condensation nuclei (CCN) is less clear. Studies have shown that fresh dust is mainly hydrophobic and a poor cloud condensation nucleus (Rosenfeld et al., 2001), while others have shown that dust can act as a cloud condensation nucleus if it has aged and mixed with anthropogenic or organic aerosols (Fitzgerald et al., 2015). The latter observation is supported by laboratory studies that have found that the CCN activity depends on the mineralogical composition and mixing state of the dust particles (Sullivan et al., 2009; Gaston, 2020). Other studies have found that dust can commonly act as a cloud condensation nucleus even from the source, as it often has hygroscopic material, and thus can influence cloud formation (Twohy et al., 2009). In contrast to these findings, Denjean et al. (2015) saw that African dust transported from Africa to the Caribbean Basin remained mainly externally mixed and that dust particles did not take up significant amounts of water when exposed to up to 94 % relative humidity, a similar finding to that of Edwards et al. (2021) from African dust transported to Florida. The larger, super-micron dust particles act as giant CCN that can form large cloud droplets that lead to the earlier formation of raindrops (Rosenfeld et al., 2008).
In the Caribbean Basin, which is frequently inundated by mineral dust picked up by surface winds over the northern half of the African continent and transported westward, studies of aerosol–cloud–radiation interactions are limited, with most having been done on the islands of Puerto Rico and Barbados. Within the northeastern part of Puerto Rico is a tropical montane cloud forest (TMCF) known as Pico del Este (PDE). PDE has been an ideal location for studying clouds, as they form frequently and last for many hours (Eugster et al., 2006; Allen et al., 2008; Spiegel et al., 2014; Raga et al., 2016). Eugster et al. (2006) studied daily cloud properties as a function of net radiation and saw that denser clouds correlated with less radiation at the surface. Allan et al. (2008) observed that clouds affected by anthropogenic pollution had a larger number of droplets with smaller diameters. The results in Spiegel et al. (2014) support the observations in Allan et al. (2008) but with clouds impacted by African dust. In contrast, Raga et al. (2016) observed that meteorology was more important in cloud formation than the aerosol source, particularly during dust events. Cloud chemical properties have also been studied at this location (Weathers et al., 1988; Asbury et al., 1994; Gioda et al., 2009a, b, 2011, 2013; Reyes-Rodríguez et al., 2009; Valle-Díaz et al., 2016). Under the influence of African dust, there was an increase in non-sea salt calcium and other crustal and organic components when clouds were formed during periods of African dust (Valle-Díaz et al., 2016). There was also an increase in non-sea salt sulfate when under the influence of anthropogenic pollution (Gioda et al., 2009; Valle-Díaz et al., 2016).
The main objective of the study being reported here is to use measurements of cloud microphysical properties in a TMCF, complemented with aerosol measurements and back-trajectory analysis, to evaluate the relationship between the source of aerosols and the formation and evolution of clouds. The working null hypothesis is that at this location, the source of the aerosols will have a statistically insignificant effect on the cloud properties.
Measurements were made at coastal and mountain sites on the Caribbean Island of Puerto Rico. The coastal site is an observatory within the nature
reserve of Cabezas de San Juan (CSJ, 18
Map of the Caribbean Basin with a zoom over Puerto Rico. The San Juan metropolitan area, CSJ, and PDE stations are highlighted in the map. This figure was generated using © Google Earth Pro 7.3.
The mountain station is located at the Pico del Este (PDE, 18
Sampling campaigns were held during the summer months, as these are the months when Puerto Rico receives the influence of African dust. The sampling campaigns were as follows: 2013 (9 June to 1 July), 2014 (20 to 30 August), and 2015 (30 May to 29 August). The sampling campaign of 2015 extended to fall with the purpose of having more data with little to no dust influence.
At CSJ, the light-scattering coefficients were measured with a nephelometer (TSI, model 3563) at three wavelengths (450, 550, and 700
At PDE, cloud water samples were collected using an aluminum Caltech active-strand cloud water collector (Al-CASCC2, Demoz et al., 1996). The sampler
was exposed only during cloud events and rinsed thoroughly with Nanopure water before each collection period. Field blanks were also collected before
each sample. Aliquots of the cloud water were stored in a freezer at
Cloud microphysical properties were measured using a backscatter cloud probe (BCP, DMT, Beswick et al., 2013) that measured over the equivalent
optical diameter range of 5–90
Meteorological data were collected at both stations using Davis VantagePro2 Plus weather stations. Total rain, rain rate, wind direction, and wind speed were measured in 15 min averaged intervals. The meteorology, aerosol, and cloud microphysical data were averaged to hourly intervals for the analysis.
Aerosol scattering coefficients and SAE measured at CSJ during the summers of
The aerosol light-scattering coefficients were used as identifiers of aerosol particles whose optical properties differ from the background marine
aerosols. This approach assumes that dust particles will have scattering coefficients significantly higher than the average background. The average
and standard deviation values for the 2013, 2014, and 2015 periods were calculated for the scattering coefficients at 550
In addition, 7 d air mass back-trajectories were calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model
(Draxler and Hess, 1998; Stein et al., 2015; Rolph et al., 2017) in order to identify the history of air masses prior to their arrival at the PDE
site. Global Data Assimilation System (GDAS) reanalysis with a 0.5
The meteorological parameters that were derived along the trajectories were temperature, pressure, relative humidity, and rainfall. The air mass origins are classified according to where the air was located 7 d before reaching the site.
The metrics selected for assessing the environmental effects of these air masses of differing origin were the scattering and absorption coefficients with derived Ångström exponents to characterize the aerosol properties, the droplet size distribution, and liquid water content to describe the cloud microphysical properties and the concentration of water-soluble ions to characterize the cloud water chemistry. The hourly latitude, longitude, altitude, and precipitation along the back trajectories are the metrics evaluated for characterizing the air mass properties.
The BCP data were recorded every second and reduced to 10 min averages. Beswick et al. (2013) provide a detailed error analysis of the BCP
measurements, in particular the uncertainty associated with deriving the size from the light-scattering signal. For this study, the BCP was installed
in a wind tunnel with constant airflow and orientation towards the prevailing wind direction. Laboratory tests of different wind speeds measured
inside the tunnel showed that this implementation enhanced the outside wind speed by a factor of 1.4 inside the tunnel. The average corrected airspeed
was 11.2
To ensure a sampling uncertainty of 10 % or less, a population of 100
At CSJ, nephelometer and CLAP data were also gathered at 1 s intervals and reduced to 6 min and hourly averages. The nephelometer data were corrected for the truncation error (Anderson and Ogren, 1998), and the CLAP data were corrected for enhanced absorption artifacts (Bond et al., 1999; Ogren, 2010). Data from both instruments were adjusted to standard temperature and pressure.
The directly measured and derived parameters were evaluated to determine if their values were Gaussian distributed in order to select parametric or non-parametric statistical methods to use in testing the null hypothesis. This was done using the Shapiro–Wilk methodology (Shapiro and Wilk, 1965). Given that the majority of the metrics had frequency distributions that were not Gaussian, the Mann–Whitney two-tailed test was used to determine if the average values of the selected parameters were statistically different with respect to the air mass origins.
Based upon the criteria described previously, using the light-scattering coefficients at a wavelength of 550
There are no regular trends in the optical properties that can be linked to daily variations in local emissions, indicating that the observed fluctuations are related to larger-scale changes in the air masses. The trends are irregular and marked by sharp increases and decreases, such as those seen in 2013 on days 165 (increase) and 167 (decrease). The durations of the events are also quite variable, several lasting more than 24 h in 2013 and 2014, while in 2015 there are many events that are only several hours long. It is possible that some of the short events that are close to each other are related, i.e., coming from the same dust event but interrupted by brief shifts in the local circulation patterns before returning to the broader-scale flows that are bringing the dust.
Box plots for periods identified as low and high dust for
Figure 3 shows box plots for the parameters studied for high- and low-dust periods. Both the scattering and absorption coefficients were considerably higher for high-dust periods. The SAE was lower for high-dust periods, which indicates the presence of larger particles than in low-dust periods. The absorption Ångström exponent (AAE) for high-dust periods was usually above 2, while in low-dust periods it was usually below 2. Both the low SAE and high AAE values confirm the presence of dust in high-dust periods (Cazorla et al., 2013).
Non-sea salt calcium (nss-
An analysis of back trajectories from the PDE site was used to track the histories of air masses that were arriving during periods of cloud at the site. Back trajectories were computed every 2 h. Those time periods when six or more consecutive trajectories of air had been over the same general region, 7 d previously, are used to identify air whose aerosol particles were in all likelihood distinctly different in physical and chemical properties. The four regions that were identified in this manner are as follows: (1) Atlantic Ocean (AO), (2) southeast (SE), (3) Saharan desert (SD), and (4) African Sahel (AS). The days that were selected were those during which there was already a cloud present at the PDE.
HYSPLIT back-trajectory analysis.
Figure 4a–c show the averages of the four air mass types as 7 d horizontal back trajectories, vertical
locations of the air along the trajectories, and accumulated precipitation, respectively. The numbers in parentheses after the air mass name in the
legend correspond to the number of consecutive 2 h trajectories averaged. Figure 4a highlights the primary difference between the AO and SE
air masses. They are both over the ocean 7 d prior, in the midlatitudes, and more than 1500
Although the SD and AS trajectories are over the African continent 7 d prior to arriving at PDE, the SD air masses were in the region over the desert, while the AS air is more tropical and over the region known as the African Sahel. The other metric that clearly distinguishes the air masses from one another is the altitude history that is documented in Fig. 4b. Of the four types, the SE air masses are the only ones that have traveled much of their 7 d trajectory near the surface, especially the 3 d before reaching the island. Although their histories vary, as illustrated by the vertical bars that mark the standard deviations about the mean, the AO, SD, and AS air masses are all descending as they reach PDE. This is an important observation due to the different forcing mechanisms that lead to cloud formation, a factor that will be discussed below.
The altitude histories are also important indicators of the possible sources of aerosols that arrive at PDE and may influence the cloud
properties. For example, SE air masses spent more than 3 d of their travel below 1000
The amount of precipitation that was derived with the HYSPLIT model is shown for the four air mass types in Fig. 4c, represented here as total accumulated rainfall along the track. The trends in accumulated precipitation reveal a number of interesting details about meteorological events along the trajectories: (1) the majority of AO, SD, and AS precipitation events occurred more than 5 d before arriving at PDE, (2) there was little precipitation along the SE air mass until 30 h before arriving at PDE, (3) the AS air masses experienced the most precipitation, the majority of which occurred within 1 d after leaving the African continent, and (4) the AO and SD air masses were generally very dry; i.e., very little precipitation events transpired over the 7 d journey. The significance of these precipitation events on the subsequent impact of these air mass is discussed below.
One point to mention is that there were no volcanic eruptions or ash plumes reported for the Soufrière Hills volcano on the island of Montserrat,
upwind of Puerto Rico, from the Montserrat Volcanic Observatory (
The hypothesis that drives the analysis in this study is that the microphysical properties of the mountaintop clouds in Puerto Rico are not strongly dependent on the air mass histories. In order to test this hypothesis, we have taken a case study approach whereby four periods were selected during which clouds were already present on the mountaintop when the air with different histories arrived. The cloud properties are evaluated as this new air arrives over a period lasting anywhere from 12 to 20 h, the time periods when the back-trajectory analysis has shown that the air is arriving from the same source.
An alternative, and perhaps preferable, analysis method would be to identify periods when clouds were formed under conditions related to the four air mass types and then evaluate the subsequent evolution; however, such conditions could not be unambiguously identified for all four types with the current data set. Hence, the analysis evaluates changes in the size distributions of cloud droplets relative to the cloud properties that existed at the time when the new air mass arrived. This is analogous to what is done during cloud seeding programs, where the material is introduced to an existing cloud in order to alter the subsequent evolution of cloud properties.
Time series of the size distribution of number concentrations for air masses with origins of
The same as Fig. 5 but for the size distribution of liquid water content (LWC). The color coding designates the value of the LWC (
Figures 5 and 6 highlight how the size distributions of number concentration,
Each of the events occurs at somewhat different times of day. The AO, AD, AS, and SE air is introduced to the clouds at 14:00, 02:00, 20:00, and 10:00 UTC (10:00, 22:00, 16:00 and 06:00 LST), respectively. Previous studies have documented that clouds can form at any time of the day and last for many hours or even days (Allan et al., 2008). Note that all times reported are in UTC.
There are changes in the cloud properties after the new air is introduced in all four cases; however, the magnitude and trends differ. Clear
differences are seen in the shapes of the size distributions over time following the introduction of air with new time histories. The AO air mass
arrives at 14:00 LST (local standard time) as a cloud is forming, and the
The SD air arrived at 02:00 LST to an already formed cloud and continued until 22:00. At the time this air
arrived, the cloud
The cloud that was already formed at PDE displayed no changes in the microphysical properties when the air mass with the AS history arrived at 20:00 LST, and these properties remain almost constant over the next 3 h with an average
The SE air mass arrived at 10:00 LST during a period of sparse cloud that remains very low in concentration with average
The times series of the microphysical properties of clouds that evolved in air with different air mass histories show that the size distributions of
number and mass concentrations, their associated average
The deviations in
The trends in the bulk properties derived from the size distributions, i.e., the
Due to the arrival of the AO air at PDE at the same time as the cloud was forming, the concentration deviation (Fig. 7a),
The AD air masses arrived at PDE and encountered forming cloud whose concentration is 50 % higher than the average during the 20 h residence of
the new air, but the
When the air of AS history reaches the mountaintop that is already in cloud, the
The trends in the
To encapsulate in a few sentences the physics behind the trends that we see in the deviations of the cloud microphysical parameters: (1) when
In this context, the impact of the air masses with four different histories is as follows:
AO – activation and growth of new droplets followed by eventual evaporation due to entrainment and mixing of subsaturated air; SD – initial entrainment of subsaturated air that led to the removal of droplets by evaporation, then new activation of CCN (either the residuals of
the evaporated droplets or those residuals in addition to CCN introduced by the SD air mass), and then further entrainment of subsaturated air that led
to droplet evaporation and the dispersion of the cloud; AS – the impact of this air mass is largely to remove the existing droplet by evaporation with only a brief period when it appears that some
new activation occurred; SE – the arrival of the new air eventually resulted in the activation of droplets in much higher concentrations than seen in the clouds under the influence of the other air masses, and these CCN are being continuously activated throughout the period, suggesting that the supersaturation is continuing to increase and smaller-sized CCN are being activated; at the same time, the droplets continue to grow, as evidenced by the increasing MVD throughout the residency of this air at PDE.
Following this summary, the trends in the microphysical properties suggest that only the AO and SE air masses introduce new CCN, whereas neither of the air masses of African history have CCN in sufficient concentration or difference in properties to generate new cloud droplets.
Previous studies at this location looking at cloud properties influenced by different air masses and aerosol types have come to mixed results. Eugster
et al. (2006) and Spiegel et al. (2014) arrived at the conclusion that the presence of African dust particles could be acting as cloud condensation
nuclei and thus must be the main aerosol responsible for different cloud microphysical properties. However, our results show otherwise and are in line
with those seen in Raga et al. (2016), whose results show that air mass history, mainly precipitation and time below 500
As previously mentioned, not only are the properties of the aerosols important when assessing the effect of the air masses, but the dynamics and thermodynamics of these air masses are also potentially of relevance. The Puerto Rico mountaintop clouds are formed from rising and cooling air brought about by surface heating that leads to convection, orographic lifting or by cold frontal passage. Only the first two mechanisms occurred during the summers of 2013–2015. Given that surface heating is a daytime phenomenon, the clouds that formed during the incursions of air masses with AO and SE histories, around 16:00 LST on both occasions, are likely the result of convective activity. In addition, in both these cases, the clouds persisted well into the evening, even after these particular air masses were no longer in residence. Hence, the clouds that were already on the mountain during the SD and AS incursions were also most probably those that had formed earlier in the day by convective activities. So before finalizing our conclusions about how the clouds in the four cases were altered by the changing air mass histories, it is important to evaluate if there were obvious differences in the winds or thermodynamic variables.
The average
Figure 8 summarizes the wind speeds and directions prior to and during the incursions of air masses, where the vertical dashed lines separate the time periods before and after the new air arrived. Other than the average wind speed being somewhat lower when the air is from the African Sahel, there are no obvious shifts in the velocity that can be associated with differences in the cloud properties. A significant increase in wind speed might lead to more turbulence and entrainment, or a large change in direction could be associated with a different magnitude of forcing; however, since neither of these shifts occurred, it suggests that atmospheric dynamics do not play a significant role in the cloud evolution in these cases.
The 7 d HYSPLIT back-trajectory analysis of average
The thermodynamic history of the air masses, i.e., the temperature and relative humidity (RH), tells a different story as is illustrated in Fig. 9, where the back-trajectory analysis summarizes how these two parameters evolved in time prior to the arrival of air at PDE.
As expected, the temperature histories follow those of altitude that were drawn in Fig. 4b, i.e., the African air masses, SD and AS, had been at lower
altitudes and warmer temperatures 7 d prior to reaching PDE, while the AO and SE over the Atlantic were much higher and colder. All the trajectories
converge on an average temperature of 18
The RH histories of the air masses are distinctly different from one another (Fig. 9b) and reflect to some degree both their temperature and altitude
trends, in particular the air mass with the SE history that was within 1000
The RH trends of the AO, SD, and AS air masses are more puzzling and would require an analysis beyond the scope of the current study to explain the rapid increase in the RH of these air mass beginning at 30 h that leads to RH values above 90 % when these parcels reach the measurement site. If the RH had been much lower than 90 % when arriving at PDE, mixing it into the existing cloud would have led to more rapid evaporation of the droplets and dissipation of the cloud. However, as the time series of the size distributions shows, the dissipation happened hours after the arrival of the air masses, so mixing will decrease the concentration, not due to the removal of droplets by evaporation but just by mixing in droplet-free air.
It is important to acknowledge that we have imposed conditions on the analysis that might have biased the results towards the higher RH air at the time of arrival or 1 h before; however, as the time traces show in Fig. 9b, there was already a strong, positive trend of humidification many hours prior to the air mass arrivals.
To conclude the discussion, we return to the analysis of the accumulation of precipitation along the air mass trajectories that was first introduced in Fig. 4c. The rainfall that is reported from the HYSPLIT model is an estimate based on the humidity fields and atmospheric cooling derived from the meteorological fields that are used when the model is run. Although these are not observed values, previously published comparisons between in situ and satellite measurements have shown that it is a reasonable approximation that can be used to evaluate precipitation along the trajectories. In the present study, we are interested in how precipitation affects the population of CCN in the air mass that will eventually reach Puerto Rico. The model predicts that the majority of rainfall is happening in the SD and AS air masses more than 5 d prior to their arrival at PDE. This suggests that some fraction of the dust and biomass burning aerosols that might have been picked up over Africa will be removed by precipitation, well before they will have a chance to influence the mountaintop clouds in Puerto Rico. On the other hand, if this precipitation is a result of deep convection, while rainfall might remove some CCN, the vertical air motions that lead to the formation of clouds and precipitations will also transport CCN that are not washed out. This latter mechanism of aerosol transport is particularly important for those air masses with AO and AS histories whose altitudes from 7 d forward never brought them near the surface as was seen with the SD and SE air masses.
The evidence that argues for the insertion of new aerosols into the air masses by deep convection lies in the RH histories because the convection that would bring particles from the boundary layer into the free troposphere would also inject additional water vapor. Indeed, we see that 6 d back in the AS history, at the same time that the accumulated rain increases, the RH also increases, so that both water vapor and particles are being lifted vertically at the same time some rainfall is removing particles. On the other hand, the increase in rainfall accumulation experience by the AO and SD air masses is not accompanied by substantial RH increases.
Thus, to revise our former observations about the impact of these four types of air masses with different histories, the clouds that are impacted by AO air masses experienced an increase in concentration that can be associated with new CCN activation; however, given the evidence that these air masses had not been near the ocean surface, or received additional particles over the previous 7 d, it is likely the increase in RH of this air mass as it approached Puerto Rico that provided additional moisture to the existing cloud. This fresh influx of water vapor conceivably could reactivate CCN residuals leftover from evaporating droplets. The evaluation of winds, temperature, and humidity do not change our original conclusion about the SE air masses that are bringing anthropogenic CCN from the boundary layer that subsequently led to higher concentrations of droplets and that the primary influence of the SD and AS air is to dilute and eventually dissipate the clouds with no new droplets activated.
In an effort to expand the current database of measurements related to the interaction of aerosol particles transported from the African continent to the Caribbean and their subsequent impact on cloud formation and evolution, we conducted measurement campaigns on the Caribbean island of Puerto Rico during the summers of 2013, 2014, and 2015. On frequent occasions, the island was clearly inundated with air masses from the Saharan desert and African Sahel. Measurements were made at a coastal location of the aerosol optical properties and during these same periods, cloud microphysical properties were measured at a mountain site that was downwind from the coastal site. The wind velocity, temperature, RH, and rain rate were measured at both sites. Based on the averaged wind directions, neither of these sites were influenced by anthropogenic emissions from local sources.
The aerosol optical properties and particle chemical composition measured at the coastal site were used to identify 16 high-dust and nine low-dust events of African dust. During high-dust periods, the scattering and absorption coefficients, AAE, and nss-
The HYSPLIT back-trajectory model was used to identify air masses with four distinctly different characteristics based on their geographic locations
prior to arriving at the Pico de Este (PDE) measurement site: midlatitude Atlantic Ocean (AO), Saharan desert (SD), African Sahel (AS), and arrival
from southeast of Puerto Rico (SE). Time periods were selected for analysis during which these air masses were arriving continuously over durations
ranging from 12–20 h when there was a cloud present on the mountain peak. The cloud droplet size distributions were measured with a droplet
spectrometer over the size range from 5–90
Based on the atmospheric dynamics and thermodynamics and the trends in the microphysical properties, we arrive at the following conclusions regarding
the impact of these air masses with four different histories on cloud evolution:
The AO air masses influence the reactivation of existing CCN as a result of the mixing of high-humidity air with the resident cloud and not due
to the introduction of new CCN. The initial entrainment of SD air parcels leads to the removal of droplets by evaporation, then new activation of CCN (either the residuals of the
evaporated droplets or those residuals in addition to CCN introduced by the SD air mass), and then further entrainment of subsaturated air that leads to
droplet evaporation and the dispersion of the cloud. The impact of air masses with AS history is largely to remove the existing droplets by evaporation with only a brief period when it appears that some
new droplet activation occurred, most likely on CCN that were residuals of evaporated droplets. The arrival of the new air along the SE trajectories eventually resulted in the activation of droplets in much higher concentrations than seen
in the clouds under the influence of the other air masses, and these CCN are being continuously activated throughout the period, suggesting that the
supersaturation is continuing to increase and smaller-sized CCN are being activated. At the same time, the droplets continue to grow, as evidenced
by the increasing MVD throughout the residency of this air at PDE.
These conclusions do not support those by Spiegel et al. (2014), who concluded that microphysical properties of clouds at PDE were significantly altered by African dust. They do support the arguments put forward by Raga et al. (2016), who used similar measurements at PDE from the summer of 2011 to conclude that the meteorology has a larger impact on the cloud microphysics than the properties of the CCN.
Clearly, the conclusions posted here are somewhat speculative in nature and require a much more detailed and long-term measurement program, coupled with cloud and chemistry models to validate these speculations. Nevertheless, the results are consistent with previous studies and, most importantly, provide a well-documented set of measurements to enhance the current data set of similar observations in the Caribbean.
Currently in progress is the next step to extend this study with a more comprehensive suite of sensors over a period that covers several years in order to take into account the year-to-year variability in synoptic and mesoscale weather patterns that modify the trajectories of dust and pollution transported to the Caribbean. Following the passage of Hurricane María, in September 2017, 2 years after the current study ended, both the mountain and coastal research sites were destroyed with all the equipment. As a result, through funding from the National Science Foundation, these sites have been rebuilt and new cloud and aerosol instrumentation purchased and installed. These new measurements, along with simulations with the Weather Research Forecast (WRF) will directly address the questions raised in the current study and begin moving the conclusions from the realm of speculation to statistically supported facts.
Data are available at
ETD, DB, and OLMB designed the field sampling campaign. OLMB designed and acquired the funding for the project. ETD, DB, and OLMB performed the data analysis and interpretation of results. ETD prepared the paper with contributions from all authors.
Darrel Baumgardner is one of the founders of Droplet Measurement Technologies as specified in the information for the editors.
Any mention of a company or product does not constitute an endorsement from the authors.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 Luquillo Critical Zone Observatory (LCZO; NSF EAR grant no. 1331841), the Long-Term Ecological Research project, Bridge to the Doctorate Program (grant no. HRD1139888), and the NASA Puerto Rico Space Grant Fellowship and Scholarship Program (grant no. NNX15AI11H).
This paper was edited by Xiaohong Liu and reviewed by four anonymous referees.