UNIVERSITY OF MIAMI CHARACTERIZATION OF THE SUMMER 2013 MIAMI SAHARAN DUST EVENTS. Sara Purdue A THESIS

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1 Purdue 1 UNIVERSITY OF MIAMI CHARACTERIZATION OF THE SUMMER 2013 MIAMI SAHARAN DUST EVENTS By Sara Purdue A THESIS Submitted to the Faculty of the Rosenstiel School of Marine and Atmospheric Science Coral Gables, Florida May 2014

2 Purdue 2 UNIVERSITY OF MIAMI A thesis submitted in partial fulfillment of the requirements for Departmental Honors in Marine and Atmospheric Science CHARACTERIZATION OF THE SUMMER 2013 MIAMI SAHARAN DUST EVENTS Sara Purdue Approved: Bruce Albrecht Name of Thesis Advisor Chair of Thesis Committee Academic Rank and Department Name of Committee Member Academic Rank and Department Name of Committee Member Academic Rank and Department

3 Purdue 3 Characterization of the Summer 2013 Miami Saharan Dust Events Abstract: The 2013 summer season in Miami was a fairly active time for dust. Between June 1 and August 31 there were three major dust events where a large presence of Saharan dust was detected over South Florida. This project looks at these periods of time, in comparison to periods when little or no dust was present in order to gain insight on the nature of these events. A variety of methods are used in the analysis of the events, including time series of CCN concentrations, HYSPLIT backwards model trajectories, and profiles of the air sampled over Miami during the summer. Introduction: Aerosols cast a large influence on global weather and climate. Dust in particular is a unique type of aerosol, in that it is capable of nucleating warm, liquid-only clouds and can serve as the nuclei for ice clouds (Carlson and Prospero 1972). The Sahara is arguably the world s largest dust source region. In South Florida, Saharan dust events are of particular importance. Dust from the Sahara regularly travel across the Atlantic, reaching areas in the western Atlantic such as Barbados and Florida primarily during June to August (Smirnov et al 2000, Carlson and Prospero 1972). By the time Saharan dust reaches Florida, it has often descended to overlie the marine boundary layer (Carlson and Prospero 1972). South Florida's CAROb (Cloud-Aerosol- Rain Observatory) at Rosenstiel School of Marine and Atmospheric Science (RSMAS) has been recording cloud condensation nuclei counts on the surface, along with depolarization lidar measurements since Over the summer of 2013, these measurements have been used to help analyze dust events in South Florida and to assess whether or not elevated dust layers are well integrated enough within the boundary layer to be observed on the surface. If the dust is

4 Purdue 4 detectable at the surface and is able to nucleate cloud droplets, this would have implications for cloud formations and other weather and climate phenomena in South Florida. Instrumentation: The primary instrument used for this project was a Droplet Measurement Technologies (DMT) cloud condensation nuclei (CCN) counter, which is a continuous-flow instrument that provides in situ measurements of CCN (Roberts and Nenes 2005). Figure 1 shows the location and set up of the instrument as it is at RSMAS. Air from the outside is directed into the instrument from an inflow pipe that is on the left side of the instrument. The air is then humidified and heated before it passes through a chamber with wetted walls and a linear temperature gradient that can be adjusted to change supersaturation values within the chamber (Roberts and Nenes 2005). The chamber reaches different supersaturation levels by taking advantage of the fact that mass diffuses more quickly than heat (Roberts and Nenes 2005). Since water molecules diffuse more quickly than the heavier air molecules (N 2 and O 2 ), and heat is dependent on the collisions of those air molecules, this relationship can be used to create different supersaturation ratios within the chamber. The ratio can be changed by varying the flow rate into the chamber or by varying the temperature gradient (Roberts and Nenes 2005). Once the air has passed through the chamber, it is directed into an optical particle counter (OPC) which uses standard light scattering techniques to detect droplets at the outlet of the growth column. The measured CCN concentrations are determined by the number of particles detected that are larger than 1µm (Roberts and Nenes 2005). Although the CCN counter is able to detect particles as small as 0.3µm, at lower supersaturations, smaller, unactivated CCN can be mistaken for activated particles (at 1µm, the lowest limit is 0.13% for (NH 4 ) 2 SO 4 particles, and 0.2% for hydrophilic

5 Purdue 5 insoluble aerosols); therefore the minimum size for classification of activated particles must be greater than 1µm (Roberts and Nenes 2005). In the case of the CCN counter at RSMAS, air from outside is directed into the supersaturation chamber, where the machine cycles through five supersaturation ratios (0.2, 0.4, 0.6, 0.8 and 1.0%) for periods of five minutes each. The ratios are changed by varying the temperature profile the temperatures are increased in 2.5 o C increments each five minute period, and are then cooled to the beginning temperatures once the CCN counter has cycled through all five supersaturation ratios. The resultant graphs of the temperatures appear as a step function, as can be seen in Figure 2. On a typical day, the graph of CCN concentrations typically has steps as well, with higher concentrations as you reach higher supersaturation levels. Typical values generally range between 400 and 1000 cm -3 depending on the supersaturation level. Results: Identifying Dust Events Days and times when dust was present in the South Florida boundary layer were identified using a time series plot of lidar depolarization ratio data in figure 3, which ranges in time from June 1 to August 31, The values shown in the plot indicate the asphericity of the particles present values closer to zero indicate spherical particles, while higher values indicate non-spherical, irregularly shaped particles. This allows us to differentiate between dust and other aerosols common in Miami such as sea salt. Sea salt is spherical and therefore has a low depolarization ratio, whereas dust is large and irregular in shape and returns a much higher depolarization ratio in lidar plots. Areas of the plot that have values greater than 0.1 and less than 0.2 indicate a dust layer is present. Higher values in figure 3 correspond to brighter colors; therefore brighter areas indicate a stronger dust presence. The most prominent dust events,

6 Purdue 6 which are circled in yellow, occurred between June 19 and June 24, July 24 and July 26, and August 10 and August 12, and are therefore the dates which are focused on in this paper. For purposes of comparison, date ranges of similar length where little to no dust is present have also been selected from the lidar these are June 26-28, July 4-9, and August (circled in red). HYSPLIT Backwards Trajectory Flow Analysis In order to confirm the source of the dust detected by the lidar plot in Figure 3, HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory Model) was used to create backwards particle trajectories for the time periods identified in the previous section. HYSPLIT is a model that is run jointly by the Air Resource Laboratory, which is a part of NOAA, and Australia s Bureau of Meteorology. It uses a Lagrangian transport method to compute aerosol dispersions and trajectories. In this paper, only trajectories were used, and the trajectories are calculated in the model through the integration of the advection of the particles by the mean wind field in three dimensions, as well as by random motions due to atmospheric turbulence (Draxler and Hess 1999). By changing initial parameters trajectories can be customized in a number of ways, including the initial height of the modeled particle(s), the number of particles initialized, whether the trajectory moves forwards or backwards in time, the length of time that is shown, the vertical motion parameter (the default is omega, however other options include isentropic, isobaric, and other motions), and output of meteorological variables at points along the trajectory (Draxler and Rolph 2014). In this project, all plots were made with backwards trajectories, a time period of 288 hours started every three hours (giving each day eight different plots per initial height from 00Z to 21Z), and run at six different starting heights ranging from m in 500 m intervals. Figure 4 shows backwards trajectories for the June event. Figures 5 and 6 show the

7 Purdue 7 trajectories for July and August respectively. In addition, Figure 7 has the same type of trajectories for all selected days with little to no dust present (June 26-28, July 4-9, and August 18-20). Plots are separated by initial height and are plotted over the month s average SLP contours. Below each panel are the corresponding height trajectories, which show the particle movement in a vertical and longitudinal cross-section. It is interesting to note that when comparing the HYSPLIT trajectories to the lidar depolarization ratio plot, you can see a relationship between the backwards trajectories that end near (and in) the Sahara as well as those that move towards the surface in this area and the heights at which the dust is most prevalent in the lidar. For example, in Figure 3 during the June period, the dust signal is fairly weak below 1.0 kilometer, while from 1.5 to 2.5 kilometers the values are much higher. In Figure 4, the back trajectories that begin from Miami at 1.0 to 1.5 kilometers show sources further north in the Atlantic and do not indicate movement from the surface. Trajectories that start above 1.5 kilometers though, show a strong tendency of starting from the surface in northern Africa, especially at 2.5 and 3.0 kilometers. Similar patterns can be seen when comparing the lidar values for July and August to the HYSPLIT results (Figures 5 and 6 respectively), although not as clearly as in Figure 4. Unsurprisingly, the July data in Figure 5 show the fewest paths coming from the Sahara many from the mid-atlantic. Those that do come from Africa do not start from the surface. This makes sense, considering in Figure 3, the July event shows the lowest depolarization ratios of the three events. In Figure 6, the August back trajectories once again reflect the strength of the event shown by the lidar. Although the paths tend to come from the surface in the Sahara more than in Figure 5, some still show origins further north or further out over the Atlantic.

8 Purdue 8 Comparing Figures 4-6 to the trajectories in Figure 7, there is more consistent movement in the latter figure from sources near Western Europe, the eastern United States, and, more frequently, near the Azores. Although some of the trajectories also move towards northern Africa, this can be explained by short lived columns of dust detected during a small portion of the period. For example, from July 4-9, the period is largely free of dust, except for a small event during July 9. Therefore, the CCN during these periods would largely be comprised of sulfates and other anthropogenic aerosols, or, in the cases when there was dust, a mix of anthropogenic aerosols and some dust. Summer 2013 CCN Time Series After identifying dust events with the depolarization ratios in Figure 3 and confirming source locations in Figures 4-7, measurements from the DMT CCN counter were used for analysis of dust behavior. Figure 8 shows the time series plots of days when dust was detected by the lidar. Data was separated by super saturation value and then averaged hourly and plotted by the day. Figure 9 was plotted by the same means and shows time series of days when lidar data indicated little to no dust was present. The counter is located in close proximity to several large sources of aerosol production (such as construction sites that blow up dust and smoke to the CCN counter location), which can cause extremely large and sudden spikes in the data. Therefore, all plots have a set maximum of 6000 on the y-axis as it was assumed values higher than this were not representative of the data set. Table 1 shows the CCN total concentrations (number of CCN per cm -3 ) for each date range in the project and includes the totals from all supersaturation levels. The trajectories in Figures 4-7 are reflected in Figures 8 and 9. Although the originally expected outcome for these plots was higher counts on days when dust was present the opposite

9 Purdue 9 ended up occurring, with generally lower counts on the dustier days. Although there are some extended periods of higher counts in the dust periods, they are few in number and have lower values than those in the counterparts in Figure 9. Furthermore, Table 1 shows that the total CCN concentrations during the dust periods were generally lower than during the days with little to no dust. This, along with the time series plots, consequently give the impression of cleaner air during the days when dust events occurred. Dust vs. Non-Dust CCN Activity Figures 10 and 11 show daily histograms from the summer, made with the normalized frequencies from the CCN counts at each supersaturation level. Figure 10 includes days when dust was present and Figure 11 has corresponding lengths of time when little to no dust was present. Plots are separated by date and supersaturation value respectively. Figure 12 shows histograms similar to those in Figures 10 and 11, except they include normalized for data from the entire summer versus individual days. Plots on the left include only data from days where dust was detected, and plots on the right include only those when no dust was detected. The histograms in Figure 10, for the most part, show a smaller spread, as well as higher and narrower peaks at the lower concentrations. Even the histograms that have a larger range of counts still have a much smaller range than those in Figure 11 (maximum range around cm -3 in Fig. 10 vs cm -3 in Fig. 11). Looking back to the trajectories in Figures 4-7, this makes sense. The back trajectories in Figures 4-6 show starting points in or near the Sahara and a path more directly across the Atlantic (with the exception of the July trajectories which reach around into the Gulf of Mexico before heading towards Miami). The paths in Figure 7 show trajectories that often originate in areas that have more anthropogenic aerosols, so that even if dust is picked up along the way, it will most likely show up in the data as polluted

10 Purdue 10 dust. The larger number of sulfates and anthropogenic aerosols would cause the shift in peak values and wider range of values in the Figure 11 histograms and the higher counts in the Figure 9 time series. Figure 13(a.) shows three curves, one from data collected in the summer of 2013 (in purple) and the other two based on the plot from Atmospheric Science: An Introductory Survey by Wallace and Hobbs (the red line is from an air mass near the Azores, which is influenced by industrial pollution from Europe, and the green line is from a more pristine marine air mass near Florida). In Figure 13(b.), the purple line includes data from the entire summer and is the same as the purple profile in 13(a.), the light blue line includes only data from days with dust, and the dark blue line only has the data from days without dust. Data in both Figures 13(a.) and (b.) was separated by supersaturation and then averaged over all days included within the plot (e.g. June 1 to August 31 for Figure 13(a.), June 19-24, July 24-26, and August for 13(b.), etc.). Additionally, values at 0.2% supersaturation were limited to the maximum the 1.0% supersaturation values. On certain days during the summer, the 0.2 supersaturation CCN concentrations would be significantly higher than at any other supersaturation. This did not occur with any other supersaturation value and did not seem to have a set pattern for days when it would or would not occur it is possible it was due to machine error (because of the lower supersaturation) as discussed in Instrumentation, therefore it was excluded from this plot. Looking at Figure 13(a.) the Wallace and Hobbs plots are fairly close in value (especially at the smaller supersaturations), while the data collected and used in this study is much higher in total. This is due to the polluted air in and around Miami. This is also apparent in the different slopes. A flatter slope, like with the marine air mass, corresponds to more easily activated aerosols, such as sea salt. Steeper slopes occur when smaller particles that are less easily

11 Purdue 11 activated at lower supersaturation levels are prevalent (Wallace and Hobbs ). This is the case with the polluted air mass above the Azores which has a flatter slope due to smaller CCN from pollution carried over from nearby Europe (Hudson and Yum 2001) and the 2013 summer data from Miami. Looking then at Figure 13(b.), we can see further differences between the periods with and without dust. The light blue profile generally has lower values than the dark blue profile. The exception to this is between 0.2 and 0.4% supersaturation, where the light blue profile has slightly higher values (although it also has a slightly flatter slope). The lower values on the rest of the plot support the idea of a cleaner air mass when there is dust present, while the flatter slope indicates the presence of larger particles (which don t require as high of a supersaturation to become activated the way that smaller particles, like sulfates, do) (Wallace and Hobbs ). The slope of the light blue line is also flatter in the rest of the profile than the slope of the dark blue line which, again, indicates a cleaner air sample in this plot, supporting the earlier plots which show a cleaner air mass in the days without dust. Comparing this to Figures 4-7, it looks like this is due to different sources across the Atlantic. As show in Figure 7 the sources further were much further north than those in Figures 4-6, and pull air from closer to Western Europe and the Azores. In fact, some of the plots even wrap around the Atlantic high and reach back to the east coast of the United States. This would explain the cleaner appearance of the air on days without dust, as the sources on these days have more anthropogenic aerosols and pollution. Summary: In this study, data from the summer of 2013 was analyzed through a variety of methods to investigate dust events in South Florida and to characterize the nature of these events. Lidar data was used to determine days when dust was present, HYSPLIT data was used to identify the

12 Purdue 12 source of air samples taken by plotting back trajectories from Miami, and CCN counter data was analyzed with time series plots and histograms to determine the nature of the air sampled during dust events. Based on the 2013 summer data from Miami, it seems that the expected results in future observations would be low concentrations of CCN and cleaner profiles because the atmospheric flow during these times causes mostly dust and sea salt to pass over South Florida. In terms of cloud drop nucleation, it is difficult to fully determine whether or not the dust during dust events could be nucleating cloud particles. The occasional time periods of higher CCN counts during the dust events would indicate it is certainly possible that the dust is nucleating cloud droplets; however, because the other CCN largely present in these samples is sea salt, which is highly effective as a CCN, it is hard to say for certain how much of the activation in the CCN counter was due to sea salt and how much was actually due to dust.

13 Purdue 13 References Carlson, Toby N., and Joseph M. Prospero. "The Large-Scale Movement of Saharan Air Outbreaks over the Northern Equatorial Atlantic." Journal of Applied Meteorology 11.2 (1972): Print. Draxler, R.R. and Rolph, G.D., HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) Model access via NOAA ARL READY Website ( NOAA Air Resources Laboratory, College Park, MD. Draxler, R.R., 1999: HYSPLIT4 user's guide. NOAA Tech. Memo. ERL ARL-230, NOAA Air Resources Laboratory, Silver Spring, MD. "EOSDIS Worldview (Alpha)." EOSDIS Worldview (Alpha). NASA, n.d. Web. 20 Jan Hudson, James G., and Seong Soo Yum. "Maritime Continental Drizzle Contrasts in Small Cumuli." Journal of the Atmospheric Sciences 58.8 (2001): Print. Roberts, G. C., and A. Nenes, "A Continuous-Flow Streamwise Thermal-Gradient CCN Chamber for Atmospheric Measurements." Aerosol Science and Technology 39.3: Print. Smirnov, A., B. N. Holben, D. Savoie, J. M. Prospero, Y. J. Kaufman, D. Tanre, T. F. Eck, and I. Slutsker. "Relationship between Column Aerosol Optical Thickness and in Situ Ground Based Dust Concentrations over Barbados."Geophysical Research Letters (2000): Print.

14 Purdue 14 Wallace, John M., and Peter Victor Hobbs, "Cloud Microphysics." Atmospheric Science: An Introductory Survey. 2nd ed. Vol. 92. New York: Academic Print. International Geophysics Ser. Zuidema, Paquita, Joseph Prospero, Sara Purdue, Bruce Albrecht, Rodrigo Delgado, and Kenneth Voss, "Dust in South Florida: Think Global, Act Local, South Florida's Cloud-Aerosol-Rain-Observatory (CAROb)." Manuscript in progress.