TMCnet News
Aerosol Indirect Effects on Idealized Tropical Cyclone Dynamics [Journal of the Atmospheric Sciences](Journal of the Atmospheric Sciences Via Acquire Media NewsEdge) ABSTRACT The desire to improve the forecasting skill of the intensity and size of tropical cyclones has prompted the investigation into numerous physical processes that can impact these quantities. The modification of cloud properties via aerosols injected into a tropical cyclone can initiate interactions between cloud microphysics and storm dynamics that ultimately lead to appreciable changes in the large-scale features of the storm. In this modeling study it is shown that the introduction of aerosols at the periphery of an idealized tropical cyclone can impact both the intensity and size of the storm. In general, the storm intensity increases and the storm size decreases with increasing aerosol number concentration. Results from a sensitivity study to the aerosol number concentration in a source located at the storm periphery reveal that the storm intensity is increased up to 17%, and the storm size is reduced up to roughly 16% for aerosol concentrations ranging from 100 to 2000 cm23. The storm response is approximately a monotonic function of the aerosol concentration amounts. Despite the increase in storm intensity for the heavily polluted case, the overall destructive potential of this case is reduced due to the significant decrease in the storm size. (ProQuest: ... denotes formulae omitted.) 1. Introduction The activity of forecasting hurricanes has numerous aspects including the prediction of how many storms will form within a season to the prediction of individual storm features such as track, intensity, and size. All of these facets are important for assessing the risk presented by hurricanes and to some degree can be influenced by aerosol indirect effects. Developing Atlantic tropical cyclones (TCs) will sometimes interact with the Saharan air layer (SAL), a dry, warm, and dusty air mass that forms offthe west coast of Africa. Braun (2010) states that it is unclear whether the SAL boosts or hinders TC development. The literature contains many studies, for dynamic or aerosol radiative effect reasons, that argue for enhancement (e.g., Alpert et al. 1998; Karyampudi and Pierce 2002; Jones et al. 2004; Ma et al. 2012) along with other studies that argue for inhibition (e.g., Dunion and Velden 2004; Evan et al. 2006; Lau and Kim 2007; Lau et al. 2009; Reale et al. 2009). Braun (2010) also comments that it is unclear what the influences of aerosol indirect effects are on TC development. Fovell et al. (2009) demonstrated that differing assumptions within bulk microphysical schemes that influence hydrometeor fall speeds can produce modifications in radial temperature gradients that consequently change the storm track during idealized simulations of TCs. This result suggests that aerosol indirect effects could potentially influence the storm track by virtue of their impacts on hydrometeor sizes and fall speeds. Observational (e.g., Dunion and Velden 2004; Jenkins et al. 2008) and modeling (e.g., Rosenfeld et al. 2007; Zhang et al. 2007, 2009; Khain et al. 2010; Carrio and Cotton 2011; Rosenfeld et al. 2011; Cotton et al. 2012; Rosenfeld et al. 2012) studies have demonstrated that aerosols interacting with TCs can impact the intensity of these storms through the modification of their cloud properties. Rosenfeld et al. (2011) showed that the effects brought on by the storm interaction with aerosols have sufficient impact to account for storm intensity prediction errors. Wang (2009) demonstrated in a modeling study, where the diabatic heating rates from a bulk microphysics scheme are systematically adjusted, that the overall heating in the outer rainbands will cause a storm to increase in size and decrease in intensity, whereas the overall cooling in the outer rainbands will cause the opposite effect. This result suggests that aerosol indirect effects could potentially modify the size and intensity of a TC through their impacts on latent heating (e.g., Khain et al. 2005; van den Heever et al. 2006). Accurate forecasting of hurricanes is of great value to those managing public safety, which leads to the desire to continue improving that skill. It appears that this improvement will come about in part by gaining a better understanding of the impacts that aerosol indirect effects may have on TCs. Therefore, the focus of this study is on the aerosol indirect effects taking place in a developing, idealized TC and how these effects ultimately impact storm intensity and size. TCs interact with aerosols under different circumstances, such as when encountering the SAL or intersecting with a plume of pollution particles from an urban region as they approach landfall. Recent TC-aerosol interaction studies (e.g., Rosenfeld et al. 2007; Zhang et al. 2007, 2009; Khain et al. 2010; Carrio and Cotton 2011; Rosenfeld et al. 2011; Cotton et al. 2012; Rosenfeld et al. 2012) have typically focused on the storm response to aerosol introduction in the outer rainbands. The introduction of aerosols acting as cloud condensation nuclei (CCN) in the outer rainbands at low levels modifies the cloud droplet size distribution (DSD) to one that contains a higher number of smaller droplets in a narrower range of diameters (Albrecht 1989). This change in the DSD tends to suppress collision and coalescence (Gunn and Phillips 1957; Squires and Twomey 1961; Warner and Twomey 1967; Albrecht 1989) in warm rain formation, which then makes many small droplets available to be lofted above the freezing level in regions where updrafts exist. The lofted droplets become supercooled and freeze, releasing latent heat resulting in enhanced updrafts (Andreae et al. 2004; Khain et al. 2005; van den Heever et al. 2006). The enhanced updrafts provide more condensate at high levels in the outer rainbands that eventually results in enhanced precipitation with associated downdrafts that form low-level cold pools in the rainband region (Rosenfeld et al. 2007; Carrio and Cotton 2011; Cotton et al. 2012; Rosenfeld et al. 2012). These cold pools tend to reduce the temperature and relative humidity in the low-level flow of air into the core of the storm, thereby reducing the storm intensity (Rosenfeld et al. 2007; Carrio and Cotton 2011; Cotton et al. 2012; Rosenfeld et al. 2012). It should be noted that in the Cotton et al. (2012) study during the initial stages of their simulations an increase in storm intensity with an increase in aerosol concentration was seen before the eventual weakening of the storm occurred. The authors attribute the initial storm intensification to aerosols penetrating to the eyewall region, but do not offer a rigorous explanation of this effect. Later on in the simulations, it is described that nucleation scavenging prevented aerosols from reaching the eyewall region, thus allowing the cold pool effects taking place in the rainband region (as outlined above) to dominate, resulting in diminishing storm intensity. The results of this study will be helpful in explaining the initial storm intensification observed in the Cotton et al. (2012) study. It is hypothesized here that if small aerosols acting as CCN are able to penetrate beyond the rainbands into the eyewall region of the storm, then these aerosols may create microphysical-dynamical interactions that result in the intensification of the surface winds and the reduction in the storm size. It is further hypothesized that this effect would increase in magnitude with increasing concentration amounts of the injected aerosols. Therefore, the goal of this study is to enhance our understanding of the impacts of the aerosols serving as CCN on TC intensity and size for the purpose of improving the forecasting of such storms. A description of the model and its configuration and the sensitivity tests employed in this study will be described in sections 2 and 3, respectively. The results of the sensitivity simulations will be presented and discussed in section 4, and conclusions will be provided in section 5. 2. Model description and configuration The model used in this study is the Regional Atmospheric Modeling System (RAMS; Cotton et al. 2003), a nonhydrostatic model that includes the capability of configuring multiple, two-way, nested, Arakawa C structured grids. The nesting feature of the grids allows for the simulation of a wide range of mesoscale phenomena including TCs at a sufficiently fine grid spacing in order to capture important convective and microphysical processes. RAMS features a bin-emulating bulk microphysical scheme (Cotton et al. 2003; Saleeby and Cotton 2004, 2008; Saleeby and van den Heever 2013) that includes three liquid (cloud droplets, large cloud droplets, and rain) and five ice (pristine ice, snow, aggregates, graupel, and hail) hydrometeor species. This microphysical scheme provides greater accuracy than a typical bulk scheme while avoiding the computational expense of a fully binned approach. The predicted quantities in the microphysics scheme are the number concentration and mixing ratio of the eight hydrometeor species. The inclusion of the large cloud droplet mode enables the representation of a bimodal cloud droplet size distribution that is often seen in clouds (Hobbs et al. 1980). The large cloud droplet mode also allows for a slower, more realistic, rate of simulated rain production compared to using only one cloud droplet mode by providing a transitional path from small cloud droplets to large cloud droplets and then to rain. The use of two cloud droplet modes also impacts ice formation in that both droplet modes participate in various forms of ice nucleation including, but not limited to, homogeneous freezing and riming. RAMS also accounts for the rapid production of secondary ice crystals through the Hallett-Mossop process (Hallett and Mossop 1974). Recent enhancements to the aerosol model in RAMS (Saleeby and van den Heever 2013) relevant to this study include nucleation scavenging, the regeneration of aerosols from evaporation-sublimation of hydrometeors, the consideration of particles greater than 0.5-mm radius as heterogeneous ice nuclei (DeMott et al. 2010), and the inclusion of dry deposition (gravitational settling) and wet deposition (precipitation scavenging). The high level of detail offered by the microphysical scheme of RAMS allows for the simulation of a wide array of microphysical mechanisms and interactions, which is of great importance to this study. The model domain was configured using threedimensional, triple-nested, two-way interactive grids with horizontal spacings of 24, 8, and 2 km (Fig. 1). A stretched vertical grid scheme (56 levels) was shared among all three grids that ranged from the surface to approximately 27 km, starting with 50-m spacing at the surface, stretching out to 1000-m spacing at approximately 11 km above ground level (AGL), and then remaining at a constant 1000-m spacing throughout the remainder of the vertical extent. Time steps of 60, 20, and 5 s were used for grids 1, 2, and 3, respectively. Aviscous ''sponge layer'' was inserted in the top six layers of the model. Radiative boundary conditions as described in Klemp and Wilhelmson (1978) were used on the lateral boundaries. These boundary conditions were selected for the purpose of damping gravity wave reflections. The surface processes were parameterized using the ocean category from the Land Ecosystem-Atmosphere Feedback 3 (LEAF-3) surfacemodel [the former version, LEAF-2, is documented in Walko et al. (2000)]. All simulations were conducted using an f plane centered on 158N. Since this f plane is located within the tropics, all simulations had their atmospheric environment set according to the mean hurricane season sounding of Jordan (1958). Because of the desire to focus on aerosol effects, the environmental wind flow was set to zero (beyond the vortex described below) so that the impacts of vertical wind shear on storm size and intensity were not included. Table 1 summarizes the model configuration used for the simulations in this study. 3. Sensitivity tests The control simulation for this study was initialized with an axisymmetric cyclonic vortex designed to mimic a pre-TC mesoscale vortex (MCV) that is in hydrostatic and gradient wind balance using a method described in Montgomery et al. (2006). The initial MCV was specified with a maximum tangential wind speed of 7ms21 located at a radius of 75km and a height of 4 km AGL. The winds within the initial MCV were allowed to taper offgently to zero well within the boundaries of the 600km by 600km inner grid. Figure 2a shows the state to which the initial vortex had grown after 1 h of simulation time. Initialization included an environmental (background) aerosol field consisting of submicron ammonium sulfate particles set to a concentration value of 100 cm23 at all locations in the domain in order to represent clean maritime air conditions (Heymsfield and McFarquhar 2001; Hudson and Yum 2002; Andreae 2009). Initialization further included setting the sea surface temperature (SST) to 268C homogeneously across the surface. This temperature was necessary to support the development of a hurricane with wind speeds between Saffir-Simpson categories 1 and 2. Weaker category 1 and 2 storms are more likely to be sensitive to aerosol indirect effects. This case study will therefore represent the upper limit on aerosol indirect effects on hurricanes. The control case was run out to 144-h (6 days) simulation time allowing for the storm to progress through a rapid intensification phase and reach a steady-state intensity (Fig. 2d). An aerosol source was added to the simulations in order to study the sensitivity of the storm intensity and size to varying concentrations of aerosol. The intent of adding such an aerosol source was to simulate the TC encountering a plume of aerosols such as dust in the SAL (Diazet al. 1976; Karyampudi et al. 1999; Adams et al. 2012). Atlantic storms would typically approach such a plume from the south before intersecting it. Therefore, the aerosol source was located inside grid 3, 150km to the north of the grid (storm) center (Fig. 2b). The aerosol profiles used in the source are shown in Fig. 2c. The aerosol source is enabled after 24 h of simulation time (Fig. 2d), and the aerosol concentrations are replenished every 60 s thereafter according to the corresponding profile (Fig. 2c). Note that the initiation time is just prior to the rapid intensification phase of the control storm. The number concentration of the aerosol source is varied using the amounts of 100, 500, 1000, and 2000 cm23 in order to examine the TC sensitivity to aerosol concentration. These values were chosen based on measurements of dust concentrations taken in various SAL dust plumes (Diaz et al. 1976; Levin et al. 2005; Lee et al. 2009). The 100 cm23 case was chosen as the control simulation due to its representation of clean air conditions. The focus of this study was on aerosol indirect effects; therefore, the aerosols were not radiatively active in the simulations. Table 2 lists the naming convention for the experimental simulations used throughout the remainder of the text. 4. Results a. Aerosol impacts on the storm structure The evolution of large-scale features of the simulated storms as a function of aerosol concentration is shown in Fig. 3. Figure 3a is a diagram that uses the maximum surface (10 m) wind velocity (V10m) to represent storm intensity and surface-integrated kinetic energy to represent storm size (Powell and Reinhold 2007; Musgrave et al. 2012). The time series of the radius of maximum surfacewinds, the radius of 34-knot (kt; 1 kt50.51ms21) surface winds (a commonly used metric of storm size), and the minimum sea level pressure are also shown in Figs. 3b, 3c, and 3d, respectively. Table 3 lists the measurements of storm structural quantities (some of which are depicted in Fig. 3) after averaging these values over the steady-state phase. With increasing aerosol concentration, the storm intensity increases (Fig. 3; Table 3) and the storm size decreases (Fig. 3; Table 3). These trends are monotonic, with the exception of only the radius of maximum surface winds (RMW) and radius of 50-kt surface winds (R50kt) measurements of the C1000 case (Table 3), suggesting that there may be a strong connection between aerosol concentration and the resulting storm structure. Low aerosol concentrations lead to storms with lower maximum winds and a larger extent, while greater aerosol concentrations lead to more compact storms with higher maximum winds. The temporal steady-state phase is marked for reference in the lower-right corners of Figs. 3b, 3c, and 3d. The steady-state phase was selected to take place between 120 and 140 h of simulation time based on the observation that this is the interval where the storm intensities, in an average sense, persist at a nearly constant value. Subsequent analyses will focus on the steady-state phase and just on the CLEAN and C2000 cases in order to provide more clarity in the figures. An empirical orthogonal function (EOF) analysis was performed on equivalent potential temperature (ue) in order to provide guidance on dividing the storm into radial sections for the subsequent analysis. Because of this specific use of the EOF, only the structure (and not the sign) of the variance from the ue differences is considered. First, ue was azimuthally averaged along radial bands from the center of the storm to a radius of 250km for each of the simulations. Then differences were formed by subtracting the azimuthally averaged ue of the CLEAN case from that of each of the aerosol sensitivity tests. Finally, an EOF analysis was performed on the resulting set of ue differences. The results of the EOF analysis on the C2000 case are shown in Fig. 4a. Only the first EOF is shown since it was the only statistically significant measurement, and this EOF explains 52%of the total variance. Several interesting regions of variance in ue are evident in this figure. The banded structures along the leftside of the plot correspond to the shifting of the radius of the eyewall. Looking at the radial structure in Fig. 4a, it appears that for radii between 40 and 70 km a fairly consistent band of variance exists that stays the same sign along the column from the surface up to approximately 4 km AGL, then switches sign and remains that sign up to 12 kmAGL. In these simulations, the freezing level between radii of 40 and 70 km is approximately 4.5km AGL that lines up closely with where the sign of the variance changes in the column. Beyond this region (radius . 70 km), the structure is different in that the sign of variance changes numerous times (both below and above the freezing point) proceeding from the surface to 12 km AGL. Inside of this region (radius , 40 km), the banded structure of the eyewall is apparent. Similar radial structures are apparent for the C500 and C1000 cases as well (not shown). Based on this structure in the variance, it was decided to split up the storm region (along the storm radius) into the three regions depicted in Figs. 4b and 4c. These three regions, storm core (0-40-km radius), rainbands (40-70-km radius), and far field (70-150-km radius), will be used in the subsequent analysis. To further study the impact of aerosols on storm structure, the radial distribution of condensate (liquid and ice) for the CLEAN and C2000 cases (Figs. 4b,c) is considered. These plots indicate that as aerosol concentration increases, the condensate in the storm tends to get focused at smaller radii. In addition, near the ends of the simulations, the radius where the maximum amount of condensate appears tends to decrease with an increase in aerosol concentration. These changes are consistent with the trends already observed regarding the storm size and intensity (Fig. 3). These changes also correspond to those depicted in Fig. 5 of Wang (2009), where the experiments with cooling in the outer storm region produce storms with nearly all of the significant rain rate located inside a radius of 60 km. Note that in the Wang (2009) study, the dividing lines between the radial regions of interest are located at 60- and 90-km radii, whereas in this study these lines are located at 40- and 70-km radii. The specific locations of these radial regions (storm core, rainband, and far field) are not as important as the structural features of the storms within them, and by comparing Figs. 4b and 4c of this study with Fig. 5 of Wang (2009), it can be seen that important structural similarities exist between the two diagrams. For example, in the C2000 case of this study (Fig. 4c), the vast majority of the condensate is found inside the storm core region (within a 40-km radius), and in the Wang (2009) study the vast majority of the rain rate is located within a 60-km radius for the experiments with cooling in the outer storm region. In summary, with increasing aerosol concentration, the storm structure tends to change from a lower intensity storm that is larger in extent to one with higher intensity, smaller radius of maximum wind, and of smaller extent. The thermodynamic structure of the simulated storms appears to be partitioned into three distinct spatial (radial) regions: the storm core, the rainbands, and the far field. In the Wang (2009) study, it was found that the region corresponding to this study's far-field region was of little interest, since the latent heating taking place in this region would dissipate quickly and thus not make a significant contribution to the results. For this reason, the subsequent investigation in this paper will be limited to the storm core and rainband regions. b. Processes in the storm core region (0-40-km radius) Figure 5 shows a Hovmeurooller plot of the cloud mixing ratio (qc), the cloud droplet number concentration, and cloud droplet mass mean diameter in the storm core region for the difference between the C2000 and CLEAN cases. Note that the plots in Fig. 5 were derived using data selected from cloudy locations (i.e., qc $ 0.01 g kg21). The plots show that at low levels (below 1 km AGL) the droplet number concentration increases and the mean droplet diameter decreases with increasing aerosol concentration. This result is consistent with the impacts of submicron aerosols on cloud DSD as found by Albrecht (1989). These results suggest that as the aerosol number concentration is increased in the aerosol source region, a greater number of these aerosols penetrate the storm core region at low levels and activate. The penetration of aerosols at low levels is consistent with the strong low-level radial inflow associated with a TC. The trend of increasing the droplet number concentration and decreasing the droplet mean diameter with increasing aerosol number concentration becomes apparent at 40-h simulation time and persists to the end of the simulations. Note that the use of a more realistic aerosol source profile, for example, representative of the dusty SAL layer with a maximum concentration occurring between 1 and 3 km AGL (Diaz et al. 1976), should not significantly change this result. This insensitivity to the idealized and realistic aerosol profiles would be expected due to the ingestion of the aerosols into the storm core taking place primarily at elevations below 1 km AGL, as implied by Fig. 5, along with the expectation that a more realistic aerosol profile would contain large enough concentrations near the surface to allow sufficient concentrations to reach the storm core. According to recent TC-aerosol interaction studies (e.g., Carrio and Cotton 2011; Cotton et al. 2012), the injection of aerosols at low levels into regions of deep convection in the rainbands produces convective invigoration primarily above the freezing level. Evidence supporting the occurrence of a similar impact in the storm core is shown in Fig. 6. The freezing level in the storm core occurs at approximately 5 km AGL. Above the freezing level, general trends of increased latent heating and enhanced updrafts with increasing aerosol number concentration are apparent. The expectation when the vertical velocity in the eyewall region is increased is that the vortex will extend in the vertical direction, contract in the horizontal direction, and the vertical vorticity will increase. This can be seen by examining the vertical component of the vorticity equation: ... where h is absolute vorticity, and it has been assumed that the fluid is incompressible and inviscid. Note that the model does not make these assumptions, yet it is helpful to examine Eq. (1) in the given (simplified) form. The third term on the right-hand side of Eq. (1) is the stretching term. As the velocity increases in the z direction, the vertical vorticity also increases. To maintain mass continuity, the fluid will then contract in the horizontal direction. Figures 7a and 7b show that the vortices extend higher into the vertical with increasing aerosol concentration, and Fig. 7c reveals that the vortices intensify, especially at low levels, with increasing aerosol concentration. Note that the positive differences at inner radii and negative differences at outer radii also indicate that the vortex is horizontally contracted compared to the CLEAN case. In the eyewall region, the results of the aerosol sensitivity tests show that with increasing aerosol concentration we see the following: low-level cloud droplet number concentration increases, low-level cloud droplet mean diameter decreases, total latent heating increases (warms) and updrafts are enhanced (especially above the freezing level), and the eyewall vortex extends in the vertical, while the eyewall radius contracts. The introduction of enhanced aerosol number concentrations serving as CCN in the lower levels of the storm core region modifies the cloud droplet size distribution to one with a larger number of smaller sized droplets. This then suppresses the collision-coalescence process and warm rain precipitation (Albrecht 1989), making available more cloud water that can be drawn aloftin the eyewall updrafts. (Note the increase of columnintegrated condensate in the storm core region during the later simulation times for the C2000 case in Fig. 4.) These lofted droplets cross the freezing level and eventually freeze releasing latent heat leading to enhanced updrafts. The enhanced updrafts work to stretch the vortex so that as aerosol concentration increases, the vortex extends in the vertical and intensifies (increased horizontal wind speeds) and the vortex radius decreases. c. Processes in the rainband region (40-70-km radius) As the vortex in the eyewall region contracts with increasing aerosol concentration, air from the outer regions of the storm will move inward in order to maintain mass continuity. This will bring lower ue environmental air into the region of the rainbands. The lower ue air occurs in association with the general subsidence in the outer area of the storm and serves to cool down the columns of air in the rainband region. As discussed in Wang (2009), the cooler columns in the rainband region will increase the low-level horizontal pressure gradient between the storm center and the rainbands, thus setting up a situation that will tend to keep the vortex in a contracted state, thereby allowing for the persistence of a higher intensity and more compact storm. Evidence of the cooling of the columns of air in the rainband region for the more heavily polluted case is apparent in Fig. 8. Figure 8a shows that with increasing aerosol concentration the equivalent potential temperature through the vertical range of 4 to 12 km AGL of the atmosphere in the rainband region tends to decrease due to the air becoming both cooler and drier (Figs. 8b,c). Figure 8a also reveals the sign of the ue variance (EOF) displayed in Fig. 4a. Figure 8d shows that downdrafts are enhanced (indicating column cooling) for the more heavily polluted case. The resulting low-level horizontal pressure gradient between the storm center and the rainbands is shown in Fig. 8e. A nearly monotonic increase in the pressure gradient with an increase in aerosol concentration exists once the sensitivity simulations reach the steady-state phase. This relationship persists throughout the steady-state phase, which is consistent with the results in Wang (2009). It is instructive to examine the timing of the dynamic responses (vertical velocity) of the storms to the injected aerosols in the sensitivity tests. Figure 9 reveals that with increasing aerosol concentration, updrafts are enhanced in the storm core region and downdrafts are enhanced in the rainband region. Around 80-h simulation time, the enhancement of the updrafts in the storm core (Fig. 9a) becomes apparent, then increases and persists throughout the steady-state phase. It is after 80-h simulation time when the sensitivity cases start to diverge in terms of RMW (Fig. 3b) and progress to the monotonic response seen in the steady-state phase. The downdraftenhancement in the rainbands (Fig. 9b) on the other hand does not become apparent until around 90-h simulation time, which then persists to the end of the simulations. The organization into the near monotonic response of the low-level horizontal pressure gradient (Fig. 8e) begins near 90-h simulation time. The fact that the dynamic response in the storm core initiates earlier than that in the rainbands suggests the following sequence of events: First, the enhancement of updrafts in the storm core of the more heavily polluted case stretches the vortex in the vertical direction (Fig. 7), then the vortex shrinks in the horizontal direction that is followed by the entrainment of lower ue air into the rainband region. This cools the columns and consequently brings about the increase of the low-level horizontal pressure gradient between the storm center and rainbands. The larger horizontal pressure gradient tends to contain the diabatic heating closer to the storm core, bringing about the persistence of a storm with a smaller extent, smaller RMW, and higher intensity. d. Impacts on storm destructive potential Recent studies (e.g., Powell and Reinhold 2007; Maclay et al. 2008) have put forward arguments that the size of a storm is at least as important a factor of a storm's destructive potential as the storm intensity (maximum sustained 10-m wind speed). The storm size is related to the magnitude of the storm surge, which can sometimes contribute more toward damage than the storm intensity. Powell and Reinhold (2007) use a comparison between the damage inflicted by Hurricanes Camille (1969) and Katrina (2005) upon landfall as an example of the importance of considering the storm surge. Camille was classified as a category 5 storm on the Saffir-Simpson scale at landfall, whereas Katrina was classified as a category 3 storm. However, Katrina caused much more damage due to its much greater size and associated larger stormsurge. Powell andReinhold (2007) follow their argument with the proposal to adopt integrated kinetic energy (IKE) as a measure of the destructive potential of a TC in order to more accurately represent the risk in cases such as Hurricane Katrina. Using the Powell and Reinhold (2007) IKE metric, Camille was measured at 63 TJ compared to Katrina being measured at 122 TJ, resulting in a much more accurate representation of the relative destructive potential between these two storms. The IKE metric of Powell and Reinhold (2007) is calculated using the formula ... where r is the density of air, U is the 10-m wind speed, and V is taken as a volume 1m deep (centered at the 10-m level) and roughly 48 in radius from the storm center. For the purpose of this study, IKE was calculated from model-diagnosed 10-m winds over a 250-km radius from the storm center. Limitations on computer resources dictated a 600km by 600km domain (Table 1) that consequently limited practical storm measurements to a 250-km radius. Calculations of the IKE for these cases (Table 3) demonstrate that despite the increasing storm intensity with increasing aerosol concentration, the trend in destructive potential is decreasing due to the corresponding decrease in IKE. The reduction of the storm IKE of 20% for the C2000 case versus the CLEAN case (Table 3) can be roughly translated to a reduction in the storm surge for that case that is a significant reduction in its destructive potential. In summary, it can be seen that the destructive potential of a storm generally decreases as the aerosol concentration increases, since the surface kinetic energy of the storm considered over a large radius is reduced, and hence the associated storm surge will also be reduced. 5. Conclusions In this study, four sensitivity simulations of an idealized TC were conducted with varying aerosol concentrations in order to evaluate the impact of aerosol indirect effects on the storm intensity and size. The aerosols were introduced into the storms via a source located on the periphery of the storms. It was found that in general with increasing aerosol concentration that storm intensity increases and the storm size decreases. This result comes about due to the following processes, which are depicted schematically in Fig. 10: First, the strong low-level radial inflow, characteristic of a TC, draws in aerosols from the source toward the core of the storm (Fig. 10a). The presence of the aerosols at low levels near the eyewall suppresses warm rain processes, resulting in more liquid water being lofted upward, freezing, and releasing latent heat that ultimately enhances the updrafts. The increased updrafts stretch the vortex in the vertical direction, thereby causing the surface winds to increase and the radius of maximum wind speed to decrease due to (near) gradient balanced flow dynamics (Fig. 10b). As the eyewall contracts, lower ue air from the surrounding outer region is drawn inward toward the eyewall (Fig. 10c). The lower ue air cools the columns of air and consequently increases the low-level pressure in the rainband region, thereby producing a larger horizontal pressure gradient between the rainband and storm center (Fig. 10c). The increased horizontal pressure gradient works to contain the diabatic heating of the storm near the eyewall, resulting in the persistence of the dynamic structure (stretched vortex, higher maximum tangential winds, and smaller radius of maximum wind speed). Despite the increase in intensity (maximum tangential wind speed) as the aerosol concentration increases, the low-level wind speeds across the extent of the storm are reduced. This results in the reduction of the destructive potential of the storm via two mechanisms. First, the slower overall wind speeds will cause less damage from the wind itself, and second, the smaller extent of the storm will cause less damage due to the reduction of the storm surge. It is interesting to compare the results of this study with those of recent research (Rosenfeld et al. 2007; Zhang et al. 2007, 2009; Khain et al. 2010; Carrio and Cotton 2011, Rosenfeld et al. 2011; Cotton et al. 2012; Rosenfeld et al. 2012). In general, the prior research found a weakening of storm intensity with increasing aerosol concentration, whereas this study found the opposite. Despite the appearance of contradictory results, there is potentially a key difference in this study that points to the discovery of a separate aerosol effect from the prior research. In the prior research, aerosols ingested in the outer rainbands lead to convective invigoration in those outer rainbands. However, in this study the aerosols penetrated to the storm core, leading to convective invigoration in the eyewall. The different locations of the convective invigoration lead to two distinct aerosol indirect effects. Convective invigoration in the outer rainbands produces enhanced low-level cold pools that impede the supply of warm moist air into the storm core, resulting in a weakening of storm intensity. Convective invigoration in the eyewall results in the vertical stretching of the vortex that strengthens storm intensity. The initial strengthening of the storm intensity seen in Cotton et al. (2012), followed by the ultimate weakening of the storm, provides evidence of this idea in that Cotton et al. (2012) reported that the presence (lack) of aerosols near the eyewall corresponded with greater (lesser) storm wind speeds. Another outcome of considering the existence of these two separate aerosol indirect effects in TCs is assistance with explaining complex responses of TCs to aerosol concentrations. It is possible that these two effects could take place simultaneously, since aerosols could be advected into both the eyewall and outer rainband regions. If this were to happen, then the two processes would compete with each other, resulting in nonlinear responses in storm intensity. For example, if it turns out that aerosols did penetrate into the storm core in past studies that reported nonmonotonic responses of intensity to aerosol concentration (e.g., Zhang et al. 2009), perhaps these results can at least be partially explained due to a strengthening effect from aerosols in the storm core competing with a weakening effect from aerosols in the outer rainbands. The goal of this study was to investigate aerosol indirect impacts on TC size and intensity. As such, other factors such as aerosol direct (radiative) effects and the dry air impacts of the SAL were deliberately omitted in order to isolate the aerosol indirect effects. It would be an interesting future study to look at these effects both individually, and in combination, in order to gain insight on the relative impacts of each effect. Finally, it should be noted that this study did not include the effects of aerosol particles that are formed from sea spray generated by the high-speed surface winds of a TC. The size distribution of sea spray- generated particles is broad enough to provide both CCN and giant CCN (O'Dowd et al. 1997). A built-in sea salt aerosol source has recently been added to RAMS (Carrio and Cotton 2011) and could be utilized to perform such a study. Therefore, the next step in this research will be to run and analyze a suite of sensitivity tests, corresponding to the tests of this study, in which the sea salt aerosol source is enabled. Acknowledgments. This work was supported by the National Science Foundation Division of Atmospheric Sciences Grant AGS-1005316. The authors thank Dr. Wayne H. Schubert (Colorado State University) for his insightful guidance in the area of tropical cyclone dynamics. The authors also thank the anonymous reviewers for their insightful and helpful comments that improved the quality of the original manuscript. REFERENCES Adams, A. M., J. M. Propsero, and C. Zhang, 2012: CALIPSOderived three-dimensional structure of aerosol over the Atlantic basin and adjacent continents. J. Climate, 25, 6862-6879, doi:10.1175/JCLI-D-11-00672.1. Albrecht, B., 1989: Aerosols, cloud microphysics, and fractional cloudiness. Science, 245, 1227-1230, doi:10.1126/ science.245.4923.1227. Alpert, P., Y. Kaufman, Y. Shay-El, and D. Tanre, 1998: Quantification of dust-forced heating of the lower troposphere. Nature, 395, 367-370, doi:10.1038/26456. Andreae, M. O., 2009: Correlation between cloud condensation nuclei concentration and aerosol optical thickness in remote and polluted regions. Atmos. Chem. Phys., 9, 543-556, doi:10.5194/acp-9-543-2009. _____,D.Rosenfeld, P.Artaxo,A.A. Costa,G. P. Frank,K.M. Longo, and M. A. F. Silva-Dias, 2004: Smoking rain clouds over the Amazon. Science, 303, 1337-1342, doi:10.1126/science.1092779. Braun, S. A., 2010: Reevaluating the role of the Saharan air layer in Atlantic tropical cyclogenesis and evolution. Mon. Wea. Rev., 138, 2007-2037, doi:10.1175/2009MWR3135.1. Carrio, G. G., and W. R. Cotton, 2011: Investigations of aerosol impacts on hurricanes: Virtual seeding flights. Atmos. Chem. Phys., 11, 2557-2567, doi:10.5194/acp-11-2557-2011. Cotton, W. R., and Coauthors, 2003: RAMS 2001: Current status and future directions.Meteor. Atmos. Phys., 82, 5-29, doi:10.1007/ s00703-001-0584-9. _____, G.M. Krall, and G. G. Carrio, 2012: Potential indirect effects of aerosol on tropical cyclone intensity: Convective fluxes and cold-pool activity. Trop. Cyclone Res. Rev., 1, 293-306, doi:10.6057/2012TCRR03.05. DeMott, P. J., and Coauthors, 2010: Predicting global atmospheric ice nuclei distributions and their impacts on climate. Proc. Natl. Acad. Sci.USA, 107, 11 217-11 222, doi:10.1073/pnas.0910818107. Diaz, H. F., T. N. Carlson, and J. M. Prospero, 1976: A study of the structure and dynamics of the Saharan air layer over the northern equatorial Atlantic during BOMEX. NOAA Tech. Memo. ERL WMPO-32, 61 pp. Dunion, J. P., and C. S. Velden, 2004: The impact of the Saharan air layer on Atlantic tropical cyclone activity. Bull. Amer. Meteor. Soc., 85, 353-365, doi:10.1175/BAMS-85-3-353. Evan, A. T., J. Dunion, J. A. Foley, A. K. Heidinger, and C. S. Velden, 2006: New evidence for a relationship between Atlantic tropical cyclone activity and African dust outbreaks. Geophys. Res. Lett., 33, L19813, doi:10.1029/2006GL026408. Fovell, R. G., K. L. Corbosiero, and H.-C. Kuo, 2009: Cloud microphysics impact on hurricane track as revealed in idealized experiments. J. Atmos. Sci., 66, 1764-1778, doi:10.1175/ 2008JAS2874.1. Gunn, R., and B. B. Phillips, 1957: An experimental investigation on the effect of air pollution on the initiation of rain. J. Meteor., 14, 272-280, doi:10.1175/1520-0469(1957)014,0272: AEIOTE.2.0.CO;2. Hallett, J., and S. C. Mossop, 1974: Production of secondary ice crystals during the riming process. Nature, 249, 26-28, doi:10.1038/249026a0. Harrington, J. Y., 1997: The effects of radiative and microphysical processes on simulated warm and transition season Arctic stratus. Ph.D. dissertation, Colorado State University, Atmospheric Science Paper 637, 289 pp. Heymsfield, A. J., and G. M. McFarquhar, 2001: Microphysics of INDOEX clean and polluted trade cumulus clouds. J. Geophys. Res., 106, 28 653-28 673, doi:10.1029/2000JD900776. Hill, G. E., 1974: Factors controlling the size and spacing of cumulus clouds as revealed by numerical experiments. J. Atmos. Sci., 31, 646-673, doi:10.1175/1520-0469(1974)031,0646: FCTSAS.2.0.CO;2. Hobbs, P. V., M. K. Politivich, and L. F. Radke, 1980: The structures of summer convective clouds in eastern Montana. I: Natural clouds. J. Appl. Meteor., 19, 645-663, doi:10.1175/ 1520-0450(1980)019,0645:TSOSCC.2.0.CO;2. Hudson, J. G., and S. S. Yum, 2002: Cloud condensation nuclei spectra and polluted and clean clouds over the Indian Ocean. J. Geophys. Res., 107, 8022, doi:10.1029/2001JD000829. Jenkins, G. S., A. S. Pratt, and A. Heymsfield, 2008: Possible linkages between Saharan dust and tropical cyclone rain band invigoration in the eastern Atlantic during NAMMA-06. Geophys. Res. Lett., 35, L08815, doi:10.1029/ 2008GL034072. Jones, C., N. Mahowald, and C. Luo, 2004: Observational evidence of African desert dust intensification of easterly waves. Geophys. Res. Lett., 31, L17208, doi:10.1029/2004GL020107. Jordan, C. L., 1958: Mean soundings for the West Indies area. J. Meteor., 15, 91-97, doi:10.1175/1520-0469(1958)015,0091: MSFTWI.2.0.CO;2. Karyampudi, V. M., and H. F. Pierce, 2002: Synoptic-scale influence of the Saharan air layer on tropical cyclogenesis over the eastern Atlantic. Mon. Wea. Rev., 130, 3100-3128, doi:10.1175/1520-0493(2002)130,3100:SSIOTS.2.0.CO;2. _____, and Coauthors, 1999: Validation of the Saharan dust plume conceptual model using lidar, Meteosat, and ECMWF data. Bull. Amer. Meteor. Soc., 80, 1045-1074, doi:10.1175/ 1520-0477(1999)080,1045:VOTSDP.2.0.CO;2. Khain, A., D. Rosenfeld, and A. Pokrovsky, 2005: Aerosol impact on the dynamics and microphysics of deep convective clouds. Quart. J. Roy. Meteor. Soc., 131, 2639-2663, doi:10.1256/qj.04.62. _____,B. Lynn, and J. Dudhia, 2010: Aerosol effects on intensity and landfalling hurricanes as seen from simulations with the WRF model with spectral bin microphysics. J. Atmos. Sci., 67, 365- 384, doi:10.1175/2009JAS3210.1. Klemp, J. B., and R. B. Wilhelmson, 1978: The simulation of three-dimensional convective storm dynamics. J. Atmos. Sci., 35, 1070-1096, doi:10.1175/1520-0469(1978)035,1070: TSOTDC.2.0.CO;2. Lau, K. M., and K. M. Kim, 2007: Cooling of the Atlantic by Saharan dust. Geophys. Res. Lett., 34, L23811, doi:10.1029/ 2007GL031538. _____, -, Y. C. Sad, and G. K. Walker, 2009:AGCMstudy of the response of the atmospheric water cycle of West Africa and the Atlantic to Saharan dust radiative forcing. Ann. Geophys., 27, 4023-4037, doi:10.5194/angeo-27-4023-2009. Lee, Y. H., K. Chen, and P. J. Adams, 2009: Development of a global model of mineral dust aerosol microphysics. Atmos. Chem. Phys., 9, 2441-2458, doi:10.5194/acp-9-2441-2009. Levin, Z., A. Teller, E. Ganor, and Y. Yin, 2005: On the interactions of mineral dust, sea-salt particles, and clouds: A measurement and modeling study from the Mediterranean Israeli Dust Experiment campaign. J. Geophys. Res., 110, D20202, doi:10.1029/2005JD005810. Lilly,D.K., 1962: On the numerical simulation of buoyant convection. Tellus, 14, 148-172, doi:10.1111/j.2153-3490.1962.tb00128.x. Ma, P.-L., K. Zhang, J. J. Shi, T.Matsui, andA.Arking, 2012:Direct radiative effect of mineral dust on the development of African easterly waves in late summer, 2003-07. J. Appl. Meteor. Climatol., 51, 2090-2104, doi:10.1175/JAMC-D-11-0215.1. Maclay, K. S., M. DeMaria, and T. H. Vonder Harr, 2008: Tropical cyclone inner-core kinetic energy evolution. Mon. Wea. Rev., 136, 4882-4898, doi:10.1175/2008MWR2268.1. Mesinger, F., and A. Arakawa, 1976: Numerical methods used in atmospheric models. GARP Publ. Series 17, WMO/ICSU Joint Organizing Committee Rep., 64 pp. Montgomery, M. T.,M.E.Nicholls, T.A. Cram, andA. B. Saunders, 2006: A vortical hot tower route to tropical cyclogenesis. J. Atmos. Sci., 63, 355-386, doi:10.1175/JAS3604.1. Musgrave, K. D., R. K. Taft, J. L. Vigh, B. D. McNoldy, and W. H. Schubert, 2012: Time evolution of the intensity and size of tropical cyclones. J. Adv. Model. Earth Syst., 4, M08001, doi:10.1029/2011MS000104. O'Dowd, C. D., M. E. Smith, I. E. Consterdine, and J. A. Lowe, 1997: Marine aerosol, sea-salt, and the marine sulphur cycle: A short review. Atmos. Environ., 31, 73-80, doi:10.1016/ S1352-2310(96)00106-9. Powell, M. D., and T. A. Reinhold, 2007: Tropical cyclone destructive potential by integrated kinetic energy. Bull. Amer. Meteor. Soc., 88, 513-526, doi:10.1175/BAMS-88-4-513. Reale, O., W. K. Lau, K.-M. Kim, and E. Brin, 2009: Atlantic tropical cyclogenetic processes during SOP-3 NAMMA in the GEOS-5 global data assimilation and forecast system. J. Atmos. Sci., 66, 3563-3578, doi:10.1175/2009JAS3123.1. Rosenfeld, D., A. Khain, B. Lynn, and W. L. Woodley, 2007: Simulation of hurricane response to suppression of warm rain by sub-micron aerosols. Atmos. Chem. Phys., 7, 5647-5674, doi:10.5194/acpd-7-5647-2007. _____, M. Clavner, and R. Nirel, 2011: Pollution and dust aerosols modulating tropical cyclone intensities. J. Atmos. Res., 102, 66-76, doi:10.1016/j.atmosres.2011.06.006. _____, W. L. Woodley, A. Khain, W. R. Cotton, G. G. Carrio, I. Ginnis, and J. H. Golden, 2012: Aerosol effects on microstructure and intensity of tropical cyclones. Bull. Amer. Meteor. Soc., 93, 987-1001, doi:10.1175/BAMS-D-11-00147.1. Saleeby, S. M., and W. R. Cotton, 2004: A large-droplet mode and prognostic number concentration of cloud droplets in the Colorado State University Regional Atmospheric Modeling System (RAMS). Part I: Module descriptions and supercell test simulations. J. Appl. Meteor., 43, 182-195, doi:10.1175/ 1520-0450(2004)043,0182:ALMAPN.2.0.CO;2. _____, and -, 2008: A binned approach to cloud-droplet riming implemented in a bulk microphysics model. J. Appl. Meteor. Climatol., 47, 694-703, doi:10.1175/2007JAMC1664.1. _____, and S. C. van den Heever, 2013: Developments in the CSURAMS aerosol model: Emissions, nucleation, regeneration, deposition, and radiation. J. Appl. Meteor. Climatol., 52, 2601- 2622, doi:10.1175/JAMC-D-12-0312.1. Smagorinsky, J., 1963: General circulation experiments with the primitive equations. I. The basic experiment. Mon. Wea. Rev., 91, 99-164, doi:10.1175/1520-0493(1963)091,0099: GCEWTP.2.3.CO;2. Squires, P., and S. Twomey, 1961: The relation between cloud drop spectra and the spectrum of cloud nuclei.Physics of Precipitation, Geophys. Monogr., Vol. 5, Amer. Geophys. Union, 211-219. van den Heever, S. C., G. G. Carrio, W. R. Cotton, P. J. DeMott, and A. J. Prenni, 2006: Impacts of nucleating aerosol on Florida storms. Part I: Mesoscale simulations. J. Atmos. Sci., 63, 1752-1775, doi:10.1175/JAS3713.1. Walko, R. L., and Coauthors, 2000: Coupled atmosphere- biophysics-hydrology models for environmental modeling. J.Appl.Meteor., 39, 931-944, doi:10.1175/1520-0450(2000)039,0931: CABHMF.2.0.CO;2. Wang, Y., 2009: How do outer spiral rainbands affect tropical cyclone structure and intensity? J. Atmos. Sci., 66, 1250-1273, doi:10.1175/2008JAS2737.1. Warner, J., and S. Twomey, 1967: The production of cloud nuclei by cane fires and the effects on cloud droplet concentration. J. Atmos. Sci., 24, 704-706, doi:10.1175/ 1520-0469(1967)024,0704:TPOCNB.2.0.CO;2. Zhang, H., G. M. McFaquhar, S. M. Saleeby, and W. R. Cotton, 2007: Impacts of Saharan dust as CCN on the evolution of an idealized tropical cyclone. Geophys. Res. Lett., 34, L14812, doi:10.1029/2007GL029876. _____, -, W. R. Cotton, and Y. Deng, 2009: Direct and indirect impacts of Saharan dust acting as cloud condensation nuclei on tropical cyclone eyewall development. Geophys. Res. Lett., 36, L06802, doi:10.1029/ 2009GL037276. STEPHEN R. HERBENER, SUSAN C. VAN DEN HEEVER, GUSTAVO G. CARRIÓ, STEPHEN M. SALEEBY, AND WILLIAM R. COTTON Colorado State University, Fort Collins, Colorado (Manuscript received 2 July 2013, in final form 8 November 2013) Corresponding author address: Stephen R. Herbener, Colorado State University, Atmospheric Science Department, 1371 Campus Delivery, Fort Collins, CO 80523. E-mail: [email protected] (c) 2014 American Meteorological Society |