Humidity impact on the aerosol effect in warm cumulus clouds

Transcription

1 GEOPHYSICAL RESEARCH LETTERS, VOL. 35, L17804, doi: /2008gl034178, 2008 Humidity impact on the aerosol effect in warm cumulus clouds O. Altaratz, 1 I. Koren, 1 and T. Reisin 2 Received 31 March 2008; revised 21 July 2008; accepted 5 August 2008; published 6 September [1] The effects of aerosols on clouds are recognized as among the most important factors affecting climate change yet these effects are poorly understood due to the complexity of cloud processes and the strong influence of other environmental conditions. A numerical cloud model is used here to study the combined influence of aerosol concentration and upper level humidity conditions on moderate-sized, coastal, convective clouds. We show that these variables are strongly linked in their effects on clouds and suggest a microphysical variable space in which their influence can be distinguished. Citation: Altaratz, O., I. Koren, and T. Reisin (2008), Humidity impact on the aerosol effect in warm cumulus clouds, Geophys. Res. Lett., 35, L17804, doi: /2008gl Introduction [2] Aerosol effects on warm cumulus clouds have been extensively studied in recent years. Xue and Feingold [2006] studied the effects of aerosol on warm trade-wind cumulus clouds using large eddy simulations (LES) with size-resolved cloud microphysics. They showed that an increase in aerosol concentration led to a reduction in cloud fraction, cloud size, cloud top-height and depth. They concluded that the complex responses of clouds to aerosols are determined by competing effects of precipitation and droplet evaporation associated with entrainment. Jiang and Feingold [2006] used a different LES model to show the effect of aerosols on warm convective clouds over land. Their results showed that in the absence of aerosol radiative effects, an increase in aerosol loading resulted in no statistically significant changes in LWP, cloud fraction and depth. On the other hand when aerosol radiative effects were included, LWP, cloud fraction and depth were reduced, primarily due to the reduction in surface forcing associated with absorbing aerosol. Xue et al. [2007] studied shallow cumulus clouds under stratocumulus and showed that cloud fraction increased with increasing aerosol (concentration) only for relatively low values (up to 100 cm 3 in their case). A further increase in the aerosol particle concentration resulted in reduced cloud fraction. They suggested that opposing effects of aerosol induced suppression of precipitation and aerosol induced enhancement of evaporation were responsible for this non monotonic behavior. [3] The evaporation of drops at cloud boundaries is especially important at cloud tops; this was shown to cause cooling and downward motion, thus enhancing mixing and entrainment. Analysis of clouds generated by LES in the 1 Department of Environmental Sciences, Weizmann Institute, Rehovot, Israel. 2 Soreq Nuclear Research Center, Yavne, Israel. Copyright 2008 by the American Geophysical Union /08/2008GL BOMEX case by Zhao and Austin [2005] supports the theory by Blyth et al. [1988] that the entrainment process in growing clouds occurs near the ascending cloud top. Mixed parcels subsequently descend around the top into a trailing wake region. A similar theory by Jonas [1990] also invokes a scenario in which air near cloud tops sinks in a thin layer around the cloud and is then laterally entrained into the cloud. Given the importance of the cloud top interface with the environmental air, it is clear that the evaporation process in this region is of great importance. [4] Important factors that influence the evaporation process near cloud boundaries are the environmental conditions. Ackerman et al. [2004] simulated a few cases of stratocumulus clouds under inversion layers with different humidities. They concluded that the response of the cloud water to changes in aerosol loading is determined by a competition between moistening from decreased surface precipitation and drying from increased entrainment of overlying air. [5] The aim of this study is to investigate the combined effect of humidity in the inversion layer, where the upper parts of warm convective clouds reside, and aerosols on the cloud properties. 2. Model Description [6] The Tel Aviv University axisymmetric non-hydrostatic numerical cloud model (TAU-CM) was used with detailed treatment of cloud microphysics [Tzivion et al., 1994; Reisin et al., 1996]. The warm microphysical processes included are nucleation of CCN, condensation and evaporation, collision coalescence, binary breakup, and sedimentation (validation of the microphysical scheme was done in the past by Reisin et al. [1998]). The microphysical processes are formulated and solved using a multimoment bin method [Tzivion et al., 1987]. [7] The drop activation scheme is based on the supersaturation and critical diameter determined by the Kohler curves [Pruppacher and Klett, 1997]. Calculations of the critical diameter for aerosol activation were done by assuming that CCN are composed of pure sea-salt (NaCl) and that all the aerosols are CCN. Wetted CCN particles provide the initial sizes for subsequent condensational growth [Kogan, 1991]. Following this approach we assumed that the initial droplet size formed on CCN equal or smaller of the equilibrium radius at 100% RH, depends on the particle size (for details see Yin et al. [2000]). The aerosol spectrum is approximated by superimposing three lognormal distributions with parameters representing a maritime air mass [Jaenicke, 1988; Altaratz et al., 2008]. The total number concentration of aerosol is 295 cm 3. [8] The simulations were initialized with a homogeneous base-state environment representing an Israeli autumn day, based on October 1st 2006, 12Z sounding of temperature L of5

2 Figure 1. Vertical cuts of the drop mass mixing ratio (kg/kg) for four clouds: (a) cloud_clean_70% (C70), (b) cloud_1600_70% (P70), (c) cloud_clean_20% (C20), (d) cloud_1600_20% (P20), at 52 minutes of simulation. and moisture data from the Bet Dagan meteorological station that is located 10 km from the shore line (without winds). A better representation of the conditions at the coast that enhance convective development was achieved by introducing some modifications to the sounding (addition of moisture, a temperature decrease near the surface, and an increase in inversion height [Altaratz et al., 2008]). The modified sounding data is representative of the conditions at a band that covers the coast and the sea near the coast, at noon time. There was a well-mixed subcloud layer between approximately 0 and 1000 m, a conditionally unstable cloud layer between 1000 and 1900 m, and an overlying inversion layer. To simulate different environmental conditions, the humidity level was modified in the inversion layer (above 1900 m), from 20% to 40% and 70%. [9] The grid resolution was set at 50 meters in both radial and vertical directions. The domain was 4000 m in the radial direction and 5000 m in the vertical. Convection was initiated by introducing a bubble 1 C warmer than the environment in a small region at the lower boundary of the grid at the domain origin, for one time step. The time step of the computation was 1 second and the total simulation time was 80 minutes. Open boundary conditions were assumed. [10] Nine simulations were performed, for three levels of pollution and three levels of humidity. The pollution aerosols were added to the background (clean) aerosol distribution as a log normal distribution in the range and 0.88 microns radius. For each humidity level simulations were run for a clean case (only background aerosols), a polluted case with 200 cm 3 pollution particles and a highly polluted cloud with 1600 cm 3 pollution particles. The clouds are marked in the text according to their level of pollution and humidity. 3. Results and Discussion [11] In all nine simulations clouds start to form after 33 minutes. The general structure of the clouds is examined for the different levels of pollution and humidity. Figure 1 presents the drop mass mixing ratio for four clouds: clean cloud at 70% (C70), polluted cloud (1600 cm 3 )at 70% (P70), clean cloud at 20% (C20) and polluted cloud (1600 cm 3 ) at 20% (P20), at 52 minutes of simulation. The top of the clouds is defined as the highest grid point with a mass mixing ratio greater than 0.01 g kg 1. C70 top height is 2250 m at 52 minutes, P70 is 2200 m, C20 is 2150 m and P20 top height is 2100 m. It can be seen that cloud tops are more sensitive to humidity than to the aerosol loading. All four clouds penetrated the inversion stable layer (above 1900 m). The straight black lines marked on Figure 1 correspond to the horizontal and vertical dimensions of the clean cloud at 70% humidity (C70) with the largest dimensions compared to the other clouds. The two clouds that developed in drier environments (20%) are smaller both in the vertical and horizontal dimensions. The entrainment of the drier air in the upper parts of the 20% clouds intensifies the evaporation process and lowers the cloud tops as well as producing narrower clouds due to the circulation of the drier air into lower parts of the clouds. Zhao and Austin [2005] showed that the entraining eddies have scales comparable to the ascending cloud top size and they directly engulf the environmental air and move it deep into the ascending cloud top center. 2of5

3 Figure 2. Vertical cuts of the condensed/evaporated mass mixing ratio (g/kg) for four clouds: (a) cloud_clean_70% (C70), (b) cloud_1600_70% (P70), (c) cloud_clean_20% (C20), (d) cloud_1600_20% (P70), at 52 minutes of simulation. The positive and negative numbers represent condensation and evaporation respectively. [12] In order to describe the combined impact of humidity and aerosol loading on the macrophysical structure of the clouds we chose to separate the clouds into two regions. The inner region is dominated by condensation and the outer one by evaporation. A value of 0 g kg 1 evaporated/condensed mass mixing ratio marks the border between the condensation and evaporation regions. The outer region is bounded by a value of 10 4 g kg 1 evaporated mass mixing ratio (which was chosen arbitrarily). To show the spatial distribution and the magnitude of the evaporation/ condensation zones, the evaporated/condensed mass mixing ratios at 52 minutes for a cross-section of the clouds is shown in Figure 2. The zone in the vicinity of the cloud top is dominated by the evaporation rates and as expected the Figure 3. The mean mass in the condensation zone (dashed lines) and in the evaporation zone (solid lines) as a function of humidity in the inversion layer, for three levels of pollution (clean, green curve; 200 cm 3, red; and 1600 cm 3, black). 3of5

4 Figure 4. The maximum horizontal wind (m s 1 ) at 52 minutes as a function of humidity in the inversion layer, for three levels of pollution (clean, green curve; 200 cm 3, red; and 1600 cm 3, black). evaporation is stronger for the drier atmosphere cases. The maximum mixing ratio of evaporated mass is larger for the clouds developed in the drier environment (0.044 g kg 1 in P20 compared to 0.02 g kg 1 in P70).This results in clean clouds condensation and evaporation zones with larger volumes (having higher tops in the vertical direction and wider dimensions in the horizontal direction) compared to the polluted clouds that developed in the same environmental conditions. For C70 the volumes are m 3 and m 3 for the condensation and evaporation zones, respectively. For P70 it is m 3 and m 3. For C20 it is m 3 and m 3. For P20 it is m 3 and m 3. These examples also show that clouds forming in a humidified environment have condensation and evaporation volumes significantly larger than those developed in a drier environment. Both humidity and aerosols change the evaporation/condensation areas. [13] Next, the liquid water mass of each zone was examined. The mean mass in the condensation (M_cond) and evaporation zones (M_evap), for three levels of pollution, as a function of humidity in the inversion layer are plotted (Figure 3). For all clouds and humidity levels M_cond is larger than M_evap. While the effect of the humidity is similar in direction and slope for M_cond and M_evap, the relative effect of aerosol (change in mass due to aerosol normalized by the total mass) appears to be less significant in M_cond. This suggests that while the entrainment is controlled by the humidity affects both the outer and inner part of the cloud (through large scale turbulence) the aerosol effect on evaporation efficiency is more local, affecting more significantly the outer part of the cloud (M_evap). [14] To illustrate the above concept we use the maximum value of the horizontal wind at cloud center as a measure of turbulence. Stronger horizontal wind is a result of larger (more organized) eddies extending the effect of the entrainment deeper into the clouds. Figure 4 presents the maximum values of the horizontal wind as a function of humidity. It shows that the change in humidity induces stronger horizontal winds and consequently a deeper entrainment. This effect is more significant than the one introduced by changes in aerosol loading. [15] The above results suggest that the space spanned by the mean mass of the evaporation zone (M_evap) and the ratio of the mass (R = M_cond/M_evap) is appropriate to show the differences in the cloud sensitivity to aerosols and cloud top humidity. While the aerosol effect is close to being parallel to R (the mass ratio x-axes) indicating strong effect on R and relatively weak (compare to the humidity) effect on the evaporated mass, the humidity follows closer the mass of evaporation (y-axes) suggesting opposite sensitivity, namely affecting significantly both the evaporated and the condensed masses. The nine clouds (three humidity levels and three pollution levels) are plotted on this space (Figure 5). The black lines connect the three clouds developed in the same environmental conditions and the red lines connect the clouds having the same level of pollution. As expected, the humidity strongly influences the masses but maintains an almost constant ratio R (R = M_cond/M_evap), while the increase in pollution levels affects the mass of the outer (evaporation) part of the cloud more than the core (as shown in Figure 3) and therefore will not maintain a constant mass ratio. The two pathways are almost orthogonal in the M_evap versus R space. 4. Summary [16] The Tel Aviv University axisymmetrical cloud model was used to study the combined influence of aerosol and inversion layer humidity on warm convective clouds of moderate size. To analyze these effects we used a method that separates the clouds into two regions: condensation and evaporation zones and we followed their characteristics. The results show that both humidity and aerosol do influence the extent of the inner and outer cloud areas but in a different manner. In order to separate the effect of the aerosols from the cloud top-humidity, the M_evap versus R 4of5

5 Figure 5. The mean mass at the external evaporation region as a function of the ratio between the mean masses in the internal and external regions for the nine simulated clouds. Black lines connect the three clouds developed in the same environmental conditions and red lines connect the clouds with same level of pollution. space was shown. While humidity affects the whole cloud and maintains similar ratios of the cloud mass ratio (R), the aerosols effect on evaporation efficiency is more local, affecting mostly the evaporation part of the cloud. [17] These results indicate fundamental differences of (and the interplay between) the effects of aerosol and humidity on warm convective clouds and should be taken in account when estimating the anthropogenic effects on clouds. [18] Acknowledgments. O. Altaratz and I. Koren acknowledge the partial support of the ISF (grant 1355/06). References Ackerman, A. S., M. P. Kirkpatrick, D. E. Stevens, and O. B. Toon (2004), The impact of humidity above stratiform clouds on indirect aerosol climate forcing, Nature, 432, Altaratz, O., I. Koren, T. Reisin, A. Kostinski, G. Feingold, Z. Levin, and Y. Yin (2008), Aerosols influence on the interplay between condensation, evaporation and rain in warm cumulus cloud, Atmos. Chem. Phys., 8, Blyth, A. M., W. A. Cooper, and J. B. Jensen (1988), A study of the source of entrained air in Montana cumuli, J. Atmos. Sci., 45, Jaenicke, R. (1988), Aerosol physics and chemistry, in Landolt-Boernstein: Zahlenwerte und Funktionen aus Naturwissenschaften und Tecknik, vol. 4b, edited by G. Fischer, pp , Springer, Berlin. Jiang, H., and G. Feingold (2006), Effect of aerosol on warm convective clouds: Aerosol-cloud-surface flux feedbacks in a new coupled large eddy model, J. Geophys. Res., 111, D01202, doi: /2005jd Jonas, P. R. (1990), Observations of cumulus cloud entrainment, Atmos. Res., 25, Kogan, Y. L. (1991), The simulation of a convective cloud in a 3-D model with explicit microphysics. Part I: Model description and sensitivity experiments, J. Atmos. Sci., 48, Pruppacher, H. R., and J. D. Klett (1997), Microphysics of Clouds, 714 pp., Springer, New York. Reisin, T., Z. Levin, and S. Tzivion (1996), Rain production in convective clouds as simulated in an axisymmetric model with detailed microphysics. Part I: Description of the model, J. Atmos. Sci., 53, Reisin, T., Y. Yin, Z. Levin, and S. Tzivion (1998), Development of giant drops and high reflectivity cores in Hawaiian clouds: Numerical simulation using a kinematic model with detailed microphysics, Atmos. Res., 45, Tzivion, S., G. Feingold, and Z. Levin (1987), An efficient numerical solution to the stochastic collection equation, J. Atmos. Sci., 44, Tzivion, S., T. Reisin, and Z. Levin (1994), Numerical simulation of hygroscopic seeding in a convective cloud, J. Appl. Meteorol., 33, Xue, H., and G. Feingold (2006), Large eddy simulations of tradewind cumuli: Investigation of aerosol indirect effects, J. Atmos. Sci., 63, Xue, H., G. Feingold, and B. Stevens (2007), Aerosol effects on clouds, precipitation, and the organization of shallow cumulus convection, J. Atmos. Sci., 65, Yin, Y., Z. Levin, T. G. Reisin, and S. Tzivion (2000), The effect of giant cloud condensation nuclei on the development of precipitation in convective clouds A numerical study, Atmos. Res., 53, Zhao, M., and P. H. Austin (2005), Life cycle of numerically simulated shallow cumulus clouds. Part II: Mixing dynamics, J. Atmos. Sci., 62, O. Altaratz and I. Koren, Department of Environmental Sciences, Weizmann Institute, Rehovot 76100, Israel. (orit.altaratz@weizmann.ac.il) T. Reisin, Soreq Nuclear Research Center, Yavne 8188, Israel. 5of5