On the precipitation susceptibility of clouds to aerosol perturbations

Transcription

1 GEOPHYSICAL RESEARCH LETTERS, VOL. 36, L13803, doi: /2009gl038993, 2009 On the precipitation susceptibility of clouds to aerosol perturbations Armin Sorooshian, 1,2,3 Graham Feingold, 2 Matthew D. Lebsock, 4 Hongli Jiang, 1,2 and Graeme L. Stephens 1,4 Received 1 May 2009; accepted 29 May 2009; published 2 July [1] Atmospheric aerosol particles act as cloud condensation nuclei, affording them the ability to influence cloud microphysics, planetary albedo, and precipitation. Models of varying complexity and satellite observations from NASA s A-Train constellation of satellites are used to determine what controls the precipitation susceptibility of warm clouds to aerosol perturbations. Three susceptibility regimes are identified: (i) clouds with low liquid water path (LWP) generate very little rain and are least susceptible to aerosol; (ii) clouds with intermediate LWP where aerosol most effectively suppress precipitation; and (iii) clouds with high LWP, where the susceptibility begins to decrease because the precipitation process is efficient owing to abundant liquid water. Remarkable qualitative agreement between remote sensing observations and model predictions provides the first suggestions that certain regions of the Earth might be more vulnerable to pollution aerosol. Targeted pollution control strategies in such regions would most benefit water availability via precipitation. Citation: Sorooshian, A., G. Feingold, M. D. Lebsock, H. Jiang, and G. L. Stephens (2009), On the precipitation susceptibility of clouds to aerosol perturbations, Geophys. Res. Lett., 36, L13803, doi: /2009gl Introduction [2] The Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) has highlighted that future warmer climate states are very likely to be accompanied by shifts in precipitation patterns, more extreme rainfall events, and more intense droughts [Intergovernmental Panel on Climate Change (IPCC), 2007]. Increased precipitation has been observed in some regions (i.e., eastern parts of North and South America, northern Europe and northern and central Asia) while decreased precipitation has been observed in others (i.e., Sahel, the Mediterranean, southern Africa and parts of southern Asia) [IPCC, 2007]. The intensification and variability of the global hydrologic cycle in a warmer planet may severely impact society in a multitude of ways including strains on regional water resources, flooding, agricultural production, and societal infrastructure. With a projected increase in global population, spatial distribution of precipitation and 1 Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado, USA. 2 Earth Systems Research Laboratory, NOAA, Boulder, Colorado, USA. 3 Now at Department of Chemical and Environmental Engineering, University of Arizona, Tucson, Arizona, USA. 4 Department of Atmospheric Sciences, Colorado State University, Fort Collins, Colorado, USA. Copyright 2009 by the American Geophysical Union /09/2009GL its availability is of growing importance. These issues motivate the need to determine the magnitude and regional nature of the effect of pollution particles on precipitation. [3] The physical basis for aerosol effects on precipitation rests on two principles: (i) an increase in aerosol results in more numerous, but smaller droplets (all else being equal) [Twomey, 1974]; and (ii) the collision-coalescence efficiency between these small droplets is significantly reduced and therefore the primary warm rain precipitation-generation mechanism is suppressed [Warner, 1968; Albrecht, 1989]. Many field experiments [Changnon, 1980; Ferek et al., 2000; Givati and Rosenfeld, 2004; Jirak and Cotton, 2006; Alpert et al., 2008], satellite remote sensing [e.g., Rosenfeld, 1999], and modeling studies [e.g., Khain et al., 2005] have attempted to quantify the magnitude of aerosol-induced reduction in surface precipitation but with limited success; there is still no statistically robust evidence of a decrease in surface precipitation in association with pollution [Levin and Cotton, 2008]. Cloud systems are characterized by a high degree of dynamical variability and the existence of multiple feedbacks makes it difficult to ascribe changes in precipitation to aerosol perturbations. [4] The goal of this work is to describe and evaluate a new construct via which the sensitivity of precipitation to changes in aerosol can be quantified. Precipitation susceptibility is defined as S o ¼ d ln R d ln N d where R is precipitation rate and N d is the cloud drop number concentration. The logarithmic form of equation (1) reduces the sensitivity of S o to the measurement accuracy of R and N d. Alternative formulations that replace N d with an aerosol measurement (cloud condensation nucleus, CCN, or proxy) can also be used. The minus sign is applied so that a positive value of S o reflects the conventional wisdom that aerosol abundance and R are negatively correlated for warmrain clouds. Precipitation susceptibility therefore attempts to identify which types of clouds are most susceptible to aerosol influences, as manifested in changes in N d [Feingold and Siebert, 2009]. Platnick and Twomey [1994] introduced cloud susceptibility, da/dn d, to relate a change in cloud albedo (A) for an incremental increase in drop concentration (N d ), holding other cloud properties (e.g., LWP, drop distribution breadth) constant. Precipitation susceptibility is an analogous construct. 2. Methods [5] We employ various independent methods of calculating S o. Numerical models of varying complexity, including a cloud parcel model and large eddy simulations, provide a ð1þ L of5

2 microphysical perspective on how aerosol perturbations influence precipitation rates. Collocated remote sensing measurements (i.e., MODIS, CloudSat, and AMSR-E) from NASA s A-Train constellation of satellites are also used [Stephens et al., 2002]. Perhaps the largest difficulty in studying aerosol-precipitation interactions is decoupling aerosol and dynamical effects. We attempt to do so by considering potential controlling factors such as LWP and lower tropospheric static stability [Klein and Hartmann, 1993] (LTSS = potential temperature difference between 700 hpa and 1000 hpa), which is an indicator of the thermodynamic state of the atmosphere. 3. Cloud Parcel Model [6] The cloud parcel model [Feingold et al., 1999] considers the growth of a population of particles by water-vapor uptake in a rising parcel of air. It accounts for drop activation, condensational growth, and collisioncoalescence. The model is applied to adiabatic conditions and also modified to include effects of continuous mixing and entrainment drying (entrainment rate = 1 km 1 ) to provide a subadiabatic vertical profile of liquid water content. Aerosol concentrations are varied between 50 and 500 cm 3 and S o is calculated as the slope between ln(n d ) and ln(r) for different values of LWP. LWP bins increase geometrically by 10%. 4. Large Eddy Simulation [7] The large eddy simulation (LES) [Jiang et al., 2008] is based on the Regional Atmospheric Modeling System (RAMS, version 6.0) coupled to a microphysical model [Feingold et al., 1999]. The LES is based on soundings obtained from the Rain In Cumulus over Ocean (RICO) field experiment. We take individual clouds in large cloud fields and follow them for the duration of their lifetime. For those clouds in the middle of their lifetime, we determine N d and R and calculate S o in various LWP bins, similar to the cloud parcel model simulations. We remove any instances of cloud merging and small non-precipitating clouds. The model grid-size is 100 m in the horizontal and 40 m in the vertical. Domain size is 6.4 km 6.4 km 4 km. Hundreds of modeled clouds are included in the analysis. 5. A-Train Measurements [8] The CloudSat cloud profiling radar (CPR) is used to select data representing warm-rain clouds over oceans and as one measure of R (2C-PRECIP-COLUMN product [Haynes et al., 2009]). This product is used to remove any instances of mixed-phase clouds and multiple cloud layers. The Advanced Microwave Scanning Radiometer (AMSR-E, Level 2B [Wentz, 1997; Kummerow et al., 2000]) data provide cloud LWP and a second measure of R. We remove R <1mmh 1 for both AMSR-E and CloudSat. Aerosol data (i.e., aerosol optical depth, AOD, and aerosol index, AI) are obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS, collection 5-level 3 [Platnick et al., 2003]). Aerosol index (AI = AOD Ångstrom exponent) has been shown to correlate better with cloud properties and columnar aerosol concentrations as compared to AOD [Nakajima et al., 2001]. We use one degree daily mean MODIS aerosol data in order to represent the large scale aerosol burden. [9] The screening methodology used to obtain the aerosol, cloud, and precipitation data is described extensively elsewhere [Lebsock et al., 2008]. It is important to note that MODIS cloud flags in the MOD35 product [Platnick et al., 2003] were used to filter out pixels that likely contained ice. Additionally, the 10.8 mm MODIS brightness temperature (T b ) was used to eliminate pixels with T b s less than 270 K. [10] Lower tropospheric static stability (LTSS) estimates are derived from the European Centre for Medium Range Weather Forecasts (ECMWF) analyses that have been matched to the CloudSat footprint (ECMWF-AUX product). [11] Caution is taken in our analysis to (i) improve the consistency of physical processes and their statistical significance by focusing on a particular meteorological regime, and (ii) strengthen the case for causality by eliminating instances of rain effects on aerosol (i.e., wet scavenging of aerosol prior to satellite overpasses). [12] To address the first issue, we focus on shallow cumulus clouds in unstable atmospheric conditions (LTSS < 15 C; R >1mmh 1 ), which allows an assessment of S o behavior over the same LWP range as the model results. We specifically focus on the tropics (15 N, 15 S; 180 W, 180 E) for June August (JJA) in In high static stability conditions (i.e. strong temperature inversion) aerosol effects on precipitation are harder to identify owing to dynamical suppression of vertical cloud development and droplet growth. [13] The second issue is addressed by employing the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) [Sorooshian et al., 2000] product (1 1 resolution) to remove instances of rain events prior to A-Train overpasses. This effectively removes biases in the results owing to wet scavenging of aerosol. PERSIANN estimates rainfall using geostationary infrared imagery of clouds (e.g., GOES-8, GOES-9/10, GMS-5, Metsat-6/7) and microwave instantaneous rainfall estimates (e.g., TRMM TMI). We use PERSIANN to remove A-Train observations where precipitation (>1 mm h 1 ) occurred in the same 1 1 grid within one day prior to a given A-Train measurement. To further strengthen our confidence that the data to be shown reveal aerosol effects on rain, as opposed to rain effects on aerosol, a separate analysis using similar A-Train sensors for the year 2007 [L Ecuyer et al., 2009] showed that the probability of precipitation was found to be similarly dependent on both MODIS AI (which includes precipitation) and model-derived aerosol fields (which do not). 6. Results and Discussion [14] The precipitation susceptibility results for the adiabatic parcel model [Feingold and Siebert, 2009] are repeated here along with sub-adiabatic calculations using the same model and results derived from the LES (Figure 1a). Dependence on LWP is expected based on empirical results in weakly precipitating clouds that show that R LWP x 1 N x 2 d with x and x [Pawlowska and Brenguier, 2003; vanzanten et al., 2005; Feingold and Siebert, 2009]; x 2 values are therefore equivalent to S o. To our knowledge, 2of5

3 x 1 and x 2 values have not been derived for strongly precipitating warm clouds. Results from the modeling approaches show that S o is a non-monotonic function of LWP and reveals the existence of three regimes. The first regime is at low LWP (]500 g m 2 ), where S o is small because clouds cannot generate much precipitation, regardless of aerosol amount. The second regime is at intermediate LWP ( g m 2 ), where S o increases steadily with increasing LWP. The increase in S o shows that the ability of clouds in this regime to generate precipitation is no longer limited by LWP, but rather that higher N d, and consequently less collision-coalescence amongst droplets, suppresses precipitation. Finally, in the third regime (LWP ^ 1000 g m 2 ) S o becomes progressively smaller as the increasing LWP dominates precipitation formation, regardless of N d. Note that these values of S o are smaller than the value of 2 often assumed for autoconversion of cloud to rain water in climate models [e.g., Khairoutdinov and Kogan, 2000]. [15] Satellite-obtained values of So 0 (= d ln R ) are calculated by replacing N d with a in equation (1), where a d ln a represents a proxy for CCN and N d a c with c < 1 [Twomey, 1974] (Figures 1b and 1c). Since the denominator in equation (1) is different for the analysis in Figure 1a (N d ) and Figures 1b and 1c (AOD and AI), our intent is to focus on the qualitative behavior of the curves, which is remarkably similar. So 0 is relatively low at smaller LWP (600 g m 2 ), but then increases to a maximum around LWP 1000 g m 2, before decreasing. Furthermore, this behavior is observed when using R from either CloudSat or AMSR-E and for AOD or AI as the CCN proxy (a). The use of AOD or AI as the CCN proxy adds complexity when trying to compare to N d since they are not linearly related. However N d a c indicates that So 0 cs o so that the lower values of So 0 (ordinate of Figures 1b and 1c compared to Figure 1a) are expected. [16] To provide further evidence for a causal chain of events, we account for the perturbation in cloud drop effective radius (r e ) as a function of aerosol concentration in the form of the following parameter representing aerosol cloud interactions: ACI r ln r ln a LWP ð2þ 3of5 Figure 1. (a) The sensitivity of precipitation to changes in drop concentration (N d ), or precipitation susceptibility, S o (equation (1)), as a function of LWP as derived from a cloud parcel model and large eddy simulations of warm trade cumulus clouds. Three distinct LWP regimes exist: (i) low LWP, where clouds do not precipitate because they have limited water content, regardless of N d ; (ii) intermediate LWP, where precipitation is progressively more effectively suppressed by increasing N d ; and (iii) high LWP, where susceptibility begins to decrease because there is sufficient cloud water to sustain precipitation, regardless of N d. So 0 as a function of LWP using A-Train satellite measurements, and replacing N d by a CCN proxy in equation (1). The CCN proxy (a) is either (b) aerosol index (AI) or (c) aerosol optical depth (AOD). Note the qualitative similarity to Figure 1a. Marker sizes are proportional to ACI r (the response of drop size to an increase in aerosol; equation (2)), with the ranges being (a = AI, Figure 1b) and (a = AOD, Figure 1c) Reported values of So 0 are statistically significant with the one-tailed t-distribution critical level set to 0.05 (i.e., the null hypothesis that results are not statistically significant at the 5% level can be rejected), with the exception of three CloudSat markers (colored black) based on the student s t-test (df = degrees of freedom, r crit = correlation required to pass student s t-test at a critical level of 0.05): (b, 800 g m 2, df = 10, r =0.37,r crit = 0.497), (c, 800 g m 2, df =16,r =0.31, r crit = 0.40), (c, 900 g m 2, df =18,r =0.28,r crit =0.378).

4 Figure 2. Global distribution of the 2007 annual averages of (a) AMSR-E LWP, (b) MODIS aerosol index (AI), and (c) % change in precipitation rate (DR) for the AI variability within a given 4 4 pixel (see text for calculation details). Measurements pertain only to precipitating cloud cases. The strongest DR are in boxes 2, 3, and 4. Note that the color schemes are chosen for ease of visualizing spatial variation; maximum values are considerably larger than indicated by the color bars (see Table 1 for maxima). where ACI r = c/3 [Twomey, 1974; Feingold et al., 2001]. This quantification of aerosol-induced reduction in droplet size is bounded by the range , with higher values indicating that pollution more effectively reduces r e. Figures 1b and 1c show that ACI r ranges between (a =AI) and (a = AOD) over the LWP range studied, with the maximum values coinciding with the greatest So 0 values. This strengthens the evidence that increasing pollution reduces r e and suppresses droplet collision-coalescence, thereby inhibiting droplets from reaching sufficiently large sizes to precipitate. [17] It is of interest to examine the potential implications for these results on reduction in warm tropical/subtropical rainfall. Figure 2 presents the annually-averaged (2007) distribution of AI, LWP, and an estimate of % changes in precipitation rate (DR), to highlight which regions might be most affected by aerosol-precipitation interactions. The calculations are performed over the oceans between 30 S and 30 N. DR is calculated as the product of two values: (i) annual average of daily S o values, as determined by AMSR-E LWP and the S o LWP relationship from the LES curve in Figure 1a; and (ii) dln(ai), as calculated from the dispersion (standard deviation/annual mean) in AI for The calculations assume that the variability in AI is similar to the variability in N d. Examples of 4 distinct regions (boxes in Figure 2c) experiencing varying degrees of DR are highlighted. Calculations of the various relevant parameters that enter into the DR calculations are included in Table 1 for the 4 regions, and for the entire domain. The largest (negative) DR appear off the coasts of west Africa ( 28.3%), Asia ( 18.5%), and southern India ( 8.9%), primarily driven by the high variability in AI. Note that in some cases the aerosol may reside above cloud, and therefore may not impact the cloud and precipitation processes to the degree shown here. The weakest reduction in R appears in the tropical Pacific ( 2.6%) where aerosol perturbations are small. Two final points are noted: (i) although we present annual means, episodic events with high LWP reach much higher values of DR (and S o ) (Table 1, last column); (ii) regions most susceptible to aerosol in a relative sense may not always coincide with those most susceptible in an absolute sense. 7. Conclusions [18] We have demonstrated with models of varying complexity and first-of-a-kind remote sensing capabilities that precipitation is most susceptible to aerosol perturbations over a finite range of cloud water conditions. Shallow clouds with low LWP are least susceptible to aerosol because they precipitate very little, whereas deep clouds become progressively less susceptible because they have ample liquid water and therefore tend to precipitate regardless. The exact bounds of the range of maximum susceptibility are still very uncertain and further analyses are required. The modeled and measured susceptibility is significantly smaller than that commonly prescribed in climate models. Preliminary results suggest annual reductions in precipitation rate on the order of 20 30% in regions most influenced by biomass burning or pollution. Field studies that target aerosol-precipitation interactions in geographical locations that produce clouds with LWPs in the range of greatest S o, accompanied by high sub-cloud aerosol variability, would help reduce uncertainties associated with the magnitude of aerosol effects on precipitation. [19] This work needs to be extended to determine whether factors other than LWP and LTSS are of importance (e.g., shear). Aerosol properties such as size and composition also need to be considered. Special cases include absorbing aerosol, which can generate local heating and lead to cloud burning [Ackerman et al., 2000; Koren et al., 2004], and giant CCN (e.g., dust and sea salt), which can enhance and 4of5

5 Table 1. Summary of DR (%) for Regions Exhibiting Various Levels of Aerosol and LWP Variability a, as Well as Domain-Wide Averages and Maxima Box 1 (Clean Pacific Outflow) Box 2 (West Africa Outflow) Box 3 (Indian Outflow) Box 4 (Asian Outflow) Entire Domain (±30 latitude) Domain Maximum Cloud Sat R (mm h 1 ) DR (%) S o S o : s/mean LWP (g m 2 ) LWP: s/mean AI AI: s/mean a See Figure 2c. s/mean represents the relative dispersion of the parameter under consideration. These values represent annual 2007 data between 30 S and 30 N for precipitating cloud scenes. CloudSat R calculations are for cases exceeding 0.1 mm h 1. expedite precipitation development [Johnson, 1982]. The behavior of S o in mixed-phase clouds is likely much more complex than that presented here for warm clouds, but the framework presented here may serve as a starting point for analysis. The climatic and societal issues associated with pressure on water resources warrant vigorous pursuit of this line of research. 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Sorooshian, Department of Chemical and Environmental Engineering, University of Arizona, JW Harshbarger Building, P.O. Box , Tucson, AZ 85721, USA. (armin@ .arizona.edu) G. L. Stephens, Cooperative Institute for Research in the Atmosphere, Colorado State University, 1371 Campus Delivery, Fort Collins, CO 80523, USA. 5of5