USA NOAA. BOX , Tucson, AZ 85721) 1 Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ,

Transcription

1 On the Relationship Between Environmental Parameters and Cloud Properties in Stratocumulus Clouds Using Four Summers of Airborne Data Off the California Coast 1 Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ, USA 2 Department of Atmospheric Sciences, University of Arizona, Tucson, AZ, USA NOAA 3 Naval Postgraduate School, Monterey, CA, USA 4 Department of Chemical Engineering, California Institute of Technology, Pasadena, CA, USA * Corresponding author (phone: , armin@ .arizona.edu, address: PO BOX , Tucson, AZ 85721) 1

2 12 13 Abstract. 2

3 Introduction Stratocumulus clouds have been studied extensively over numerous decades in part due to both the challenge of explaining their nature and their importance to the planet s energy balance and other environmental issues (e.g., Wood et al., 2012). These clouds have a significant impact on radiative forcing, physicochemical properties of aerosol particles, and biogeochemical cycling of nutrients. Factors governing the microphysical and macrophysical properties of warm clouds are a major source of uncertainty in climate models (e.g. Levin and Cotton, 2008; Stevens and Feingold, 2009; Tao et al., 2012; Wood 2012; Boucher et al., 2013; Wang et al., 2013) owing to a high degree of dynamical variability and the existence of multiple feedbacks. The largest source of uncertainty in climate change prediction is rooted in the effective radiative forcing linked to aerosol-cloud interactions (IPCC, 2013). As a result, aggressive research has focused on examining relationships between aerosol concentrations and cloud properties such as the incidence and magnitude of precipitation (L Ecuyer et al., 2009; Wang et al., 2012), cloud microphysical parameters such as droplet effective radius, cloud drop number concentration, and cloud optical depth (e.g., Feingold et al., 2003; McComiskey et al., 2009), cloud fraction and macrophysical properties such as liquid water path (Albrecht et al., 1989; Nakajima et al., 2001), albedo (Twomey et al., 1977a; Platnick and Twomey, 1994), and process timescales such as converting cloud water to rain water (e.g., Sorooshian et al., 2013). Widely accepted principles include that, at fixed LWP, cloud albedo increases with more aerosol due to smaller but more numerous drops (Twomey et al., 1977a), and that collision-coalescence between smaller drops is suppressed for more polluted clouds leading to suppressed precipitation and changes in LWP (Albrecht et al., 1989). A strategy employed to investigate the influence of aerosol on the aforementioned cloud characteristics has been to examine correlations, quantification of physically-relevant metrics (e.g., susceptibilities), and regression techniques to test and improve parameterizations employed in models. A critical issue in these investigations has been the aerosol proxy used as there are limitations associated with each type of measurement platform; for example, remote sensing retrievals struggle to provide information strictly near cloud base while airborne measurements struggle to provide broad spatiotemporal coverage and statistics. Furthermore, past studies have mostly been focused on using only a proxy for aerosol concentration and did not probe deeper to examine characteristics of the aerosol beyond just number concentration using in-situ measurements. Characteristics that may have an impact, depending on environmental conditions, include aerosol size distribution and chemical composition. This study aims to use a multi-campaign dataset to examine interrelationships between numerous cloud properties and aearosol physicochemical properties. A unique aspect of this work is the extensive statistics collected over the course of four field campaigns in the exact same region (coastal California coast) with nearly the same payload in the same months of the year (July-August) when stratocumulus cloud cover is at a maximum. The structure of this paper is as follows: (i) overview of campaigns and instrument datasets; examination of factors governing (ii) cloud drop concentration, (iii) drop effective radiust, (iv) drizzle rate, and (v) relationship between cloud thickness and LWP; and (vi) summary of results. 2. Experimental Methods Airborne data are used from four field experiments based out of Marina, California using the Center for Interdisciplinary Remotely-Piloted Aircraft Studies (CIRPAS) Twin Otter. The first Marine Stratus/Stratocumulus Experiment (MASE-I; Lu et al., 2007) included 13 flights in 3

4 July 2005, the second Marine Stratus/Stratocumulus Experiment (MASE-II; Lu et al., 2009) included 16 research flights in July 2007, the Eastern Pacific Emitted Aerosol Cloud Experiment (E-PEACE; Russell et al., 2013) included 30 flights between July and August in 2011, and the Nucleation in California Experiment (NiCE) included 23 flights between July and August in 2013 (Coggon et al., 2014). During these experiments, the Twin Otter conducted ~4-4.5 h flights at an airspeed of ~50 m s -1 in the area encompassed by 34º N 40º N and 121.5º W 125º W with nearly the same payload including the following: (i) particle number concentrations in the different size ranges were observed with a condensation particle counter (CPC 3010; TSI Inc.; D p > 10 nm), a passive cavity aerosol spectrometer probe (PCASP; D p ~ μm), and a scanning mobility particle sizer (SMPS; D p ~ 15 nm 800 nm) comprising a differential mobility analyzer (DMA Model 3081, TSI Inc.) coupled to a CPC (Model 3010, TSI Inc.); (ii) droplet size distributions between 0.4 μm and 1.6 mm were obtained with a cloud aerosol spectrometer (CAS), forward scattering spectrometer probe (FSSP), and cloud imaging probe (CIP); (iii) sub-micrometer mass concentrations of non-refractory aerosol constituents were obtained with an Aerodyne Aerosol Mass Spectrometer (AMS; Drewnick et al., 2005); (iv) meteorological data (e.g., temperature, humidity, winds, Gerber probe liquid water content (LWC; Gerber et al., 1994)). As presented in numerous past studies (e.g., Prabhakar et al., 2014; Crosbie et al., 2016), the general flight pattern used in the marine atmosphere is as follows: level legs below cloud base, immediate above cloud base, at mid-cloud altitude, immediately below cloud top, immediately above cloud top (called wheels-in leg), and a few hundred feet higher as part of a free troposphere leg. In addition to the leg profiling, vertical soundings, either as a slant or spiral, were conducted before and after each of these sets of leg patterns, from which column-integrated could be calculated. Details of calculations of cloud parameters such as LWP, drop effective radius (r e ), cloud optical depth (τ) and rain rate are summarized in past studies (e.g., Chen et al., 2012). Table 1 reports a summary of nomenclature used to represent the aforementioned (and other) measurement parameters. Numerous calculations were made that required choices. Clear air is identified as having liquid water content (LWC) values below 0.01 g m -3, and thus values exceeding this value were used to identify cloud base and top heights. Inversion strength is calculated here as the difference of potential temperature between the inversion-top and -base heights; inversion-base height is defined as the altitude where temperature first reaches a minimum above the surface and the top is defined as the height that the 5-s running mean of dθ/dz reaches a maximum. 3. Results and Discussion In the subsequent discussion we examine interrelationships between microphysical and macrophysical parameters relevant to aerosol particles and clouds, includng the following: (i) cloud drop number concentration (N d ), (ii) cloud drop effective radius (r e ), (iii) drizzle rate (R), and (iv) LWP. The number of data points used in the following analyses ranges between , depending on the variables being used as some parameters may have been unavailable or below detection limits. 3.1 Cloud Drop Number Concentration (N d ) Two common ways field data are often used to examine the relationship between subcloud aerosol concentraiton (N a ) and cloud drop number concentration is with the ACI metric,, and by power law regression fits,. ACI N is related to, with values 4

5 ranging from 0 to 1. A characteristic value of ~0.7 has been proposed based on heuristics (Twomey, 1977b). Reported values from MASE I and MASE II were and 0.594, respectively (Lu et al., 2009). Factors that have been documented that increase the value of include increases in updraft velocity and adiabaticity, while reduced values are observed when there is more active collision-coalescence and high Angstrom exponents (i.e., smaller aerosol) (e.g., McComiskey et al., 2009). Leaitch et al. (1996) showed that N d variance could be explained better by N a and cloud water sulfate concentrations when data were separated into categories of turbulence as quantified by the standard deviation of updraft velocity w (σ w ). Lu et al. (2007) also confirmed that inclusion of updraft velocity variabiity to N a values improved predictions of N d. Here we examine the ranking of the most important parameters in our dataset to predict values of N d. Regression analysis was conducted to identify (i) the best single parameter to predict N d, (ii) the best two-parameter models, and (iii) the highest level of improvement in a two-parameter model as compared to the individual single-parameter models (Table 2). Model rank is based on values of the coefficient of determination (r 2 ), which quantifies how much of the variabile in a response variable is explained by the predictive variable(s). A P value is reported for each regression to quantify the significance level of an individual model, with values < 0.05 considered here to be significant; values above 0.05 mean that the null hypothesis cannot be rejected that the model predicts the response variable better than the intercept-only model Single Parameter Models Within the three strongest single parameter models were the predictive variables drop effective radius (r e ) and rain rate (R), but since these parameters are considered to be a consequence of N d values, more focus is placed on models with parameters that are thought to be precursor factors in altering N d. Among the other single parameter models, the strongest ones included an aerosol number concentration proxy, followed by above-cloud thermodynamic variables, and sub-cloud σ w. More specifically, the second best model was for sub-cloud N a, as measured by the PCASP (D p > 100 nm), which is a better proxy measurement for CCN concentration as compared to CPC (D p > 10 nm; rank = 5) measurements since the activation diameter of particles in the region is much closer to 100 nm than 10 nm (e.g., Wonaschuetz et al., 2013). The power law exponent, when using N a from PCASP and CPC, is 0.54 and 0.25, respectively, with the lower value from the CPC being due to the considerably large amount of number concentration between nm that may not be at CCN-relevant sizes as compared to particles above 100 nm. Values of r 2 dropped significantly after the N a model (r 2 = 0.47), with other notable models being for σ w (r 2 = 0.12) and thermodynamic variables above cloud top (inversion strength and temperature: r 2 = 0.17). In terms of aerosol mass and composition, the r 2 of models considering mass concentration of total non-refractory sub-micrometer constituents, inorganics, and sulfate was similar (0.11), whereas that for organics was lower (0.06). Models containing size distribution parameters (D 0.5, GSD) were even weaker (r 2 = ), followed by surface-level GCCN and mass fractions of sulfate and either organics or inorganics (r ); only D 0.5 exhibited a P value less than 0.05 of these last six models Double Parameter Models The 55 highest ranked double parameter models included re as one of the two predictive variables along with a suite of other parameters such as LWP and sub-cloud N a which exhibited 5

6 the two highest r 2 values (0.88 and 0.84, respectively). The first two models without re include N a and either R or R cb as predictors (r 2 = ), with the latter two exhibiting a negative exponent due to the coincidence of more rain and with enhanced collision-coalescence to decrease N d in cloud. The next notable model includes N a and σ w (r 2 = 0.54), with the latter having a positive exponent due to higher supersaturations achieved and thus greater ease of particle activation into drops. In terms of models with other aerosol parameters in addition to Na were three with mass fractions for sulfate (r 2 = 0.52), organics (r 2 = 0.49), and inorganics (r 2 = 0.49). Models exhibiting the greatest level of r 2 improvement due to a second variable added included those with either N a or CN and either R or R cb. Therefore, although mass fractions ranked lower than mass concentrations, GSD, and D 0.5 in the single parameter models, they strengthened two parameter models the most in terms of an aerosol parameter to add to N a or CN; however, the level of r 2 improvement is weak when adding any information about composition to aerosol number concentration ( 0.02) with the exception of adding MF SO4 to N a (improvement of 0.09) Binning Analysis It is of interest to look more closely at how ACI depends on potentially influential factors. Figure 2 shows how ACI is relatively stable as a function of D 0.5 until reaching the very largest bin, suggestive of how the largest aerosol probed exhibited increased likelihood to activate into drops. The reduction of ACI as a function of R is further supportive of previous reports of how collision-coalescence in precipitating clouds can obfuscate the desired relationship that ACI serves to probe. ACI increases as a function of σ w due to higher supersaturations expected under such conditions. Previous modeling work has shown that in regimes with high ratios of updraft velocity (w) to aerosol concentration, that N d is proportional to aerosol concentration and practically independent of w (Reutter et al., 2009); conversely, they showed that low ratios are characterized by N d being directly proportional to w and independent of aerosol concentration. Figure 3 attempts to validate such results with observations by examining the relationship between Nd and CN in bins of σ w :CN. In agreement with Reutter et al. (2009), our data shows that low ratios coincide with Nd being positively related to σ w and with a lack of relationship with CN. At high ratios, N d is more strongly related to CN, and, while still positively related, it is more weakly related now to σ w. 3.2 Cloud Drop Effective Radius Analagous to N d, relationships between aerosol and r e have been studied extensively using the ACI metric, (at fixed cloud macrophysical conditions) and power law regression fits such as. Since LWP is often the macrophysical cloud property held fixed when quantifying ACI, the value of ACIre is directly related to β 2 in the power law. With basic assumptions of a homogeneous cloud with constamt N d and LWC, β 1 = 0.33 and β 2 = /3 (Feingold et al., 2003). A wide range of ACI values has been reported in the literature (e.g., Kim et al., 2003; Garrett et al., 2004; Tang et al., 2011; Zhao et al., 2012), with higher values usually associated with in-situ and ground-based measurements as compared to satellite remote sensing observations (McComiskey and Feingold, 2008); that study showed that a wide range of radiative forcing estimates (from -3 to -10 W m -2 ) can result simply from a difference in ACI of

7 Single Parameter Models When excluding models with R (r 2 = 0.63) and R cb (r 2 = 0.41), which are due to them being a consequence of high r e values, regressions relying on either drop or aerosol number concentration were the highest ranked (Table 3). N d was the best predictive variable (r 2 = 0.82), followed by, in decreasing r 2 value, sub-cloud N a, surface level N a, above-cloud N a, and abovecloud CN from the CPC (r 2 = ). Of note is that inverstion strength is negatively related to re in the 10 th ranked model (r 2 = -0.12), consistent with its positive associated with N d in Table 2. The 13 th ranked model included σ w, with a negative dependence owing likely to lower supersaturations at lower values of σ w that limit drop activation to larger particles, thereby suppressing N d. While a number of mass concentration variables are negatively related to r e, owing to their positive association with aerosol concentration, GCCN concentration is instead positively associated with re in the 15 th ranked model; this could be owing to enhanced collision coalescence leading to larger r e values Double Parameter Models The top ranked models include N d and either R, R cb, LWP, depth, or adiabaticity (r 2 = ); N d always exhibits a negative exponent while the second variable is positive. Models exhibiting the strongest improvement in r 2 with the inclusion of a second variable included those with N a and either depth, R, R cb, and q. Detailed aerosol physicochemical properties (D0.5, GSD, composition) only appeared in poorly ranked models as their effects were contained within N d and also could have been much weaker than the impact of just using aerosol concentration Binning Analysis 3.3 Drizze Rate Of relevance to warm clouds is the power law representing autoconversion, R LWP N (e.g., Khairoutdinov and Kogan, 2000), where LWP is liquid water path and N d is cloud drop number concentration. Some studies have used multivariate linear regression fits to the logarithms of values for R, LWP, and N d to test and suggest methods of improving this parameterization for specific cloud types. Jiang et al. (2013) did such an exercise and concluded that if another term is added for cloud lifetime that R could be more reliably predicted for warm trade cumulus clouds. Other studies have focused exclusively on specific exponents in the power law for R. For example, to study the nature of the β parameter, the precipitation susceptibility metric (S o = ) was introduced, which relates a change in drop number concentration N d (or related proxies) to a change in precipitation rate (R) at fixed cloud macrophysical conditions (e.g., LWP or cloud thickness) (Feingold and Siebert, 2009). Numerous studies have examined how S o depends on a macrophysical property assumed to include meteorological influences on the cloud, including LWP (e.g., Lu et al., 2009; Wood et al., 2009; Sorooshian et al., 2009, 2010; Jiang et al., 2010; Bangert et al., 2011; Duong et al., 2011; Gettelman et al., 2013; Mann et al., 2014) and cloud thickness (Terai et al., 2012). Studies differ in reported absolute values of S o and their behavior as a function of the chosen 7

8 macrophysical proxy. For example, some studies report that S o typically increases up to a specific LWP at which point it drops in value reflecting a switch from a dominant autoconversion process to an accretion process. The other subset of studies, focused mainly on stratocumulus clouds, report a general reduction in S o as a function of LWP or cloud thickness. Differences in these studies include (but are not limited to) differences in the following: (i) cloud type (Lebo and Feingold, 2014), (ii) choices of how to calculate parameters included in quantifying S o (Duong et al., 2011), (iii) minimum threshold value for rain rate (Duong et al., 2011), (iv) lower tropospheric static stability (Sorooshian et al., 2009), (v) and cloud contact time, defined as the time an air parcel spends in cloud (Feingold et al., 2013). The choice of whether to include non-precipitating clouds in the analysis also can lead to conflicting results between studies, and thus, the use of S I (henceforth referrred to as S o ) introduced by Terai et al. (2012) most closely matches S o examined in this study for precipitating clouds. Terai et al. (2015) concluded in their recent investigation of S o across different regions that N d and LWP are insufficient in terms of governing factors and that other controls exist that need to be identified to connect regional and global estimates. Interestingly, they observed negative values of S o for thin and polluted clouds, and speculated that other factors can help explain such unintuitive results including turbulence, giant cloud condensation nuclei (GCCN), and satellite retrieval artifacts. These other factors and others require warrant attention in a focused study examining one cloud regime with a decent amount of statistics Single Parameter Models The highest ranked models to predict R, aside from the related R cb parameter, include r e (r 2 = 0.63), Nd (r 2 = 0.34), depth (r 2 = 0.27), and LWP(r 2 = 0.16) (Table 4). In terms of aerosol parameters, interestingly the best model was for GCCN (r2 = 0.13) with a positive exponent indicative of enhancement of collision-coalesence, which is consistent with its positive relationship with r e in Table 4. The next strongest models included various mass concentration variables (total, sulfate, organic, inorganic: r 2 = ) that were negatively related owing to their covariance with aerosol number concentration Double Parameter Models The highest ranked model that excludes the obvious variable R cb, includes re and either LWP (r 2 = 0.77), N a (r 2 = 0.75), depth (r 2 = 0.74), and N d (r 2 = 0.73). The commonly employed autoconversion parameterization relying on N d and LWP registered as the 82 nd best model but with a similarly high r 2 value of 0.63 and the highest improvement of any model when adding a second variable. 3.4 LWP Liquid water path is signifcant as not only a key macrophysical cloud property driving reflectivity and precipitation, but also as a key variable held fixed in studies of how aerosol perturbations impact cloud microphysical properties. Owing to the importance of holding macrophysical cloud properties constant when examining relationships between aerosol properties and cloud properties (i.e., to hold meteorological factors constant), we examine relationships between cloud thickness and LWP as these two are the most commonly used macrophysical property that is held constant. An 8

9 adiabatic assumption is often employed to relate these two parameters (e.g., Albrecht et al., 1990; Austin et al., 1995; Wood and Taylor, 2001; Zhou et al., 2006) (1) where is precentage of adiabaticity and A is the adiabatic rate of change of height with LWC. While adiabatic (or near-adiabatic) liquid water content profiles are well documented (e.g., Slingo et al., 1982; Gerber et al., 1994), (i) sub-adiabatic and (ii) super-adiabatic profiles have also been reported due to (i) entrainment or drizzle (Wood and Taylor, 2001) and (ii) decoupled boundary layers where detraining cumulus plumes enhance stratocumulus layers (Miller et al., 1998), respectively. Others have noted different relationships between LWP and H such as based on CloudSat LWP and CALIPSO thickness data (Brunke et al., 2010). Better understanding the nature of this relationship will benefit model parameterizations that are used for radiation calculations and for the purposes of intercomparisons of aerosol-cloud interaction studies that use both LWP and H as the macrophysical cloud property held fixed to account for meteorological influences on clouds Single Parameter Models As expected, depth is the strongest predictive variable for LWP (r 2 = 0.69). The next best models exhibited a steep drop off in r 2 values: cloud top height (0.19), R (0.16) Double Parameter Models The strongest two models with two parameters contained depth and eiehter N d or GCCN (r 2 = 0.78). The model including depth and adiabaticity was the 35 th best model (r 2 = 0.71). Aside from the obvious improvement in r2 expected when coupling cloud base and top height (0.42), the next few models exhibiting the highest r2 improvement included r e -N d, R-N d, R-r e, and R- H top ( ). 4. Conclusions This study Acknowledgements All data and results are available from the corresponding author (armin@ .arizona.edu). This work was funded by NASA grant NNX14AM02G and Office of Naval Research grants N , N , N , and N References Albrecht, B. (1989), Aerosols, cloud microphysics, and fractional cloudiness, Science, 245, , doi: /science Albrecht, B. A., C. W. Fairall, D. W. Thomson, A. B. White, J. B. Snider, and W. H. Schubert (1990), Surface-Based Remote-Sensing of the Observed and the Adiabatic Liquid Water-Content of Stratocumulus Clouds, Geophys Res Lett, 17(1),

10 Austin, P., Y. N. Wang, R. Pincus, and V. Kujala (1995), Precipitation in Stratocumulus Clouds - Observational and Modeling Results, J Atmos Sci, 52(13), Bangert, M., C. Kottmeier, B. Vogel, and H. Vogel (2011), Regional scale effects of the aerosol cloud interaction simulated with an online coupled comprehensive chemistry model, Atmos. Chem. Phys., 11(9), Boucher, O., D. Randall, P. Artaxo, C. Bretherton, G. Feingold, P. Forster, V.-M. Kerminen, Y. Kondo, H. Liao, U. Lohmann, P. Rasch, S.K. Satheesh, S. Sherwood, B. Stevens and X.Y. Zhang, 2013: Clouds and Aerosols. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Brunke, M.A., de Szoeke, S.P., Zuidema, P., Zeng, X., A comparison of ship and satellite measurements of cloud properties with global climate model simulations in the southeast Pacific stratus deck. Atmos Chem Phys 10, Chen, Y. C., M. W. Christensen, L. Xue, A. Sorooshian, G. L. Stephens, R. M. Rasmussen, and J. H. Seinfeld (2012), Occurrence of lower cloud albedo in ship tracks, Atmos Chem Phys, 12(17), Crosbie, E., Z. Wang, A. Sorooshian, P. Y. Chuang, J. S. Craven, M. M. Coggon, M. Brunke, X. Zeng, H. Jonsson, R. K. Woods, R. C. Flagan, and J. H. Seinfeld (2016). Stratocumulus cloud clearings and notable thermodynamic and aerosol contrasts across the clear-cloudy interface, J. Atmos. Sci., 73, Duong, H. T., A. Sorooshian, and G. Feingold (2011), Investigating potential biases in observed and modeled metrics of aerosol-cloud-precipitation interactions, Atmos. Chem. Phys., 11(9), Feingold, G., W. L. Eberhard, D. E. Veron, and M. Previdi (2003), First measurements of the Twomey indirect effect using ground-based remote sensors, Geophys. Res. Lett., 30(6), 1287, doi: /2002gl Feingold, G., and H. Siebert (2009), In Clouds in the Perturbed Climate System: Their Relationship to Energy Balance, Atmospheric Dynamics, and Precipitation, Strüngmann Forum Reports, vol. 2, edited by J. Heintzenberg and R. J. Charlson, pp. 597, The MIT Press, Cambridge, Mass. Feingold, G., A. McComiskey, D. Rosenfeld, and A. Sorooshian (2013), On the relationship between cloud contact time and precipitation susceptibility to aerosol, J. Geophys. Res., 118(18),

11 Garrett, T. J., Zhao, C., Dong, X., Mace, G. G., and Hobbs, P. V. (2004). Effects of varying aerosol regimes on low-level Arctic stratus, Geophys. Res. Lett., 31, L17105, doi: /2004gl Gerber, H., B. G. Arends, and A. S. Ackerman (1994), New microphysics sensor for aircraft use, Atmos. Res., 31(4), Gettelman, A., H. Morrison, C. R. Terai, and R. Wood (2013), Microphysical process rates and global aerosol-cloud interactions, Atmos. Chem. Phys., 13(19), Jiang, H. L., G. Feingold, and A. Sorooshian (2010), Effect of aerosol on the susceptibility and efficiency of precipitation in warm trade cumulus clouds, J. Atmos. Sci., 67(11), Kim, B.-G., Schwartz, S. E., Miller, M. A., and Min, Q. (2003). Effective radius of cloud droplets by ground-based remote sensing: Relationship to aerosol, J. Geophys. Res., 108(D23), 4740, doi: /2003jd Leaitch, W. R., C. M. Banic, G. A. Isaac, M. D. Couture, P. S. K. Liu, I. Gultepe, S. M. Li, L. Kleinman, P. H. Daum, and J. I. MacPherson (1996), Physical and chemical observations in marine stratus during the 1993 North Atlantic Regional Experiment: Factors controlling cloud droplet number concentrations, J Geophys Res-Atmos, 101(D22), Lebo, Z. J., and G. Feingold (2014), On the relationship between responses in cloud water and precipitation to changes in aerosol, Atmos. Chem. Phys., 14(21), L Ecuyer, T. S., W. Berg, J. Haynes, M. Lebsock, and T. Takemura (2009), Global observations of aerosol impacts on precipitation occurrence in warm maritime clouds, J. Geophys. Res., 114, D09211, doi: /2008jd Lu, M. L., W. C. Conant, H. H. Jonsson, V. Varutbangkul, R. C. Flagan, and J. H. Seinfeld (2007), The Marine Stratus/Stratocumulus Experiment (MASE): Aerosol-cloud relationships in marine stratocumulus, J Geophys Res-Atmos, 112(D10). Lu, M. L., A. Sorooshian, H. H. Jonsson, G. Feingold, R. C. Flagan, and J. H. Seinfeld (2009), Marine stratocumulus aerosol-cloud relationships in the MASE-II experiment: Precipitation susceptibility in eastern Pacific marine stratocumulus, J. Geophys. Res., 114. Mann, J. A. L., J. C. Chiu, R. J. Hogan, E. J. O'Connor, T. S. L'Ecuyer, T. H. M. Stein, and A. Jefferson (2014), Aerosol impacts on drizzle properties in warm clouds from ARM Mobile Facility maritime and continental deployments, J. Geophys. Res., 119(7), McComiskey, A., and G. Feingold (2008), Quantifying error in the radiative forcing of the first aerosol indirect effect, Geophys Res Lett, 35(2). 11

12 McComiskey, A., G. Feingold, A. S. Frisch, D. D. Turner, M. A. Miller, J. C. Chiu, Q. L. Min, and J. A. Ogren (2009), An assessment of aerosol-cloud interactions in marine stratus clouds based on surface remote sensing, J Geophys Res-Atmos, 114. Miller, M. A., M. P. Jensen, and E. E. Clothiaux (1998), Diurnal cloud and thermodynamic variations in the stratocumulus transition regime: A case study using in situ and remote sensors, J Atmos Sci, 55(13), Nakajima, T., A. Higurashi, K. Kawamoto, and J. E. Penner (2001), A possible correlation between satellite-derived cloud and aerosol microphysical parameters, Geophys. Res. Lett., 28, , doi: /2000gl Platnick, S., and S. Twomey (1994), Determining the susceptibility of cloud albedo to changes in droplet concentration with the Advanced Very High-Resolution Radiometer, J. Appl. Met., 33, , doi: / (1994)033. Prabhakar, G., B. Ervens, Z. Wang, L. C. Maudlin, M. M. Coggon, H. H. Jonsson, J. H. Seinfeld, and A. Sorooshian (2014), Sources of nitrate in stratocumulus cloud water: Airborne measurements during the 2011 E-PEACE and 2013 NiCE studies, Atmos Environ, 97, Reutter, P., Su, H., Trentmann, J., Simmel, M., Rose, D., Gunthe, S.S., Wernli, H., Andreae, M.O., Poschl, U., Aerosol- and updraft-limited regimes of cloud droplet formation: influence of particle number, size and hygroscopicity on the activation of cloud condensation nuclei (CCN). Atmos Chem Phys 9, Slingo, A., S. Nicholls, and J. Schmetz (1982), Aircraft Observations of Marine Stratocumulus during Jasin, Q J Roy Meteor Soc, 108(458), Sorooshian, A., G. Feingold, M. D. Lebsock, H. L. Jiang, and G. L. Stephens (2009), On the precipitation susceptibility of clouds to aerosol perturbations, Geophys. Res. Lett., 36. Sorooshian, A., G. Feingold, M. D. Lebsock, H. L. Jiang, and G. L. Stephens (2010), Deconstructing the precipitation susceptibility construct: Improving methodology for aerosolcloud precipitation studies, J. Geophys. Res., 115. Sorooshian, A., Z. Wang, G. Feingold, and T. S. L'Ecuyer (2013), A satellite perspective on cloud water to rain water conversion rates and relationships with environmental conditions, J Geophys Res-Atmos, 118(12), Tang, J. P., Wang, P. C., Duan, M. Z., Chen, H. B., Xia, X. A., and Liao, H. (2011). An evidence of aerosol indirect effect on stratus clouds from the integrated ground-based measurements at the ARM Shouxian site, Atmos. Oceanic Sci. Lett., 4(2),

13 Terai, C. R., R. Wood, D. C. Leon, and P. Zuidema (2012), Does precipitation susceptibility vary with increasing cloud thickness in marine stratocumulus?, Atmos. Chem. Phys., 12(10), Terai, C. R., R. Wood, and T. L. Kubar (2015), Satellite estimates of precipitation susceptibility in low-level, marine stratocumulus, J. Geophys. Res. Twomey, S. (1977a), Influence of Pollution on Shortwave Albedo of Clouds, J Atmos Sci, 34(7), Twomey, S., Atmospheric Aerosols, Elsevier Sci, New York, 1977b. van de Hulst, H. C., Light Scattering by Small Particles, Dover, Mineola, New York, Wang, M. H., et al. (2012), Constraining cloud lifetime effects of aerosols using A-Train satellite observations, Geophys Res Lett, 39. Wonaschutz, A., et al. (2013), Hygroscopic properties of smoke-generated organic aerosol particles emitted in the marine atmosphere, Atmos. Chem. Phys., 13(19), Wood, R. (2012), Stratocumulus Clouds, Mon. Weather Rev., 140(8), Wood, R., and J. P. Taylor (2001), Liquid water path variability in unbroken marine stratocumulus cloud, Q J Roy Meteor Soc, 127(578), Wood, R., T. L. Kubar, and D. L. Hartmann (2009), Understanding the importance of microphysics and macrophysics for warm rain in marine low clouds. Part II: Heuristic models of rain formation, J. Atmos. Sci., 66(10), Zhao, C. F., Klein, S. A., Xie, S. C., Liu, X. H., Boyle, J. S., Zhang, Y. Y. (2012). Aerosol first indirect effects on non-precipitating low-level liquid cloud properties as simulated by CAM5 at ARM sites, Geophys. Res. Lett., 39, L08806, doi: /2012gl Zhou, M.Y., Zeng, X.B., Brunke, M., Zhang, Z.H., Fairall, C., An analysis of statistical characteristics of stratus and stratocumulus over eastern Pacific. Geophys Res Lett

14 Table 1. Summary of variable names. Acronym Description Aerosol Size Distribution N a Sub-cloud PCASP concentration (> 100 nm) CN Sub-cloud CPC concentration (> 10 nm) D 0.5 Geometric median diameter of size distribution (DMA) GSD Geometric standard deviation of size distribution (DMA) GCCN Near surface GCCN concentration above 5 µm (CAS) Aerosol Composition (AMS) SO 4 Sulfate NO 3 Nitrate NH 4 Ammonium Inorg Inorganic (SO 4 + NH 4 + NO 3 ) Org Organic Total Inorganic + Organic MF Mass fraction Mass Mass concentration Cloud Adia Adiabaticity LWP Liquid water path r e Drop effective radius H base Cloud base height H top Cloud top height R Columnar rain rate R cb Cloud base rain rate N d Columnar cloud drop concentration Met/Thermo T Temperature q Specific humidity Wind Wind speed Depth Cloud thickness RH Relative humidity SST Skin surface temperature InvStrength Inversion strength InvDepth Inversion layer depth σ w Standard deviation of sub-cloud vertical wind velocity Location SF Near surface measurement AT Above cloud top measurement 14

15 Table 2. Summary of 1- and 2-parameter regression models to predict N d. In terms of model rankings, based on r2 values, there were XX and YY total models for the single- and doubleparameter models, respectively. Regression Rank β r 2 P value r 2 improvement n N d ~r e E N d ~N a E N d ~R E N d ~N a,sf E N d ~CN E N d ~N AT E N d ~R cb E N d ~T AT E N d ~InvStrength E N d ~CN SF E N d ~RH SF E N d ~σ w E N d ~Mass Total E N d ~Mass Inorg E N d ~Mass SO E N d ~CN AT E N d ~RH AT E N d ~Mass Org E N d ~D E N d ~GSD E N d ~GCCN SF E N d ~MF SO E N d ~MF Inorg E N d ~MF Org E N d ~LWP β r e E β N d ~N a r e E N d ~N a R β E β N d ~N a R cb E β N d ~N a σ w E β N d ~N a MF SO E β N d ~N a MF Org E β N d ~N a MF Inorg E N d ~CN R β E N d ~InvStrength InvDepth β E N d ~CN β R cb E N d ~CN β D E N d ~CN β σ w E N d ~CN β MF SO E N d ~CN GSD β E N d ~CN β MF Org E N d ~CN β MF Inorg E

16 Table 3. Summary of 1- and 2-parameter regression models to predict r e. In terms of model rankings, based on r2 values, there were XX and YY total models for the single- and doubleparameter models, respectively. Regression Rank β r 2 P value r 2 improvement n r e ~N d E r e ~R E r e ~R cb E r e ~N a E r e ~N a,sf E r e ~N a,at E r e ~CN AT E r e ~RH SF E r e ~Mass Total E r e ~InvStrength E r e ~Mass Inorg E r e ~Mass Org E r e ~σ w E r e ~Mass NO E r e ~GCCN E r e ~N d R β E r e ~N d LWP β E β r e ~N d R cb E r e ~N d Depth β E r e ~N d Adiabaticity β E r e ~N a R β E r e ~LWP R β E β r e ~N a R cb E r e ~N a Depth β E r e ~InvStrength InvDepth β E r e ~N a q β E

17 Table 4. Summary of 1- and 2-parameter regression models to predict R. In terms of model rankings, based on r2 values, there were XX and YY total models for the single- and doubleparameter models, respectively. Regression Rank β r 2 P value r 2 improvement n R~r e E R~R cb E R~N d E R~Depth E R~LWP E R~GCCN E R~Mass Total E R~Mass SO E R~D E R~Mass Org E R~Mass Inorg E β R~r e R cb E R~r e LWP β E β R~r e N a E R~Depth β R cb E R~r e Depth β E β R~r e N d E R~LWP β N d E R~Depth β N d E

18 Table 4. Summary of 1- and 2-parameter regression models to predict LWP. In terms of model rankings, based on r2 values, there were XX and YY total models for the single- and doubleparameter models, respectively. Regression Rank β r 2 P value r 2 improvement n LWP~Depth E LWP~H top E LWP~R E LWP~Wind AT E LWP~Adiabaticity_H E LWP~H base E LWP~Depth β N d E LWP~Depth GCCN β E LWP~Depth Adiabaticity β E β LWP~H top H base E LWP~R β N d E β LWP~r e N d E LWP~R β H top E LWP~R β r e E

19 Figure 1. Dependence of ACI (using sub-cloud CN from CPC as aerosol proxy) on geometric median diameter, rain rate, and sub-cloud vertical wind standard deviation. 19

20 Figure 2. Relationship between Nd and sub-cloud CPC aerosol concentration (CN) in bins of σ w /CN. The inset boxes show results of two-parameter regression models for each bin. 20

21 Figure Y. S o -LWP relationship based on airborne data from four field campaigns. 21