Giant nuclei (GN) concentrations (NGN) below RICO small cumulus clouds were substantially correlated with drizzle drop concentrations (Nd), especially at higher cloud altitudes. The NGN-Nd correlation coefficients (R) progressively increased with altitude whereas R for CCN concentrations with Nd were negative with mostly decreasing magnitudes at increasing altitudes. These results indicate that the positive influence of GN [or CCN with low critical supersaturations (Sc)] on Nd is greater than the negative influence of high Sc CCN on Nd at high cloud altitudes where there are more drizzle drops. This work has implications not only for fundamental cloud physics but also for climate change; i.e., global warming and the indirect aerosol effect as well as geoengineering and hygroscopic cloud seeding.
 Nonfreezing (warm) clouds rain more easily in maritime than continental air [Battan and Braham, 1956]. The lower submicrometer cloud condensation nuclei (CCN) concentrations (NCCN) of maritime air produce fewer and thus larger droplets that are more likely to precipitate [Hudson and Yum, 2001]. However, maritime precipitation is often so rapid that it seems necessary to invoke further explanations [Mason, 1971] involving either dynamics [e.g., Telford et al., 1984; Pinsky and Khain, 2002] or giant nuclei (GN > 1 μm) [e.g., Johnson, 1993; Blyth et al., 2003], which more directly produce precipitable drops. Although GN are known to be produced by the action of wind at the ocean surface [Woodcock and Blanchard, 1955] their role in warm rain has been debated for decades though many recent publications have been negative [e.g., Hudson and Yum, 2001; Göke et al., 2007; Hudson and Mishra, 2007 (hereafter HM7); Knight et al., 2008]. The GN-warm rain hypothesis has implications beyond cloud physics. The most uncertain aspect of the indirect aerosol effect (IAE), 2nd IAE (precipitation inhibition by anthropogenic NCCN) would be further complicated by precipitation enhancement by increased concentrations of GN (NGN). Schickedanz  noted that urban areas are a source of GN while Mather  demonstrated positive influence of anthropogenic GN on warm rain. Latham and Smith  showed that global warming could enhance maritime wind velocities, which could perturb precipitation by increasing NGN.
 During the Rain in Cumulus over the Ocean (RICO) project [Rauber et al., 2007] NCCN were highly correlated with total cloud droplet concentrations (Nc; larger than 2.4 μm diameter) at altitudes up to 3 km (Figure 1a) [Hudson et al., 2009] (hereafter H9). Precipitation inhibition of higher NCCN was demonstrated by negative correlations between NCCN active at a supersaturation (S) of 1% (N1%) with cumulative concentrations of cloud droplets larger than specified threshold sizes (Nt) (here diameter > 20 μm in Figure 1a, except 3–5 km band). Moreover, the maxima of these negative linear correlation coefficients (R) were even greater at higher altitudes and occurred at larger cumulative threshold sizes at higher altitudes (Figure 1a, except 3–5 km). These extensions of negative R at higher altitudes are due to the typical overall larger drop sizes at higher altitudes. Negative R for N1%-Nt extended to drizzle sizes (diameter > 50 μm, Nd), especially at higher altitudes where there was more drizzle (greater Nd, Table 1, columns 8–10). However, the magnitudes of these negative R values decreased with larger drizzle drop diameters (Figure 1a).
Table 1. Altitude, Number of Flights, Flight Numbers, Correlation Coefficients (R) Between N1% and NGN, Mean CCN, GN, Total Droplet, and Drizzle Drop Concentrations (Nd) in Each Altitude Banda
Last column shows the difference between R for NGN and N1% with Nd larger than 245 μm. Last 3 rows consider only those flights with a monotonic relationship between NGN and N1%.within 3 altitude bands. N1%, NGN and Nc units are cm−3; drizzle drop concentration units are per liter.
1, 3, 5, 7, 8, 10, 12–15,18
3, 5, 13–15
3, 7, 13, 15
3, 5, 13–15
3, 5, 13–15
Hudson and Noble  (hereafter HN9) and Hudson et al.  (hereafter H10) also observed similar N1%-Nt R patterns in other cloud systems. Negative R for N1%-Nt was explained by greater competition among droplets for condensed water when N1% were higher. Higher Nc due to higher N1% produced greater limitations to droplet sizes when there was more competition. Thus, the smaller droplets of higher Nc situations meant that there were fewer droplets exceeding various larger size thresholds. The maximum negative R of N1%-Nt occurred at threshold sizes slightly larger than the mode of the average droplet spectrum (HN9; H10). These negative R for N1%-Nt sharply contrast with the positive R for N1%-Nc (left side of Figure 1a). However, lower Nt for even larger threshold drop sizes; i.e., threshold sizes well beyond the mode of the average droplet spectrum produced less competition for condensate among these sparse drop concentrations. This resulted in a smaller negative R tendency for these Nt with N1%. Therefore, R for these lower Nt at these much larger threshold sizes reverted back toward positive with N1% because, like Nc, the sparse concentrations of the very large drops tend to be in proportion to the concentrations of the nuclei upon which they had condensed. The nuclei of these very large drops, however, should be a smaller CCN subset, only CCN with lower critical supersaturations (Sc) than the CCN that were responsible for Nc (all drops); i.e., NCCN at high S; i.e., 1% (N1%). GN are the lowest Sc CCN with the smallest concentrations. Nt at the largest threshold sizes should be proportional to NGN.
 When NGN are proportional to N1% there should also be a positive R tendency for N1% with the low Nt at very large drop size thresholds. These explanations are then consistent with observations of positive R for N1% with Nc (i.e., small droplet size thresholds) and with Nt at large cumulative drop sizes where there is little competition for condensate; and negative R for N1%-Nt at intermediate size thresholds where there is the largest effect of competition among droplets for condensate. H10 demonstrated adiabatic droplet growth model predictions that reproduced these observed R patterns when similar CCN spectral shapes were assumed for each of the various cloud situations that were considered. This means NCCN in the same proportions for all S for the entire data set under consideration. The extreme of NCCN proportionality is NGN ∝ N1%.
Colón-Robles et al.  (hereafter CR6) examined the influence of GN on RICO cloud droplet spectra (diameter < 50 μm) within the lowest cloud altitude band (<900m) and determined that the concentrations of largest cloud droplets were opposite of that expected for a GN influence. Now H9 analysis is extended above 3 km and the GN measurements of CR6 are included. As in most of H9, cloud is defined as liquid water content (LWC > 0.1 gm−3).
3. Results of Analysis
Figure 1 shows R for N1% (panel A) and NGN (panel B) with Nt within seven altitude bands. The uniformity of the below cloud aerosol during each RICO flight allowed consideration of flight averages of N1%, NGN, Nc and Nt [HM7 and H9]. NGN are averages of the morning and afternoon concentrations in Figure 1a of CR6. Another flight is added by using GN measurements of Reiche and Lasher-Trapp . The R of 0.97 between these NGN and NGN of CR6 for the 9 flights with common data made this possible.
 The 3–5 km altitude band shows a different R pattern for Nd from that of the lower altitudes (right hand side of Figure 1a). This apparent positive influence of N1% on Nd is inconsistent with the negative influence of N1% on large threshold Nt observed and described by H9, HN9 and H10. The positive R for NGN with Nd at the highest altitude band in Figure 1b is consistent with the explanations of the last section and HN9 and H10. The similarity of the GN (B) and CCN (A) R patterns for the 3–5 km altitude band is only due to the 0.996 R for NGN-N1% for these 5 flights (Figure 2 and Table 1, column 4). As described in the previous section this is the only reason for the positive R observed for N1%-Nd. For all altitudes R for N1%-Nc (left end of Figure 1a) is high whereas R for NGN-Nc (left end of Figure 1b) is low, except 3–5 km. However, NGN should not influence Nc any more than N1% can positively influence Nd. Order of magnitude differences between N1% and Nd and between NGN and Nc are consistent with this (Table 1, columns 5–10). It is only the high NGN-N1% R for these 5 flights that forces equal R for N1% and NGN with anything. The pattern of high positive R for N1% with Nt at large size thresholds when NCCN are in the same proportions at all S was observed and predicted by HN9 and H10. The proportionality of NGN with N1% for this 3–5 km band is an example of this proportionality.
Table 1, column 4 shows that NGN and N1% were uncorrelated for the other cloud altitude bands. That decoupling of N1% from NGN is reflected in the R differences between Figures 1a and 1b. Figure 1a shows negative R for all cumulative drop sizes larger than 40 μm for 5 of the 7 altitudes whereas Figure 1b shows positive R for all cumulative sizes above 120 μm at all altitudes. The positive R drizzle exceptions in Figure 1a are an artifact for the 3–5 km band (NGN ∝ N1%) and very small positive R for the 600–900 m band, which has least Nd (Table 1, columns 8–10).
Figure 3 demonstrates the contrasting CCN and GN R patterns within each altitude band. For drizzle (right side of Figure 3) these R differences progressively increase for higher altitude bands except the 3–5 km band. This progressive increase with altitude of the differences between the drizzle R values of GN and CCN and the increase of R for NGN–Nd with altitude, parallel the increase in Nd with altitude (Table 1, columns 8–10). The last column of Table 1 quantifies the R differences between NGN-Nd and N1%-Nd by altitude.
 The effects of CCN and GN on precipitation are opposite; precipitation is favored by higher NGN while higher NCCN impedes precipitation. This suggests that the most direct opposition of these influences when NGN ∝ N1% would convolute these influences. Although this might impair deconvolution of these influences by some types of analyses, here the monotonic NGN-N1% relationship offers the most direct comparison of these opposing influences. For the highest RICO altitude band the NGN positive influence on Nd clearly dominates the negative influence of N1% on Nd. The low NGN-N1% R (Table 1, column 4) of other altitude bands can be converted to this most direct comparison by considering only flights that have a monotonic NGN-N1% relationship. For the 2nd highest altitude band (2.4–3 km) 4 of the 6 flights; (Figure 2 and Table 1, row 8) have a high NGN-N1% R (column 4). They display a higher positive NGN-Nd R and significance level (Figure 4a) than NGN-Nd for all 6 flights (Figure 3b). Figure 4a shows the same compulsory concurrence of the two sets of R as Figure 3a. Since NCCN should only have a negative influence on Nd, this further demonstrates the dominance of the GN influence on drizzle at high cloud altitudes where there is more drizzle (Table 1, columns 8–10). N1%-Nd is negative for all altitude bands where NGN is not proportional to N1% except the small positive R for the lowest band where there is least Nd.
 Two altitude bands had cloud data for the same 5 flights with clouds above 3 km (last two rows of Table 1). The R patterns for these considerations where NGN ∝ N1% are in Figures 4b and 4c, which demonstrates dominance of the negative influence on Nd by N1%. The greater negative influence of N1% on Nd than the positive influence of NGN on Nd at lower altitudes is emphasized by the significantly greater negative R in Figure 4b than Figure 3d and Figure 4c compared to Figure 3g. This analysis demonstrates that the direct aerosol competition shown in Figure 3a and Figure 4 provides a more definitive assessment of the relative influences of the two aerosols on Nd.
4. Discussion and Conclusions
 The RICO flight plans were designed to capture clouds in early precipitation stages. The progressive increase of Nd with altitude (Table 1, columns 8–10) indicates success in that pursuit. The consistent positive R between NGN and Nd progressively increases with altitude and size while the consistently negative R between N1% and Nd mostly decreases in magnitude with altitude and size. This describes greater GN influence on larger drizzle drops at higher altitudes where there are more drizzle drops. Although the number of flights (data points) is smaller for the higher altitudes where R for NGN-Nd is highest, the statistical significance levels are 95% for the two highest altitude bands and exceed 90% for the third and fourth highest altitudes. Although RICO fortuitously provided wide aerosol and cloud microphysics variations, it was not a controlled experiment. R for NCCN or NGN with Nc, Nt or Nd is reduced by other variables namely vertical velocity (W) and entrainment, which independently influence cloud microphysics.
 The NGN used in this analysis (e.g., Table 1, column 6) are not exactly the particles upon which most of the drizzle drops condensed; i.e., different drizzle concentrations at various drop sizes and altitudes condensed upon different particle size or Sc ranges. But CR6 indicated that NGN in all size ranges are probably in similar proportions among the flights because they all come from the same wind driven ocean source. Thus, the actual NGN used in this analysis are probably reasonable surrogates for NGN at various sizes or Sc that the various drizzle drops condensed upon; some may be smaller than GN.
 This analysis assumes that CCN and GN measurements well below cloud base are relevant up to 5 km. This is indicated by the consistently high R for N1%-Nc (H9 and Figures 1, 3, and 4) and the very high R between NGN at 100m [CR6] and 450m [Reiche and Lasher-Trapp, 2010] altitude. The wider range of altitudes within the higher altitude bands is a weakness, but there is no correlation between each average flight cloud altitude or average LWC with Nd.
 The NGN correlation with surface wind is a complicating factor because of the horizontal wind coupling with W, which independently influences cloud microphysics. CR6 found low altitude cloud W correlated with horizontal wind velocity, R = 0.79 for 12 flights. This R is 0.90 for the 5 flights of the 3–5 km band. However W at the highest altitudes was not correlated with low altitude W or with low altitude horizontal wind velocity. This argues against the possibility that dynamic effects were the cause of the apparent GN influence on high altitude drizzle.
 This observational work will be expanded to include CCN spectra (i.e., intermediate of 1% and GN), comparisons between aerosol and drop number concentrations, and droplet growth model predictions (i.e., H10) that will provide more explanations. Although correlation is not cause, this analysis indicates viability of the GN warm rain hypothesis. Recent RICO articles by Gerber et al.  and Lowenstein et al.  also lend some support to the GN warm rain hypothesis. This analysis also has implications for the geoengineering proposal to counteract global warming by brightening clouds because injection of particles of too large a size range could have opposite effects such as those of deliberate hygroscopic cloud seeding [Bruintjes, 1999], which is supported by these results. This study indicates the importance CCN spectra not just total CCN concentrations.
 Support was from the U.S. National Science Foundation grant ATM-0342618. Cloud measurements were provided by the Research Aviation Facility of NCAR, which provided the C-130 airplane, the platform for all measurements.