Hudson et al. 2009 (hereafter H9) and Hudson and Noble (2009) (hereafter HN9) displayed patterns of correlation coefficients (R) between CCN and cumulative droplet concentrations (Fig. 1). These went from positive to negative and then to less negative (RICO) or positive (PASE; Fig. 2) for increasing droplet size thresholds. The first transition was explained by droplet competition, which restricts droplet sizes. The second transition was explained by the decreased competition of the lower concentrations of large droplets, resulting in a return to proportionality with nuclei concentrations. The less negative or positive R for the low concentration large drops with total CCN concentrations suggested proportionality among CCN concentrations at various S.
HN9 showed that the maximum negative R occurred just beyond the average mode of the droplet distributions where droplet competition is greatest especially for higher CCN concentrations (Fig. 2). However, since even the most numerous intermediate-sized droplets have little effect on the growth of the sparse large droplets they revert to proportionality with nuclei concentrations. The smaller overall droplet sizes of PASE compressed the R pattern so that it went positive at smaller droplet sizes (Fig. 2). The larger overall drop(let) sizes of the larger clouds and the lower CCN concentrations of RICO expanded the R pattern so that positive R was not observed (Fig. 1).
Panel A of Fig. 3 displays cumulative droplet spectra grown upon six different CCN spectra that are exact multiples of each other at all supersaturations; identical spectral shapes. All other factors namely temperature, pressure, cloud base height, distance above cloud base, and updraft speed are identical inputs to the Robinson (1984) adiabatic model predictions that are displayed. Panel B of Fig. 3 displays R between CCN and cumulative droplet concentrations. R is positive for diameters less than 8 µm. Between 8 and 10.5 µm the spectra cross and thus reduce R to negative. At 12 µm the spectral lines cross again so that above 13 µm R is again positive. When the model is applied to several measured RICO CCN spectra that have different concentrations and shapes they produce the observed RICO R patterns of H9 (Fig. 4).
Comparisons of giant nuclei (GN) concentrations with cloud microphysics in RICO resulted in a negative test result for the GN warm rain hypothesis (Colon-Robles et al. 2006; hereafter CR6). However, the microphysical measurements were only for small cloud droplets near cloud base. H9 found similar R patterns for CCN with cumulative droplet concentrations for six altitude bands up to 3 km (Fig. 1). However, the R pattern above 3 km is different; it goes to strong positive values of 0.9 at drizzle drop sizes above 250 µm (Fig. 5). We then discovered that the CR6 GN concentrations were extremely well correlated with the CCN concentrations for the five flights with cloud data above 3 km. Therefore, the GN-drop R values go to +0.8 for sizes above 250 µm (Fig. 5), which indicates a positive test result for the GN warm rain hypothesis. These results are also consistent with the explanations of the R patterns presented by HN9 and the adiabatic model results shown in Panel B of Fig. 4 though expanded in terms of drop size. We also find positive R for large nuclei (LN; CCN at low S) intermediate of total CCN and GN (Fig. 6). Furthermore, it seems more plausible that the very low GN concentrations would not be correlated with the much higher concentrations of small cloud droplets examined by CR6. But since GN concentrations are closer to the lower concentrations of drizzle drops, a positive R between them seems more likely, especially at higher altitudes where in RICO there were more drizzle drops (H9).
Similar R patterns have been observed in the entirely different environments of ICE-L (supercooled continental) and POST (maritime and polluted stratus). The positive R and diminished negative R that we are finding at large droplet or drizzle sizes with CCN, LN and GN suggests that the 2nd indirect aerosol effect (the largest climate uncertainty) may be more complicated than simple pluvial inhibition. These results indicate influence of lower S CCN concentrations on cloud microphysics. CCN influence has generally been thought to be confined to only determining total cloud droplet concentrations, which meant that only CCN concentrations at the initial cloud S were important (usually high S). Now we seem to be learning that the entire CCN spectrum (LN and even GN) may be important for determining the full cloud droplet and drizzle drop spectra.
Work supported by NSF grant ATM-0342618
References:
Colon-Robles, M., R.M. Rauber, J.B. Jensen, 2006: Geophys. Res. Lett., 33, L20814, doi:10.1029/2006GL027487.
Hudson, J.G. and S. Noble, 2009: Geophys. Res. Let., 36, L13812, doi:10.1029/2009GL038465.
Hudson, J. G., S. Noble, V. Jha, and S. Mishra, 2009: J. Geophys. Res., 114, D05201, doi:10.1029/2008JD010581.
Robinson, N.F., 1984: J. Atmos. Sci., 41, 697-700.
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