P2.24 Stratus microphysics correlations

Wednesday, 30 June 2010
Exhibit Hall (DoubleTree by Hilton Portland)
Vandana Jha, DRI, Reno, NV; and S. Noble and J. G. Hudson

             The large variation of measured cloud microphysics is the basis of the indirect aerosol effect (IAE), the largest climate uncertainty.  IAE derives from the influence of anthropogenic cloud condensation nuclei (CCN) on cloud microphysics. Cloud microphysics is determined not only by CCN spectra but also by dynamic processes; the foremost of which is the upward velocity (W) at cloud base.  The CCN spectrum, W and the temperature at cloud base determine initial cloud droplet concentrations, which generally remain constant until significantly impacted by entrainment or collection by larger drops.  The relative influence of CCN and W on cloud microphysics is critical for understanding IAE.  But the W influence is really another manifestation of the CCN influence, which is thus tempered by W; i.e., higher W produces higher cloud supersaturations (S), which activate more CCN to cloud droplets.                 The Physics of Stratocumulus Tops (POST) project in July-August, 2008 off the central California coast provided the wide range of CCN concentrations and cloud microphysics needed to advance cloud physics and IAE research.  These marine stratus clouds are especially pertinent to IAE because they provide a large albedo contrast with and over wide expanses of ocean.  Furthermore, typical low CCN and droplet concentrations of maritime air masses are especially susceptible to anthropogenic intrusions.  The low altitudes of these clouds provide not only higher radiative temperatures that enhance their contribution to global cooling but also a shallower boundary layer to perhaps simplify sub cloud aerosol measurements.  On the other hand the latter also provides more opportunity for cloud effects on the aerosol that can make for a more complex boundary layer aerosol. 

            Unlike the two most recent maritime cloud presentations by the authors (Hudson et al. 2009 [hereafter H9], RICO; Hudson and Noble 2009 [hereafter HN9], PASE) POST provided intraflight and interflight contrasts.  The differences within each flight combined with the differences among 15 flights provided an order of magnitude more data points--144. 

            All measurements were made on board the CIRPAS Twin Otter based in Marina, California. CCN were measured by a Desert Research Institute (DRI) CCN spectrometer (Hudson 1989), cloud droplet measurements were made with a Cloud Aerosol Spectrometer (CAS) probe (diameter 0.58-51 µm), W was GPS corrected. 

Order of magnitude differences and nonlinearity of the relationship between CCN and droplet concentrations are apparent in Figure 1.  Figure 2 shows the pattern of linear correlation coefficients (R) for CCN concentrations with cumulative droplet concentrations larger than various threshold sizes. An important matter for cloud microphysics is the threshold for defining what constitutes a cloud. Figures 1 and 2 employ CAS liquid water contents (LWC) as thresholds.  Figure 2 shows R patterns similar to H9 where R plunges from positive for all cloud droplets to negative for droplets larger than 18 µm diameter, and then relaxes to smaller negative values for larger thresholds.  The R patterns closely resemble those of the lowest altitude band in RICO (H9) especially for the same LWC threshold of 0.1 gm-3 except that the positive R at small sizes is not quite as high.  The R similarities are probably due to the similarity of the cloud thicknesses—300 m in RICO and 287 m average thickness in POST.  H9 explained that the negative R are due to greater droplet size limitations due to greater competition for condensate when CCN concentrations are higher.  HN9 explained that the diminished negative R at larger sizes are due to less competition among the lower concentrations of the larger droplets, which then tend to revert toward proportionality with the concentrations of the nuclei upon which they condensed.  Since the CCN concentrations at various S are often in the same proportions (HN9) this makes a tendency for positive R with CCN at 1 % S.  The positive R tendency thus vies with the negative R tendency due to competition and this results in intermediate R values. 

            Figure 3 shows a similar R pattern for W with cumulative droplet concentrations except that the positive R values are significantly lower.  This indicates that CCN concentrations play a more significant role than W in determining total cloud droplet concentrations.  Nevertheless, the positive R for W and total droplet concentrations indicates that higher W activate more CCN to cloud droplets than lower W.  The negative R for larger droplets suggests that the lower droplet concentrations of clouds with lower W allow larger droplets.  The R for W with droplets shows more variability with LWC threshold than CCN. 

            In Figure 4 the soundings have been restricted to only those with linear LWC profiles with altitude.  These clouds are closer to adiabatic, i.e., less microphysical modification by entrainment or drop collection.  Nonetheless, the R values are only slightly different from Fig. 1 except for 0.01 gm-3.  This suggests rather minimal modification of microphysics by entrainment.  This is also revealed in Figure 5, which also reveals the contrasting Rs for CCN and W.  Figure 6 shows minimal altitude differences in R for CCN and droplets except near the base where many droplets are too small for detection by the CAS.   

Hudson, J.G., 1989:  J. Atmos. & Ocean. Techn., 6,  1055-1065.

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.


- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner