J6.3 Stratus cloud supersaturations

Tuesday, 25 January 2011: 11:45 AM
3A (Washington State Convention Center)
James G. Hudson, DRI, Reno, NV; and S. R. Noble and V. Jha

Conventional wisdom is that stratus cloud supersaturations [S] are < 0.3% (100.3% RH) (Hegg et al. 2009) and that higher CCN concentrations (NCCN) restrict cloud S (i.e., Twomey 1959). The Physics of Stratocumulus Tops (POST) field experiment in July-August, 2008 off the central California coast provided a wide enough range of NCCN and droplet concentrations (Nc) to test these concepts. Condensation nuclei (CN) were measured with a TSI 3010, CCN were measured by a Desert Research Institute CCN spectrometer (Hudson 1989), cloud droplets were measured with a Cloud Aerosol Spectrometer (CAS) probe (diameter 0.58-51 µm), W was measured with a GPS corrected C-MIGITS III all aboard the CIRPAS Twin Otter. Nc are averages of droplets larger than 0.58 µm diameter for those one second periods where CAS liquid water content (LWC) exceeded 0.1 gm-3. NCCN are measured below the clouds.

Row 1, Table 1 shows the high correlations (R) between N1% (NCCN at 1% S) and Nc. Although below-cloud CCN provide the greatest influence on cloud microphysics the often-observed high NCCN immediately above California coastal stratus (e.g., Hudson and Frisbie 1991) might also play a role in cloud microphysics by entrainment. In an attempt to ascertain this influence, the 69 vertical cloud penetrations are divided according to vertical aerosol profiles of CN concentrations (NCN). These are used because the faster response of CN makes for easier discrimination from within-cloud measurements. Columns 3 and 4 of row 1, Table 1 indicate high and similar R of Nc-N1% for the 40 cases with higher NCN immediately above the stratus than below cloud NCN (designated HA) and the 29 cases with just above cloud NCN lower or similar to below cloud NCN (designated LSA). These facts indicate that there was minimal influence of the above cloud CCN on Nc. This is also indicated by a division of each cloud penetration into altitude quartiles, which revealed the highest Nc-N1% R for the highest cloud altitude quartile.

Table 2 uses the entire CCN spectra between 1.5 and 0.04% S to estimate effective cloud S (Seff) by determining the S of the various NCCN that match mean Nc of each of the corresponding 69 vertical cloud penetrations (i.e., Hudson 1983). The last column of Table 1 is an attempt to minimize the effects of entrainment and droplet coalescence that reduce Nc and thus obscure Nc-NCCN relationships that usually establish Nc at cloud base. Such reductions of Nc could be independent of NCCN , which would perturb the Nc-NCCN relationship and thus also the relevance of Seff, which is the most pertinent when it reflects the influence of NCCN on Nc at cloud base. Therefore, penetrations with linear LWC altitude profiles that are characteristic of adiabatic unmixed clouds should show higher Nc-NCCN R. Since they do not this indicates that entrainment does not seem to disrupt the Nc-NCCN relationship.

Table 2 shows mean Seff within various N1% intervals. This is consistent with row 5, Table 1 in showing that Seff is lower for higher NCCN. R for Seff-Nc is also negative (with one small exception), but with smaller absolute values (Row 6, Table 1) than R for Seff-N1%. R for Seff-Nc are less negative than R for Seff-N1% because both Seff and Nc are functions of NCCN.

W also determines Nc; i.e., higher W produces higher S and Nc. R for Nc-W is positive in row 3, Table 1 (except last column) but is much lower than R for Nc-N1% (row 2). Variations of W thus contribute to the scatter of Nc-N1% because W variations ought to be independent of N1%. But N1% and W are positively correlated (row 4; except last column). But since the effects of NCCN and W on Seff are opposite (higher NCCN reduces Seff whereas higher W increases Seff) this positive albeit small coupling of NCCN with W is probably the reason that Seff does not display the expected positive correlation with W (last row, Table 1). Apparently this coupling allowed the greater effect of NCCN variations on Seff to overwhelm the effect of the W variations on Seff. This means that the decrease of Seff with N1% shown in Table 2 could have been even greater had W been constant or independent of NCCN.

Consistent with this is the fact that for the 17 adiabatic clouds W and N1% were not correlated (R = -0.28; last column of row 4) and this probably contributed to the greater negative R of Seff-N1% for the adiabatic clouds compared to all 69 vertical clouds (0.84 versus 0.74) in spite of the more limited N1% range of the adiabatic clouds, which thus prevented observations of the greatest reductions of Seff caused by the highest N1%.

Table 2 shows the decrease of cloud S with NCCN and higher stratus cloud S than conventional wisdom especially since these are underestimates of cloud S. This means that much smaller particles can nucleate stratus clouds (e.g., 30 nm rather than 60 nm ammonium sulfate particles). This has important implications for the largest climate uncertainty—the indirect aerosol effect (Alley et al., 2007) and for geoengineering schemes to brighten marine stratus clouds. Smaller particles are easier to produce either deliberately or inadvertently.

Alley, R.B., and Coauthors, 2007: Cambridge University Press, 1-18.

Hegg, D.A., D.S. Covert, H.H. Jonsson, and R. Woods, 2009: Tellus, 61B, 669-676.

Hudson, J.G., 1983: J. Atmos. Sci., 40, 480-486.

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

Hudson, J.G. and P.R. Frisbie, 1991: J. of Geophys. Res., 96, D11, 20,795-20,808.

Twomey, S., 1959: Pure. Appl., 43, 243-249.

Table 1. Correlation coefficients (R) between various parameters. First row-number of clouds.








































Table 2. Mean Seff within various average N1% ranges.

N1% (cm-3)

< 100




> 400

mean Seff







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