An assessment of CALIOP polar stratospheric cloud composition classification

Abstract

<p><strong>Abstract.</strong> This study assesses the robustness of the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) polar stratospheric cloud (PSC) composition classification algorithm – which is based solely on the spaceborne lidar data – through the use of nearly coincident gas-phase HNO₃ and H₂O data from the Microwave Limb Sounder (MLS) on Aura and Goddard Earth Observing System Model, Version 5 (GEOS-5) temperature analyses. Following the approach of Lambert et al. (2012), we compared the observed temperature-dependent HNO₃ uptake by PSCs in the various CALIOP composition classes with modeled uptake for supercooled ternary solutions (STS) and equilibrium nitric acid trihydrate (NAT). We examined the CALIOP PSC data record from both polar regions over the period from 2006 through 2011 and over a range of potential temperature levels spanning the 15–30 km altitude range. We found that most PSCs identified as STS exhibit gas phase uptake of HNO₃ consistent with theory, but with a small temperature bias, similar to Lambert et al. (2012). Ice PSC classification is also robust in the CALIOP optical data, with the mode in the ice observations occurring about 0.5 K below the frost point. We found that CALIOP PSCs identified as NAT mixtures exhibit two distinct preferred modes which reflect the fact that the growth of NAT particles is kinetically limited. One mode is significantly out of thermodynamic equilibrium with respect to NAT due to short exposure times to temperatures below the NAT existence temperature, <i>T</i>_NAT, with HNO₃ uptake dominated by the more numerous liquid droplets. The other NAT mixture mode is much closer to NAT thermodynamic equilibrium, indicating that the particles have been exposed to temperatures below <i>T</i>_NAT for extended periods of time. With a few notable exceptions, PSCs in the various composition classes conform well to their expected temperature existence regimes. We have a good understanding of the cause of the minor misclassifications that do occur and will investigate means to correct these deficiencies in our next generation algorithm.</p>