Cloud droplet number concentration in VOCALS-REx

Prediction of the CDNC is complicated by sensitivity to the cloud condensation nuclei (CCN) activity spectrum and to updraft velocity. Here, we use data collected during VOCALS-REx to examine the predictability of CDNC based on in situ and remote-sensing measurements. The VOCALS-REx dataset is well-suited to this analysis because of the suite of instrumentation onboard the NSF/NCAR C-130 aircraft and the range of conditions – CCN concentrations at supersaturation = 0.4% ranged from 500 per cubic centimeter, near the Chilean coast, to 50 per cubic centimeter 500 km to the west. This work compares three estimates of the CDNC during VOCALS-REx. The first comes from in-cloud measurements made with the C-130 cloud droplet spectrometers (CDP and FSSP); the second is from sub-cloud values of the CCN activity spectrum (Wyoming CCN instrument, PCASP and aerosol chemistry), and measurements of the updraft probability distribution function (gust probe), input to a parcel model; and the third is from Wyoming Cloud Lidar via a CDNC retrieval algorithm. We refer to the CDNC values derived from these three approaches as the “measured”, the “modeled” and the “retrieved.” The three approaches are complimentary. The measured values are affected by two processes (entrainment/evaporation and precipitation scavenging) not accounted for in the modeled values; the retrieved values are available coincident with the modeled values; and the modeled/measured comparison is complicated by displacements in time (~15 min). The presentation will discuss the sub-cloud and in-cloud vertical velocity probability distribution functions, the departure of cloud liquid water content and in-cloud temperature from adiabatic, the cloud droplet closure problem, and comparison of the retrieved CDNC values to those derived via the two other approaches.