Comparison of Antarctic polar stratospheric cloud observations by ground-based and space-borne lidar and relevance for chemistry–climate models

• Published in: Atmospheric Chemistry and Physics
• Main Authors: Marcel Snels, Andrea Scoccione, Luca Di Liberto, Francesco Colao, Michael Pitts, Lamont Poole, Terry Deshler, Francesco Cairo, Chiara Cagnazzo, Federico Fierli
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2019
• Subjects: NSF; Division of Polar Programs; Directorate for Geosciences; National Science Foundation; Atmospheric Science; Antarctic; Antarc*; Antarctica
• Online Access: https://www.openaccessrepository.it/record/68248; https://doi.org/10.5194/acp-19-955-2019

Abstract. A comparison of polar stratospheric cloud (PSC) occurrence from 2006 to 2010 is presented, as observed from the ground-based lidar station at McMurdo (Antarctica) and by the satellite-borne CALIOP lidar (Cloud-Aerosol Lidar with Orthogonal Polarization) measuring over McMurdo. McMurdo (Antarctica) is one of the primary lidar stations for aerosol measurements of the NDACC (Network for Detection of Atmospheric Climate Change). The ground-based observations have been classified with an algorithm derived from the recent v2 detection and classification scheme, used to classify PSCs observed by CALIOP. A statistical approach has been used to compare ground-based and satellite-based observations, since point-to-point comparison is often troublesome due to the intrinsic differences in the observation geometries and the imperfect overlap of the observed areas. A comparison of space-borne lidar observations and a selection of simulations obtained from chemistry–climate models (CCMs) has been made by using a series of quantitative diagnostics based on the statistical occurrence of different PSC types. The distribution of PSCs over Antarctica, calculated by several CCMVal-2 and CCMI chemistry–climate models has been compared with the PSC coverage observed by the satellite-borne CALIOP lidar. The use of several diagnostic tools, including the temperature dependence of the PSC occurrences, evidences the merits and flaws of the different models. The diagnostic methods have been defined to overcome (at least partially) the possible differences due to the resolution of the models and to identify differences due to microphysics (e.g., the dependence of PSC occurrence on T−TNAT). A significant temperature bias of most models has been observed, as well as a limited ability to reproduce the longitudinal variations in PSC occurrences observed by CALIOP. In particular, a strong temperature bias has been observed in CCMVal-2 models with a strong impact on PSC formation. The WACCM-CCMI (Whole Atmosphere Community Climate Model ...