4.3 Temperature Control of the Variability of Tropical Tropopause Layer Cirrus Clouds

Monday, 26 June 2017: 4:00 PM
Salon G-I (Marriott Portland Downtown Waterfront)
Hsiu-Hui Tseng, University of Washington, Seattle, WA; and Q. Fu

This study examines the temperature control of variability of tropical tropopause layer (TTL) cirrus clouds that are the clouds in the tropics with bases higher than 14.5 km. Eight years (2006-2014) observational data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) measurements are used. It is found that the vertical structure of TTL cirrus cloud fraction averaged between 15oN and 15oS can be well explained by the vertical temperature gradient for the variability in both seasonal and interannual scales. It is shown that the TTL cirrus cloud fraction at a given altitude is best correlated with the temperature at a higher altitude and this vertical displacement increases with the decrease of height. It is also shown that the TTL cirrus at all levels is well correlated with tropical cold point tropopause temperature. The plausible mechanisms responsible for the robust relations between TTL cirrus and temperature-derived variables will be discussed, which include (not limited to) ice particle sediments, cooling related to the large-scale wave propagations, atmospheric instability change, and vertical water vapor mixing ratio gradient.

We further examine the spatial co-variability of total TTL cirrus fraction and cold point tropopause temperature by using the maximum covariance analysis (MCA) and explore their relation to large scale dynamical variability. Our result suggests that the El Niño Southern Oscillation (ENSO) and quasi-biennial oscillation (QBO) are the leading factors in controlling the spatial variability of the TTL cirrus clouds and temperatures while the Brewer-Dobson circulation plays a secondary role. In terms of the zonal mean TTL cirrus and temperatures, the variabilities are dominant by QBO signal because the spatial positive and negative signals due to ENSO is largely cancelled out.

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