Analyzing dynamical circulations in the tropical tropopause layer through empirical predictions of cirrus cloud distributions

Abstract

We explore the use of nonlinear empirical predictions of thin cirrus for diagnosing transport through the tropical tropopause layer (TTL). Thirty day back trajectories are calculated from the locations of CALIPSO cloud observations to obtain Lagrangian dry and cold points associated with each observation. These historical values are combined with "local" (at the location of the CALIPSO observation) temperature and specific humidity to predict cloud probability using multivariate polynomial regression. We demonstrate that our statistical sample (seven seasons) is sufficient to retrieve the full nonlinear relationship between cloud probability and its predictors and that substantial information is lost in a purely linear analysis. The best cloud prediction is obtained by the two-variable combination of local temperature and humidity, which reflects the close relationship between clouds and relative humidity. However, single-variable predictions involving air parcel histories are better than those based solely on the individual local fields, indicating the existence of reliable dynamical information content within parcel trajectories. Thermal fields are better cirrus predictors during boreal winter than summer primarily due to poor predictions over the Asian summer monsoon region, revealing that the functional relationship over southern Asia differs from the rest of the tropics; in short, TTL cirrus formation over regions of active maritime convection, such as the West Pacific, is thermally dominated, indicating an environment in which in situ cirrus are readily formed, while TTL cirrus of southern Asia is moisture dominated, indicating a more direct connection between convective injection of moisture and thin cirrus.

Identifying robust transport features of the upper tropical troposphere

“…To address these questions, our first challenge is to distinguish reliable from unreliable trajectory statistics. Ultimately, trajectories should be validated with predictions of constituent concentrations that can be verified with satellite and aircraft data [e.g., Fueglistaler et al, 2005;James et al, 2008;Ploeger et al, 2011;Bergman et al, 2014;Ueyama et al, 2014;Jensen et al, 2015]. However, these predictions require knowledge of the constituent sources and process models that alter concentrations along the trajectories.…”