Probability Distribution Function of a Forced Passive Tracer in the Lower Stratosphere

作     者：胡永云 

作者机构：Department of Atmospheric Sciences School of PhysicsPeking University 

出 版 物：《Advances in Atmospheric Sciences》 (大气科学进展（英文版）)

年 卷 期：2007年第24卷第2期

页      面：163-180页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 0816[工学-测绘科学与技术] 0825[工学-航空宇航科学与技术] 

基　　金：This work is supported by the National Natural Science Foundation of China (NSFC) under Grants Nos. 40575031 and 40533016 by the Ministry of Education of China under Grant No. 106002 

主　　题：chaotic mixing probability distribution function stratosphere turbulence passive tracer 

摘      要：The probability distribution function （PDF） of a passive tracer, forced by a ＂mean gradient＂, is studied. First, we take two theoretical approaches, the Lagrangian and the conditional closure formalisms, to study the PDFs of such an externally forced passive tracer. Then, we carry out numerical simulations for an idealized random flow on a sphere and for European Center for Medium-Range Weather Forecasts （ECMWF） stratospheric winds to test whether the mean-gradient model can be applied to studying stratospheric tracer mixing in midlatitude surf zones, in which a weak and poleward zonal-mean gradient is maintained by tracer leakage through polar and tropical mixing barriers, and whether the PDFs of tracer fluctuations in midlatitudes are consistent with the theoretical predictions. The numerical simulations show that when diffusive dissipation is balanced by the mean-gradient forcing, the PDF in the random flow and the Southern-Hemisphere PDFs in ECMWF winds show time-invariant exponential tails, consistent with theoretical predictions. In the Northern Hemisphere, the PDFs exhibit non-Gaussian tails. However, the PDF tails are not consistent with theoretical expectations. The long-term behavior of the PDF tails of the forced tracer is compared to that of a decaying tracer. It is found that the PDF tails of the decaying tracer are time-dependent, and evolve toward flatter than exponential.