Transport and mixing in the stratosphere: the role of Lagrangian studies

  • Bernard Legras
  • Francesco d’Ovidio
Conference paper
Part of the ERCOFTAC Series book series (ERCO, volume 11)


The stratosphere is an important component of the climate system which hosts 90% of the ozone protecting life from the ultra-violet radiations and, through the region called upper troposphere / lower stratosphere (UTLS) that encompasses the tropopause, has some control on the weather, the chemical composition of the atmosphere and the radiative budget. Because the temperature grows with altitude in the stratosphere, convection is inhibited by stratification, and the motion is mainly layer-wise on isentropic surfaces, with time scales of the order of weeks to months. The cross-isentropic adiabatic circulation is slow with time scales of the order of the season to several years. Below 30km, many chemical species, among which ozone, do not have significant sources or sinks and exhibit a chemical life-time of the order of several months to years. Such species can be treated as passive scalars transported by the flow. Their distribution is then dependent on the transport and mixing properties. Two useful quantities are the potential temperature θ = T (p 0/p)R/Cp which is related to entropy by S = C p ln θ and the Ertel potential vorticity (or PV) P = (∇ × u · ∇θ)/ρ which is a passive tracer under adiabatic and inviscid approximation. Owing to the separation between fast horizontal adiabatic motion and slow vertical diabatic motion, the potential temperature is often used as a vertical coordinate. PV is not practically measurable by in situ or remote instruments unlike many chemical tracers but can be easily calculated from model’s output. It is most often used as a diagnostic of transport and dynamical activity.


Lyapunov Exponent Unstable Manifold Polar Vortex Lagrangian Coherent Structure Isentropic Surface 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer 2007

Authors and Affiliations

  • Bernard Legras
    • 1
  • Francesco d’Ovidio
    • 1
  1. 1.Laboratoire de Meteorologie DynamiqueEcole Normale Supérieure and CNRSParis Cedex 05France

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