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Tracing the convective sources of air at tropical tropopause during the active and break phases of Indian summer monsoon

  • C. Sivan
  • , Ajil Kottayil (Korresp. Autor*in)
  • , Bernard Legras
  • , Silvia Bucci
  • , K. Mohanakumar
  • , K. Satheesan

Veröffentlichungen: Beitrag in FachzeitschriftArtikelPeer Reviewed

Abstract

The Indian summer monsoon rainfall shows intraseasonal oscillations manifested as active and break phases, and depending on the phase, the water vapor concentration in the tropical tropopause layer (TTL) varies. Using dedicated Lagrangian backward trajectory analysis and circulation dynamics within the Asian monsoon anticyclone, this study investigates in detail the factors governing water vapor in the TTL during the active and break phases. Backward trajectory simulations have been performed using wind field and heating rate from ERA5 re-analysis data and the convective sources in the TTL are estimated from high resolution satellite measurements. A water vapor maximum in the TTL is found from Iran to the Tibetan Plateau during the active phase, whereas it is concentrated mostly over the Tibetan Plateau during the break phase. This study shows that the differences in the water vapor concentration in the TTL between these phases are primarily caused by the variability in the strength of Asian monsoon anticyclone and convective sources. At TTL, the influence of convective sources from the ocean on water vapor is small and continental regions dominate, mainly from the Indian subcontinent and the Tibetan plateau. The time taken (age) for the vertical transport of air parcels from major convective sources to the 380 K isentropic level, that is close to the thermal tropopause, is estimated to be around 3 weeks.

OriginalspracheEnglisch
Seiten (von - bis)2717-2734
Seitenumfang18
FachzeitschriftClimate Dynamics
Jahrgang59
Ausgabenummer9-10
DOIs
PublikationsstatusElektronische Veröffentlichung vor Drucklegung - 21 März 2022

Fördermittel

We sincerely acknowledge and thank the Indo French Centre for the Promotion of Advanced Research (CEFIPRA) for the research grant (Project no. 5607-1) for undertaking this study. Additional support was provided by the TTL-Xing ANR-17-CE01-0015 project of the Agence Nationale de la Recherche. ERA-5 trajectory computations were generated using Copernicus Climate Change Service Information. MLS data were provided by the NASA Goddard Space Flight Center earth Sciences (GES) Data and Information Services Center (DISC). GridSat-B1 is a product of NOAA Climate Data Record (CDR) Program. Supporting computational resources were provided by Institut Pierre-Simon Laplace (IPSL) mesocenter. We thank the reviewers for their useful comments that have resulted in improving the content and quality of the paper.

ÖFOS 2012

  • 105206 Meteorologie

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