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GNSS radio occultation in-filling of the vast radiosonde data gap of the UTLS over Africa reveals global, regional and coupled climate drivers of tropopause variability
  • +5
  • Tong Ding,
  • Joseph Awange,
  • Barbara Scherllin-Pirscher,
  • Michael Kuhn,
  • Richard Ochieng Anyah,
  • Khandu Khandu,
  • Ayalsew Zerihun,
  • Luyen K. Bui
Tong Ding
Curtin University

Corresponding Author:tong.ding@student.curtin.edu.au

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Joseph Awange
Curtin University of Technology
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Barbara Scherllin-Pirscher
Zentralanstalt für Meteorologie und Geodynamik (ZAMG)
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Michael Kuhn
Curtin University
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Richard Ochieng Anyah
University of Connecticut
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Khandu Khandu
Landgate, Western Australia
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Ayalsew Zerihun
Curtin University
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Luyen K. Bui
University of Houston
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Over much of Africa, radiosonde data are lacking; consequently, the African UTLS is understudied, and potential proxies such as climate models and reanalyses fail to capture the behaviour of the UTLS fully. This study pioneers the use of Global Navigation Satellite System Radio Occultation (GNSS-RO) data from 2001 to 2020 to address the radiosonde data gaps over Africa and contributes to a better understanding of the tropopause (TP) characteristics under the influence of multiple climate drivers. The analyses show that GNSS-RO data from CHAMP, GRACE, MetOp, COSMIC, and COSMIC-2 agree with radiosonde measurements with differences being smaller than 1 K in the UTLS; thereby enabling in-filling of 80% of the missing radiosonde data in Africa during 2001-2020. By contrast, the smoothed vertical temperature profiles of reanalysis products lead to a warm bias of +0.8K in ERA5 and +1.2K in MERRA-2, and these biases alter some vertical and temporal structure details, with possible implications on climate change detection and attribution. Furthermore, the analysis of GNSS-RO data over Africa revealed: 1) influences of global climate drivers on TP temperature, with QBO > ENSO > IOD > NAO > SAM > MJO, and on TP height with ENSO > QBO > NAO > MJO > IOD > SAM; 2) multiple coupled global climate drivers such as ENSO-MJO, ENSO-NAO etc.; 3) coupled global and regional climate drivers that influence the TP variability, e.g., ENSO-ITCZ; and 4), the deep convection associated with the Asian Summer Monsoon and Tropical/African Easterly Jet locally influence TP height.