GNSS radio occultation in-filling of the African radiosonde data gaps reveals drivers of tropopause climate variability

Abstract

Radiosonde data are important for understanding and monitoring the upper troposphere and lower stratosphere (UTLS) region. Over much of Africa, however, such data are lacking; consequently, the African UTLS is understudied, and potential proxies such as climate models and reanalysis products fail to fully capture the behavior of the UTLS. This study pioneers the use of Global Navigation Satellite System-Radio Occultation (GNSS-RO) data from 2001 to 2020 to address the radiosonde data gap over Africa and contributes to a better understanding of the tropopause (TP) characteristics under the influence of global and regional climate drivers over the continent. As a first step to using GNSS-RO for infilling the radiosonde data gap over Africa, we analyzed the performance of GNSS-RO (2001–2020) and reanalysis products (European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) and Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2)) against radiosonde observations applying the Kling-Gupta Efficiency metric. The analyses show that GNSS-RO data from Challenging Mini-satellite Payload, Gravity Recovery and Climate Experiment, Meteorological Operational, Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC), and COSMIC-2 are in good agreement with radiosonde measurements with differences being smaller than 1 K in the UTLS, thereby enabling infilling of missing radiosonde data in Africa during 2001–2020. By contrast, the smoothed vertical temperature profiles of reanalysis products lead to a warm bias of +0.8 K in ERA5 and +1.2 K 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: (a) the teleconnections of El Niño-Southern Oscillation (ENSO), Quasi-Biennial Oscillation (QBO), Indian Ocean Dipole (IOD), Madden-Julian Oscillation (MJO), North Atlantic Oscillation (NAO) and Southern Annular Mode (SAM) at the tropopause boundary; (b) multiple coupled global climate drivers such as ENSO-IOD, ENSO-MJO, ENSO-NAO, QBO-IOD, and ENSO-NAO-MJO; (c) coupled global and regional climate drivers that influence the TP variability, for example, ENSO-Inter Tropical Convergence Zone; and (d), the deep convection associated with the Asian Summer Monsoon and Tropical/African Easterly Jet also locally influence TP height. In conclusion, this study demonstrates the capability of GNSS-RO to fill the vast radiosonde data gap over Africa. This opens the opportunity for further detailed studies toward a better understanding of the tropopause characteristics including localization, quantification of trends, and influences of global, regional, and coupled climate drivers.