Atmospheric Measurement Techniques (Mar 2018)

Atmospheric QBO and ENSO indices with high vertical resolution from GNSS radio occultation temperature measurements

  • H. Wilhelmsen,
  • H. Wilhelmsen,
  • H. Wilhelmsen,
  • F. Ladstädter,
  • F. Ladstädter,
  • B. Scherllin-Pirscher,
  • A. K. Steiner,
  • A. K. Steiner,
  • A. K. Steiner

DOI
https://doi.org/10.5194/amt-11-1333-2018
Journal volume & issue
Vol. 11
pp. 1333 – 1346

Abstract

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We provide atmospheric temperature variability indices for the tropical troposphere and stratosphere based on global navigation satellite system (GNSS) radio occultation (RO) temperature measurements. By exploiting the high vertical resolution and the uniform distribution of the GNSS RO temperature soundings we introduce two approaches, both based on an empirical orthogonal function (EOF) analysis. The first method utilizes the whole vertical and horizontal RO temperature field from 30° S to 30° N and from 2 to 35 km altitude. The resulting indices, the leading principal components, resemble the well-known patterns of the Quasi-Biennial Oscillation (QBO) and the El Niño–Southern Oscillation (ENSO) in the tropics. They provide some information on the vertical structure; however, they are not vertically resolved. The second method applies the EOF analysis on each altitude level separately and the resulting indices contain information on the horizontal variability at each densely available altitude level. They capture more variability than the indices from the first method and present a mixture of all variability modes contributing at the respective altitude level, including the QBO and ENSO. Compared to commonly used variability indices from QBO winds or ENSO sea surface temperature, these new indices cover the vertical details of the atmospheric variability. Using them as proxies for temperature variability is also of advantage because there is no further need to account for response time lags. Atmospheric variability indices as novel products from RO are expected to be of great benefit for studies on atmospheric dynamics and variability, for climate trend analysis, as well as for climate model evaluation.