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

Abstract

Abstract. 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.

Observed Temperature Changes in the Troposphere and Stratosphere from 1979 to 2018

“…This is also seen in the stratospheric temperature structure as positive and negative temperature anomalies of several degrees; anomalies are proportional to the vertical gradient of the zonal winds (Randel et al 1999;Baldwin et al 2001). This distinctive thermal structure makes it possible to investigate the QBO with RO temperature anomalies (Wilhelmsen et al 2018). Here, we use the QBO index of monthly mean zonal winds of the Freie Universität of Berlin (FU Berlin 2019) produced by combining observations of three radiosonde stations: Canton Island, Gan/Maldives, and Singapore (Naujokat 1986).…”

The COSMIC/FORMOSAT-3 Radio Occultation Mission after 12 Years: Accomplishments, Remaining Challenges, and Potential Impacts of COSMIC-2

“…3). Scherllin-Pirscher et al 2012;Wilhelmsen et al 2018) and climate trends (Ho et al 2009b;Lackner et al 2011;. Dense RO sampling through all local times has also allowed quantification of the diurnal temperature cycle and its seasonal evolution in the upper troposphere and stratosphere (Zeng et al 2008;Pirscher et al 2010;Xie et al 2010a).…”

“…Thus, RO observations are useful to quantify the moisture distribution within and outside clouds in the middle and lower troposphere (below about 400 hPa), including regions with extremely moist and dry layers such as the subtropical Pacific (Rieckh et al 2017(Rieckh et al , 2018 except for superrefraction regions dominated by sharp boundary layer over oceans . Steiner et al (2018) demonstrated the use of RO for the evaluation of temperature and humidity in climate models in tropical convection regimes.…”

Evaluation of the 15-year ROM SAF monthly mean GPS radio occultation climate data record

“…However, despite differences between the RO missions, there is an expectation that measurements from different missions can be combined without any adjustments or intercalibrations to form long time series of RO data, provided that they use the same processing scheme (e.g., Foelsche et al, 2011;Angerer et al, 2017). Multi-mission RO time series have been used in several studies, implicitly assuming inter-mission consistency, e.g., in studies of atmospheric temperature trends (Ladstädter et al, 2011;Khaykin et al, 2017;Leroy et al, 2018), in climate model evaluation studies (Lackner et al, 2011;Ao et al, 2015;Schmidt et al, 2016), and in studies of atmospheric structure and dynamics (Scherllin-Pirscher et al, 2012Rieckh et al, 2014;Wilhelmsen et al, 2018).…”