Stratospheric temperature changes during the satellite era

Abstract

Satellite‐based layer average stratospheric temperature (T) climate data records (CDRs) now span more than three decades and so can elucidate climate variability associated with processes on multiple time scales. We intercompare and analyze available published T CDRs covering at least two decades, with a focus on Stratospheric Sounding Unit (SSU) and Microwave Sounding Unit (MSU) CDRs. Recent research has reduced but not eliminated discrepancies between SSU CDRs developed by NOAA and the UK Meteorological Office. The MSU CDRs from NOAA and Remote Sensing Systems are in closer agreement than the CDR from the University of Alabama in Huntsville. The latter has a previously unreported inhomogeneity in 2005, revealed by an abrupt increase in the magnitude and spatial variability of T anomaly differences between CDRs. Although time‐varying biases remain in both SSU and MSU CDRs, multiple linear regression analyses reveal consistent solar, El Niño–Southern Oscillation (ENSO), quasi‐biennial oscillation, aerosol, and piecewise‐linear trend signals. Together, these predictors explain 80 to 90% of the variance in the near‐global‐average T CDRs. The most important predictor variables (in terms of percent explained variance in near‐global‐average T) for lower stratospheric T measured by MSU are aerosols, solar variability, and ENSO. Trends explain the largest percentage of variance in observations from all three SSU channels. In MSU and SSU CDRs, piecewise‐linear trends, with a 1995 break point, indicate cooling during 1979–1994 but no trend during 1995–2013 for MSU and during 1995–2005 for SSU. These observational findings provide a basis for evaluating climate model simulations of stratospheric temperature during the past 35 years.

Climatology and interannual variability of dynamic variables in multiple reanalyses evaluated by the SPARC Reanalysis Intercomparison Project (S-RIP)

“…The satellite weighting functions for these three channels can be found in Fujiwara et al (2017, their Fig. 7) and Seidel et al (2016, their Fig. 1 (Zhao et al, 2016).…”

“…Other studies use these temperature data records to monitor changes in the Brewer-Dobson circulation (Young et al, 2011(Young et al, , 2012. Randel et al (2016) compared global and latitudinal trends from SSU with Aura MLS and SABER temperatures. Simmons et al (2014) discuss the impacts of the MSU, SSU, AMSU-A, HIRS, and AIRS channels assimilated in the ERA-I.…”

Properties of the Long-Term Oscillations in the Middle Atmosphere Based on Observations from TIMED/SABER Instrument and FPI over Kelan

“…Above all, we believe that the ENSO signal may also modulate the amplitude of the TO, mainly in the lower stratosphere. Several studies have revealed that the El Nino-Southern Oscillation (ENSO) can influence the tropical stratospheric temperature [48][49][50]. The ENSO variations can be characterized by the Multivariate ENSO Index (MEI) [51].…”

In situ temperature measurements in the upper troposphere and lowermost stratosphere from 2 decades of IAGOS long-term routine observation

“…The robustness of the temperature trends in the lower stratosphere derived from radiosondes (since 1958) and from satellites (since 1979) suffers from instrumental uncertainties such as sensor changes, drifts, etc., implying large uncertainties in the trend estimates (Simmons et al, 2014). In recent years, several studies assessed the uncertainty of temperature trends in the lower stratosphere and the impact on changing trends of radiatively active constituents (such as ozone) or atmospheric dynamics (e.g., Fueglistaler et al, 2014;Seidel et al, 2016).…”

“…Since 1995, the cooling turned into a neutral trend with a larger increase (not significant) over the Antarctic region than over the tropics (Randel et al, 2009;Blunden and Arndt, 2014;Seidel et al, 2016). The robustness of the temperature trends in the lower stratosphere derived from radiosondes (since 1958) and from satellites (since 1979) suffers from instrumental uncertainties such as sensor changes, drifts, etc., implying large uncertainties in the trend estimates (Simmons et al, 2014).…”