Intensity of climate variability derived from the satellite and MERRA reanalysis temperatures: AO, ENSO, and QBO

Abstract

Satellite measurements (Atmospheric InfraRed Sounder/Advanced Microwave Sounding Unit-A, MODerate resolution Imaging Spectroradiometer) and the Modern Era Retrospective-analysis for Research and Applications (MERRA) reanalysis have been utilized to analyze the relative influence of the climate variability (AO: Arctic Oscillation, ENSO: El Niño-Southern Oscillation, QBO: Quasi-Biennial Oscillation) on the zonal-mean temperature and wind variations over the globe from September 2002 to August 2011. We also extended the usage of MERRA data for the period of 1979-2011; furthermore, three climate indices of AO, NINO3.4, and QBO were used as the corresponding climate indicators. The correlations between the temperature anomalies and the climate indices indicate that the tropospheric temperature variability in the mid-latitude (30-60N) linked to both AO and ENSO has been more pronounced over ocean than over land. However, the low stratospheric temperature variability in the mid-latitude is mainly associated with ENSO and QBO. The north-south symmetric patterns over the globe are seen in the wind anomaly distributions for ENSO and QBO, but not for AO. The ENSO events are globally vigorous but also localized during the recent 9 years compared with those based on the period of 1979-2011. The tropospheric warming and stratospheric cooling phenomena during this period are more remarkable in the recent 9 years, although according to IPCC (2012). their linkage to the ENSO variability is still uncertain. The ENSO is found to have more significant impact on the tropospheric and low stratosphere temperature variability over the tropics in the recent period, consistent with more active zonal wind meridional circulations. The discrepancies between satellite observations and MERRA are also discussed. The estimated relative impact of the three major concurrent large-scale climate phenomena on regional temperature variability can be of great use in its long-term predictability. © 2013 Elsevier Ltd.