Areal analysis of oscillations in 500‐hPa temperature field: a pseudo‐2D wavelet transform approach

Abstract

ABSTRACT:This article introduces areal analysis of regular changes in 500-hPa temperature fields with pseudo-2D wavelet transform (p2D-WT). p2D-WT is a new technique, a practical approach based on the 1D continuous wavelet transform (CWT), designed to describe frequency characteristics of studied datasets in terms of their evolution in both time and space. The algorithm transforms datasets defined in a grid into a multidimensional object, which is subsequently interpreted by time and frequency slices. In this study, we describe the transformation fundamentals and application of the transform on the 500-hPa temperature field. Two types of reanalysis data are studied: time series from the European Centre for Medium-Range Weather Forecasts (denoted as ERA-40) and datasets from National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR). The analysis of the climatological data shows the presence of various periodicities among which the most distinct are the annual cycle (AC), semi-annual cycle, quasiquadrennial oscillation (QQO) and quasi-decadal oscillation (QDO). The article also provides a comparison of frequency characteristics of the NCEP/NCAR and ERA-40 reanalysis datasets; it shows that the frequency patterns found in the particular reanalysis datasets differ only in limited areas.

Manifestation of reanalyzed QBO and SSC signals

“…To study the QBO and SSC areally and to describe latitudinal, longitudinal, and vertical variations of the oscillations, we applied an extension of the CWT-the pseudo-2D wavelet transform (p2D-WT, Pisoft et al 2011). For more details about the methodology, please see the online resource.…”

“…Previous studies using p2D-WT to describe horizontal distribution of an oscillation's amplitudes (Pisoft et al 2009(Pisoft et al , 2011(Pisoft et al , 2012 showed that the studied reanalyzed datasets varied considerably from each other in some cases and the results were complicated to compare. In this study, we decided to present our results mainly in the form of vertical profiles of the zonal means because those are constructed of values averaged along particular latitudes representing general nature of the results and they are relatively easier to compare.…”

“…To overcome this deficiency, a spatial component needs to be included in the analysis. That can be implemented using the pseudo-2D wavelet transform that analyzes selected oscillations areally (Pisoft et al 2009(Pisoft et al , 2011(Pisoft et al , 2012. The study is supplemented by additional comments that can be found in the online resource.…”

Analysis of 24 years of mesopause region OH rotational temperature observations at Davis, Antarctica. Part 2: Evidence of a quasi-quadrennial oscillation (QQO) in the polar mesosphere

“…Liu and Xue (2010) investigated the relationship between ENSO and the Antarctic Oscillation (AAO) index with empirical orthogonal function analysis in sea level pressure anomalies from 1951-2002 concluded that ENSO plays a key role in the phase transition of AAO at the quasiquadrennial timescale. Pisoft et al (2011) point out that the quasi-quadrennial oscillations that have been reported are almost always associated with the ENSO phenomenon and https://doi.org/10.5194/acp-2019-1097 Preprint. Discussion started: 5 February 2020 c Author(s) 2020.…”

Temperature and precipitation variability in regional climate models and driving global climate models: Total variance and its temporal‐scale components

“…The strengths of the forcing mechanisms and resulting spectral characteristics of observed time series often vary in time, which cannot be detected by traditional Fourier analysis. Various methods, frequently the wavelet transform (e.g., Brázdil et al, 2012;Šácha et al, 2014;Kuchař et al, 2017) and its modifications (e.g., Pišoft et al, 2011) have been used in climatology to overcome this hurdle. Eskridge et al (1997) and Hogrefe et al (2003) compared several methods of separating atmospheric variables into different temporal components.…”