Abstract
Abstract. We study daily surface air temperature (SAT) reanalysis in a grid over the Earth's surface to identify and quantify changes in SAT dynamics during the period 1979–2016. By analysing the Hilbert amplitude and frequency we identify the regions where relative variations are most pronounced (larger than ±50 % for the amplitude and ±100 % for the frequency). Amplitude variations are interpreted as due to changes in precipitation or ice melting, while frequency variations are interpreted as due to a northward shift of the inter-tropical convergence zone (ITCZ) and to a widening of the rainfall band in the western Pacific Ocean. The ITCZ is the ascending branch of the Hadley cell, and thus by affecting the tropical atmospheric circulation, ITCZ migration has far-reaching climatic consequences. As the methodology proposed here can be applied to many other geophysical time series, our work will stimulate new research that will advance the understanding of climate change impacts.
Highlights
As the methodology proposed here can be applied to many other geophysical time series, our work will stimulate new research that will advance the understanding of climate change impacts
We find that, when increasing the noise intensity in the synthetic series, the Hilbert amplitude decreases while the frequency increases, which shows that this trend is observed in real surface air temperature (SAT) time series
We have used Hilbert analysis to quantify the changes in SAT dynamics, on a global scale, that have occurred over the last 3 decades
Summary
Quantifying variations in surface air temperature (SAT) dynamics over several decades is a challenging problem because of non-stationarity and the presence of trends, measurement noise, multiple timescales, memory, and correlations in the data (Franzke, 2012; Massah and Kantz, 2016); in addition, reanalysis data can be unreliable (due to the lack of observational constraints in many geographical regions), and reanalysis time series are insufficiently long (as reanalysis starts at the beginning of the satellite era) These challenges have motivated the use, for climate data analysis, of data-driven approaches that have been commonly used for investigating observed complex signals in other fields of science (e.g. neurological, physiological, financial, etc.).
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