Abstract
The identification of causal effects is a fundamental problem in climate change research. Here, a new perspective on climate change causality is presented using the central England temperature (CET) dataset, the longest instrumental temperature record, and a combination of slow feature analysis and wavelet analysis. The driving forces of climate change were investigated and the results showed two independent degrees of freedom —a 3.36-year cycle and a 22.6-year cycle, which seem to be connected to the El Niño–Southern Oscillation cycle and the Hale sunspot cycle, respectively. Moreover, these driving forces were modulated in amplitude by signals with millennial timescales.
Highlights
The identification of causal effects is a fundamental problem in climate change research
By using the slow feature analysis (SFA) approach, we analyzed the reconstructed the driving force of climate change based on the central England temperature (CET) dataset
A new investigation on climate change causality is given using the longest instrumental temperature record — the CET dataset— which was analyzed using SFA and wavelet analysis. This investigation into the driving forces of climate change reproduces a 3.36-year cycle and a 22.6-year cycle, which may be connected to the El Niño-Southern Oscillation (ENSO) cycle and the Hale sunspot cycle, respectively
Summary
The identification of causal effects is a fundamental problem in climate change research. Using SFA and the wavelet transformation technique, Yang et al.[11] (hereafter, Yang16) reconstructed and analyzed the driving forces for the monthly mean surface air temperature anomaly time series in the Northern Hemisphere, and found that the driving forces for this temperature climate system included two independent degrees of freedom that represented the effects of a 22-year solar cycle and the Atlantic Multidecadal Oscillation (AMO) on the climate system. They found that the driving forces are modulated in amplitude by signals with much longer time periods, this is, a long-term natural trend determined by the modulating amplitude signals
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