New study identifies natural driving forces of climate change

The El Niño of 1997-8

Another paper attempting to shed some light on the mysteries of long-term cyclical climate patterns is brought to our attention by the GWPF. The abstract looks fair but there are a few nods in the direction of ‘greenhouse gases’ later in the paper, in particular related to what they identify as millennial signals.

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.

Introduction
Causality analysis in climate change is an active and challenging research area that remains highly uncertain. The Intergovernmental Panel on Climate Change (IPCC) advocates that human activity is the most important driving force of climate change, while some researchers have argued that natural forces might be the main cause.

These different views are mainly due to a lack of methods to address the complexity of climate system and insufficiency in observational climate data. Global circulation model (GCM) simulations are generally used to investigate the causality of climate change.

However, due to the limited knowledge of the climate system, large uncertainties are still associated with GCMs; therefore, the improvement of current GCMs to meet the requirements for causality analysis is still an urgent issue.

An alternative method to GCMs is to use long-term observational climate data to study the driving forces of climate change, a method that has recently benefited from the great progress made by physical and biological scientists in studying the driving forces in non-stationary time series. The main advantage of this approach is that observational data can be used to directly extract the driving forces of an unknown dynamical system.

This can be achieved by two techniques. The first technique involves finding the driving forces by studying the connections among different physical factors. These types of relations cannot be established using general correlation analysis, but only in dynamical directional influences. Granger causality is a pioneering approach for achieving this task.

Continued here.
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Full paper