New study identifies natural driving forces of climate change 

Posted: September 3, 2017 by oldbrew in climate, IPCC, modelling, Natural Variation, research
Tags:

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

Comments
  1. Ned Nikolov says:

    There is still little understanding of the fact that Earth’s climate cannot be figured out without considering our Planet in the context of a larger cosmic continuum of drivers involving other bodies in the Solar System as well. What we observe on Earth in terms of “climate change” is not a result of some unique physical laws applicable exclusively to our planet as many scientists tend to think …

    For example, the presence of liquid water on Earth is considered by most as an agent that creates unique conditions on this planet. According to the mainstream Greenhouse theory, water vapor is a key controller of Earth’s climate. However, our analysis (Nikolov & Zeller 2017, https://tinyurl.com/ydxlfwn7) showed that liquid water & water vapor on Earth is a consequence of the atmospheric thermal effect determined by total atmospheric pressure and solar irradiance!

  2. oldbrew says:

    ‘Causality analysis in climate change is an active and challenging research area that remains highly uncertain.’

    But there’s no shortage of alarmist climate propaganda :/

  3. erl happ says:

    Quote: ‘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.’

    Refreshingly realistic,

    Source: LAGEO Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Comment: The Chinese are excellent mathematicians.

    But ENSO is not the major natural mode of change in climate. That can be attributed to the annular mode phenomenon that ultimately originates in the Antarctic stratosphere, a link appreciated by other Chinese researchers who use sophisticated mathematical analysis.

    Interesting to see Tsonis cited for his work on the influence of cosmic rays the cycles being attributed directly to change in solar activity, as is ‘well understood’ as Leif Svalgaard is wont to say.

    Despite all this the underlying mechanisms of change have not been apprehended. This will not happen until climatologists get to grips with the forces driving polar cyclones and Jet Stream activity. It is beyond the scope of mathematicians.

  4. Now that the EL NINO environment is ending let us see how much warmth will be present as we move forward. Answer nada.

    In the meantime the recent burst of solar activity (a surprise solar flux 120) lends credence to the idea that this cycle is going to be not only weak but long.

    Global ocean temperatures last check were +.293 c.

    My solar climate play is very low solar will result in overall lower ocean temperatures and an increase in albedo .

    Thus far this year the global temperatures have been trending down and I expect this trend to continue with global temperatures at 30 year means within a year.

    AGW will be in more trouble to justify this development if it occurs which is looking more likely.

  5. Gamecock says:

    ‘The identification of causal effects is a fundamental problem in climate change research.’

    Climate change research is the fundamental problem. The above declaration is an assertion of importance. It’s not.

    We know that natural variability overwhelms any man made influence. And a simple graph of CO2 concentration and global mean temperature over the last 40 years shows no correlation.

    Climate change research is basic research, with no real life implications. In a word, esoterica. GWPF does us no favors by saying that climate change is relevant.

    ‘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.’

    In other words, GCMs are junk. The improvement of current GCMs will still yield junk. GCMs are a total waste of money. They don’t work, and even if they did, there is no real world value in their output. They are an expensive toy. And propaganda prop. We are threatened with their outputs.

  6. oldbrew says:

    ‘due to the limited knowledge of the climate system, large uncertainties are still associated with GCMs’

    Bit of a Catch-22? GCMs need better knowledge of the climate system, but GCMs are supposed to help scientists to gain better knowledge of the climate system.

  7. pochas94 says:

    Now, if we could only figure out what drives the ENSO oscillation.

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