Gas giants of the solar system [image credit: Wikipedia]
Wavelet transforms reveal solar system footprints in climate time series, says Prof. Harald Yndestad. He explains how TSI (total solar irradiance) has a mean growth from 1700 to 2014. We believe the ideas here have links with this recent Talkshop post. (For the full technical discussion and wavelet examples see the linked article. Some extracts here.).
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In the mid-1980s, the mathematician Yves Mayer from the University of Marseille and the petroleum engineer Jaean Morlet worked with the analysis of data from petroleum surveys at Elf-Aquitaine, writes
Harald Yndestad @ The Climate Clock.
In their efforts to find better methods for frequency analysis, they rediscovered a set of a new type of transformations which they called Wavelets.
The wavelet transform solved some of the weaknesses of the Fourier transform. It required less computing power; it was possible to identify period and phase relations in time-series, and non-stationary periodic variations in nature.
When the method was presented, Morlet received the comment, “A method, not described in any textbook, cannot be of great importance”.
In 1988, Ingrid Daubechies published the article “Orthogonal Bases of Compactly Supported Wavelets”. This is perhaps the most important contribution to frequency analysis of time series since Fourier published his book in 1822.
I started using wavelet spectrum analysis around the year 2000. The problem was to find coincidences between the cod recruitment and the temperature variations in the Barents Sea. There are, however, a variety of wavelet functions, which calculate results variations.
The best choice of wavelet function had to be adapted to the statistical properties from the time series. This led to my own investigations of all wavelet function in the MATLAB Toolbox.
When I submitted the manuscript for review, wavelets spectrum analysis was unknown to editor. To find a reviewer, I visited the EGU conference in Vienna. Here I found two young PhD students who studied the wavelet transform.
Gradually, more people began to use the wavelet transform to study climate time series. Still most scientists are using the wavelet transform as a substitute for Fourier analysis. To me this is a wrong use of this powerful method.
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[Some technical discussion omitted here – see linked article].
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Example 2: Total solar irradiation
Figure 5: Total solar irradiance from A.D. 1700 to 2013 (Scafetta & Willson, 2014).
The Total solar irradiation (TSI) represents the measured irradiation Wm-2 at the average distance from the Sun to the Earth. Figure 5 shows an annual mean Total solar irradiance time series that covers the period from A.D. 1700 to 2013 (Scafetta & Willson, 2014).
A simple visual inspection of this data series shows that TSI from the sun has variations, and TSI has a mean growth from 1700 to 2014. The research question is to reveal the source of TSI variations.
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The computed wavelet specter revealed, for the first time, that the Jovian planets are the source of TSI variations. The result was confirmed by a wavelet spectrum analysis of the sun’s movement around the solar system Barycenter.
A study of Jovian planet movements has revealed a direct relations between Jovian planet movement, solar positions movement and TSI variations in periods up to 4450 years (Yndestad 2022).
Full article here.
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[Talkshop note: 3 dots indicate an omission from the original article.]
Tallbloke's Talkshop 
Figure 5 does not represent the “measured” TSI. It is reconstructed TSI. No one can agree on what TSI is over the last four solar cycles using spaced-based sensors, much less over the 300+ years using proxy data. For more info refer to “Empirical assessment of the role of the Sun in climate change using balanced multi-proxy solar records”, Scafetta, 2023
That is a lot of analysis on almost nothing that was not even a measured change.
It looks as if they have been playing computer games again to get the numbers from 1700, therefore most of the analysis is very suspect.
I wish these so called ‘scientists’ would do real science with what they actually know.
My 2c worth on other thread.
For reference:
ACRIM total solar irradiance satellite composite validation versus TSI proxy models (2014)
Nicola Scafetta • Richard C. Willson
Click to access 1403.7194.pdf
Note – Appendix B: ‘The importance of the TSI satellite debate for solar physics and climate change’
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Jovian Planets and Lunar Nodal Cycles in the Earth’s Climate Variability (2022)
– Harald Yndestad
This study utilizes time-series data devised to measure solar irradiation, sea surface temperatures, and temperatures in the lower atmosphere to gain a better understanding of how gravitational effects from the moon and Jovian planets (Jupiter, Saturn, Uranus, and Neptune) influence solar activity and climatic conditions on Earth. Then, standard statistical methods are used to determine the degree of correlation among these time series and construct a Jovian gravitational model. The study reveals a direct relationship between JSUN perihelion coincidences and TSI amplitude variations in cycles up to 4,450 years.
https://www.frontiersin.org/articles/10.3389/fspas.2022.839794/full
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The Influence of Solar System Oscillation on the Variability of the Total Solar Irradiance (2016)
– Harald Yndestad, Jan-Erik Solheim
Click to access JP%202016%20NA-TSI%20%202.pdf
Re. Stefani’s solar magnetic cycle proposal – see here under paragraph heading: ‘Rosette-shaped movement of the sun can produce a 193-year cycle’.
— https://tallbloke.wordpress.com/2021/06/17/the-suns-clock-new-calculations-support-and-expand-planetary-hypothesis/
We think Yndestad’s ~4450 year period is an exact number (23) of these cycles, which would be the number of beats derived from the solar Hale cycle (201 mean occurrences) and Jupiter-Saturn conjunctions (224) in the period (224 – 201 = 23). More to say about that in a future Talkshop post.
Stefani used Ian Wilson’s formula, explained in the Phys.org article as follows:
‘Mathematically, the 193 years arise as what is known as a beat period between the 19.86-year cycle and the twofold Schwabe cycle, also called the Hale cycle.’
Ian Wilson’s 2013 PRP paper (see Figure 12 and notes).
Click to access prp-1-147-2013.pdf
Solar Irradiance Variability: Comparisons of Models and Measurements (2019)
NOAA National Centers for Environmental Information (NCEI) Climate Data Record Program established the Solar Irradiance Climate Data Record.
Key Points
>> The Naval Research Laboratory’s solar variability models, NRLTSI2 and NRLSSI2, establish the Solar Irradiance Climate Data Record (CDR)
>> The CDR total and spectral (between 265 and 500 nm) solar rotational estimates are validated by observations on 27-day solar rotations
>> On solar cycle timescales and longer, particularly in the spectrum, differences in observational data sets preclude model validation
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019EA000693
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If the observations don’t agree the models have no chance of doing so.
From Judith Curry’s blog – upward trend in solar activity proxy since 1700…
Figure 4. The millennial solar-climate cycle over the past 2000 years. The anomaly in 14C production levels (black curve), a proxy for solar activity, is compared to iceberg activity in the North Atlantic (dashed blue curve), a climate proxy. The pink sine curve shows the millennial frequency. It defines two warm and two cold periods, supported by a large amount of evidence, some of which are represented by red and blue bars (see main text).
Author: ‘The problem can be summarized as follows: If we do not acknowledge the substantial effect of low solar activity, we are left without a satisfactory explanation for the occurrence of the Little Ice Age.’
January 19, 2021
Solar activity reconstructed over a millennium
https://phys.org/news/2021-01-solar-reconstructed-millennium.html