Rain trends vs -ENSO-IPO: Manilla NSW by surlybond

From 1999, rainfall at Manilla NSW matched ENSO only up to 2011, before the IPO became positive. This graphical log compares the rainfall at Manilla NSW with the El Niño-Southern Oscillation (ENSO) and the Inter-decadal Pacific Oscillation (IPO) through the 21st century to date. Values shown are anomalies, smoothed. (See Notes below on “Data”, “Smoothing”, […]

via 21-C Rain-ENSO-IPO: Line graphs — climate by surly


Climate shifts…natural variation

I have started this blog post because today l have found out all major search engines are re routing the search string ‘climate shift’.

If you enter this term into any search engine, it will respond with pages and pages of ‘climate change’

We are being prevented from viewing alternative theories to man made climate change theories or facts, folks.

I will make an attempt to collect some links to climate regime shift sites that focus on natural variability.

I have tried alternatives to google and they ALL redirect the term ‘climate shift’

…You can get around this by..

Using google scholar…

which will accept the string ‘climate shift’ and lead you to alternative research on the reasons for global temperature trends other than AGW


on on the main google search engine page use talking marks on the search string which over rides the ban on the term… climate shift

“climate shift”






Complete record of droughts at Manilla NSW from 1884.

By ‘surly bond’,an independent climate analyst at Manilla NSW , Australia.

Posted on ‘weather zone forum’ July 2019


Red colour marks times when rainfall was in the lowest one percent of occurrences. The graph is novel, in that the beginning of drought is marked as well as the end.

There were just six great droughts lasting more than one year: 1902, 1911, 1940, 1946, 1965, 2018.
Very long droughts persisted for forty years from 1910 to 1950, then they ceased for the next forty years.
The pattern does not match a model of climate change that supposes that droughts have been getting (a) more frequent or (b) more extreme.

Details are in posts such as:


Australian weather research

Have you ever gone on to a blog and wanted to quote about a research paper you read and couldn’t find it to quote it.

Here l would like to do myself a favor and record their links and title and points of interest. Feel free to do the same in the comment section below.

Click title if they don’t load

Thanks to ‘colin maitland’ of Australian weather forum


for providing this link to Blair Trewin ‘s research gate page with a plethora of research to read

Here is a link to my favorite moon cycle physicist. and the amazing links to Earths climate he has found. BTW Ian Wilson is not an  astrologer


Ian Wilsons’ home page



Obviously this thread is in progress.Go to comments section for further research links. They are not A to Z but just posted randomly


Ken what is the difference between deterministic and ensemble?

Ken Kato is a senior meteorologist on the Qld coast with BOM. He runs a facebook page

south Brisbane storms



is a frequent and long term  contributor to the ‘weatherzone forum’


Many people ask Ken excellent questions

Here a forum member asks Ken about model runs

and here is Kens’ answer


In the context of a single model ensemble like the EC ensemble, it’s when a single model like the EC (or any other model) is run multiple times to generate a range of scenarios (members).
The EC ensemble always generates 50 members. Each member starts off with slightly different initial states of the atmosphere and oceans. This is because we don’t have obs data for every square inch of the whole planet (satellite data covers most of the planet but it has its own degree of uncertainties) – it’s these uncertainties in the exact initial state of the atmosphere and oceans that grow and grow as you look further into the future and are one of the biggest contributions to increasing forecast error over time (chaos theory, butterfly effect, etc). So the model uses a sophisticated system to deliberately inject variations (weighted towards the uncertainties which are most likely to cause the maximum forecast errors) into the starting state of the atmosphere/ocean for each member.
If most ensemble members are tightly clustering around a particular scenario (e.g. max temp of 35C for a particular day, 25-50mm of rain, etc), it generally means that the weather setup concerned is insensitive to influences that can throw that forecast off which implies there’s a high confidence in that model for that scenario. But if there’s a big spread in the ensemble’s members, it implies the setup’s sensitive to even small changes and uncertainty is high.

In contrast, the deterministic version of a model (the forecasts from models like EC, GFS, ACCESS-G, etc whose forecasts you see on most websites) is just a single scenario.

Most deterministic models have an ensemble version.

The advantage of ensembles is that they give a great idea of how confident or uncertain a model is for particular scenarios. Comparing an ensemble to a deterministic version of a model is a bit like asking a big bunch of doctors for a diagnosis on a hard-to-diagnose disease compared to asking just a single doctor.
One disadvantage of ensembles is that they have lower resolution than their deterministic versions (due to the computational resources they take up) so they can sometimes miss smaller scale details, underestimate the intensity of a smaller than normal intense TC, etc.

There’s also multimodel ensembles and grand ensembles… the former are ensembles consisting of multiple deterministic models (WATL and OCF are examples) and the latter are ensembles of ensembles.

So in a nutshell, think of ensembles by their common definition such as that used for furniture i.e. a group of things that are treated as a whole thing. Ensemble = multiple scenarios from a model or models. Deterministic = single scenario from a model.

One thing to note is that a lot of forecast products from ensembles show the average of all the scenarios (e.g. WATL, OCF rainfall amounts, etc). While this is useful, it doesn’t show anything about how the scenarios are distributed, if they’re skewed, what outliers there are, etc. Therefore I prefer to look at probability forecasts from ensembles (percentage of an ensemble’s members going for a particular scenario) and preferably multimodel ensembles because a single model ensemble often tends to be more representative of its deterministic version rather than giving an appreciation of the true range of possible scenarios. ”




Nicola Scafetta: On the astronomical origin of the Hallstatt oscillation found in radiocarbon and climate records throughout the Holocene.

NICOLA SCAFETTA SEPTEMBER 2016 “Hallstatt oscillation (about 2318 year period), which is observed in climate and solar records is a major stable resonance of the solar system. The paper also evaluates the other major planetary stable resonances and we found all other typical oscillations found in climate and solar records such as a quasi 20-year oscillation, a quasi 60-year oscillation, the 82-97 year Gleissberg oscillation and the 159-185 year Jose oscillation (and others).”

Tallbloke's Talkshop


Nicola Scafetta writes:

Dear all,

it was a pleasure to meet you at London. Some of you asked me about my paper in press about a link between astronomical, solar and climate oscillations. Here it is:

Scafetta, N., Milani, F., Antonio Bianchini, A., Ortolani, S.: On the astronomical origin of the Hallstatt oscillation found in radiocarbon and climate records throughout the Holocene. Earth-Science Reviews 162, 24–43, 2016. There is a free access to the article, and is valid for anybody until November 10, 2016 by using this link  http://authors.elsevier.com/a/1TlSB2weQTZcD

(Permanent copy here)

The importance of the article is that it demonstrates quite clearly that the long Hallstatt oscillation (about 2318 year period), which is observed in climate and solar records is a major stable resonance of the solar system. The paper also evaluates the other major planetary stable resonances and we found all other typical oscillations found in climate and…

View original post 121 more words


Fibonacci and the earths climate

It is well known that the universe is not a chaotic system but governed by laws that are predictable. All of creation exhibits order. The Fibonacci series and the ‘golden numbers’ are found in all ordered and stable components of all living and non living things.


So why wouldn’t we find Fibonacci numbers in the weather and earths climate.

I have started this post on Tom Mangos request and posted his table of Fibonacci and conjunction cycles as requested. Tom studies the sun, moon  and the large planets Jupiter and Saturn and  there links to the earths climate .





The suns acceleration linked to the earths ice age cycles by TOM MANGO





Hi Sue,
These two graphs indicate that there are times when solar
minimum occurs at or near the same location on the ecliptic.

When the Sun’s acceleration decreases during an Ice-age cycle
it’s orbit becomes more circular and average solar cycle length
becomes longer (approaching 11.86 years).

During the 12,000 year inter-glacial periods there is evidence
that this is happening to a lesser degree. Notice the 143 year
separation (Bretagnon wave?) between 1843 and 1986.


CRIKEY said”feel freee to ask TOM questions in the comments section below.
This post is an ongoing study of Toms’
Use the search facility to find his previous posts

l store all of Toms graphs in my solar system and the earths climate folder found here



TOM MANGO supports Scafetta 2016 research on the role of the planetary gas giants on earths climate

Dr Scafetta has a new paper out in 2016


Tom has contacted me with some of his own calculations to support Scafettas findings

He wrote

“Hey Sue,

Scafetta’s new paper is all about validating the 60 year
cycle using highly technical methods.
I use an extremely simple method of sums with

very interesting results.

Here’s a table and a graph I’ve put together:’
PLEASE FEEL FREE TO CONTRIBUTEjup_sat_inequality2btom2bmango2bmay2b2016
The above works belongs to TOM MANGO

Readers can contact me at TLMango10@gmail.com
or via this post in the comments section below..

Nicola Scafetta: High resolution coherence analysis between planetary and climate oscillations

extract from abstract
“… using the canonical correlation analysis at least five coherent frequencies at the 95% significance level are found at the following periods: 6.6, 7.4, 14, 20
and 60 years. Thus, high resolution coherence analysis confirms that the climate system can be partially modulated by astronomical
forces of gravitational, electromagnetic and solar origin. A possible chain of the physical causes explaining this coherence is briefly
2016 COSPAR. Published by Elsevier Ltd. All rights reserved”SCAFETTA 2016′




click on the totle of this post to load all comments below

Tallbloke's Talkshop

Image credit: NASA Image credit: NASA
Note from the author: I am sending you my new paper. It has been just published.

Scafetta, N.: High resolution coherence analysis between planetary and climate oscillations.
Advances in Space Research 57, 2121-2135, 2016.
DOI: 10.1016/j.asr.2016.02.029

To help access and share the article, there is the following article link, which will provide free access to the article until June 9, 2016.

View original post 172 more words