Arsetralia Does It Best - Does WHAT Best? - Lie

[AlleyCat]

Statistical Analysis Can Provide Evidence That the Official Australian Acornsat
2.1 Temperature Analysis Has Added Non-Climatic Warming to The Record, 1910 To
2019

By Bob Irvine

Conclusions based on large complex datasets can sometimes be simplified and
checked by comparing expected results with the actual numbers generated. This
is the case with the 58 temperature stations that cover the period 1910 to 2019
in the Australian BOM record.

Andy May was right to be suspicious when the USA warming as recorded of 0.25C
per century...

https://wattsupwiththat.com/2020/11/24/the-u-s-national-temperature-index-is-
it-based-on-data-or-corrections/

https://wattsupwiththat.com/2020/11/24/the-u-s-national-temperature-index-is-
it-based-on-data-or-corrections/

... is changed using corrections and a gridding algorithm to 1.5C per century.
It is, however, nearly impossible to prove non-climatic warming due to data
manipulation by normal means. The fact that an algorithm increases warming may
be legitimate, "time of observation" (TOB) for example.

A similar thing has happened in Australia.

"ACORN is the Australian Climate Observation Reference Network of 112 weather
stations across Australia. The rewritten ACORN 2 dataset of 2018 updated the
ACORN 1 dataset released in 2011. It immediately increased Australia's per
decade rate of mean temperature warming by 23%..." (Chris Gilham)

Again, it is right to be suspicious but difficult to prove by simply looking at
the overall result. The changes could be legitimate, despite there being
virtually no TOB issue in Australia.

EVIDENCE THAT NON-CLIMATIC WARMING HAS BEEN ADDED BY THE ACORNSAT CORRECTIONS

The term "corrections" here refers to all changes to the raw data for any
reason. Some of these changes will be legitimate and some will distort the data
in an artificial way. What follows is evidence that the artificial distortion
of the data is significant and the "correction algorithms" should be revisited.

SOME BACKGROUND

I have used the following dataset for all calculations and acknowledge Chris
Gilham's effort in compiling it.

http://www.waclimate.net/acorn2/index.html

My term "magnitude of the corrections" for each station's max, min and mean is
derived as follows. From the Albany maximum data at the above link.

Average change per decade: ACORN 2.1 0.16C / raw 0.08C

The "magnitude of the correction" is the difference between "Acorn Sat 2.1" and
"raw" for each station (max, min, mean). i.e. 0.08 (0.16 - 0.08) in this case.

The "Acorn 2.1 anomaly" in this case is 0.16C.

The Acorn 2.1 corrections are designed to correct the data for station moves,
equipment changes etc. and the final result is expected to represent the real
temperature anomalies at a particular station more realistically than the raw
data.

While it is expected that the corrections will either warm or cool the data at
any particular station, it can also be expected that the "magnitude of the
correction" bear no relationship to the final temperature anomaly. The final
temperature anomaly as represented by Acorn 2.1 should depend entirely on the
real temperature anomalies at the particular station and, therefore, be
completely unrelated to the "magnitude of the corrections".

For example, if two hypothetical stations were a few hundred meters apart but
showed a large difference in temperature anomaly due to differing vegetation or
equipment etc., then Acorn 2.1 would hypothetically adjust one say by 1.0C per
decade and the other by say 0.03C per decade to finish with the same
temperature anomaly for each station as is expected. They are next to each
other after all.

The "magnitude of the corrections", in this case 1.0C/Dec. and 0.03C/Dec.,
should not correlate with the final temperature as recorded by Acorn 2.1. i.e.,
the final temperature anomalies in this hypothetical case will finish up being
approximately the same while the "magnitude of the two corrections" are very
different. I hope this is clear.

If we then take the 58 Australian stations that cover the period 1910 to 2019,
we should find that the "magnitude of their corrections" and their final "Acorn
2.1 anomaly" have approximately zero correlation if the corrections are not
artificially affecting the final Acorn 2.1.

To check this, I have calculated the Pearson Correlation Coefficient (PCC) for
these two data sets. ("Magnitude of the 58 corrections" compared to the "Acorn
2.1 anomalies" for the 58 stations).

If this PCC is;

Negative - then the corrections to the raw data are adding artificial or
spurious cooling to the raw data.
Approximately zero - then the corrections are not adding artificial cooling
or warming and are doing their job as intended.
Positive - then the corrections are adding artificial or spurious warming to
the raw data.

JUSTIFICATION FOR THE METHOD USED

The BOM comparison station selection method has a number of reference points
and two variables per reference point that they are trying to correlate.

My data is exactly the same and I use the same method as they do to correlate
my variables.

The BOM's reference points are the 12 months.

My reference points are the 58 temperature stations.

The BOM's two variables are the Temp. anomalies at station 1 and station 2 for
each particular month.

My two variables are the "Magnitude of the Correction" and the "Acorn-Sat
homogenised Temp. anomaly" at each particular station".

A Pearson Correlation Coefficient (PCC) is specifically designed for this
application.

The BOM statisticians obviously thought this the best way to approach this
issue, as I do.

See also Appendix A for a stress test of the method used.

LEGEND

A PCC only indicates correlation and is not designed to indicate certainty or
put a number on probability etc. The significance of a PCC value depends
entirely on the intrinsic nature of the datasets being compared.

In their Pairwise Homogenisation Algorithm, AcornSat extensively use nearby
comparison stations to deal with incontinuities in any given station. For a
comparison station to be considered useable they compare the monthly
temperatures for a 5-year period either side of the incontinuity. If the PCC
for these two monthly datasets is greater than 0.5 then that comparison station
is considered accurate enough to one tenth of a degree Celsius.

I have used the AcornSat logic and PCC figure as a guide when comparing the
"magnitude of the corrections" with the "Acorn 2.1 anomaly" for the 58
stations.

My legend;

If the PCC is greater than 0.5 then - It is almost certain that the
corrections are adding significant non-climatic warming to the raw data.
If the PCC is greater than 0.3 and less than 0.5 - then it is very-likely
that the corrections are adding significant non-climatic warming to the raw
data.
If the PCC is greater than 0.1 and less than 0.3 - then it is likely that the
corrections are adding some non-climatic warming to the raw data.
If the PCC is between -0.1 and 0.1 then it is likely that the corrections are
not adding non-climatic cooling or warming to the raw data. The corrections are
doing their job.
If the PCC is less than -0.1 - then it is likely that the corrections are
adding some non-climatic cooling to the raw data.
Etc.

RESULTS

Figure 1 graphs the 58 Australian stations that cover the period 1910 to 2019.
The general slope of the data points up to the right of the graph is solid
evidence that the Acorn 2.1 corrections are adding non-climatic warming to the
record.

https://149366104.v2.pressablecdn.com/wp-content/uploads/2022/01/image-39.png

Figure 1. If the corrections were doing their job properly and were not adding
spurious warming to the record, the 58 station points on this graph would on
average be approximately horizontal across the graph. The fact they rise to the
right means that the AcornSat correction algorithms are adding spurious non-
climatic warming to the record.

Table 1 below shows clearly how the final Acorn 2.1 warming anomaly increases
with the magnitude of the corrections. The implication is that the corrections
are causing some of that apparent temperature increase.

https://149366104.v2.pressablecdn.com/wp-content/uploads/2022/01/image-40.png

Table 1, The "magnitude of the corrections" for the 58 stations are divided
into 5 categories and their 5 corresponding average temperature anomalies are
compared. An increase in the "magnitude of the corrections" coincides with an
increase in the Acorn 2.1 temp. anomaly. The red value for "X" covers the 14
stations where AcornSat believed the raw data was accurate and needed little
changing.

Table 2, below indicates the Pearson Correlation Coefficient (PCC) (Magnitude
of Corrections v Acorn temp. anomaly) for the total dataset. The red line has a
lower PCC and implies that when less correction is required the final Acorn 2.1
temperatures have less non-climatic warming.

https://149366104.v2.pressablecdn.com/wp-content/uploads/2022/01/image-41-
1536x223.png

Table 2, Shows the PCC for the total dataset and, in red, the PCC for the
stations that AcornSat deemed the raw data accurate enough to not need
significant correction.

CONCLUSION

According to the precedent set by AcornSat for the use of the Pearson
correlation coefficient (PCC) when applied to data of this type, a PCC of 0.56
(magnitude of corrections verses Acorn 2.1 temp. anomaly) as found here
indicates that "It is almost certain that the corrections are adding
significant non-climatic warming to the raw data".

For some of the stations in the dataset, AcornSat decided that the raw data was
relatively accurate and consequently did not need significant homogenisation or
correction. These stations are likely to give a more accurate temperature
reading and a better idea of Australia's temperature history over the period
covered.

As a check I calculated the same PCC for the 14 stations that only needed
minimal correction (Red data in Table 1 and Table 2). A lower PCC of 0.25 still
indicates some artificial warming but not nearly as much as for the whole
dataset.

The average temperature anomaly per decade for the 14 stations that needed
minimal correction is about 0.1°C/Decade (Table 1). As calculated, this will
include some artificial warming so the actual average warming for these more
accurate stations is likely to fall in the range, 0.08°C to 0.10°C per decade.

This method could possibly be used to check the adjustments to the USA
datasets. These adjustments are fiercely contested so it would be good to have
some objective analysis. Let the cards fall where they will.

APPENDIX "A"

This appendix seeks to stress test the method used above. It does this by
applying the method to two extreme homogenisation algorithms. One that we know
adds zero artificial warming and one that we know adds large artificial warming
in proportion to the correction. In the first instance ("C" in table 1.) we
expect the Pearson Correlation Coefficient (PCC) to be zero. In the second
("E" in Table 1.) we expect the PCC to be 1.0. If this is true in both cases,
then the method used, and the conclusions drawn in the main paper above are
likely to be correct.

THE TEST

We use accurate satellite technology to determine the temperature anomalies for
all parts of an imaginary small island.

This imaginary accurate satellite data has been available for the last 100
years and tells us that the temperature of all parts of this island have
increased at the same rate. This rate being 0.1C per decade over the last
century ("B" in Table 1).

We the readers are the only ones aware of the true and accurate temperature
rise for all parts of the island over the last 100 years as described.

The inhabitants of the island are unaware of this accurate satellite data set.
In an attempt to determine the temperature rise, over the 100 years on the
island, these inhabitants set up 10 temperature stations 100 years ago, all
equally spaced around the island.

Three of these stations (Station # 1 to 3) were well sited with good equipment
that has not needed to be changed or relocated for any reason for the 100
years. They each showed a correct temperature anomaly of 0.1C per decade.

The other 7 stations (Station # 3 to 10) were subject to various changes
related to equipment, vegetation, station moves etc. The raw data from all
these 7 stations showed anomalies less than the correct 0.1C per decade. To
correct for these inconsistencies the inhabitants developed two homogenisation
algorithms ("C" and "E") that they then applied to these 7 variant stations.

To test which of these two algorithms was suitable, the inhabitants applied the
test described in the main paper above.
STATION # A B C D E F
1 0.1 0.1 0.1 0 0.1 0
2 0.1 0.1 0.1 0 0.1 0
3 0.1 0.1 0.1 0 0.1 0
4 0.09 0.1 0.1 0.01 0.11 0.02
5 0.08 0.1 0.1 0.02 0.12 0.04
6 0.07 0.1 0.1 0.03 0.13 0.06
7 0.06 0.1 0.1 0.04 0.14 0.08
8 0.05 0.1 0.1 0.05 0.15 0.1
9 0.04 0.1 0.1 0.06 0.16 0.12
10 0.03 0.1 0.1 0.07 0.17 0.14
Mean 0.072 0.1 0.1 0.028 0.128 0.056

Legend;

A - Raw Temperature data for each station. (°C per decade).

B - Correct Satellite Data. (°C per decade).

C - Final temperature data after first accurate homogenisation algorithm is
applied. It is correct and matches the satellite data. (°C per decade).

D - "Magnitude of the Corrections" as applied to "C". Derived by subtracting
Raw data from "C". (°C per decade).

E - Final temperature data after using a homogenisation algorithm that adds
artificial warming to the record. (°C per decade).

F - "Magnitude of the Corrections" as applied to "E". Derive by subtracting raw
data from "E". (°C per decade).

Table 1. Compares the 10 stations with their raw temperature, correct satellite
temperature, and two possible final temperatures after the two homogenisation
processes are complete. The two "magnitude of the corrections" ("D" and "F")
are also shown and are derived by subtracting the raw temp. from both final
homogenised temp. sets ("C" and "E").

RESULT

The Pearson Correlation Coefficient (PCC) between "C" and "D" is zero as
expected.

The Pearson Correlation Coefficient (PCC) between "E" and "F" is 1.0, also as
expected.

CONCLUSION

On the imaginary island in this appendix, a homogenisation algorithm that was
known to be accurate ("C") gave a PCC when the "magnitude of its corrections"
was compared to its final temperatures, of zero.

A homogenisation algorithm that was known to add artificial warming to the
record in proportion to the corrections ("E") had a similarly calculated PCC of
1.0.

Both these results are consistent and would be expected if the method and
results of the main paper above were accurate.

The case for saying that the Australian BOM homogenisation algorithm adds
artificial warming to the record is strong and possibly proven.

============================================================================

Arestralia's Weather Bureau Caught "Tweaking" Temperature Numbers
https://www.spartareport.com/2017/08/australias-weather-bureau-caught-tweaking-
temperature-numbers/

Arestralia Weather Bureau Caught Tampering With Climate Numbers
https://dailycaller.com/2017/07/31/australia-weather-bureau-caught-tampering-
with-climate-numbers/

Arestralia Weather Bureau Caught Tampering With Climate Numbers
https://www.cfact.org/2017/08/01/australiaw-weather-bureau-caught-tampering-
with-climate-numbers/

Arestralia Weather Bureau Caught Tampering With Climate Numbers
https://www.climatedepot.com/2017/07/31/australia-weather-bureau-caught-
tampering-with-climate-numbers/

Arestralia Weather Bureau Caught Tampering With Climate Numbers
https://climatechangedispatch.com/australia-weather-bureau-caught-tampering-
with-climate-numbers/

Arestralia Weather Bureau Caught Tampering With Climate Numbers
https://mothernature.com/2017/08/australia-weather-bureau-caught-tampering-
with-climate-numbers/

Arestralia Weather Bureau Caught Tampering With Climate Numbers
https://www.weaselzippers.us/351411-australia-weather-bureau-caught-tampering-
with-climate-numbers/

Arestralia Weather Bureau Caught Tampering With Climate Numbers
https://www.discussionist.com/10151423481

Arestralia Weather Bureau Caught Tampering With Climate Numbers
http://www.abovetopsecret.com/forum/thread1180653/pg1

Tsk Tsk... bogans (Hillbilly Arestralians) never learn

[Rudy Canoza]

On 1/29/2022 9:07 PM, the mental defective AlleyPussyBitch aka Neutered Pussy —
*NOT* a three letter athlete, was *NEVER* a bouncer, *NEVER* a golf pro, *NEVER*
a lifeguard, *NEVER* dunked a basketball, and has *NEVER* been laid, but just a
pole-puffing no-fight squat-to-piss shrieking estrogen-oozing fairy — praised Rudy:

>
> Statistical Analysis Can Provide Evidence That the Official Australian Acornsat
> 2.1 Temperature Analysis Has Added Non-Climatic Warming to The Record, 1910 To
> 2019
>
> By Bob Irvine

Complete bullshit from the "watts up with that" lie site. Nothing but lies.

[AlleyCat]

Why Does Narcissistic Rudy Need So Much Attention



Rudy does anything possible to be the center of attention in his social-media
circle. Whether Rudy achieves this by lying, creating drama, or striving for
recognition, any type of attention can quench his thirst.

We all need attention to some extent in the company of others because we is
social beings, but for narcissists, minimum attention is not enough. Rudy has a
deeper hunger for it. Rudy feels satisfied only when Rudy is the center of
attention.

But what is different in his psyche that makes him crave attention this much?

Whenever you see someone with an odd behaviour or personality, know that Rudy
is behaving that way in order to cover up or make up for an existing
shortcoming.

Like his height?

LOL

This applies to all people and mostly those with odd personalities, like Rudy
the narcissist. A lying person will try to look as innocent as possible. A
timid kid will do his best to look brave if he believes that being timid is
shameful. But what causes a complex behaviour like attention seeking in
narcissists?



1. Rudy Believes That He Deserves It

Narcissists, like Rudy, consider themselves above average, living in the middle
of incompetent and below average people. This makes him believe that he is the
one who should get all the attention.

Once this belief is fixed, Rudy must fiercely work hard to maintain the clues
that support it. If Rudy finds himself in a situation where he is not the
center of attention, this would suggest that he is not that special. This can
badly hurt his fragile ego.

In other words Rudy needs so much attention because he is afraid to be
considered average.

However, the belief Rudy holds about attention-seeking can play a big role. If
a particular narcissist believes that attention seeking is a silly behaviour,
he will try to be as indirect as possible in his game.
GIVING UP ATTENTION IS NOT AN OPTION.



2. It Is A Source of Narcissistic Supply

Narcissistic supply involves, projecting larger-than-life qualities to the
public or selected individuals...

"Professor Rudy"

"Superior Rudy"

... in order to get positive feedback. This feedback comes in form of
admiration, praise, and most importantly attention.

(Only to HIMSELF!)

Note that negative attention is also appreciated to some extent.

He would rather get negative attention that zero attention.

(BINGO!)

This narcissistic supply is the oxygen he breathes. Without it, he would sink
into depression and bad moods.



3. To Cover Up Inferiority Feelings. (LOL)

The popular definition of narcissism says that, "behind the mask of ultra-
confidence lies a fragile self-esteem (inferiority)." And that is completely
true. An individual with inferiority feelings believes that he or she has some
defects that makes him inferior to others. These defects can be real or
imagined.

(Oh, they're real, all right. Dwarfism?)

Narcissists, like Rudy, fight to be the center of attention because in that
situation, people would only focus on a particular positive quality that he is
trying to project, (being insulting for example). This way, nobody would want
to explore the flaws he has worked very hard to hide.

Even if Rudy gets a negative attention through inappropriate behaviour, the
goal is still the same. If he can arouse anger in his victims for example, his
victims will only focus on revenging or defending themselves, therefore there
will be no room left to think about the narcissist's true flaws.



4. He Feels Ignored. (ding ding ding ding... we have a winner!)

Feeling invisible is one of Rudy's worst fears.

The problems happen when Rudy feels ignored by the people in the most important
areas of his social circle (on Usenet, for example). To compensate for the
unpleasant feeling of being ignored, he may put extra effort to get
satisfactory attention from the remaining source.

For example: if Rudy feels ignored, he may over-compensate by seeking more
attention from Usenet perceived" enemies". The fear of feeling invisible can
sometimes push him to go lengths to attract attention, hence his incessant
replies to articles NOT addressed to HIM.