**Australian Climate Data used for creating trends by BOM is analysed and dissected. The results show the data to be biased and dirty, even up to 2010 in some stations, making it unfit for predictions or trends.In many cases the data temperature **

*sequences are*strings of duplicates and duplicate sequences which bear no resemblance to observational temperatures.

**This data would have been thrown out in many industries such as pharmaceuticals and industrial control, and many of the BOM data handling methologies are unfit for most industries. **

Dirty data stations appear to have been used in the network to combat the *scarcity of climate stations *argument made against the Australian climate network. (Modeling And Pricing Weather-Related Risk, Antonis K. Alexandridis et al)

We use a forensic exploratory software (SAS JMP) to identify fake sequences, but also develop a technique which we show at the end of the blog that spotlights clusters of these sequences in time series data. This technique, as well as Data Mining Bayesian and Decision Tree analysis **prove the causality of BOM adjustments creating fake unnatural temperature sequences** that no longer function as observational data, making it unfit for trend or prediction analysis.

*"These (Climate) research findings contain circular reasoning because in the end the hypothesis is proven with data from which the hypothesis was derived."*

Circular Reasoning in Climate Change Research - Jamal Munshi

**Before We Start -- The Anomaly Of An Anomaly:One of the persistent myths in climatology is:**

*"Note that temperature timeseries are presented as anomalies or departures from the 1961–1990 average because temperature anomalies tend to be more consistent throughout wide areas than actual temperatures." --BOM*

This is complete nonsense. Notice the weasel word **"tend"** which isn't on the NASA web site. Where BOM use weasel words such as "perhaps", "may", "could", "might" or "tend", these are red flags and provide useful investigation areas.

Using an offset value **arbitrarily** chosen, a 30 year block of average temperatures, does not make them "normal", nor does it give you any more data than you already have.

Plotting deviations from an **arbitrarily** chosen offset, for a limited network of stations gives you no more insight and it most definitely does not mean you can extend analysis to areas without stations, or make extrapolation any more legitimate, if you haven't taken measurements there.

Averaging temperature anomalies *"throughout wide areas"* if you only have a few station readings, doesn’t give you any more an accurate picture than averaging straight temperatures.

### Think Big, Think Global:

Lets look Annual Global Temperature Anomalies. This is the weapon of choice when creating scare campaigns. It consists of averaging nearly a million temperature anomalies into a single number. (link)

Here it is from the BOM site for 2022.

Data retrieved using the Wayback website consists of the years 2014 and 2010 and 2022 from BOM site (actual data is only to 2020). Nothing is available earlier.

Below is 2010.

Looking at the two graphs you can see differences. There has been warming but by how much?

Overlaying the temperature anomalies for 2010 and 2020 helps.

BOM always state that their adjustments and changes are small, for example:

*"The differences between ‘raw’ and ‘homogenised’ datasets are small, and capture the uncertainty in temperature estimates for Australia." -BOM*

Let's create a hypothesis: Every few years the temperature is warmed up significantly, at the 95% level (using BOM critical percentages).

Therefore, 2010 > 2014 <2020.**The null hypothesis is that the data is from the same distribution therefore not significantly different.**

To test this we use:

### Nonparametric Combination Test

For this we use NONPARAMETRIC COMBINATION TEST or NPC. This is a permutation test framework that allows accurate combining of different hypothesis.

Pesarin popularised NPC, but Devin Caughey of MIT has the most up to date and flexible version of the algorithm, written in R. (link).

Devin's paper on this is here.

"Being based on permutation inference, NPC does not require modeling assumptions or asymptotic justifications, *only that observations be exchangeable* (e.g., randomly assigned) under the global null hypothesis that treatment has no effect. It is possible to combine p-values parametrically, typically under the assumption that the component tests are independent, but nonparametric combination provides a much more general approach that is valid under arbitrary dependence structures." --Devin Caughey, MIT

As mentioned above, *the only assumptions we make for NPC are that the observations are exchangeable*, and it allows us to combine two or more hypothesis, while accounting for multiplicity, and to get an accurate total p value.

NPC is also used where a large number of contrasts are being investigated such as brain scan labs. (link)

The results of after running NPC in R, and our main result:

**2010<2014 results in a p value = 0.0444**

This is less than our cutoff of p value = 0.05 *so we reject the null *and can say that the Global Temp. Anomalies between 2010 and 2014 have had warming increased **significantly in the data, and that the distributions are different.**

The result of 2020 > 2014 has a p value = 0.1975

We do not reject the null here, so 2014 is not significantly different from 2020.

If we combine p values using hypothesis (2010<2014>2020 ie increases in warming in every version) with NPC we get a p value of 0.0686. This just falls short of our 5% level of significance, so we don't reject the null, although there is considerable evidence supporting this.

*The takeaway here is that Global Temperature Anomalies have been significantly altered by warming up, between the years 2010 and 2014, after which they stayed essentially similar.*

### I See It But I Don't Believe It....

*" If you are using averages, on average you will be wrong."* **(**link**)** -- Dr. Sam Savage on The Flaw Of Averages

I earlier posts I showed the propensity of the BOM to copy/paste or alter temperature sequences, creating blocks of duplicate temperatures and sequences lasting a few days or weeks or even a full month. They surely wouldn't have done this with Global Temperature Anomalies, a really tiny data set, would they?

As an incredible as it seems, we have a duplicate sequence even in this *small sample*. SAS JMP calculates the probability of seeing this at random given this sample size and number of unique values, is equal to seeing 10 heads in a row in a coin flip sequence. In other words, unlikely. More likely is the dodgy data hypothesis.

### The Case Of The Dog That Did Not Bark

Just as the dog not barking on a specific night was highly relevant to Sherlock Holmes in solving a case, so it is **important with us knowing what is not there.**

We need to know what variables disappear and also which ones suddenly reappear.

*"A study that leaves out data is waving a big red flag. A*

*decision to include or exclude data sometimes makes all the difference in*

*the world."*-- Standard Deviations, Flawed Assumptions, Tortured Data, and Other Ways to Lie with Statistics, Gary Smith.

This is a summary including missing data from Palmerville as an example. Looking at maximum temps first, the initial data the BOM works with is raw, so minraw has 4301 missing temps, then minv1 followed as the first set of adjustments and now we have 4479 temps missing. Around 178 temps went missing.

A few years later and more tweaks are on the way, thousands of them, and in version minv2 and we now have 3908 temps missing, so now 571 temps have been imputed or infilled.

A few more years later technology has sufficiently advanced for BOM to bring out a new fandangled version, minv2.1 and now we have 3546 temps missing -- a net gain of 362 temps that have been imputed. By version minv22 there are 3571 missing values and so a few more go missing.

Max values tell similar stories as do other temperature time series. Sometimes temps get added in with data imputation, sometimes they are taken out. You would think that if you are going to use advanced techniques for data imputation you would do all the missing values, why only do some. Likewise, why delete specific values from version to version.

Its almost as if the missing/added in values *help* the hypothesis.

Below -- Lets stay with Palmerville for August. All the Augusts from 1910 to 2020. For this we will use the most basic of all data analysis graphs, the good old scatterplot. This is a data display that shows the relationship between two numerical variables.

Above -- This is a complete data view of the entire time series, minraw and minv22. Raw came first (bottom in red) so this is our reference. There is clustering at the ends of the raw graph as well as missing values around 1936 or so, and even at 2000 you see horizontal gaps where decimal values have disappeared, so you only get whole integer temps such 15C, 16C and so on.

But minv22 is incredibly bad -- look at the long horizontal "gutters" or corridors that exist from 1940's to 2000 or so. There are complete **temperature ranges** that are missing, so 14.1, 14,2,14.3 for example might be missing for 60 years or so. It turns out that these "gutters" or missing temperature ranges were added in! Raw has been adjusted 4 times with 4 versions of state of the art BOM software and this is the result - a worse outcome.

January has no clean data, massive "corridors" of missing temperature ranges until 2005 or so. No predictive value here. Raw has a couple of clusters at the ends, but this is useless for the stated BOM goal of observing trends. Again, the data is worse after the adjustments.

March data is worse after adjustments too. They had a real problem with temperature from around 1998-2005.

Below -- Look at *before and after* adjustments. This is very bad data handling procedures and it's not random, so don't expect this kind of manipulation to cancel out.

### More Decimal Drama:

You can clearly see decimals problems in this histogram. The highest dots represent the most frequently occurring temperatures and they all end in decimal zero. This is from 2000-2020.

*"18700111-20121231 Correction for urban heat island trend and other inhomogeneities. This gives an average adjustment by -0.3 C both May and August and -0.7 C for June and July. This adjustment is in agreement with conclusions drawn by Moberg et al. (2003), but have been determined on an ad hoc basis rather than from a strict statistical analysis."*

**The data is all still there after adjustment,**just the adjusted months were "slightly lowered" indicating a cooler temperature adjustment.

**Decimals have not gone missing in action and complete temperature ranges have not been altered or deleted.**

### Sunday at Nhill = Missing Data NOT At Random

*A bias is created with missing data not at random.*(link).

Below - Nhill on a **Saturday** has a big chunk of data missing in both raw and adjusted.

Below: Now watch this trick -- my hands dont leave my arms -- it becomes **Sunday**, and voila -- thousands of raw temperatures now exist, but adjusted data is still missing.

**Monday**, and voila -- now thousands of adjusted temperatures appear!

The temperatures all reappear!

I know, you want to see more:

Below -- Mildura data Missing NOT At Random

Above -- Mildura on Friday with raw has a slice of missing data at around 1947, which is imputed in the adjusted data.

Below -- Mildura on a Sunday:

Above - The case of the disappearing temperatures, raw and adjusted, around twenty years of data.

Below -- Mildura on a Monday:

Above - On Monday, a big chunk disappears in adjusted data, **but strangely the thin stripe at 1947 missing data in raw is filled in at the same location at minv22.**

Even major centres like Sydney get affected with missing temperature ranges over virtually the entire time series up to around 2000:

Below -- The missing data forming these gashes is easily seen in a histogram too. Below is November in Sydney with a histogram and scatterplot showing that you can get 60-100 years with some temps virtually **never** appearing!

More problems with Sydney data. My last posts showed two and a half months of data that was copy/pasted into different years.

This kind of data handling is indicative of many other problems of bias.

Sydney Day-Of-Week effect

Taking all the September months in the Sydney time series from 1910-2020 shows Friday to be at a significantly different temperature than Sunday and Monday.

The chance of seeing this at random is over 1000-1:

Saturday is warmer than Thursday in December too, this is highly significant.

### Never On A Sunday.

Moree is one of the best *worst* stations. **It doesn't disappoint with a third of the time series disappearing on a Sunday!** But first Monday to Saturday:

Below -- Moree on a Monday to Saturday looks like this. Forty odd years of data is deleted going from raw to minv1, then it reappears again in versions minv2, minv21 and minv22.

Below -- But then Sunday in Moree happens, and a third of the data disappears! (except for a few odd values).

**A third of the time series goes missing on Sunday!**It seems the the Greek comedy film

*Never On A Sunday*with Greek prostitute Ilya attempting to relax Homer (but never on a Sunday) has rubbed off onto Moree.

### Adjustments create duplicate sequences of data

The duplicated data is created by the BOM with their state-of-the-art adjustment software, they seem to forget that

*this is supposed to be observational data*. Different raw values turn into a sequence of duplicated values in maxv22!

### Real Time Data Fiddling In Action:

Maxraw (above) has a run of 6 temperatures at 14.4 (others too above it, but for now we look at this), and at version minv1 the sequence is faithfully copied, at version minv2 the duplicate sequence changes by 0.2 (still dupes though)

**and a value is dropped off on Sunday 18**. By version minv21, the "lost value" is still lost and the duplicate sequence goes down in value by 0.1, then goes up by 0.3 in version minv22. So that single solitary value on Sunday 18 becomes a missing value.

### A Sly Way Of Warming:

*"Watch out for unnatural groupings of data.In a fervent quest for publishable theories—nomatter how implausible—it is tempting to tweak the data to providemore support for the theory and it is natural to not look too closely ifa statistical test gives the hoped-for answer.*

-- Standard Deviations,Flawed Assumptions, Tortured Data, and Other Ways to Lie with Statistics, Gary Smith.

-- Standard Deviations,Flawed Assumptions, Tortured Data, and Other Ways to Lie with Statistics, Gary Smith.

*"In biased research of this kind, researchers do not objectively seek thetruth, whatever it may turn out to be, but rather seek to prove the truth of what they already know to be true or what needs to be true to support activism for a noble cause (Nickerson, 1998)." -- Circular Reasoning In Climate Change Reasearch, *

*Jamal Munishi*

### The Quality Of BOM Raw Data

*"Reference to Raw is in itself a misleading concept as it often implies*

*some pre-adjustment dataset which might be taken as a pure*

*recording at a single station location. For two thirds of the ACORN SAT*

*there is no raw temperature series but rather a composited series*

taken from two or more stations." -- the BOM

taken from two or more stations." -- the BOM

*"Homegenization does not increase the accuracy of the data - it can be no higher than the accuracy of the observations. "*(M.Syrakova, V.Mateev, 2009)

You don't have to be a data scientist to see that this is a problem, there appear to be two histograms superimposed. We are looking at maxraw on the X axis and frequency or occurrences on the Y axis.

These histograms consists of the entire time series. Now we know decimalisation came in around the 70's, so this shouldn't happen with the more recent decades, correct?

Here we have what appears to be three histograms merged into one, and that is in the decade 2010-2020. Even at that late stage in the game, BOM is struggling to get clean data.

**different rounding scenarios exist where different decimal scarcities and excesses were created in the same time series!**

*"Decoding The Precision Of Historical Temperature Observations"*-- Andrew Rhimes, Karen A McKinnon, Peter Hubers.

### Adjustments, Or Tweaking Temperatures To Increase Trends.

*"For example, imagine if a weather station in your suburb or town had to be moved because of a building development. There's a good chance the new location may be slightly warmer or colder than the previous. If we are to provide the community with the best estimate of the true long-term temperature trend at that location, it's important that we account for such changes. To do this, the Bureau and other major meteorological organisations such as NASA, the National Oceanic and Atmospheric Administration and the UK Met Office use a scientific process called homogenisation." --*BOM

Look at the intricate overlapping adjustments- the different colours signify different sizes of adjustments in degrees Celsius (see table on right side of graph).

*BOM would have us believe that these chaotic adjustments for just 1960 in this example, are exact and precise adjustments needed to correct biases.*

1-- most of the warming trends are created by adjustments, and this is easy to see.

2--BOM tell us that they are crucial because they have so many cases of vegetation growing, moving to airports, observer bias, unknown causes, cases where they

**think**it should be adjusted because it doesn't look right and so on.

*less compliance with adjustments indicating data problems.*

*"Systematic bias as long as it does not change will not affect the changes in temperature. Thus improper placement of the measuring stations result in a bias but as long as it does not change it is unimportant. But any changes in the number and location of measuring stations could create the appearance of a spurious trend." --*

*Prof Thayer Watkins, San Jose University.*

### The Trend Of The Trend

*"Analysis has shown the newly applied adjustments in ACORN-SAT version 2.2 have not altered the estimated long-term warming trend in Australia." -- BOM*

*".....pooled data may cancel out different individual signatures of manipulation."*

### Adjustments: Month Specific And

Add Outliers + Trends,

*Columns are months and frequencies (occurrences).*

**The above shows how the largest cooling adjustments at Bourke get**This shows months and frequencies, how often adjustments of this size were done. It makes the bias adjustments look like what they are - warming or cooling enhancements.

*hammered*into a couple months.*These outliers and values, by themselves, have an upward trend.*In other words, the imputed/created data has a warming trend.(below)

Adding outliers is a no-no any any data analysis. The fact is that only some values are created, which seem to suit the purpose of warming, but there are still missing values in the time series. As we progress to different versions of tweaking software, it is possible

*new missing values*will be imputed, or other values disappear.

### First Digit Of Temperature Anomalies Tracked For 120 years

*Technological improvements or climate change? Bayesian modeling of time-varying*

conformance to Benford’s Law, Junho Lee and Miquel de Carvalho (link)

conformance to Benford’s Law, Junho Lee and Miquel de Carvalho (link)

### The German Tank Problem

**Dark Data**. This is data that many industries have, never use, but leaks interesting information that can be used. (link)

**Dark Data**in the context of Australian Climate data would allow us extra insight to what the BOM is doing behind the scenes with the data....that they are not aware of. So if dodgy work was being done, they would not be aware of any information "leakage."

*If the difference between two days is zero, then the two paired days have the same temperature.*So this is a quick and easy way to spot paired days that have the same temperature.

*meaning that the 2 days have identical temperatures.*

The paired days with same temperatures are clustered in the cooler part of the graph, and taper out after 2010 or so.

This data is varying with adjustments, in many cases there are very large difference before and after adjustments.

**hundreds**of these dodgy sequences!

Data that is missing NOT at random creates a bias.

*_____________________________________________________________*

And:

*"... a systematic
shift of observing sites from post offices to airports,
leads to apparent and spurious trends in the data."*

But as Prof. Thayer Watkins from San Jose University notes:

*"A perplexing aspect of the global temperature data is that there is no measure of accuracy associated with each datum. Surely the earlier years with their fewer weather stations and less accurate instruments have less accurate values than the later years. However systematic but constant bias in the measurements is not really an issue. The concern is not with the level of the temperature but with the change in the level of the temperature. Systematic bias as long as it does not change will not affect the changes in temperature. Thus the improper placement of a measuring station results in a bias but as long as it does not change it is unimportant."*

* "Homegenization does not increase the accuracy of the data - it can be no higher than the accuracy of the observations. The aim of adjustments is to put different parts of a series in accordance with each other as if the measurements had not been taken under different conditions."* (M.Syrakova, V.Mateev, 2009)

*"Bourke: the major adjustments (none of them more than 0.5 degrees Celsius) relate to site moves in 1994 (the instrument was moved from the town to the airport), 1999 (moved within the airport grounds) and 1938 (moved within the town), as well as 1950s inhomogeneities that were detected by neighbour comparisons which, based on station photos before and after, may be related to changes in vegetation (and therefore exposure of the instrument) around the site."*

*For two-thirds of the ACORN-SAT station series there is no raw temperature series, but rather a composited series taken from two or more stations."*

*3 - "*

*Reference to ‘raw’ data is in itself a misleading concept...."*

Bootstrapping is a modern powerful statistical computation technique that overcomes many problems and assumptions of the old methods of estimating statistics on a population. I use this on all comparisons as well as creating Confidence Intervals, especially since the assumption of normality (used by older methods) is not true in much climate data, see for example Port Macquarie in February, the histogram shows "fat tails" or leptokurtic tails.

These are extreme weather event tails, where Normal Distribution and Standard Deviation are misleading or wrong-- Prof Thayer Watkins:

*"...because of this skewness it does not have a finite standard deviation and thus any sample estimates of the standard deviation of annual changes is meaningless." *

This is very evident in raw and exists but is weaker in adjusted data. The histogram has highly repeated temps interspersed with low freq (occurring) temps, see raw pic above. There is a consistent pattern of high low. I have been told it may be conversion from Fahrenheit to Celsius during metrication in the 70's, but this appears in some stations after 1980 and even after 2000.

*above what is expected.*

### Simonsohn Number Bunching Test For Sydney Histograms, Month By Month.

**It can be seen that the temperature frequency histograms are extremely heavily manipulated and/or fabricated.**We know some data has been fabricated in the Sydney time series with my last post showing some of the incidences of weeks and months being copy/pasted into subsequent years.

**Now we can see conclusively that the temperatures have been heavily tampered with, by changing the frequency of occurrence, where some temperatures are overused and appear far too much, some far too little.**

*September, June and May are the only months where the null is not rejected at the 95% level of significance, in other words, they have temperature frequencies as we would expect.*

*The other 9 months have heavily manipulated temperature frequencies*, with many temperatures appearing far more often than they should.

**This further backs up Benford's Law that the temperature time series is not observational data.**

*Thus the probability of large scale tampering is virtually certainty.*

*even if the data were clean!*

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