One Minute Data and Extremes Part 1: Thangool

In 2017 I purchased from the Bureau of Meteorology (BOM) a slab of one minute data from 16 country Queensland stations with Automatic Weather Stations (AWS).  One minute data is the temperature of the final second of every minute- 1,440 of them each day.  I posted a few times about this, and now I return to it to check on some recent claims by the BOM.

They repeatedly assert that the difference between AWS temperatures and those measured by mercury thermometers (LIG) is less than 0.1 degree Celsius.

The one minute data, infuriatingly, is NOT published by the BOM for more than 72 hours, and is NOT used for any daily temperature recording.  The AWS reads the temperature every second in each minute, but only the highest, lowest and final second temperatures are kept.  The highest of those highest one second values, from 9:00 a.m. to 8:59 a.m. next day, becomes the maximum (Tmax) of the day, and the lowest (also 9:00 a.m. to 8:59 p.m.) becomes the minimum (Tmin).  Tmax and Tmin are freely available, published at Climate Data Online (CDO).  One minute data is available at a cost, and at the time of my purchase did not include one minute high and low values.  Therefore, I can only compare daily data for final seconds of 1,440 minutes with the one highest and one lowest seconds, and can only estimate their time of recording.  Grrr!

A further source of frustration is that daily temperatures at CDO for many places have not passed Quality Assurance checks more than six years later- but that doesn’t stop them from calculating monthly means for them, claiming the monthly means are quality controlled.

Therefore in this series of analyses I only use daily data that is quality controlled.

Thangool is a very small town about 120km south-west of Gladstone and has the airport for Biloela.   Figure 1 shows the difference of the daily Tmin (one second value) minus the lowest one minute (final one second value) for February 2017.

Figure 1:  Daily Minimum Difference

Note that no daily minimum value is more than 0.1C below the lowest one minute value on any day in February.  No apparent issue there.

Figure 2:  Daily Maximum Difference

Clearly the difference is greater for maximum temperatures.  On 11 out of 28 days (39.3%) the difference between the maximum temperature and the highest temperature in the final second of any minute was greater than +0.1C.  The greatest difference was on 19 February when Tmax was +0.7C higher.  And that is at least, as I will show.

That is not comparing AWS readings with the old mercury LIG thermometers- we need parallel data for that, which the BOM is extremely reluctant to release.

However, we can draw some inferences.

Figure 3 is a plot of 1-minute temperature at Thangool Airport between 11:00 a.m. and 2:00 p.m.  on 19 February 2017 as measured by the AWS, the maximum recorded by the AWS, and an illustration of what an LIG thermometer might have recorded.  If we assume the AWS accurately simulates a mercury thermometer, I have shown how the mercury would have risen in steps: it would not have fallen after these steps until reset at 9:00 a.m. next day.  The maximum was reached after 1:00 p.m. and was recorded by the AWS as 35.7C.

Figure 3: One minute and Maximum Temperature at Thangool

Note I show the “theoretical” temperature a mercury thermometer might have recorded as following the peaks of the one minute values.  It may well have been higher than these steps, but below 35.7C- but we don’t know because those previous Tmax values were discarded.  It is most likely near one of the two spikes between 1:30 and 2:00 p.m.  In any case, Tmax of 35.7C is 0.7C above the highest one minute temperature of the day.  But the change is supposed to have been up  by 0.7C (at least) and back down again in one minute- it is not just one step up.

By the way, the BOM do quality checks on 1 second data, discarding any value that differs from those either side of it by more than 0.4C.  So the AWS could record a temperature increase of 4 degrees in 10 seconds without causing any alert.

Figure 4 shows the likely times when the AWS would have measured 35.7C.

Figure 4: One minute and Maximum Temperature at Thangool, 1:30 p.m. to 2:00 p.m.

Figure 5 shows temperatures from 1:30 p.m. to 1:35 p.m.- the time when the minute to minute temperature change is less..

Figure 5: One minute and Maximum Temperature at Thangool, 1:30 p.m. to 1:35 p.m.

Tmax was probably in either of the minutes indicated.  If it was at B (between 1:33 and 1:34) the difference was 0.7C.  If it was at A (between 1:32 and 1:33) the difference was 0.8C.  That’s why I say the real difference between highest 1 minute temperature and Tmax on any day is a minimum estimate. At any previous or later minute (such as the second peak at 1:52 p.m. in Figure 4) the difference would be much greater.  The important difference is between Tmax and the next highest 1 minute temperature: that is in this case the previous minute.

BOM apologists assert that the difference between LIG and AWS is negligible.  They also assert that each 1-second reading, because of the probe design, is really an average of the previous 40 to 80 seconds.

If that is true, then for the minute from 1:32:01 p.m. to 1:33:00 p.m. the running smoothed average of all the fluctuations between 1:31:01 and 1:33:00 rose from 34.9C to 35.7C then fell to 35C.  Therefore the real (unsmoothed) temperature must have fluctuated very rapidly to values much higher and much lower in that 120 second period. 

Further, could any human or animal detect such changes in less than one minute, and would it matter to anyone?  For example, would aircraft preparing for take-off need such precision?

That is why we say that AWS temperature data is over-precise and inaccurate.

However, only parallel observations will prove whether AWS simulates LIG to within +/- 0.1C.

The next post will look at Sunshine Coast Airport.

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Who’s Laughing Now?

In The Guardian last Sunday, Graeme Readfearn wrote a defense of the BOM headlined

Climate scientists first laughed at a ‘bizarre’ campaign against the BoM – then came the harassment

“This has frankly been a concerted campaign,” says climate scientist Dr Ailie Gallant, of Monash University. “But this is not about genuine scepticism. It is harassment and blatant misinformation that has been perpetuated.”

And

“It’s just someone’s opinion until it’s published. That’s why I would argue this is harassment. They need to put up or shut up.”

Dr Greg Ayers, a former director of the bureau and leading CSIRO atmospheric scientist is quoted:

“There’s a lot of assertion [from sceptics] but I haven’t seen much science,” said Ayers. “If you are going to make claims then we need to do peer-reviewed science, not just assertion.”

Well let’s take a look at this supposedly peer reviewed science form esteemed climate scientist Ayers.

Ayers examined “if the bureau’s recording method could generate a bias towards higher temperatures…..

Ayers took all the data recorded at two locations to see if taking extra readings across a minute made any difference to the temperatures recorded. While tiny differences were found, the study concluded the bureau’s method was “not at risk of bias”.

Here’s the paper in question:

Response time of temperature measurements at automatic weather stations in Australia

G. P. Ayers ^A B and J. O. Warne ^A

Journal of Southern Hemisphere Earth Systems Science 70(1) 160-165 https://doi.org/10.1071/ES19032
Submitted: 20 July 2019  Accepted: 3 March 2020   Published: 5 October 2020

The authors use selected data for Darwin and Noarlunga in 2018.

So with all the computer power, human resources, and money available to BOM and CSIRO scientists, no doubt their data and results are beyond reproach?

A simple check at Climate Data Online shows how good.

Figure 1 shows the daily Tmax at Darwin for 2018.  Note the two values I have circled.

Figure 1: Darwin Tmax 2018

And Figure 2 shows Tmin for 2018:

Figure 2: Darwin Tmin 2018:

Figure 3 is Table 1 from Ayers and Warne’s paper, I have noted the values shown in Figures 1 and 2.

Figure 3: Data Table from Ayers and Warne (2020)

On three occasions their values are different from those on the BOM website by, 1 degree Celsius, 0.5 C, and 0.3 C.

Here is Ayers’ previous paper, quoted by Readfearn:

A comment on temperature measurement at automatic weather stations in Australia

G. P. Ayers

Journal of Southern Hemisphere Earth Systems Science 69(1) 172-182 https://doi.org/10.1071/ES19010
Submitted: 17 January 2019  Accepted: 19 July 2019   Published: 11 June 2020

In this paper he analyses data from Mildura in September 2017. (Hardly exhaustive I know, but who cares?) 

Ayers says

“the response time of its automatic probes means the recorded measurement is effectively an average of the temperature over the previous 40 seconds to 80s.”

Figure 4 is Table 1 in his paper, for September 2017.

Figure 4: Data Table from Ayers (2020)

And Figure 5 is the 2017 Tmin data for Mildura from Climate Data Online:

Figure 5: Mildura Tmin 2017:

Note September 2. Another discrepancy, this time 1.5 C.

So much for accuracy!

There are three possibilities: 

Ayers and Warne haven’t bothered to double check before publishing;

they used faulty data;

or the data was correct when they used it but has since been “adjusted” by the Bureau in its ongoing pursuit of (ahem) “excellence”.

Whichever, it’s not a good look.

No doubt the papers’ authors only used limited data samples, so that skeptics wouldn’t find more faults. We couldn’t have that!

So Readfearn, Gallant, Ayers, and Warne: despite your denials, obfuscation, delaying tactics, and misinformation, who’s laughing now?

More Indications of Bureau of Meteorology Temperature Nonsense- Update

In recent weeks Jennifer Marohasy has demonstrated that the BOM’s preferred method of temperature measurement (its Automatic Weather System, AWS, of probe and data logger) delivers temperatures that are often substantially different from the old Liquid In Glass (LIG) thermometers at the same times in the same Stevenson screen at Brisbane Airport.

The BOM has denied this, as reported in The Guardian:

Plummer says it also aligns with the warming seen in the ocean around the continent, and with “18 other independent data sets around the world, including from satellites looking at the lower atmosphere”.
In one paper, Ayers, who left the bureau 13 years ago, compared the Acorn-Sat warming trend with four other international data sets that use weather balloons, satellites and raw data from the bureau. In all cases, Ayers found a comparable warming trend.

Following from my much older posts in 2015, 2021, and 2022, here is another way of showing how the BOM’s temperature record has thus diverged from reality.

I use the Bureau’s Acorn monthly Tmax data for Australia, their monthly rainfall data, and satellite data for Australia from UAH (the University of Alabama- Huntsville), for the period from December 1978 to March 2023.

I have recalculated Tmax and rainfall anomalies from 1991 to 2020, the same period as the UAH dataset.

Of course, the BOM and other Global Warming Enthusiasts will insist that Acorn and UAH both show similar warming since 1978, and they are (mostly) right, as Figure 1 shows:

Figure 1: Monthly Surface Tmax and Atmospheric Temperatures from UAH

A scatterplot of Acorn Tmax against UAH shows they are “roughly” similar:

Figure 2: BOM Tmax vs UAH

There is correlation, but there are many differences.

As I showed back in 2015, the relationship between Tmax and UAH is governed by rainfall. Figure 3 shows how closely the difference between surface Tmax and atmospheric (UAH) temperatures follows inverted rainfall. I have smoothed the data with a 12 month running average.

Figure 3: 12 month running averages, Tmax minus UAH and Inverted Rainfall

Note the close match! Yet you may also note that before about 1998 the inverted rain value is often above the difference value, while after about 2012 it is mostly below. This implies that the relationship between Tmax and UAH has changed. Which is at fault?

Figure 4 shows the running 120 month correlation between the Tmax-UAH difference and rainfall:

Figure 4: 120 Month Running Correlation between Tmax-UAH Difference and Rainfall

Note that better correlation is at the bottom (closer to -1). The best correlation is in the 10 year period to February 2015. Figure 5 plots the Tmax-UAH difference against rainfall for that period:

Figure5: Rainfall as a factor of the BOM-UAH difference

The equation for the trendline is

Tmax – UAH = (-0.0339 x Rainfall) + 0.1546

So,

Tmax = (-0.0339 x Rainfall) + 0.1546 + UAH

This allows us to calculate an approximation of what the surface Tmax should be for given rainfall.

Figure 6: Monthly Tmax and Theoretical Tmax

Similar, but slightly different. Figure 7 shows the difference:

Figure 7: Tmax minus Theoretical Tmax

The 12 month running mean may help show how the relationship changes:

Figure 8: Tmax minus Theoretical Tmax 12m Averages

No difference is zero. Clearly the official Acorn TMax is too high, and much too high in the last few years- roughly 0.4C to 1C higher than what would be expected given rainfall and atmospheric temperatures recorded by UAH.

The reason? The Bureau’s AWS data collection increased from the 1990s. Before 2000 adjustments have been increasingly applied to original LIG temperatures to match.

The Bureau of Meteorology’s Tmax dataset is a crock.

More Problems With Australia’s Temperature Record: Part 3

We have seen in Parts 1 and 2 that every extra year of annual data can decrease the temperature trend at a weather station by from -0.02 to -0.03℃ per decade, and that less than half (47% actually) of Australia’s weather stations used for climate analysis have data from 1910, and three of them have insufficient data to calculate trends.

Figure 1 shows a map of non-urban Acorn stations with enough data to calculate trends, at 1910.  The others I have blanked out.

Figure 1: Acorn stations with data for 1910

The network is very sparse.  To estimate a national temperature for 1910 enormous weighting must be given to the values of a few remote stations like Alice Springs, Boulia and Kalgoorlie, so we hope they got the adjustments right!  Unfortunately, in 2015 I found adjustments at Kalgoorlie and Alice Springs were very problemmatic.

The Bureau explains the process of calculating average temperatures here.

Figure 2 shows the BOM map of trends from 1910 to 2020:

Figure 2:  Australian Tmean trends 1910-2020

Note that there a few “bullseyes” which surround stations whose temperature trends are out of phase with areas around them- e.g. Boulia is warmer, Marble Bar is cooler. 

Now here is a paradox.  As the years go by and more stations have data available, the area weighting for each station will decrease, however trends at the newer stations will show increased warming compared with the older ones.  However they will also have more variability.  This will result in oddities as I shall show, and reveals something of the difficulties with the BOM methods.

 Figure 2 is a plot of mean temperature from 1970 to 2020.

Figure 2:  Australian Tmean 1970-2020

The Acorn 2 trend is now +0.23℃ per decade or +2.3℃ per 100 years- a full degree more than the trend from 1910.

Now let’s look at the trend map for 1970 to2020:

Figure 3:  Australian Tmean trends 1970-2020

Note the little “bullseye” around Victoria River Downs, the little “balloon” around Halls Creek to the south-west of VRD, and the little surge to the south-southwest of VRD of 0.05 to 0.1℃ per decade.  Note also that north-eastern Arnhem Land, with no stations, has a warmer pocket.  Figure 4 is the BOM data for VRD.

Figure 4: Annual mean temperature at Victoria River Downs

VRD opened in 1965 and has too much data missing for BOM to calculate a trend.  The area weighting algorithm still gives it a cooling trend of between minus 0.05 and 0℃ per decade (Figure 3).  Que?

With more than 27% of data missing I wouldn’t calculate a trend either, but with only six of 43 years missing I can calculate a trend from 1978:

Figure 5: Annual mean temperature at Victoria River Downs

The trend is -0.09℃ per decade, which is a bit more cooling than the trend map (Figure 3) shows.  Now let’s look at trends from 1980 to 2020.

Figure 6:  Australian Tmean trends 1980-2020

There are more bullseyes, and I have shown temperature trends for some- Carnarvon, Meekatharra, Forrest, Thargomindah, and Gayndah.  But remember Figure 3’s little surge to the SSW?   It now has its own bullseye, and that is Rabbit Flat.

Figure 7: Annual mean temperature at Rabbit Flat

Rabbit Flat opened in 1970 and has a trend of +0.08℃ per decade, which agrees with the trend map in Figure 3.  Now from 1980:

Figure 8: Annual mean temperature at Rabbit Flat

What a difference a few years make in a short timeseries.  The trend of -0.06℃ per decade also agrees with the 1980-2020 trend map.

However, just 328km away Halls Creek shows a warming trend of +0.17C per decade from 1980 – 2020:

Figure 9: Annual mean temperature at Halls Creek 1980-2020

But from 1970 to 2020 Halls Ck is warmer still at +0.19C per decade:

Figure 10: Annual mean temperature at Halls Creek 1970-2020

And at Tennant Creek 441km away the 1970-2020 trend is +0.19C per decade:

Figure 11: Annual mean temperature at Tennant Creek 1970-2020

From 1980 it is +0.06C per decade.

Figure 12: Annual mean temperature at Tennant Creek 1980-2020

Temperatures are trending in different directions and wildly different rates at the closest stations: they can’t all be right!

The method of drawing trend maps is to use anomalies of temperatures of all years of all stations whether or not an individual trend can be calculated, then calculate a gridded average, and from that calculate trends, then spread those trends hundreds of kilometres in every direction- even across the Gulf of Carpentaria from Horn Island to Arnhem Land, as seen in Figures 3 and 6- averaged with the trends propagated by other stations.  If a site has data missing, the grid is infilled with the weighted data from other sites.  

In recent decades this causes great variability because of the short records, which leads to grave doubts about the reliability of some records.  Further back in time, there is less variability because there are more stations, and the longer records smooth and decrease the trends- however the weighting has to be much greater because of the large areas with no data at all for many years. 

The problem is: we can have either a long record, or an accurate record, but not both.

This leads to the obvious conclusion:

The official temperature record since 1910 is just a guesstimate.

More Problems With Australia’s Temperature Record: Part 2

My colleague Chris Gillham at WAClimate uses 58 long term weather stations for his analyses.

And with good reason.  Here’s why.

Figure 1 is a screenshot of the annual mean temperature record at a typical Acorn station, Longreach (Qld) with the linear trend shown.

Figure 1: Annual mean temperature at Longreach

The linear trend is +0.12℃ per decade.  Nine (9) of the 111 years of data from 1910 to 2020 are missing, leaving 102 years.

Australia’s official climate record is based on 112 sites like Longreach.  Of those, 8 are not used for seasonal and annual analyses because they are affected by Urban Heat Island (UHI) effect.  Five (5) of the non-urban stations have more than 20% of their data missing, so the BOM does not calculate trends for them. Of those remaining, only 50 started in 1910, and another 8 before 1915.  What is the effect of different length records on our understanding of how temperatures have changed over the years?

Figure 2 is a plot of the trends of mean temperatures per decade as a factor of the number of years of annual temperature data on record at those 107 Acorn stations with enough data to calculate trends.

Figure 2:  Trend as a factor of amount of data

Stations with  longer data records have lower trends.  The trends at stations with shorter records vary wildly, with some obvious outliers. 

At those stations with UHI effect, the relationship is even stronger.

Figure 3:  Trend as a factor of amount of data at sites with UHI

These sites are in larger towns and cities, possibly with better maintenance and observation practices (although not necessarily better siting).

The slope of the trendlines in the above two figures show that for every additional year of data, temperature trend decreases by about -0.02 to -0.03℃ per decade. In 100 years that could make a difference of as much as three degrees Celsius 0.3C at a well maintained site.

Figure 4 is a map of trends across Australia from 1910 to 2020.  I have shown the years of available data at each site (locations only approximate) and I have circled in blue those 5 sites that have insufficient data.

 Figure 4:  Years of data contributing to 1910 to 2020 trend map

Trends in different regions vary from less than 0.1C per decade to up to 0.3C per decade.  As you can see there is a large variation in the amount of available data in each different coloured band.  That’s for 1910 to 2020.  Note that there are only three (3) non-urban stations with no missing years- Carnarvon, Esperance, and Mt Gambier- which I have circled in red.  There are some big gaps.

In Part 3 I will look at some individual stations and how trends vary in the 51 years from 1970 to 2020.

More Problems With Australia’s Temperature Record: Part 1

Since 2010 I have been documenting problems with different versions of Australia’s official temperature record as produced by the Bureau of Meteorology (BOM).  Since the High Quality (HQ) dataset was quietly withdrawn in 2012 we have seen regularly updated versions of the Australian Climate Observation Reference Network- Surface Air Temperature (ACORN-SAT or Acorn).  We are now up to Version 2.2.  In this Part I shall show the effect of these changes on temperature trends.  In Part 2 I will show how record length affects trends, and in Part 3 I will look at the record since 1970 at some individual stations.

Figure 1 is from the BOM Climate Change Time Series page.

Figure 1:  Australian Official Temperature Record 1910 to 2021

The linear trend is shown as +0.13℃ per decade, or 1.3C per 100 years.  My colleague Chris Gillham of WAClimate has provided me with archived Acorn 1 annual mean temperature data to 2013 which allows this comparison:

Figure 2:  TMean: Acorn 1 and Acorn 2

The result of introducing Acorn 2 has been a much steeper trend:  Acorn 1 trend to 2013 was 0.9℃ per decade.  The trend has now become 0.13℃ per decade. (The extra 9 years have added an extra 0.017C per decade to the trend.)

Figure 3 shows when and how large the changes were:

Figure 3:  Difference between Acorn 1 and Acorn 2

Acorn2 is cooler than Acorn 1 before 1971 and warmer in all but three years since.  Since these were based on the same raw temperatures (with some small additions of digitised data and a couple of changes to stations) the changes were brought about entirely by adjustments to the data.

I calculated running trends from every year to 2013 for both datasets.  As trends shorter than 30 years become less reliable I truncated the running trends at 1984.  Figure 4 compares thre trends to 2013 of Acorn 1 and Acorn 2.

Figure 4:  Acorn 1 and Acorn 2 running trends per decade to 2013

The weather fluctuations of the mid-1970s to 1980s played havoc with trends.

Figure 5 shows the difference between the trends.

Figure 5:  Difference between Acorn 1 and Acorn 2 Trends

The difference ranges from +0.024C per decade for 1910 to 2013, to +0.039C for 1950 to 2013.  Having increased warming by from 0.25C to 0.4C per 100 years (just by making different adjustments) Acorn 2’s trend is much more alarming than Acorn 1’s.

Conclusion:

This is from the BOM’s explanation for Acorn:  

“A panel of world-leading experts convened in Melbourne in 2011 to review the methods used in developing the dataset. It ranked the Bureau’s procedures and data analysis as amongst the best in the world. ‘The Panel is convinced that, as the world’s first national-scale homogenised dataset of daily temperatures, the ACORNSAT dataset will be of great national and international value. We encourage the Bureau to consider the dataset an important long-term national asset.’” ACORN-SAT International Peer Review Panel Report, 2011.

 Acorn 1.0 was apparently such an important long-term asset that it was quickly superseded by Acorn 2 with a much more alarming trend.

Diurnal Temperature Range and the Australian Temperature Record: More Evidence

In an earlier post, I demonstrated through analysing Diurnal Temperature Range (DTR) that the Bureau of Meteorology is either incompetent or has knowingly allowed inaccurate data to garble the record.

A couple of readers suggested avenues for deeper analysis. 

Siliggy asked, “Is the exaggerated difference now caused by the deletion of old hot maximums and or whole old long warmer records?”

Graeme No. 3 asked, “Is there any way of extracting seasonal figures from this composition?”

This post seeks to answer both, and the short answer is “Yes”.

Using BOM Time Series data (from the thoroughly adjusted Acorn dataset) I have looked at data for Spring, Summer, Autumn, and Winter (although those seasons lose their meaning the further north you go).

DTR is very much governed by rainfall differences as shown by this plot.

Figure 1:  Winter DTR anomalies plotted against rainfall anomalies- all years 1910-2020

This shows that in winter DTR decreases with increasing rainfall.  The R squared value of 0.79 means that for the whole period, rainfall explained DTR 79% of the time on average.  However, the average conceals the long term changes in the relationship.

To show this, I simply calculated running 10 year correlations between DTR and Rainfall anomalies for each season, and squared these to show the “R squared” value.  This is a good rule of thumb indicator for how well DTR matches rainfall over 10 year periods.  A value of 0.5 indicates only half of the DTR for that decade can be explained by rainfall alone.  As you will see in the following figures, there are plenty of 10 year periods when the relationship was 0.9 or better, meaning it is ideally possible for 90% of DTR variation to be explained by rainfall.  Here are the results.

Figure 2:  Spring Running R-squared values: DTR vs Rain

There was a good relationship before 1930.  In the decades from then to the mid-1970s it was much worse, and very poor in the decade to 1946. It was poor again in the decade to 2001, and the 10 years to 2020 shows another smaller dip, showing something not quite right with 2020.

Figure 3: Summer Running R-squared values: DTR vs Rain

Summer values were very poor before the 1960s, especially the decades to 1944 and 1961, and dipped again in the 1990s.

Figure 4:  Autumn Running R-squared values: DTR vs Rain

The DTR/Rain relationship was very poor in the decades to 1928, and again before 2001.  The recent decade has also been poor- less than half of DTR to 2020 can be explained by rainfall.

Figure 5:  Winter Running R-squared values: DTR vs Rain

The DTR/rainfall relationship was fairly good, apart from two short episodes, until the 1990s.

I now turn to the northern half of the continent.

A large area of Northern Australia is dominated by just two seasons, wet and dry.  Here is the plot of northern DTR vs Rain for the wet season (October to April).

Figure 6:  Northern Australia Wet Season Running R-squared values: DTR vs Rain

Apart from the 1950s, the late 1970s-early 1980s, and 1998 to 2020, the DTR : Rainfall relationship is very poor, with a long period in the 1930s and 1940s in which rainfall explains less than half of DTR variation (only 13% in the decade to 1943). 

Because the northern half of Australia accounts for the bulk of Australian rainfall, and the wet season is from October to April, this perhaps explains the problems in spring, summer, and autumn for the whole country.

We can get some clues as to the reasons by comparing long term average maximum temperatures with inverted rain (as wet years are cool and dry years are warm).

Figure 7:  Northern Australia Wet Season Decadal Maxima and Rain

The divergence before 1972 and after 2001 is obvious.

The above plots show how poorly DTR (and therefore temperature, from which it is derived) has matched rainfall over the past 111 years.  Low correlations indicate something other than rainfall was influencing temperatures.

In reply to Siliggy, who asked “Is the exaggerated difference now caused by the deletion of old hot maximums and or whole old long warmer records?” the answer appears to be: both, however Figure 7 shows old temperatures (before 1972) appear incorrect, but recent temperatures are at fault too.

The mismatch shows that the Acorn temperature record is not to be trusted as an indicator of past temperatures- and even recent ones.

More Evidence That The Australian Temperature Record Is Complete Garbage

The Bureau of Meteorology is either incompetent or has knowingly allowed inaccurate data to garble the record.

My colleague Chris Gillham at http://www.waclimate.net/ has alerted me to growing problems with the BOM’s record for Diurnal Temperature Range (DTR).  DTR is the difference between daytime temperature (Tmax) and night-time temperature (Tmin). 

According to Dr Karl Braganza’s paper at https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2004GL019998 , “an index of climate change” is that DTR should decrease as greenhouse gases accumulate. To oversimplify, greenhouse gases will enhance daytime temperature while at night greenhouse gases will slow down cooling.  With increasing greenhouse gas concentration, daytime maxima are expected to increase, certainly, but the effect on night-time minima will be relatively greater.  Thus, minimum temperatures will increase faster than maxima, and DTR will decrease.  While Dr Braganza was referring to global values, Australia is a large dry continent where DTR should show up clearly.

We now have 111 years of temperature data in ACORN-SAT (Australian Climate Observation Reporting Network- Surface Air Temperatures).  In this post I only use Acorn temperature data and corresponding rainfall data.  Skeptics have been bagging Acorn ever since it was introduced, and for good reasons as you will see.

Figure 1 is straight from the Bureau’s climate time series page, and shows how DTR has varied over the years.  There is a centred 15 year running mean overlaid. 

Figure 1: Official plot of annual DTR

Melbourne, We Have A Problem… DTR has been increasing recently.

I have used BOM data to make plots that show this more clearly.  First, Figure 2 shows annual DTR from 1910 to 2020 has no trend.  It should be decreasing.

Figure 2:  Annual DTR

There appears to be a distinct step up around 2000-2002.

Figure 3 shows the same data for the last 70 years, broken into two periods, from 1951 to 2000, and 2001 to 2020.

Figure 3:  DTR since 1951

From 1951 to 2000, DTR behaves as it should, with a long term decrease.  After 2000, DTR steps up well above expected values.  The average from 1981-2000 is -0.12 C.  From 2001-2020 the average is +0.35C.  DTR suddenly increases by nearly 0.5C. Why?

DTR is very much governed by that other greenhouse gas, H2O.  Dry days, months and years produce hot days and cooler nights; wet periods result in cooler than average days and warmer than average nights.  This relationship is shown in Figure 4.

Figure 4:  DTR anomalies plotted against rainfall anomalies- all years 1910-2020

As rainfall increases, DTR decreases.  The effect is more marked in very wet (>100mm above average) and very dry (100mm or more below average) years.

Figure 5 shows time series of DTR (as in Figure 2) and rainfall.  Rainfall has been inverted and scaled down by a factor of 250.

Figure 5:  DTR and Inverted, Scaled Rainfall

There is close match between the two.

Using 10 year averages in Figure 6 makes the change after 2001 much clearer.

Figure 6:  Decadal means of DTR and inverted, scaled rainfall

The 10 year average rainfall to 2020 is about the same as the 1961-1990 average (the period the BOM uses for calculating anomalies).  The 10 year average DTR should be about the same value- not at a record level.

As DTR decrease due to greenhouse gas accumulation is caused by minimum temperatures increasing faster than maximum temperatures, Figure 7 shows 10 year averages of maxima and minima for all years to 2020.

Figure 7:  10 year running means of Tmax and Tmin

Tmax has clearly accelerated in the last 20 years, increasing much faster than Tmin.

This is NOT what should be happening: indeed it is the exact opposite of what greenhouse theory predicts.

Something happened to Australian maximum temperature recording or reporting early this century.  I suspect that the BOM changed from using the highest one-minute average of temperatures recorded in Automatic Weather Systems to the current highest one-second value for the day becoming the reported maximum; or else the design of a significant number of AWS changed, with new, faster-responding probes replacing old ones.

I also suspect I know why this was allowed to happen and continue.

Warmer minimum temperatures at night and in winter are not very scary, but record high temperatures and heatwaves make headlines.

It would suit the Global Warming Enthusiasts in the Bureau for apparently rapidly rising maxima and ever higher records being broken to make headlines, frighten the public, put pressure on governments, and generally support The Narrative.

But someone forgot to tell the left hand what the right hand was doing.

The result is that they are now faced with a contradiction- Diurnal Temperature Range is not decreasing as it should. 

The Bureau is either incompetent or has knowingly allowed inaccurate data to garble the record.

How Accurate Is Australia’s Temperature Record? Part 3

In previous posts (here and here) I have shown how maximum temperatures (Tmax) as recorded by ACORN-SAT (Australian Climate Observations Reference Network- Surface Air Temperature- ‘Acorn’ for short) have diverged from other measures of climate change, in particular, rainfall.

I’ll continue looking at the Tmax ~ Rainfall relationship, and show how the Bureau of Meteorology (BOM) must never have used it as a quality control measure for temperature recording.  Result: garbage.

Tmax is negatively correlated with rainfall.  Wet years are cooler, dry years are warmer.  If the incoming solar radiation, the landscape and the measuring sites remain the same, over a number of years this physical relationship remains constant.  What is true of rainfall and temperature in my lifetime was also true in my grandfather’s lifetime, and will still be true in my grandchildren’s lifetime.  If the relationship appears to vary, it must be as a result of some other cause, such as:

changes in solar radiation;

changes in the landscape (urban development, tree clearing, irrigation);

changes in the weather station sites (movement to new sites, tree growth, proximity to heat sources,  buildings, or areas of pavement, change of screen size);

changes in measuring equipment or methods (electronic probes instead of mercury in glass, time of observation, recording in Celsius instead of Fahrenheit, millimetres instead of inches); or

changes in the recorded data (wrong dates applied, or adjustments).

Solar exposure has not changed (and it would be a huge problem for Global Warming Enthusiasts and the whole Climate Change industry if it had).  Across Australia, urban development is a minuscule fraction of land area; tree clearing and irrigation have affected a larger area in several regions, but the vast arid interior remains largely unchanged.  We do know however that weather station sites, observation methods, and equipment have changed, and temperature data (and to a much smaller extent, rainfall data) have been “homogenized” in an attempt to correct for these changes.

The 1961 – 1990 Tmax ~ rainfall relationship

The Bureau of Meteorology uses the period from 1961 to 1990 as the baseline for calculating temperature and rainfall means and anomalies.  Figure 1 shows the relationship between Tmax and Rainfall for all years from 1961 to 1990.  I am using BOM data from their Climate Change Time Series page.

Fig. 1:  Annual Tmax plotted against Rainfall, 1961 – 1990

The x-axis represents rainfall, the y-axis represents maximum temperatures.  The trendline is marked, showing Tmax decreases with rainfall. 

In the top left is the trendline label, showing the value for Tmax (y) for the any value of rainfall (x).   I have magnified this in Figure 2.

Fig. 2:  Trendline label, Tmax vs Rain 1961-90

Circled in red is the slope of the trendline (-0.0029:  there is an inverse relationship, with temperature decreasing 0.0029 of a degree Celsius for every extra millimeter of rain). 

Circled in green is the intercept (+29.9476: if rainfall was zero, the trendline would intercept the y-axis at 29.9476 degrees). 

Circled in blue is the R-squared value (0.3744: R^2 indicates how well Tmax and rainfall match, 0 being not at all and 1 being perfectly.  This value indicates a correlation of about -0.61.  Another way of thinking about it is that 37% of Tmax is explained by rainfall.)

Now this is important: the relationship shown in the trendline label should be similar for the whole record, or else something other than the climate has changed.

I will now show how we can use the information in Figure 1 to test the accuracy of Tmax data in three separate ways.

Comparison of Acorn Tmax with theoretical values derived from rainfall.

Using the trendline equation (Tmax = -0.0029 x Rain + 29.9476) we can calculate an estimate of Tmax from rainfall in any given year.  Figure 3 shows the result.

Fig. 3:  1910 – 2020 Acorn Tmax and Theoretical Tmax calculated from rainfall

For most of the 1961-90 period there is a fair match (37%, remember).  Before the mid-1950s Tmax is mostly lower than the rainfall derived estimate, and after 1990 is nearly always very much higher than we would expect for the rainfall. 

A plot of annual differences between Acorn Tmax and Theoretical Tmax (the residuals if you like), the Tmax variation that is not explained by rainfall, shows how much they differ.

Fig. 4:  Annual Differences: Acorn Tmax minus Theoretical Tmax

We would expect some random differences, but not that much and not strongly trending up.

Correlation between Tmax and rain over time

The next figure shows the “goodness of fit” between Tmax and Rainfall from any given year to 2020.  (The plotline stops at 2010 as correlation fluctuates too much with only a few datapoints.)

Fig. 5:  Running R-squared values of Tmax vs Rain for all years to 2010, from any given year to 2020

The relationship plainly changes (and improves) with time. From 1998 to 2020 there is a good correlation between Tmax and rainfall: before this it is woeful. 

The next figure shows running 21 year calculations of R-squared ‘goodness of fit’ between annual maxima and rainfall, and is included for your entertainment. 

Fig. 6:  Centred running 21 year R-squared values of Tmax vs Rainfall

In 1989, the 21 year period from 1979 to 1999 has a correlation between Tmax and Rain of -0.35: less than 12 % of temperature change is explained by rainfall.  20 years later, the value for 1999 to 2019 is -0.9, or 81% of Tmax explained by rainfall.  That amount of difference is farcical.

Moreover, recent data shows a completely different Tmax~Rain relationship from Figure 1. 

Which brings me to the third point.

Change in Tmax ~ Rainfall relationship over time

From the trendline equation in Figure 2, the Tmax ~ Rainfall relationship may be calculated as

(Tmax – intercept)/ Rainfall.   The value for 1961-90 is -0.29C per 100mm.

This plot of the 21 year moving average shows how much this changes.

Fig. 7:  21 Year Centred Running Average of ((Tmax – intercept)/Rainfall) x 100

The expected value of -0.29C/ 100mm is reached from 1973 to 1976, in the middle of the 1961 – 1990 period, as expected.  Based on the 1961-1990 trendline equation, over the last 21 years 100mm of rain reduces Tmax by one tenth of a degree Celsius (and if we extrapolate- not a good idea- that figure will approach zero in about 10 years’ time).   100 years ago that same 100mm of rain would have reduced Tmax by 0.38 degrees.  Why has rain lost its power?

Conclusion

Three plots of the Tmax ~ Rainfall relationship- Figures 4, 5, and 7- show a similar pattern of change in the difference between recorded and theoretical Tmax, the correlation between Tmax and rainfall, and the Tmax ~ Rainfall equation. 

Why has rain lost its power?  It hasn’t- Tmax has become relatively too high.  Historical maximum temperatures as reported in Acorn are not just inaccurate but deeply flawed. 

The Acorn dataset is garbage.

How Accurate Is Australia’s Temperature Record? Part 2

In my last post I showed that Australia wide the Tmax ~ rainfall relationship has remained constant for the past 110 years (as it should) but Tmax reported in the Acorn dataset has increased by more than 1.5 degrees Celsius relative to rainfall.  Consequently, the ACORN-SAT temperature dataset is an unreliable record of Australia’s maximum temperatures.

Of course there are other aspects of climate besides rainfall.   In this post I will compare annual ACORN-SAT Tmax data with:

Rainfall

Sea Surface Temperatures (SST)

The Southern Annular Mode (SAM)

Cloudiness

Evaporation

all for the Australian region.

I have sourced all data from the Bureau of Meteorology’s Climate time Series pages

except for SAM data from Marshall, Gareth & National Center for Atmospheric Research Staff (Eds). Last modified 19 Mar 2018. “The Climate Data Guide: Marshall Southern Annular Mode (SAM) Index (Station-based).” 

Tmax, Rainfall, and SST data are from 1910; SAM and Daytime Cloud from 1957, and Pan Evaporation from 1975.  Cloud observations apparently ceased after 2014, and Evaporation after 2017, possibly because of staffing cuts.

Because Pan Evaporation data are only available from 1975 and are reported as anomalies from 1975 to 2004 means, I have recalculated Tmax, Rainfall, SST, SAM, and Daytime Cloud anomalies for the same period so all data are directly comparable.

As in the previous post, I have calculated decadal averages for all indicators to show broad long term climate changes.  Decadal averages show how indicators perform over longer periods.  Each point in the figures below shows the average of the 10 years to that point.  This can then indicate times of sudden shifts or questionable data. (For example in Figure 1 SAM (the green line) makes a sudden jump in 2015.  Was this a climate shift or a data problem?)

Figure 1 shows the 10 year means for all climate indicators.  I have scaled Rain and SST to match Tmax at 2019, Cloud and SAM to match Tmax at 1966, and Evaporation to match Tmax in 1984.  Rain and Cloud are inverted as they have an inverse relationship with temperature.

Figure 1:  10 Year Means of Climate Indicators

Tmax has stayed close to SST until 2001.  Clearly Tmax has increased far more than any of the others, especially since 2001.

The next plots show the difference between decadal averages of Tmax and the other indicators.  Zero difference means an excellent relationship with Tmax.

Figure 2:  Difference: 10 Year Means of Tmax minus Rain and SSTs.

Starting from 1919 (zero difference), Rainfall is close to Tmax until 1957, after which Tmax takes off until it is 1.6 degrees Celsius greater than expected in the 10 years to 2020.  Tmax diverges from SST values in 2001 and in 2020 is 0.7 degrees greater than expected.

In Figure 3, Rain, SST, SAM, and Cloudiness are scaled to match Tmax at 1966.

Figure 3:  Difference: 10 Year Means of Tmax minus Rain, SST, SAM, and Cloud

Figure 3 shows how closely Rain and Cloud are related: differences from Tmax are almost identical.  Compared with 1966, Tmax is 1.3 degrees more than rainfall would suggest in the 10 years to 2020.  SST and the SAM index are less different from Tmax but Tmax divergence is still clear.  You may notice that Tmax differences from all climate indicators seem to change at similar times, apart from SAM in 2015.

In Figure 4, all indicators are scaled to match Tmax at 1984.

Figure 4:  Difference: 10 Year Means of Tmax minus Rain, SST, SAM, Cloud and Evaporation

Differences increase rapidly after 2001, so in Figure 5 indicators match Tmax at 2001.

Figure 5:  Difference: 10 Year Means of Tmax minus Rain, SST, SAM, Cloud and Evaporation

There appears to be a problem with SAM in 2015, and it’s a shame that the BOM have discontinued Cloud and Evaporation observations.  In the last 20 years, it is obvious that Tmax has diverged from other indicators.

Conclusion:

All factors- Rainfall, SAM, SST, Clouds, and Pan Evaporation- point to a clear divergence of temperature nationwide, especially in the last 20 years.  In other words, ACORN-SAT, our official record of temperatures, is unreliable.