Archive for the ‘DTR’ Category

Diurnal Temperature Range and the Australian Temperature Record: More Evidence

January 19, 2022

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

December 8, 2021

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.

BEST Adjustments

February 11, 2018

Two years ago I wrote a post about changes in Diurnal Temperature Range (DTR) and whether these were a “Fingerprint of enhanced greenhouse warming”, as claimed by Dr Karl Braganza in an opinion piece at The Conversation in 2011, and in his 2004 paper.

It being time to check more recent data (in 2016 the BEST data finished at December 2015), I went to the BEST site and downloaded the most recent monthly data for maxima and minima, which now extends to July 2017.

I should not have been surprised to find that the two datasets, produced 18 months apart, are different.  The differences are not large enough to be immediately apparent (from 1850 to 2015 the increase in trend per 100 years is only 0.023 degrees Celsius for maxima and 0.007C for minima), but they are none-the-less influential.

Here’s why.

Fig. 1: BEST Tmax 2016 minus 2017 (above zero means the data has been cooled, below zero means it has been warmed.)

BEST max diff

Note the large corrections before 1910, but the overall effect is minor.

Fig. 2:  BEST Tmin 2016 minus 2017

BEST min diff

I have shown the zero value, meaning no adjustment.  Note the large adjustments pre-1910 (but at different times to maxima); apart from two short periods, the whole series is WARMED by about 0.1C; I have marked with arrows the period from the late 1950s to the early 1980s when adjustments were minimal; but note the sudden drop (from January 1983) with recent minima WARMED by about 0.1C.

They have warmed the present and pre-1950, but left the cool 1950 – 1980 period largely alone.   What effect would this have?

Not much if you are looking only at temperature- they certainly can’t be accused of the more usual cooling the past and warming the present.  But if you are looking to find fingerprints of greenhouse warming, this is gold.  One of the fingerprints of enhanced greenhouse warming is greater warming at night than during the day, such that the Diurnal Temperature Range decreases.

The effect is subtle.  There is virtually no change in the long term DTR trend from 1850.

Fig. 3:  Diurnal Temperature Range calculated from BEST 2016:

BEST dtr 1850 2015

Fig. 4:  DTR calculated from BEST 2017:

BEST dtr 1850 2015 2017 version

But there is much uncertainty in data before 1910 as we are told, which is why BOM climate datasets start from 1910.

Fig. 5:  DTR 1910 – 2015 from BEST 2016:

BEST dtr 1910 2015 2016 version

Fig. 6:  DTR 1910 – 2015 from BEST 2017:

BEST dtr 1910 2015 2017 version

Again, virtually no change.  Aha, I hear Global Warming Enthusiasts chortle, gotcha!

The real effect of the adjustments is on the period from 1950, when man-made atmospheric carbon dioxide began increasing rapidly.

Fig. 7:  DTR 1950 – 2015 from BEST 2016:

BEST dtr 1950 2015 2016 version

Note the linear trend value: that equates to less than -0.1C per 100 years- a clear fault with the 2016 BEST data.  But with the new, improved 2017 version, the downward trend in DTR becomes:

Fig. 8:  DTR 1950 – 2015 from BEST 2017:

BEST dtr 1950 2015 2017 version

A three-fold increase in the downward trend in DTR.  This is much better support for the narrative of strong greenhouse warming since 1950.  How convenient.  We just have to wait for the papers and publicity about new evidence for decreasing DTR.

But Global Warming Enthusiasts wouldn’t want us to look at shorter time frames, particularly starting from the dog-leg which still exists from 1983, despite BEST’s warming of the minima data since then by about 0.1C.  This graph includes data to July 2017.

Fig. 9:  DTR 1983 – 2017

BEST dtr 1983 2017 2017 version

That looks like a rather long period of increasing DTR- not good evidence for the meme.  Don’t worry, they’ll explain that by claiming it’s due to “increased cloud and rain” since 1983, and besides, you have to look at the long term trend.

So be prepared for papers and press releases spruiking new confirmation that greenhouse warming is real, as evidenced by strong DTR decrease since 1950.

And all because of almost undetectable changes to the BEST datasets.

DTR, Cloud, and Rainfall

September 19, 2016

In my last brief post I showed how Diurnal Temperature Range is related to rainfall in Northern and Southern Australia in Northern and Southern wet seasons (which correspond roughly to summer and winter).

In this post I show the relationship between DTR and daytime cloud, and between rainfall and daytime cloud, and something very peculiar about South-Western Australia.

All data are taken straight from the Bureau’s Climate Change Time Series page.

DTR is affected by rainfall through Tmax being cooled by cloud albedo, evaporation and transpiration, and Tmin warmed by night cloud and humidity.  There must be a relationship between clouds and rain, although it is (rarely) possible to have rain falling from a clear sky with no visible cloud.  Rain is easily measured in standard rain gauges.  Cloud is calculated by trained observers, and we only have data for 9 a.m., 3 p.m., and daytime cloud.  The data give no indication of cloud type, thickness, or altitude, just amount of sky covered (in oktas, or eighths).

Here I show scatterplots for Australia as a whole annually, and for Northern, South-Eastern, and South-Western Australia in summer and winter.  I calculate both rainfall and cloud as percentage differences from their means.

Fig. 1:  DTR vs Rain for Australia annually:

dtr-vs-rain-oz-ann

Fig. 2:  DTR vs Cloud for Australia annually:

dtr-vs-cloud-oz-ann

Notice much better correlation between DTR and Cloud.

Now let’s look at the relationship between rainfall and daytime cloud.

Fig. 3:  Percentage difference in Rainfall vs percentage difference in Cloud for Australia annually:

rain-v-cloud-oz-ann

Note a 10% increase in cloud cover could be expected to be associated with a 25% increase in rainfall.

Fig. 4: Percentage difference in Rainfall vs percentage difference in Cloud North Australian summers:

rain-v-cloud-n-oz-summ

Fig. 5: Percentage difference in Rainfall vs percentage difference in Cloud North Australian winters:

Note how rainfall in the North Australian dry season varies proportionally more, but has a slightly lower correlation (>0.8 vs 0.9).

Fig. 6: Percentage difference in Rainfall vs percentage difference in Cloud South-East Australian summers:

rain-v-cloud-se-oz-summ

Note the much greater effect of cloud on rainfall in the southern dry season.

Fig. 7: Percentage difference in Rainfall vs percentage difference in Cloud South-East Australian winters:

rain-v-cloud-se-oz-wint

Now, get ready for a surprise.

Fig. 8: Percentage difference in Rainfall vs percentage difference in Cloud South-West Australian summers:

rain-v-cloud-sw-oz-summ

Fig. 9: Percentage difference in Rainfall vs percentage difference in Cloud South-West Australian winters:

rain-v-cloud-sw-oz-wint

What’s going on in the south-west?

Here’s how DTR compares:

Fig. 10:  DTR vs percentage difference in rainfall: South-west Australia

dtr-vs-rain-sw-oz-ann

Similar relationship to everywhere else.

Fig. 11:  DTR vs percentage difference in cloud cover: South-west Australia

dtr-vs-cloud-sw-oz-ann

And this graph clearly shows the relationship between rain and cloud is closer in the wet seasons, but also clearly shows that South-west Australia is an extreme outlier.

Fig. 12:  R-squared comparison between rain and cloud in wet and dry seasons

chart-seasonal-r2

Why the huge difference?  There is no relationship between cloud and rain in south-west Australia, unlike everywhere else.  The South-West has seen a marked decline in rainfall since the late 1960s, but an increase in cloud cover.  It seems counter intuitive, but there you go.

Any suggestions are welcome.

DTR and Rainfall

September 12, 2016

I’ve been looking at DTR and rainfall relationships for Northern and Southern Australia.  I’ve also analysed them by winter and summer (southern and northern wet seasons).

I’ve used a different approach.  Instead of comparing DTR with rainfall anomalies (differences from the mean) I’ve converted these to percentage differences from the mean rainfall.

Data are from the BOM climate change page, so DTR is based on Acorn.  DTR before 1950, and especially before 1932, may be suspect.  However the data are useful for this comparison.

Propositions to test:

DTR which is supposed to decrease as a fingerprint of greenhouse warming, is strongly related to rainfall variation.

There is an unexplained increase in DTR around 2001.

In the time series plots below, rainfall has been inverted, so ‘up’ is dry and ‘down’ is wet.  The rainfall anomalies are expressed as percentages difference from the mean and scaled down by 50.

dtr-rain-oz-ann

dtr-vs-rain-oz-ann

dtr-rain-n-oz-ann

dtr-vs-rain-n-oz-ann

dtr-rain-s-oz-ann

dtr-vs-rain-s-oz-ann

Now comparisons during northern wet season (November to April, basically summer), and southern wet season (May to October- winter and spring).

dtr-rain-oz-summ

dtr-vs-rain-oz-summ

dtr-rain-oz-wint

dtr-vs-rain-oz-wint

dtr-rain-n-oz-summ

dtr-vs-rain-n-oz-summ

dtr-rain-n-oz-wint

dtr-vs-rain-n-oz-wint

dtr-rain-s-oz-summ

dtr-vs-rain-s-oz-summ

dtr-rain-s-oz-wint

dtr-vs-rain-s-oz-wint

Results:

table

 

 

 

 

 

 

Notice that Southern Australian winters dominate DTR.  The impact of rainfall on DTR in Southern Australian winters is twice that in Northern Australian winters, and correlates better as well.  Also note that Southern summers have very slightly higher DTR change per rainfall change and slightly better correlation than Northern.  No doubt you realise winters up here can’t really be compared with southern winters, being mild and very dry.  In many places it is not very difficult to double the mean rainfall in winter with not many millimetres of rain, and zero rain for many months in winter is not unusual.

This plot shows Cusums of DTR and inverted, scaled rainfall.

cusums

The turning points line up exactly, including 2001.  There is no visible unusual change in 2001.  There are however times when the Cusums diverge: 1932, 1958, 1985, and 2003 and 2011.

DTR is strongly related to rainfall variation, especially in southern Australia in winter.

There is no unexplained increase in DTR in 2001.