BEST Adjustments

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.)

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

Fig. 2:  BEST Tmin 2016 minus 2017

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:

Fig. 4:  DTR calculated from BEST 2017:

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:

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

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:

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:

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

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.

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DTR, Cloud, and Rainfall

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:

Fig. 2:  DTR vs Cloud for Australia annually:

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:

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:

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:

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:

Now, get ready for a surprise.

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

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

What’s going on in the south-west?

Here’s how DTR compares:

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

Similar relationship to everywhere else.

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

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

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

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.

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

Results:

 

 

 

 

 

 

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.

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.