UAH, ACORN and Rainfall: Something’s Wrong

Tom Quirk had an interesting article posted by Jo Nova this week, at

http://joannenova.com.au/2018/04/bom-homogenization-errors-are-so-big-they-can-be-seen-from-space/

questioning the large number of adjustments coincident with the changeover to automatic weather stations in the 1990s, which appear to have had a large impact on the correlation between BOM’s monthly ACORN mean temperatures and UAH’s Lower Troposphere data for the Australian region.

However, using a different comparison something very strange appears.

For me, his killer plot was this one, showing a huge drop in centred running 13 month correlations between UAH and BOM mean anomalies:

Figure 1: Tom’s plot of monthly correlations:

Using the same methodology, but with maxima instead of mean temperature anomalies (as tropospheric data better reflect daytime temperatures when there is deep convective overturning), I have replicated his findings.  Note that BOM maxima and rainfall are converted to anomalies from 1981 to 2010, the same as UAH.

Figure 2 is my plot of the running centred 13 month correlations between BOM maxima anomalies and UAH Australian region anomalies for all months of data from December 1978 to February 2018.

Figure 2:  Centred running 13 month correlation between BOM maxima and UAH:

There are some differences, but like Tom, I find a distinctly low, in fact, negative, correlation in the mid-nineties, centred on April 1996.

However, as I showed in my post “Why are surface and satellite temperatures different?”  in 2015, most of the difference between UAH and BOM maxima can be explained by rainfall variation alone.

Figure 3 is a plot of the monthly difference between UAH and BOM data plotted against rainfall anomalies (also calculated from 1981-2010 means).

Figure 3:

R-squared of 0.54 means a correlation coefficient of 0.73.

This is how the correlation varies over time:

Figure 4:

I have a problem.

There is a major drop in July 1995, but other big ones- October 1998, July 2003, December 2009, September 2015, and the most recent figure, August 2017.   Correlations are much more variable from 1995.  What can be the reason for these poor correlations?

There is also a general decrease in correlation over the years since 1978.

What’s wrong?  Surely rain gauges can’t be faulty?

Has there been a drift in accuracy of the UAH data?

Or has there been a drift in accuracy of BOM temperature measurement?

Any suggestions would be most welcome.

Post Script:

The major drops may occur at about the same time as major ENSO changes, though not always.  This graph plots the above correlations and 13 month centred averages of the SOI (scaled down) together.

Figure 5:

The SOI has not been lagged in this plot.  Perhaps the major changes in trade winds, monsoons, and the sub-tropical ridge affect tropospheric temperatures differently from surface temperatures at these times.  But that doesn’t explain the gradual decrease over time.

 

 

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Pretty Patterns

Most people like pretty patterns.  They are pleasing to the eye.  But that’s no reason to create them when homogenising data, as the Bureau of Meteorology does when creating its ACORN-SAT datasets for a number of sites.

I am indebted to Bob Fernley-Jones, who noticed this and has been trying without success to point out to the Bureau that they need to address this issue.

For example, the Bureau found problems with maximum data from Darwin, especially before the Post Office and its thermometer were blown to bits by a Japanese bomb in February 1942.  Adjustments were needed as the data source moved from the town to the RAAF base.  Before this, apparently the Stevenson screen had become partially shaded by vegetation.  The problem is that the only other stations available for comparison for identifying and adjusting for discontinuities in the data were hundreds of kilometres away- Port Keats Police Station is 243 km away, Katherine is 270 km away, and Wyndham Port is 446 km away.  Port Keats and Katherine have monthly data from 1938 and 1937 respectively (but with many months of data missing from Katherine), and Wyndham Port has daily data available for the whole 1910-1942 period.  So these three distant sites were used to adjust Darwin’s raw data before 02/02/1941, but only Wyndham Port was used to make adjustments for all data before 01/01/1937 and 01/01/1916.

Here is the result.

Figure 1:  Adjustments to Darwin’s daily maxima 1910 to 1942

Now isn’t that a very pretty and pleasing pattern?  The red line shows the difference between Darwin Acorn Tmax and Darwin raw Tmax, for every day from 01/01/1910 to 31/01/1942, revealing a repeating oscillation in values.  Note that from 2 February 1941 there are no adjustments.

The next plots analyse the three distinct periods by month of the year.

Figure 2:  Daily adjustments to Darwin’s maxima 01/01/1937 to 31/01/1941

Note that these are not mean values:  every single day in each month was adjusted by exactly the same amount as every other day in that month.  Every day in June 1937 was cooled by -0.5 degrees C, and likewise every day in June 1938, 1939, and 1940.  Days in April and December were not adjusted, while the Wet months were warmed and the Dry and Build-up months were cooled.  So much for the Bureau’s explanation that only Winter (-0.47) and Spring (-0.57) were adjusted.

Figure 3:  Daily adjustments to Darwin’s maxima 01/01/1916 to 31/12/1936

Again, every single day in each month has been adjusted by exactly the same amount as every other day in that month.  Days in the Wet were cooled by from -0.2C to -1.2C, while days in the Dry and Build-up months were cooled by -1.2C to -2.2C.  That’s some pretty savage adjusting, and does not vary from the first to the last day of each month.

Figure 4:  Daily adjustments to Darwin’s maxima 01/01/1910 to 31/12/1915

Note again that while the adjustments are not as large as 1916-1936, only February has no adjustment to raw data, and all other months have daily cooling adjustments which are the same from the start to the finish of the month.

Unbelievable.

Time for a clean out.

 

 

 

Fingerprints of Greenhouse Warming: Poles Apart

If global warming is driven by the influence of carbon dioxide and other man made greenhouse gases, it will have certain characteristics, as explained by Karl Braganza in his article for The Conversation (14 June 2011).

As water vapour is a very strong greenhouse gas, it will tend to mask the influence of man made greenhouse gases, and because solar radiation is such a powerful driver of temperature, this also must be taken into account.  Therefore, the characteristic greenhouse warming fingerprints are best seen where solar and water vapour influences can be minimised: that is, at night time, in winter, and near the poles.  So we would look for minimum temperatures rising faster than maxima; winter temperatures rising faster than summer, and polar temperatures rising faster than the tropics.  Indeed, polar temperature change in winter should be an ideal metric, as in Arctic and Antarctic regions the sun is almost completely absent in winter, and the intense cold means the atmosphere contains very little water vapour.  We can kill three birds with one stone, as winter months in polar regions are almost continuously night.

So let’s look at the evidence for greater winter and polar warming.

Figure 1: North Polar Summers:

Figure 2:  North Polar Winters:

Yep, North Polar winters are warming very strongly, at +2.58C/100 years, and much faster than summers (+1.83C/100 years)- strong evidence for anthropogenic global warming.  And warming is much faster than the Tropics (+1.023C/100 years):

Figure 3: Tropics

Unfortunately for the theory, the opposite happens in the South Polar region:

Figure 4: South Polar Summers

Figure 5:  South Polar Winters:

While summers are warming (+0.58C/100 years), winters are cooling strongly at -1.66C/100 years.  Over land areas, with little influence from the ocean, very low moisture, and very little solar warming, winters are cooling even faster:

Figure 6:  Antarctic winters over land:

This is the exact opposite of what is supposed to happen in very dry, cold, and dark conditions- at night, in winter, at the poles.  Can this be because carbon dioxide and other greenhouse gases are NOT well mixed, and are in fact decreasing in concentration near the South Pole?

Figure 7: Carbon Dioxide concentration at Cape Grim (Tasmania):

Figure 8:  South Polar region TLT (all months) as a function of CO2 concentration:

No, while Cape Grim data show CO2 concentration to be increasing in the Southern Hemisphere, but without the marked seasonal fluctuations of the Northern Hemisphere, there is NO relationship between CO2 and temperature in the South Polar region.

Is it because the oceans around Antarctica are cooling?

Figure 9: South Polar Ocean TLT:

Nope- -0.01C/100 years (+/- 0.1C).  Neither cooling nor warming.

The cold, dry, dark skies over Antarctica are getting colder in winter.  Summers show a small warming trend.

Conclusion:  The fingerprints of man made greenhouse warming are completely absent from the South Pole, and differences between North and South Polar regions must, until shown otherwise, be due to natural factors.

Data sources:

https://www.nsstc.uah.edu/data/msu/v6.0/tlt/uahncdc_lt_6.0.txt

http://www.csiro.au/en/Research/OandA/Areas/Assessing-our-climate/Latest-greenhouse-gas-data

Mandated disclaimer:-

“Any use of the Content must acknowledge the source of the Information as CSIRO Oceans & Atmosphere and the Australian Bureau of Meteorology (Cape Grim Baseline Air Pollution Station) and include a statement that CSIRO and the Australian Bureau of Meteorology give no warranty regarding the accuracy, completeness, currency or suitability for any particular purpose and accept no liability in respect of data.”

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.

Summer Temperatures in South-Central Queensland Part 2: Weather Events and Spikes

In my last post I showed how on average temperature changed diurnally across a number of Queensland BOM stations.  In this post I will show examples of temperature change at some of these stations.  I am using “one minute data’, which despite its name, is really the value at the last second of every minute, in other words, sampling at 60 second intervals.

We know that temperatures spike up and down every few seconds, but these spikes are not captured by the Bureau unless they are the highest and lowest for each minute, and only noticed by a keen observer if the highest or lowest temperature spike so far that day occurs in the same minute (usually on the hour or half hour) as final second temperature reported at the Station Observations page.

Let us begin with this plot of a temperature spike at Maryborough Airport on 15 February, kindly reposted by Anthony Watts.  This was one of many examples from different locations around Australia of times when the maximum temperature of the day occurred in the same minute as a half-hourly recording, but exceeded it by a large amount (1.5 degrees in this example).

Figure 1:

Please note that for this plot I only had access to the half hourly data from the Bureau, supplemented with some time offset data from the UK Met Office, usually 10 minutes before the BOM values.  With the higher resolution given by one minute data, we can gain a better appreciation of what was happening on this day.

Figure 2:

Note the spike at 13:00.  It is just part of the constant fluctuation during daylight hours which is not apparent from the data available for Figure 1.

Let’s have a closer look at the period from 12:00 to 14:00.

With the caveat that we can only guess at the 59 one second values in between the final second samples, we can use the latter values to investigate temperature response by day and night to various influences.  Assuming that the intervening one second fluctuations are approximately equally above and below a 60 second de facto mean represented by the value at the final second (as the Bureau’s Fast Facts would have us believe), a centred 5 minute mean of one minute (final second) data would approximate a mean of the complete 300 seconds.  I use a centred 5 minute mean to compare with the one minute data, but please understand this is an approximation, a best guess, when applied to short time lengths.  Its real value will be with all 115,200 data points- more later.

Figure 3:

Firstly, note how well the five minute centred mean represents most of the larger fluctuations, while considerably smoothing the final second data.

Secondly, note that the day’s maximum, 33.7C, was reached in the final second of 12:59, and was still at 33.7C at some second of the next minute, before falling 1.5 degrees to 32.2C in the final second of 13:00.

Thirdly, note that if this was a station in the USA, where 5 minute means are used, the maximum for the day would have been approximately 32.5C, still 1.2C less than the official value.

The temperature also fell 1.6C in the 60 seconds to 12:53.   And here are all the minute to minute temperature changes at Maryborough on 15 February (large outliers circled).

Figure 4:

As shown in the previous post, this is the typical diurnal pattern.  Figure 5 shows one minute temperature fluctuation for the whole period, 1 January to 21 March 2017.

Figure 5:

Note the swelling of fluctuation in daylight hours, the constriction at sunset and sunrise as heating/ cooling regimes change, and the outliers: values can change by up to +2.3C or -2.1C in 60 seconds.

And here is an example of how a day’s temperature can change quite naturally, but we have to ask: would a mercury thermometer be able to match this?

Figure 6:

I now turn to other stations.  Hervey Bay Airport is about 30km from Maryborough Airport, only a couple of kilometres from the sea.  Firstly, how temperature changes from one minute to the next for the whole period.

Figure 7:

Note that the daily increase in fluctuation is much less than at Maryborough.  Hervey Bay Airport is only a couple of kilometres from Sandy Strait, and proximity to a water body may be a tempering influence.

Note also the large outlier of -2C in one minute- still less than the 2.2C downwards spike on 22 February in less than a minute, which prompted my first query to the Bureau!  What could have caused such an outlier?  Here’s the one minute temperature plot for 16 March:

Figure 8:

This outlier was the result of an entirely natural weather event, a sudden cool change, possibly a storm front: 4.4mm of rain was recorded at 09:00 on the 17^th.  Would a mercury thermometer be sensitive enough to capture that?

And here’s 22 February:

Figure 9:

Note the unusual spiking between about 04:30 and 06:30.  Something was going on.  Note also that the minimum temperature at 06:00 was far below at 23.2C, 1.6 degrees below any other temperature that day- for one second.

I now turn to Thangool Airport, a few kilometres from Biloela in the Callide Valley, 150km from the coast.

Figure 10:

Note the same shape, and though much further inland, not apparently different range from Maryborough.  Most of the change between 09:00 and 15:00 is within the bounds of +/- 1 degree each minute, but there are many outliers.

I shall now look at how temperature changed on a sample of days.  Firstly, 31 January shows a typical temperature curve for a clear sunny day.

Figure 11:

Figure 12 shows 7 January, a day with a mid-morning drop.  0.2mm of rain was recorded on the 8^th.

Figure 12:

Note how after the sudden plunge the temperature quickly returns to “normal” as if nothing has happened.

28 January shows a late afternoon drop with a smaller recovery until sundown.

Figure 13:

Figure 14:

Note the typical warming curve which lasts until 16:47 when there is a sudden drop of 2.3 degrees in 3 minutes, with continued cooling.  I suspect a wind change was the cause.

Figure 15:

This shows a midday weather event, with the rapid return to the “normal” curve.  6mm was measured next morning.

Figure 16:

Note the sudden spike mid-morning.  The temperature spikes nearly 4 degrees in a few minutes to a value not expected for another hour or two.  This is odd and I cannot think of a natural weather event that could be the cause.  Whatever the cause, I doubt a mercury thermometer would track this change.

The final station for this post is at Lady Elliott Island, about 80km off the coast in the Coral Sea.  The screen is on white coral sand, about 100 metres from the water to the east.  First, one minute change over the whole period.

Figure 17:

Note again the typical shape, but with much smaller daytime range of changes than inland sites.  Upward outliers are muted (there is only one instance of a temperature change in one minute of more than one degree).  However, downwards outliers are large and occur throughout the 24 hour period.

Here are some plots of several days on a tropic island.

Figure 18:

Note the early morning downward spikes: rain showers.

Figure 19:

Note the sudden drop just before midday: another rain shower.  But note how the temperature quickly returns to nearly what it was before.

Figure 20:

Again, morning showers (quite normal near the sea in the wet season).

Now for the largest one minute temperature drop of -2.3 degrees just before midnight on 14 March.

Figure 21:

Now watch the temperature recovery next day.

Figure 22:

So, with a drop of nearly 6 degrees in a few minutes, this was a perfectly natural weather event.  Apart from sudden weather generated decreases like those shown above, it seems that there is a floor to minima of about 26C to 27C, due of course to the sea temperature.

While these examples are interesting, what about a day with sunny, fine weather?  Here’s the plot for 16 February.

Figure 23:

Note a much more regular daytime curve (with rapid large spikes between 09:00 and 15:00), peaking only just after midday- except for a spike at about 14:30.  Here’s a closer look at the time from 12:00 to 15:00.

Figure 24:

The second largest downwards spike (-1.3C) of the whole record occurred at 14:32.  This was purely a spike, not due to any weather event.  Could a mercury thermometer possibly match this?  If not, it would not reach the same maximum (30.8C).  On a hot sunny day on a coral island 100 metres from the sea, daytime temperature spikes up and down rapidly by up to a degree (or more) at a very high frequency.  Compare this with Maryborough in Figure 3.

This confirms generalisations I made in my last post:

“Temperatures in daylight hours are very volatile, while at night temperatures change very little except in unusual weather events.  Fastest and most sustained warming is in the hour after sunrise.  Fastest and most sustained cooling is also in daylight hours.  Night time cooling is much more gradual.  Cooling is on average more rapid than warming.  Rapid warming occurs when the sun suddenly appears.  Rapid cooling is associated with weather events such as rain storms.”

The Bureau of Meteorology have claimed that their AWS sensors are so designed that they mimic the mercury in glass thermometers they have replaced.   They claim a mercury in glass thermometer would track the above fluctuations closely.  However they have as yet provided no papers or comparative data to back this up.  From analysis of these stations’ data, I find that hard to believe.

Again we say, show us the data.

Summer Temperatures in South-Central Queensland Part 1: Diurnal Patterns of Temperature Change

In March of this year I purchased from the Bureau of Meteorology one-minute temperature data for the period 1 January to 21 March 2017, for a number of Queensland stations within 250km of Bundaberg.  “One-minute temperature data” is not the temperature of the whole minute, but means temperatures at  of the final second of each minute, so are spot samples taken at regular intervals.  Temperatures can be higher and lower in the intervening seconds, and so for example daily maxima can be several tenths of a degree or more above the final second values, as I demonstrated in earlier posts.

I have analysed data from these stations:  Maryborough, Hervey Bay Airport, Gayndah Airport, Thangool Airport, Bundaberg Airport, Rosslyn Bay, Gladstone Radar, Gladstone Airport, Rundle Island, Nambour, Kingaroy, Tewantin, Maroochydore, Gympie, Double Island Point Lighthouse, and Lady Elliott Island.  Most of these have few missing observations, but all still needed tedious checking.  Kingaroy’s record is atrocious, with days and weeks of intermittent data drop out.

I looked at: one minute temperature change, that is, from one data point to the next; temperature change after 10 minutes; the number of minutes of uninterrupted rise; the number of minutes of uninterrupted fall; and the number of minutes the temperature remained at the same value.

In this post I firstly plot averages of the above metrics across all 16 stations by time of day, to show the range of temperature variation from one minute to the next throughout the day and night, in distinctive diurnal patterns.

Figure 1:  One minute temperature change:-

All stations show this distinctive shape, with some variance in range from island to inland stations.

Remember, this plot shows the average of 16 stations every minute of every day for 80 days.

Note the narrow range (averaging less than +/-0.1C) between sunset and sunrise, and the much larger swings from one minute to the next in daylight hours, especially between 09:00 and 15:00.  Outlier points are from weather events at individual stations.

The next plot shows the range of temperature change over 10 minute periods:

Figure 2:  10 minute temperature change:-

Note the sharp increase from shortly after sunrise to an early morning peak, then a gradual decrease in the mean to a small dip at around 6 p.m..  Note again the small variation in the absence of the sun, and the many individual weather events shown by outliers.

The next plot counts the number of minutes when the temperature increases each minute at least +0.1C.

Figure 3:  Uninterrupted temperature increase:-

As you might expect, temperatures rise predominantly during daylight hours, with a sudden jump up just after sunrise, and a dip at sunset.

The next plot counts the number of minutes when the temperature decreases each minute at least -0.1C.

Figure 4:  Uninterrupted temperature decrease:-

Temperatures generally don’t fall very much just after sunrise.  However note that between 0900 and 1800 it is very rare for the temperature to be falling for zero minutes.  Most long temperature falls occur in daylight hours.  Surprising? What goes up must come down.

The next plot shows the length of time when the temperature does not change from one minute to the next:

Figure 5:  Unchanged temperature:-

Note that during the night on average temperatures are never the same for zero minutes (i.e. they are frequently the same), while in daylight hours temperatures are much less stationary, with a gradual rise from 1500.

The next graphs show the range of these metrics for individual stations.  This will be explored further in a future post.

Figure 6:  One minute temperature change:-

This shows the fastest minute to minute temperature change, both up and down.

Figure 7:  10 minute temperature change:-

Note that there was much faster cooling than warming over 10 minute periods, mostly associated with rain showers, storms, or cool changes.

Figure 8:  Uninterrupted temperature increase:-

Figure 9:  Uninterrupted temperature decrease:-

Note that Lady Elliott Island (far out to sea) and Rundle Island (in Gladstone Harbour) both had shorter periods of constantly rising and falling temperature.

Figure 10:  Unchanged temperature:-

On the night of the 6^th March at Maroochydore Airport the temperature was 26.1 degrees for 118 minutes.  As you can see nearly all stations had stable temperatures for nearly an hour on at least one occasion.

These results confirm that temperatures in daylight hours are very volatile, while at night temperatures change very little except in unusual weather events.  Fastest and most sustained warming is in the hour after sunrise.  Fastest and most sustained cooling is also in daylight hours.  Night time cooling is much more gradual.  Cooling is on average more rapid than warming.  Rapid warming occurs when the sun suddenly appears.  Rapid cooling is associated with weather events such as rain storms.

In Part 2 (probably not for a week or two) I will look at daily warming and cooling at individual stations.

Replicating Lewis et. al. (2017): Another Junk Paper

The recently released scarey predictions about “50 degree temperatures for Sydney and Melbourne” touted by Sophie Lewis are hardly worth wasting time on.  The paper is

Australia’s unprecedented future temperature extremes under Paris limits to warming, Sophie C. Lewis , Andrew D. King  and Daniel M. Mitchel, (no publication details available).

The paper is junk.  It has some very sciencey sounding words but is at heart pure speculation.  Like most “projections” by Global Warming Enthusiasts, the predictions are untestable.  Scarey temperatures are possible IF (and only if) IPCC scenarios are valid and we get either 1.5C or 2C warming by the last decade of the century.  That’s what the paper rests on.

The paper looks at Australian summer means, Coral Sea autumn means, and New South Wales and Victorian daily January maxima.  AWAP data are used for Australia and NSW and Victoria, and HadCruT4 for the Coral Sea region (which includes most of Queensland).

I have just looked at Australian Summer Means, and that was enough for me.  Lewis et.al. say that the decadal mean from 2091-2100 may have Australia wide summer means of 2 to 2.4 degrees above the mean of 2012-13, or 30.1 to 30.5C, with resultant very high daily maxima in southern cities.

I could have saved them the trouble, and at considerably less cost.

All I needed was the AWAP data for summer means (I purchased monthly AWAP data up to 2013 a couple of years ago), and plotted it with a 2^nd order polynomial (quadratic) trend line:

And also showing decadal means (although the first and last decades have several missing summers):

There: the trend line goes smack through the higher (+2 degrees) projection, so it must be right!

Only trouble is, extrapolating with a quadratic trend is not a good idea. Lots can go wrong in the meantime.

So my plot is about as useful as the Lewis et.al. paper, and that’s not much.

Australian Temperature Data Are Garbage

From the Bureau’s hastily published “Fast Facts”:

“This means that each one second temperature value is not an instantaneous measurement of the air temperature but an average of the previous 40 to 80 seconds.”

That is complete nonsense.

At the end of each minute, the following data are recorded:

1. Lowest one second reading of the previous 60 seconds
2. Highest one second reading of the previous 60 seconds
3. Reading at the final second of the minute.

Firstly, 40 seconds is not one minute, the integration period recommended by the WMO in 2014 and by the Bureau’s own officers in 1997.  Anything less than 60 seconds is not compliant.

Secondly, consider this plot, which is from actual 1 minute temperatures recorded at Hervey Bay Airport on 22 February 2017.  (Data purchased by me from the Bureau).

Fig. 1:

Sunrise was at about 5:40 a.m.  Temperatures do not increase until about 6:30 a.m.  Note the strangely low temperature- the daily minimum- which was reported as occurring sometime in the 60 seconds before 06:00:00.  The BOM would have us believe that each of the values in Figure 1, including the low of 23.2C, are “averages” of the previous 40 to 80 seconds.

Next consider what happens in that minute from 5:59 to 6:00, as per the following plot.

Fig. 2:

We don’t know in which seconds the high and low readings for that minute occurred, so I have shown them for each of 59 seconds.  I have shown the 5:59 and 6:00 readings: both were 25.3C.

Consider how the value at 06:00 was obtained:

If by an “average” (however derived) of less than 60 seconds, the methodology is non-compliant.

If by an “average” of the previous 60 seconds, it must include values that contributed to the High of 25.4C and the Low of 23.2C.

If by an “average” of anything greater than 60 seconds, it must include values that contributed to both the Low and High values, and as well, values that contributed to the 5:59 reading- which is the same as the 06:00 reading.

Similar logic applies to the Low and High readings.

It follows that the intermediate instantaneous atmospheric temperatures that contributed to all three reported “average” values must have ranged from much higher than 25.4C to very much lower than 23.2C.

Look at Figure 1 again.  The air temperature at Hervey Bay on 22 February must have spiked down very much lower than the 23.2C plotted.

Really?

In the early morning there is very little near ground turbulence so temperatures do not fluctuate from one minute to the next by very much.  In How Temperature Is Measured in Australia Part 2 I showed that 91% of low temperatures vary from final second temperatures in the same minute by 0.2C or less.  A difference of 2.1C is extraordinary.  Fluctuations greater than that are difficult to believe.

However, in a comment at How Temperature Is Measured In Australia Part 1, Tony Banton, a retired meteorologist, says that the BOM explanation of cooler ground level air mixing upwards is correct.  If we accept that explanation, we must then face the problem of “comparability”.

In 61 seconds, the Hervey Bay AWS has reported temperatures of 25.3, 25.4, 23.2, and finally 25.3 degrees.  The BOM asserts that a liquid-in-glass thermometer will be able to respond as quickly and show similar temperatures- and remember, 23.2C was the morning’s official minimum.

My response: rubbish.  The data for 22 February at Hervey Bay show that no averaging is used at all, and the Low Temperature of 22.3C  23.2C is an instantaneous one second recording from a rogue downwards spike, whatever the cause, whether a natural event or other (e.g. electrical) factor.

Temperatures reported by the BOM are not fit for purpose of accurate reporting of maxima and minima, identifying records, or identifying warming or cooling by comparison with historic liquid-in-glass data.

Watch an AWS Fail

(With thanks to Lance, Phill, and others)

A week ago, a colleague alerted me to strange behaviour at an Automatic Weather System at Borrona Downs in NSW.  This is a brand new weather station, with its first observation on 21 July.

Phill writes in an email:  Do you ever wonder why you get a shiver down your spine?  Pity the poor folks in the NSW far west.  

 From this mornings (20^th  August) NSW observation list: The minimum temperature at Borrona Downs AWS was -62.5C at 9:59pm last night.  Probably some clowns with a bucket of dry ice or liquid nitrogen.  Perhaps Odin’s host crossed the night sky or maybe death just walked on by…  The individual reads don’t show anything lower than -37.5C also at 9:59 so the cold spike was quite sudden.  It went from -62.5C sometime between 21:58:00 and 21:59:00 to -37.5C at exactly 21:59:00 to -4.4C at 22:00:00.

I was too busy and preoccupied until now to follow this up, but I have a few days now.

Borrona Downs Station is in sandhill and claypan country in the far northwest of NSW:

Here is the Climate Data Online minima record (note minima indicated on two days):

The following plots show the deterioration in the performance of the AWS.  Firstly, the comparison with Tibooburra, 110km away, showing a sudden change at 29 July:  Subtracting Borrona Downs data from Tibooburra shows that Borrona Downs Tmin is too high from this date.  The whole (brief) record should be scrapped.

But the devil, as Phill found, is in the detail.  Here is part of the record for the 19^th:  Note the Low Temp at 9.59 pm, and I have indicated the official minimum for the day which would have occurred early that morning.

The Bureau has the minimum at 4.6C, but how was this value obtained?  The erroneous values, (including that of liquid nitrogen), are flagged, then manually removed, and the previous lowest temperature is retrieved from the one minute data for the day.  This also happened on the 26^th:

Things got much worse on August 27^th:

Why could no minimum be found?  Did the BOM realise that none of the data were reliable, and were essentially random errors?  Remember that the AWS records values every second, and the highest, lowest, and final second values for each minute are stored.  My guess is that many of these values were unreliable as well, even though many of the final second half hour values seem reasonable- for example 4.4C at 5.30 am.

This continued on August 28^th   with an all time low of -69.5C:

And the BOM ceased reporting values at 3:30 pm.

This description of events was confirmed by the Bureau’s response to a query:

“Do you know what is causing the very low temperature recordings?

There is a hardware fault within the AWS which is generating spurious values. The Bureau’s technicians are investigating but a site visit will be required.

Why was the August 19 low temperature recording not left blank?

Manual quality checking confirmed that the spiking on 19 August did not occur near the minimum  temperature for that day, as a result, the minimum temperature was recorded.”

This begs the question: is this what happened at Goulburn Airport on 2 July ? The initially reported figure of -10.4C was flagged as suspicious, so the previous low temperature of -10C was then reported, then this was removed , then the initial -10.4C was reinstated.  Perhaps.

-10.4C certainly should not have been flagged as too low for that location, as many other  values below 10C have been observed, including the record -10.9C recorded on 17 August 1994.  However, perhaps it was flagged as suspicious by comparison with the series of values before and after: too large a change in temperature from second to second.  But if so, why didn’t the BOM CEO just say so, instead of getting tangled in a web of conflicting explanations?

The AWS at Borrona Downs has failed.  So has the Bureau of Meteorology.

 

Garbage In, Garbage Out

(By Ken Stewart, assisted by Bill Johnston and Phill Goode; and cross-posted with Jo Nova)

Early ABC Radio news bulletins last Wednesday morning were led by this item, which you can read in full at ABC Online.

More climate scientists needed to avoid expensive mistakes, review urges

Apparently we urgently need 77 climate scientists to predict the future of areas like the Murray-Darling Basin with climate modelling.

Interestingly, Professor McDougall of the Australian Academy of Science points out that one of those “expensive mistakes” was the $2 billion desalination plant built in Queensland as a response to the millennium drought, “which really wasn’t an indication of climate change at all”.   Why didn’t the good professor raise his voice before the money was wasted?

But I digress.

Reliable modelling and projections for the future are surely desirable.

But such modelling must be based on reliable data, and the reliability of temperature data in Australia is demonstrably poor.

Example 1:  As has been widely reported in The Australian, and by Jennifer Marohasy and Jo Nova, cold temperatures at two separate sites (and possibly many others) were altered to appear warmer, then changed back, then deleted.  The BOM gave two conflicting explanations, both of which cast grave doubt on the reliability of “raw” temperature data from an unknown number of stations.

Example 2:  After enquiring why there are frequently two different temperature readings for exactly the same minute at various weather stations, a Bureau spokesperson told me that:

Firstly, we receive AWS data every minute. There are 3 temperature values:
1. Most recent one second measurement
2. Highest one second measurement (for the previous 60 secs)
3. Lowest one second measurement (for the previous 60 secs)

(See here and here.)

In other words, Australian maximum and minimum temperatures are taken from ONE SECOND readings from Automatic Weather Stations.  Spikes due to localised gusts of hot air, or instrument error, become the maximum for the day.  (This rarely has a large effect on minima, as night time temperatures are fairly smooth, whereas during the day temperature bounces rapidly up and down.  This is shown in this plot of temperatures at Thangool Airport in Queensland on Australia Day this year.)

And this is for the same day between 3.00pm and 4.00pm.

As you can see the temperature spikes up and down in the heat of the day by up to one degree between one minute and the next.  But these are the temperatures at the final second of each minute: during the intervening 59 seconds the temperature is spiking up and down as well, which we know because occasionally the highest or lowest temperature for the day occurs in the same minute as a final second recording on the BOM database (usually on the hour or half hour).  This can be up or down by two or three degrees in less than 60 seconds.

This is in contrast to the rest of the world.  The WMO recommends 1 minute (60 second) averages of temperature to be recorded to combat this very problem of noisy data, and this is followed in the UK.  In the USA 5 minute (300 second) averages are calculated.

From THE WEATHER OBSERVER’S HANDBOOK by Stephen Burt (Cambridge University Press, 2012):

Even without software or human interference as in Example 1, this means Australian temperature data, in particular maxima, are not reliable.

Example 3:  Historically, temperatures were observed from Liquid In Glass (LIG) thermometers.  From the 1990s, Automatic Weather Stations (AWS) were installed using Platinum Resistance Thermometers (PRT) and are now the source for daily data.  AWS thermometers are very precise, but as I showed in Example 2, their data is used idiosyncratically to record 1 second spikes, frequently resulting in higher maxima and less often slightly lower minima than a 1 or 5 minute average.

One would think that with such a major change in technology there would be comparative studies reported in the BOM’s meteorological journal or other “peer reviewed” literature.  Apparently not.

Dr Bill Johnston has investigated this and says:

Parallel data were collected all over Australia for over a decade, some until last year when thermometers were removed, at manned sites, mainly major airports (Ceduna, Sydney, Hobart, Perth, Darwin, Alice Springs, Albany, Norfolk Island, Wagga to name a few) and also met-offices such as Cobar and Giles. However, comparisons between screens were done at one site only (Broadmeadows, Melbourne, which is not even an official weather station) using PRT only and reported as a “preliminary report”, which is available (https://www.wmo.int/pages/prog/www/IMOP/WebPortal-AWS/Tests/ITR649.pdf) however, after AWS became primary instruments, as I’ve reported before, the Bureau had an internal policy that parallel liquid-in-glass thermometer data were not databased. Furthermore, they had another policy that paper-data was destroyed after 2-years. So there is nothing that is easily available…. there is also no multi-site replicated study involving screen types and thermometers vs. PRT probes ….

Deliberate destruction of data is scandalous; the only way now to compare Automatic Weather Stations (AWS) and Liquid in Glass, is to hunt for sites where there is overlap between two stations; where the AWS is given a new number. This is possible BUT the problem is that the change-over is invariably confounded with either a site move or the change to a small screen.

Therefore we suspect that the introduction and reliance on AWS has led to artificially higher maxima (and thus record temperatures) than in the past, but we have no way of knowing for sure or how much.

So we now have (1) temperatures that are altered before they even become ‘raw’ data; (2) use of one second spikes for recording daily maximum and minimum temperatures, very probably resulting in artificially high maxima and slightly lower minima; and (3) no way of telling how the resulting data compare with those from historical liquid-in-glass thermometers.

How can the CSIRO hope to produce reliable climate modelling with any number of climate scientists when the BOM cannot produce reliable temperature data?  Garbage in, garbage out.