Posts Tagged ‘Australia’

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

October 30, 2017

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:

Mboro 15 Feb

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:

1 min T Mboro 15 feb

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:

1 min T Mboro 15 feb 12 to 2

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:

1 min T change by hr of day Mboro 15 Feb

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:

1 min T change by hr of day Mboro 1 Jan 21 Mar 2017

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:

1 min T Mboro 6 mar

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:

1 min T change by hr of day Hervey Bay 1 Jan 21 Mar 2017

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:

1 min T Hervey Bay 16 March 2017

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 17th.  Would a mercury thermometer be sensitive enough to capture that?

And here’s 22 February:

Figure 9:

1 min T Hervey Bay 22 Feb 2017

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:

1 min T change by hr of day Thangool 1 Jan 21 Mar 2017

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:

1 min T Thangool 31 jan

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

Figure 12:

1 min T Thangool 7 jan

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:

1 min T Thangool 28 jan

Figure 14:

1 min T Thangool 24 jan

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:

1 min T Thangool 20 mar

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

Figure 16:

1 min T Thangool 17 feb

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:

1 min T change by hr of day L Elliott Is 1 Jan 21 Mar 2017

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:

1 min T L Elliott Is 7 jan

Note the early morning downward spikes: rain showers.

Figure 19:

1 min T L Elliott Is 16 jan

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:

1 min T L Elliott Is 28 jan

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:

1 min T L Elliott Is 14 mar

Now watch the temperature recovery next day.

Figure 22:

1 min T L Elliott Is 14 15 mar

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:

1 min T L Elliott Is 16 feb

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:

1 min T L Elliott Is 16 feb 12 to 3

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.

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

October 9, 2017

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 2nd order polynomial (quadratic) trend line:

lewis predictions summers1

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

lewis predictions summers2

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

September 14, 2017

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:

Hervey Bay 1 min 5 to7am 22 Feb

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:

Hervey Bay 1 min 0559 to 0600am 22 Feb

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

August 30, 2017

(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 (20th  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:

Borrona Dns map

Borrona Dns aerial

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

Borrona Dns cdo

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.

Borrona Dns Tibooburra comp

But the devil, as Phill found, is in the detail.  Here is part of the record for the 19th:  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.

Borrona Dns 19 Aug

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 26th:

Borrona Dns 26 Aug

Things got much worse on August 27th:

Borrona Dns 27 Aug

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 28th   with an all time low of -69.5C:Borrona Dns 28 Aug

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.

 

How Temperature is “Measured” in Australia: Part 2

March 21, 2017

By Ken Stewart, ably assisted by Chris Gillham, Phillip Goode, Ian Hill, Lance Pidgeon, Bill Johnston, Geoff Sherrington, Bob Fernley-Jones, and Anthony Cox.

In the previous post of this series I explained how the Bureau of Meteorology presents summaries of weather observations at 526 weather stations around Australia, and questioned whether instrument error or sudden puffs of wind could cause very large temperature fluctuations in less than 60 seconds observed at a number of sites.

The maximum or minimum temperature you hear on the weather report or see at Climate Data Online is not the hottest or coldest hour, or even minute, but the highest or lowest ONE SECOND VALUE for the whole day.  There is no error checking or averaging.

A Bureau officer explains:

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)

Relating this to the 30 minute observations page: For an observation taken at 0600, the values are for the one minute 0559-0600.

Automatic Weather Station instruments were introduced from the late 1980s, with the AWS becoming the primary temperature instrument at a large number of sites from November 1 1996.  They are now universal.

An AWS temperature probe collects temperature data every second; there are 60 datapoints per minute.  The values given each half hour (and occasionally at times in between) at each station’s Latest Weather Observations page are samples: spot temperatures for the last second of the last minute of that half hour, and the Low Temp or High Temp values on the District Summary page are the lowest and highest one second readings within that minute of reporting.  The remaining seconds of data are filtered out.  There is no averaging to find the mean over say one minute or ten minutes.  There is NO error checking to flag rogue values.  The maximum temperatures are dutifully reported in the media, especially if some record has been broken.  Quality Control does not occur for two or three months at least, which then just quietly deletes spurious values, long after record temperatures have been spruiked in the media.

In How Temperature is “Measured” in Australia: Part 1 I demonstrated how this method has resulted in large differences recorded in the exact same minutes at a number of stations.

What explanation is there for these differences? 

The Bureau will insist they are due to natural weather conditions.  Some rapid temperature changes are indeed due to weather phenomena.  Here are some examples.

In semi-desert areas of far western Queensland, such as in this example from Urandangi, temperatures rise very rapidly in the early morning.

Fig. 1:  Natural rapid temperature increase

urandangi

For 24 minutes the temperature was increasing at an average of more than 0.2C per minute.  That is the fastest I’ve seen, and entirely natural- yet at Hervey Bay on 22 February the temperature rose more than two degrees in less than a minute, before 6 a.m., many times faster than it did later in the morning.

Similarly, on Wednesday 8 March, a cold change with strong wind and rain came through Rockhampton.  Luckily the Bureau recorded temperatures at 4:48 and 4:49 p.m., and in that minute there was a drop of 1.2C.

Fig. 2:  Natural rapid temperature decrease

Rocky 8 March

That was also entirely natural, and associated with a weather event.

For the next plots, which show questionable readings, I have supplemented BOM data with data from an educational site run by the UK Met Office, WOW (Weather Observations Worldwide).  The Met gets data from the BOM at about 10 minutes before the hour, so we have an additional source which increases the sample frequency.  The examples selected are all well-known locations in Queensland, frequently mentioned on ABC TV weather.  They have been selected purely because they are examples of large one minute changes.

This plot is from Thangool Airport near Biloela, southwest of Rockhampton, on Friday 10 March.  The weather was fine, sunny, and hot, with no storms or unusual weather events.

Fig. 3:  Temperature spike and rapid fall at Thangool

Thangool 10 march

This one is for Coolangatta International Airport on the Gold Coast on 20th February.

Fig. 4:  Temperature spike and rapid fall at Coolangatta

Coolangatta 20 Feb bom met

And Maryborough Airport on 15th February:

Fig. 5:  Temperature spike and rapid fall at Maryborough (Qld)

Mboro 15 Feb

Figure 5(b):  The weirdest spike and fall:  Coen Airport 21 March 

Coen 21 March

Thanks to commenter MikeR for finding that one.

All of these were in fine sunny conditions in the hottest part of the day.  It is difficult to imagine a natural meteorological event that would cause such rapid fluctuations- in particular rapid falls- as in the above examples.  It is possible they were caused by some other event such as jet blast or prop wash blowing hotter air over the probe during aircraft movement, quickly replaced by air at the ambient surrounding temperature.  It is either that or random instrument error.  Either way, the result is the same: rogue outliers are being captured as maxima and minima.

How often does this happen?

Over one week I collected 200 instances where the High Temps and Low Temps could be directly checked as they occurred in the same minute as the 30 minute observation.

The results are astounding.  The differences occurring in readings in the same minute are scattered across the range of temperatures.  Most High Temp discrepancies are of 0.1 or 0.2 degrees, but there is a significant number (39% of the sample) with 0.3C to 0.5C decreases in less than one minute, and five much larger.

Fig. 6:  Temperature change within one minute from maximum

Count diffs hi T graph

Notice that 95% of the differences were from 0.1C to 0.5C, which suggests that one minute ranges of up to 0.5C are common and expected, while values above this are true outliers.  The Bureau claims (see below) that in 90% of cases AWS probes have a tolerance of +/-0.2C, whereas the 2011 Review Panel mentioned the “the present +/- 0.5 °C”.  Is the tolerance really +/-0.5C?

Fig. 7:  Temperature change within one minute from minimum

Count diffs lo T graph

There was one instance where there was no difference.  The vast majority have a -0.1C difference, which is within the instruments’ tolerance.

This next plot shows the differences (temperature falls in one minute from the second with the highest reading to that of the final second) ordered from greatest to least.

Fig. 8:  Ordered count of temperature falls

Count diffs hi T

The few outliers are obvious.  More than half the differences are of 0.1C or 0.2C.

One minute temperature rises:

Fig. 9:  Ordered count of temperature rises

Count diffs lo T

Note the outlier at -2.1C: that was Hervey Bay Airport.  Also note only one example with no difference, and the majority at -0.1C.

Is there any pattern to them? 

The minimum temperature usually occurs around sunrise, although in summer this varies, but very rarely when the sun is high in the sky.  Therefore rapid temperature rise at this time will be relatively small, as the analysis shows: 80% of the differences between the Low Temps and corresponding final second observations were zero or one tenth of a degree, and 91% two tenths of a degree or less.  As the instrument tolerance of AWS sensors is supposed to be +/- 0.2C, the vast majority of Low Temps are within this range.  Therefore, the Low Temps are not significantly different from the Latest Observation figures.  Yet as it is the Lowest Temperature that is being recorded, all but one example have the Low Temp, and therefore daily minimum, cooler than the final second observation.  9% are outside the +/-0.2C range and show real discrepancy, i.e. very rapid temperature rise within one minute, that is worth investigating.  Remember, the fastest morning rise I’ve found averaged about 0.2C per minute.

The High Temps have 56% of discrepancies within the +/-0.2C tolerance range.  Day time temperatures are much more subject to rapid rise and fall of temperatures.  The 44% of discrepancies of 0.3C or more are worth investigation.  Many are likely due to small localized air temperature changes, the AWS probes being very sensitive to this, but the rapid decreases shown in the examples above, as well as the rapid rises in the Low Temp examples, mean that random noise is likely to be a factor as well.

Have they affected climate analysis? 

Comparison of values at identical times has shown that out of 200 cases, all but one had higher or lower temperatures at some previous second than at the last second of that minute, with a significant number of High Temp observations (39% of the sample) with 0.3C to 0.5C decreases in less than one minute, and five much larger.  There is a very high probability that similar differences occur at every station in every state, every day.

In more than half of the sample of High Temps, and over 90% of the Low Temps, the discrepancy was within the stated instrumental tolerance range, and therefore the values are not significantly different, but the higher or lower reading becomes the maximum or minimum, with no tolerance range publicised.

This would of course be an advantage if greater extremes were being looked for.

Nearly 10 percent of minimum temperatures were followed by a rise of more than 0.2C, and 44 percent of maxima were followed by a fall of more than 0.2C.  While many of these may have entirely natural causes, none of the very large discrepancies examined had an identifiable meteorological cause.   It is questionable whether mercury-in-glass or alcohol-in-glass thermometers used in the past would have responded as rapidly as this.  This must make claims for record temperatures questionable at best.

If you think that the +/- 0.2C tolerance makes no difference in the big picture, as positives will balance negatives and errors will resolve to a net of zero, think again.  Maximum temperature is the High Temp value for the day, and 44% of the discrepancies were more than +0.2C.  If random instrument error is the problem causing the apparent temperature spikes, (and downwards spikes in the hot part of the day are not reported unless they show up in the final second of the 30 minute reporting period), only the highest upwards spike, with or without positive error, is reported.  Negative error can never balance any positive error.

Further, these very precise but questionable values then become part of the climate monitoring system, either directly if they are for ACORN stations, or indirectly if they are used to homogenise “neighbouring” ACORN stations. They also contribute to temperature maps, showing for example how hot New South Wales was in summer.

Again, temperature datasets in the ACORN network are developed from historic, not very precise, but (we hope) fairly accurate data from slow response mercury-in-glass or alcohol-in-glass thermometers observed by humans, merged with very precise but possibly unreliable, rapid response, one second data from Automatic Weather Systems.  The extra precision means that temperatures measured by AWS probes are likely to be some tenths of a degree higher or lower than LIG thermometers in similar conditions, and the higher proportion of High Temp differences shown above, relative to Low Temp differences, will lead to higher maxima and means in the AWS era.  Let’s consider maxima trends:

Fig. 10:  Australian maxima 1910-2016

graph max trend

There are no error bars in any BOM graph.  Maxima across Australia as a whole have increased by about 0.9 C per 100 years according to the Bureau, based on analysis of ACORN data.  Even if across the whole network of 526 automatic stations the instrument error is limited to +/- 0.2C, that is 22.2% of the claimed temperature trend.  In the past, indeed as recently as 2011 (see below), instrument error was as high as +/-0.5C, or about half of the 107 year temperature increase.  No wonder the Bureau refuses to show error bands in its climate analyses.

There have been NO comparison studies published of AWS probes and LIG thermometers side by side.  Can temperatures recorded in the past from liquid-in-glass thermometers really be compared with AWS one second data?  The following quotes are from 2011, when an Independent Review Panel gave its assessment of ACORN before its introduction.

Report of the Independent Peer Review Panel p8 (2011)

Recommendations: The Review Panel recommends that the Bureau of Meteorology should implement the following actions:

A1 Reduce the formal inspection tolerance on ACORN-SAT temperature sensors significantly below the present ±0.5 °C. This future tolerance range should be an achievable value determined by the Bureau’s Observation Program, and should be no greater than the ±0.2 °C encouraged by the World Meteorological Organization.

A2 Analyse and document the likely influence if any of the historical ±0.5 °C inspection tolerance in temperature sensors, on the uncertainty range in both individual station and national multidecadal temperature trends calculated from the ACORN-SAT temperature series.

And the BoM Response: (2012)

… … …   An analysis of the results of existing instrument tolerance checks was also carried out. This found that tolerance checks, which are carried out six-monthly at most ACORN-SAT stations, were within 0.2 °C in 90% of cases for automatic temperature probes, 99% of cases for mercury maximum thermometers and 96% of cases for alcohol minimum thermometers.

These results give us a high level of confidence that measurement errors of sufficient size to have a material effect on data over a period of months or longer are rare.

This confirms LIG thermometers have more reliable accuracy than automatic probes, and that 10% of AWS probes are not sufficiently accurate, with higher error rates.  That is, at more than 50 sites.  If they are in remote areas, their inaccuracy will have an additional large effect on the climate signal.   It is to be hoped that Alice Springs, which contributes 7-10% of the national climate signal, is not one of them.

Conclusion

It is very likely that the 199 one minute differences found in a sample of 200 high and low temperature reports are also occurring every day at every weather station across Australia.  It is very likely that nearly half of the High Temp cases will differ by more than 0.2 degree Celsius.

Maxima and minima reported by modern temperature probes are likely to be some tenths of a degree higher or lower than those reported historically using Liquid-In-Glass thermometers.

Daily maximum and minimum temperatures reported at Climate Data Online are just noise, and cannot be used to determine record high or low temperatures.

These problems are affecting climate analyses directly if they are at ACORN sites, or indirectly, if they are used to homogenise ACORN sites, and may distort regional temperature maps.

Instrument error may account for between 22% and 55% of the national trend for maxima.

A Wish List of Recommendations (never likely to be adopted):

That the more than 50 sites at which AWS probes are not accurate to +/- 0.2 degree Celsius be identified and replaced with accurate probes as a matter of urgency.

That the Bureau show error bars on all of its products, in particular temperature maps and time series, as well as calculations of temperature trends.

That the Bureau of Meteorology recode its existing three criteria filter, to zero-out spurious spikes and preferably send them as fault flags into a separate file in order to improve Quality Control.

That the Bureau replace its one second spot maxima and minima  reports with a method similar to wind speed reports: the average over 10 minutes.  That would be a much more realistic measure of temperature.

Putting Temperature in Context: Pt 2

December 14, 2016

To show how handy my Excel worksheet is, here’s one I did in the last 15 minutes.

Apparently Sydney has had its warmest December minimum on record at 27.1 C.  The record before that was Christmas Day, 1868 at 26.3C.

The following seven plots show this in context.

Fig. 1:  The annual range in Sydney’s minima:

whole-yr-sydney-min

Extremes in minima can occur any time between October and March.

Fig. 2:  The first 2 weeks of December

14d-sydney-min

Plainly, a new record was set this morning, but apart from Day 340 the other days are within the normal range.

Fig. 3:  7 day mean of Tmin in this period

7d-avg-sydney-min

Extreme, but a number of previous years had warmer averages.

Fig. 4:  Consecutive days above 20C Tmin.

days-over-20-sydney

But there have been longer periods of warm minima in the past.

Now let’s look at the same metric, but for all of December.

Fig. 5:  All Decembers (including leap years).

december-sydney-min

A record for December, with 1868 in second place.

Fig. 6:  7 day mean of Tmin for Decembers

7d-avg-sydney-min-december

Seven day periods of warm nights are not new.  The horizontal black line shows the average to this morning (20.6C) is matched or exceeded by a dozen other Decembers.  (Of course this December isn’t half way through yet.)  Also note what appears to be a step change about 1970.

Fig. 7:  Consecutive days above 20C Tmin in December.

days-over-20-sydney-december

I doubt if 15 December will be as warm as today, but could still be over 20C.

This is weather, not global warming.

 

Land and Sea Temperature: South West Australia Part II: TMin

November 29, 2016

This is a quick follow up to my last post, as an update:  I’ve been reminded to show Tmin as well.   My apologies.

In this post I examine minimum temperature for Winter in South-Western Australia, and Sea Surface Temperature data for the South West Region, all straight from the Bureau of Meteorology’s Climate Change time series page .

All temperature data are in degrees Celsius anomalies from the 1961-90 average.

Fig. 1:   Southwestern Australia Winter TMin Anomalies & SST

sw-tmin-sst

Note that TMin roughly matches SSTs, but there are differences from TMax.  CuSums will show this:

Fig. 2:  CuSums of Winter TMin and SST compared:

sw-tmin-cusums

Note that TMin has completely different change points, marked in red.  The major different ones are at 1949, 1956, 1964, 1990, 2000, and 2010.  There is a barely discernible point at 1976 (not 1975), so the next plots will use 1976 to show trends since then.

Fig. 3:  Trends in TMin:

sw-tmin-trends

Cooling since 1976 at -0.36C/100 years.

Detrending the data allows us to see where any of the winters “bucks the trend”.  In the following plots, the line at zero represents the trend as shown above.

Fig. 4:  TMin Detrended:

sw-tmin-detrended

2016 winter TMin is 0.5C below trend, and 0.38C below average, however winter this year in southwest WA was not as cold as 1986, 1990, 2001, 2006, 2008, or 2010- according to Acorn of course.

The action is with TMax.

Land and Sea Temperature: South West Australia

November 29, 2016

This year, the south-west of Western Australia has recorded some unexpectedly low temperatures.  Has this been due to rainfall, cloud, winds, or the cooler than normal Leeuwin Current and Sea Surface Temperatures in the South West Region?

In this post I examine maximum temperature and rainfall data for Winter in South-Western Australia, and Sea Surface Temperature data for the South West Region, all straight from the Bureau of Meteorology’s Climate Change time series page .

All temperature data are in degrees Celsius anomalies from the 1961-90 average.

Figure 1 is a map showing the various Sea Surface Temperature monitoring regions around Australia.

Fig. 1

sst-regions

The Southwest Region is just to the west and southwest of the Southwest climate region, and winter south westerlies impact this part of the continent first.  2016’s winter has seen maxima drop sharply.  In fact, it was the coldest winter since 1993:

Fig. 2:  Southwestern Australia Winter TMax Anomalies

sw-tmax

There is a relationship between rainfall and Tmax- as rain goes up, Tmax goes down, so here south west rainfall is inverted and scaled down by 100:

Fig. 3:  TMax and Rain:

sw-tmax-rain

The next plot shows TMax and the South West Region’s Sea Surface Temperature anomalies (SST):

Fig. 4:  TMax & SST:

sw-tmax-sst

Again, related: both have strong warming from the 1970s.  Next I check for whether there was a real change in direction in the 1970s, and if so, when.  To do this I use CuSums.

Fig. 5:  CuSums of Winter TMax and SST compared:

sw-tmax-sst-cusums

Both have a distinct change point: 1975, with SST warming since, but TMax appears to have a step up, with another change point at 1993 with strong warming since.  Rainfall however shows a different picture:

Fig. 6:  CuSums of Winter Rainfall

sw-rain-cusums

Note the major change at 1968 (a step down: see Figure 3), another at 1975 with increasing rain to the next change point at 2000, after which rain rapidly decreases.

I now plot TMax against rainfall and SST to see which has the greater influence.  First, Rain:

Fig. 7:  TMax vs Rain:

sw-tmax-vs-rain

100mm more rain is associated with about 0.5C lower TMax, but R-squared is only 0.22.

Fig. 8:  TMax vs SST:

sw-tmax-vs-sst

A one degree increase in SST is associated with more than 1.1C increase in TMax, and R-squared is above 0.51- a much closer fit, but still little better than fifty-fifty.

TMax is affected by rain, but more by SSTs.

I now look at data since the major change points in the 1975 winter.  The next three figures show trends in SST, Rain, and TMax.

Fig. 9:  Trends in SST:

sw-sst-trends

Warming since 1975 of +1.48C/ 100 years.

Fig. 10:  Trends in Rainfall:

sw-rain-trends

Decreasing since 1975 at 89mm per 100 years (and much more from 2000).

Fig. 11:  Trends in TMax:

sw-tmax-trends

Warming since 1975 at +2.14C per 100 years.

Detrending the data allows us to see where any of the winters “bucks the trend”.  In the following plots, the line at zero represents the trend as shown above.

Fig. 12:  SST Detrended:

sw-sst-detrended-75-to-16

Fig. 13:  Rainfall Detrended:

sw-rain-detrended-75-to-16

Fig. 14:  TMax Detrended:

sw-tmax-detrended-75-to-16

Note that SST in 2016 is just below trend, but still above the 1961-90 average.  Rainfall is only slightly above trend, and still below average.  However TMax is well below trend, and well below average, showing the greatest 12 month drop in temperatures of any winter since 1975.

My conclusions (and you are welcome to comment, dispute, and suggest your own):

  • Maximum temperatures in winter in Southwestern Australia are affected by rainfall, but to a much larger extent by Sea Surface Temperature of the South West Region.
  • The large decrease in winter temperature this year cannot be explained by rainfall or sea surface temperature.  Cloudiness may be a factor, but no 2016 data are publicly available.  Stronger winds blowing from further south may be responsible.

Water World

November 15, 2016

Readers may be aware of the “Cold Blob” which is moving across the northern Pacific Ocean.  In this post I shall show sea surface temperature anomalies, and currents, in all of the world’s oceans, as shown by nullschool.

This is the colour scale for all figures, from -6C to +6C.  Zero anomaly is black.

scale

The Arctic Ocean

arctic-ocean

The Southern Ocean

sthn-ocean

Note the large area of sea ice around Antarctica (black) surrounded by a ring of below average SSTs, with another ring of swirling eddies of warmer SSTs.  Note also the cold blob just below south-western Australia which is working its way east.

The Atlantic Ocean

atlantic-ocean

The North Atlantic is predominantly unusually warm- especially the Gulf Stream.  However the South Atlantic is largely covered by a very large pool of cold water.

The Indian Ocean

indian-ocean

The Indian Ocean Dipole between the west and the east is plain to see.  Note the colder than normal SSTs near south-western Australia which have led to some unusually cold land temperatures this winter and spring.

The Pacific Ocean

pacific-ocean

The El Nino has ended and La Nina appears to be building as the surge of cold water moves west along the Equator.  Note the cold blobs in the North Pacific, and less well defined in the South Pacific.  Note also the high SSTs near South America and around the International Date Line at 30 degrees North.

Note there are large areas of above and below normal SSTs in all ocean basins except the Arctic, where sea ice cover tends to hide water temperature below.  The Arctic ocean atmospheric temperature anomalies have recently shot up to record highs.

I now turn to the seas close to Australia.

australia-sst

Waters around the northern, north-western, and eastern coasts of Australia are generally 1.0 to 1.8C above normal.  This includes the area of the Great Barrier Reef.  The East Australian Current runs down the east coast and can be seen as a warm tongue spilling into the Tasman Sea.  (This is what led to the ABC’s reports about high temperatures in the Tasman Sea.)  But the Tasman Sea has several eddies of cold and warm water.  Note also the cold area to the south of Western Australia, and the cool area just to the east of Tasmania.

Warm waters around northern Australia are likely to generate extra rainfall and probably cyclones, and a strong gradient between north and south will likely lead to strong weather changes and storms.

Conclusion:  Once again, the difference between the Northern and Southern Hemispheres shows itself in sea temperatures.  Apart from the cold blob in the northern Pacific, Northern Hemisphere oceans are predominantly warmer than usual, while those of the Southern Hemisphere have large regions of both warmer and cooler water.  There is a very large cold blob in the South Atlantic, and another surrounding Antarctica.  Ocean currents constantly move thermal energy around, releasing it by radiation and evaporation mainly, and governing land temperatures hundreds of kilometres away.

The next six months should be interesting.

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.