One Minute Data and Extremes Part 1: Thangool

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

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

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

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

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

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

Figure 1:  Daily Minimum Difference

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

Figure 2:  Daily Maximum Difference

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

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

However, we can draw some inferences.

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

Figure 3: One minute and Maximum Temperature at Thangool

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

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

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

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

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

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

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

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

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

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

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

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

The next post will look at Sunshine Coast Airport.

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Who’s Laughing Now?

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

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

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

And

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

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

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

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

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

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

Here’s the paper in question:

Response time of temperature measurements at automatic weather stations in Australia

G. P. Ayers ^A B and J. O. Warne ^A

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

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

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

A simple check at Climate Data Online shows how good.

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

Figure 1: Darwin Tmax 2018

And Figure 2 shows Tmin for 2018:

Figure 2: Darwin Tmin 2018:

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

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

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

Here is Ayers’ previous paper, quoted by Readfearn:

A comment on temperature measurement at automatic weather stations in Australia

G. P. Ayers

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

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

Ayers says

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

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

Figure 4: Data Table from Ayers (2020)

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

Figure 5: Mildura Tmin 2017:

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

So much for accuracy!

There are three possibilities: 

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

they used faulty data;

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

Whichever, it’s not a good look.

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

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

More Indications of Bureau of Meteorology Temperature Nonsense- Update

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

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

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

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

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

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

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

Figure 1: Monthly Surface Tmax and Atmospheric Temperatures from UAH

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

Figure 2: BOM Tmax vs UAH

There is correlation, but there are many differences.

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

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

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

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

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

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

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

The equation for the trendline is

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

So,

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

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

Figure 6: Monthly Tmax and Theoretical Tmax

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

Figure 7: Tmax minus Theoretical Tmax

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

Figure 8: Tmax minus Theoretical Tmax 12m Averages

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

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

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

Coal Generation Sets New Record After Liddell Closes!

The National Electricity Market lost 2,000 MW of generating capacity last Friday.  In spite of this, coal fired generation increased its share of total generation, to a record for the year to 30 April, of 67.52%, as Figure 1 shows:

Figure 1: Percentage of Total NEM Generation: Coal, Wind, Solar

The other immediate result was that the Capacity Factor of the remaining coal generators suddenly increased by about 5%. 

Figure 2: Running Average Coal Capacity Factor % 1 April -1 May 2023

The remaining coal fired stations ramped up their generation to make up for the shortfall- mainly Eraring in NSW:

Figure 3:  Eraring Electricity Generation 27-29 April: average 69%

Eraring maintained a Capacity Factor of around 95% for most of Saturday until Sunday morning when it dropped to 37% during daylight, then back up Sunday night and most of Monday.

Figure 4:  Eraring Electricity Generation 30 April – 2 May: average 72.1%

Why couldn’t wind and solar fill the gap left by Liddells’s closure?  Because there was not much wind or sunshine!  Figures 5 and 6 show Saturday to Monday generation at Stockyard Hill wind farm and the New England solar farm- two of the biggest:

Figure 5:  Wind Generation at Stockyard Hill: average 3.4% Capacity Factor

Figure 6:  Solar Generation at New England: average 6.7% Capacity Factor

Of course, in the coming winter there will be increased demand, and coal generators will need to be maintained.  We are not out of the woods, but the above graphs show how resilient, reliable, and efficient our much-maligned coal fired power stations are.

Could we lose 2,000 MW of solar or wind generation and have the rest immediately increase production?  Not likely!

And are Batteries and Hydro capable, and how efficient are they?

Figure 7: Battery Capacity Factor (Percent)

Batteries nearly reached 0.1 % of their stated capacity.

Figure 8: Hydro Capacity Factor (Percent)

Hydro did better- but even when producing over 28% of total NEM generation could only reach a Capacity Factor of nearly 0.4%. 

These dams and batteries are very inefficient for their cost.

Let’s see what the future holds!

(Source: OpenNEM)

Electricity Generation: The Impact of Rooftop Solar

Capacity Factor of an electricity generator is its actual generation as a percentage of its installed capacity.  A generator with an installed capacity of 1,000 Megawatts that generates 500 Megawatts has a Capacity Factor of 50%.  Obviously it is a good idea to have CF as high as possible as that will give a better return for the time, money, and effort used to build and run it.

In this post I am looking at Capacity Factors of all generators in the National Electricity Market (NEM), firstly excluding rooftop solar, then looking at CF when rooftop solar is included.

I use data available from Open NEM for the week from 8^th to 15^th March.

Firstly, Figure 1 shows the total of all major generators in Queensland, New South Wales, Victoria, Tasmania, and South Australia.

Figure 1:  Total NEM Generation 8-15 March

Solar and wind get preference, such that coal is curtailed when the sun is shining, but has to ramp up to meet demand from late afternoon to breakfast time.  Hydro and gas follow the same pattern at a much lower level, while wind generation adds its two bob’s worth at unpredictable times.

Figure 2 shows the Capacity Factor for the whole network (if there was no rooftop solar):

Figure 2: Capacity Factor NEM (excluding rooftop solar)

During this week CF varied in a regular cycle, from 27.9% to 43.8%.  Figure 3 shows this daily cycle:

Figure 3: Capacity Factor by Time of Day- NEM excluding rooftop solar

The NEM is at its most efficient- makes best use of generation resources- between 6pm and 7pm at night.  There is a lower peak in CF at 7am to 7.30am.  There is a drop in CF in the early morning (at baseload time), but the lowest CF is between about 11.30am and 12.30pm on several days.

Capacity Factors for coal, gas, and hydro have cycles reflecting that of the NEM without rooftop solar.

Figure 4: Capacity Factor by Time of Day: Coal, Gas, Hydro

By contrast, wind’s CF, which on the afternoon of the 8^th was briefly over 50%, could be as low as 2.4% and averaged 20.5% for the week.

Figure 5: Capacity Factor by Time of Day: Wind

Decidedly unreliable and inefficient.

Solar generation is much more reliable (in the sense of predictable) as we see in Figure 6.

Figure 6: Capacity Factor by Time of Day:  Solar

Solar CF is between about 40% and 60% in the middle of the day.  Note that utility solar, with tracking panels, reaches close to maximum CF by mid-morning and maintains higher CF than rooftop at nearly every 30 minute period of daylight.  Between sunset and sunrise, CF is zero.  All those millions of panels are useless.

When we include rooftop solar in the generation mix, see what happens to the CF for the whole NEM grid:

Figure 7: Capacity Factor by Time of Day- NEM excluding rooftop solar

Maximum CF is now in the middle of the day.  Figure 8 shows the difference rooftop solar makes to the CF of the whole network:

Figure 8: Change in Capacity Factor by Time of Day with Rooftop Solar

Before 9am and after 3.30pm the system is worse off. While the CF for the whole network has been increased in the middle of the day by between 2% and 6%, the average has been reduced by 4.5%, at baseload times by about 6.5%, and in the evening by nearly 10%.  Every additional panel will reduce CF even further, and this is not even considering the additional network capacity needed to keep the system balanced with such a wildly fluctuating supply.  Not a bad effort for a generating system with an average CF last week of 14.9%.

The final two figures compare actual generation at 12 noon and 4am.

Figure 9: 12 Noon Generation 8-15 March 2023

That’s all the renewables enthusiasts see: solar outperforming coal.  They are willfully blind to baseload needs:

Figure 10: 4:00 a.m. Generation 8-15 March 2023

When the remaining 1,500 MW of Liddell are lost in April, and 2,880 MW at Eraring in August 2025, the 4,330 MW gap in supply at 4:00 in the morning won’t be filled by rooftop solar or by solar farms: it will be made up by the remaining coal units working even harder (giving coal an even higher CF) until the strain is too much and they break down, and by gas and hydro.  Inevitable result: higher prices and probable blackouts (sorry- load shedding).

People of my generation often say we have lived through the best of times.

What will the coming generation say?

(Source: OpenNEM)

The Surprising Cost of Electricity

Using data from OpenNEM here is a plot of the cost per MegaWatthour of the main sources of electricity across eastern Australia since 1999.

Figure 1: Historical Cost of Electricity

Plainly the price of electricity supplied by major generators rocketed up in 2022.  Gas and coal were far more expensive than wind and solar. 

QED, would say Chris Bowen and Albo.

But hydro was more expensive than coal- and has been for most of the last 24 years.  Snowy Hydro 2.0 might not be such a good idea.

However, which generation had the biggest percentage increase in price from 2021 to 2022?  Gas?  Get ready for a surprise!

Figure 2:  Percentage Increase in Market Value per Megawatthour from 2021 to 2022

Blame the Russians or evil gas and coal exporters as much as you like- our saintly renewable generators had the largest increases.  Wind generated electricity increased the most- by a country mile.

They’re not above making a fast buck at the expense of Australian consumers.

(Source: OpenNEM)

A Snapshot of the National Electricity Market

Here is a point in time snapshot of electricity generation across the eastern states of Australia, in five simple plots.

Figure 1:  Total Installed Capacity of all Electricity Generators at 14 February 2023

Note that while coal is still king, rooftop solar capacity is rapidly gaining.  Figure 2 shows relative capacity in a pie chart:

Figure 2:  Percentage of Total Capacity

If you are any good at Maths you will see that fossil fuels account for just over 45% of generation capacity while renewables (including hydro) account for almost 55%.

Figure 3 looks at actual generation for the year from 14/2/22 to 6/2/23- 52 weeks- in a pie chart.

Figure 3:  Percentage of Total Generation over 52 weeks

Now that is interesting: coal supplied 58% of electricity generation from just 30% of generating capacity.  Renewables, with 55% of capacity could only manage 36% of actual supply.  Gas made up the remaining 6%.

What about in one 24 hour period?  Monday 13 February had close to ideal conditions for renewables: fine, sunny weather with the monsoon far to the north, moderate winds, and dams full.  Figure 4 shows the percentage of total generation for one day:

Figure 4:  Percentage of Total Generation on one day

Coal has slipped by one percent, gas by two percent for a total of 61%.  In ideal conditions, renewables provided 39%. 

Knowing the installed capacity for all generators and the actual electricity supplied we can calculate the capacity factor of each:

Figure 5:  Capacity Factor for all Generators 13/2/23

Coal                       62.13%

Wind                     32.93%

Solar (Utility)       32.85%

Hydro                    19.60%

Rooftop Solar      17.29%

Gas                        8.24%

Battery                  3.38%

Distillate               1.14%

Bioenergy           -0.15%

(-0.15% for bioenergy?  That’s not a typo: when sugar mills are not crushing, bioenergy is a drain on the network.)

Solar farms are nearly twice as efficient as roof top solar, for the simple reason that rooftop panels are usually fixed while panels in solar farms track the sun.  Maximum capacity factor for a solar farm could in theory approach 50%, while that of household solar, no matter how much installation increases, won’t get much higher than now.

You can be assured that wind and solar are generating as much as possible.  Coal and gas must reduce supply to allow for this- if it wasn’t for renewables they would have a much higher capacity factor.  This is a problem renewables will never be able to solve- wind and solar energy are too diffuse to be much more efficient.

I hate waste.

We will see how this compares in winter, with much less sunshine and Liddell coal fired power station closed.

(Source: OpenNEM)

Extreme Weather Events 3: Sydney

Are extreme weather events showing up in Australia’s largest city?

Floods and bushfires might affect smaller areas, but droughts, heatwaves, and very heavy rainfall from large weather systems affect large areas. All of the above have occurred near Sydney in the past few years: surely there should be visible signs in temperature and rainfall.
First, rainfall.

In July and October 2022 flooding affected the western Sydney region again, with The Conversation of course saying “climate change is projected to bring far worse extreme rain events than in the past.”

For long term rainfall I look at Sydney’s longest rain records, at Observatory Hill and the Botanic Gardens. Figure 1 shows their location.

Figure 1: Central Sydney, courtesy of Google Maps

Observatory Hill rain records start in July 1858, but the original data ends in August 2020. I choose not to splice data from old and new gauges. Botanic Gardens start in 1885 but there is a large gap, with continuous data from late 1909 to the present. Figures 2 and 3 plot daily rainfall for each:

Figure 2: Observatory Hill daily rain

Figure 3: Botanic Gardens daily rain (1910 to 2022)

Long term means:

Figure 4: 10 year running means of rainfall at Observatory Hill and Botanic Gardens

Note that the means are similar until about 2010 when they start to diverge. Reasons might include changes to the sites. Rainfall was clearly higher in several previous decades.

Figure 5: 10 year running Standard Deviations

There was much greater variability in Sydney’s rainfall for most of the 50 years from 1950 to 2000. To show Standard Deviation relative to mean rainfall:

Figure 6: 10 year running Standard Deviations divided by 10 year means

Which shows there is little daily variability in rainfall in recent years, and both sites are comparable.

I will now analyse Botanic Gardens data in more detail.

Figure 7: Running 365 day means

2022 was the wettest year on record, followed by 1950.

Rainfall accumulated over several days is a factor in large scale riverine flooding such as occurred in Sydney’s west.

Figure 8: Four day total rainfall

Clearly there were many much greater 4 day rain events in the past than in the latest floods.

I measure “droughts” by counting the number of days with less than 4mm of rain in running 365 day periods.

Figure 9: Running 365 day counts of days with under 4mm of rain

2022 was by far the most consistently wet. There is no sign of increased drought in Sydney.

Conversely, do recent years have more days with high rainfall?

Figure 10: Running 365 day counts of days with over 100mm of rain

No. Only 3 days in 2022, while 1999 had 5, and many others in previous years had more than 2022. It seems that the Sydney region, going by the Botanic Gardens rain gauge, has less extreme rainfall than the past.

I now analyse temperature at Sydney Observatory Hill, using the latest version of Acorn to 2021, and Climate Data Online for 2022 and January 2023 up to Australia Day.

Figure 11: Daily Maxima Sydney Observatory Hill 1910 to 26/1/2023

Maximum temperatures in Sydney, according to the best the Bureau can provide, have warmed at 0.9 degrees Celsius per 100 years. Decadal means show an almost identical trend.

Figure 12: 10 year mean Tmax

Standard Deviation measures daily variability, and 10 year mean Standard Deviations show some interesting patterns:

Figure 13: 10 year running Standard Deviation, Sydney Tmax

Variability is greater with higher temperatures and less with lower temperatures, and temperatures should be related to rainfall- because a dry period will have hotter days and usually cooler nights. Temperature adjustments might interfere with this.

Whatever, there were several past periods with higher Standard Deviations than the past decade, and when divided by the 10 year means the contrast is even greater:

Figure 14: 10 year running Standard Deviations divided by 10 year means

Are days getting hotter? Well, years are, mostly:

Figure 15: 365 day running means of Tmax

Highest and lowest daily maxima in 365 day periods are not co-operating:

Figure 16: Highest Tmax in 365 day periods

The hottest day was back in 1939, and 2022 had the lowest “hottest day” in a 365 day period on record, with the hottest day being 31.9 degrees.

Figure 17: Lowest Tmax in 365 day periods

Several past winters had cooler maxima.

But is Sydney getting more frequent hot and very hot days?

Figure 18: Running 10 year counts of days over 34.9 degrees

Figure 19: Running 10 year counts of days over 39.9 degrees

The last 10 years have had fewer hot and very hot days than in the past.

What about heat waves, when there are strings of hot days? The definition appears to have changed, but if we consider three hot days in a row to be a heat wave:

Figure 20: Running 10 year counts of 3 consecutive days over 34.9 degrees

There is a very small trend (0.8 in 100 years) but there were many more 3 day heatwaves in the past.

Figure 21: Running 10 year counts of 3 consecutive days over 39.9 degrees

There is a decreasing trend of very hot heat waves (more than 3 less per 100 years), with nearly three times as many 3 day heatwaves of 40 degrees or more in the 10 years to 1982 as in the past 10 years.

Conclusion:

Contrary to popular belief encouraged by politicians and the media, in Australia’s largest city it is clear that:

Rainfall and temperature variability is LOWER than in the past

Droughts are NOT increasing

Extreme rainfall is NOT increasing

Dry years are NOT increasing

Very hot days are DECREASING in frequency

Heatwaves are NOT increasing and are very much LESS COMMON than 40 years ago.

If anything, Sydney’s weather is becoming less extreme and more benign. That should be good news.

We are still waiting for the “projections” of more extreme weather to arrive.

Extreme Weather Events: 2

Further to my post yesterday about the Climate Council’s recent fear mongering, with my look at whether the recent flooding at Fitzroy Crossing could be due to increasingly severe rain events, here are two more locations.

I calculate the 10 year running standard deviation of daily rainfall, the 10 year mean, and because the standard deviation must change as the mean changes, I divide the 10 year standard deviation by the 10 year mean.

Early this year there was sever flooding in northern New South Wales. Brays Creek is near Mt Warning about 40 km north of Lismore. Here is the standard deviation divided by average rainfall:

Rainfall over the past 10 years is less extreme than it was 40 to 50 years ago.

The Bruce Highway to north Queensland was blocked for several days, as it normally is every Wet season, by flooding at Goorganga Plains just south of Proserpine. Is rainfall becoming more extreme? Here is the raingauge at Lethebrook, using the same technique.

Nothing exciting to see there either.

Extreme Weather Events: 1

Last night On Wednesday night 18 January, the Climate Council released their latest doomsday publication, with the support of Beyond Blue (they’re now off my list of charities to donate to.)

“HIDDEN MENTAL HEALTH TOLL OF WORSENING CLIMATE DISASTERS ON AUSTRALIANS REVEALED WITH NEW NATIONAL POLL”

Climate Councillor, climate scientist at the Australian National University and author of Humanity’s Moment: a Climate Scientist’s Case for Hope, Dr Joelle Gergis said: “The results of this poll are confronting. It’s heartbreaking to realise that many Australians are living with significant levels of distress related to the reality of our changing climate. It shines a light on this invisible mental health crisis that is undermining the stability of our local communities all over the country.

“We need to have a national conversation about climate change adaptation and listen to the experiences of people who have lived through these disasters.

“Extreme weather events are going to escalate as our planet continues to warm, so the impacts we have witnessed in recent years are really just the tip of the iceberg. We urgently need to develop plans that protect and support our local communities as climate change-fuelled disasters continue to upend the lives of countless Australians.”

Time for a reality check:

Is there evidence of increasing climate extremes?  Rainfall and temperature are easily measured and data is freely available from the BOM.

First example:  The recent flooding at Fitzroy Crossing. 

A useful measure of extremes is Standard Deviation.  For this technique I am indebted to Willis Eschenbach whose recent post at WattsUpWithThat sparked my interest.

I calculate the 10 year running standard deviation of daily rainfall, the 10 year mean, and because the standard deviation must change as the mean changes, I divide the 10 year standard deviation by the 10 year mean.

The nearest rain gauge with a reasonably long record is Fossil Downs.  Here is the 10 year average daily rainfall:

As you can see average daily rainfall (which nearly all falls in the Wet) has nearly doubled since the decades to the 1960s.

10 year standard deviation:

No wonder people are anxious!  The 10 year figure is very high (but not as high as the 1980s!  Was it more extreme 40 to 50 years ago?)

But here is the standard deviation divided by average rainfall:

This shows that relative to the average, rainfall extremes are actually getting smaller.

Over the next few days I will show rainfall and temperature plots for several Australian cities.  Stay tuned.