Blowin’ in the Wind

June 22, 2022

The energy crisis seems to be ongoing- the new normal apparently.  Is it the fault of old, rundown coal fired power stations with breakdowns?  Is it the fault of greedy, profit hungry energy suppliers gaming the system?  Is it the fault of the Ukraine war pushing up coal and gas prices?  Is it the fault of the previous coalition government for not having the correct climate policy, resulting in not enough investment in renewables?  Or all of the above?

Nope.

Breakdowns last week in under-funded power stations didn’t help, nor a shortage of high priced coal and gas.  And you can’t blame companies wanting to keep their income above their costs. 

But no amount of climate ambition, and no possible amount of renewable capacity, could have averted the problems we’ve had last week and are likely to continue to have.

Figure 1 shows our electricity consumption for the two weeks from 3rd to 17th June. 

Figure 1:  All NEM electricity consumption 3- 17 June

Coal is the heavy lifter.

Figure 2 shows the main energy sources as a percentage of the total usage.

Figure 2:  All sources as a percentage of NEM electricity consumption 3- 17 June

Note again it is coal followed by daylight- and I don’t mean solar!  Note also that coal’s relative contribution increased despite breakdowns and supply difficulties.

The next plot shows the percentage contribution of fossil fuels and all non-fossil sources- batteries, hydro, wind and solar.  I’ve also included the negative contribution of pumped hydro, when dams are refilled using excess electricity- except on 13th and 14th when it was too expensive.

Figure 3:  Fossil and non-fossil generation as a percentage of consumption

Renewable energy advocates like averages- they hide a multitude of sins.  Here are the averages of all sources for each 30 minutes of the day for the last two weeks:

Figure 4:  Average 30 minute NEM electricity consumption 3- 17 June

Coal varies between 12,000 and 16,000 MW per half hour as it responds to the twice daily peaks in demand, and the daily peak in solar output.  Solar is useless for meeting baseload around 4:00 a.m., or either of the daily peaks.  Wind averages a touch over 4,000 MW all day so is also no help with extra demand.  Battery discharge at peak times can barely be seen.  Gas and hydro vary at similar rates to meet demand when needed, though gas output remains higher throughout the night.

How reliable was wind generation, which averaged over 4,000 MW per half hour?  Here is a plot of actual wind generation at 30 minute intervals from 3 June to 17 June:

Figure 5:  Actual wind generation 3- 17 June for each half hour

“Fickle” is not an adequate description.

Of course renewables can provide 18,000 MW at maximum capacity- but at the wrong time of the day.  When the need was greatest, they could provide only 6,880 MW- and 90% of that was hydro.

Our entire electricity generation, including fossil generation, depends on the reliability or otherwise of renewable generation.

Our energy crisis last week was not caused by breakdowns, fossil fuel prices, greedy power companies, coalition governments, or lack of investment in renewables.

It was caused by a lack of wind.

Figure 6:  Actual wind generation 3- 17 June

We are hostages to the weather.  Bob Dylan was right.  The answer is blowin’ in the wind.

(Source: OpenNEM)

The Gap

June 18, 2022

Here is a simple plot to demonstrate the challenge facing our new government, and all future governments, if they want to transition to a zero carbon economy.

This is the gap between all non-fossil fuel generated electricity- solar, wind, and hydro- and total consumption in eastern Australia over the past two weeks (3rd to 17th of June) for every 30 minutes of the day.

That gap- 12,000 to 16,000 MW for base load and 16,000 to 30,000 MW for peak load- is now filled by gas and coal.  Snowy 2.0 will only provide an extra 2,000 MW of storage.

That’s just for electricity- don’t forget electric vehicles and hydrogen!

(Source: OpenNEM)

The Real Cost of Renewables

June 13, 2022

Electricity prices are increasing, we know.  Here is a plot of electricity prices across the eastern states in the National Electricity Market.

Fig. 1:  NEM Prices 2009-2022

There is a shortage of available coal and gas generation, resulting in record prices.

Fig. 2:  NEM Coal & Gas Prices 2009-2022

Of course wind and solar are much cheaper:

Fig. 3:  NEM Wind & Solar Prices 2009-2022

See?  Renewables are cheaper.

Not so fast.

Figure 4 shows electricity consumption for the eastern states last week (Friday 3 June to Friday 10 June).

Fig. 4:  NEM Total Consumption 3 June – 10 June

Note the daily cycle between baseload and peak load.  Figure 5 is a plot of consumption for each 30 minutes of the day:

Fig. 5:  NEM Total Consumption by Time of Day

The baseload- the minimum amount of electricity to meet the needs of streetlights, hospitals, smelters, and households- occurs every day between about 3.30 a.m. and 5.00 a.m., and last week was from 20,600 to 22,300 MW.

Peak load rose to 35,386 MW.

Figure 6 shows how wind and solar performed last week:

Fig. 6:  NEM Wind & Solar Consumption 3 June – 10 June

The bleeding obvious is that while solar provided more than 10,000 MW for 30 minutes on Saturday 4 June, it produced absolutely zero every night.  Wind never reached 7,000 MW.

That’s the reason we need storage.  If we can store the excess from solar, we could use it to supplement wind when needed.  Much money has been invested in large scale batteries.  However, batteries provided a maximum output of 324 MW last week- pathetic really.

We do have hydro-electricity, mainly in Tasmania and the Snowy Mountains.  Figure 7 shows how hydro contributed last week:

Fig. 7:  NEM Hydro Consumption 3 June – 10 June

Hydro helped twice a day at peak times, and also provided a substantial supply in daylight hours- over 2,000 MW on 8 June.  The previous week- at 6 p.m. on Thursday 2 June- wind could manage only 3% of the NEM load, and hydro provided 19.33%, or 5,382 MW.  Last Thursday 9 June at 6 p.m. hydro provided 5,519 MW.

That’s why we need more storage.  Forget batteries- the only realistic storage is pumped hydro, where excess off-peak electricity is used to pump water to storage dams.  Wivenhoe Dam in Queensland has been doing this for 40 years.

So the politicians dreamed up Snowy 2.0.  This scheme, whose timeline for completion has blown out to the end of 2026 according to Chris Bowen (Weekend Australian June 11-12), will cost 4.5 billion dollars to build, plus another $1.5 billion to $2 billion for extra transmission lines.

 “Snowy 2.0 will provide an additional 2,000 megawatts of dispatchable, on-demand generating capacity and approximately 350,000 megawatt hours of large-scale storage to the National Electricity Market. To provide context, this is enough energy storage to power three million homes over the course of a week.”

That’s a cost of $3.25 million per MW.

That’s the “good” news.  Now for the interesting news.

As we saw above, baseload last week was 20,000 to 22,000 MW- and winter has only just started.  If fossil fuels are removed eventually, baseload at 4 a.m. must be met by some combination of wind and hydro as there is no sun at that time of day. 

The current hydro capacity is 9,285 MW.  Snowy 2.0 will provide an extra 2,000 MW.

The current installed capacity of wind generation is 9,202 MW- and that is going full bore day and night, with optimum wind conditions and no stops for maintenance.  32% of capacity is the average reached.

The total installed capacity of wind, current hydro, and Snowy 2.0 is 20,487 MW.  That is still short of baseload with winter to come, and peak load last week was 35,386 MW.  That doesn’t allow for population increase or economic growth either.  Where will the extra 15,000 MW of wind powered and pumped hydro electricity come from?  It’s an impossible dream.

But wait, there’s more.

Here’s the bad news:  Hydro electricity is the most expensive electricity in Australia- more expensive than either coal or gas.  In May 2022 it reached $315.91 per MW.

Because it is rapidly despatchable it is sold at times of very high demand, so the operators get top dollar.  Much more than coal or gas.

Figure 8 shows the average price of hydro for each month to May 2022.

Fig. 8:  NEM Hydro Prices 2009-2022

The real cost of renewables will include the cost of storage and emergency supply.

Don’t hold your breath hoping for electricity prices to come down.

Energy Crisis or Ideology Crisis?  The Rubber hits the Road

June 7, 2022

Australia faces an energy crisis as electricity prices escalate, as we were told by the media last week.  Last Thursday evening at 6:00 p.m. the spot price for electricity hit $4,335 per Megawatt Hour.  Blame was immediately cast on our aging fleet of coal fired power stations.  Several were operating well under capacity through planned maintenance or unexpected failures.  Gas powered stations ramped up, but gas costs an arm and a leg because of the Ukraine war.  The weather had turned very cold in southern states and the wind had dropped. 

What shall we do?

Well, firstly, don’t panic.

Secondly, don’t depend on renewables.

Thirdly, enjoy the abundance of fossil fuel powered electricity- don’t restrict it.

And finally, if you must insist on Net Zero by 2050, go nuclear.

Here’s why.

Don’t panic:

Despite our apparently aging, decrepit, obsolete fleet of coal fired power stations operating at only 58% of capacity for the last seven days (10:30 a.m. Tuesday 31 May to Tuesday 7 June) the lights stayed on- just.  Gas and hydro came to the rescue.  With better maintenance and planning, there would have been no problem at all, and no need for cutbacks to industrial production such as aluminium.  So there’s no need to panic- we have ample energy supply.

Figure 1 shows electricity generation for the seven days to Sunday 5 June (p.m.) from OpenNEM.

The vertical line shows 6:00 p.m. Thursday night when the spot price peaked at $4,335.  It was after sundown so no solar, and there was little wind.  The plot shows how gas and hydro ramped up.  Also note the peaks on Monday, Tuesday and Wednesday were higher, and on the weekend demand was lower, so excess electricity could be used to pump water for hydro.

Don’t depend on renewables:

Figure 2 shows the same data as Figure 1 but not stacked, so comparison is easier.

Figure 2: All generation 29 May to 5 June (2:30 p.m.)

Coal of course stands out- nothing comes near.  Note that daylight hours are easy to see from the solar peaks.  Wind varies up and down as weather systems move across.  To fill the gaps on either side of solar, hydro and gas peak together in early mornings and evenings.  And finally you can barely see the contribution of batteries and diesel generators.

Figure 3 shows the relative contributions to the total of fossil fuels and renewables.

Figure 3:  Fossil fuels and wind, solar, and hydro:

The total generation has a daily cycle to match demand, but never dropped below 18,600 MW at night.  That is the minimum that the eastern Australian network must supply at this time of year.  The total rose to a touch under 31,300 MW in the early evening of Monday, Tuesday, and Wednesday, with excess power used to pump water for hydro, reducing to about 30,000 on Thursday and Friday as industries and commerce cut back, and much lower peaks on the weekend.  Note that renewables fluctuate much more than coal and gas.

Figure 4 looks at the percentage contribution of the “old” generation- coal, gas, and hydro- compared with the new- wind and solar.

Figure 4:  Old and new as a percentage of total generation:

Wind plus solar only exceed 50% on sunny, windy days.  Because they get preferential treatment coal stations must cut back at these times.  When wind or solar- or both- cannot meet demand, fossil fuels and hydro must quickly ramp up.  On Thursday night wind and solar contributed just 3% of electricity.

That’s why we cannot depend on renewables.

Enjoy the abundance of fossil fuels:

Coal capacity is 23,049MW.

Gas capacity is 10,967MW.

Together that is 8 percent more than the maximum of all generation on any day of the last week.  Coal alone could easily meet night-time needs.

Hydro-electricity averaged another 9% (peaking at 19.4%).

With proper planning and maintenance that should be a decent buffer for unexpected breakdowns.

Of course gas is very expensive because of global demand.  With more coal generation (HELE power stations) we could have a reliable and cheaper electricity network, without any need for solar or wind power except in remote or special locations.

If you must insist on Net Zero by 2050, go nuclear:

There is no other realistic choice.

34,000 Megawatts of fossil fuelled electricity can be phased out, but on last week’s figures we MUST have at least 31,000 MW or we will have cut backs in industry, commerce, services, and domestic supply.  And last week, at one stage only 665 MW was being generated by wind turbines, and each night there is zero from all the rooftop and solar farm capacity in the country.   Hydro?  We have frequent droughts, so that cannot be relied upon in the long term.

Even in the sunniest continent on earth, and with the usually strong winds across southern Australia, renewables cannot be relied on when needed.  If there is to be limited fossil fuel use, the only alternative is nuclear energy.

I look forward to watching the Greens and Labor squirm over the next few years.

I have included as an Appendix a sample of the major electricity facilities so you can see how their generation varied over the last week.

Appendix:  Electricity generation from a sample of coal, gas, hydro, wind and solar facilities last Friday, Saturday, and Sunday

Coal can ramp up and down as needed- but is hard to do and harder on the equipment.

Gas can quickly fill the gap but is expensive- and sits idle for a long time too.

Hydro is quick to ramp up and down but dams have to have enough water.

Wind is free but doesn’t always blow!

Likewise, sunshine is free but not always there!

OpenNEM Crashes- Atlassian Software Fault?

May 27, 2022

Remember on Monday the OpenNEM showed Rooftop Solar generation dropping out?  It has happened again just three days later:

Not only that, but the whole OpenNEM reporting system seems to have crashed.  The last update was at 1:40 pm on Thursday 26th.  This screenshot was at 9.00 am this morning Friday.

It looks like a software system crash.  Not in electricity production or we’d have noticed, but in the reporting.

This website (OpenNEM) is not that of the actual NEM, but has been set up to make NEM data “more accessible to a wider audience”.  That’s very commendable.  Note who has set it up:

Simon Holmes a Court was instrumental in the Teal wins over moderate Liberals.  He’s pushing rapid transition to renewables.

Dr Dylan McConnell is an energy systems researcher at the Climate and Energy College at the University of Melbourne.

Nik Cubrilovic is an internet security blogger, best known for computer hacking, according to Wilipedia.

And the platform is driven by Atlassian, founded and largely owned by Michael Cannon-Brookes, who is the largest shareholder of AGL and is trying to stop the proposed AGL demerger so that he can get rid of fossil fuels faster.

Couldn’t happen to nicer blokes….

Or maybe they have interests in uranium mines and know that nuclear is the only hope for Net Zero?

What Happened To Rooftop Solar Yesterday?

May 24, 2022

Yesterday, 23 May, something strange happened to electricity supplies across the National Energy Market (NEM).

Figure 1 shows total electricity generation for the last three days across Queensland, New South Wales, Victoria, South Australia, and Tasmania.  Notice the huge drop in generation early yesterday afternoon.

Figure 1:  3 Day Generation, NEM

The drop was entirely due to Solar Rooftop generation going from gangbusters at 1:00 pm to zero from 2:00 pm to 3:00 pm.

Figure 2:  All NEM Generation Monday 23rd.

Figures 3, 4, and 5 show the drop in closer detail.

Figure 3:  All NEM Generation 1:00 pm

Figure 4:  All NEM Generation 2:00 pm

Figure 5:  All NEM Generation 3:30 pm

It happened in every state, from Queensland, producing the most solar power (1,376 MW or 18.6% of the Queensland total):

Figure 6:  Queensland Rooftop Solar:

to South Australia, whose paltry 814 MW was 48.4% of total power used.  Interesting that solar in SA fell off from 12:30 pm.

Figure 7:  South Australia Rooftop Solar (12:30 pm):

At 2:00 pm, the drop in energy supply was nearly half (1,659 MW to 849 MW)- and they were still charging batteries.

Figure 8:  South Australia Rooftop Solar (2:00 pm):

By 3:30 pm, SA solar had recovered to 28% of supply- which was also helped by an almost equal amount of imported electricity:

Figure 9:  South Australia Rooftop Solar (3:30 pm):

In case you think this was caused by the cloudy weather over eastern Australia, it wasn’t:  it was mostly clear.

Figure 10:   BOM radar map at 1.30 pm 23rd May

Network generation fell by 16.5% from 1:00 pm to 2:00 pm.  Did no one notice?  Were there no blackouts?  Why was all rooftop solar in eastern Australia closed down for an hour?  Did you know they could do that?  If rooftop solar can be completely shut down without any ill effects why have it in the first place?

I think this will remain a mystery.

Australian Temperature- Satellites or Surface Stations?

May 13, 2022

For years we have been very sceptical about the official Bureau of Meteorology (BOM) temperature record which is based on 104 surface stations in the ACORN-SAT (Acorn) network.  In this post I look at one of the main reasons for doubting the veracity of the surface record- the increasing divergence from the satellite record.

First up I should say that the two records should not necessarily agree, because they measure two completely different things.  Surface stations measure the temperature of the air 1.2 metres above the ground and report the highest and lowest one second samples each day at 104 locations.  These are combined in a grid average to give monthly, seasonal, and annual temperatures.  Satellites measure temperatures of the atmosphere from the ground to many kilometres up, every second, over a wide area for each pass.  These are similarly combined by algorithms to calculate a monthly average for (in this case) the land area of Australia’s Temperature of the Lower Troposphere (TLT). 

They are both useful for different purposes but are not easily compared.  Because minimum surface temperatures poorly match TLT, mean surface temperature is also a poor match.  Maxima are a better match, but still not perfect.

For this post I use data from the University of Alabama (Huntsville) (UAH) which calculates anomalies from 1991 to 2020 means.  I have converted Acorn data from anomalies from 1961-1990 means, to anomalies from 1991-2020 means, to match.

Figure 1 shows monthly Acorn maxima data and UAH means from December 1978.

Figure 1: Monthly Surface Tmax and UAH data

Although surface maxima have a much larger range than TLT anomalies, they plainly follow similar trajectories.  12 month running means smooth the data and allow easier visual comparison.

Figure 2: Running 12 Month Means: Surface Tmax and UAH data

Similar, but different at several times.   Annual means show that in some years Tmax and TLT are close to identical, while in other years they have large differences.

Figure 3: Annual Means: Surface Tmax and UAH data

In 2015 I showed the reason for these differences (but not the difference in trends).  The differences between the two datasets are very largely due to variations in rainfall.  In wet years surface maxima are relatively much cooler than TLT; in dry years surface maxima are much warmer.  In Figure 4 I have calculated rainfall anomalies scaled down by a factor of 20 and inverted, to compare with the difference between Tmax and TLT.

Figure 4: Running 12 month Means: Surface Tmax minus UAH and Inverted Rainfall

The match is close.  Figure 5 shows annual values, and trend lines.

Figure 5: Annual Means: Surface Tmax minus UAH and Inverted Rainfall

While annual rain has been slightly increasing (it’s inverted, remember) the relative difference between surface temperature and atmospheric temperature has been increasing at a rate of one degree per hundred years.  That’s odd.  Figure 6 shows the relationship between the temperature difference and rainfall.

Figure 6: Annual Surface Tmax minus UAH versus Scaled Rainfall

For every extra 20mm of rainfall, the difference between surface maxima and TLT decreases by 0.85 degrees Celsius.  The trend lines in Figure 5 should be close to parallel, not diverging.

As well, as rainfall increases, Tmax should decrease, as Figure 7 shows.

Figure 7: Surface Tmax as a Product of Rain

But as we saw in Figure 3, Tmax is increasing faster than UAH.

Furthermore, as surface Tmax increases, TLT should be increasing as well, which it is, but at a slower rate.

Figure 8:  Atmospheric Temperature as a Product of Surface Tmax

Is the atmospheric temperature lagging behind surface temperature?  Figure 9 shows the last two years of monthly values.

Figure 9:  Monthly Atmospheric Temperature and Surface Tmax, January 2020-March 2022

The values are mostly synchronous, with sometimes a delay in one or the other of one month.  (Remember, we are comparing data from 104 stations scattered across the continent, with that of the atmosphere with constantly changing and circulating winds).  When the land warms, the atmosphere warms with it; when the land cools, so does the atmosphere.

Conclusion:

Tmax should not be increasing faster than atmospheric temperature.  There is no real delay in any temperature change, as the atmosphere is heated each day by the land.  Therefore it appears that there must be some fault with the maximum temperatures reported by ACORN-SAT, which appears to be warming too rapidly.

Explanation of the mechanism for rainfall moderation of surface-atmospheric temperature differences:

In wet years more moisture carried upwards condenses, releasing heat, thus warming the atmosphere, while the surface is cooled by cloud cover, evaporation, and transpiration.  In dry years much less moisture is convected, so less heat is released in the atmosphere, while the surface is hotter because of less cloud cover and less evaporation and transpiration.  Thus dry years have a greater relative difference between Tmax and TLT than wet years.

The only energy source is solar radiation heating the land surface in daylight hours, which in turn heats the atmosphere by conduction and convection.  At night as radiation to space rapidly cools the earth, convection also rapidly decreases, so maxima, not minima, are responsible for the relationship with TLT. 

A complication is that in summer (and more so in very wet La Nina years) large volumes of very moist air from the tropical seas to the north converge over northern Australia and penetrate even into southern Australia.  This warm moist air cannot heat the surface but through condensation transfers heat to the upper atmosphere- therefore the difference between surface and atmosphere is even smaller.

Is Climate Change Threatening the Solomon Islands?

April 23, 2022

Since the first talk of an agreement between China and the Solomon Islands to establish a Chinese presence there, accusations have flown thick and fast between the Australian government and their opponents.

One of the points of contention is whether Australia’s supposed lack of urgency in addressing climate change has led to distrust of Australia by Pacific island nations, thus encouraging them to seek help from China.  Considering China’s record and plans for emissions, that is hardly likely.  However, The Guardian thinks so, saying two days ago:

There might not be a direct link between Australia’s climate policy and the security deal – Morrison certainly thinks there isn’t, dismissing such a connection as “nonsense” today – but it is without doubt that Australia’s climate policy has contributed to the dimming of Australia’s reputation in the region, especially given Australia claims to be family.

So is climate change – specifically sea level rise- threatening the Solomons?

Time for a reality check.  Here is a map courtesy of Google, showing where the tide gauge in the Solomons is in relation to Australia.

Figure 1:  Solomons tide gauge location

Not that far away.

Over the last 28 years since the BOM began monitoring sea level at Honiara, sea level has definitely risen.  Figure 2 shows monthly anomalies of mean tidal data.

Figure 2:  Monthly mean sea level, Honiara

Oh no!  Climate change!

Figure 3 shows inverted mean barometric pressure anomalies plotted with mean sea level.

Figure 3:  Monthly sea level and barometric pressure (inverted)

Hmm.  As air pressure falls, sea level rises, and vice versa.  Figure 4 shows 12 month means (from July to June, which covers most ENSO events):

Figure 4:  12 month means of monthly sea level and inverted barometric pressure

Still not a close match, but let’s include the effect of the trade winds (data from NOAA).

12 month means of trade wind anomalies, scaled down by a factor of 10 show a much better match:

Figure 5:  12 month means of monthly sea level and scaled trade winds index

Now we see the connection, and cause of the apparent trend in sea level- the combination of air pressure and trade winds.  Barometric pressure has been decreasing, and trade wind strength has increased.  These are symptoms of the El Nino Southern Oscillation (ENSO).  When atmospheric pressure is unusually high (as in very big El Ninos), sea levels are lower, mainly because the normal trade winds slacken and less water than normal is pushed westwards across the Pacific.  As trade winds strengthen, more water is pushed westwards and sea level rises.  (This also affects the eastern coast of Australia, and strengthens the East Australian current as well.) 

When we get the next big El Nino (cue droughts, bushfires, and wailing and gnashing of teeth) it is likely that the sea level trend will mysteriously flatten.

Sorry, guys, unless climate change predicts fewer and weaker El Ninos, climate change is not to blame: and certainly not the Australian government.

It’s all about the money.

Gladstone Rejects Domestic Hydrogen

April 20, 2022

Gladstone Regional Council in Central Queensland has rejected a proposal to distribute a blend of 10% hydrogen and LNG from the Gladstone hydrogen park to residential and commercial customers.

The council received 100 submissions regarding the project.  All but one were against it, citing safety concerns.

The Australian Gas Infrastructure Group has been distributing a blend with 5% hydrogen through plastic pipes at Mitchell Park in South Australia since 2021 and is planning another hydrogen park to supply Albury-Wodonga in 2024.

Colorants and odorants are added to the blend.  The Gladstone plant was only to operate in daylight hours when solar power is relatively abundant.  Water was to be sourced from the Gladstone water supply.

With residents not convinced by safety assurances (the blend was to have twice the concentration of hydrogen as the South Australian scheme), it’s back to the drawing board for AGIG.

The future of Australia’s hydrogen industry is by no means assured.

Listen to the ABC Radio news item from 1:18.

More Problems With Australia’s Temperature Record: Part 3

April 13, 2022

We have seen in Parts 1 and 2 that every extra year of annual data can decrease the temperature trend at a weather station by from -0.02 to -0.03℃ per decade, and that less than half (47% actually) of Australia’s weather stations used for climate analysis have data from 1910, and three of them have insufficient data to calculate trends.

Figure 1 shows a map of non-urban Acorn stations with enough data to calculate trends, at 1910.  The others I have blanked out.

Figure 1: Acorn stations with data for 1910

The network is very sparse.  To estimate a national temperature for 1910 enormous weighting must be given to the values of a few remote stations like Alice Springs, Boulia and Kalgoorlie, so we hope they got the adjustments right!  Unfortunately, in 2015 I found adjustments at Kalgoorlie and Alice Springs were very problemmatic.

The Bureau explains the process of calculating average temperatures here.

Figure 2 shows the BOM map of trends from 1910 to 2020:

Figure 2:  Australian Tmean trends 1910-2020

Note that there a few “bullseyes” which surround stations whose temperature trends are out of phase with areas around them- e.g. Boulia is warmer, Marble Bar is cooler. 

Now here is a paradox.  As the years go by and more stations have data available, the area weighting for each station will decrease, however trends at the newer stations will show increased warming compared with the older ones.  However they will also have more variability.  This will result in oddities as I shall show, and reveals something of the difficulties with the BOM methods.

 Figure 2 is a plot of mean temperature from 1970 to 2020.

Figure 2:  Australian Tmean 1970-2020

The Acorn 2 trend is now +0.23℃ per decade or +2.3℃ per 100 years- a full degree more than the trend from 1910.

Now let’s look at the trend map for 1970 to2020:

Figure 3:  Australian Tmean trends 1970-2020

Note the little “bullseye” around Victoria River Downs, the little “balloon” around Halls Creek to the south-west of VRD, and the little surge to the south-southwest of VRD of 0.05 to 0.1℃ per decade.  Note also that north-eastern Arnhem Land, with no stations, has a warmer pocket.  Figure 4 is the BOM data for VRD.

Figure 4: Annual mean temperature at Victoria River Downs

VRD opened in 1965 and has too much data missing for BOM to calculate a trend.  The area weighting algorithm still gives it a cooling trend of between minus 0.05 and 0℃ per decade (Figure 3).  Que?

With more than 27% of data missing I wouldn’t calculate a trend either, but with only six of 43 years missing I can calculate a trend from 1978:

Figure 5: Annual mean temperature at Victoria River Downs

The trend is -0.09℃ per decade, which is a bit more cooling than the trend map (Figure 3) shows.  Now let’s look at trends from 1980 to 2020.

Figure 6:  Australian Tmean trends 1980-2020

There are more bullseyes, and I have shown temperature trends for some- Carnarvon, Meekatharra, Forrest, Thargomindah, and Gayndah.  But remember Figure 3’s little surge to the SSW?   It now has its own bullseye, and that is Rabbit Flat.

Figure 7: Annual mean temperature at Rabbit Flat

Rabbit Flat opened in 1970 and has a trend of +0.08℃ per decade, which agrees with the trend map in Figure 3.  Now from 1980:

Figure 8: Annual mean temperature at Rabbit Flat

What a difference a few years make in a short timeseries.  The trend of -0.06℃ per decade also agrees with the 1980-2020 trend map.

However, just 328km away Halls Creek shows a warming trend of +0.17C per decade from 1980 – 2020:

Figure 9: Annual mean temperature at Halls Creek 1980-2020

But from 1970 to 2020 Halls Ck is warmer still at +0.19C per decade:

Figure 10: Annual mean temperature at Halls Creek 1970-2020

And at Tennant Creek 441km away the 1970-2020 trend is +0.19C per decade:

Figure 11: Annual mean temperature at Tennant Creek 1970-2020

From 1980 it is +0.06C per decade.

Figure 12: Annual mean temperature at Tennant Creek 1980-2020

Temperatures are trending in different directions and wildly different rates at the closest stations: they can’t all be right!

The method of drawing trend maps is to use anomalies of temperatures of all years of all stations whether or not an individual trend can be calculated, then calculate a gridded average, and from that calculate trends, then spread those trends hundreds of kilometres in every direction- even across the Gulf of Carpentaria from Horn Island to Arnhem Land, as seen in Figures 3 and 6- averaged with the trends propagated by other stations.  If a site has data missing, the grid is infilled with the weighted data from other sites.  

In recent decades this causes great variability because of the short records, which leads to grave doubts about the reliability of some records.  Further back in time, there is less variability because there are more stations, and the longer records smooth and decrease the trends- however the weighting has to be much greater because of the large areas with no data at all for many years. 

The problem is: we can have either a long record, or an accurate record, but not both.

This leads to the obvious conclusion:

The official temperature record since 1910 is just a guesstimate.

More Problems With Australia’s Temperature Record: Part 2

April 10, 2022

My colleague Chris Gillham at WAClimate uses 58 long term weather stations for his analyses.

And with good reason.  Here’s why.

Figure 1 is a screenshot of the annual mean temperature record at a typical Acorn station, Longreach (Qld) with the linear trend shown.

Figure 1: Annual mean temperature at Longreach

The linear trend is +0.12℃ per decade.  Nine (9) of the 111 years of data from 1910 to 2020 are missing, leaving 102 years.

Australia’s official climate record is based on 112 sites like Longreach.  Of those, 8 are not used for seasonal and annual analyses because they are affected by Urban Heat Island (UHI) effect.  Five (5) of the non-urban stations have more than 20% of their data missing, so the BOM does not calculate trends for them. Of those remaining, only 50 started in 1910, and another 8 before 1915.  What is the effect of different length records on our understanding of how temperatures have changed over the years?

Figure 2 is a plot of the trends of mean temperatures per decade as a factor of the number of years of annual temperature data on record at those 107 Acorn stations with enough data to calculate trends.

Figure 2:  Trend as a factor of amount of data

Stations with  longer data records have lower trends.  The trends at stations with shorter records vary wildly, with some obvious outliers. 

At those stations with UHI effect, the relationship is even stronger.

Figure 3:  Trend as a factor of amount of data at sites with UHI

These sites are in larger towns and cities, possibly with better maintenance and observation practices (although not necessarily better siting).

The slope of the trendlines in the above two figures show that for every additional year of data, temperature trend decreases by about -0.02 to -0.03℃ per decade. In 100 years that could make a difference of as much as three degrees Celsius 0.3C at a well maintained site.

Figure 4 is a map of trends across Australia from 1910 to 2020.  I have shown the years of available data at each site (locations only approximate) and I have circled in blue those 5 sites that have insufficient data.

 Figure 4:  Years of data contributing to 1910 to 2020 trend map

Trends in different regions vary from less than 0.1C per decade to up to 0.3C per decade.  As you can see there is a large variation in the amount of available data in each different coloured band.  That’s for 1910 to 2020.  Note that there are only three (3) non-urban stations with no missing years- Carnarvon, Esperance, and Mt Gambier- which I have circled in red.  There are some big gaps.

In Part 3 I will look at some individual stations and how trends vary in the 51 years from 1970 to 2020.

More Problems With Australia’s Temperature Record: Part 1

April 8, 2022

Since 2010 I have been documenting problems with different versions of Australia’s official temperature record as produced by the Bureau of Meteorology (BOM).  Since the High Quality (HQ) dataset was quietly withdrawn in 2012 we have seen regularly updated versions of the Australian Climate Observation Reference Network- Surface Air Temperature (ACORN-SAT or Acorn).  We are now up to Version 2.2.  In this Part I shall show the effect of these changes on temperature trends.  In Part 2 I will show how record length affects trends, and in Part 3 I will look at the record since 1970 at some individual stations.

Figure 1 is from the BOM Climate Change Time Series page.

Figure 1:  Australian Official Temperature Record 1910 to 2021

The linear trend is shown as +0.13℃ per decade, or 1.3C per 100 years.  My colleague Chris Gillham of WAClimate has provided me with archived Acorn 1 annual mean temperature data to 2013 which allows this comparison:

Figure 2:  TMean: Acorn 1 and Acorn 2

The result of introducing Acorn 2 has been a much steeper trend:  Acorn 1 trend to 2013 was 0.9℃ per decade.  The trend has now become 0.13℃ per decade. (The extra 9 years have added an extra 0.017C per decade to the trend.)

Figure 3 shows when and how large the changes were:

Figure 3:  Difference between Acorn 1 and Acorn 2

Acorn2 is cooler than Acorn 1 before 1971 and warmer in all but three years since.  Since these were based on the same raw temperatures (with some small additions of digitised data and a couple of changes to stations) the changes were brought about entirely by adjustments to the data.

I calculated running trends from every year to 2013 for both datasets.  As trends shorter than 30 years become less reliable I truncated the running trends at 1984.  Figure 4 compares thre trends to 2013 of Acorn 1 and Acorn 2.

Figure 4:  Acorn 1 and Acorn 2 running trends per decade to 2013

The weather fluctuations of the mid-1970s to 1980s played havoc with trends.

Figure 5 shows the difference between the trends.

Figure 5:  Difference between Acorn 1 and Acorn 2 Trends

The difference ranges from +0.024C per decade for 1910 to 2013, to +0.039C for 1950 to 2013.  Having increased warming by from 0.25C to 0.4C per 100 years (just by making different adjustments) Acorn 2’s trend is much more alarming than Acorn 1’s.

Conclusion:

This is from the BOM’s explanation for Acorn:  

“A panel of world-leading experts convened in Melbourne in 2011 to review the methods used in developing the dataset. It ranked the Bureau’s procedures and data analysis as amongst the best in the world. ‘The Panel is convinced that, as the world’s first national-scale homogenised dataset of daily temperatures, the ACORNSAT dataset will be of great national and international value. We encourage the Bureau to consider the dataset an important long-term national asset.’” ACORN-SAT International Peer Review Panel Report, 2011.

 Acorn 1.0 was apparently such an important long-term asset that it was quickly superseded by Acorn 2 with a much more alarming trend.

What’s The Best Electric Vehicle For Me?

March 29, 2022

Pictured: Hundai Ioniq

So, you’re thinking about whether to get an electric car.  You’re worried about the cost of fuel, and you know you should be concerned for the environment.  Will it be practical for you?

Are you single, or have a partner but no kids, and live and work in the south-east of Queensland, or one of the other metropolitan areas of Australia?  If so, then you may take advantage of state subsidies and choose from a range of smaller EVs that may suit.

You have no doubt heard about the latest Queensland subsidy scheme:

“Queensland offers $3,000 subsidy to EVs priced under $58,000, excludes Tesla”

Unfortunately this policy is pure political window dressing, and is deliberately aimed at metropolitan voters (not necessarily drivers), as the only cars that can theoretically be of practical use outside Brisbane are outside the scheme.  Unlike other states, Tesla, the car best suited to roads outside the suburbs, is specifically excluded.

The Queensland government said that cars that will qualify for the rebate include the Nissan Leaf, the MG ZS EV, the Hyundai Ioniq, the Hyundai Kona, the new Atto 3 model being released by BYD, and the Renault Kangoo.

Never mind, I’ll attempt to list the pros and cons of a range of vehicles, including Tesla.

Car Base priceClaimed Range
Hyundai Ioniq$49,970311km
Hyundai Kona$54,500305km
Nissan Leaf   $49,990270km
MG ZS EV  $40,990263km
Renault Kangoo $50,290?
Atto 3  N/AN/A
Not subsidized in Qld
Tesla Model 3$59,900491km
Tesla Model S$162,559652km
Kia EV6$62,990484km

Remember that these prices do not include on-road costs.  However, with the subsidy taking $3,000 off the base price of the smaller ones, they are within reach of many people.

If you are serious, you should check reviews at reputable sites such as carsguide. Here most are described as, for example, “easy-going, comfortable, and has plenty of range to work with for city drivers, so charging doesn’t become much of an inconvenience…” (the Nissan Leaf).  They are nice small cars, ideal for the city.  Except the Kangoo.  It’s a van.

If you sometimes escape the city, for example to the Sunny Coast, beware.  The range shown above may not be achieved in practice.  You will need to plan your trip very carefully including possible recharging stops.  At a 50 kW DC charger, you will need from 45 minutes to just over an hour to charge from 20% to 80% of battery capacity.  Well, I suppose you could have lunch while you wait, but the Cooroy train station with its one 50 kW chargepoint might not be your desired destination.  And why 20% to 80%? Apart from not wanting to be stranded with a flat battery (“range anxiety”) you should be aware that lithium ion batteries degrade if the charge is allowed to be above or below these levels too often or too long.  So to protect your battery, the vehicles able to get the subsidy will have a range between charging of from 180km (the MG) to 290km (the Kona)- maximum.  Keep your wits about you.

You can also recharge at home of course, where charging times can be from 6 hours for the Kona, to 25 hours for the MG, to “up to 60% overnight” for the Leaf.  Oops.

If you have a family, or if you live outside the south-east corner of Queensland or other metropolitan area, or if you would like to take a road trip from time to time, none of these vehicles are for you.  They are too small for a family, have limited luggage space, and limited range.  No subsidy for you.

The cheapest EV option would be the Tesla Model 3, at $59,900, plus on road costs.  For that you get a beautiful car that will fit a small family, with a range of 491km (or 296km if you want to protect your battery). More options will cost $84,900, for a range of 614km (or 368km if you want to protect your battery).  It will take 60 minutes to charge at a fast charger, but if you charge at home the quoted figure is “10km per hour”.  And at the moment in Queensland Tesla has superchargers at Brisbane, Gold Coast, Maroochydore, Toowoomba, and Gympie.  One is planned for Rockhampton- but you might not get to Rockhampton from Gympie (467km).    Wider travel in regional areas is out of the question unless you use much slower recharging stations.

If you have a spare $162,559 plus on road costs you could buy a Tesla Model S, with a range of from 637km to 652km which means you could get from Brisbane to Rockhampton in one go (theoretically) if you started out with 100% charge.  But you should know that an EV performs worse on the highway, and the stated range is the upper limit on a full charge on average- so I would still recharge at Gympie, taking from 40 to 60 minutes.

Another option is the Kia EV6 ($62,990 to $82,990) with a range from 484km to 528km (290km to 317km if battery saving), but you will still need recharging stops of over 70 minutes.  Fortunately there are charging stations at Cooroy, Gympie, Maryborough, Childers, Miriam Vale, and Rockhampton (and all the way to Cairns).   If you wish to go west they are at Gatton and Toowoomba.  Another 18 are planned in the inland.

Existing and planned charging stations in southern Qld

I drive a Hyundai Tucson.  I can easily drive between Rockhampton and Brisbane (621km) on one tank, with 100km of range to spare.  With rest stops it usually takes well under 8 hours.  If we do choose to refuel on the way it takes about 15 minutes.  The 2022 price is $36,500 plus on road costs.  That is still $1,490 cheaper than the smallest of the subsidized vehicles (the MG- which would need three or four recharging stops, and is still $16,000 dearer than the petrol MG, even with the subsidy.) At my average economy of 7.5 litres/100km, petrol at $2.10 a litre, and including service charges it would take 4 years and 4 months for the cheaper Tesla 3 to be better value than a Tuscon- and it would need at least two recharges to go 621km .

Now, about emissions.  The only benefit to the environment of an EV is less exhaust fumes in the city.   Unless you are completely off grid with solar panels and batteries, no matter where or when you recharge your emissions will be no less and no more than the whole electricity grid- and if you recharge at night there is no solar.  An EV is just another (expensive) electrical appliance.

Your choice (for now).  But I won’t be going electric.

Is Australia Getting Harder To Live In?

March 23, 2022

Update: see link below kindly supplied by Big M

According to Scomo it is.

And are natural disasters becoming worse and more frequent?

If you listen to or look at commentary in the mass media and social media, largely fuelled by politicians and journalists with no contact with nature and no life experience, you might think so.

The Conversation says:

It’s too soon to say whether the current floods are directly linked to climate change. But we know such disasters are becoming more frequent and severe as the climate heats up.

Time for a reality check.

Flood and fire and famine are the three great normals of Australia, as so well expressed by Dorothea McKellar in My Country, and we in the north also have cyclones.   

First, floods.  Brisbane was hit hard by floods last month.  Figure 1 is from a previous post, showing historic floods in the Brisbane River with the 2022 flood inserted.  No cause for alarm there.

Figure 1: Historic Brisbane Flood heights 

What about fatalities?  Figure 2 shows the 2022 floods compared with some historic floods from all over Australia.  Fatalities are totalled if several floods occurred in one year.

Figure 2:  Death tolls of flooding events

Are flood disasters getting deadlier? No.

Fatalities and housing damage are the result of people living in flood prone areas- or from being trapped in vehicles in rising waters.   After the 1916 flood, the people of Clermont in Queensland moved their town to higher ground- without any government assistance.  This photo from Bonzle shows the Commercial Hotel being moved on log rollers by a steam traction engine.  The Commercial is still standing- I’ve had a few coldies there.

Figure 3: Moving the Commercial Hotel to higher ground

And no one asked where Billy Hughes was.

What about fires?

Figure 4 shows the area of land burnt by bushfires by notable fires across Australia.  I have marked some fires that are fairly well known- but does anyone mention the fires of the 1960s and 1970s?  These were in largely savannah country of WA, Queensland, and the NT.

Figure 4:  Area Burnt by Bushfires

Figure 5 shows fatalities due to bushfires.

Figure 5:  Bushfire Fatalities 1920-2020

Despite the terrible 2009 fires, fatalities due to bushfires in the last 100 years have been trending down.  Lessons must be learned from these tragic events.  We should remember that fire is part of the Australian bush.  Many fatalities occur where housing is surrounded by bushland, with poor escape routes.

The downtrend in fire fatalities is even more apparent when you consider Australia’s population has grown enormously since 1920.  The following plot shows how the risk of death by bushfire has changed.

Figure 6:  Bushfire Fatalities per 1,000 people 1920-2020

No, by no measure are bushfires getting worse, or making Australia harder to live in.

Droughts are also in decline across most of Australia.  The following plots use BOM data.

Figure 7:  Percentage of Land in Severe Drought (lowest 10% of rainfall)

Even though 2019 was an extremely dry year, over 120 years the area of land in drought is decreasing at the rate of 0.23% per decade.

The only areas where drought has increased are Southwestern Western Australia, Victoria, and southern South Australia. 

In southern Australia as a whole, there is no trend in droughts, even with the 2018-2019 drought.

Decadal averages are an excellent way of showing long term patterns.  In southern Australia the worst period of long lasting dry years was the 60 years from 1920 to 1980.

Figure 8:  Percentage of Land in Severe Drought- Decadal Averages Southern Australia

But are dry periods getting drier, and wet periods wetter?  And are dry areas getting drier, and wet areas wetter?  Here are long term rainfall records for Sydney, Cairns (very wet) and Alice Springs (very dry), and Adelaide (drying trend) again with decadal means.  Values are anomalies from months of overlap of weather stations, in millimetres of rain.

Figure 9:  Decadal Mean Rainfall- Sydney

The three major droughts stand out, as does the major reset of the 1950s.  Note the decreasing values to the 1940s, and again from the 1960s.  There is no indication of wet periods getting wetter and dry periods drier.

Figure 10:  Decadal Mean Rainfall- Cairns

Figure 11:  Decadal Mean Rainfall- Alice Springs

It seems that dry periods are getting wetter at Cairns and Alice Springs, and apart from the 1970s-1980s, wet periods show no great difference.

Figure 12:  Decadal Mean Rainfall- Adelaide

Here we see the gradual fall off in rainfall in southern SA, gradually since the 1930s but more rapidly since the 1970s.  The shift in the Southern Annular Mode has caused drying in southern parts of the continent.  It is too early to draw any conclusions from that.

The alternately wet – dry feature of Australian climate is obvious from all the above plots.  However, wet periods are not getting wetter, and dry periods are not getting drier.

What about cyclones?  Here is a plot straight from the Bureau:

Figure 13:  Tropical Cyclones 1970-2021

Cyclones are NOT becoming more frequent or more severe.  The trend is clearly downwards.

Finally, heatwaves.  In reality we have no idea, as the temperature record managed by the Bureau is so bastardised- as shown here, here, here, here, here, and here.  We just don’t know, no matter what they claim.

Those who live in the cities, who have little contact with nature, and who have no knowledge of the history of Australia’s climate, will accept whatever they’re told about natural disasters as gospel.  The truth is different.

Scomo has nothing to worry about (apart from the next election).  Australia is NOT getting harder to live in: floods, fires, droughts, and cyclones are NOT getting worse or more frequent. 

UPDATE: Big M has kindly supplied this link, which I missed.

https://www.abc.net.au/news/2021-05-26/australias-hidden-history-of-megadroughts/100160174

The 1760s WA drought seems to match data from the Barrier Reef showing a 30 year drought in NQ.

Why Is Business Investment Sluggish: An Alternative View to Alan Kohler

March 8, 2022

On ABC News on Sunday night, Alan Kohler in his regular spot showed how business investment, especially in plant and equipment, has  been sluggish for the past several years.  Despite acknowledging a number of theories, of course he blamed it on the lack of a coherent bi-partisan climate policy- his favourite hobby-horse.

Time for a reality check.

Firstly, Figure 1 shows the Australian All Ordinaries Index with the key dates of proposal, adoption, deferral, re-proposal, and eventual scrapping of all versions of carbon tax, with the 2014 and 2019 elections when Labor’s climate dreams were roundly rejected.  It is important to realise that various Federal and State renewable energy incentives have also been introduced during this time.

Figure 1:  All Ordinaries Index 2007-2022 (per Westpac)

The share market seems to have been largely oblivious to climate policy.  What about business investment?

I checked the recently released ABS data, here and here.

Alan Kohler used 3 data points (decadal annual growth rates).  I looked at the 124 quarterly values of private investment in 2021 dollar values, from March 1991 to December 2021.

Figure 2:  Quarterly Private Capital Investment, 1991-2021

While Construction boomed from 2011 to 2015, it is true that investment in plant, equipment, and machinery has barely moved since 2010.

These categories can be further broken down into Mining and all others except for mining:

Figure 3:  Capital Investment in Construction, Mining and Non-mining

That big bump was the mining boom, which also shows but to a lesser extent in investment in Plant and Equipment:

Figure 4:  Capital Investment in Plant & Equipment, Mining and Non-mining

Note that the total figure for Plant and equipment is nearly all from non-mining activity.  Note the peak was reached in the December quarter of 2009, before the big reduction brought about by the GFC of 2008 and 2009.

Rather than annual growth or actual quarterly investment, an alternative comparison is with GDP.

Figure 5:  Australia’s Gross Domestic product

Despite the sluggish early 1990s, the GFC and the pandemic, GDP has been growing at an increasing rate, especially in the last five years.

Figure 6:  Quarterly Private Capital Investment as a percentage of GDP, 1991-2021

Mining investment in construction has been huge, and the economy has been reaping the benefit since 2016. 

Figure 7 shows investment in plant and equipment (which Alan Kohler says has been flat since 2011 as a result of not having certainty in climate policy) outside the mining industry.  The dates from Figure 1 are shown.

Figure 7:  Quarterly Plant and Equipment Investment as a percentage of GDP, 1991-2021

Alan Kohler’s explanation is obviously wrong. Perhaps he could explain why plant and equipment expenditure relative to GDP has been steadily decreasing since 1996- well before any mention of climate policy.  That would seem to be a much more serious problem.

But I don’t think he will- there’s an election coming up.

How Unusual Is All This Rain We’ve Had?

March 3, 2022

Yesterday, 2nd March, ABC weather reporter Kate Doyle posed this question on the ABC website about the recent rain event in SE Queensland and Northern NSW.

Her answer to the above question was:

Very unusual.

The rainfall totals from this event have been staggering. 

From 9am Thursday to 9am Monday three stations recorded over a metre of rain:

– 1637mm at Mount Glorious, QLD 
– 1180mm at Pomona, QLD
– 1094mm at Bracken Ridge “

She goes on to say:  “South-east Queensland and northern NSW are historically flood prone and have certainly flooded before but this event is definitely different from those we have seen in the past.”  And of course climate change is involved.

Time for a reality check. 

My answer to Kate’s question:  Not very unusual at all.

I went looking at Climate Data Online for four day rainfall totals over one metre, to compare with the recent totals above at Mount Glorious, Pomona, and Bracken Ridge. 

For a start, Pomona’s BOM station has been closed for years, and Bracken Ridge is not listed at all, so those reports are from rain gauges external to the BOM network and can’t be checked. 

That’s OK.  In about half an hour I found the following four day rainfall records.

Crohamhurst4/2/18931963.6mm
Yandina3/2/18931597.8mm
Tully Sugar Mill13/02/19271421.3mm
Palmwoods4/2/18931244.6mm
Buderim3/2/18931150.3mm
Bloomsbury20/01/19701141.8mm
Dalrymple Heights6/04/19891141mm
Innisfail3/04/19111075.8mm
Nambour11/1/18981013mm

1893 was a wet year!  Crohamhurst had 2023.8 in five days, and Brisbane had three floods in two weeks in February and another in June.

And there is no such thing as a “rain bomb”, a term invented to make it sound unprecedented.  This was an entirely natural and normal rain event.  Slow moving tropical lows drift south every few years in the wet season, producing a large proportion of Queensland’s average rainfall.

Floods have affected Brisbane and surrounds since before European settlement.  The Bureau has an excellent compilation of accounts of past floods at

http://www.bom.gov.au/qld/flood/fld_history/brisbane_history.shtml

It includes this graphic showing the height of known floods.  I have added an indication of the height of the 2022 flood.

Here are some notable Brisbane floods:

1825       a flood probably as high as the 1893 flood

1841       8.43m

1844       about1.2 metres lower than 1841

1864       ?

1887       ?

1889       ?

1890       ?

1893       8.35m

“              8.09m

“              ?

“              ?

1908       4.48m

1974       5.45m

2011       4.46m

2022       3.85m

Every flood is different- water backs up higher in unexpected places, or gets away faster, so for many people this flood was worse than 2011.  However it is beyond any doubt that this flood, heartbreaking as it was for many people, could have been much worse.  It was nowhere near as big as several in the past.  Wivenhoe Dam worked as planned this time, which greatly lessened the impact.

Another thing worth remembering:  floods were more frequent and higher in the 19th Century than they have been in the last 100 years.

ABC journalists need to do a lot more research.

Covid in Context: the Eastern States

February 3, 2022

In this post I am looking at the pandemic experience across New South Wales, Victoria, Queensland, and South Australia, since the Queensland border was opened on 17 December 2021.  Tasmania, the Northern Territory, and the ACT are excluded because their numbers so far are too low for useful analysis, and WA of course is still a hermit kingdom.

I use data from the excellent site, Covid-19 in Australia

That site has excellent comparative charts, however I wanted to pick up on some points which are not so clear.

For some time Chief Health Officers have been warning that case numbers are a poor metric of Covid infections.  Here’s why:

Figures 1 to 4 show 7 day running means of reported daily positive cases of Covid-19 for each state.

Figure 1:  Queensland cases

Figure 2:  New South Wales cases

Figure 3:  Victorian cases

Figure 4:  South Australian cases

Notice that the high point for all states was reached at about the same date, and cases in all states plummeted after the 20th January.  (Victoria plummeted from the 15th.)  All states gave up trying to keep up with the testing demand and Rapid Antigen Tests were as rare as hens’ teeth.

Case numbers we can then ignore:  they may be two, three, or more times higher.

A better metric will be the  seven day rolling mean number of people in hospital, in Intensive Care, or dying.

Figure 5:  Queensland daily numbers in hospital

Hospitalisations peaked on Australia Day and are slowly falling.

Figure 6: NSW daily numbers in hospital

In NSW there was no distinct peak but hospitalisations have been gradually falling since 25 January.

Figure 7:  Victoria daily numbers in hospital

Victoria’s peak was on 21 January.

Figure 8:  South Australia daily numbers in hospital

South Australian hospitalisations stopped rising on 25 January with a slow fall since.

Figure 9:  Queensland daily ICU and mortality numbers

Although Qld hospitalisations have declined, ICU numbers have remained at about 50 for two weeks.  Deaths are also plateauing.

Figure 10:  NSW daily ICU and mortality numbers

Despite a fall in the number of ICU patients, deaths are high, and it is still too early to see a peak.

Figure 11:  Victoria daily ICU and mortality numbers

There is a similar situation in Victoria.  While ICU numbers have fallen, deaths have plateaued over the last six days.

Figure 12:  South Australia daily ICU and mortality numbers

Only in South Australia do we see a distinct fall in deaths, with a corresponding fall in ICU numbers.  Let’s hope this continues.  However, it is possible there is something different about the data reporting.

Across these states there appears to be a delay of from 7 to 10 days from the suspected peak in case numbers to hospital admission, and 14 to 16 days from peak in cases to death.

Of those admitted to hospital, the chance of going into ICU is:

Queensland:      1 in 17

NSW:                1 in 15

Victoria:            1 in 9

Sth Australia:    1 in 5 – 6

Once in ICU, the chance of dying is:

Queensland:      1 in 4 -5

NSW:                 1 in 6

Victoria:             1 in 5

Sth Australia:     1 in 8

In Queensland, based on official case numbers, an individual testing positive (all ages and all vaccination states) has a 1 in 20 chance of being sick enough to go to hospital; 1 in 345 of being admitted to ICU; and 1 in 1,500 of dying.  (For healthy, fit individuals under the age of 60 the chances will be considerably smaller.)

Conclusion 1:  In these four states, we are almost over the worst, and the health systems have managed to cope (albeit with leave being cancelled and great stress on staff). 

Conclusion 2:  Covid-19 loves people to live in big cities, or to live in crowded conditions, or to have lowered immunity and chronic health conditions, or to be elderly.  Nursing homes fit those last three conditions nicely. Many nursing home inmates also have Advanced Health Directives, many probably stipulating they do not wish to have resuscitation or ventilation. A high death toll in nursing homes is to be expected with a highly transmissible and nasty flu like Covid.

Covid in Context

January 24, 2022

With the recent surge in Covid-19, here is a progress report without the hype from the media, and without the commentary from those who doubt the impact of the disease.

I am attempting to show how Covid-19 compares with other major diseases in one important aspect: mortality.  How deadly is it?

I use data from the Australian Bureau of Statistics reports Provisional Mortality Statistics, Australia, Jan 2020 – Oct 2021 and Covid-19 Mortality, released 22 December 2021, and Our World in Data.  

To be certified as a Covid-19 fatality, Covid-19 must be the underlying cause of death- not dying of another condition while being positive for Covid.  According to Covid-19 Mortality, 71.2% of people dying from Covid had pre-existing chronic conditions.  The overall Case Fatality Rate (CFR) for Australia for COVID-19 as of 31 October 2021 was 1.0%, but while the CFR for those aged under 60 years was 0.1%, the CFR for males aged 90 years and over was close to 50%.   83% of people who died of Covid were over 70.  It is therefore a relatively mild disease for younger people, but very severe for elderly and sick Australians.

I shall now tease out mortality statistics to show Covid in context.

Figure 1 shows weekly death tallies of deaths in which doctors certified Covid as being the underlying cause of death, and from November weekly death tallies from Our World in Data.

Figure 1:  Weekly Covid Deaths from January 2020

Those who doubt the severity of Covid-19 often say that deaths from Covid are far less than from other causes.  Figure 2 shows total deaths for the past two years to October as well as the average from 2015-2019 (as 2020 was very unusual), together with Covid deaths.

Figure 2:  Covid-19 compared with all deaths per week

They have a point- to a point.  Weekly deaths from Covid in 2020 and 2021 were tiny in comparison, but in 2022 have risen to be a fifth of the average number for this time of year.  Breaking down the death toll to show separate diseases shows a different picture again.

Figure 3:  Covid-19 and other major diseases

Clearly, Covid’s weekly death toll is already greater than all other major killers except cancer, and may overtake cancer in another couple of weeks.  Thankfully we are close to the peak in eastern states.

Covid is a respiratory disease, but counted separately.  How does it compare with other respiratory diseases?  The next figure tracks Covid and total respiratory deaths, together with the average weekly deaths from respiratory illness from 2015 to 2019.

Figure 4:  Covid-19 and respiratory disease mortality

Covid already not only exceeds the weekly respiratory deaths for any time in the last two years (which had very little influenza), but also the highest average for 2015-2019.

I used to think Covid-19 was just another nasty infectious flu.  Not anymore.  Here’s a comparison of Covid deaths with deaths due to influenza leading to pneumonia.

Figure 5:  Covid-19 and influenza mortality

Already Covid-19 deaths are nine times the average for this time of year, and are also more than three times higher than the average in the peak of the winter flu season.

And WA has yet to open its border!

To compare mortality from diseases, the ABS calculates age-standardised death rates (SDRs) which “enable the comparison of death rates between populations with different age structures”.  Rates are calculated per 100,000 population.  Figure 2 shows death rates for the major diseases causing fatalities, including approximate (caution: not age- standardised) figures for Covid. 

Figure 6:  Death rates for Covid-19 and other major killers

Deaths will not stay at this high level for much longer.  There are signs we are close to the peak of new cases, and deaths will peak a week or two after that.  With Covid endemic in the community, mortality will fall to an unknown rate, and hospitalisations will become more easily manageable.

Make no mistake:  this is a deadly disease!  Take care!

Post Script: Here is another excellent resource:

https://www.covid19data.com.au/deaths

Diurnal Temperature Range and the Australian Temperature Record: More Evidence

January 19, 2022

In an earlier post, I demonstrated through analysing Diurnal Temperature Range (DTR) that the Bureau of Meteorology is either incompetent or has knowingly allowed inaccurate data to garble the record.

A couple of readers suggested avenues for deeper analysis. 

Siliggy asked, “Is the exaggerated difference now caused by the deletion of old hot maximums and or whole old long warmer records?”

Graeme No. 3 asked, “Is there any way of extracting seasonal figures from this composition?”

This post seeks to answer both, and the short answer is “Yes”.

Using BOM Time Series data (from the thoroughly adjusted Acorn dataset) I have looked at data for Spring, Summer, Autumn, and Winter (although those seasons lose their meaning the further north you go).

DTR is very much governed by rainfall differences as shown by this plot.

Figure 1:  Winter DTR anomalies plotted against rainfall anomalies- all years 1910-2020

This shows that in winter DTR decreases with increasing rainfall.  The R squared value of 0.79 means that for the whole period, rainfall explained DTR 79% of the time on average.  However, the average conceals the long term changes in the relationship.

To show this, I simply calculated running 10 year correlations between DTR and Rainfall anomalies for each season, and squared these to show the “R squared” value.  This is a good rule of thumb indicator for how well DTR matches rainfall over 10 year periods.  A value of 0.5 indicates only half of the DTR for that decade can be explained by rainfall alone.  As you will see in the following figures, there are plenty of 10 year periods when the relationship was 0.9 or better, meaning it is ideally possible for 90% of DTR variation to be explained by rainfall.  Here are the results.

Figure 2:  Spring Running R-squared values: DTR vs Rain

There was a good relationship before 1930.  In the decades from then to the mid-1970s it was much worse, and very poor in the decade to 1946. It was poor again in the decade to 2001, and the 10 years to 2020 shows another smaller dip, showing something not quite right with 2020.

Figure 3: Summer Running R-squared values: DTR vs Rain

Summer values were very poor before the 1960s, especially the decades to 1944 and 1961, and dipped again in the 1990s.

Figure 4:  Autumn Running R-squared values: DTR vs Rain

The DTR/Rain relationship was very poor in the decades to 1928, and again before 2001.  The recent decade has also been poor- less than half of DTR to 2020 can be explained by rainfall.

Figure 5:  Winter Running R-squared values: DTR vs Rain

The DTR/rainfall relationship was fairly good, apart from two short episodes, until the 1990s.

I now turn to the northern half of the continent.

A large area of Northern Australia is dominated by just two seasons, wet and dry.  Here is the plot of northern DTR vs Rain for the wet season (October to April).

Figure 6:  Northern Australia Wet Season Running R-squared values: DTR vs Rain

Apart from the 1950s, the late 1970s-early 1980s, and 1998 to 2020, the DTR : Rainfall relationship is very poor, with a long period in the 1930s and 1940s in which rainfall explains less than half of DTR variation (only 13% in the decade to 1943). 

Because the northern half of Australia accounts for the bulk of Australian rainfall, and the wet season is from October to April, this perhaps explains the problems in spring, summer, and autumn for the whole country.

We can get some clues as to the reasons by comparing long term average maximum temperatures with inverted rain (as wet years are cool and dry years are warm).

Figure 7:  Northern Australia Wet Season Decadal Maxima and Rain

The divergence before 1972 and after 2001 is obvious.

The above plots show how poorly DTR (and therefore temperature, from which it is derived) has matched rainfall over the past 111 years.  Low correlations indicate something other than rainfall was influencing temperatures.

In reply to Siliggy, who asked “Is the exaggerated difference now caused by the deletion of old hot maximums and or whole old long warmer records?” the answer appears to be: both, however Figure 7 shows old temperatures (before 1972) appear incorrect, but recent temperatures are at fault too.

The mismatch shows that the Acorn temperature record is not to be trusted as an indicator of past temperatures- and even recent ones.

Tonga Volcano Shock Wave

January 17, 2022

The volcano that erupted near Tonga (and I won’t pretend I can spell let alone pronounce its name) sent a shock wave racing around the world.

It was detected at weather stations across Australia as a sudden spike followed by sharp drop about half an hour afterwards, as in this screenshot from Rockhampton.   

I used Google Maps to plot the course of the shock wave from the volcano across the widest part of Australia.

Showing just Australia:

I used BOM’s Weather Graphs of weather stations close to that line and found the times of the spikes and dips as the wave passed over.

It took roughly 3 hours and 24 minutes to cross the country.  That’s 3,953km at about 1,160 kph.

Here’s a quick plot of the speed of the shock wave as it crossed Australia.

It’s not often you get to see such a phenomenon.

The Challenge Ahead For Renewables: Part 3

January 16, 2022

In Part 1 I showed how the low Capacity Factors of wind and solar mean enormous wastage of resources and money has been incurred over the past 20 years. 

In part 2, I showed the impact of the policies of the major parties, with the costs of replacing fossil fuels in electricity generation, and the enormous cost of using renewables for all our energy use.

However, Net Zero is the goal of the whole developed world, not just Australia.  There are many, and not just the Greens, who say that replacing fossil fuel for all energy is not enough.  We must also ban all exports of coal and gas.

We produce far more energy than we consume- mainly coal (cue wailing and gnashing of teeth).  Most is exported.

According to the Department of Industry, Science, Energy and Resources (2021) total energy production (for domestic consumption plus exports of coal and gas) in 2019-2020 was 20,055 PetaJoules. 

Figure 1:  Australian energy production 2019-2020

All renewables and hydroelectricity amounted to a little over 2% of energy produced in Australia.

Figure 2:  Relative share of energy production

Therefore if we are to maintain our role as an energy exporter (of electricity or hydrogen), and thus our standard of living, then just to keep up with our 2019-2020 production, renewables will have to produce 48 times current production- an EXTRA 19,636 PJ. 

Figure 3: All renewables compared with energy consumption and production

Can this be achieved?

19,636 PJ is 5.45 billion MegaWattHours, which will need 622,227 MW generation (at 100% capacity).

If the extra generation is to come from solar (wind would require far too much land- over 6% of Australia’s land area), we will need an extra 4.149 million MW- 290 times 2020 solar capacity.

Therefore the cost would be at least

$7.47 TRILLION (if all solar).

And that figure doesn’t include storage, extra infrastructure like transmission lines and substations, charging points for vehicles, building hydrogen plants, and losses involved in electrolysis of water, conversion to ammonia and back again, and conversion of hydrogen to motive power.  Neither does it include the costs of decommissioning and replacement, safe burial of non-recyclable solar panels, turbine blades, and used batteries, nor the human costs of child labour in Congolese mines supplying cobalt for batteries.

(Australia’s nominal GDP will be around $2.1 trillion in 2022.)

Figure 4 shows the comparison between Australian GDP and the cost of solar generation needed.

Figure 4:  Cost of extra solar generation needed for Net Zero compared with the whole of the economy

So can it really be achieved?

In the minds of some, yes.

The report from the Australian Energy Market Operator (AEMO) containing the Draft 2022 Integrated System Plan (ISP) makes interesting (and scary) reading.  The favoured scenario is called “Step Change” which involves a rapid transformation of the Australian energy industry (rather than “Slow Change” or “Progressive Change”), which relates more to my analysis in Part 2.

However the scenario called “Hydrogen Superpower” received 17% of stakeholder panellists’ votes in November 2021 and must be considered a possible political goal.

Here is a summary of the Step Change and Hydrogen Superpower scenarios:

• Step Change – Rapid consumer-led transformation of the energy sector and co-ordinated economy-wide action. Step Change moves much faster initially to fulfilling Australia’s net zero policy commitments that would further help to limit global temperature rise to below 2° compared to pre-industrial levels. Rather than building momentum as Progressive Change does, Step Change sees a consistently fast-paced transition from fossil fuel to renewable energy in the NEM. On top of the Progressive Change assumptions, there is also a step change in global policy commitments, supported by rapidly falling costs of energy production, including consumer devices. Increased digitalisation helps both demand management and grid flexibility, and energy efficiency is as important as electrification. By 2050, most consumers rely on electricity for heating and transport, and the global manufacture of internal-combustion vehicles has all but ceased. Some domestic hydrogen production supports the transport sector and as a blended pipeline gas, with some industrial applications after 2040.

• Hydrogen Superpower – strong global action and significant technological breakthroughs. While the two previous scenarios assume the same doubling of demand for electricity to support industry decarbonisation, Hydrogen Superpower nearly quadruples NEM energy consumption to support a hydrogen export industry. The technology transforms transport and domestic manufacturing, and renewable energy exports become a significant Australian export, retaining Australia’s place as a global energy resource. As well, households with gas connections progressively switch to a hydrogen-gas blend, before appliance upgrades achieve 100% hydrogen use.

Household gas switching to 100% hydrogen? What could possibly go wrong?

Here are the AEMO projections:

“The ISP forecasts the need for ~122 GW of additional VRE by 2050 in Step Change, to meet demand as coal-fired generation withdraws (see Section 5.1). This means maintaining the current record rate of VRE development every year for the decade to treble the existing 15 GW of VRE by 2030 – and then double that capacity by 2040, and again by 2050.”  (VRE= Variable Renewable Energy)

 “In Hydrogen Superpower, the scale of development can only be described as monumental. To enable Australia to become a renewable energy superpower as assumed in this scenario, the NEM would need approximately 256 GW of wind and approximately 300 GW of solar – 37 times its current capacity of VRE. This would expand the total generation capacity of the NEM 10-fold (rather than over three-fold for the more likely Step Change and Progressive Change scenarios). Australia has long been in the top five of energy exporting nations. It is now in the very fortunate position of being able to remain an energy superpower, if it chooses, but in entirely new forms of energy. “ (p.36)

Figure 5:  Projections of different renewable needs from the draft report

And capacity factors have not been considered!

And here are the “future technology and innovation” ideas for reducing emissions:

Figure 6: How to achieve emissions reductions

I’m glad I won’t be around to see this play out.

The Challenge Ahead For Renewables: Part 2

January 13, 2022

In Part 1 I showed how the low Capacity Factors of wind and solar mean enormous amounts of wastage of resources and money have been incurred over the past 20 years. 

I also said that the wastage can only get worse.  Here’s how.

In Part 1, I only looked at historical electricity generation.  What of the future according to the major political parties? (The Greens don’t count because they can’t count.)

The major parties are committed to Net Zero emissions by 2050, which will require massive changes to our energy use.

I use data from the BP Statistical Review of World Energy 2021, the National Energy Market website, and the report of the Department of Industry, Science, Energy and Resources (2021).

To replace 2020 fossil fuel electricity with renewable electricity will require an extra 200.6 TeraWattHours:

Figure 1:  Total Electricity Generation

That’s an extra 22,884 MegaWatts of renewable capacity at 100% capacity factor.  Remember, wind’s capacity factor is about 32%, and solar is about 15%.  At $1.8 million per MW, that will cost somewhere between 129 and 275 billion dollars. 

That is of course entirely achievable.  Costly, but achievable.

However, electricity makes up only a small part of Australia’s total energy use.  Transport alone uses much more.  That is why there is a push for more electric vehicles: the ALP wants 89% of new car sales to be electric vehicles by 2030.

Australia’s 2020 energy consumption was 5,568.59 PetaJoules, a decrease of 5.25% on 2019.  One PetaJoule is the equivalent of 0.278 TeraWattHours, or 277,778 MegaWattHours, which is the power generated by 31.7 MW over one year.

Figure 2:  Total Energy Consumption in Australia

Renewables of all sorts accounted for just 8% of energy consumed in Australia in 2020.  Include hydro and that rises to 10.4%.  Figure 2 shows the amount for each.

Figure 3:  Energy Consumption by Type

Note the complete absence of nuclear energy.

If Australia is to be completely fossil fuel free (with no increase on 2020 consumption, which was reduced because of Covid), renewables will have to produce an extra 4,990.9 PetaJoules.  Our consumption will look like this:

Figure 4:  Energy Consumption without Fossil Fuels

4,990.9 PJ is 1.387 billion MegaWattHours, which will need 158,152 MW generation (at 100% capacity)- only 27.8 times 2020 generation.

If this is to be supplied by wind alone, we will need an additional 494,225 MW of installed capacity in wind farms- 52 times 2020 wind capacity- at 24 Hectares per MW.  An extra 118,600 square kilometres of suitable land for wind farms will be difficult to find.

Solar at 2-3 Hectares per MW would probably be a better proposition.  If the extra generation is to come from solar, we will need an extra 1,054,357 MW- 60 times 2020 solar capacity.

Therefore the cost of meeting our current energy consumption- transport, domestic, commercial, and industrial- with no allowance for growth, and ignoring the cost of converting our entire domestic, commercial, industrial, mining, and air transport capacity to some form of electric vehicles, would be between:-

$ 889.6 BILLION  (if all wind)

and

$1.898 TRILLION (if all solar).

(Australia’s nominal GDP will be around $2.1 trillion in 2022.)

That’s up to $73,700 for every man, woman, and child in Australia.

Figure 5 shows the comparison between Australian GDP and the cost of solar generation needed.

Figure 5:  Cost of extra solar generation compared with the whole of the economy

How much of that investment would be in wasted capacity? Between 68% and 85%-from $605 Billion to $1.613 Trillion.

Moreover, the life of a wind turbine is 20 to 25 years, and 25 years for solar panels, so we can look forward to more expense in decommissioning and replacement in the future.

(By the way- do you think that “future technology and innovation” will be any cheaper?)

That’s just what would be the result of the major parties’ commitment to Net Zero.

But wait- there’s more. Stand by for Part 3.

The Challenge Ahead For Renewables: Part 1

January 11, 2022

As we are committed by all major parties to the goal of Net Zero emissions by 2050 perhaps we need to reflect on the scale of the challenge ahead.

I shall first deal with electricity, as that is the only thing that renewables such as wind and solar can produce (except perhaps for a warm inner glow in those who love them.)

Being less of a romantic, I prefer facts and figures.  In this post I use data from the BP Statistical Review of World Energy 2021, the National Energy Market website, and by tracking down opening and closing dates for various facilities.

Figure 1 shows the total generating capacity for coal, wind, and solar electrical generation for the last 20 years.  (Gas is excluded as it makes up less than 8% of generation over a year.)  This is the maximum possible output if all plants are operating at 100% of their rated capacity.

Figure 1: Generating Capacity 2021 – 2020

Note how coal fired electrical capacity fell below 25,000 MegaWatts (MW) with the closure of power stations in SA, WA, and Victoria.  Meanwhile from a very low base wind capacity rose steadily and accelerated from 2018.  Solar generating capacity has exceeded wind since 2012 and really took off in 2019 and 2020.  Wind and solar combined now exceed coal generating capacity.

Now let’s look at how much electricity was actually produced

Figure 2: Coal Capacity and Generation 2021 – 2020

Note how coal generation is falling steadily.  The gap between generation and capacity may be regarded as wasted resources (and money).  This has remained fairly constant over the years.

Figure 3: Wind Capacity and Generation 2021 – 2020

Despite the large increase in capacity, generation is not increasing as fast.  The gap is widening.

Figure 4: Solar Capacity and Generation 2021 – 2020

Again, the gap (i.e. waste) is increasing even faster.  More on this later.

Here’s another way of looking at this problem, for solar.

Figure 5: Solar Generation as a Factor of Installed Capacity 2021 – 2020

Over the last 20 years there has been a fairly constant and close relationship between the amount of electricity generated and the installed capacity it is produced from.  This illustrates the low capacity factor of renewables.  Capacity factor is average actual generation divided by the nameplate capacity, usually expressed as a percentage. 

Figure 6: Capacity Factor 2021 – 2020

Coal has a capacity factor of between 65% and 80%.  Hydro depends on rainfall and has averaged 21% over the last 10 years.  Wind averaged 32% over the last 10 years, but solar struggles to get above 15%- mainly because it sits idle at night, there are large losses in conversion from DC to AC, and also because it produces more than the grid can handle in the middle of the day so supply is curtailed. 

Investors take heed: for every MegaWatt of solar electricity you may wish to generate, you will need to install 6.7 MW.  Every 1 MW of wind electricity needs 3.125 MW installed.  But wind takes up about 24 Hectares of land per Megawatt as against 2-3 Hectares for solar.

Figure 7 shows how much investment has been wasted over the years.

Figure 7: Wasted Capacity

Waste costs money.  In the case of wind and solar, $1.8 MILLION per MW.

I hate waste- but it can only get worse. 

More Evidence That The Australian Temperature Record Is Complete Garbage

December 8, 2021

The Bureau of Meteorology is either incompetent or has knowingly allowed inaccurate data to garble the record.

My colleague Chris Gillham at http://www.waclimate.net/ has alerted me to growing problems with the BOM’s record for Diurnal Temperature Range (DTR).  DTR is the difference between daytime temperature (Tmax) and night-time temperature (Tmin). 

According to Dr Karl Braganza’s paper at https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2004GL019998 , “an index of climate change” is that DTR should decrease as greenhouse gases accumulate. To oversimplify, greenhouse gases will enhance daytime temperature while at night greenhouse gases will slow down cooling.  With increasing greenhouse gas concentration, daytime maxima are expected to increase, certainly, but the effect on night-time minima will be relatively greater.  Thus, minimum temperatures will increase faster than maxima, and DTR will decrease.  While Dr Braganza was referring to global values, Australia is a large dry continent where DTR should show up clearly.

We now have 111 years of temperature data in ACORN-SAT (Australian Climate Observation Reporting Network- Surface Air Temperatures).  In this post I only use Acorn temperature data and corresponding rainfall data.  Skeptics have been bagging Acorn ever since it was introduced, and for good reasons as you will see.

Figure 1 is straight from the Bureau’s climate time series page, and shows how DTR has varied over the years.  There is a centred 15 year running mean overlaid. 

Figure 1: Official plot of annual DTR

Melbourne, We Have A Problem… DTR has been increasing recently.

I have used BOM data to make plots that show this more clearly.  First, Figure 2 shows annual DTR from 1910 to 2020 has no trend.  It should be decreasing.

Figure 2:  Annual DTR

There appears to be a distinct step up around 2000-2002.

Figure 3 shows the same data for the last 70 years, broken into two periods, from 1951 to 2000, and 2001 to 2020.

Figure 3:  DTR since 1951

From 1951 to 2000, DTR behaves as it should, with a long term decrease.  After 2000, DTR steps up well above expected values.  The average from 1981-2000 is -0.12 C.  From 2001-2020 the average is +0.35C.  DTR suddenly increases by nearly 0.5C. Why?

DTR is very much governed by that other greenhouse gas, H2O.  Dry days, months and years produce hot days and cooler nights; wet periods result in cooler than average days and warmer than average nights.  This relationship is shown in Figure 4.

Figure 4:  DTR anomalies plotted against rainfall anomalies- all years 1910-2020

As rainfall increases, DTR decreases.  The effect is more marked in very wet (>100mm above average) and very dry (100mm or more below average) years.

Figure 5 shows time series of DTR (as in Figure 2) and rainfall.  Rainfall has been inverted and scaled down by a factor of 250.

Figure 5:  DTR and Inverted, Scaled Rainfall

There is close match between the two.

Using 10 year averages in Figure 6 makes the change after 2001 much clearer.

Figure 6:  Decadal means of DTR and inverted, scaled rainfall

The 10 year average rainfall to 2020 is about the same as the 1961-1990 average (the period the BOM uses for calculating anomalies).  The 10 year average DTR should be about the same value- not at a record level.

As DTR decrease due to greenhouse gas accumulation is caused by minimum temperatures increasing faster than maximum temperatures, Figure 7 shows 10 year averages of maxima and minima for all years to 2020.

Figure 7:  10 year running means of Tmax and Tmin

Tmax has clearly accelerated in the last 20 years, increasing much faster than Tmin.

This is NOT what should be happening: indeed it is the exact opposite of what greenhouse theory predicts.

Something happened to Australian maximum temperature recording or reporting early this century.  I suspect that the BOM changed from using the highest one-minute average of temperatures recorded in Automatic Weather Systems to the current highest one-second value for the day becoming the reported maximum; or else the design of a significant number of AWS changed, with new, faster-responding probes replacing old ones.

I also suspect I know why this was allowed to happen and continue.

Warmer minimum temperatures at night and in winter are not very scary, but record high temperatures and heatwaves make headlines.

It would suit the Global Warming Enthusiasts in the Bureau for apparently rapidly rising maxima and ever higher records being broken to make headlines, frighten the public, put pressure on governments, and generally support The Narrative.

But someone forgot to tell the left hand what the right hand was doing.

The result is that they are now faced with a contradiction- Diurnal Temperature Range is not decreasing as it should. 

The Bureau is either incompetent or has knowingly allowed inaccurate data to garble the record.

The World’s Biggest Thermometer

August 23, 2021

Are temperatures today unprecedented and dangerously high?  Apparently- the IPCC’s 6th Assessment Report says that current temperatures are higher than at any time in the last 125,000 years

But that is wrong.  Temperatures today are cooler than they were in the past.

In making that statement I am not referring to data from ice cores (as in my previous posts here and here), but a simple and accessible temperature measurement device: the biggest thermometer in the world.

The following statements are uncontroversial:

1 Sea level rise is largely due to melting of glaciers and thermal expansion of the oceans.

2 Thermal expansion and glacial melting are symptoms of temperature increase.

3 Higher sea level indicates warmer conditions, lower sea level indicates colder conditions.

4 Sea levels are currently rising (by a small amount- NOAA says Fort Denison, Sydney, has a rise of 0.65mm per year).

5 This indicates temperatures have been rising.

6 But sea levels and therefore temperatures were higher than now about 4,000 to 7,000 years ago.

If you doubt point 6, you can easily tell whether it was warmer or cooler in the past relative to today.

How?  By looking for evidence of sea level change in areas that are not affected by tectonic rising or falling coastal land, or by large scale water run off or glacial melting, or by very large underground water extraction.

Areas such as the eastern coastline of Australia- the world’s biggest thermometer.

The continent of Australia is very old and flat.  It is in the middle of its continental plate with very little tectonic activity.  Australia’s coastlines are therefore largely stable with little vertical movement, apart from a small tilt down at the northern edge and a small uplift along the southern coast.  Australia is also a very long way from ancient ice sheets.

Evidence of higher sea level is plain to see in many places around Australia.  For example, at Phillip Island in Victoria, Victorian Resources Online describes raised Holocene beaches at Chambers Point, 0.5m and 3 to 5m above high water mark.  Arrows on this Google Maps image show where to find them.

More evidence at Wooloweyah Lagoon, near Maclean in NSW:

And Bulli, NSW:

There are many, many other locations where you can find Holocene beaches well above current sea level. 

Some of the height of these stranded beaches is probably due to the weight of deeper seawater from the melting ice sheets gradually tilting up continental coastlines as the sea floor deepened leading to an apparent drop in sea level at the coast.  However, as Lewis et al (2013) and Sloss et al (2018) (see Appendix below) show, this was of lesser importance especially in northern Australia.  Sea level fall was largely due to climatic influences- in particular, cooling and drying since the Holocene Optimum.

To conclude:  Sea levels were higher in the past, so temperatures must have been higher. 

Therefore there is no evidence that current temperature rise is anything unusual.  Just check the world’s biggest thermometer.

Appendix:  Here are a few of many references to higher Australian sea levels in the Holocene, and reasons for variation.

Sloss et al (2007)  Holocene sea-level change on the southeast coast of Australia: a review

“Present sea level was attained between 7900 and 7700 cal. yr BP, approximately 700—900 years earlier than previously proposed. Sea level continued to rise to between +1 and +1.5 m between 7700 and 7400 cal. yr BP, followed by a sea-level highstand that lasted until about 2000 cal. yr BP followed by a gradual fall to present. A series of minor negative and positive oscillations in relative sea level during the late-Holocene sea-level highstand appear to be superimposed over the general sea-level trend.”

ABC TV catalyst 19/6/2008

Even the ABC says sea levels were higher in the Holocene!

Lewis et al (2008) Mid‐late Holocene sea‐level variability in eastern Australia

“We demonstrate that the Holocene sea-level highstand of +1.0–1.5 m was reached ∼7000 cal yr bp and fell to its present position after 2000 yr bp.”

Moreton Bay Regional Council, Shoreline Erosion Management Plan for Bongaree, Bellara, Banksia Beach and Sandstone Point (2010)

“Sea levels ceased rising about 6,500 years ago (the Holocene Stillstand) when they reached approximately 0.4 to 1m above current levels. By 3,000 years before present they had stabilised at current levels”

Switzer et al (2010) Geomorphic evidence for mid–late Holocene higher sea level from southeastern Australia

“This beach sequence provides new evidence for a period of higher sea level 1–1.5 m higher than present that lasted until at least c. 2000–2500 cal BP and adds complementary geomorphic evidence for the mid to late Holocene sea-level highstand previously identified along other parts of the southeast Australian coast using other methods.”

Lewis et al (2013) Post-glacial sea-level changes around the Australian margin: a review

“The Australian region is relatively stable tectonically and is situated in the ‘far-field’ of former ice sheets. It therefore preserves important records of post-glacial sea levels that are less complicated by neotectonics or glacio-isostatic adjustments. Accordingly, the relative sea-level record of this region is dominantly one of glacio-eustatic (ice equivalent) sea-level changes. ….Divergent opinions remain about: (1) exactly when sea level attained present levels following the most recent post-glacial marine transgression (PMT); (2) the elevation that sea-level reached during the Holocene sea-level highstand; (3) whether sea-level fell smoothly from a metre or more above its present level following the PMT; (4) whether sea level remained at these highstand levels for a considerable period before falling to its present position; or (5) whether it underwent a series of moderate oscillations during the Holocene highstand.”

Leonard et al (2015) Holocene sea level instability in the southern Great Barrier Reef, Australia: high-precision U–Th dating of fossil microatolls

“RSL (relative sea level) was as least 0.75 m above present from ~6500 to 5500 yr before present (yr BP; where “present” is 1950). Following this highstand, two sites indicated a coeval lowering of RSL of at least 0.4 m from 5500 to 5300 yr BP which was maintained for ~200 yr. After the lowstand, RSL returned to higher levels before a 2000-yr hiatus in reef flat corals after 4600 yr BP at all three sites. A second possible RSL lowering event of ~0.3 m from ~2800 to 1600 yr BP was detected before RSL stabilised ~0.2 m above present levels by 900 yr BP. While the mechanism of the RSL instability is still uncertain, the alignment with previously reported RSL oscillations, rapid global climate changes and mid-Holocene reef “turn-off” on the GBR are discussed.”

Sloss et al (2018) Holocene sea-level change and coastal landscape evolution in the southern Gulf of Carpentaria, Australia

“ By 7700 cal. yr BP, sea-level reached present mean sea-level (PMSL) and continued to rise to an elevation of between 1.5 m and 2 m above PMSL. Sea level remained ca. + 1.5 between 7000 and 4000 cal. yr BP, followed by rapid regression to within ± 0.5 m of PMSL by ca. 3500 cal. yr BP. When placed into a wider regional context results from this study show that coastal landscape evolution in the tropical north of Australia was not only dependent on sea-level change but also show a direct correlation with Holocene climate variability….  Results indicate that Holocene sea-level histories are driven by regional eustatic driving forces, and not by localized hydro-isostatic influences. “

Dougherty et al (2019)  Redating the earliest evidence of the mid-Holocene relative sea-level highstand in Australia and implications for global sea-level rise

“The east coast of Australia provides an excellent arena in which to investigate changes in relative sea level during the Holocene…. improved dating of the earliest evidence for a highstand at 6,880±50 cal BP, approximately a millennium later than previously reported. Our results from Bulli now closely align with other sea-level reconstructions along the east coast of Australia, and provide evidence for a synchronous relative sea-level highstand that extends from the Gulf of Carpentaria to Tasmania. Our refined age appears to be coincident with major ice mass loss from Northern Hemisphere and Antarctic ice sheets, supporting previous studies that suggest these may have played a role in the relative sea-level highstand. Further work is now needed to investigate the environmental impacts of regional sea levels, and refine the timing of the subsequent sea-level fall in the Holocene and its influence on coastal evolution.”

Helfensdorfer et al (2020) Atypical responses of a large catchment river to the Holocene sea-level highstand: The Murray River, Australia

“Three-dimensional numerical modelling of the marine and fluvial dynamics of the lower Murray River demonstrate that the mid-Holocene sea-level highstand generated an extensive central basin environment extending at least 140 kilometres upstream from the river mouth and occupying the entire one to three kilometre width of the Murray Gorge. This unusually extensive, extremely low-gradient backwater environment generated by the two metre sea-level highstand….”

Climate Change in Context

August 17, 2021

In my last post I showed some plots of temperature data derived from ice cores at Vostok base in Antarctica, which indicate we are close to the end of the Holocene.

Here are some more plots from the same data so we can put present concerns about warming in some context.  Please remember- temperatures calculated from ice cores have a resolution of from 20 years recently to 40 to 50 years in the mid-Holocene, to 80 to 85 years in the glacial maximum.  Temperatures shown may be regarded as a rough average of conditions over those intervals.  Also note this dataset is for one point on the earth’s surface, not a global average.  Nevertheless it is a very important dataset as it shows polar conditions over a very long period.

Figure 1:  Vostok temperatures relative to 1999 over the last 20,000 years

The previous glacial maximum had temperatures in the Antarctic about 9 degrees colder than now.  This was followed by a strong warming, the Termination of glacial conditions, resulting in 11,000 years of warm conditions, the Holocene.  The Holocene was not uniformly warm but featured fluctuations of up to 2 degrees above and below current temperatures.  I will look at this later, but first I shall take a closer look at the Termination.  

Figure 2:  Vostok temperatures during the Termination

Point A marks the start of the Termination warming.  Temperatures rose from A to B (by about 6.5 degrees in 3,000 years- about 0.2 degrees per 100 years- so not exactly “rapid” warming).  Temperatures then fell about 2 degrees, before rising even more sharply from C to D, the start of the Holocene.  Figure 3 shows temperatures in this final part of the Termination.

Figure 3:  Vostok temperatures in the steepest part of the Termination

Temperatures increased by about 5 degrees over a bit more than 1,100 years.  Yes, the warming rate was indeed steeper- 0.44 degrees per 100 years on average.  However, the temperature rose 1 degree in less than 50 years at the end of this period.

During the Termination, long term temperature rise was gradual, but punctuated by short periods of much more rapid rise.

Now let’s look at temperature change in the Holocene.

Figure 4:  Vostok temperatures 7,000 to 9,000 years ago

Conditions were not uniformly warm, with fluctuations from -1 to +.5C relative to 1999 over hundreds of years.  But there was one episode with a rise of 2.93 degrees in less than 100 years- now that’s rapid warming.

Figure 5:  Vostok temperatures in the last 2,020 years

More recently, temperatures rose 1.94 degrees in 155 years to 1602, and again 2.2 degrees in 44 years to 1809.

You will notice I have shown 3 datapoints showing 21 year mean annual surface air temperatures at Vostok (1970, 1990, and 2010, with zero at 1990).  This is merely for interest- instrumental air temperatures should never be appended to ice core data.  What it does show is that the rate of present temperature change is well within the range of natural variation.

This is also evident when a Greenland ice core series is compared with modern surface air temperatures.

Figure 6:  Greenland (GISP2) temperatures in the last 4,000 years

I have inserted the decadal average of -29.9 C at the GISP borehole from 2001-2010.  Notice how unremarkable that is.

As the fluctuations at GISP and Vostok have been occurring for thousands of years something other than carbon dioxide emissions must be responsible.

So what about carbon dioxide? Data in the next figure is from Dome Fuji, also in Antarctica.

Figure 7:  Insolation, temperature, and CO2 in the last 350,000 years

Notice that at no time in previous interglacials did carbon dioxide concentration exceed 300ppm, (and despite the higher temperatures than now there was no “runaway” warming.)    And as the Carbon Dioxide Information Analysis Centre says

There is a close correlation between Antarctic temperature and atmospheric concentrations of CO2 (Barnola et al. 1987). The extension of the Vostok CO2 record shows that the main trends of CO2 are similar for each glacial cycle. Major transitions from the lowest to the highest values are associated with glacial-interglacial transitions. During these transitions, the atmospheric concentrations of CO2 rises from 180 to 280-300 ppmv (Petit et al. 1999). The extension of the Vostok CO2 record shows the present-day levels of CO2 are unprecedented during the past 420 kyr. Pre-industrial Holocene levels (~280 ppmv) are found during all interglacials, with the highest values (~300 ppmv) found approximately 323 kyr BP. When the Vostok ice core data were compared with other ice core data (Delmas et al. 1980; Neftel et al. 1982) for the past 30,000 – 40,000 years, good agreement was found between the records: all show low CO2 values [~200 parts per million by volume (ppmv)] during the Last Glacial Maximum and increased atmospheric CO2 concentrations associated with the glacial-Holocene transition. According to Barnola et al. (1991) and Petit et al. (1999) these measurements indicate that, at the beginning of the deglaciations, the CO2 increase either was in phase or lagged by less than ~1000 years with respect to the Antarctic temperature, whereas it clearly lagged behind the temperature at the onset of the glaciations. (My emphasis).

Therefore, carbon dioxide did not drive, but followed, temperature change in the past; past rapid warming did not lead to positive feedbacks and runaway warming; and the instrumental record is far too short to draw any definitive conclusion about recent warming, which cannot be differentiated from past Antarctic and Greenland temperature fluctuations.

There is no climate crisis.

Global Warming or Global Cooling: Keep an Eye on Greenland

July 30, 2021

Here are four graphs that governments should think about.

The first graph is of ice core temperature data from Vostok in Antarctica for the past 422,000 years.  Temperatures are shown as variation from surface temperature in 1999 of -55.5 degrees Celsius.

(From:- Petit, Jean-Robert; Jouzel, Jean (1999): Vostok ice core deuterium data for 420,000 years. PANGAEA, https://doi.org/10.1594/PANGAEA.55505)

 We are living in an inter-glacial period of unusual warmth, the Holocene, but previous interglacials were 2 to 3 degrees warmer than the present.  Between these brief interglacials are 100,000 year long glacial periods.  As the US National Climatic Data Centre says, “Glacial periods are colder, dustier, and generally drier than interglacial periods.”

We are lucky to be living now- life would be pretty hard for the small population the world could support in a glacial period.

Graph 2 shows just the last 12,000 years.  We are at the extreme right hand end.

Note that Vostok temperatures have fluctuated between +2 and -2 degrees relative to 1999.

There are several ways of identifying the start and end of interglacials.  I have chosen points when Antarctic temperatures first rise above zero and permanently fall below zero relative to 1999.  Graph 3 shows the length of time between these points for the previous three interglacials compared with the Holocene.

The Holocene has lasted longer than the previous three interglacials: and is colder.

Many scientists think glacial periods start when summer insolation at 65 degrees North decreases enough so that winter snowfall is not completely melted and therefore year by year snow accumulates.  Eventually the area of snow (which has a high albedo i.e. reflects a lot of sunlight) is large enough to create a positive feedback, and this area becomes colder and larger.  Ice sheets form, and a glacial period begins.  This is a gradual process that may take hundreds of years.

Well before global temperatures decrease, the first sign of a coming glacial inception will be an increasing area of summer snow in north-eastern Canada, Baffin Island, and Greenland.

I could find no data for northern Canada or Baffin Island, but it is possible to deduce summer snow area for Greenland.

Graph 4 shows the minimum area of snow at the end of summer in Greenland.  (Data from Rutgers University, calculated from North America including Greenland minus North America excluding Greenland.)

The area of unmelted snow at the end of summer in Greenland has grown by about 100,000 square kilometres in the past 30 years.  At this rate Greenland will be completely covered in snow all year round in about 45 years.

Caution: there was no glacial inception in the Little Ice Age- other factors may be involved, cloudiness being one.  Further, a 30 year trend is just weather, and may or may not continue- but with the Holocene already longer and colder than previous interglacials, summer snow cover is one indicator we ignore at our peril.

Cold is not good for life.

How Accurate Is Australia’s Temperature Record? Part 3

March 17, 2021

In previous posts (here and here) I have shown how maximum temperatures (Tmax) as recorded by ACORN-SAT (Australian Climate Observations Reference Network- Surface Air Temperature- ‘Acorn’ for short) have diverged from other measures of climate change, in particular, rainfall.

I’ll continue looking at the Tmax ~ Rainfall relationship, and show how the Bureau of Meteorology (BOM) must never have used it as a quality control measure for temperature recording.  Result: garbage.

Tmax is negatively correlated with rainfall.  Wet years are cooler, dry years are warmer.  If the incoming solar radiation, the landscape and the measuring sites remain the same, over a number of years this physical relationship remains constant.  What is true of rainfall and temperature in my lifetime was also true in my grandfather’s lifetime, and will still be true in my grandchildren’s lifetime.  If the relationship appears to vary, it must be as a result of some other cause, such as:

changes in solar radiation;

changes in the landscape (urban development, tree clearing, irrigation);

changes in the weather station sites (movement to new sites, tree growth, proximity to heat sources,  buildings, or areas of pavement, change of screen size);

changes in measuring equipment or methods (electronic probes instead of mercury in glass, time of observation, recording in Celsius instead of Fahrenheit, millimetres instead of inches); or

changes in the recorded data (wrong dates applied, or adjustments).

Solar exposure has not changed (and it would be a huge problem for Global Warming Enthusiasts and the whole Climate Change industry if it had).  Across Australia, urban development is a minuscule fraction of land area; tree clearing and irrigation have affected a larger area in several regions, but the vast arid interior remains largely unchanged.  We do know however that weather station sites, observation methods, and equipment have changed, and temperature data (and to a much smaller extent, rainfall data) have been “homogenized” in an attempt to correct for these changes.

The 1961 – 1990 Tmax ~ rainfall relationship

The Bureau of Meteorology uses the period from 1961 to 1990 as the baseline for calculating temperature and rainfall means and anomalies.  Figure 1 shows the relationship between Tmax and Rainfall for all years from 1961 to 1990.  I am using BOM data from their Climate Change Time Series page.

Fig. 1:  Annual Tmax plotted against Rainfall, 1961 – 1990

The x-axis represents rainfall, the y-axis represents maximum temperatures.  The trendline is marked, showing Tmax decreases with rainfall. 

In the top left is the trendline label, showing the value for Tmax (y) for the any value of rainfall (x).   I have magnified this in Figure 2.

Fig. 2:  Trendline label, Tmax vs Rain 1961-90

Circled in red is the slope of the trendline (-0.0029:  there is an inverse relationship, with temperature decreasing 0.0029 of a degree Celsius for every extra millimeter of rain). 

Circled in green is the intercept (+29.9476: if rainfall was zero, the trendline would intercept the y-axis at 29.9476 degrees). 

Circled in blue is the R-squared value (0.3744: R^2 indicates how well Tmax and rainfall match, 0 being not at all and 1 being perfectly.  This value indicates a correlation of about -0.61.  Another way of thinking about it is that 37% of Tmax is explained by rainfall.)

Now this is important: the relationship shown in the trendline label should be similar for the whole record, or else something other than the climate has changed.

I will now show how we can use the information in Figure 1 to test the accuracy of Tmax data in three separate ways.

Comparison of Acorn Tmax with theoretical values derived from rainfall.

Using the trendline equation (Tmax = -0.0029 x Rain + 29.9476) we can calculate an estimate of Tmax from rainfall in any given year.  Figure 3 shows the result.

Fig. 3:  1910 – 2020 Acorn Tmax and Theoretical Tmax calculated from rainfall

For most of the 1961-90 period there is a fair match (37%, remember).  Before the mid-1950s Tmax is mostly lower than the rainfall derived estimate, and after 1990 is nearly always very much higher than we would expect for the rainfall. 

A plot of annual differences between Acorn Tmax and Theoretical Tmax (the residuals if you like), the Tmax variation that is not explained by rainfall, shows how much they differ.

Fig. 4:  Annual Differences: Acorn Tmax minus Theoretical Tmax

We would expect some random differences, but not that much and not strongly trending up.

Correlation between Tmax and rain over time

The next figure shows the “goodness of fit” between Tmax and Rainfall from any given year to 2020.  (The plotline stops at 2010 as correlation fluctuates too much with only a few datapoints.)

Fig. 5:  Running R-squared values of Tmax vs Rain for all years to 2010, from any given year to 2020

The relationship plainly changes (and improves) with time. From 1998 to 2020 there is a good correlation between Tmax and rainfall: before this it is woeful. 

The next figure shows running 21 year calculations of R-squared ‘goodness of fit’ between annual maxima and rainfall, and is included for your entertainment. 

Fig. 6:  Centred running 21 year R-squared values of Tmax vs Rainfall

In 1989, the 21 year period from 1979 to 1999 has a correlation between Tmax and Rain of -0.35: less than 12 % of temperature change is explained by rainfall.  20 years later, the value for 1999 to 2019 is -0.9, or 81% of Tmax explained by rainfall.  That amount of difference is farcical.

Moreover, recent data shows a completely different Tmax~Rain relationship from Figure 1. 

Which brings me to the third point.

Change in Tmax ~ Rainfall relationship over time

From the trendline equation in Figure 2, the Tmax ~ Rainfall relationship may be calculated as

(Tmax – intercept)/ Rainfall.   The value for 1961-90 is -0.29C per 100mm.

This plot of the 21 year moving average shows how much this changes.

Fig. 7:  21 Year Centred Running Average of ((Tmax – intercept)/Rainfall) x 100

The expected value of -0.29C/ 100mm is reached from 1973 to 1976, in the middle of the 1961 – 1990 period, as expected.  Based on the 1961-1990 trendline equation, over the last 21 years 100mm of rain reduces Tmax by one tenth of a degree Celsius (and if we extrapolate- not a good idea- that figure will approach zero in about 10 years’ time).   100 years ago that same 100mm of rain would have reduced Tmax by 0.38 degrees.  Why has rain lost its power?

Conclusion

Three plots of the Tmax ~ Rainfall relationship- Figures 4, 5, and 7- show a similar pattern of change in the difference between recorded and theoretical Tmax, the correlation between Tmax and rainfall, and the Tmax ~ Rainfall equation. 

Why has rain lost its power?  It hasn’t- Tmax has become relatively too high.  Historical maximum temperatures as reported in Acorn are not just inaccurate but deeply flawed. 

The Acorn dataset is garbage.

How Accurate Is Australia’s Temperature Record? Part 2

January 19, 2021

In my last post I showed that Australia wide the Tmax ~ rainfall relationship has remained constant for the past 110 years (as it should) but Tmax reported in the Acorn dataset has increased by more than 1.5 degrees Celsius relative to rainfall.  Consequently, the ACORN-SAT temperature dataset is an unreliable record of Australia’s maximum temperatures.

Of course there are other aspects of climate besides rainfall.   In this post I will compare annual ACORN-SAT Tmax data with:

Rainfall

Sea Surface Temperatures (SST)

The Southern Annular Mode (SAM)

Cloudiness

Evaporation

all for the Australian region.

I have sourced all data from the Bureau of Meteorology’s Climate time Series pages

except for SAM data from Marshall, Gareth & National Center for Atmospheric Research Staff (Eds). Last modified 19 Mar 2018. “The Climate Data Guide: Marshall Southern Annular Mode (SAM) Index (Station-based).” 

Tmax, Rainfall, and SST data are from 1910; SAM and Daytime Cloud from 1957, and Pan Evaporation from 1975.  Cloud observations apparently ceased after 2014, and Evaporation after 2017, possibly because of staffing cuts.

Because Pan Evaporation data are only available from 1975 and are reported as anomalies from 1975 to 2004 means, I have recalculated Tmax, Rainfall, SST, SAM, and Daytime Cloud anomalies for the same period so all data are directly comparable.

As in the previous post, I have calculated decadal averages for all indicators to show broad long term climate changes.  Decadal averages show how indicators perform over longer periods.  Each point in the figures below shows the average of the 10 years to that point.  This can then indicate times of sudden shifts or questionable data. (For example in Figure 1 SAM (the green line) makes a sudden jump in 2015.  Was this a climate shift or a data problem?)

Figure 1 shows the 10 year means for all climate indicators.  I have scaled Rain and SST to match Tmax at 2019, Cloud and SAM to match Tmax at 1966, and Evaporation to match Tmax in 1984.  Rain and Cloud are inverted as they have an inverse relationship with temperature.

Figure 1:  10 Year Means of Climate Indicators

Tmax has stayed close to SST until 2001.  Clearly Tmax has increased far more than any of the others, especially since 2001.

The next plots show the difference between decadal averages of Tmax and the other indicators.  Zero difference means an excellent relationship with Tmax.

Figure 2:  Difference: 10 Year Means of Tmax minus Rain and SSTs.

Starting from 1919 (zero difference), Rainfall is close to Tmax until 1957, after which Tmax takes off until it is 1.6 degrees Celsius greater than expected in the 10 years to 2020.  Tmax diverges from SST values in 2001 and in 2020 is 0.7 degrees greater than expected.

In Figure 3, Rain, SST, SAM, and Cloudiness are scaled to match Tmax at 1966.

Figure 3:  Difference: 10 Year Means of Tmax minus Rain, SST, SAM, and Cloud

Figure 3 shows how closely Rain and Cloud are related: differences from Tmax are almost identical.  Compared with 1966, Tmax is 1.3 degrees more than rainfall would suggest in the 10 years to 2020.  SST and the SAM index are less different from Tmax but Tmax divergence is still clear.  You may notice that Tmax differences from all climate indicators seem to change at similar times, apart from SAM in 2015.

In Figure 4, all indicators are scaled to match Tmax at 1984.

Figure 4:  Difference: 10 Year Means of Tmax minus Rain, SST, SAM, Cloud and Evaporation

Differences increase rapidly after 2001, so in Figure 5 indicators match Tmax at 2001.

Figure 5:  Difference: 10 Year Means of Tmax minus Rain, SST, SAM, Cloud and Evaporation

There appears to be a problem with SAM in 2015, and it’s a shame that the BOM have discontinued Cloud and Evaporation observations.  In the last 20 years, it is obvious that Tmax has diverged from other indicators.

Conclusion:

All factors- Rainfall, SAM, SST, Clouds, and Pan Evaporation- point to a clear divergence of temperature nationwide, especially in the last 20 years.  In other words, ACORN-SAT, our official record of temperatures, is unreliable.

How Accurate Is Australia’s Temperature Record? Part 1

January 7, 2021

In my last post I showed that maximum temperature (Tmax) as reported by ACORN-SAT (Australian Climate Observations Reference Network-Surface Air Temperature) appears to be responsible for the growing divergence of the difference between Tmax and tropospheric temperatures from Australia’s rainfall.

In this post I show how Tmax is related to rainfall, and show that while this relationship holds for discrete periods throughout the last 110 years, Tmax has apparently diverged from what we would expect.  In other words, the Acorn Tmax record is faulty and unreliable.

For much of this analysis I am indebted to Dr Bill Johnston who has posted a number of papers at Bomwatch using the relationship between Tmax and rainfall.

At any land based location annual maximum temperature varies with rainfall: wet years are cooler, dry years are warmer.  More rainfall (with accompanying clouds that reflect solar radiation) brings cooler air to the ground; provides more moisture in the air, streams, waterholes, and the soil which cools by evaporation; causes vegetation to grow, the extra vegetation shading the ground and retaining moisture, with transpiration providing further cooling; and in moist conditions deep convective overturning moves vast amounts of water and heat high into the troposphere- especially evident in thunderstorms.  Less rainfall means the opposite: more solar radiation reaches the ground with fewer clouds and less vegetation; there is less moisture available to evaporate; less vegetation growth and transpiration; and much less heat is transferred to the troposphere through convective overturning.

While more rainfall than the landscape can hold results in runoff in rivers and streams, thus removing some moisture from the immediate area, this affects large regions only in tropical coastal catchments- the Kimberleys, the Gulf rivers, the Burdekin and Fitzroy.  Across the bulk of Australia there is very little discharge of water to the oceans.  In the Murray-Darling Basin, on average less than 0.005% of rainfall is discharged from the Murray mouth. (BOM rainfall data and 1891-1985 discharge data from Simpson et al (1993))

This temperature ~ rainfall relationship is particularly evident in desert areas far from any marine influence.  Alice Springs provides a good example.  Figure 1 shows how annual maximum temperatures at Alice Springs Airport vary with rainfall since 1997.  Data are from ACORN.

Fig. 1: Tmax and Rainfall, Alice Springs

The slope of the trend line shows that for every extra millimetre of rain, Tmax falls by 0.0047 of a degree Celsius, which is about half a degree less for every 100 mm.  The R-squared value shows that there is a good fit for the data- 79% of temperature change is due to rainfall.

I said above that this relationship holds for land locations.  An island, with a little land surrounded by water, is mostly affected by sea temperature and wind direction.  Locations near the coast are also affected by marine influence.  At Amberley in south-east Queensland daily maximum temperature can be moderated by the time of arrival of a sea breeze or whether it arrives at all.  (Site changes also can change Tmax recorded.)

Fig. 2: Tmax and Rainfall, Amberley

Further inland, the relationship is strong: at Bathurst, there is 0.4C temperature variation per 100mm of rainfall and 61% of temperature change is due to rainfall alone.

Fig. 3: Tmax and Rainfall, Bathurst

The BOM has sophisticated algorithms for area averaging temperature and rainfall across Australia and provide national climate records back to 1900 for rainfall and 1910 for maxima.  Averaged across Australia individual station idiosyncrasies are submerged so that the 1997 to 2019 relationship between Tmax and rainfall is very strong (and similar to that of Alice Springs):

Fig. 4: Tmax and Rainfall, Australia 1997-2019

However, the relationship is not strong throughout the whole record:

Fig. 5: Tmax and Rainfall, Australia 1910-2019

The relationship from 1910 to 2019 is poor.

In the next figure I compare the Tmax – rainfall relationships for the first 10 years of the record with the last 10 years.

Fig. 6: Tmax and Rainfall, Australia, first and last decades

The trendlines are almost exactly parallel, with tight fits, showing strong relationships 100 years apart- but the trendline for 2010 to 2019 is about 1.7 degrees above that for 1910 – 1919.  How can that be?

It is possible to compare rainfall and temperature throughout the last 110 years.  In the next figure, rainfall is inverted and scaled down so as to match Tmax at 1910.

Fig. 7: Tmax and Inverted Scaled Rain, Australia

Running 10 year means allow us to see long term patterns of rainfall and temperature more easily:

Fig. 8: Tmax and Inverted Scaled Rain, Decadal Means, Australia

Rainfall has increased over the last 110 years (despite what you might hear in the media), and so apparently have maximum temperatures.  In the above figures Tmax and rainfall track roughly together until the mid-1950s, then Tmax takes off.

I calculated an “index” of temperature ~ rainfall variation by subtracting scaled, inverted rainfall from Tmax, commencing at zero in 1919.  This allows us to identify when temperature appears to diverge markedly from inverted rainfall:

Fig. 9: Index of Temperature ~ Rainfall Variation: Tmax minus Inverted Scaled Rain, Decadal Means, Australia

There is a small increase from the mid-1950s, but the really large divergence commences in the 1970s, with the decade from 1973 to 1982 about 0.6 units above the decade to 1972.  The index decreases to 1995, then there is another steep increase to 2007, and a final surge to 2019.

This index alone shows how poorly the official temperature record represents the temperature of the past.

 While there are other times, in the next figures I compare four periods: 1910 to 1972, 1973 to 1995, 1996 to 2007, and 2008 to 2019.  Here I use annual data.

Fig. 10: Tmax and Rainfall, Australia, four periods

Again, trendlines are almost parallel with similar slopes, showing that the Tmax ~ rainfall relationship is fairly constant for all periods- (about 0.5C per 100mm after 1995 and about 0.4C per 100mm before 1995).  However, the lines are separated.  Temperature for each later period is higher than the ones preceding, such that the temperature recorded now is about 1.5 degrees Celsius higher than it would have been for similar rainfall before 1973. And rainfall has increased in that time.

Global Warming Enthusiasts and apologists for the BOM will claim that these breaks between separate periods are real and caused by changes in circulation patterns due to climate change- in particular the Southern Annular Mode.  That will be the subject of Part 2.

Whatever the reasons, Australia wide the Tmax ~ rainfall relationship has remained constant for the past 110 years (as it should) but the temperatures reported in the Acorn dataset have increased by more than 1.5 degrees Celsius relative to rainfall.

Conclusion:

The ACORN-SAT temperature dataset is an unreliable record of Australia’s maximum temperatures.

Surface and Satellite Temperatures: 2020 Update

December 19, 2020

What’s gone wrong?

In November 2015 in my post “Why are Surface and Satellite temperatures Different?” and two follow up posts I showed that the difference is very largely due to rainfall.  You are urged to read these posts in full.

I repeat a key paragraph:

Firstly, surface temperatures are supposed to be different from atmospheric temperatures. Both are useful, both have limitations. The TLT is a metric of the temperature of the bulk of the atmosphere from the surface to several kilometres above the whole continent, in the realm of the greenhouse gases- useful for analysing any greenhouse signals and regional and global climate change. Surface temperature is a metric of temperature 1.5 metres above the ground at 104 ACORN-SAT locations around Australia, area averaged across the continent- useful for describing and predicting weather conditions as they relate to such things as human comfort, crop and stock needs, and bushfire behaviour.

Here are three plots from my 2015 post.

Fig.1:  Tmax and Scaled, Inverted Rain (from Figure 7 from my 2015 post)

Dry periods are hotter, wet periods are cooler.

Fig. 2:  Surface maxima minus atmospheric temperatures and inverted rain (Figure 10 from my 2015 post)

Fig. 3:  Temperature difference compared with rainfall (from Figure 12)

The difference between Australian surface and satellite temperatures was very largely explained by rainfall. However, after five more years of satellite and surface data there is a problem (and I thank Chris Gillham for alerting me to this.)

Fig. 4:  Surface maxima minus atmospheric temperatures and inverted rain

Since about 2013 the difference between surface Tmax and satellite data has visibly increased above rainfall.

Now I have a confession to make.

In previous analyses I used running 12 month means in calculating correlation.  This can lead to inaccuracy as the means can be highly auto-correlated.  From now on I will use annual data, either with calendar years or, as in this post, annual means from December to November (so that summer months and most of the northern Wet season are included in the one datapoint).

I downloaded data from:

Monthly maxima

Monthly rainfall

Temperature of the Lower Troposphere- Australia Land

As with my 2015 post, I have recalculated rainfall and maxima from 1981-2010 means to match UAH.

In the past five years there have been changes:  the Australian maximum temperature record is now based on ACORN-SAT Version 2 instead of Version 1, including new adjustments and some station changes.  No doubt UAH has been tweaked a little as well.

However correlation between the difference between the surface maxima as recorded by Acorn and temperature of the lower troposphere (TLT) as recorded by UAH, and rainfall, has decreased.

Fig. 5:  Temperature difference compared with rainfall

The close connection between the temperature differences and rainfall became broken from about 2005, as can be seen in Figure 4.  Another step up occurred in 2013.

So there appear to be three distinct periods: 1979 to 2004, 2005 to 2012, and after 2013.  Plotting temperature differences against rainfall allows us to compare each period.

 Fig. 6:  Temperature difference compared with rainfall

From 1979 to 2004 and from 2005 to 2012 slopes are identical at 0.4 degrees lower temperature for each 10 mm of rain, with 76% and 93% of temperature variance explained by rainfall. The trend lines are parallel but offset by 0.26 degrees indicating either atmospheric temperatures have reduced or surface maxima have increased in the middle period.  From 2013 the relationship is different with closer to 0.5 degrees lower temperature per 10mm of rainfall, with rainfall explaining 78% of the variance.  Again, the offset shows either UAH has suddenly decreased or Acorn has suddenly increased.

Conclusion:  Something has gone wrong with the relationship between rainfall and temperature in Australia.  In recent years, and certainly since 2013, the surface- atmospheric temperature difference has rapidly increased relative to rainfall.  That should not have happened.

My suspicion is that Acorn’s maxima are to blame.   Figure 1 showed Acorn appeared to step up relative to rainfall in 2001 or 2002, or perhaps earlier in 1997, and again in 2013.  There can be no meteorological explanation for this.

The accuracy, and therefore usefulness, of the ACORN-SAT adjusted temperature record will be the topic of my next post.

Stay tuned.

Acorn Mish-Mash Part 2: Scone

December 13, 2020

In Part 1 we saw that Scone in NSW has the fastest increase in 120 month mean maximum temperatures of all 112 Acorn stations.  The Station Catalogue shows a recent photo of the site with long grass at least 60cm high surrounding the screen- not a very good advertisement for compliance with siting specifications.

Fig. 1:  BOM photograph of Scone site

However the Metadata for this site reveals how much the site has changed.  Before 2005 the screen was close to the runway and a service road, and there was considerable earthworks nearby in 2001.  By April 2005 the screen had been moved to its current location.  In 2012 the grass was 60cm high as in the above photo, and was whipper-snipped during the annual inspection.  In 2015 and 2019 the grass around the instruments was “sparse” as weed control had been used i.e. it had been sprayed out with herbicide.  Temperature data for the airport may be questionable based solely on site information.

The Acorn record has been created by merging data from 01/01/1995 to 31/12/1995 from the present site at the airport with that of a Soil Conservation Research Station (SCS) 10 km away from 1965 to 1994.

Data before 1975 were adjusted downwards because of a change or repair to the screen.

Fig. 2:  Adjustments to annual data at Scone

This resulted in an increase in trend of +0.43C per 100 years.

Fig. 3:  Scone raw and Acorn annual data

However, comparison with the average of the Bureau’s nominated neighbouring stations used to make this adjustment shows the adjustment was much too great.  While the raw record from 1965 to 1973 shows Scone warming 0.29C per 100 years faster than the neighbours, the Acorn record is warming at 1.46C per 100 years- much faster than the neighbours.

Fig. 4:  Difference between Scone and average of neighbours, 1965 – 1973

While that alone is enough to cast doubt on the Acorn adjustments, an analysis of the relationship between maxima and rainfall shows that little reliance can be placed on temperature data before 1974, and after 1995.

At every well maintained site there is a relationship between maximum temperature and rainfall: periods of dry weather are hotter and periods of wet weather are cooler, because of the effects of more or less cloud cover, evaporation and transpiration.  (Wind direction will also have an influence, especially in dry seasons.)  At a well maintained station much more than half of temperature variation is due to rainfall. Therefore, if this relationship varies markedly we can deduce that either temperature or rainfall data are questionable.  This is shown by Dr Bill Johnston at his website, BomWatch, which I urge you to visit, and my analysis is loosely based on his methods.

I calculated 12 month running means of temperature and rainfall for the Airport and the Soil Conservation (SCS) sites.  Figures 5 to 7 show 12 month average temperature plotted against 12 month average rainfall for the three periods, 1965 – 1973 (in which Acorn temperatures are adjusted), 1974 – 1994 (when Acorn and raw are the same), and 1995 – 2018 (when the temperature record switches from the SCS to the airport).

Figure 5:  Scone adjusted maxima plotted against local rainfall

That is a very poor relationship: either temperature data or rainfall data are unreliable.

Figure 6:  Scone unadjusted maxima plotted against local rainfall

Here, more than half of temperature variation can be explained by rainfall.  It is not brilliant, but much better than what comes before and after.

Figure 7:  Scone Airport maxima plotted against local rainfall

While not as bad as pre-1974, less than 30% of temperature variation is explained by rainfall.  Either temperature or rainfall data, or both, are dubious.  Considering the site history and varying vegetation, this is not surprising.

It is unlikely that Acorn is a true record of temperatures at this location. Scone Acorn data are not reliable and should not be included in regional and national climate analysis.

Acorn Mishmash- Part 1: They can’t all be right

November 23, 2020

The Bureau of Meteorology (BOM) produces climate analyses and forecasts based on their best efforts at estimating long term climate trends around the nation- the latest being their suitably scary State of the Climate 2020. 

The main datasets used are ACORN-SAT (Australian Climate Observations Recording Network- Surface Air Temperature) Version 2.1, Daily and Monthly Rainfall Networks, Monthly Pan Evaporation Network, and Monthly Cloud Amount Network.  In future posts I hope to look at some of the BOM’s claims in more detail, however in this series of posts I will look at climate trends at individual stations.  In this post I will look solely at monthly maximum temperatures at all 112 ACORN-SAT sites.  This information is freely available at http://www.bom.gov.au/climate/change/index.shtml#tabs=Tracker&tracker=site-networks and is adjusted and homogenized Acorn V.2.1 data.

Like the Bureau, in order to compare data from all stations I calculate anomalies from monthly means for all months from 1981 to 2010.  I then calculate 120 month running means.  120 month (decadal) means allows us to see long term patterns and changes.  For example, Figure 1 shows decadal monthly means of rainfall that fell at Alice Springs since October 1900. 

Figure 1:  Decadal rainfall at Alice Springs

I would not use the term “cycles” to describe what we see, but clearly there are wetter and drier periods: rainfall is not random from year to year at Alice Springs.

The same decadal averaging when applied to maximum temperatures should show how temperature changes over years, and because Acorn 2.1 is homogenized using neighbouring stations for adjustments, there should be similarities between stations in the same regions.  Let’s see.

I have made all means zero at December 2019 (except Boulia, which ends in June 2013, Point Perpendicular, ending in January 2017,and Gunnedah, ending in June 2019), so in the following plots all data points are relative to the most recent available.  Each data point is the mean of all monthly maxima of the previous 10 years.

Figure 2:  Running 120 month means, maxima anomalies (from 1981-2010 means), relative to most recent data (mostly December 2019), all 112 Acorn stations

That spaghetti plot shows decadal Tmax for all 112 Australian stations, with a few stations identified.  What a mess.  There is a range of 1.5 to 2.5 degrees between highest and lowest in most years before 2000.  We need to look at different regions to make more sense of it.  I will show a map for each region.

Figure 3: Tasmanian stations

Figure 4: Decadal anomalies, Tasmania

Tasmania is a small, compact region, and all stations appear to show the same decadal climate variations.  However, Grove seems to have much less increase than the others, and Larapuna has a much greater increase than its close neighbours, Low Head and Launceston.

Figure 5: East coast of Queensland

Figure 6: Decadal anomalies, east coast of Queensland

Similarity between stations barely extends back as far as 2005.  There is little sign of common climatic patterns except in very recent years.  Brisbane Aero and its closest neighbour Cape Moreton Lighthouse diverge between 1986 and 2007.  And Mackay in particular is an outlier: what reason can there be for Mackay to be more than one degree cooler in all decades to 1940 than Bundaberg to the south and Cairns to the north?

Figure 7:  North-east NSW stations

Figure 8: Decadal anomalies, north-east NSW

Again, while there are some similarities, there is much variety.  Inverell is more than one degree cooler than neighbouring Moree in the decade to the early 1920s, then their decadal means converge to within 0.3 of a degree in the 1950s.  And Scone has had a meteoric rise from 1.6 degrees less than now in November 2001- faster than anywhere else in Australia.

Figure 9:  South-west Western Australian stations

Figure 10: Decadal anomalies, South-west Western Australian stations

This climate region has fairly consistent records, at least back to the 1930s, when Perth’s diverges from the others.  Perth goes from coolest in the 1920s to warmest (relative to now) in the 1980s.

The northern part of Western Australia is messier.

Figure 11:  Northern Western Australian stations

Figure 12: Decadal anomalies, northern Western Australia

Halls Creek and Broome are much cooler than Port Hedland, Marble Bar, and Carnarvon in the decades before the 1930s.  There is a range of 1.3 degrees between decadal means of Marble Bar and neighbouring Karijini North (the former Wittenoom) in 1969, and there is a large divergence between Kalumburu and Carnarvon (at opposite ends of the coast), and the rest of the stations, between 2000 and 2008.

Central Australian stations, because of their remoteness, have a large impact on our climate signal.

Figure 13:  Central Australia

Figure 14: Decadal anomalies, Central Australian stations

While there are similar decadal patterns in maximum temperatures, you will note that Alice’s record rises from the coolest in the 1920s and 1930s to warmest from the 1940s to 1970s, in steps rather than rises and falls.

The Top End is subject to the annual north-west monsoon, with climatic seasons alternating between Wet and Dry.

Figure 15:  Top End stations

Figure 16: Decadal anomalies, Top End

Again we see in most stations rises in the 1970s and 1990s, and falls in the 1980s and early 2000s.  The exception is Darwin, with an almost linear increase with a small acceleration in the 1990s.  Normanton in the far east is an outlier before the 1980s, and VRD in the 1990s.

Inland New South Wales is another region showing common climate patterns, but a few surprises.

Figure 17:  Western NSW stations

Figure 18: Decadal anomalies, western NSW

Here is a good example of many stations showing common climate patterns, rising and falling almost in unison.  However there is still well over one degree between highest and lowest in nearly every year before 1990.  Further, it is not perfect unison: not all stations show similar responses to regional climate swings.  In 1956 and 1957 Canberra at 2.4 degrees cooler than now and Walgett at more than 2.5 degrees cooler are clear outliers, and are well below the pack from 1950 to 1972, and again from 1980 to 2002.  Walgett in particular shows little response to the 1980s surge shown by most other stations.  These two are joined by West Wyalong in the 1970s, and are just under 1.5 degrees cooler than now in 2000 before surging rapidly.

Finally, for comparison, the next plot shows some of the big movers in the Acorn stations, most of which we have seen before.

Figure 19: Decadal anomalies, big hitters

Linearly rising Darwin and recent rapid riser Scone we have met before.  Alice Springs and Perth are joined by Geraldton and Eucla, both in Western Ausralia, in rising from about 2 degrees cooler than now in the decade to the 1920s.  In the 1930s another WA station, Morawa, is almost 2.5 degrees cooler than now.  In the previous figure we saw Canberra in the 1950s 2.4 degrees cooler than now and Walgett more than 2.5 degrees cooler than now: the coolest of any station in Australia. 

Conclusion:

Decadal means show broad patterns of climate change in various regions but there are many examples of individual stations within these regions standing out from these patterns.  They can’t all be right.  The accuracy of the BOM’s ACORN-SAT dataset for maximum temperatures must therefore be called into question at a number of its stations.  This must then throw doubt on the Bureau’s climate analyses and future projections.

In future posts I will look more closely at some of these individual stations’ records.

The Mexican Wave: Covid19 in Australia to October

November 2, 2020

Postscript: For more detailed information and graphs that support/ augment/ supersede my analysis, see https://www.health.gov.au/news/health-alerts/novel-coronavirus-2019-ncov-health-alert/coronavirus-covid-19-current-situation-and-case-numbers

In Queensland we refer to people in the southern states as “Mexicans” (because they’re from “south of the border, down Mexico way” as sung by Gene Autry, Patsy Kline, Patti Page and many others.)

Read on to find why I describe the Australian Covid19 experience from June to October as the Mexican wave.

Worldometers has these plots illustrating the Australian experience:

Figure 1:  Daily new cases

There were (apparently) two waves in Australia.

Figure 2: Cumulative death toll

In four months the death toll increased by 803- more than 770 %! 

We know what went wrong, but the following plots might illustrate it more clearly.

These plots are from statistics from State government websites, such as this one from Victoria: https://www.dhhs.vic.gov.au/victorian-coronavirus-covid-19-data .

All are correct as of 31 October.  They speak for themselves so I will keep my comments to a minimum.

The next figure compares seven day averages of Victorian and all Australian new cases from 25 July to 6 August, at the peak of the “second wave”.

Figure 3: National and Victorian new cases

Until 5 June, Victoria had 1,681 cases.  From then, the new cases began increasing, adding another 18,666 cases to 31 October.  92% of Victorian cases were in this period.

Comparing all states:

Figure 4:  Total cases

Figure 5:  Mortality:

I estimated population figures from March ABS figures.  With almost zero overseas net immigration and very little interstate migration, natural growth remains, which does not change the rates per million by very much at all.

If Victoria was a separate country, its case rate per million would rank it at 127th, just ahead of Bangla Desh.   

Figure 6:  Case Rate per million people

Its Death Rate per million would rank it at 76th, just ahead of Turkey.

Figure 7:  Mortality Rate per million people

The next figure shows Case Fatality Rate, the number of deaths per total cases, which is not complete until the pandemic is over.  These figures are for the CFRs to 31 October.

Figure 8: Covid19 Case Fatality Rate

CFR is affected by whether the virus gets into nursing homes and hospitals which have high proportions of vulnerable people.  There was an outbreak of Covid19 in hospitals in northern Tasmania which affected the Tasmanian CFR.

 4.03% of all Victorian cases so far resulted in death.

The figure for all of Australia is 3.29%.

The figure for Australia excluding Victoria is 1.22%.

The virus first entered Australia via overseas travellers, then spread by local transmission.  The next plot compares infections acquired overseas with those acquired locally in Victoria.

Figure 9: Victorian overseas and locally acquired infections

The contrast is stark.  Victoria compares most unfavourably with other states with over 95% of all cases locally acquired. (Data not available for Tasmania and Territories.)

Figure 10:  Percentage of local transmission in larger states

And Victoria has more than 90% of total national local transmission.

Figure 11:  Percentage of national local transmission

Therefore it can be clearly seen that Australia’s “second wave” was really all about Victoria.  This was easily avoidable with strict hotel quarantine and better contact tracing.  There was no second wave in other states, with small outbreaks mostly due to travellers from Victoria.

Perhaps “Mexican” should from now on describe the government of Victoria, but not their long suffering people, and not governments of NSW, Tasmania, or South Australia.

The Mexican Wave is not something we wish to see repeated.

First Wave Covid19 Mortality in Context

October 22, 2020

Key takeaway points:

  • It is likely that the real Covid19 death toll was at least double the official tally, and possibly hundreds more.
  • Despite this, there were 1,457 fewer deaths in the first six months of this year than last year.
  • The first lockdown worked- until the Victorian fiasco.

In this post I use the most recent Mortality data (released 1 October 2020) from the Australian Bureau of Statistics (ABS), up to 30 June 2020, and the most recent ABS Population data, to examine the effect of the Covid19 pandemic on Australian deaths.  This period covers the whole of the first wave of the pandemic and gives interesting insights.  Future data releases covering the second wave (with another 800 Covid19 deaths) will provide further illumination.

The ABS advises that the data are provisional and not complete as deaths subject to coroners’ inquests are not included, but with completeness percentages in the high 90s “meaningful comparison with historic counts” may be made.

Key statistics from the ABS:

  • 68,986 doctor certified deaths occurred between 1 January 2020 and 30 June 2020.
  • Numbers of deaths have been below historical averages since mid May and below baseline minimums since the week ending 9 June.
  • Deaths from respiratory diseases and heart diseases were below historical minimum counts throughout June.

Figure 1:  ABS chart of deaths and Covid19 infections

The peak of new coronavirus infections was in the week ending 31 March, with 2,428 new infections in that week (Week 13), and the peak in all mortality also occurred in that week.  The following plot shows official Covid19 mortality (from Worldometers) peaking in Week 14.

Figure 2:  Covid19 first wave deaths

The ABS says that the World Health Organisation (WHO) early in 2020 “directed that the new coronavirus strain be recorded as the underlying cause of death, i.e. the disease or condition that initiated the train of morbid events, when it is recorded as having caused death……..

……. Deaths due to COVID-19 are included in the total for all deaths certified by a doctor. They are not included in deaths due to respiratory diseases or any of the other specified causes.”

The first reported Covid19 death was on 1 March, (Week 9).  In Week 14, one week after the peak in new infections, the peak in the first wave deaths occurred.  In this post I define the first wave of the pandemic as Weeks 9 to 21.  (The second wave commenced in Week 24.)  Figure 3 shows Covid19 deaths in context.  The duration of first wave deaths is indicated by the horizontal red line.

Figure 3: Covid19 and total deaths

Note the increase in total deaths in Weeks 12 to 15, and the insignificance of official Covid19 mortality by comparison.  (Australia closed borders on 16 March- Week 11- and began restricting movement in the days following.)

The next graph compares 2020 mortality so far with the five previous years.

Figure 4: Total Australian deaths 2015 – 2020

This year’s peak in deaths also occurred in Weeks 12 to 15, at the height of the first wave infections.

You will also note Australia’s 2020 mortality levelled off well below previous years’ figures, which usually continue rising to peak in Winter and early Spring.  Mortality figures for Weeks 27 to 52 will be very interesting.  There was an unusual early surge in 2019, and a very large increase in deaths in Winter and Spring of 2017.

I now look at excess deaths.  The ABS says:

Measuring ‘excess’ deaths

Excess mortality is an epidemiological concept typically defined as the difference between the observed number of deaths in a specified time period and the expected numbers of deaths in that same time period. Estimates of excess deaths can provide information about the burden of mortality potentially related to the COVID-19 pandemic, including deaths that are directly or indirectly attributed to COVID-19.

… counts of deaths for 2020 are compared to an average number of deaths recorded over the previous 5 years (2015-2019). These average or baseline counts serve as a proxy for the expected number of deaths, so comparisons against baseline counts can provide an indication of excess mortality. “

However, Australia’s population has increased by nearly two million from March quarter 2015 to March quarter 2020 (from 23,745,629 to 25,649,985).  This has a large impact on calculations.  Mortality rate per 1,000 head of population is a better measure. Figure 5 shows mortality rates for recent years.

Figure 5:  Australian mortality rates, 1st 26 weeks, 2015 – 2020

The method I have used is different from the ABS methodology because of the population increase and is based on mortality rates rather than absolute numbers of deaths. 

I have calculated the mortality rate per 1,000 people for each of the 2015-2019 years (using the population for the March quarters of those years), and similarly for the 2020 data.  I then multiply the average of the 2015-2019 mortality rates by the 2020 March quarter population to obtain an estimate of predicted deaths for 2020.  Subtracting this from the actual 2020 number gives an estimate of excess deaths.  An excess death figure of zero indicates the mortality rate is no different from previous years.  The next figure shows plots of actual and expected deaths for the first half of 2020.

Figure 6:  Predicted and actual deaths

Figure 7 is my plot of excess deaths to 30 June.

Figure 7: Estimated Excess Mortality

Excess and actual deaths peaked in Weeks 12 to 15, with weeks 13 and 14 nearly 200 above the expected level- but there were only 56 official Covid19 deaths in those weeks.  Officially, Covid19 was involved in 29 deaths in Week 14, 12 each in Weeks 13 and 15 and only 3 in Week 12.  It is possible that Covid19 deaths were being vastly under-reported in March. 

By the end of June estimated excess deaths were at minus 349, 11.5% below the expected number for Week 26.  Actual deaths in the first half of the year were 1,457 fewer than for the same period in 2019.

States and Territories:

Figure 8 shows actual numbers of deaths for all states and territories.

Figure 8:  2020 mortality numbers for each state

Mortality figures are dominated by New South Wales, followed by Victoria and Queensland.  Figure 9 shows excess deaths.

Figure 9:  Excess mortality by states

Smaller states had smaller changes in excess mortality, although Western Australia had a peak of 54 excess deaths in Week 13.   Figure 10 shows excess deaths for the larger states only.

Figure 10:  Excess deaths in the large eastern states

Peaks in excess deaths occurred between Weeks 9 to 17, but note earlier peaks in New South Wales and Queensland 7 or 8 weeks before the pandemic peak, with Queensland much higher than New South Wales, largely counteracted by Victoria, and a peak in Victoria in Weak 11, counteracted by New South Wales.  There was a third peak in Weeks 17 to 19, coinciding with another peak in Covid19 deaths.  Remember these numbers are additional to Covid19 deaths.  And officially Queensland had only seven Covid19 deaths, almost certainly due to under-reporting.

Age at death

Figure 11 shows the ages at which excess deaths occurred.

Figure 11:  Excess mortality by age

People aged from 0 to 44 years were not affected by the large changes in death rates in older age groups, but there was an increase in excess deaths in the 45 to 64 age bracket in Week 13, at the height of Covid19 infections, as Figure 12 shows.  That looks suspicious, but may be chance.

Figure 12:  Excess deaths for younger cohorts

The majority of excess deaths were in older age groups, as Figure 13 shows.

Figure 13:  Excess deaths for older Australians

There was a peak of 132 excess deaths in those 85 years and over in Week 14, but in Week 13 there were 146 excess deaths in those aged 65 to 84.  There were additional substantial peaks in earlier weeks as well.  It was not a good first half of the year for senior citizens, but excess deaths for all age groups were well below expected numbers by June.

Cause of death

  A death certificate lists all causes of death, and with elderly people these can be three or more.  It is very likely that a person over 85 may die of pneumonia (classified as a respiratory illness), but may also have any or all of dementia, diabetes, cerebrovascular disease, ischaemic heart disease, and cancer.  However, the ABS asks doctors to report the (one) underlying cause of death, and since earlier this year, Covid19 as the underlying cause “when it is recorded as having caused death.

 Figure 14 compares all respiratory deaths with Covid19.

Figure 14:  Covid19 and respiratory deaths

Influenza and pneumonia are subsets of respiratory illness, and the next figure shows interesting excess mortality data for 2020.

Figure15:  Excess deaths due to respiratory causes

Note the peak in respiratory deaths at the height of pandemic infections, but an earlier peak some four weeks previously.  It is likely that Covid19 was not correctly reported to the ABS by all doctors until Week 14 or 15- doctors are human too.  Since the first wave and the increase in personal hygiene, social distancing and little travel, deaths have remained well below previous years.

Figure 16:  Ischaemic heart and cerebrovascular disease excess deaths

This plot illustrates the advances in medicine:  ischaemic heart disease in 2020 had fewer deaths than expected for all of the first six months apart from a peak in Week 7.  Cerebrovascular disease (chiefly strokes) also had fewer deaths than expected except for Week 14 (so was potentially related to Covid19), and another peak in Week 24. 

Figure 17 plots excess deaths caused by the common co-morbidities of Covid19, dementia and diabetes.

Figure 17:  Excess deaths caused by dementia, diabetes, and Covid19

Diabetes and Dementia excess deaths were also higher than expected during the first wave, but there was another large surge in excess deaths with dementia as a cause weeks earlier.

Conclusions:

With the caveat that the ABS mortality figures are provisional, and putting together figures for various states, ages, and causes of death, some conclusions may be drawn:-

Either a mystery respiratory illness or undiagnosed Covid19 was widespread in the eastern states amongst elderly people weeks before the peak of first wave deaths, possibly arriving from cruise ships.

There were probably many more Covid19 deaths and infections than reported.  It is likely that the real Covid19 death toll was at least double the official tally, and possibly hundreds more.

Social distancing, good hygiene, and travel restrictions have caused a large decrease in mortality in May and June by restricting the spread of many common illnesses.  The first lockdown worked- until the Victorian fiasco.

The net effect of the first wave of the Covid19 pandemic on Australian mortality was negative.  Covid19, and public health responses to it, resulted in a lower death toll in the first half of 2020.  This lower death toll was not just in relative (mortality rate) terms but also in absolute terms: there were 1,457 fewer deaths in the first six months of this year than last year.

ABS data for the second half of the year will be released around April 2021 and will provide much better information about excess mortality for all states (and Victoria in particular), for all age groups, and for all causes.

I include an appendix with raw mortality data for 2015 -2020.

Appendix:  Raw mortality data for all causes 2015 – 2020.

Figure 18:  Respiratory mortality

Note the typical winter and spring surge in respiratory deaths, mainly due to influenza outbreaks in cold months.  There was an early surge in 2019 and a very large surge in 2017 which will skew means for those weeks.  Median mortality rate may be more appropriate than means.

Figure 19:  Ischaemic heart disease mortality

Heart disease mortality has been below previous years for most of the first 26 weeks.

Figure 20:  Cerebrovascular disease mortality

Cerebrovascular disease (stroke) deaths peaked during the first wave of Covid19 but have been mostly near the bottom of the range of previous years, with a second peak in June.

Figure 21:  Dementia mortality

Deaths with dementia as a cause have increased over the years.  A peak in dementia deaths coincided with Covid19 but deaths have been in the normal range since then.

Figure 22:  Diabetes mortality

A peak in diabetes deaths coincided with the peak in Covid19 infections and deaths, and was much higher than expected.  At the end of June deaths were in the range of previous years.

Figure 23:  Cancer mortality

Cancer deaths have increased over the years and 2020 remains within the expected range.  You may note there is no winter increase in cancer mortality.

Distance Records for Temperature Adjustments

October 6, 2020

Trigger Warning:  ridicule of the Australian Bureau of Meteorology below!

The official Australian climate record is developed from ACORN-SAT– the Australian Climate Observation Reporting Network- Surface Air Temperatures.  This is relied on by governments and industry and so should be completely trustworthy and free from any problems that might lead to lack of confidence.

The Acorn stations have had their temperature records adjusted to account for any discontinuities or irregularities.  This is done by comparing Acorn stations’ data with those from a selection of comparative stations. 

The Bureau says:

The process of homogenisation seeks to answer a very simple question: what would Australia’s long-term temperature trend look like if all observations were recorded at the current sites with the current available technology? Homogenisation means we can compare apples with apples when it comes to temperature trends.

One might expect that, with the aim being to “compare apples with apples”, stations used for comparison and making adjustments would be physically not too distant- ideally, neighbouring.  

Not so.

For many stations, not even remotely so.

Australia is a very big country with vast areas of sparsely inhabited desert.  There are very large distances between towns in the outback, so it is not surprising that it is often difficult to find suitable comparative stations.

But the Acorn Station Catalogue, which has helpful lists of comparative stations used for adjustments, has some absolute doozies.  Here are some for your amusement.  (Obviously most stations have many comparative sites.)

Carnarvon, in Western Australia, has been adjusted by reference to a number of stations hundreds of kilometers away, including Southern Cross, only 897km away.

Camooweal, Queensland, “ “ “  Thargomindah, 1,067km away.

Boulia, Qld, “ “ “  Walgett, New South Wales, 1,130km

Halls Creek, WA, “ “ “  Boulia, Qld, 1,370km

Tennant Creek, Northern Territory, “ “ “   Charleville, Qld,  1,443km

Mount Gambier, in South Australia, has been adjusted with the help of Lismore in northern New South Wales, 1,526km away.  (And it’s not as if there is a shortage of sites in this well populated part of South Australia.)

But the gong, the gold medal, the record breaking achievement for the Bureau, goes to…….

Alice Springs, in the Northern Territory, which has been adjusted using data from Collarenebri in New South Wales,  1,590 kilometres away.

And they want the public to trust them.

More Questionable Adjustments- Cape Moreton

October 5, 2020

Here’s another Acorn station with interesting adjustments- Cape Moreton (40043) minimum temperatures.

Cape Moreton Lighthouse is on the north-eastern tip of Moreton Island, 65 km north-east of Brisbane.  It is not compliant with siting specifications. 

Figure 1 is the adjustment summary shown by the Bureau in its Station Catalogue.

Figure 1:  Adjustment summary for Cape Moreton

Two points to note:  The Bureau has TWO adjustments applied to the same date- 1/01/1946; and there are four comparative stations used to make these adjustments at this Acorn station.

Figure 2 shows the neighbours the BOM used for comparison. 

Figure 2:  Google Maps image showing Cape Moreton and its neighbours

There are many neighbouring stations the Bureau could have used for comparison, but the Bureau chose those with the “best correlation” during the comparison period (the late 1940s):  Brisbane Regional Office 65km away, and probably affected by Urban Heat Island effect; Yamba, also coastal but 267km south; Dalby Post Office on the Darling Downs 220km west; and Miles Post Office 330km west.

Figure 3 shows the annual average minima for these weather stations.

Figure 3:  Annual minima, Cape Moreton and neighbours

UHI at Brisbane is visible as the plot line rises faster than the others after 1950.

The next figure shows Acorn’s adjustments have increased the rate of warming from +1.2 degrees Celsius per 100 years to more than +1.5 C.

Figure 4:  Cape Moreton Minima

Figure 5 shows the difference between the original raw record and Acorn.

Figure 5:  Cape Moreton adjustments

It is plainly obvious that the Acorn adjustment summary shown in Figure 1 is wrong.  The first adjustment was applied from 01/01/1948 (not 1946) and decreased the annual minima for 1946 and 1947 by -1.2C.  The second adjustment was applied from 01/01/1946 and increased previous annual minima by +0.8C or +0.9C. The raw minima were decreased by -0.3C or -0.4C, but that is not how the Bureau describes the adjustment process:

Date applied: data prior to this date was adjusted for the reason (cause) cited. Adjustments are superimposed on each other; for example, if two adjustments are shown, one for 1/1/2000 and one for 1/1/1988, data prior to 1/1/1988 has both adjustments applied to it, data between 1/1/1988 and 1/1/2000 only has the first adjustment applied, and data after 1/1/2000 is not adjusted at all.”

The documentation of Acorn is a mess.

In order to compare data from stations with varying temperatures we need to calculate their anomalies from their means for the same period.  Figure 6 shows Cape Moreton’s and comparison stations’ anomalies from their 1931-1960 means.

Figure 6:  Minima anomalies, Cape Moreton and “neighbours”

Hard to follow, there is too much variability.  You may note that by comparison with the periods before 1948 and after 1960, the 1950s show much agreement.  The next figure shows the period from 1930 to 1960.

Figure 7:  Minima anomalies, Cape Moreton and “neighbours” 1930-1960

Notice that in 1946 and 1947 (indicated by the arrow) Cape Moreton is far too warm- the reason for the adjustment; however Yamba’s record is just as erratic or more so, being too low in 1933, 1934, 1940-1944,  and 1947; and too high in 1950.  This suggests firstly that the 1946 and 1947 adjustments were justifiable for those two years, and secondly that Yamba is not a good comparison station.  The next figure, with Yamba excluded, clearly illustrates this point.

Figure 8:  Cape Moreton and comparison stations, excluding Yamba

Apart from 1946 and 1947 Cape Moreton’s record is not greatly dissimilar from the three remaining stations. 

The object of adjusting temperatures using neighbours for comparison is to endeavour to produce a record that more truly reflects climate trends of the area.  The resulting record should be more like the neighbours than the original raw record.  We can test this by plotting the differences between Acorn and the raw record and the average of the neighbours.  If the comparison is good, while individual years’ differences may vary, the trend should be close to zero: the station should not be warming or cooling more than the neighbours.  Figure 9 shows the results for Cape Moreton minima for the period before the 1946 adjustment, excluding Yamba from the average.

Figure 9:  Differences between Cape Moreton and Qld neighbours

You will note that the blue trend line, showing the trend of the difference between Cape Moreton’s annual data and the average of Brisbane, Dalby, and Miles, has a trend of about +0.3 degrees per 100 years, indicating Cape Moreton is already warming more than the others.  The “raw” record already compares fairly well with the neighbours, considering that they are inland stations, unlike Cape Moreton.  In contrast the red trend line shows the adjusted data is warming more than three times faster, indicating a poor reflection of the climate of the area.

Conclusion

The Bureau has not followed its own methodology in its adjustment summary.

Documentation of adjustments is incorrect.

Three comparison stations are hundreds of kilometres away and another is subject to Urban Heat Island effect.

One comparison station (Yamba) has a record more erratic than Cape Moreton’s and should not have been used.

The adjustments have increased the difference between Cape Moreton and its neighbours, and has increased the warming trend by 30%.

Garbage in, garbage out.

Sources for annual minima data:

Acorn: http://www.bom.gov.au/climate/change/hqsites/data/temp/minT.040043.annual.txt

Raw:

Cape Moreton:

http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?p_nccObsCode=38&p_display_type=dataFile&p_startYear=&p_c=&p_stn_num=40043

Brisbane Regional Office: http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?p_nccObsCode=38&p_display_type=dataFile&p_startYear=&p_c=&p_stn_num=40214

Dalby Post Office:

http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?p_nccObsCode=38&p_display_type=dataFile&p_startYear=&p_c=&p_stn_num=41023

Miles Post Office:

http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?p_nccObsCode=38&p_display_type=dataFile&p_startYear=&p_c=&p_stn_num=42023

Yamba Pilot Station:

http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?p_nccObsCode=38&p_display_type=dataFile&p_startYear=&p_c=&p_stn_num=58012

Garbage In, Garbage Out- Horn Island

September 27, 2020

After last year’s major project of checking the compliance of 666 Australian weather stations with the guidelines set out by the Bureau of Meteorology, and finding that nearly half did not meet siting specifications, I decided to take a break from analysing BOM climate data.

It might now be time to re-enter the fray.

The pride of the BOM climate fleet, Version 2 of the Australian Climate Observation Recording Network- Surface Air Temperatures (ACORN-SAT, or Acorn 2) was launched with no fanfare at all in late 2018 and now is the basis for the Bureau’s climate claims and predictions.  I summarised many of its faults in May 2019.

Now I am going to look at some individual examples of Acorn 2 nonsense- firstly, Horn Island’s minimum temperatures.

Horn Island is our most northerly Acorn site.  It is the airport for Thursday Island and is closer to Port Moresby than any other Australian town except Weipa and of course TI.  Figure 1 shows the station’s only neighbours on Cape York Peninsula the BOM used for comparison. 

Figure 1:  Google Maps image of Cape York Peninsula showing Horn Island and its neighbours

Figure 2 shows the annual average minima for all of these weather stations.

Figure 2:  Annual minima, Horn Island and neighbours

Horn Island 27058 has a very limited data record commencing in March 1995.  To construct a longer record back as far as 1951, BOM merged Horn Island’s observations with those of Thursday Island Township 27021 and Thursday Island Meteorological Office 27022.  Figure 3 shows the result of the BOM’s merge.

Figure 3:  Horn Island and Thursday Island minima

The 27022 TI Met Office annual average Tmin has been reduced in Acorn by 0.5 or 0.6 degrees for all years before 1993.  (The Adjustment Summary claims there was an annual impact of -0.53 degrees.)  There were other adjustments at 01/01/1967 and 01/01/1958, but the 1993 adjustment has the largest effect.

Unfortunately, there are only 6 months of overlap between 27021 and 27022 (September 1992 to February 1993), and 11 months between 27021 and 27058 (March 1995 to January 1996), as figure 4 shows.

Figure 4:  Extent of overlaps in monthly minima

In September, October, and November 1992 TI Township 27021 recorded minima 1.1 to 1.4 degrees cooler than the Met Office site in “a relatively exposed location” on top of the hill 900 metres west.  However, Acorn tracks 27021 and approximately splits the difference from 27022.

Figure 5:  Extent of overlaps in monthly minima, including Acorn 2

To do this, the BOM uses two comparative stations, Lockhart River 28008 and Coen Airport 27006.  Coen is 385km from Horn Island. 

Figure 6 shows a plot of monthly anomalies from 1981 to 2010 means for these stations.

Figure 6:  Monthly minima anomalies from 1981-2010 means, all stations

Note that 27022 is close to 27021 from December 1992 to February 1993, but not before, while Coen is nothing like either.  I cannot see the justification for the adjustment.

I constructed a merge of annual data using 27021 for 1993, 1994, and 1995, joined to 27022 and 27058 with no adjustment to construct a “raw” record.  This is entirely artificial, but no more so than Acorn.  Figure 7 shows plots of both with annual trends.

Figure 7:  Horn Island minima, Acorn 2 and “raw”

Note Acorn has enormously increased the warming trend.  Figure 8 plots the differences between Acorn and my “raw” record.

Figure 8:  Horn Island minima adjustments

The earlier adjustments were also large and based on “statistical” breakpoints.

The object of adjusting temperatures using neighbours for comparison is to endeavour to produce a record that more truly reflects climate trends of the area.  The resulting record should be more like the neighbours than the original raw record.  We can test this by plotting the differences between Acorn and the raw record and the average of the neighbours.  If the comparison is good, while individual years’ differences may vary, the trend should be close to zero: the station should not be warming or cooling more than the neighbours.  Figure 9 shows the results for Horn Island minima for the period that the 1993 adjustment applied.

Figure 9:  Differences between Horn Island and two neighbours

You will note that the blue trend line, showing the trend of the difference between annual data merged with no adjustment and the average of Coen and Lockhart River, is almost flat, indicating the “raw” record already compares well with the neighbours.  In contrast the red trend line shows the adjusted data is warming faster than the neighbours, indicating a very poor reflection of the climate of the area.

Conclusion

The Horn Island record should never have been merged because of the lack of suitable overlap.

Once merged, it should never have been adjusted downwards so much.

 Lockhart River and Coen are far too distant to be suitable for comparison.

The result is nonsense.

Garbage in, garbage out.

CO2vid Watch: August

September 10, 2020

I have been wondering whether the largest real-life science experiment in history will show whether atmospheric carbon dioxide concentrations will decrease as a result of the Covid19-induced economic slowdown.

Earlier I concluded:  “I expect there may be a small decrease in the rate of CO2 concentration increase, but it won’t be much, and I will be surprised if it turns negative.  A large La Nina later this year will lead to a CO2 increase a few months later, in which case there will be a larger downturn in annual CO2 change in 2021.

However, if the major cause of CO2 increase is fossil fuel consumption, there will be an extra large decrease in CO2 change in 2020 and 2021- and a noticeable jump if the global economy rebounds.”

The CO2 concentration number for August is now published: 412.55 p.p.m. (parts per million).  The seasonal drawdown of CO2 has begun, but CO2 concentration is still 2.61 ppm above the figure for August last year.  Figure 1 shows the 12 month change in CO2 at Mauna Loa since 2015-that is, January to January, February to February, March to March.

Fig. 1:  12 month change in CO2 concentration since 2015 to August 2020- Mauna Loa

Figure 2 is a monthly update for 2020 I will show as each month’s CO2 figures become available (and 2021 if necessary):

Fig. 2:  Updated 12 month changes in CO2 concentration for 2020- Mauna Loa

Figure 3 shows the 12 month change in CO2 concentration since the record began.

Fig. 3:  12 month change in CO2 concentration since 1958 to August 2020- Mauna Loa

Annual growth has been above zero since the mid 1970s, and has not been below 1 ppm since 2011. The annual rate of change is increasing, in other words CO2 concentration growth is accelerating.

Note that so far this year, 12 month changes continue to remain firmly in the normal or even upper range, and there is no sign of any slow down. And there won’t be!

This paper by J. Reid explains why.

http://blackjay.net/?p=1021%20%3Chttp://blackjay.net/?p=1021%3E

CO2 growth appears to be an entirely natural process.

Unless something dramatic happens, I don’t think I will continue this series any longer. There’s nothing to see.

An Impossibility of Windmills

September 9, 2020

There are many strange collective nouns for groups of animals, people, and things. For example, a parliament of owls, a murder of crows, a convocation of eagles, an intrusion of cockroaches, an audience of squid are for groups from the animal kingdom.

A company of archers, an eloquence of lawyers, and a poverty of pipers describe some groups of people.

What about things? A distraction of smartphones, a smug of Priuses, a Hilary of pantsuits I have heard of.

But Jan Smelik from the Netherlands has sent me a link to his Youtube video and we now have collective noun for a group of windmills.

No, not the old windmills for pumping water and grinding grain we know from paintings and tourist brochures- the modern variety which will save the world from global warming.

Very appropriately, an impossibility of windmills.

Here’s his video:

Even more so for Australia!

CO2vid Watch: July

August 7, 2020

I have been wondering whether the largest real-life science experiment in history will show whether atmospheric carbon dioxide concentrations will decrease as a result of the Covid19-induced economic slowdown.

Earlier I concluded:  “I expect there may be a small decrease in the rate of CO2 concentration increase, but it won’t be much, and I will be surprised if it turns negative.  A large La Nina later this year will lead to a CO2 increase a few months later, in which case there will be a larger downturn in annual CO2 change in 2021.

However, if the major cause of CO2 increase is fossil fuel consumption, there will be an extra large decrease in CO2 change in 2020 and 2021- and a noticeable jump if the global economy rebounds.”

The CO2 concentration number for July is now published: 414.38 p.p.m. (parts per million).  The seasonal drawdown of CO2 has begun, but CO2 concentration is 2.61 ppm above the figure for July last year.  Figure 1 shows the 12 month change in CO2 at Mauna Loa since 2015-that is, January to January, February to February, March to March.

Fig. 1:  12 month change in CO2 concentration since 2015 to July 2020- Mauna Loa

Notice the amount of 12 month change has increased a bit more.

Figure 2 is a monthly update for 2020 I will show as each month’s CO2 figures become available (and 2021 if necessary):

Fig. 2:  Updated 12 month changes in CO2 concentration for 2020- Mauna Loa

Note that so far this year, 12 month changes continue to remain firmly in the normal or even upper range, and there is no sign of any slow down.

Watch for next month’s update, and enjoy the ride!

CO2vid Watch: June

July 13, 2020

I have been wondering whether the largest real-life science experiment in history will show whether atmospheric carbon dioxide concentrations will decrease as a result of the Covid19-induced economic slowdown.

Earlier I concluded:  “I expect there may be a small decrease in the rate of CO2 concentration increase, but it won’t be much, and I will be surprised if it turns negative.  A large La Nina later this year will lead to a CO2 increase a few months later, in which case there will be a larger downturn in annual CO2 change in 2021.

However, if the major cause of CO2 increase is fossil fuel consumption, there will be an extra large decrease in CO2 change in 2020 and 2021- and a noticeable jump if the global economy rebounds.”

(In a coming post I will update my expectations for the end of the year and next year.) 

The CO2 concentration number for June is now published: 416.39 p.p.m. (parts per million).  The seasonal drawdown of CO2 has begun, but CO2 concentration is 2.47 ppm above the figure for June last year.  Figure 1 shows the 12 month change in CO2 at Mauna Loa since 2015-that is, January to January, February to February, March to March.

Fig. 1:  12 month change in CO2 concentration since 2015 to June 2020- Mauna Loa

Notice the amount of 12 month change has increased a little.

Figure 2 is a monthly update for 2020 I will show as each month’s CO2 figures become available (and 2021 if necessary):

Fig. 2:  Updated 12 month changes in CO2 concentration for 2020- Mauna Loa

Note that so far this year, 12 month changes are in the normal or even upper range, and there is no sign of any slow down.

Watch for next month’s update, and enjoy the ride!

Hottest Day Ever in Australia Confirmed: Bourke 51.7°C, 3rd January 1909

July 11, 2020

reposted from Jennifer Marohasy

The Australian Bureau of Meteorology deleted what was long regarded as the hottest day ever recorded in Australia – Bourke’s 125°F (51.7°C) on the 3rd January 1909. This record* was deleted, falsely claiming that this was likely some sort of ‘observational error’, as no other official weather stations recorded high temperatures on that day.

However, Craig Kelly MP has visited the Australian National Archive at Chester Hill in western Sydney to view very old meteorological observation books. It has taken Mr Kelly MP some months to track down this historical evidence. Through access to the archived book for the weather station at Brewarrina, which is the nearest official weather station to Bourke, it can now be confirmed that a temperature of 50.6°C (123°F) was recorded at Brewarrina for Sunday 3rd January 1909. This totally contradicts claims from the Australian Bureau of Meteorology that only Bourke recorded an extraordinarily hot temperature on that day.

Brewarrina Meteorological Observations Book, January 1909 — photographed by Craig Kelly MP. Note 123F recorded at 9am on 4th January 1909.

Just today, Friday 10th July 2020, Mr Kelly MP obtained access to this record for Brewarrina, the closest official weather station to the official weather station at Bourke.

He has photographed the relevant page from the observations book, and it shows 123°F was recorded at 9am on the morning of Monday 4th January 1909 – published here for the first time. This was the highest temperature in the previous 24 hours and corroborates what must now be recognised as the hottest day ever recorded in Australia of 51.7°C (125°F) degrees at Bourke on the afternoon of Sunday 3rd January 1909.

The Meteorological Observations Book for Bourke for January 1909 records 125°C for 3rd January. Photograph taken on 26th June in 2014 at the Chester Hill archive by Jennifer Marohasy.

That the Bureau of Meteorology denies these record hot days is a travesty. Is it because these records contradict their belief in catastrophic human-caused global warming?

The temperature of 50.6°C (123°F) recorded back in 1909 which is more than 100 years ago, photographed by Mr Kelly today at the National Archives in Chester Hill, is almost equivalent to the current official hottest day ever for Australia of 50.7 degrees Celsius at Oodnadatta on 2nd January 1960. These are in fact only the fourth and third hottest days recorded in Australia, respectively.

Not only has Mr Kelly MP tracked-down the meteorological observations book for Brewarrina, but over the last week he has also uncovered that 51.1°C (124°F) was recorded at White Cliffs for Wednesday 11th January 1939. This is the second hottest ever!

The evidence, a photograph from the relevant page of the White Cliff’s meteorological observations book, is published here for the first time.

This photograph from the White Cliffs Meteorological Observation Book shows the second hottest temperature ever recorded in Australia using standard equipment in a Stevenson screen.

Until the efforts of Mr Kelly MP, this second hottest-ever record was hidden in undigitised archives.

It is only through the persistence of Mr Kelly to know the temperatures at all the official weather stations in the vicinity of Bourke that this and other hot days have been discovered.

If we are to be honest to our history, then the record hot day at Bourke of 51.7°C (125°F) must be re-instated, and further the very hot 50.6°C (123°F) recorded for Brewarrina on the same day must be entered into the official databases.

Also, the temperature of 51.1°C (124°F) recorded at White Cliffs on 12th January 1939 must be recognised as the second hottest ever.

For these temperatures to be denied by the Bureau because they occurred in the past, before catastrophic human-caused global warming is thought to have come into effect, is absurd.

At a time in world history when Australians are raising concerns about the Chinese communist party removing books from Libraries in Hong Kong, we should be equally concerned with the Australian Bureau of Meteorology removing temperature records from our history.

If global warming is indeed the greatest moral issue of our time, then every Australian regardless of their politics and their opinion on greenhouse gases and renewable energies, must be honest to history and these truths.

____

* This temperature (125°F/51.7°C on the 3rd January 1909) was recorded at an official Bureau weather station and using a mercury thermometer in a Stevenson screen. Hotter temperatures were recorded in 1896 but the mercury thermometers were not in Stevenson screens, which is considered the standard for housing recording equipment.

The feature image shows Craig Kelly MP at The Australian National Archive, Chester Hill, just today examining the Brewarrina Meteorological Observations book.

The following YouTube video is of me being interviewed on Sky Television by Chris Smith last December 2019.

I have previously blogged on the record hot day at Bourke being deleted by the Bureau here:
https://jennifermarohasy.com/2017/02/australias-hottest-day-record-ever-deleted/

Covid-19 and Global Warming: Two Problems, Two Responses

June 24, 2020

Skeptics have often faced the argument, “You trust medical experts, so you should trust the climate experts”.  The science, after all, is settled.

That argument is nonsense- there is no comparison between them.

Medical researchers, in the fight against Covid-19, are using the time honoured scientific method used for decades in the search for treatments, vaccines, or cures for a host of crippling and deadly diseases- cancer, diabetes,  HIV, to name a few.

This usually involves years of careful examination of patient data and all existing information and literature, forming an hypothesis to test, designing studies, writing protocols, implementing and evaluating laboratory trials, designing and conducting animal trials, designing and conducting clinical trials, analyzing results, and then reporting findings.  It is a continuous process built on past and current evidence. 

The sought-after treatment or vaccine must pass the tests of safety and efficacy.  Doctors are enjoined: First, do no harm.  As well, the treatment must be effective.  There are many examples of trials that were stopped because they were causing higher risk of harm or were showing no benefit. 

It would be too much to expect automatic success from any of the programs under way around the world to find a safe and effective Covid-19 vaccine.

The same approach is not used in climate science:-

It is assumed that the patient (the world) has an unusually high and increasing temperature, even though patient records indicate periods of higher temperature in the past.

It is assumed that this will continue and will worsen.

It is assumed that this is dangerous and must be treated.

It is assumed that we know the cause, because of an untested hypothesis that increasing concentrations of greenhouse gases in the atmosphere, caused by the burning of fossil fuels, lead to increasing temperatures.

It is assumed that “the science is settled”, (and, even more dangerously, conflicting opinions have been actively suppressed.)

Based on these assumptions, all manner of treatments have been rushed into service, with no testing and no thought for safety or efficacy.   Unwanted and dangerous side-effects have been ignored.  Enormously expensive treatments with no proven or even possible benefit have been implemented, while other treatments (e.g. nuclear energy) are beyond consideration.

Why do I trust medical experts?

When discussing a cancer diagnosis, I trusted my specialist because he showed me the evidence, welcomed a second opinion, discussed the benefits and side-effects of different treatments (and none), gave me research papers on the safety and efficacy of the recommended treatment, and gave me time to think about it.  Nearly three years later the treatment is (so far) successful.

Thank God climate experts are not involved in the search for a Covid-19 vaccine- or cancer treatment.

A Closer Look at CO2 Growth

June 11, 2020

For a while I have been looking at atmospheric carbon dioxide data from stations around the world.  This post draws together some observations, many of which are pretty much common knowledge- but some of what I’ve found is surprising.

So I’ll start by listing some of this common and not so common knowledge:-

-The often quoted figures for global CO2 levels are not at all global, but are the local readings at Mauna Loa in Hawaii.

-The long term carbon dioxide record shows continuing increase at all stations, indicating greater output than sinks can absorb. 

-Southern Hemisphere CO2 concentration is increasing but more slowly than the Northern Hemisphere.  Their trends are diverging.

-Seasonal peaks in CO2 concentration occur in late winter and spring in both hemispheres.

-There is very great inter-annual variation in the seasonal cycle of CO2, which can be even more than the average annual increase.

-This inter-annual variation occurs at the same time in both hemispheres, even though the seasonal cycles are 6 months apart.  This implies a global cause, such as the El Nino Southern Oscillation (ENSO).  Large volcanic eruptions also have an impact.  There are likely to be other factors.

-Sea surface temperature change precedes CO2 change by 12 to 24 months.  It is difficult to reconcile this with ocean out-gassing as a cause of the inter-annual CO2 changes.  It is nonsense to claim that CO2 change leads to sea surface temperature change.

-ENSO changes occur at about the same time as CO2 changes.

-CO2 concentration increases during La Ninas. 

-El Ninos precede higher sea temperatures by 4 to 6 months.

-Because of the “oscillation” part of ENSO events, strong events are followed by opposite conditions 16 to 24 months later.  In this way a strong El Nino will lead to strong ocean warming often followed by La Nina conditions and higher CO2 concentration.

-The slowing Southern Hemisphere trend and flattening curve at the South Pole lacks satisfactory explanation.

CO2 measuring stations

Geoffrey Sherrington has shown differences existing between NOAA and Scripps daily CO2 data at Mauna Loa, and that uncertainty in daily data must be much greater than the claimed 0.2 part per million.  His article confirmed my decision to use Scripps instead of NOAA data.  In this post I use Scripps monthly data from many stations across the Pacific, and data from the CSIRO station at Cape Grim in Tasmania, to compare observations from different locations.

Figure 1 shows the locations of stations in the Scripps network, and Cape Grim.

Figure 1:  Scripps stations and Cape Grim

Point Barrow is the most northerly part of the USA, and Alert is the most northerly part of Canada.

The often quoted figures for global CO2 levels are not at all global.  They are not the global average, nor are they representative of other locations.  They are in fact the local CO2 concentration from the slopes of Mauna Loa in Hawaii.  The trend in CO2 increase is similar to, but not the same as, those in other locations.

Figure 2 shows monthly CO2 concentrations from all of the Scripps stations.

Figure 2:  Monthly CO2 at all locations

It is clear that all stations show a similar rising trend, and all show seasonal variation of varying degrees.  However, few stations have long term records, and most have periods of missing data. 

Differences, similarities, and divergence

Figure 3 shows monthly differences from the Mauna Loa record of stations with fairly complete records. 

Figure 3:  Six stations’ difference from Mauna Loa

Monthly differences show huge seasonal variation, so Figure 4 shows 12 month average differences.

 Figure 4:  Six stations’ difference from Mauna Loa, 12 month averages

Clearly, there are major differences between the different records: 

-La Jolla has too many gaps for further analysis. 

-There are differences between Cape Grim and South Pole from about 1980 to the early 1990s.

-Southern Hemisphere stations (American Samoa, Cape Grim, and South Pole) are diverging from Mauna Loa, and from Barrow Point and Alert.  Figure 5 shows these trends more clearly.

Figure 5:  Barrow Point and South Pole difference from Mauna Loa, 12 month averages

While South Pole and Mauna Loa are strongly diverging, Barrow Point and Mauna Loa are becoming slightly more similar.

In Figure 6, the divergence of South Pole data is evident in monthly readings.

Figure 6:  Monthly CO2 concentrations, Mauna Loa, Barrow Point, and South Pole

Note how much larger the Barrow Point seasonal range is.  More importantly, note how South Pole data begin well within the Mauna Loa range, but 50 years later barely reach the bottom of the Mauna Loa range, as Figures 7 and 8 show.

Figure 7:  Monthly CO2 concentrations, Mauna Loa and South Pole 1965-1975

Figure 8:  Monthly CO2 concentrations, Mauna Loa and South Pole 2010 -2020

Why the divergence?  How can a well-mixed gas show a lower trend at the South Pole?  Why is it that the South Pole summer draw down has decreased and is now a plateauing?

Seasonal change

Now zooming in to look at seasonal swings in just two years, 2011 and 2012:

Figure 9:  Monthly CO2 concentrations, Mauna Loa, Barrow Point and South Pole

The Barrow Point range from low to high is nearly three times the size of the Mauna Loa range, and the South Pole range is tiny.  The peak concentrations at Barrow Point and Mauna Loa are in late spring, with a sharp drop at Barrow Point to August and a smoother curve at Mauna Loa to lows in autumn; while at the South Pole the annual curve is better described as a shallow rise in winter followed by a “peak” in spring and a long plateau over summer, with a very small decrease in late summer.  The next three plots show the timing of highs and lows at these three stations for the whole record.

Figure 10:  Timing of seasonal high and low CO2 concentrations, Mauna Loa

Annual lows are in September or October, and highs are almost always in May.

Figure 11:  Timing of seasonal high and low CO2 concentrations, Barrow Point

Lows are always in August, while highs are spread across late winter to late spring, with a plateau from February to May (and extending twice into June).

Figure 12:  Timing of seasonal high and low CO2 concentrations, South Pole

At the South Pole, seasonal highs are reached in spring or early summer, with lows in late summer and early autumn, with one instance in June.

Inter-annual changes

While the seasonal cycles appear to be regular, the timing and size of seasonal changes can vary considerably from year to year.

The next plots show detrended data since 1985 for several locations (few have good data before 1985).  Detrending allows us to compare inter-annual variation more easily.  We do this for each record by subtracting the trend.

Figure 13:  Detrended monthly CO2, Mauna Loa

Figure 14:  Detrended monthly CO2, Barrow Point and Alert

Figure 15:  Detrended monthly CO2, South Pole and Cape Grim

While the seasonal range is different for each location, there is remarkable similarity in timing of changes, for example the late 1980s- early 1990s and 2009-2013.  Note how close Cape Grim and South Pole are, although Cape Grim is at 40.68 degrees South, 49 degrees north of the South Pole.  The South Pole data appear to be representative of a large part of the Southern Ocean.

Because the detrended data retain enormous seasonal variations, it is necessary to show the detrended data (this time from 1979) with monthly means subtracted, for Barrow Point in the far north, Mauna Loa in the middle, and South Pole at the extreme south.  Here are the seasonal signals:

Figure 16: Seasonal signals of monthly CO2 data

As an example, Figure 17 compares detrended data from Barrow Point with monthly means:

Figure 17:  Detrended monthly CO2 with monthly means, Barrow Point

Subtracting the monthly means shows the residual variation in carbon dioxide for Barrow Point:

Figure 18:  Detrended monthly CO2 with seasonal signal removed, Barrow Point

Figure 19 combines the three stations:

All three records follow the same pattern, with a large increase from 1979 to the late 1980s, followed by decrease in the 1990s.  There appears to be another steep increase from 2012 to the present.  Notice that Mauna Loa and South Pole values can be from 1 ppm below to 2 ppm above the trend, while at Barrow Point the range can be from 4ppm below to 5 ppm above the trend, which is about 2.5 ppm per year. 

However, there is still a large amount of variation in the monthly figures.  A centred 13 month rolling mean makes comparison much easier.

Figure 20:  Centred 13 month mean of detrended monthly CO2 with seasonal signal removed

The similar pattern followed by stations from north to south, from the Arctic Ocean, across the Pacific, to the Antarctic, far from any industrial or cropping contamination, is immediately obvious.  The Barrow Point record appears to lag behind Mauna Loa and South Pole data by from one to five months.  South Pole can be a few months ahead to a few months behind Mauna Loa, even though South Pole absolute monthly concentration peaks are from four to seven months later.

Ocean temperature effects

In Figure 14 of my post on 2nd May, Will Covid-19 Affect Carbon Dioxide Levels? I showed that CO2 change lags one year behind sea surface temperatures (SSTs).  The next plot shows the centred 13 month mean of HadSST4 data, scaled up to compare with CO2 data.

Figure 21:  Scaled, centred 13 month mean of detrended monthly HadSST4 and CO2 data with seasonal signal removed

Now the same data with SSTs lagged 12 months…

Figure 22:  Scaled, centred 13 month mean of detrended monthly HadSST4 and CO2 data with seasonal signal removed, HadSST4 lagged 12 months

Large change in CO2 concentrations appears closely linked with sea surface temperature a year before- (or even two years, as between 2002 and 2010).  Sea surface temperatures have a global effect.

ENSO effects

Another cause of CO2 variation is the El Nino- Southern Oscillation (ENSO) which appears in the swings between El Nino and La Nina conditions.  ENSO has a great effect on weather conditions globally, affecting winds, clouds, rainfall and temperature.  Figure 18 shows how CO2 levels respond to the Southern Oscillation Index (SOI), which is a good indicator of ENSO conditions.

Figure 23:  Centred 13 month means, scaled SOI and detrended CO2 levels

CO2 increases in La Ninas.  The pattern becomes more intriguing when we plot inverted SOI levels with sea surface temperatures, as in Figure 19.

Figure 24:  Scaled, centred 13 month mean of detrended monthly HadSST4 with seasonal signal removed and scaled inverted SOI

Inverted SOI data indicate SST data 4 to 6 months later.  (The early 1980s and early 1990s don’t match because of the huge volcanic eruptions of El Chichon and Pinatubo.)  In other words, an El Nino will raise ocean temperatures, and a La Nina will lower ocean temperatures, 6 months later.  Because of the oscillating nature of ENSO, El Ninos and La Ninas approximately reflect each other 16 to 24 months later, as Figure 20 shows.  (Again, El Chichon and Pinatubo have a large impact.)

Figure 25:  Scaled SOI, normal and inverted

That pattern recurs, with varying lag times, throughout the whole 144 year SOI history.

Which is why SSTs will probably increase to about February of 2021…

Figure 26:  Scaled SOI, normal and inverted, and detrended HadSST4

…and with them, CO2 concentration.

Figure 27:  Scaled SOI, normal and inverted, and detrended HadSST4, with South Pole CO2 data

This image has an empty alt attribute; its file name is soi-inv-sst-co2-1.jpg

Discussion

The long term carbon dioxide record shows continuing increase at all stations, indicating greater output than sinks can absorb. 

CO2 concentrations and trends, while similar, have discernible differences at different locations, notably between the hemispheres.

CO2 concentrations at Southern Hemisphere stations are increasing, but more slowly than those in the Northern Hemisphere, such that their trends are diverging.

On the long term CO2 rise are seasonal rises and falls, most likely due to seasonal vegetation, crop, and phytoplankton growth and decay. 

Peaks in CO2 concentration occur after winter and spring in both hemispheres- February to May at Barrow Point, April and May at Mauna Loa, and September-December at the South Pole.  This is not due however to a six month delay in CO2 mixing from sources in the Northern Hemisphere to the Southern, otherwise the South Pole trend would be the same.  It is lower, and becoming more so. 

There is great variety in seasonal range of CO2 at different locations, with greatest variation in the Arctic and the least in the Southern Hemisphere.

The amount and timing of these seasonal rises and falls varies from year to year.  These inter-year changes in CO2 concentrations can be as much as or greater than the normal annual increase.

Even though the South Pole station is far from the Southern Ocean, especially in winter when sea ice extends further, and even further from any vegetated land areas, its data appear representative of a great part of the Southern Hemisphere.

Small inter-annual changes in sea surface temperatures have a large impact on these changes in CO2 concentrations at South Pole and Mauna Loa about 12 to 24 months later.  There can be a further delay of up to five months in the effect at Point Barrow. 

This is not controversial.  According to the CSIRO, these variations “have been shown to correlate significantly with the regular El Niño-Southern Oscillation (ENSO) phenomenon and with major volcanic eruptions. These variations in carbon dioxide are small compared to the regular annual cycle, but can make a difference to the observed year-by-year increase in carbon dioxide.”

While sea surface temperature rise precedes CO2 concentration increase, there is no evidence at all of CO2 concentration change preceding sea surface temperature change.

With an apparent approximate 12 – 24 month delay between ocean temperature change and inter-annual CO2 change, changes in ocean out-gassing and absorption rates appears to be an unlikely mechanism.  Changes in land vegetation, forests, crops, and oceanic phytoplankton, moderated by the changing circulation, rainfall, cloud, and temperature patterns of ENSO events, appears to be a more likely mechanism, with the much smaller land area of the Southern Hemisphere accounting for the much smaller changes. 

The unresolved problem

This does not however explain the decreasing amount of summer draw down at the South Pole, and the divergence from Northern Hemisphere data.   Perhaps Southern Ocean phytoplankton are not decreasing as much during winter, so the CO2 sink is slightly increasing, slowing the CO2 growth trend a little and smoothing the CO2 growth curve.  Who knows?  I have yet to see a satisfactory- or any- explanation.

CO2vid Watch: May

June 8, 2020

I have been wondering whether the largest real-life science experiment in history will show whether atmospheric carbon dioxide concentrations will decrease as a result of the Covid19-induced economic slowdown.

Earlier I concluded:  “I expect there may be a small decrease in the rate of CO2 concentration increase, but it won’t be much, and I will be surprised if it turns negative.  A large La Nina later this year will lead to a CO2 increase a few months later, in which case there will be a larger downturn in annual CO2 change in 2021.

However, if the major cause of CO2 increase is fossil fuel consumption, there will be an extra large decrease in CO2 change in 2020 and 2021- and a noticeable jump if the global economy rebounds.”

(In a coming post I will update my expectations for the end of the year and next year.) 

The CO2 concentration number for May is now published: 417.07 p.p.m. (parts per million).  That’s an increase of 0.86 ppm over the April figure, and 2.41 ppm above the figure for May last year.  Figure 1 shows the 12 month change in CO2 at Mauna Loa since 2015-that is, January to January, February to February, March to March.

Fig. 1:  12 month change in CO2 concentration since 2015 to May 2020- Mauna Loa

Notice the amount of 12 month change has decreased a little.

Figure 2 is a monthly update for 2020 I will show as each month’s CO2 figures become available (and 2021 if necessary):

Fig. 2:  Updated 12 month changes in CO2 concentration for 2020- Mauna Loa

Note that so far this year, 12 month changes are in the normal or even upper range, and there is no sign of any slow down.

Watch for next month’s update, and enjoy the ride!

Mysterious Jump in Ocean Temperatures

May 31, 2020

Back in 2018 Jo Nova publicised Dr John McLean’s exposé of the many ridiculous flaws in HadCruT4, the global temperature dataset, which included until a year ago the oceanic component, HadSST3. That was bad enough, with some data from positions 100km inland from the nearest sea. But in June 2019 the long awaited HadSST4 was released, in which many corrections were made to reduce “problems” in the sea surface temperature record.


Corrections indeed.


Figure 1 is a comparison of HadSST4 with HadSST3.

Figure 1: HadSST3 and HadSST4 since 1850

And figure 2 shows the extent of the “corrections”.

Figure 2: Adjustments: HadSST4 minus HadSST3

You will no doubt note how the “corrections” have made the past cooler, as is standard practice for all those curating temperature records. Indeed, apart from a small foray in the 1940s, the whole 100 years from 1875 to about 1975 has been made ever so slightly- up to a tenth of a degree- cooler.


But in an interesting move, all temperatures since then have been corrected, and, would you believe, upwards. Who would have thought that the average sea surface temperature measured just a couple of years ago in September 2017 was 0.1875 degrees too cool, and needed revising upwards?

Figure 3: HadSST3 and HadSST4 since 2010

Modern thermometers just aren’t what they used to be.

CO2vid Watch: April

May 7, 2020

In my last post I wondered whether the largest real-life science experiment in history will show whether atmospheric carbon dioxide concentrations will decrease as a result of the Covid19-induced economic slowdown.

I concluded:  I expect there may be a small decrease in the rate of CO2 concentration increase, but it won’t be much, and I will be surprised if it turns negative.  A large La Nina later this year will lead to a CO2 increase a few months later, in which case there will be a larger downturn in annual CO2 change in 2021.

However, if the major cause of CO2 increase is fossil fuel consumption, there will be an extra large decrease in CO2 change in 2020 and 2021- and a noticeable jump if the global economy rebounds.”

 Figure 1 shows the 12 month change in CO2 at Mauna Loa since 2015-that is, January to January, February to February, March to March (as in Figure 6 of my previous post):

Fig. 1:  12 month change in CO2 concentration since 2015- Mauna Loa

The CO2 concentration number for April is now published: 416.21 p.p.m. (parts per million).  That’s an increase of 1.71 ppm over the March figure, and 2.89 ppm above the figure for April last year.  Figure 2 is the April update on Figure 1.

Fig. 2:  Updated 12 month change in CO2 concentration since 2015- Mauna Loa

Notice the amount of 12 month change has increased, despite at least two months of downturn in China and at least a month in most other countries.

Figure 3 is a monthly update for 2020 I will show as each month’s CO2 figures become available (and 2021 if necessary):

Fig. 3:  Updated 12 month changes in CO2 concentration for 2020- Mauna Loa

Figure 4 shows the range of 12 month changes for each decade since the record began in 1958:

Fig. 4:  Updated 12 month changes in CO2 concentration all decades- Mauna Loa

Figure 5 shows the same, but just since 2000:

Fig. 5:  Updated 12 month changes in CO2 concentration since 2000- Mauna Loa

Note that so far this year, 12 month changes are in the upper range, and there is no sign of any slow down.

Watch for next month’s update, and enjoy the ride!

Will Covid-19 Affect Carbon Dioxide Levels?

May 2, 2020

The Coronavirus pandemic has already caused a huge downturn in many industries world-wide- especially tourism, manufacturing, and transport.  Prices of oil and thermal coal have fallen dramatically.  The first impact was on China, as this plot from the World Economic Forum shows:

Fig. 1:  Industrial production in China

Industrial production has fallen by 13.5% in January and February, and exports have dropped by 17%.  While China may be recovering from the virus, the rest of the world is not and knock-on effects from low Chinese production of essential inputs will hold back recovery in other countries.

So the question is: if atmospheric concentrations of carbon dioxide and other greenhouse gases are largely a product of fossil fuel emissions, and if fossil fuel emissions decrease, will we see a reduction in the rate of increase of CO2, and if so, how much?

This is the biggest real life experiment we are ever (I hope) likely to see.

Background:

The concentration of CO2 in the atmosphere is increasing, as in Figure 2.

Fig. 2:  CO2 measurements at Mauna Loa

Cape Grim in Tasmania samples the atmosphere above the Southern Ocean and shows a similar trend, with much smaller seasonal fluctuations:

Fig. 3:  CO2 measurements at Cape Grim

But what we are vitally interested in, is how much we may expect CO2 concentration to change.  We can show change, and remove the seasonal signal, by plotting the 12 month differences, i.e., March 2020 minus March 2019.  Thus we can see how much real variation there is even without an economic downturn.  And it is huge.

Fig.4:  12 month change in CO2 concentration- Mauna Loa

Fig. 5:  12 month change in CO2 concentration- Cape Grim

Not very much smaller at Cape Grim.

However, the Mauna Loa record is the one commonly referred to.  Figure 6 shows the 12 month changes since 2015.

Fig.6:  12 month change in CO2 concentration since 2015- Mauna Loa

We will keenly watch the values for the remaining months of 2020, and then 2021.

My expectation?

I will be very surprised if there is much visible difference from previous years at all, as the following plots show.  Figure 7 shows the time series of annual global CO2 emissions and scaled up atmospheric concentration from 1965 to 2018 (the most recent data from the World Bank):

Fig. 7:  Carbon Dioxide Emissions and Concentration to 2018

Fig. 8:  Carbon Dioxide Emissions as a Function of Energy Consumption to 2018

There is a very close match between emissions and energy consumption of all types- including nuclear, hydro, and renewables.

Fig. 9:  CO2 Concentration as a Function of Carbon Dioxide Emissions to 2018

Again, it is close, they are both increasing, but with some interesting little hiccups….

So what is the relationship between change in atmospheric concentration and change in emissions?

Fig. 10:  Percentage Change in CO2 Concentration as a Function of Percentage Change in Carbon Dioxide Emissions to 2018

Not very good correlation: 0.01.

Fig. 11:  Percentage Change in Energy Use, GDP, and Carbon Dioxide Emissions to 2018

GDP fluctuates much more than energy or emissions, which are very close, and if anything tends to follow them.

Figure 12 is a time series of annual percentage change in energy and emissions and absolute change in CO2 concentration.

Fig. 12:  Percentage Change in Energy Use and Carbon Dioxide Emissions and Absolute CO2 Change to 2018

You will note that during the three occasions (1974, 1980-82, and 2008-09) when global emissions growth went negative (as much as minus two percent), CO2 concentration barely moved, and still remained positive, and on two occasions when CO2 concentration increased by 3 ppm or more (1998 and 2016), emissions increase was much reduced. 

Ah-ha, but that’s because the volume of the atmosphere is so huge compared with the amount of greenhouse gases being pumped out- according to the Global Warming Enthusiasts.

In Figure 10 I showed that there was little relationship between annual change in CO2 emissions and atmospheric concentration.  Figure 13 shows what appears to have a much greater influence on CO2 concentrations: ocean surface temperature. 

Fig. 13:  Annual Change in CO2 Concentration as a Function of Change in Sea Surface Temperature (lagged 1 year)

Remember the correlation of CO2 with emissions in Figure 10 was 0.01.  The correlation between CO2 and lagged SSTs is 0.59.  That’s a pretty devastating comparison.

Figure 14 shows how in most years SST change precedes CO2 change throughout the entire CO2 record.

Fig. 14:  Annual Change in CO2 Concentration and Sea Surface Temperatures

There is little evidence for CO2 increase causing SST increase, while there is evidence that SST change (or something closely associated with it) leads to CO2 change.   The largest changes coincide with large ENSO events.

Conclusion:

Therefore, I expect there may be a small decrease in the rate of CO2 concentration increase, but it won’t be much, and I will be surprised if it turns negative.  A large La Nina later this year will lead to a CO2 increase a few months later, in which case there will be a larger downturn in annual CO2 change in 2021.

However, if the major cause of CO2 increase is fossil fuel consumption, there will be an extra large decrease in CO2 change in 2020 and 2021- and a noticeable jump if the global economy rebounds.

As I said, a very large real life experiment. So watch this space!

Trade Winds and Australian Sea levels

May 1, 2022

In my post Is Climate Change Threatening the Solomon Islands? I showed that sea levels at Honiara are predominantly caused by variations and strengthening of the south-east trade winds blowing across the Pacific.

Trade wind strength is also an indicator of sea levels all around Australia- as far south as Tasmania.

I use scaled trade wind index data from NOAA, and mean sea level data from the BOM’s Australian Baseline Sea Level Monitoring Project.  Sea level is in metres and all data are monthly anomalies.

Here’s a map showing the location of the ABSLMP stations.

Figure 1:  Sea level stations

I did not use those stations with large gaps (e.g. Thevenard) or very short records (Thursday Island).

Figure 2 shows sea level and the trade wind index (scaled down by a factor of 60).

Figure 2:  Trade Winds and East Coast Sea Levels

Sea levels appear to loosely match trade winds (a symptom of the El Nino- Southern Oscillation-ENSO).  Sea levels are averaged in Figure 3.

Figure 3:  Trade Winds and Averaged East Coast Sea Levels

Across the north of Australia, the match is close and strong.

Figure 4:  Trade Winds and North Australian Sea Levels

Figure 5 shows the average of the tide gauges, including Cocos Island, far out in the Indian Ocean.

Figure 5:  Trade Winds and Average North Australian Sea Levels

Figure 6:  Trade Winds and Average North Australian Sea Levels Excluding Cocos Island

The surprise is that the same effect is seen across southern Australian ports, with the TWI scaled down by 30.

Figure 7:  Trade Winds and Southern Australian Sea Levels

Figure 8:  Trade Winds and Average Southern Australian Sea Levels

When the trades are weak, sea level is lower, and vice versa, with a delay of one or two months.  The trade winds have become stronger over the last 40 years, and sea levels have increased.

Across southern Australia the intensity of high pressure systems has also increased:

Figure 9:  Strength of southern high pressure systems

The strength of high pressure systems in the sub-tropical ridge has increased.  On the southern side blow the Roaring Forties, and on the northern side the South-East Trades.  Stronger winds in the Pacific roughly match stronger winds in the Southern Ocean, pushing the sea up against the coastlines in the north and south.

It could be that stronger circulation is a symptom of global warming (which you may remember I don’t doubt, just the amount and cause).   However water finds its own level.   Sea level rise at Australian ports and some Pacific islands that has been caused by wind-driven water movement has to be matched by sea level fall across broad areas elsewhere.  That’s why coastal tide gauges are not good at measuring global sea level.

There’s more to sea level than you might think.