Posts Tagged ‘climate’

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!

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!

The Wacky World of Weather Stations: No. 60- Bombala AWS (NSW)

August 29, 2019

Time for some comic relief.  Only in Australia!

Please refer back to my first post for site specifications.

Station:  Bombala AWS 70328

Opened: 1988

Daily Temperature data from: 1990

Data used to adjust Acorn sites at: —

Location: Co-ordinates  -37.0016 149.2336

BombalaAWS map

About 370km south of Sydney, and about 6km from Bombala.

2019 Google satellite image:

BombalaAWS aerial

Looks perfect?  Looks can be deceiving.

BOM site plan 2014:

BombalaAWS plan

Far from having vegetation trimmed to a few centimetres, the screen is surrounded by grass up to half a metre high.  The tipping bucket rain gauge and screen are often both mounted on a metal arm.  Cattle rubbing on the arm and/or screen will cause vibration at the least, not to mention heat from bovine body temperature and possible fresh manure and urine.

So we have a problem.  There are many stations not in an enclosure, and in many of them livestock graze nearby.  This is the first I have seen where the annual inspection reveals cattle rubbing on the apparatus.  That was in 2014.  I wonder if anything has been done about it?  As they have evidently recognised the Buffalo Fly problem, and a fence is out of the question, perhaps the Bureau has added an insecticidal back rubber to the bar?  Or recommended introduction of Brahman bloodlines?

Shh!  The Bureau would not want word getting out, people might laugh at them.

This station is not compliant, with temperatures reported at Latest Weather Observations but not used to adjust data at Acorn sites.

FAIL

The Wacky World of Weather Stations: No. 7- Adelaide (SA)

August 2, 2019

Please refer back to my first post for site specifications.

Station:  Adelaide 23090

Opened: 1977

Daily Temperature data from: 1977

Data used to adjust Acorn sites at:  Port Lincoln, Ceduna, Nuriootpa

Location: Co-ordinates  -34.9211  138.6216

Adelaide map

2019 satellite image:

Adelaide aerial

The screen is the white dot in the red ellipse.

2018 BOM Site Map:

Adelaide plan

The screen is on a flat lawn between two bitumen streets and above a bitumen carpark.

Street view from College Rd:

Adelaide collegeroad

Street view from Little Young Street:

Adelaide LittleYoungSt

This site is recognised by the BOM as being subject to Urban Heat Island (UHI) effect.  It is non-compliant, so temperatures recorded here are not reliable.  Adelaide’s temperatures are published at the BOM Latest Weather Observations page, and also used by the BOM in weather reports, potentially including extremes of heat or cold. Adelaide’s UHI does not prevent it from being one of the sites used to adjust temperatures at ACORN-SAT sites at Port Lincoln, Nuriootpa, and Ceduna. Acorn sites are used for climate analysis- whether winters are getting warmer and summers hotter for example.  So the lack of quality at any site DOES MATTER!

Another FAIL.

The Renewable Energy Transition

July 11, 2019

The Australian Greens’ number one aim in their Climate Change and Energy Policy is:

“Net zero or net negative Australian greenhouse gas emissions by no later than 2040.”

And the Lowy Institute believes that Australia can set an example for the rest of the world.  In their article ‘An Australian model for the renewable-energy transition’ published on 11 March 2019, they assert that across the world “A very rapid transition to renewables is in process” and that “Most countries can follow the Australian path and transition rapidly to renewables with consequent large avoidance of future greenhouse emissions.”

Time for a reality check.

In this assessment I use energy consumption and carbon dioxide emissions data from the 2019 BP Statistical Review of World Energy.

First of all, greenhouse gas emissions.  In the BP Review,

…carbon emissions … reflect only those through consumption of oil, gas and coal for combustion related activities, and are based on ‘Default CO2 Emissions Factors for Combustion’ listed by the IPCC in its Guidelines for National Greenhouse Gas Inventories (2006).  This does not allow for any carbon that is sequestered, for other sources of carbon emissions, or for emissions of other greenhouse gases. Our data is therefore not comparable to official national emissions data.

Excluded sources would include for example cement production and land clearing.  However, given that we are focussing on the transition away from fossil fuels towards renewables, that is not a problem.

Figure 1 shows the growth in carbon dioxide emissions (from fossil fuels) since 1965.

Fig. 1: Global CO2 emissions in millions of Tonnes

CO2 emissions global

The big hitters are China, the USA, and India, who together account for more than half of the world total.

Fig. 2: CO2 emissions by the Big Three and the rest

CO2 emissions top3 rest

Note that America’s emissions peaked in 2007 and have since declined.  China’s emissions rose rapidly from 2002 to 2013.  From a low base, India’s emissions growth rate is practically exponential.

Figure 3 shows how Australia “compares”.

Fig. 3: CO2 emissions by the Big Three and Australia

CO2 emissions top3 Oz

Australia’s emissions from fossil fuels peaked in 2008.

The BP Review’s CO2 emissions data are based on fossil fuel combustion, so I now look at energy consumption since 1965.  Energy units are million tonnes of oil equivalent (MTOE), from the BP Review, “Converted on the basis of thermal equivalence assuming 38% conversion efficiency in a modern thermal power station.”

Fig. 4: Global energy consumption by fuel type in millions of tonnes of oil equivalent

World energy cons 65 to 18

(Note:

Apart from 2009 (the GFC) gas has risen steadily, especially the last five years.

Since the oil shocks of the seventies and early eighties and apart from the GFC, oil has mostly enjoyed a steady rise.

Coal consumption increased rapidly from 2002 to 2013 (mostly due to Chinese expansion) followed by a small decrease to 2016.

Hydro power has seen a steady increase.

Nuclear power peaked in 2006 and declined slightly before increasing over the last six years.

Wind and Solar are in the bottom right hand corner.  Both are increasing rapidly but are dwarfed by other forms of energy.)

How close are we to the renewable energy transition?  Figures 5 to 9 show 1965 – 2018 energy consumption for conventional sources (fossil fuels plus hydro and nuclear) and the total.  The gap between conventional and total energy use is filled by renewables OF ALL TYPES- solar, wind, geothermal, bio-waste (e.g. sugar cane bagasse), and bio-mass used for electricity production, (but excluding firewood, charcoal, and dung).  I have highlighted the gaps with a little green arrow.

Fig. 5: Total and conventional energy consumption in millions of tonnes of oil equivalent

World energy cons 65 to 18 fossil hydro nuclear

In 2018, renewables of all types accounted for just 4.05% of the world’s energy, fossil fuels 83.7%.  So much for rapid transition to renewables.

The next three plots show energy consumption of the big emitters.

Fig. 6: Total and conventional energy consumption- China

CO2 emissions China

4.38% of Chinese energy came from renewables in 2018.  Nuclear and hydro power have increased enormously over the past 15 years and make up 10.35% of usage but fossil fuels (mostly coal) make up 85.3% of energy consumption.

Fig. 7: Total and conventional energy consumption- USA

CO2 emissions USA

Renewables accounted for 4.51% of US energy.  Fossil fuel and total energy consumption peaked in 2007 but has recently started increasing mostly due to gas and oil use.   (Coal has slipped from more than a quarter of the fossil fuel total in 2007 to less than a sixth in 2018.)  Fossil fuels make up 84.3% of energy use.

Fig. 8: Total and conventional energy consumption- India

CO2 emissions India

Only 3.4% of India’s energy comes from renewables.  India’s energy consumption is growing very rapidly, and 91.6% of consumption is from fossil fuels.

What of Australia, supposedly setting an example for the rest of the world to follow?

Fig. 9: Total and conventional energy consumption- Australia

CO2 emissions Australia

After years of building solar and wind farms, and at enormous expense, renewable energy of all types accounts for just 5% of Australia’s energy use- and the Greens aim to have zero net emissions in 21 years from now.

In the past 10 years, renewable consumption has increased by 5.5 million tonnes of oil equivalent- but fossil fuels have increased by 6.4 million tonnes.  While coal use has dropped by 12 million tonnes, this has been more than replaced by 18.4 million tonnes of oil and gas.  That’s not much of a rapid transition.

Figure 10 shows in order renewables consumption in all countries.  Remember, this includes all types including geothermal energy and bio-mass.

Fig. 10: Comparative penetration of renewables

Renewable cons %

Australia at 5 % renewable consumption is 19th and ahead of the big emitters, the USA, China, and India.

Perhaps the Extinction Rebellion activists who are unhappy with lack of action against climate change in Germany, the UK, and Australia, could glue themselves to the roadways in China, India, or Russia.

There is no rapid renewable energy transition.   Oil, coal, and gas are cheap and readily available and are powering growth in developing economies.  At some time in the future there will not be enough accessible fossil fuel to sustain the world’s economies alone; uranium too will one day be in short supply.  However, necessity and technological innovation, not legislation, will drive the adoption of alternative fuels.

Rumours of the imminent death of fossil fuels appear to be greatly exaggerated (with apologies to Mark Twain).

More Footprint Comparisons

June 18, 2019

In my previous post I showed different ways of comparing carbon dioxide emissions.

Here are some more, unashamedly with an Australian focus, in different formats.

As in my last post I use data from the Global Carbon Atlas for fossil fuel emissions for 2017 (the most recent data available), and Gross Domestic Product (GDP) data from the World Bank, also for 2017. GDP for each nation is calculated in current US dollars.

Percentages

Figure 1 shows cumulative percentages of 2017 fossil fuel emissions for all 202 countries with available data.

Fig. 1:  Cumulative CO2 emissions 2017 expressed as percentages

Globalco2 cum %

China, the USA, and India are the big hitters.  China produces 28.5% on its own.  Australia, in 16th place, produces 1.2% of global emissions, a bit behind Canada at 1.66%, and just ahead of the UK at 1.12%.  France and Italy are just over 1% each.  The remaining 183 countries each produce less than 1% – many much less.

Earth Hours

Earth Hour, where some people show how virtuous they are by switching off their lights for an hour in order to reduce emissions, might provide another way of comparing emissions.  I next compare emissions by units of “Earth Hours”.  One Australian Earth Hour is the amount of CO2 emissions reduced when:

Across Australia, all lights powered by fossil fuels; all stoves, fridges, air conditioners, and other appliances; all battery chargers; all street lights, traffic lights, and emergency lighting; all hospitals, schools, shopping centres, and telecommunications including computers; all mining operations; all transport- cars, trucks, trains, and aircraft; all farming operations; all water pumping; all manufacturing industry small and large, including steel and aluminium; all building and construction:  are shut down for one hour.

That is one Australian Earth Hour.

One Chinese Earth Hour is equal to 23.82 Australian Earth Hour units- Australia could run for 23 hours and 48 minutes on the equivalent amount of emissions. The value for America in Australian Earth Hours is 12 hours and 45 minutes; India, 6 hours; Russia, 4 hours; Japan, 2 hours 54 minutes. The value for the UK is 55 minutes and 53 seconds worth of Australian emissions output.

At the other end of the scale, El Salvador’s hourly emissions would last Australia for one minute.  Tuvalu’s total emissions are the equivalent of one tenth of one second of Australia’s emissions.

Efficiency

Here’s another idea.  Australia is the world’s 13th largest economy, and achieves this with emissions per dollar of GDP that put us in 105th place.  For all nations the average CO2 emissions per US dollar of GDP is 485 grams per dollar.  What if all countries were as efficient as Australia?  That is, they all had the same amount of emissions as Australia: 312 grams of CO2 per dollar of GDP.

Figure 2 shows what global emissions would look like if all nations were as efficient as Australia.

Fig. 2:  Global fossil fuel emissions currently and at Australia’s rate per dollar GDP

Global Oz efficiency

Or, to put it another way, Figure 3 shows the effect on the global economy for the same level of emissions.

Fig. 3:  Global GDP currently and at Australia’s emissions rate per dollar GDP

Global GDP Oz efficiency

That’s a potential increase of 37.7%.

Conclusion

Australia is punching above its weight in regard to efficiency of fossil fuel emissions per dollar of GDP.  Our carbon footprint is tiny compared with the big three- China, the USA, and India.  While there is always room for us to improve, if every country behaved as well as we do, the world would be a better place.

ACORN-SAT 2.0: Nation-wide Summary

May 20, 2019

This is the eighth in a series of posts in which I directly compare the most recent version of Australia’s temperature record, ACORN-SAT 2, with that of the previous version, ACORN-SAT 1.  Results for the whole network are summarised below.

Introduction:

The Bureau of Meteorology has released its latest revision of the Australian temperature record back to 1910.  Previous versions of our historic temperatures included “High Quality”, which I revealed in 2010 to have major flaws, not least being the strong warming bias; and ACORN-SAT 1, released in March 2012, proudly touted as being “World’s Best Practice”, which I (along with others) found to have very many severe problems.  (If you like, check these posts, hereherehere, and here.  There are many others.)

Stung by the public and media criticism which this generated, the Bureau set up a supposedly independent Technical Advisory Forum, which met on one day per year for three years and basically rubber-stamped Acorn.  They did, however, make some recommendations, particularly about transparency.  In the light of this recommendation, this latest release without any publicity at all is perplexing.

Nearly all of Australia’s climate analysis and modelling is based on the previous version, Acorn 1, including monthly, seasonal, and annual means, extremes, and trends.  Sometime in the near future, this will be based on Acorn 2 data.

As this an upgrade to an existing dataset, we might expect there would be a few small tweaks of maybe a few tenths of a degree in some records and any changes to temperature trends would be fairly small.  Perhaps there might be some extra stations in remote areas to improve the density of the sparse network, perhaps some records starting earlier because of newly digitized data, hopefully a sensible fix for the dreadful situation of many daily minimum temperatures being higher than the maximum.

Not so.

No wonder the Bureau has released Acorn 2 so quietly- it is a confusing mess, and completely alters Acorn 1.  Trends are vastly different, some temperatures altered by more than 10 degrees Celsius, and new records established.

The basis for the new version is in the Research Report.  The Bureau has published a new station catalogue with more detailed information, the adjustment summary for each station, plus lists of comparative stations for adjustments and all comparison stations for each site, with explanations of adjustment terminology.  Well worth a look.

It is important to highlight this paragraph on the new ACORN-SAT home page:

The purpose of updating datasets like ACORN-SAT is principally to incorporate data that has been recorded since the last analysis was released, as well as historical paper records that have been recently digitised. ACORN-SAT version 2 also incorporates the findings and recommendations of the Technical Advisory Forum, applies the latest scientific research and understanding and, where applicable, introduces new methodologies. The overall aim of the update to ACORN-SAT is to provide improved estimates of historical changes in climate.

As well, in the ACORN-SAT FAQs, the Bureau says:

“… The important question is not which one (version) represents the absolute truth, but whether those estimates produce wildly different results, and whether the range of estimates provides a reasonable guide to what has actually occurred.”

Therefore, the Bureau has set their own criterion for whether Acorn 1 and Acorn 2 are at all useful and valuable.  To repeat:

“whether those estimates produce wildly different results, and whether the range of estimates provides a reasonable guide to what has actually occurred.”

Daily data were directly downloaded from the Bureau of Meteorology for maxima and minima for each of the 112 stations.

The Context

Figure 1:  Australian ACORN-SAT stations

Oz map all

There are 112 Acorn stations in the BOM database.  Differences between Acorn 1 and Acorn 2 are summarized in the following sections.

Largest temperature differences between Version 1 and Version 2

All temperatures are shown in degrees Celsius.

The five stations with the largest increases to daily maxima were:

Orbost (Victoria) 14.60
Wandering (W.A.) 10.90
Alice Springs  (N.T.) 10.10
Port Lincoln  (S.A.) 9.70
Scone  (N.S.W.) 8.30

The five stations with the largest decreases in daily maxima were:

Wandering  (W.A) -10.90
Cabramurra  (N.S.W.) -9.60
Esperance  (W.A.) -9.40
Alice Springs  (N.T.) -9.20
Wyalong  (N.S.W.) -8.60

Gunnedah (NSW) was the only station that had no changes made to the Version 1 values for maxima.

The five stations with the largest increases to daily minima were:

Merredin  (W.A.) 14.40
Butlers Gorge  (Tas.) 11.30
Alice Springs  (N.T.) 11.00
Scone  (N.S.W.) 9.60
Snowtown   (S.A.) 9.10

Horn Island (Qld) was the only station with no increases to daily minima.

The five stations with the largest decreases in daily minima were:

Wagga Wagga (N.S.W.) -13.40
Merredin  (W.A.) -12.60
Alice Springs  (N.T.) -11.50
Esperance  (W.A.) -10.80
Butlers Gorge  (Tas.) -9.70

Adjustments made to daily data were mostly of the order of +/- 1 or 2 ℃.  However the figures in the above tables show how enormous some adjustments were at many stations- including a range of 27 ℃ in adjustments to minima at Merredin!  That must surely rank as “wildly different results”.

New temperature extremes:

New records were set.

In Acorn 2 40 stations had increased record high maxima, 35 had their record highs decreased, and the remaining 37 were unchanged.  In minima, there were 36 stations whose lowest temperatures were increased, and 66 had new record lows. 10 were unchanged.

The old Australian record for highest maximum in Acorn Version 1 was (improbably) 51.2 ℃ at Albany in the far south of Western Australia.  In version 2, that has been reduced to 49.5 ℃.  The Version 2 highest maximum is now 51.1 ℃ at Oodnadatta in the South Australian desert.

The lowest temperature in Version 1 was -12.7 ℃ at Butlers Gorge in Tasmania.  That has been surpassed in version 2 by Inverell in northern inland New South Wales with -13 ℃.  (In raw data, the lowest at Inverell was -10.6 ℃ in July 1882.)

Here are the five stations with highest daily maximum temperatures.

Oodnadatta 51.10
Carnarvon 51.00
Forrest 50.10
Marble Bar 49.80
Port Hedland 49.7

Coldest minima:

Inverell -13.00
Butlers Gorge -12.70
Bathurst -11.50
Canberra -11.50
Cabramurra -10.70

Warmest minima stations are all coastal or islands:

Cape Moreton 5.10
Cairns 6.00
Weipa 9.10
Darwin 10.50
Horn Island 15.00

In Acorn version 2 there are some other peculiarities:  the “improved estimate” of climate change in Australia shows that Nhill, in western Victoria, has probably never had a frost, as its coldest morning has only been 2.7 ℃, the same as Sydney and Tennant Creek.   Alice Springs has the 69th hottest temperature at 45 ℃: far cooler than Albany, Eucla, Ceduna, or Port Lincoln far to the south.

Changes to temperature trends

With such enormous changes made to the daily data at so many stations, there have also been some major changes to temperature trends, both at individual stations and across the whole network.  (Trends are shown as degrees Celsius per 100 years).

Highest trends in maxima in Acorn 1

Cabramurra 4.75
Birdsville 3.21
Wyalong 3.00
Cunderdin 2.97
Cape Borda 2.90

Highest trends in maxima in Acorn 2

Wyalong 3.78
Cabramurra 3.66
Cunderdin 2.96
Birdsville 2.82
Ceduna 2.78

A trend of +4.75 ℃ per 100 years at Cabramurra high in the mountains is astounding- and the trend has been decreased by over one whole degree in Acorn 2.  The top four warming stations are the same in both datasets, but Cape Borda has been replaced by Ceduna in Acorn 2.  (Cape Borda went from 5th fastest warming to 15th; Ceduna went from 22nd to 5th.)

Highest trends in minima in Acorn 1

Rockhampton 3.10
Barcaldine 3.03
Laverton RAAF 3.03
Moree 3.01
Townsville 2.96

Highest trends in minima in Acorn 2

Rockhampton 3.41
Camooweal 3.27
Coffs Harbour 3.17
Horn Island 2.90
Laverton RAAF 2.81

Rockhampton maintains top position as fastest warming, and is warming even more in Acorn 2.  Laverton RAAF slips from third to fifth, while the other three places are completely changed.  Barcaldine slips from second fastest warming to 49th, replaced by Camooweal which rises from 41st!  Coffs Harbour has risen from 46th to third, but the gong for “most improved” must go to Horn Island, rising to +2.9 ℃ per 100 years in fourth place from +1.03 ℃ per 100 years in 72nd place,.    Those are definitelywildly different results”.

Greatest warming change in trends in maxima from Acorn 1 to Acorn 2

Eucla 1.50
Wittenoom 1.43
Giles 1.10
Ceduna 1.05
Port Macquarie 0.98

It seems that the improved methods used to create Acorn 2 has changed Eucla’s record to the extent thata reasonable guide to what has actually occurred” means an increase of +1.5 ℃ per 100 years in the rate of warming- an increase of 557%.

Greatest warming change in trends in minima from Acorn 1 to Acorn 2

Horn Island 1.87
Scone 1.86
Camooweal 1.71
Coffs Harbour 1.70
Tarcoola 1.51

The same applies to minima, with Horn Island’s warming trend increasing from +1.03 ℃ to +2.9 ℃ per 100 years.  Five stations had warming trends increase by more than 1.5 ℃, and 14 increased by more than 1.0 ℃ per 100 years.

Greatest cooling change in trends in maxima from Acorn 1 to Acorn 2

Victoria River Downs -1.07
Sale -1.07
Cabramurra -1.08
Moree -1.29
Rabbit Flat -1.64

The changes were not all in the same direction.  Rabbit Flat’s very patchy record takes the gong for the greatest cooling change in trend.  Rabbit Flat went from 24th fastest warming (+1.69 ℃ per 100 years) in Acorn 1 to 110th (third last) in Acorn 2- at +0.05 ℃ per 100 years.

Greatest cooling change in trends in minima from Acorn 1 to Acorn 2

Moree -0.89
Rabbit Flat -0.94
Halls Creek -1.32
Barcaldine -1.42
Giles -1.55

Again there were some big movers.  Rabbit Flat and Moree were again in the top five for most cooling change.  Giles had a similar fall from grace to record the greatest change: from 71st fastest warming (+1.03 ℃ per 100 years in Acorn 1) to second last place in Acorn 2 at -0.52 ℃, but Barcaldine went from second fastest warming to 49th, and Halls Creek from 58th fastest to 110th.

National Trends

In order to aggregate data into a national mean, all stations’ data were converted to anomalies calculated from their 1981 -2010 means.  There are 112 Acorn stations, but the Bureau insists that Urban Heat Island (UHI) warming makes eight of them (Townsville, Rockhampton, Sydney, Richmond RAAF (NSW), Melbourne, Laverton RAAF (Vic), Adelaide and Hobart) unsuitable for regional and national analysis.  The next plots show the change in trend from Acorn 1 to Acorn 2: first in maxima.

Figure 2: National mean of maxima at all stations

Acorn trends tmax all

Acorn 2 produces an increase in trend from +0.88 ℃ to + 0.99 ℃ per 100 years- an increase of 12.5%.

Figure 3: National mean of maxima at 104 stations, excluding those with UHI effect

 Acorn trends tmax nonUHI

Excluding eight UHI warmed stations produces virtually no difference from the trend of all 112 stations: +0.88 to +1.00 ℃.

Figure 2: National mean of minima at all stations

Acorn trends tmin all

In minima, the trend increases from +1.16 ℃ to +1.35 ℃ per 100 years- an increase of 16.4%.

Figure 3: National mean of minima at 104 stations, excluding those with UHI effect

 Acorn trends tmin nonUHI

Again, exclusion of UHI warming stations has a small effect, with the trend for non-UHI stations increasing from +1.15 ℃ in Acorn 1 to +1.37 ℃ per 100 years in Acorn 2.  That’s an increase of 19.1%.

Conclusion:

There are no additional stations, but additional digitised data at several stations has a large impact on annual trends.  As well, several Acorn 1 stations closed and their data merged with data from new sites in Acorn 2.

Large differences between Acorn 1 and Acorn 2 daily data of many degrees Celsius are found at many stations.  The largest changes ranged from -10.9 ℃ to +14.6 ℃ in maxima and -13.4 ℃ to +14.4 ℃ in minima.  Interestingly, no changes were made to Version 1 in Gunnedah maxima, and to Horn Island in minima.

New record maxima were established at 40 stations, with the remaining stations’ records being reduced or unchanged.  Australia’s “new” record high temperature is 51.1 ℃ at Oodnadatta.  The largest increase was of +2.5 ℃ at Carnarvon.  Our “new” record low temperature is -13 ℃ at Inverell.  The largest decrease in record low is -2.7 ℃ at Alice Springs.

Trends at individual stations in maxima and minima have often seen spectacular changes: changes in trend over Acorn 1 of from -1.64 ℃ to +1.87 ℃ per 100 years.  These changes resulted in very large changes in relative placing of fastest warming or cooling.  Eucla’s trend in maxima was increased more than six and a half times: 557%.  Broome’s minima trend increased more than five and a half times: 461%.

The size of the adjustments only seven years after the “world’s best practice” dataset was launched, is incredible.  The explanation that Acorn Version 2 “applies the latest scientific research and understanding and, where applicable, introduces new methodologies”, is beyond belief, as most datasets are vastly different from Acorn Version 1.  This is not incremental improvement.

In the ACORN-SAT FAQs, in the answer to:

“Why should the adjustments change, weren’t they correct the first time?”

the Bureau says:

“… The important question is not which one (version) represents the absolute truth, but whether those estimates produce wildly different results, and whether the range of estimates provides a reasonable guide to what has actually occurred.”

By their own words they have condemned themselves- “wildly different results” is exactly what has been produced.  Adjustments made in Version 1 were apparently made in error as they have been “corrected” by adjustments in version 2.  Will these adjustments be in error and corrected in version 3?

The Bureau officers responsible for Acorn version 2 appear to be blissfully unaware that they have made adjustments of up to 14.6 ℃ to temperatures in the dataset they proudly claimed to be world’s best practice just seven years ago.

Acorn 2, as the best estimate of Australia’s temperature record, is a failure.

ACORN-SAT 2.0: New South Wales- What a mess

April 10, 2019

This is the seventh in a series of posts in which I directly compare the most recent version of Australia’s temperature record, ACORN-SAT 2, with that of the previous version, ACORN-SAT 1.  Daily data are directly downloaded from the Bureau of Meteorology. I do not analyse against raw data (available at Climate Data Online), except for particular examples, as I am interested in how different Acorn 2 is from Acorn 1.  The basis for the new version is in the Research Report.  The Bureau has published a new station catalogue with more detailed information, the adjustment summary for each station, plus lists of comparative stations for adjustments and all comparison stations for each site, with explanations of adjustment terminology.  Well worth a look.

See my previous posts for Western Australia, the Northern TerritoryQueensland,  South Australia, Tasmania, and Victoria for a general introduction.  It is important to highlight this paragraph on the new ACORN-SAT home page:

The purpose of updating datasets like ACORN-SAT is principally to incorporate data that has been recorded since the last analysis was released, as well as historical paper records that have been recently digitised. ACORN-SAT version 2 also incorporates the findings and recommendations of the Technical Advisory Forum, applies the latest scientific research and understanding and, where applicable, introduces new methodologies. The overall aim of the update to ACORN-SAT is to provide improved estimates of historical changes in climate.

As well, in the ACORN-SAT FAQs, the Bureau says:

“… The important question is not which one (version) represents the absolute truth, but whether those estimates produce wildly different results, and whether the range of estimates provides a reasonable guide to what has actually occurred.”

Therefore, the Bureau has set their own criterion for whether Acorn 1 and Acorn 2 are at all useful and valuable.  To repeat:

“whether those estimates produce wildly different results, and whether the range of estimates provides a reasonable guide to what has actually occurred.”

The Context – New South Wales

Figure 1 is a map of Australia showing all of the Bureau’s ACORN-SAT climate monitoring stations.  New South Wales is the oldest and most populous state with climates varying from semi-desert to montaine.

Figure 1:  Australian ACORN-SAT stations

NSW map all

There are 25 Acorn stations in the NSW BOM database.  Differences between Acorn 1 and Acorn 2 are summarized in the following sections.

Additional data

An extra 27 years of data have been digitised for Canberra, and 45 years for Moree, which has had an enormous effect on annual temperature trends (see below).  Some locations had changes to new sites, with Acorn 1 data merged to Acorn 2 data, including Tibooburra and Wilcannia.

Largest temperature differences

In maxima, changes to Acorn 1 daily data ranged from +8.3 ℃ at Scone in 1996 to -9.6 ℃ at Cabramurra in 1998 applied to individual daily figures.

Remarkably, there were NO changes from Acorn 1 to Acorn 2 at Gunnedah.

Figure 2:  Daily changes in maxima from Acorn 1 to Acorn 2 at Cabramurra

Cabramurra max adj

Minima adjustments ranged from -13.4 ℃ at Wagga Wagga in 1946 to +9.6 ℃ at Scone in 1996 on individual days but with many days adjusted by -2 ℃ or greater.

Figure 3:  Daily changes in minima from Acorn 1 to Acorn 2 at Wagga Wagga:

Wagga min diffs

(Remember, these are adjustments to Acorn 1, which was supposed to be “world’s best practice” seven years ago.  How did the Bureau get it so wrong the first time?  Has world’s best practice changed so much in seven years?)

Record temperatures

New record maxima were established at nine stations, with the highest at Bourke (48.9 ℃) while other stations’ record highs were unchanged or reduced.  There were two notable changes.  Figure 4 shows maxima at Sydney in 1939, where the record was increased by 2.5 ℃ to 47.9 ℃.

Figure 4:  Three versions of maxima at Sydney in 1939

Sydney record max

(The temperature was below 20 ℃ on 16th and 17th.)

Figure 5 shows Port Macquarie, whose record maximum was reduced by -4.1 ℃ from 48.1 ℃ to 44 ℃ in 1944.

Figure 5:  Two versions of maxima at Port Macquarie in 1944

PtMcquarie record max

There is NO daily raw data for any Port Macquarie site from 1921 to 1956 at Climate Data Online, so there is no way of replicating these adjustments.

Such “wildly different results” are beyond rational explanation.

New record low temperatures were established at 15 stations, and a new record low for Acorn stations was set, not at Cabramurra in the Snowy Mountains, but at Inverell in the north: -13 ℃.  Canberra’s minimum was reduced by 2.9 ℃ to -11.5 ℃.

Figure 6:  Three versions of minima at Inverell

Inverell record min

Raw minimum of -10 ℃ is cold enough.  Acorn version 1 had cooled this further by 1.4 ℃, but version 2 cools version 1 by another 1.6 ℃, making it three degrees cooler than the raw figure.  Strange things happen in the past!

Quality Control: especially minimum temperatures higher than maximum.

In Acorn 1, 15 out of the 25 stations had at least one example of minimum higher than maximum- including 12 times at Bourke and Sydney, 15 at Tibooburra, and 212 times at Cabramurra.  The worst example was minimum 2.2 ℃ above maximum in October 1913 at Tibooburra.  Blair Trewin claims he has “fixed” this problem (which he concedes was “physically unrealistic”) by adjusting temperatures in Acorn 2 so that the maximum and minimum are the same, so that DTR for the day is zero.  In his words:

A procedure was therefore adopted under which, if a day had a negative diurnal range in the adjusted data, the maximum and minimum temperatures were each corrected to the mean of the original adjusted maximum and adjusted minimum, creating no change in the daily mean.

That is almost how he “corrected” the worst NSW example in Acorn 1 (minimum 2.2 ℃ above maximum at Tibooburra).  Here is a plot of the raw data and changes made by Acorn 1 and Acorn 2 at Tibooburra in 1913.

Figure 7:  Tibooburra temperatures October-November 1913

Tibooburra DTR 1913

Acorn 1 maxima (orange line) were reduced too far below Raw (brown). Acorn 1 minima (grey) were too far above raw minima (light blue).  Result: garbage. Acorn 2 has changed maxima (dark red) back to 0.1 ℃ below the raw value, and reduced minima (dark blue) from 17 ℃ to 16 ℃.  This is not the “mean of the original adjusted maximum and adjusted minimum”- but at least the DTR is not negative.

The problem was caused by far too large adjustments to both maxima and minima, and was fixed by more arbitrary adjustments.

Not all Acorn 2 adjustments resulted in an increase in warming- in several, the warming trend was reduced.  For example, Figure 8 shows annual temperature trends at Sydney.

Figure 8:  Maxima Trends in Sydney 1910-2017

Sydney max ann trends

The warming rate of +1 ℃ per 100 years in Acorn 1 has been reduced to +0.79 ℃ in Acorn 2.

However, at Coffs Harbour the warming trend in minima was more than doubled, from +1.47 ℃ to +3.17 ℃ per 100 years.

Figure 9:  Minima trends at Coffs Harbour 1952-2017

CoffsHbr min ann trends

Figure 10 shows the effect of including an extra 27 years of data on annual trends at Canberra, with Acorn 1 adjusted downwards from 2011.

Figure 10:  Trends in Canberra minima 1914-2017

Canberra min ann trends

Acorn 1 starts in 1940.  Canberra’s warming trend has been increased from +1.48 ℃ to +2.18 ℃ per 100 years.

Conclusion:

There are no additional stations, but additional digitised data at several stations has a large impact on annual trends.  As well, several Acorn 1 stations closed and their data merged with data from new sites in Acorn 2.

Large differences between Acorn 1 and Acorn 2 daily data of many degrees Celsius are found at several stations.  Interestingly, no changes were made to Version 1 in Gunnedah maxima, and only a few in minima.

New record maxima were established at nine stations, with the remaining stations’ records being reduced or unchanged.  The largest increase was of +2.5 ℃ at Sydney, and the largest decrease was at Port Macquarie where the record high was reduced by -4.1 ℃.

The issue of instances of minima being higher than maxima caused by too vigorous adjustments at 15 stations (including 12 times at Bourke and Sydney, 15 at Tibooburra, and 212 times at Cabramurra) has been “fixed”- only seven years after the problem was pointed out.

Not all Acorn 2 adjustments resulted in an increase in warming- in several, the warming trend was reduced.  However, excessive adjustments have resulted in Coffs Harbour’s Acorn 1 minima trend of +1.47 ℃ per 100 years being more than doubled to +3.17 ℃ in Acorn 2.

The size of the adjustments only seven years after the “world’s best practice” dataset was launched, is incredible, and demands explanation.  The explanation that Acorn Version 2 “applies the latest scientific research and understanding and, where applicable, introduces new methodologies”, is beyond belief, as most datasets so far examined are vastly different from Acorn Version 1.  This is not incremental improvement.

In the ACORN-SAT FAQs, in the answer to:

“Why should the adjustments change, weren’t they correct the first time?”

the Bureau says:

“… The important question is not which one (version) represents the absolute truth, but whether those estimates produce wildly different results, and whether the range of estimates provides a reasonable guide to what has actually occurred.”

By their own words they have condemned themselves- “wildly different results” is exactly what has been produced.  Adjustments made in Version 1 were apparently made in error as they have been “corrected” by adjustments in version 2.  Will these adjustments be in error and corrected in version 3?

The Bureau officers responsible for Acorn version 2 appear to be blissfully unaware that they have made adjustments of up to 13.4 ℃ to temperatures in the dataset they proudly claimed to be world’s best practice just seven years ago.

What a mess.

I will next show a summary of Version 2 changes across the whole network, and then look at annual trends at all stations.