How Accurate Is Australia’s Temperature Record? Part 2

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.

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Mysterious Jump in Ocean Temperatures

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.

Will Covid-19 Affect Carbon Dioxide Levels?

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!

The Chicken or the Egg?

Climate scientists assert that increasing concentrations of carbon dioxide and other greenhouse gases in the atmosphere have caused and will continue to cause global temperature to increase.  Real world evidence to support this is sadly lacking.

I use CO2 data from NOAA at Mauna Loa and HadSST3  Sea Surface data to compare both over the same period, as oceans cover most of global surface.

There have been 60 years of continued and accelerating CO2 increase.

Figure 1: 60 years of carbon dioxide concentration

Ocean temperatures have also increased:

Figure 2:  HadSST3 Sea Surface Temperature from 1958

While you may note the distinct lack of warming before the mid 1970s, and that although a quadratic trend line fits the data, the increase is not smooth but a series of steps with some large spikes at about the time of ENSO events, climate scientists insist that it is the overall trend that is important.

The following plot appears to support the greenhouse warming theory.

Figure 3:  Global Sea Surface Temperature anomalies as a function of CO2 concentration

It seems that nearly three quarters of the temperature change since 1958 can be explained by the increase in CO2 concentration.  This accords with the theory.

But what if we reverse the axes in Figure 3?

Figure 4:  CO2 concentration as a function of Sea Surface Temperature anomalies

It is equally valid to propose that nearly three quarters of the increase in carbon dioxide concentration can be explained by increasing sea surface temperatures, although that is not the point of this exercise.

To determine if CO2 is the cause of increasing temperature, or vice versa, we need to compare SST anomalies and CO2 concentration as a function of time.  If SST and CO2 both change at the same time, we are no further advanced, but if CO2 changes before SST (due to thermal inertia of the oceans), then that would be evidence for CO2 increase being the driver of temperature increase.

Both CO2 concentration and SST anomalies have pronounced trends, so for comparison both datasets are detrended, and the large seasonal signal is removed from CO2 data to calculate monthly “anomalies”.

Remember, it is increasing CO2 which is supposed to cause increasing temperature, not a static amount, so change in CO2 and SST must be our focus.

My measure of change in SST and CO2 is 12 monthly difference: for example January 2000 minus January 1999.  The next plot shows 12 monthly difference in both SST and CO2 anomalies from 1959 to 2018.  (SST is scaled up for comparison).

Figure 5:  12 monthly change in detrended SST and CO2 anomalies

SST appears to spike before CO2.  In the next plot, SST data have been lagged by seven months:

Figure 6:  12 monthly change in detrended SST (lagged 7 months) and CO2 anomalies

There appear to be differences in some decades- the lag time varies from four months to eight or nine months.

Here’s the plot of CO2 vs lagged SST:

Figure 7:  12 month change in CO2 as a function of 12 month change in SST, lagged 7 months

Correlation co-efficient of 0.57 is not bad considering we are comparing all ocean basins and the atmosphere.

As SST change generally precedes CO2 change by about seven months (sometimes less, sometimes more), there is NO evidence that CO2 increase causes temperature increase.

But we are still left with the increase in CO2 from 1958 while SST paused or decreased for 19 years.

Figure 8:  Sea Surface Temperature and CO2 concentration, 1958-1976

While it is difficult to attribute decadal CO2 increase to non-existent SST rise, there is no evidence for CO2 driving temperature increase in this period.

However, plotting 12 month change of CO2 and SST clearly reveals their relationship.

Figure 9: 12 month change in detrended CO2 and SST anomalies

Figure 10: 12 month change in detrended CO2 and SST anomalies, lagged 7 months

It is clear that 12 monthly change in temperature drives 12 monthly change in CO2 concentration.

The continual rise in CO2 from 1958 to 1976 while SST declined indicates there must be an underlying increase in CO2 unrelated to immediately preceding temperature, but there is definitely no evidence that it causes sea surface temperature increase at any time.

Summary:

1. Increase in CO2 concentration is supposed to be the cause of the increase in temperature we see in the SST data (and satellite data).
2. However, analysis shows that CO2 changes about four to seven months (and longer) after sea surface temperature changes.
3. Therefore, atmospheric CO2 increase cannot be the cause of surface temperature increase. Real world data disproves the theory.

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

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

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

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

Fig. 1:   Southwestern Australia Winter TMin Anomalies & SST

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

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

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

Fig. 3:  Trends in TMin:

Cooling since 1976 at -0.36C/100 years.

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

Fig. 4:  TMin Detrended:

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

The action is with TMax.

Land and Sea Temperature: South West Australia

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

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

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

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

Fig. 1

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

Fig. 2:  Southwestern Australia Winter TMax Anomalies

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

Fig. 3:  TMax and Rain:

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

Fig. 4:  TMax & SST:

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

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

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

Fig. 6:  CuSums of Winter Rainfall

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

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

Fig. 7:  TMax vs Rain:

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

Fig. 8:  TMax vs SST:

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

TMax is affected by rain, but more by SSTs.

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

Fig. 9:  Trends in SST:

Warming since 1975 of +1.48C/ 100 years.

Fig. 10:  Trends in Rainfall:

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

Fig. 11:  Trends in TMax:

Warming since 1975 at +2.14C per 100 years.

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

Fig. 12:  SST Detrended:

Fig. 13:  Rainfall Detrended:

Fig. 14:  TMax Detrended:

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

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

• Maximum temperatures in winter in Southwestern Australia are affected by rainfall, but to a much larger extent by Sea Surface Temperature of the South West Region.

• The large decrease in winter temperature this year cannot be explained by rainfall or sea surface temperature.  Cloudiness may be a factor, but no 2016 data are publicly available.  Stronger winds blowing from further south may be responsible.

Water World

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

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

The Arctic Ocean

The Southern Ocean

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

The Atlantic Ocean

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

The Indian Ocean

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

The Pacific Ocean

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

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

I now turn to the seas close to Australia.

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

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

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

The next six months should be interesting.