The last post showed that there was little change in the timing of, or the seasonal maximum of temperatures in Australia. Here I look at the long term trends in 3 different locations Yongala in SA, Alice Springs in central Australia and Townsville near the coast in Queensland.
Maximum daily temperatures and the Diurnal range in temperatures for Yongala, SA Click for large version.
The top signal is the daily maximum temperature recorded for over a 73 year period (73 summers). There are some very hot days but overall the signal is remarkably stable with no evident warming trend. The bottom trace shows the daily range in temperatures between night and day. There is a clear tendency for higher daily temperature swings in winter but there is no long term trend evident.
Alice Springs located in central Australia shows a much stronger seasonal change in maximum temperatures
Daily Maximum and Diurnal temperature range for Alice Springs. Note how there appears to be no change in Maximum temperatures.
Townsville is located in Northern Queensland next to the sea and the Great Barrier reef . It has a semi tropical climate and shows a much smaller seasonal cycle, with a smaller diurnal temperature range, inversely proportional to the seasons. Presumably this is because the ocean is cooler in winter reducing overnight temperatures.
Townsville daily maximum temperatures and diurnal temperature range.
None of these stations show any significant warming signal in this data. So why does ACORN show a warming trend ? The main reason is that they all use a selected set of “anomalies” based on the monthly differences to their 30 year temperature average. These values are called station “normals”. However before all this another bit of black magic called “homogenisation” is applied. Finally just 112 stations are selected for this warming trend analysis
Homogenisation is based on the assumption that nearby stations all follow the same warming trend, So there is an inbuilt tendency to correct or even reject any negative trends. Systematic differences can indeed be due to a site relocation, inducing a positive trend. When I looked at this earlier I found that this increased the average temperature anomaly trend by about 0.3C
Difference between the homogenised result (ACORN-SAT) and Raw measurements (1805 stations). Note the linear increase of about 0.3C with time.
One more problem is that anomalies don’t tell you whether it is maximum temperatures that are rising or instead minimum temperatures that are rising.
I found previously that in Australia it is nights (Tmin) that are warming rather than days (Tmax) !
Average maximum land temperatures in Australia compared to minimum temperatures
” One more problem is that anomalies don’t tell you is whether it is maximum temperatures that are rising or instead minimum temperatures that are rising. ”
*
Wow! Clive, this is a very, very strange statement, unless I have completely misunderstood you.
What does that have to do with anomalies?
You can easily generate absolute data or anomalies based respectively on the daily minima and maxima:
Among other things, we clearly see in the above graph that the minima anomalies have been increasing less than the maxima lately.
The average temperature each day is Tav = (Tmin + Tmax)/2
The average monthly temperature is Sum(Tav)/Ndays
So Tmin and Tmax play an equal role in defining the average temperature.
Check out my analysis of GHCN Daily. which finds the opposite.
https://clivebest.com/blog/?p=8464
Sorry, I still don’t understand what you mean when saying that anomalies “don’t tell you is whether it is maximum temperatures that are rising or instead minimum temperatures that are rising.”
When you process e.g. GHCN daily station temperature data, you can select which part of data you use: TMIN, TMAX, TAVG, TOBS.
Thus you can construct a first time series with TMIN data, and a second one based on TMAX data, and compare them.
Where is the problem?
*
By the way, I’m also wondering about this TMIN lowering for the Globe. Higher TMINs are after all typical for both GHE effect increase and… UHI, aren’t they?
Maybe you generate your GHCN daily again, this time in a TMIN vs. TMAX variant?
Your difference if any might then be due to the fact that you interpolate, if I well remember.
And since 2018, many GHCN daily stations have been added, perhaps mostly in regions where UHI is less present.
Please read this post
https://clivebest.com/blog/?p=8464
Otherwise it seems we basically agree so I am not sure what your point is.
Yes UHI has some effect superimposed on GHE.
But Clive… I have read this 8464 post years ago.
At that time, it was possible to download your monthly GHCN daily evaluation, and I was happy not to be too far away from it, despite lacking your much higher scientific education:
https://drive.google.com/file/d/168OPlUViZl-abCfft6iREQccGVV3C7_e/view
*
“Otherwise it seems we basically agree so I am not sure what your point is. ”
Regarding today’s discussion, I see again that in post 8464 you mention the decrease in the diurnal range over time, but nowhere there have you separated Tmin and Tmax on any graph.
My point keeps the same: superposing Tmin and Tmax time series shows very well which of the two increase or decrease.
We’re probably talking past each other.
Thanks for your excellent work anyway!
Sorry I misunderstood you. Yes you can deal with the daily Tmax and Tmin separately or as the daily average Tav = (Tmax + Tmin)/2
So yes you’re right and your results look good.
What I should have said was that the diurnal temperature difference (Tmax – Tmin) “anomaly” appears to have remained constant despite Tav anomaly rising.
CO-2 is nonsense. Points of measure is chaos.
average temperature is impossible to measure.
Water vapor and Fluor combinations
Refrigerant R410a has the same greenhouse impact as two tonnes of carbon dioxide, which is the equivalent of running your car for six months
That is something.
Killing big land and sea grazers. Cutting down rain forest. Burning of sugar plantages.
We would be bound to go towards cooling. Milankovitch.
Fossil carbohydrates are natural.. They were in the system.
The planet was bound to go towards cooling.
And then the magnetic influence. Air / gasses are attracted by a magnetic field.
If the flux goes we are in trouble. And the flux has many cycles.
Mars lost its magnetic… and its atmosphere.
But hey what is better warm or Cold….
Like Cern billions to find a statistical higgs Boson. Which we have no clue about what it does or is. and for years and years the found nothing.
We do not even have a clue about light. photons nonsense.
it’s not a particle it’s a wave a bubble which collapses on point of measure. like a soap bubble you touch it and it collapses.
Coral bleeching based on a reef a million years old. We jump into conclusions we do not apprehand. And yet the flood and megalithic buildings which we can not build….. We oversee no past.
Fear is the basis of climate “research” funding. Fear is government and taxes.
As always, great analysis Clive.
Presumably this means that in Australian winter days are less cold and evenings remain warmer for longer. The built environment, which absorbs and stores energy would delay cooling in this manner, however I suspect that you chose these sites because they are not influenced by UHI effects. This then raises the question of where this extra heat is stored and how the heat got there?
Is the cooling curve for air with 280ppm CO2 sufficiently different from air with 400ppm CO2 to cause this effect?
Is there an increase in atmospheric water content? Moist air certainly will cool slower than dry air.
Since the land and the atmosphere are intimately linked, is this caused by the land warming more during the day? What would cause this?
Lowered ability for land to transfer heat to air?
A change in the radiation input to the land due to:
Atmospheric forcing?
Increased radiation input from the sun, or from space? Perhaps due to decreased magnetic field and more energy coming from the sun and cosmos? Wobbles in the earths orbit? The aftermath of operation goldfish bowl?
Perhaps oceanic circulation? Even a dry place like Australia is greatly influenced by the sea.
The big takeaway from me is just how complex this all is.
Any tentative conclusions must be hedged around by numerous caveats?
Atmospheric science really is wonderfully engaging, but it is complex science rather than settled science?
Absolutely Correct. The current narrative is far too naive because it focusses on a simplistic cause (CO2) and effect (1C rise in average surface temperature (anomalies).
“Homogenisation is based on the assumption that nearby stations all follow the same warming trend”
No, it isn’t. No-one seems to understand this, but it’s very simple.
You examine a single station to see if there are things that seem statistically wrong – usually a change in mean temperature. Then you look at neighboring stations for confirmation. If they show that change, accept it. If not, correct it. That is the role of neighboring stations. Not sharing trend.
Yes it is more complicated than that. Sometimes weather stations move location so indeed do need merging eg.
https://clivebest.com/blog/?p=8327
However I looked at 10s of examples from Australia and I never found one where the warming trend was reduced. That may not be deliberate but perhaps examples of the opposite were rejected as being “non physical”.
Here is Dubbo. Notice how the adjustments vary with time inducing a warming trend not present before. It is not a simple linear shift down.
On my short journey home each day, there can be as much a 10C difference in temperature along the route, dew pockets, temperature inversions, sun traps… This is in boring old Scotland where the Atlantic controls the temperature and the annual range is only 30C. Temperature can also vary widely across the day depending on the presence of cloud, type of cloud, fog etc.
I remain skeptical about the methods used to gather data and to analyze dubious information.