Theory and Reality- Part 1: DTR

February 2, 2016

It was two years ago in 2013 that I last posted on the difference between climate scientists’ expectations and reality, so in this series of posts I bring these points up to date, and add a couple of related points.

What the climate scientists tell us:

Dr Karl Braganza in The Conversation on 14/06/2011 lists the “fingerprints” of climate change (my bold).

These fingerprints show the entire climate system has changed in ways that are consistent with increasing greenhouse gases and an enhanced greenhouse effect. They also show that recent, long term changes are inconsistent with a range of natural causes…..
…Patterns of temperature change that are uniquely associated with the enhanced greenhouse effect, and which have been observed in the real world include:
• greater warming in polar regions than tropical regions
• greater warming over the continents than the oceans
• greater warming of night time temperatures than daytime temperatures
• greater warming in winter compared with summer
• a pattern of cooling in the high atmosphere (stratosphere) with simultaneous warming in the lower atmosphere (tropopause).

and later

Similarly, greater global warming at night and during winter is more typical of increased greenhouse gases, rather than an increase in solar radiation.

This post will examine “greater global warming at night” and whether it can be attributed to increased greenhouse gases.

If night time temperatures (minima) increase faster than day time temperatures (maxima), then the difference between these, the Diurnal Temperature Range (DTR) will decrease.

I use BEST global land temperature data,

and annual CO2 concentration data from NOAA.

Fig. 1: Global DTR (derived from BEST Land Tmax and Tmin)

DTR globe

Yes, the long term linear trend shows globally DTR has decreased, at a rate of more than half a degree Celsius per century.

Case closed! That is, if you ignore the sudden turnaround in the early 1980s. Since then DTR has been increasing at +1.1C per 100 years.

The plot showing the relationship with CO2 concentration is even more revealing:

Fig. 2: Global DTR vs CO2 concentration

globe dtr v co2 all

If we break the series in two at the dogleg, we get the following plots:

Fig. 3: Global DTR vs CO2 concentration to 1982

globe dtr v co2 1

Fig. 4: Global DTR vs CO2 concentration 1982 to 2015

globe dtr v co2 2

Calling Global Warming Enthusiasts! I am puzzled:

Is DTR decreasing at 1.14 C/ 100 ppm CO2 or increasing at 0.61 C/ 100 ppm?
Can there be any logical explanation for this distinct turnaround?
Is there a problem with (a) the CO2 concentration data? (b) BEST data? (c) the theory behind decreasing DTR being an indicator of enhanced greenhouse warming? (d) all of these?

I now turn to the Australian context, with Australian surface data.

Fig. 5: Annual DTR Australia (from ACORN)

DTR Aust

While averaged across Australia, DTR has decreased since 1910, there has been a marked increase recently. As well, the pattern is different in different regions.

Fig. 6: DTR North Australia

DTR Aust nth

Fig. 7: DTR Southwest Australia


Fig. 8: DTR South Australia


Fig. 9: DTR Victoria

DTR Aust Vic

Fig. 10: DTR Tasmania

DTR Aust Tas

The effect is strongest in the tropical northwest and northeast, and weakest in the southwest and South Australia, Victoria, and Tasmania.

Moreover, the dominant influence on DTR is rainfall:

Fig. 11: DTR vs Rainfall

DTR Aust vs rain

Definitely not CO2!

Fig. 12: DTR vs CO2 concentration

DTR Aust vs CO2

Assessment of decreased DTR as evidence for the enhanced greenhouse effect: Fail.

Other factors- especially rainfall- overwhelm the enhanced greenhouse effect.


Perhaps I should be more blunt:  If Global Warming Enthusiasts stick to decreasing DTR as an indicator of greenhouse warming, then this shows BEST and ACORN surface data are completely unreliable.  If they stick to claiming ACORN and BEST are “world’s best practice” then they must accept that DTR as an indicator of greenhouse warming is a dead duck.

Trending trends

January 17, 2016

In this post I demonstrate my template that shows linear trends in data from any given point in time to the most recent month, (which is how I determine the starting point and length of The Pause.) It can be quickly seen how trends change over time and where these changes occur so they can be investigated. This can be used for any data at all, from monthly TLT anomalies to road fatalities. In future posts I will use this with Australian surface temperatures and rainfall. It does not replace, but supplements, normal time series graphs.
Up to now I have used monthly UAH temperature anomalies to study The Pause, but I have recently learnt that there can still be a weak seasonal signal, so from now on I will use 12 month running means of monthly anomalies. This leads to some changes in trends and the start of The Pause in some regions, notably Australia, but overall gives similar results. Importantly it reduces the impact of outlier individual months, especially at the start of the record and as each new month is added.
As well, my previous Pause criterion (a linear trend of less than +0.01 degree Celsius / 100 years) has been too strict. While UAH data are to two decimal places, the uncertainty range is +/- 0.1C. Accordingly, for 2016 my Pause criterion will be a trend of less than +0.1C per 100 years. (This is still far too lenient on Global Warming Enthusiasts- compared with trends above 1C per 100 years, anything below about +0.3C is an embarrassing slowdown.) Further, it is important to be transparent. All available data should be shown, not just those that create The Pause.
Finally I note, thanks to Christopher Monkton, that

In 2008, NOAA’s report on the State of the Global Climate, published as a supplement to the Bulletin of the American Meteorological Society, said: “The simulations rule out (at the 95% level) zero trends for intervals of 15 yr or more, suggesting that an observed absence of warming of this duration is needed to create a discrepancy with the expected present-day warming rate.”

A look at some of the graphs shown below will show why this is a valid statement. Certainly trends of 10 to 15 years give an indication of what has been happening, but I will agree that 15 years is about the length of time needed for trend values to settle without too much undue impact from the short term fluctuations in recent values.
Let’s begin.
Fig. 1: Running linear trend values in degrees Celsius per 100 years in Global UAH TLT anomalies from December 1978 to December 2015 (12 month means)

Trend whole
The plot shows the value of the linear trend from any given month to the most recent.
It should be plainly obvious that trends constructed from less than 10 years of data are spectacularly meaningless. This is weather.
The trend for the whole data series is about +1C/ 100 years.
The trend line crosses the zero value in 1997-98, so The Pause starts there.
Now I reduce the scale, and demonstrate how the graph may be interpreted.
Fig. 2: Running trend in degrees Celsius per 100 years in Global UAH TLT anomalies from December 1978 to December 2015 (12 month means)

Trend globe all

The trend for the entire record is +1.11C per 100 years.
A higher bounce in the trend indicates that earlier Temperatures were cooler relative to recent values, and a lower trend, a dip, the reverse. If recent temperatures are low enough compared with past values, the trend will reduce to zero or below, as it has above.
I have drawn a horizontal line showing +0.1 C, below which the trend cannot be distinguished from zero, unless it is below -0.1, in which case it is definitely negative.
The trend line crosses +0.1C in 1997. I have drawn in a horizontal black line from 2015 back to this point showing the length of The Pause. I can now refer to my spreadsheet table to find the exact month for the commencement of The Pause- April 1997- and graph it.
Fig. 3: UAH v6.0 anomalies for the Globe in blue, with data since April 1997 in orange.

Globe graphs all

The Pause is highly dependent on the El Nino generated 1998-99 spike. However showing the whole record makes the following plateau plainly obvious.
Now, what about the mysterious disappearing Northern Hemisphere Pause? In graphs of 12 month means, it’s back!
Fig. 4: Running trend in degrees Celsius per 100 years in Northern Hemisphere UAH TLT anomalies from December 1978 to December 2015 (12 month means)

Trend NH all

Of course, this is very much dependent on values in the next few months, as it will probably disappear again!
You will note the series of bumps and dips in the trend values. The small upward bounces coincide with cooling events such as La Ninas or explosive volcanoes, while the dips coincide with warming events such as El Ninos.
So, we have a Northern Hemisphere Pause again, if only briefly, and Global Warming Enthusiasts will surely accuse me of cherry picking. But remember, values will continue to be applied to the right hand end.
Fig. 5: UAH v6.0 anomalies for the Northern Hemisphere with the whole series in blue, and with data since October 1997 in orange.

NH graphs all

The next graph illustrates how using 12 month means can alter the Pause length. Monthly data had Australia’s Pause lasting for 18 years and 1 month, but this has shortened to 15 years and 3 months (which still meets NOAA criteria).
Fig. 6: Running trend in degrees Celsius per 100 years in Australian UAH TLT anomalies from December 1978 to December 2015 (12 month means)

Trend Aust

As the next graph shows, the Australian Pause starts from near the bottom of a La Nina cooling. No cherry picking there.
Fig. 7: Australian crawl: UAH v6.0 anomalies for Australia with the whole series in blue, and with data since October 2000 in orange.

Aust graphs all

I’ll conclude with a warning that as each month’s data point is appended, the trend graph will change (unlike temperature graphs where all past data points are fixed.) Don’t be confused by this- we are simply re-calculating linear trends.

Earth and Water

January 13, 2016

Graphs of The Pause are valuable as a means of confounding Global Warming Enthusiasts by showing how little temperatures have increased in the past couple of decades, but there are many other gems in monthly Temperature of the Lower Troposphere (TLT) anomalies. In this post I take a different look at monthly data using UAH v.6.0 for various regions.
Click on images to expand them.
First, here is the complete TLT record for the globe from December 1978 to December 2015.

Globe all
A trend of +1.14C/ 100 years, although anomalies have definitely flattened (the Pause) since about 2002.
But here are the Land and Ocean data separately:

Global land

Global ocean

Due to the oceans’ greater thermal inertia, it is to be expected that land areas would warm faster than oceans in any warming scenario no matter its cause. The Pause remains as well.
Notice the arrow at the beginning of 1998, marking the spike of the 1997-98 El Nino.  Note that the Land data after this are flatter and slightly stepped up from the data before this. The Ocean data give no hint of this, where since June 1994 the trend has been less than +0.1C (+/- 0.1C) per 100 years. Globally, Oceans have contributed nothing to global warming for well over half the satellite era.
Is this step change evident in other Land regions?

Northern Hemisphere:

NH land
Southern Hemisphere:

SH land
There is no sign of a step change in these data.   The step change is limited to the Northern Hemisphere.

Trop land

There is a flattening in the Land data from about 2001-2002, but no apparent step change.  The step change is limited to the Northern Hemisphere, but outside the Tropics.

North Polar:

NP land
No step change in 1998, although temperatures began changing in the mid-1990s.
Therefore, the 1998 step change must be in the data from the Northern Extra-Tropics (20-90 North), and specifically from 20N to 60N.

Nextr land

There’s the culprit. There is a clear discontinuity at the beginning of 1998. This graph shows it more clearly, with plots of data before and after this step change.

Nextr 2 parts
The whole record for the Northern Extra Tropics Land shows a linear trend of +2.04 degrees Celsius per 100 years. But the trend for the first half of the record (229 out of 445 months) is only +0.6C/ 100 years, and for the past 18 years only +0.36C/ 100 years. The rapid rate of warming overall is largely due to a step change in early 1998.
Here is the plot for the Northern Extra Tropics Ocean data:

Nextr ocean
The step change is not clearly defined, but the trend change is dramatic: +0.84C/ 100 years to zero.
This graph shows Land and Ocean data on the one plot, together with mean temperatures for both of them before and after the step change. The scale has been changed to highlight the differences.

Nextra land and ocean
Land data steps up by +0.48C and Ocean data by +0.26C.
What have we learnt?
The different behaviours of Land and Ocean data suggest that global warming trends are difficult to interpret.
Land TLT is warming faster than Ocean TLT.
North of 20S, Tropical and Northern Extra Tropical Land TLT data show warming above +2C, nearly 50% more than Southern Extra Tropical Land. (There is not much land compared with water south of 20S).
Global warming, by whatever cause, is dominated by Land warming, and by the Northern Hemisphere (which has most of the land area).
Warming in the Northern Hemisphere is dominated by a step change of nearly +0.5C at the beginning of 1998 in data for the Lower Troposphere over Land areas between 20N and 60N- by far the largest Land area on the planet, and the most heavily populated and industrialised region.
Significantly, this warming step change also contributed to the Pause, as temperatures since then have flattened.
We live in interesting times. Indeed, we are on the cusp of finding, over the next 4 to 5 years, whether the Pause has been a temporary slowdown as temperatures step up to a higher level, a longer period of levelling temperatures, or a brief plateau before a cooling phase.

The Pause: Further Update December 2015, including Northern Hemisphere

January 9, 2016

Complete UAH v6.0 data for December for all regions were released yesterday- sooner than I expected! Here are graphs for the remaining regions showing the furthest back one can go to show a zero or negative trend (less than +0.01C/ 100 years) in lower tropospheric temperatures. (See my previous post for Global, Southern Hemisphere, and Tropical regions). Note: The satellite record commences in December 1978. The entire satellite record is now 37 years 1 month long- 445 months.


Tropical Oceans:

dec tropic ocean
One month shorter.

North Polar:

dec NP
The Pause has lengthened again, by 10 months.

South Polar:

dec SP
For the whole of the satellite record, the South Polar region has had a negative trend. So much for a fingerprint of warming due to the enhanced greenhouse effect being greater warming at the Poles!


dec Aus
No change.

USA 49 states:

dec USA
No change.

Northern Hemisphere:
Now, about the disappearance of the Pause in the Northern Hemisphere- here’s a curious thing. While there is no Pause overall, Northern Hemisphere Land data do show a Pause, albeit short:

dec NH Land
Only 6 years 6 months- but still a pause. Northern Hemisphere Ocean data show a much more impressive Pause:

dec NH Ocean

18 years and 11months. So the Pause hasn’t disappeared- it’s just hiding in the ocean!
In my next post, later today or tomorrow, I’ll compare Land and Ocean data for various other regions.

The Pause: Interim Update December 2015

January 6, 2016

UAH v6.0 data for December were released last night.  Here is an interim post with updated graphs for some regional data (Globe, Southern Hemisphere, Tropics) as released by Roy Spencer, showing the furthest back one can go to show a zero or negative trend (less than +0.01C/ 100 years) in lower tropospheric temperatures.   For the third month of the climb towards the El Nino peak, there is still NO pause in the Northern Hemisphere trend.  Note: The satellite record commences in December 1978.  The entire satellite record is now 37 years 1 month long- 445 months.



dec Globe

The length of the Pause has remained the same, with zero trend for one month short of half the record.  While CO2 has increased by 37 ppm, energy consumption by 187 billion tons of oil equivalent, and population by 1.3 billion people, temperatures have remained flat.

Northern Hemisphere:  No Pause

Southern Hemisphere:

dec SH

The Pause has shortened by four months.  For more than half the record the Southern Hemisphere has zero trend.


dec tropics

The Pause has shortened again as the El Nino peaks, but is still more than half the record.

The remaining charts will be posted when data for the remaining regions are released later in the month.


The Pause: November 2015 Update

December 18, 2015

UAH v6.0 data for November were released a couple of days ago.  Here are updated graphs for various regions showing the furthest back one can go to show a zero or negative trend (less than +0.01C/ 100 years) in lower tropospheric temperatures.   For the second month of the climb towards the El Nino peak, there is still NO pause in the Northern Hemisphere trend.  However, in some regions the pause has lengthened.  Note: The satellite record commences in December 1978.  The entire satellite record is now 37 years long- 444 months.



nov globe

There has been zero trend for exactly half the record, (and for an increase in CO2 concentration of 37 ppm).

Northern Hemisphere:  No Pause

Southern Hemisphere:

nov SH

The Pause has lengthened again.  For more than half the record the Southern Hemisphere has zero trend.


nov tropics

The pause has shortened significantly.

Tropical Oceans:

nov tropic oceans

Unchanged from last month.

North Polar:

nov N Pol

The Pause has lengthened by two months.

South Polar:

nov S Pol

At -0.11C/ 100 years, the cooling trend is now undeniable.  For the whole of the satellite record, the South Polar region has had a negative trend.  So much for a fingerprint of warming due to the enhanced greenhouse effect being greater warming at the Poles!


nov aus

No change.

USA 49 states:

nov usa

One month longer!

The Pause lives!

Energy, Carbon Dioxide, and The Pause

December 16, 2015

Here’s an alternative way to view The Pause. Rather than analysing temperature trends over time, here I compare temperature with carbon emissions and carbon dioxide concentration, and on the way look at a couple of interesting facts that need highlighting.

I use energy data from the BP Statistical Review of World Energy 2015, CO2 data from NOAA, and Temperature data from UAH.

I need to get two important issues out of the way.

Firstly, total energy consumption. Figure 1 shows global energy consumption from all sources for 2014.

Fig. 1: Global Energy Consumption in Million Tonnes of Oil Equivalent
energy 1965 2014

I aggregated coal, oil, and gas into one fossil fuel category. It is plainly obvious that fossil fuels are going to be around for a long time, unless there is a massive multiplication of (a) nuclear energy production, which may not appeal to some environmentalists, or (b) hydro-electricity dams, but that may not appeal either, and are there enough rivers?, or (c) windfarms and large scale solar, with storage, to produce 30 times what they produce now just to meet current demand. Cheap, reliable energy supply is going to depend on technological breakthroughs in the next 100 years and fossil fuels in the meantime.

Secondly, the recent increase in carbon dioxide concentrations is almost entirely anthropogenic.

Figure 2: CO2 concentration as a function of global energy consumption from 1965 to 2014:
Energy vs co2

99% of CO2 increase can be explained by energy use in all forms.

Now, before Global Warming Enthusiasts drool all over their keyboards, let’s look at how this relates to temperature.
I have calculated 12 month running means of CO2 concentration and TLT anomalies. From November 1979 to November 2015- CO2 concentration increased from 336.6 ppm to 400.57 ppm. What happened in this period to global lower troposphere temperatures- arguably a better indicator of global warming than surface temperatures because they show what the bulk of the atmosphere is doing?

Fig. 3: Tropospheric temperature anomalies vs CO2 concentration:
TLT vs CO2 78-15

43.5% of the temperature increase over the satellite era can be explained by/ is associated with the increase of about 64 ppm of CO2. The relationship is anything but linear, however the linear trend indicates, if warming continues at the same rate while CO2 increases by 100 ppm, that temperature anomalies will increase by about 0.63C. By this estimate, doubling CO2 concentration from 280 ppm (what many believe to be pre-industrial concentration) will result in a temperature increase from whatever the global temperature was 250 years ago, of 1.76C. According to HadCruT4, we’ve already seen about 0.8C increase since 1850, so we’re nearly halfway there! Not only that, but we’ll stay below 2 degrees of warming without the need for any emissions reductions!

But the temperature increase is not linear. The next plot shows the tropospheric temperature/ CO2 relationship while temperatures have paused.

Fig. 4: TLT vs CO2, from 363 ppm to 400 ppm:
TLT vs CO2 Pause

That, my friends is the true indicator of The Pause: while CO2 has increased by almost 37 ppm (out of 64 ppm), temperature has remained flat. The trend is +0.01C per 100 ppm CO2.

Finally, I’ve separated the record into three phases: before, during, and after the large step change in the 1990s culminating in the 1997-98 El Nino and the following La Nina.

Fig. 5: Temperature vs CO2 during the first phase, when CO2 increased by 20 ppm:
Phase 1

Fig. 6: Temperature vs CO2 during the second phase, when CO2 increased by about 14 ppm:
Phase 2
Fig. 7: Temperature vs CO2 during the last phase, when CO2 increased by about 29.3 ppm:
Phase 3

Therefore I conclude:

Barring a miraculous breakthrough, renewable energy has no hope of replacing cheap, reliable fossil fuels in the foreseeable future- thankfully!
Greenhouse gas increase is anthropogenic;

CO2 increase has probably caused some small temperature increase;

The relationship between CO2 and temperature in the satellite era is weak, with 58% of the CO2 increase occurring while temperatures have paused;

Therefore temperature change is probably caused mainly by natural factors;

Even if the long term “linear” trend continues, this rate is not alarming, and would lead to a temperature increase during a doubling of CO2 of less than 1.8C.

I find it amusing that Global Warming Enthusiasts pin their hopes for an end to The Pause on a strong El Nino- in other words, on natural variability, the very thing that is supposed to have been overwhelmed by greenhouse warming.

The end of the scam is nigh!

How Significant Is This El Nino?

December 3, 2015

For months we have been told how this is a strong El Nino, similar to the “Super El Nino” of 1997-98. How does it really stack up?

As data for sea surface temperatures are not available before 1950, the Southern Oscillation Index (SOI) data from 1876 are the best for long term analysis. In this post I am using SOI data from the BOM archive.

The Bureau uses sustained (three month mean) SOI values of 7 or less as an indication of El Nino conditions. This plot shows three month mean SOI values from 1876:

Fig. 1: Three month mean SOI values from 1876

3m soi
It is plain that as of November 2015 the three month mean is still nowhere near as low as it has been in several past El Ninos (and 1997-98 was not the lowest either!)

The next graph compares the length of El Ninos.

Fig. 2:  El Nino length

EN length -7

Plainly 1941-42 was the one to beat, and El Nino conditions will need to persist for another 18 months to compare. Another four to six months is more likely, and of course there could be a double up of another El Nino next year (as happened in the 1990s).

I next calculate the relative strength of El Nino conditions, by summing the (inverted) SOI values of all months in El Nino i.e. that have a three month mean of -7 or less.

Fig. 3:  El Nino cumulative strength

EN strength -7

Unless we get another six months of values below -20 we won’t beat 1997-98 into fourth place.

Of course, we are only in the seventh month of this El Nino- how does it compare with this stage of previous El Ninos?

Fig. 4: Three month mean SOI value for seventh month of cycle

EN strength 7th mth -7

The November 2015 value is the black dot- in sixth place.

Compared with the strength of previous El Ninos, the seven month value of this one is also in sixth place:

Fig. 5:  Cumulative strength in seventh month of cycle

EN strength 7th mth integral -7

Another interesting method of comparison is to change the definition of “El Nino” to “El Nino or Neutral” i.e. periods between La Ninas.

Fig. 6:  Length of El Nino or Neutral conditions

EN length EN or neut

Note the two periods of nearly seven years without La Ninas in the 1980s and 1990s, separated by a 12 month La Nina- immediately followed by the 1997-98 event, and then another five year period. 2014-15 is not unusual.

The integral of SOI values, as a measure of the strength of El Nino:

Fig. 7:  Cumulative strength, El Nino or Neutral conditions

EN strength EN or neut

Currently this event is in 12th place, and if it runs strongly for another six months it could sneak into seventh place.

Compared with other events, at the 22nd  month this event ranks fourth.

Fig. 8:  Cumulative strength at 22nd month of cycle

EN strength 7th mth integral EN or neut


The current El Nino event is not going to break any records, unless it continues for several years!

It is nowhere near the most intense, nor the longest, nor the strongest.

It cannot compare with the intensity of previous El Ninos, as measured by three month average values, such as in 1896, 1905, or 1983.

It cannot compare with the length of previous El Ninos, such as the 1941-42 event, or the series of years of El Nino and neutral conditions in the 1980s and 1990s.

Depending on the measure used, it is fourth or sixth strongest for this stage of the cycle. If it continues strongly, its final strength might reach seventh or perhaps even fourth place. But that is unlikely. According to the Bureau, this event will peak before the end of 2015, and finish by mid-Autumn.

Fig. 9:  Model outlooks for El Nino end

Despite the hopes of the global warming enthusiasts, this is just another moderately strong El Nino which may cause a spike in world temperatures in the first half of next year, but is nothing to get excited about.

Rain and Surface Temperature Part 3

December 2, 2015

I have recently shown how the difference between surface maxima for Northern Australia and Temperature of the Lower Troposphere (TLT) for Australia as a whole is very largely due to rainfall variation in the Northern Australian region alone.
Fig. 1:  Northern Australian rainfall compared with the difference between North Australian surface maxima and Australian TLT (120 month means)

Nth rain v nth diff 120m
Now I turn to comparison with another region: that of Tropical Land.  All but six degrees of Latitude of the Northern Australian region is in the tropics, so most of it will be covered by TLT for Tropical Land. How much influence does Northern Australian rainfall have on the difference between Northern Australian surface maxima and TLT for all land in the tropics around the globe?
Fig. 2: Northern Australian rainfall compared with the difference between North Australian surface maxima and Global Tropical Land TLT (12 month means)

Nth rain v tropic land diff 12m

Fig. 3: Northern Australian rainfall compared with the difference between North Australian surface maxima and Global Tropical Land TLT (decadal means)

Nth rain v tropic land diff 120m

Considering that the Tropical Land TLT measures temperature above large tracts of Africa, South Asia, and Central and South America as well as tropical Australia, this result is amazing: on a decadal timescale, Northern Australian rainfall variation alone accounts for the same proportion of the surface- tropospheric difference of northern Australia surface maxima- Australia TLT as northern Australia surface maxima- tropical land TLT.

Surface temperatures cannot be understood separately from rainfall, and especially tropical rainfall. We can also conclude that as the decadal comparison of North Australian rain and surface-atmospheric differences have similar results for both Australia and Tropic Land datasets, UAH Version 6.0 represents TLT in various regions very well. Further, if the rest of the world’s tropical land areas behave as Australia does, then the world’s climate is dominated by tropical rainfall.

Rainfall and Temperatures Part 2

November 29, 2015

In my last post I showed how in Australia more than three quarters of the difference between surface maxima and tropospheric anomalies can be explained by variation in rainfall alone. Figure 12 from that post showed that clearly:

Fig. 1: 12 month rainfall vs surface maxima – TLT difference: Australia

max diff v rain scatterplot

In this post I am looking at the different contributions made by the north and the south of Australia. This map shows the regions used for climate analysis by the Bureau.

Fig. 2: Climate regions

Climate regions
Northern Australia and Southern Australia have vastly different climates, as shown by the following graphs of mean monthly rainfall:

Fig. 3: Mean Monthly Rainfall: Northern Australia

Nthn rain av

Fig. 4: Mean Monthly Rainfall: Southern Australia

Sthn rain av

The sheer volume of wet season rain in the north dominates, and distorts, the national means, therefore it is sensible to analyse northern and southern influences separately.

Northern Australia, north of 26 degrees South, has less than 3 degrees outside the Tropics, so may be considered the tropical northern half of Australia. Do not think of this as a lush tropical paradise. Far from it. There are a couple of very small areas of wet tropics, and some softer country in the far south-east, but the rest is either monsoonal wet/dry (very wet summers, almost completely dry for the rest of the year), or desert. The rainfall graph for Northern Australia is for the whole region- most of which is desert. If the monsoon is weak or fails altogether we get drought.

Southern Australia is largely influenced by the Southern Annular Mode. Each winter the southern high pressure systems move north, and cold fronts sweep across the south bringing winter rains. If these systems don’t move far enough north, the rain systems dodge the bottom of the continent, resulting in drought conditions. The far north-east of Southern Australia (southern Queensland and northern New South Wales) gets a wet summer/ dry winter pattern, and Tasmania and coastal New South Wales get rain in all seasons.

Comparing the influences of northern and southern rainfall on the national surface- TLT differences:

Fig. 5: Northern rain vs national maxima- TLT difference

Nthn rain v nat max diff

More than two thirds of the national maxima-TLT difference can be explained by variation in the northern rainfall alone.

Southern rainfall has a much weaker correlation with national maxima- TLT difference:

Fig. 6: Southern rain vs national maxima- TLT difference

Sthn rain v nat max diff

Now let’s plot Northern rain vs northern maxima- TLT (for the whole of Australia) difference, firstly for each month:

Fig. 7: Northern rain vs northern maxima- national TLT difference: monthly

Nth rain v nth diff monthly

Nearly two thirds of the monthly difference can be explained by monthly rainfall alone.

Fig. 8: Northern rain vs northern maxima- national TLT difference: 3 monthly

Nth rain v nth diff 3m

Three quarters of the 3 month mean surface maxima minus national TLT difference can be explained by rainfall.

Fig. 9: Northern rain vs northern maxima- national TLT difference: 12 monthly

Nth rain v nth diff 12m

R squared value of 0.8348 corresponds to a correlation of -0.91! But wait- there’s more! The 24 month means give an even better fit!

Fig. 10: Northern rain vs northern maxima- national TLT difference: 24 monthly

Nth rain v nth diff 24m

And 120 month means show an extremely close fit:

Fig. 11: Northern rain vs northern maxima- national TLT difference: decadal

Nth rain v nth diff 120m

97% of decadal northern surface maxima- national TLT difference is explained by decadal northern rainfall variation.

Fig. 12: Southern rain vs southern maxima- TLT difference: monthly

Sth rain v Sth diff monthly

Fig. 13: Southern rain vs southern maxima- TLT difference: 3 monthly

Sth rain v Sth diff 3m

Fig. 14: Southern rain vs southern maxima- TLT difference: 12 monthly

Sth rain v Sth diff 12m

More than half the difference between southern Australian maxima and TLT can be explained by southern rainfall variation.

However, 24 month means are not as good a fit:

Fig. 15: Southern rain vs southern maxima- TLT difference: 24 monthly

Sth rain v Sth diff 24m

And the long term means are a much poorer fit:

Fig. 16: Southern rain vs southern maxima- TLT difference: decadal

Sth rain v Sth diff 120m

Only 34% of the decadal southern Australian maxima-TLT difference is due to rainfall variation.

In tabular form:

Fig. 17: Range of rainfall anomalies and R-squared values for regional rainfall vs regional surface maxima- national TLT differences

Table rain r2


Australian climate is dominated by the tropics, and tropical rainfall variation dominates the national surface- troposphere differences, and even more so the tropical surface – national troposphere temperature differences: the greater the rainfall variation, the greater the difference between surface and tropospheric temperatures.

For a better analysis, we would need UAH anomalies for Australia separated into north and south of 26 degrees South.


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