Archive for the ‘temperature’ Category

Temperature Variation Due to ENSO

July 25, 2016

In this post I use the Multivariate ENSO Index (MEI) supplied by NOAA at http://www.esrl.noaa.gov/psd/enso/mei/index.html and lower tropospheric temperature data supplied by UAH to show how much of temperature variation over the past 20 years is due to ENSO and how little is due to CO2.  I will keep words brief and let graphics do the talking.

Firstly, here is the MEI data from 1950:

Fig. 1:  Monthly MEI from 1950

mei monthly

As an aside, this is how it compares with SOI data.  The SOI is inverted and both are scaled for comparison.

Fig. 2:  MEI compared with SOI inverted

mei vs soi

Now compare scaled MEI with Global UAH:

Fig. 3: MEI (scaled) and UAH

mei monthly w uah

Notice tropospheric temperatures appear to lag the MEI by some 5 months:

Fig. 4: MEI advanced 5 months and UAH

mei monthly advd 5m w uah graph

Notice both datasets are noisy, and there is a clear discrepancy in the early 1990s.  12 month running means show this more clearly:

Fig. 5:  12 month means of UAH and MEI advanced 5 months:

mei advd w uah 12m

The slump in UAH data is shown by the arrow.  Mt Pinatubo’s main eruption was in June 1991. (Without El Chichon in 1982, there may well have been a much higher spike in the mid-1980s).

Now let’s look at the correlation between monthly MEI and UAH.  Firstly, the whole period from December 1978:

Fig. 6:  UAH vs MEI advanced 5 months 1978 – 2016

mei monthly advd 5m w uah

About 13% of temperature variation is associated with MEI variation.  Doesn’t tell us much does it.  What if we exclude the UAH data for two years from April 1982, and from July 1991 to December 1995?

Fig. 7:  UAH vs MEI advanced 5 months 1978 – 2016 with periods after volcanic eruptions excluded

mei monthly advd 5m v uah excl volcanoes

Considering the fluctuations in both datasets, that shows a fairly strong correlation.

Next, we examine the periods, before, during, and after the Pinatubo influence.

Fig. 8:  :  UAH vs MEI advanced 5 months December 1978 – June 1991, excluding April 1982 to March 1984

mei monthly advd 5m w uah 78-91

Again we see a similar correlation.

Fig. 9:  UAH vs MEI advanced 5 months July 1991 – December 1995

mei monthly advd 5m w uah 91-95.jpg

The strong positive correlation of the previous plots has broken down.

Fig. 10:  UAH vs MEI advanced 5 months January 1996 – June 2016

mei monthly advd 5m w uah 96-16

The correlation is even higher.  Over half of temperature variation is associated with ENSO variation five months previously.  Here is the same 1996-2016 plot but with 12 month running means:

Fig. 11  UAH vs MEI advanced 5 months January 1996 – June 2016, with 12 month running means

mei  advd 5m w uah 96-16 12m

74% of temperature variation for the past 20 years and 6 months can be explained by previous ENSO variation alone.  In the same period, carbon dioxide concentration at Mauna Loa has increased by 44.77 ppm, which is more than 49% of the entire increase from 1958, and Global temperature as measured by UAH has increased by a little over 0.1 degree C.

No wonder Global Warming Enthusiasts were pinning their hopes on the 2015-16 El Nino to put an end to the Pause, but they must also hope for the ENSO- temperature correlation to break down shortly, as a deep La Nina will mean cooler temperatures and further embarrassment for them.  However, the correlation breaks down when volcanoes cause lower temperatures in El Nino conditions as we have seen, but what mechanism could there be for higher temperatures in La Nina conditions?  Perhaps that magical greenhouse gas CO2?  That would indeed be spectacular- there are no outliers at the low end of any of the above plots.  The most UAH has been higher than expected with low MEI is about +0.2C to +0.3C, and those values cannot be described as outliers.  Besides, UAH for June is already down to +0.34C, and we are only four months past the peak- the cooling has barely begun.

Finally, this is a plot of the centred 37 month mean MEI (because La Ninas can last for three years).

Fig. 12: 37 month centred mean MEI

mei 37m avg

Notice that before 1975 the 37 month average never exceeded +0.5, the majority of the time was in negative territory, and in the 1950s and 1970s reached below -1.0.  Since 1975 the MEI has dropped below -0.5 only once in 2000 and approached -0.5 in 2012, but has been in positive territory for the vast majority of the time, exceeded +0.5 in six events, and was above +1.0 in the early 1990s.  It would be surprising if global temperatures had not seen a large increase.

How low will the monthly MEI go with the coming La Nina, and how low will the following global temperatures go?  All depends on La Nina’s length and strength, but the monthly MEI data are falling fast.  Stand by.

Temperature and Mortality

May 24, 2016

We are all going to die, nothing is surer. “Nobody knows the day or the hour”, but one thing is clear: we are more likely to die in winter than in summer.

Death by unnatural causes (suicide, accident, bushfire, disaster, even acute illness) can come to otherwise healthy people of any age. Death by natural causes is more predictable.

Those vulnerable to death are the elderly, very young babies, those with chronic illness (e.g. asthma, diabetes) and weakened immunity, and those with respiratory and circulatory illness.

Analysing mortality is made difficult because the sample population is always changing. Excess deaths in one month may be followed by further excess deaths in the following month, or because so many vulnerable people have already died, there will be fewer than expected deaths in the next month or months, or even the next couple of winters. Similarly, if fewer than expected deaths occur, there will be a larger cohort of the vulnerable in the following months, getting older and with probably poorer health. Population growth, aging, migration, improved vaccines, and public education programs all play a part as well.

In this analysis, I use mortality and population data from the Australian Bureau of Statistics (ABS), and temperature data from the Bureau of Meteorology (BOM), for Victoria, as it is a small and compact state which is subject to large temperature changes and also severe heat waves. Monthly mortality data are difficult to find, so this study is restricted to the period January 2002 to December 2011. A 10 year period is hardly sufficient for meaningful averages, however some useful insights can be found.

Mortality statistics are available by month, but population figures are by quarter, therefore I interpolated estimated monthly population figures based on three month growth.

Firstly, this plot shows the total deaths for every month from January 2002 to December 2011.

Fig. 1:

act D per mnth
Note the seasonal spikes and dips. The apparent increase in deaths can be compared with Victoria’s population increase:

Fig.2:

Population Vic
By dividing the total deaths by the population in thousands we can calculate the death rate:

Fig. 3:

Death rate per yr

Note the mortality rate has decreased, and that, in spite of heatwaves, bushfires, and flu pandemics, 2009 had a lower death rate than 2008.

Because months have varying numbers of days, a better analysis can be made by calculating the Daily Death Rate for each month (by dividing each monthly rate by 31, 30, 29, or 28 days).

Fig. 4:

mortality per month

For the state of Victoria for the 10 years to 2011, on average more deaths occurred for each day in August than for any other month. The lowest Daily Death Rate was in February.

Now compare with monthly averages (2002 to 2011) for maximum and minimum temperatures:

Fig. 5:

Tmax Tmin avg

The death rate peak lags July temperature by about a month. Cooler months (June to September) are deadlier than warmer (December to April).

The relationship with temperature can be shown with scatter plots:

Fig. 6:

DDR v Tmax

Fig. 7:

DDR v Tmin

Which merely reinforce that deaths are more likely in winter!

Now we look at the question of estimating how many deaths are likely in a given period, by multiplying the average daily death rate for each month by the number of days in each month and by the estimated total population for each month. By subtracting this figure from the actual number of deaths we get a mortality “anomaly”.  The following graph shows this anomaly for each year:

Fig. 8:

Act minus exp deaths per year

And each month:

Fig. 9:

Diff act minus exp Deaths per mnth

Note the peaks in the winters of 2002 and 2003, and also in the summer of 2008-2009. Note also that both graphs show that in spite of a killer heatwave, the Black Saturday bushfire, and the swine flu pandemic, deaths in 2009 were below what could be expected.

To put the anomaly for January 2009 into context, we can compare actual daily deaths per 1,000 population for all months from 2002 to 2011:

Fig. 10:

act daily D per mnth

Note that the extreme figure for January 2009, while extremely high for January, is still below those of the lowest extremes of June, July, and August.

Perhaps higher mortality in the winter months is coincidence and due to some other factor than temperature- seasonal flu incidence for example. I now look at the month of August with the highest average mortality rate:

Fig. 11:

Act minus exp deaths vs Tmin August

There is fairly decent correlation showing that for every degree warmer in minima, the August death toll will be around 150 less than expected.

February, with the lowest rate:

Fig. 12:

Act minus exp deaths vs Tmin Feb

Even in summer, warmer minima mean fewer deaths.

In summer, do higher maxima cause more deaths?

Fig. 13:

Act minus exp deaths vs Tmax Feb

Even including the 173 deaths in the Black Saturday bushfires in the 200 extra deaths for February 2009, there is no trend.

January, whose data include the 2009 heatwave:

Fig. 14:

Act minus exp deaths vs Tmax Jan

A very small trend, but the 2009 heatwave outlier is obvious and skews the data. (Victorian health authorities say there were 374 excess deaths in the week to 1 February 2009).

Extreme heatwaves are indeed killers. Normal hot summers up to two degrees above average are not.

Conclusion:

Improved public health measures, influenza vaccines, and improved public awareness – plus warmer winters- have led to a decrease in the Victorian mortality rate in the period 2002-2011.

Extreme heatwaves are dangerous in Victoria and cause hundreds of extra deaths especially amongst the elderly (>75 years old). However, these are rare events. Severe and Extreme Heatwaves are newsworthy precisely because they are unusual.

Normal Victorian winters are even more dangerous with on average 17.5% more deaths in winter than summer every year, but because this is normal and expected, this regular annual spike in deaths is unremarkable and not newsworthy- much less regarded as a natural disaster. While 374 excess deaths in a week in a heatwave is shocking, even with these included, the highest January’s Daily Death Rate (in 2009) is below that of the lowest of any winter month.

Warmer minimum temperatures are associated with lower death rates at all times of the year, but especially in August in Victoria, where for every degree of extra warmth, about 150 fewer deaths can be expected. I hope, for the sake of those who are sick or elderly, that we have a warm winter this year.

Antarctic Trends

April 17, 2016

Data from UAH Version 6.0 show the South Polar region to be unique in that it has a Pause, if not very mild cooling, for the whole of the satellite record, since December 1978. In this post I dig in a little deeper, and also look at surface data from Australia’s Antarctic bases.

Fig.1: Monthly TLT for the South Polar region (60- 85 S)

SP monthly

Fig. 2: Three Monthly TLT

SP 3m

Both plots show no evidence of any warming. However, Land areas are warming:

Fig. 3: SP Land: 3 month means

SP land 3m

While the Ocean area is cooling:

Fig. 4: SP Oceans: 3 month means

SP ocean 3m

Summers are warming:

Fig. 5: South Polar Summers (Yearly)

SP summer

While winters are cooling rapidly:

Fig. 6: South Polar Winters

SP winter

Especially Ocean winters, when the sea ice is at its greatest and thickest extent.

Fig.7:  SP Ocean Winters

SP ocean winter

Perhaps the sea ice insulates the atmosphere from the water below the ice? If so, in summer, with sea ice extent much reduced, the atmosphere above the ocean should be warmed much more than above the land, which is almost totally covered by ice. Let’s check:

Fig.8:  SP Ocean Summers

SP summer ocean

Fig.9:  SP Land Summers

SP summer land

Nope- TLT above land area is warming at four times the rate of ocean areas.

It’s not a great mystery. Here’s why.

We should not read too much into whether individual months create records or not, nor should we stress about the seasonal differences. Here’s an example of individual Octobers.

Fig.10: Octobers from 1979-2015

SP land october

Note the rising and falling pattern: a series of below average Octobers is followed by a series of above average Octobers.  A trend using only Octobers would show warming, as the record starts with below average Octobers and ends with above average. (Just like some global datasets!)

These patterns are evident, but with different values, in all months, which is why winters appear to be cooling and summers appear to be warming.

Fig.11:  SP Ocean Junes from 1979-2015

SP ocean junes

The most we can say is that the long term trend of ALL months shows no evidence of any warming, i.e. a Pause.

So is this just an artefact of the fairly short satellite record? We can check against surface data from Australia’s Antarctic stations at Mawson and Davis. (There is insufficient overlap to make a useful splice between closed and open sites at Casey.) These stations are on the coast far from the Antarctic Peninsula.

Fig. 12:  Monthly mean temperatures, Mawson Base

mawson mean

There is a Pause, or slight cooling, over the past 62 years.

Fig. 13: Monthly mean temperatures, Davis Base

davis mean

At Davis, a Pause, or slight warming, over the past 47 years.

The Pause in the South Polar region is real.

The Disconnect Between Theory and Reality- Part 2: Winters vs Summers

February 12, 2016

UPDATE:  PLEASE NOTE UAH DATA FOR THIS POST ARE FROM 6.0 BETA 4.  BETA 5 WILL GIVE DIFFERENT RESULTS.

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.

In this post I look at whether there is a pattern of greater warming in winter than summer.

This indicator appears to be FALSIFIED for both Northern and Southern Hemispheres:

Fig. 1:  Winter vs Summer, Northern Hemisphere (UAH)

summ win NH

Fig. 2:  Winter vs Summer, Southern Hemisphere (UAH)

summ win SH

And at the Poles:

Fig. 3: Winter vs Summer, Northern Polar region (UAH)

summ win NP

Summers warming faster than winters.  And in Antarctica:

Fig. 4: Winter vs Summer, Southern Polar region (UAH)

summ win SP

Winters (which are mostly night) are cooling much faster than summers.

In Australia overall however, winters are warming faster than summers.

Fig. 5: Winter vs Summer, Australia (UAH 1979-2015):

summ win Oz uah

And Acorn surface data since 1979:

Fig. 6: Winter vs Summer, Australia (Acorn 1979-2015):

summ win Oz acorn 7915

And since 1911:

Fig. 7: Winter vs Summer, Australia (Acorn 1911-2015):

summ win Oz acorn 19112015

However, the patterns are very different in different Australian regions.  North Australia has winters warming faster than summers:

Fig. 8: Winter vs Summer, Northern Australia (Acorn 1911-2015):

summ win Oz nth

While Southern Australia has exactly the reverse:

Fig. 9: Winter vs Summer, Southern Australia (Acorn 1911-2015):

summ win Oz sth

Let’s look at different parts of the South, first the South East:

Fig. 10: Winter vs Summer, South Eastern Australia (Acorn 1911-2015):

summ win Oz SE

And the South West:

Fig. 11: Winter vs Summer, South Western Australia (Acorn 1911-2015):

summ win Oz SW

This shows a particularly strong summer warming effect.

In the North, the pattern seems driven by greater summer rainfall and drier winters:

Fig. 12:  Summer and Winter rainfall anomalies, Northern Australia

summ win Oz rain Nth

There has been much less winter rain in the Southwest (in the Southeast, there has not been as much variation):

Fig. 13:  Summer and Winter rainfall anomalies, South Western Australia

summ win Oz rain SW

In both the North and Southwest, there are distinct changes in rainfall in the late 1960s or early 1970s:

Fig. 14:  Northern Summer rainfall changes

summ rain Nth

Note the long term slow decrease to 1973, the wet 1970s and dry 1980s, and all except 6 wetter than average seasons since 1991.

By contrast, the South Western rainy season shows a long term slow increase with great variability until the 1960s, with a sharp step down in 1969, and another in 2001, with less year to year variability.

Fig. 15:  South Western Winter rainfall changes

winter rain SW

This shows up in trend maps of summer and winter rainfall 1970-2014:

Fig. 16:  Trends in summer rainfall

summ rain 19702014

Fig. 17:  Trends in winter rainfall

winter rain 19702014

The effect of less winter rain on temperatures in the following summer in South Western Australia is clearly seen in this scatterplot:

Fig. 18:  Summer means and previous winter rain:

summ T vs win rain SW

While the IPCC and its acolytes in the Climate Council predict less rainfall for southeastern and southwestern Australia, this would not be difficult given the trend for southwestern Australia had been established for 20 years before the IPCC was even formed, and 45 years before AR5. Northern Australian rainfall is not mentioned.

Assessment of this evidence for the enhanced greenhouse effect: FAIL.  Tropospheric data show this to be falsified in both Hemispheres and both Poles.  Australia appears to go against this pattern, but drastic changes in rainfall patterns in the Northwest and Southwest appear to be involved in the difference between north and south.

Theory has been mugged by reality yet again.

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.
 
Tropics:

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: 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.

[CLICK ON IMAGES TO ENLARGE]

Globe:

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.

Tropics:

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!

Australia:

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!

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

Conclusion:

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.

Why Are Surface and Satellite Temperatures Different?

November 20, 2015

Many are puzzled by the difference between surface temperature, measured in Stevenson screens, and atmospheric temperature, as measured by satellites. Some sceptics suspect surface temperatures cannot be trusted; some global warming enthusiasts claim satellite data are not accurate. The truth is both are accurate enough to be useful for their own purposes. But why the difference?

I have used data from the Bureau’s Climate Change Time Series site for monthly rainfall and surface temperatures for Australia, and from University of Alabama-Huntsville (UAH) for Temperature of the Lower Troposphere (TLT) anomalies for Australia, from December 1978 to October 2015. I converted rainfall and surface temperatures to anomalies from monthly means 1981 – 2010, the same as UAH. Throughout I use 12 month running means.

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

The context:

This map shows the location of the Acorn surface temperature observing sites.

Fig.1: ACORN-SAT sites

Acorn network

Note the scale at bottom left, and that they are concentrated in the wetter, more closely settled areas. As with rainfall observing sites:

Fig.2: Rainfall observation sites

Rainfall network awap

Fig.3: mean annual rainfall:

Avg ann rain map

The scale is in millimetres: divide by about 25 to get inches. Consequently a very large area of Australia is desert, and another large area is grassland with few or scattered trees. Very little of Australia is green for more than a few months of the year. More on this later.

The data:

Here are 12 month running means of the Bureau’s Acorn maxima and minima since December 1978.

Fig. 4: 12 month running means of monthly maxima and minima anomalies (from 1981-2010 means) for Australia

Max v min

Note that minima frequently lags several months behind maxima- which is why mean temperature doesn’t give us very much useful information.

Now compare surface temperature with the lower troposphere:

Fig. 5: Minima vs TLT anomalies

min v uah

Fig. 6:  Maxima vs TLT anomalies

max v uah

TLT approximately tracks surface temperature, but with smaller variation. So what causes the difference between surface and atmospheric temperatures?

The culprit is that wicked greenhouse gas, H2O.

In the following graphs 12 month mean rainfall is scaled down by a factor of 25, and inverted: dry is at the top and wet is at the bottom of these plots.

Fig. 7: Maxima vs Inverted Rain

max v rain

It is plainly obvious that very wet periods mostly coincide with low maxima, and dry periods with high maxima.

Fig. 8: Minima vs Inverted Rain

min v rain

Again, minima has no immediate relation with rainfall (although cloudy nights are warmer), lagging many months behind.

Next I calculate the difference in anomalies- surface temperature minus TLT- to analyse the difference between surface and satellite data. As minima lags many months behind rainfall a close relationship is not expected.

Fig.9: Acorn minima anomalies minus TLT anomalies compared with rainfall anomalies

min diff v rain

However, Acorn maxima minus UAH matches rainfall remarkably well.

Fig.10: Acorn maxima anomalies minus TLT anomalies compared with rainfall anomalies

max diff v rain

It is not an exact match of course. The next graph plots the surface maxima- TLT anomaly difference against 12 month mean rainfall anomaly sorted from smallest to largest, with the horizontal axis showing monthly percentile rank by rainfall:

Fig.11: Comparison of maxima-TLT anomaly difference with ranked rainfall anomalies

Max-UAH v rain%

Note that the surface- atmosphere difference tracks rainfall quite closely (+/- about 0.5C), with the largest positive and negative differences at the rainfall extremes, and also that the 12 month period where the rainfall anomaly crosses from negative to positive is at the 59th percentile: there are more dry months than wet months.

Another way of showing the relationship is with a scatterplot:

Fig.12: Surface maxima- TLT difference compared with rainfall

max diff v rain scatterplot

Note the R squared value: 0.76! At least three quarters of the difference can be explained by rainfall variation alone- not bad across a whole continent with a northern wet summer / dry winter and a southern wet winter / dry summer pattern.

An over simplified explanation of a complex process:

In wetter than normal weather, more and thicker clouds reflect sunlight and shade the surface, keeping it cooler than normal. Moisture from the surface (and vegetation) is evaporated, also cooling the surface. Deep convective overturning occurs during the day and evaporated moisture ascends in the atmosphere, where it condenses, releasing heat. The troposphere anomaly is thus relatively warmer than the surface anomaly in moist conditions such as during wet weather.

In a drought, fewer clouds allows more sunlight to heat the surface. The ground is dry; surface water is scarce; vegetation is thinner, drier, and shades less of the ground. Therefore the surface is hotter than normal. Less evaporated moisture means less condensation releasing heat in the troposphere, and therefore the troposphere anomaly will be relatively cooler than the surface anomaly.  As well, as the Bureau explains, ” the rate at which temperatures cool with increasing altitude (known as the lapse rate) is greater in dry air than it is in moist air.”  Thus in dry weather, ignoring convection, the atmosphere will be cooler than normal.

Yes, but…

So how does this explain why the October 2015 surface maximum anomaly was a record +3.08C above the 1981-2010 mean, while the UAH anomaly was a mere +0.71C, and the rainfall anomaly was only -12.75mm, nowhere near the lowest?

This map shows the Normalised Difference Vegetation Index for October. The Bureau explains the index as a measure of “the fractional cover of the ground by vegetation, the vegetation density and the vegetation greenness”.

Fig.13: Normalised Difference Vegetation Index (NDVI) October 2015

Vegetation Oct 2015

What do the dark brown areas look like on the ground? Here’s a photo I took recently around about the area circled red:

Fig.14: Droughted country, Western Queensland, September 2015

Bare, dry dirt with scattered tussocks of dead grass- scattered prickly acacia in the distance.

A large area of Australia is relatively bare and bone dry, therefore hotter. Over wide areas, much less moisture is convected into the atmosphere, which will thus be relatively cooler than surface anomalies. North winds blowing from the interior towards the south will bring hot dry air even to green areas, causing much hotter surface temperatures there as well. Much of the moisture evaporated from these wetter areas is blown out to sea (outside the UAH Australian grids) so the TLT over even these green areas is relatively cooler than expected.

Conclusion:

Atmospheric temperature anomalies are necessarily different from surface anomalies. Usually, atmospheric anomalies are less than surface maxima in hot periods and higher than surface anomalies in cool periods.

There is no conspiracy: over three quarters of the difference between surface and atmospheric temperature anomalies is due to rainfall variation alone.


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