Archive for the ‘temperature’ Category

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.

The Pause: October 2015 Update

November 11, 2015

UAH v6.0 data for October were released on today.  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.  The strongest El Nino since 1997-98 has struck down its  first victim!  There is now NO pause in the Northern Hemisphere data.  However, in some regions it has lengthened.  Note: The satellite record commences in December 1978.  The entire satellite record is 36 years and 11 months long.

[CLICK ON IMAGES TO ENLARGE]

Globe:

Zero trend oct 2015 globe

There has been zero trend for over half the record.

Northern Hemisphere:

It might be something I’m getting wrong in my calculations, but the Pause has suddenly disappeared.

The trend since December 1997 is +0.17C/ 100 years +/-0.1C.

Southern Hemisphere:

Zero trend oct 2015 SH

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

Tropics:

Zero trend oct 2015 tropics

Unchanged from last month.

Tropical Oceans:

Zero trend oct 2015 tropic oceans

Unchanged from last month.

North Polar:

Zero trend oct 2015 N Polar

The Pause has lengthened by one month.

South Polar:

Zero trend oct 2015 S Polar

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

Australia:

Zero trend oct 2015 oz

The Pause has lengthened by two months.

USA 49 states:

Zero trend oct 2015 USA49

Four months shorter.

And now, in breaking news, for those who were waiting to hear whether satellite data confirm October 2015 as Australia’s hottest ever………

Oz October rankings

Sorry, but this October ranked 12th out of 37.  It made the hottest third (just).

The Pause continues.

Case Studies in “World’s Best Practice” 2: Kerang

November 5, 2015

Introduction:  This series of posts is intended to show that despite Greg Hunt’s loyalty, all is not right at the Bureau of Meteorology.

Please refer to my first post, Case Studies in “World’s Best Practice” 1:  Wilsons Promontory, for a complete description of the Bureau’s claims, the problems, data sources, and my methods.

Here are some further examples of “World’s Best Practice”.

********************

Kerang is on the Murray River, about 250 km from Melbourne.  The story of temperature adjustments here illustrates much that is wrong with the Bureau: misinformation, incompetence, lack of transparency, and unscientific behaviour.  This post took longer than expected because the more I looked, the more problems I found.

Note: Both maxima and minima at Kerang are warming. I have no comment on whether the adjustments are justified.  I am only interested in the methods used.

Problem 1: Missing data

The Bureau’s claim that they provide raw data as well as adjusted data is a half-truth, and completely misleading- some would say, dishonest.

The Bureau has adjusted Kerang maxima at 01/06/1957 and 01/01/1922, and minima at 18/01/2000 and 01/08/1932, and provides daily adjusted temperatures from 1/1/1910.

Unfortunately, there are NO daily raw data for Kerang before 1/1/1962.

Where are 52 years of daily temperatures?  How is it possible to have adjusted digitised data but no raw digitised data for half of the record?

Another issue brought to my attention is that there is an enormous amount of data missing even from Acorn: a large proportion every year before 1960, especially from 1932 to 1949, when 100 to 180 days are missing every year.

null days kerang

This lack of transparency makes it impossible to replicate and analyse the adjustments at Kerang.  If it can’t be replicated, with all data made available, it isn’t science.

Problem 2: Nonsense temperatures

There is only one instance when Acorn shows that the minimum temperature, the lowest temperature for the 24 hour period, was higher than the maximum temperature.

min max kerang

That dot at ‘0.6’ shows that on 2nd February 1950 the coldest temperature was 0.6C hotter than the hottest temperature!  Unfortunately it is impossible to compare with the missing raw data.

Any organisation that can’t perform a basic quality control test on its product is incompetent, as is any Review Panel or Technical Advisory Forum that endorses it.

Problem 3: Artificial warming 

Even though UHI makes Melbourne unsuitable for use in climate analysis, the Bureau still uses it to adjust the early data at Kerang!

Problem 4:  Neighbours

One of the neighbours used to adjust Kerang is Broken Hill, 477 km away, and another is Snowtown in South Australia, 565 km away.

Problem 5:  Results of adjustment

Comparison of differences between Kerang and its neighbours, pre- and post adjustment, using annual temperatures.

Firstly, minima, from the 2000 adjustment: Kerang minus neighbours, annual anomalies from 1985-2014.

Kerang comp 2000 min

The adjustment of -0.4C applied to years before 2000 is too great.  The slope of the mean difference from the neighbours is much too steep.

Next, for the 1932 adjustment (annual anomalies from 1917-1946 means):

Kerang comp 1932 min

Again, the adjustment is too great, as they make the differences from neighbours much greater.

The same pattern follows with maxima.  The 1957 adjustment (anomalies from 1944-1973):

Kerang comp 1957 max

And the 1922 adjustment (anomalies from 1910-1938):

Kerang comp 1922 max

In both cases Kerang is cooling compared with neighbours, but the adjustments reverse this and make Kerang compare less well with its neighbours.

Problem 6:  Undocumented adjustments

The Bureau lists only two adjustments to minima at Kerang:  -0.4 on 18/01/2000 and -0.61 on 01/08/1932.  This is not the whole story, as a plot of the actual annualised adjustments shows:

Kerang adjustments min

If the adjustments were as stated, the difference between adjusted and raw temperatures would be indicated by the blue lines.  The actual adjustments are shown by the brown lines.

The queried adjustments are not mentioned in the Bureau’s list here.

Similarly, there are two documented adjustments to maxima: -0.71 on 01/06/1957 and +0.33 on 01/01/1922.  These are visible in the next graph, but note the extra adjustment before 1950, and a series of adjustments from 1948 back to 1925.

Kerang adjustments max

I understand why these are needed: to adjust for the steadily increasing difference between Kerang and neighbours in this period.  But why was this not documented?

Thus we see at Kerang further misinformation and lack of transparency through failure to supply digitised raw data to allow replication; incompetence through not using basic checks for data integrity, resulting in publication of the “world’s best practice” temperature dataset with minimum temperatures higher than maximum; use of UHI contaminated sites when making adjustments; use of distant neighbours from different climate regimes; over-zealous adjustments resulting in worse comparison with neighbours than before; and undocumented adjustments.

Half-truths, incompetence, lack of transparency, and unscientific practices are evident at many other sites.  A proper investigation into the Bureau is overdue.

Case Studies in “World’s Best Practice” 1: Wilsons Promontory

October 26, 2015

Introduction: This series of posts is intended to show that despite Greg Hunt’s loyalty, all is not right at the Bureau of Meteorology.

The Bureau describes its methodology for creating the ACORN-SAT temperature reconstruction as “world’s best practice”, as it was described thus by the 2011 International Review Panel. The recent Report of the Technical Advisory Forum accepts this claim, reporting that “the Forum did not prioritise further international comparison of the Bureau’s curation methods in this report. However, the Forum will revisit this issue at its next meeting in 2016.”

In light of this endorsement, here are some examples of “World’s Best Practice”.
**********************************************************

Wilsons Promontory Lighthouse is on the southernmost tip of the Australian continent, about 170 km from Melbourne. The story of temperature adjustments here illustrates much that is wrong with the Bureau: misinformation, incompetence, lack of transparency, and unscientific behaviour.

Note: Both maxima and minima at Wilsons Promontory are warming. The Minima trend has been cooled, the maxima warmed.  I have no comment on whether the adjustments are justified. I am only interested in the methods used.

ACORN-SAT, (Australian Climate Observation Reference Network- Surface Air Temperatures), was introduced in March 2012, with several revisions mainly to bring the series up to date. It is a daily dataset of minima and maxima, from which monthly and annual means are derived, for 112 sites around Australia. Raw temperature data at these sites were homogenised by a complicated algorithm by comparison with neighbouring sites.

After much criticism, the Bureau has been forced to provide some answers, and agreed to ‘checking’ by a Technical Advisory Forum. The Bureau has provided additional information at the Acorn website, and in September 2014 released a list of the sites with adjustment dates, amounts, and the neighbour sites used for adjustment (see http://www.bom.gov.au/climate/change/acorn-sat/documents/ACORN-SAT-Station-adjustment-summary.pdf). Unfortunately, this additional information has raised more questions than it has unsuccessfully answered.

Problem 1: Missing data
The Bureau says at its FAQ No. 6 at http://www.bom.gov.au/climate/change/acorn-sat/#tabs=FAQs ,
the Bureau provides the public with raw, unadjusted temperature data for each station or site in the national climate database, as well as adjusted temperature data for 112 locations across Australia”, and at No. 8, “Daily digitised data are now available back to 1910 or earlier at 60 of the 112 ACORN-SAT locations, as well as at some non-ACORN-SAT locations.

This is a half-truth, and completely misleading- some would say, dishonest.

The Bureau provides raw data at Climate Data Online at http://www.bom.gov.au/climate/data/, and adjusted data at http://www.bom.gov.au/climate/change/acorn-sat/#tabs=Data-and-networks.

The Bureau has adjusted all Wilsons Promontory maxima before 1/1/1950, and minima before 1/1/1930, and provides daily adjusted temperatures from 1/1/1910.

Unfortunately, there are NO daily raw data for Wilsons Promontory before 1/1/1957.

Where are 47 years of daily temperatures? How is it possible to have adjusted digitised data but no raw digitised data?

Likewise, of the 10 neighbouring sites used for the pre-1950 maxima adjustments, only five have daily raw data before 1957, and for minima, only two (and one is Melbourne- more later). Were the adjustments made with only two comparisons? Otherwise, where are the data for the others?

This lack of transparency makes it impossible to replicate and analyse the adjustments at Wilsons Promontory. If it can’t be replicated, with all data made available, it isn’t science.

Problem 2: Nonsense temperatures
There are 79 instances when Acorn shows that the minimum temperature, the lowest temperature for the 24 hour period, was higher than the maximum temperature.

min max wils promThat dot at ‘1’ shows that on 5th December 1911 the coldest temperature was one degree hotter than the hottest temperature!

All of these occurred before 1950, so it is impossible to compare with the raw data.

The Bureau dismisses this as a minor hiccup of no importance, as an artefact of the adjustment process. The Bureau goes to great pains to explain how carefully the raw data was checked to remove any glaring errors and mistakes. On page 31 of CAWCR Technical Report No. 049, the section “Quality control checks used for the ACORN-SAT data set” describes a test for internal consistency of daily maximum and minimum temperature, which was carried out on the raw data of the ACORN-SAT sites. This test for minima greater than maxima, the first and most important quality control check, obviously was not applied to the adjusted data at all, and these nonsensical values remain years after sceptics made the Bureau aware. Any organisation that can’t perform a basic quality control test on its product is incompetent, as is any Review Panel or Technical Advisory Forum that endorses it.

 

Problem 3: Artificial warming
Here are the neighbouring sites used.

Maxima: East Sale Airport, Geelong SEC, Laverton RAAF*, Orbost, Queenscliff, Cape Otway Lighthouse, Melbourne Regional Office*, Essendon Airport, Currie, and Ballarat Aerodrome.

Minima: Cape Otway Lighthouse, Kerang, Melbourne Regional Office*, Eddystone Point, Geelong SEC, Bendigo Prison, Swan Hill PO, Cape Bruny Lighthouse, Currie, and Ballarat Aerodrome.

On page 71 of CAWCR Technical Report No. 049 is the statement, “the potential still exists for urbanisation to induce artificial warming trends relative to the surrounding region, and it is therefore necessary to identify such locations to prevent them from unduly influencing assessments of background climate change.

Included in the eight stations not used in climate analysis because their records exhibit Urban Heat Island effects are Laverton RAAF and Melbourne. Even though UHI makes Melbourne and Laverton unsuitable for use in climate analysis, the Bureau still uses them to adjust the data at Wilsons Promontory!

 

Problem 4: Neighbours
Cape Bruny Lighthouse is on the far south east coast of Tasmania, and is 509 km south of Wilsons Promontory. Kerang is on the Murray River, 413 km northwest, in a dry inland area, as is Swan Hill, 468 km away. Were there no better correlated sites nearer?

 

Problem 5: Results of adjustment.
To compare the temperature record at Wilsons Promontory with its neighbours, as we don’t have daily data, we can only use monthly or annual data. A simple but reliable method is to calculate the difference between Wilsons Promontory and each neighbour. This is done for raw and adjusted anomalies from the mean of a common baseline period. If Wilsons Promontory compares well with its neighbours, the differences should be close to zero, and most importantly, in spite of any short fluctuations, there should no trend: Wilsons Promontory should not be warming or cooling relative to its neighbours.

 

Unfortunately there are no monthly or annual data before 1957 for Eddystone Point or Bendigo Prison, so comparison is further restricted.

 

Firstly, minima: Wilsons Promontory minus neighbours, annual anomalies from 1916-1945, raw data.
raw min diffs wils prom

The differences range from +2 degrees to – 2 degrees, so there is plenty of variance, but the bulk of differences are +0.5 to -0.5 degrees. The spaghetti lines can be averaged to show the mean difference.
raw min avg diff wils prom

While there are periods of significant differences (1924-26, 1958-60, and 1974) it is plain that the raw data difference shows zero trend, indicating good comparison between Wilsons Promontory and its neighbours. Now compare the differences following the 1930 adjustment:
raw v adj min wils prom

The Acorn adjusted record preserves the periods of large differences, but has Wilsons Promontory cooling relative to its neighbours by more than half a degree per 100 years. The adjustment was too large.
Here is the comparison for maxima (anomalies from 1936-1965).
raw v adj max wils prom

The raw data show Wilsons Promontory cooling a little (-0.13C per 100 years) relative to the neighbours, but Acorn overcorrects, resulting in warming (+0.18C per 100 years) too much compared with the neighbours.

 
Problem 6: Site quality
On pp. 22-23 of Techniques involved in developing the Australian Climate Observations Reference Network – Surface Air Temperature (ACORN-SAT) dataset (CAWCR Technical Report No. 049) by Blair Trewin, March 2012, we find:-
Standards for instrument exposure and siting in Australia are laid down by Observations Specification 2013.1 (Bureau of Meteorology, 1997). Among the guidelines are:
• Sites should be representative of the mean conditions over the area of interest (e.g., an airport or climatic region), except for sites specifically intended to monitor localised phenomena.
• The instrument enclosure (if there is one) should be level, clearly defined and covered with as much of the natural vegetation of the area that can be kept cut to a height of a few centimetres.
• The distance of any obstruction should be at least four times the height of the obstruction away from the enclosure. (This criterion is primarily directed at elements other than temperature; for temperature the last guideline is more important.)
• The base of the instrument shelter should be 1.1 metres above the ground, with the thermometers approximately 1.2 metres above the ground.
• If no instrument enclosure is provided, the shelter should be installed on level ground covered with either the natural vegetation of the area or unwatered grass, and should be freely exposed to the sun and wind. It should not be shielded by or close to trees, buildings, fences, walls or other obstructions, or extensive areas of concrete, asphalt, rock or other such surfaces – a minimum clearance of five times the width of the hard surface is recommended.

 
The following photos are from Dayna’s Blog, a fascinating blog about bushwalking in SE Australia. (Interested readers are encouraged to visit https://daynaa2000.wordpress.com/ for some excellent walking tour information and photographs.)

 
The first view is towards the southwest, towards the direction of the prevailing south-westerly winds.
WilsonPLighthousenSolarPanels notes

Note the large areas of concrete under and near the Stevenson Screen; the nearby rock walls, the nearby solar panels almost directly to the south of the screen.

 
The second photo is in the opposite direction and shows the proximity of a building, another rock wall, and the steep slope of the site.
wilspromphoto east

These photographs make a mockery of the Station Catalogue description, which calls it “a very exposed location”. There are several man made features which surely influence temperatures recorded. Jennifer Marohasy recently asked the Bureau whether the solar panels would reflect onto the screen. The reply was,
“The angle of the panels means that any reflection from the panels is likely to only intersect the instrument shelter for a small part of the day during a limited part of the year. As the instrument shelter is fitted with double-louvered wall panels, it is virtually impossible that a direct beam of light would be able to enter the screen. Further, it is unlikely that the solar panels are influencing the instrument shelter as the shelter is painted to reflect direct and indirect radiation.”

 
Yet in the Station Catalogue for Alice Springs we find this statement “The site was enclosed by a rock wall about 1 m high and painted white that would have interrupted wind flow and reflected heat.”

 
They cannot have it both ways. If a 1m high rock wall interrupts wind flow and reflects heat in Alice Springs, then surely rock walls and buildings, large areas of concrete, and solar panels, all on a downward sloping lee side of a hill, will cause artificial warming at Wilsons Promontory.
Wilsons Promontory is a far from ideal site.

 
Thus we see at Wilsons Promontory misinformation and lack of transparency through failure to supply digitised raw data to allow replication; incompetence through not using basic checks for data integrity, resulting in publication of the “world’s best practice” temperature dataset with minimum temperatures higher than maximum; use of UHI contaminated sites when making adjustments; use of distant neighbours from different climate regimes; over-zealous adjustments resulting in worse comparison with neighbours than before; all at a very poor quality site.
Half-truths, incompetence, lack of transparency, and unscientific practices are evident at many other sites. A proper investigation into the Bureau is overdue.

The Pause September Update

October 16, 2015

UAH v6.0 data for September were released on Wednesday.  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.  The strongest El Nino since 1997-98 is affecting some regions more than others.  Note: The satellite record commences in December 1978.  The entire satellite record is 36 years and 10 months long.

[CLICK ON IMAGES TO ENLARGE]

Globe:

global sep

There has been zero trend for exactly half the record.

Northern Hemisphere:

NH sep

Southern Hemisphere:

S hemis sep

For more than half the record the Southern Hemisphere has zero trend.

Tropics:

tropics sep

Ditto!

Tropical Oceans:

tropic oceans sep

Even longer!

North Polar:

N Pole sep

Only 13 years and 7 months worth of Pause here.

South Polar:

S Pole sep

So much for a fingerprint of warming due to the enhanced greenhouse effect being greater warming at the Poles!

Australia:

aust sep

USA 49 states:

USA sep

The Pause continues.  To borrow a phrase, our children won’t know what warming looks like!


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