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.

Pacific Sea Levels- Warming, ENSO, or Wind?

November 1, 2015

Apparently our Opposition Leader Bill Shorten, his deputy Tanya Plibersek, and immigration spokesman Richard Marles are heading off to the Pacific to discuss “the dangerous consequences of climate change”, all paid for by media baron Harold Mitchell, according to yesterday’s Weekend Australian.
They will visit Papua New Guinea, the Marshall Islands, and Kiribati (pronounced “Kiri-bahss”).
The President of Kiribati leads the complaints about the threat of global warming to his island nation. Kiribati has indeed seen foreshore erosion and salt water intrusion in recent years- just don’t mention causeway construction and underground water extraction.
Time for a reality check.
Sea level rise in Kiribati and the Marshalls has nothing to do with climate change and everything to do with the ENSO cycle, and winds in particular.
In this post I use data from the Australian Bureau of Meteorology’s Pacific Sea Level Monitoring Project at http://www.bom.gov.au/pacific/projects/pslm/. I also use NINO 4 data from http://www.cpc.ncep.noaa.gov/data/indices/sstoi.indices and Trade Wind Index data from http://www.cpc.ncep.noaa.gov/data/indices/wpac850.
Fig. 1: Island nations in the Pacific Sea Level Monitoring Project, also showing the area of the NINO 4 index. (Click graphics to enlarge).

Pacific MSL map
I have converted raw mean sea level data to monthly anomalies from 1995-2014 means, and scaled down NINO 4 and Trade Wind data, in order to make comparison easier.
This figure shows sea level data at all of the islands in the BOM’s Pacific Sea Level Monitoring Project.
Fig. 2: 12 month smoothed sea level anomalies, 1992-2015, for all islands in the Sea Level Monitoring Project. The vertical axis is in metres.

MSL graph all
Point 1: While there is broad agreement on rises and falls, the timing of the rises and falls is very mixed- some rise at the same time as others fall.

This is clearly shown by Kiribati and the Marshalls- when one is rising the other is falling.
Fig. 3: 12 month sea level anomalies at Kiribati and Marshalls, 1992 to 2015.

MSL M & K
Perhaps Mr Shorten can discuss why sea level changes at the Marshalls precede those at Kiribati by many months, such that they are presently moving in opposite directions.
Point 2: There is no doubt that from 1992 to 2015, throughout this region sea levels have been rising: both the Marshalls and Kiribati by 4.8 mm per year. However, since sea level rise occurs at different times, it cannot be due to temperature change. This is further reinforced by the next graphic.
Fig. 4: NINO sea surface temperature anomalies, January 1982- September 2015, 12 month means.

NINO indices
NINO 4 sea surface temperature anomalies since 1982 have almost zero (+0.01C per decade) trend (and the other indices show negative trend in sea surface temperatures). The tropical Pacific has not warmed.
At first glance, sea level change at these islands appears to correlate well with El Ninos and La Ninas, however close analysis shows a much more complex picture.
Fig. 5: 12 month running means of Kiribati sea level anomalies compared to NINO 4

K MSL v NINO4
Note that sea level changes several months before the NINO 4 index.
Fig. 6: 12 month running means of Marshall Islands sea level anomalies compared to the Trade Winds Index.

Tr Winds v MSL Marshalls
Note that the Trade Winds mostly change simultaneously with or some months before sea levels change.
Point 3: Sea level changes at Kiribati precede ENSO events, as measured by the NINO 4 index, but follow or match the Trades at the Marshalls (just to the north of the NINO 4 region).
However, in Figure 3 above, note that sea level anomalies at the Marshalls PRECEDE Kiribati.
So what is the cause of sea level rise in the western Pacific, which is an alarming 4.8mm per year at Kiribati and the Marshall islands?
Fig. 7: Trade Wind Index, December 1992 to September 2015

Trades trend
The Trade Winds have been increasing throughout the period of the Sea Level Monitoring Project, pushing surface water from east to west across the Pacific.
Fig. 8: Western Pacific winds at 1 November 2015

Pacific winds 1Nov 15(From http://earth.nullschool.net/#current/wind/surface/level/orthographic=163.83,1.50,277 )
At Kiribati westerly wind pulses, which help to initiate and strengthen El Ninos, push water against the low coral cay and raise the sea level at the tide gauge, (located on the western side of Tarawa, which is in normal and La Nina years the leeward side).

 

Fig. 9:  Tarawa Atoll

Tarawa atoll
When these winds slacken and trade winds strengthen, the sea level drops. In the rising sea level phase, the westerly winds push warmer water eastwards, and the weaker trades do not bring in as much cooler water from higher latitudes. Thus the rise in sea level at Kiribati precedes a rise in sea surface temperature, and the peak in sea level occurs about 5 months before the peak in NINO 4, one of the El Nino indicators.
Conversely, weaker trade winds bottom out some months before the bottom of sea levels at the Marshalls, which are to the north of the area affected by westerly wind pulses.
Therefore we can reassure Mr Shorten and his pals that sea level rise has little to do with climate change (unless alarmists claim that global warming will lead to stronger La Ninas, which lead to cooler temperatures- but I thought the plan was for more El Ninos).
Sea level rise is largely due to wind. And no doubt Mr Shorten will contribute to that.

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!

If the climate change debate was like a Rugby League Grand Final

October 5, 2015

After 21 seasons, finally the North Queensland Cowboys have won a nail biting RL Grand Final, beating the Brisbane Broncos 17-16 in Golden Point time.  J.T.’s under there somewhere!

It was a hard, fast, clean game right to the end, and in the end, the best team won.

It got me thinking, what would the climate change debate look like if it was conducted like a Rugby League game?

  • Both teams would play hard but fairly.
  • There would be a salary cap, so no team would have an unfair financial advantage.
  • The referees would be impartial, and would not award penalties unfairly against one side.
  • Anyone who infringed the rules (say by not letting an opposing player play the ball/ have his say) would be penalised.
  • Media commentators would give coverage to both sides, and show all plays both good and bad.
  • Referees, spectators, and the media would not try to prevent one team from even getting onto the field, or ban individual players from running on.
  • The game would be decided by points scored, not by the number of supporters of one team.
  • Politicians would not show up to be seen with the winning team…. Oh, wait….

Ah well, I can still enjoy the footy.

More on the absurd ACORN adjustment process

September 29, 2015

This is a Letter to the Editor of The Australian I sent recently, but not published.

Sir

Dr Jennifer Marohasy (Ideology adds heat to the debate on climate change, 29/9)  claims that sites prone to Urban Heat Island effect, such as Melbourne, have been used to adjust the temperature records at sites such as Cape Otway.

This is indeed absurd, but true.  Of the 104 sites used for climate analysis, 22 have been adjusted at least in part by comparison with sites whose artificially raised temperatures make them unsuitable for use in that same climate analysis.

The Bureau of Meteorology lists eight sites which are not used in climate analysis because their records exhibit Urban Heat Island effects: Townsville, Rockhampton, Sydney, Richmond (NSW), Melbourne, Laverton RAAF, Adelaide, and Hobart.

According to the Bureau’s “ACORN-SAT Station adjustment summary”, seven of these sites are still used as comparison sites when adjusting raw temperatures at other locations.  Adelaide is used at Snowtown and Port Lincoln; Townsville at Cairns, Mackay and Charters Towers; Rockhampton at Townsville, Mackay, Bundaberg and Gayndah; Sydney at Williamtown, Bathurst, Richmond, Nowra, and Moruya Heads; Laverton at Orbost, Sale, Wilson’s Promontory, Melbourne and Cape Otway; Melbourne at Orbost, Sale, Wilson’s Promontory, Laverton, Kerang, and Cape Otway; and Hobart at Launceston, Eddystone Point, Cape Bruny, Grove, and Butlers Gorge.

Richmond (NSW) is apparently the only site not used in the adjustment process.

Greg Hunt’s faith in the credibility of the Bureau of Meteorology is touching, but just as absurd.

Pause Update September 2015

September 11, 2015

UAH v6.0 data for August 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.

Globe:

global aug

Due to the strong El Nino, global temperatures are expected to continue to increase until May or June of 2016 (at least until February).  This will shorten the Pause.

Northern Hemisphere:

NH

Southern Hemisphere:

S hemis aug

Tropics (20N – 20S):

tropics aug

Tropical Oceans:

tropic oceans aug

The bulk of solar heating of the Earth occurs in the tropics, which is  mostly ocean, and ENSO events occur here.  Since October 1992, very much before the 1997-98 Super El Nino, there has been no warming at all.

North Polar:

N Pole aug

South Polar:

S Pole aug

Oops!  For the whole of the satellite record, there has been NO warming in the atmosphere above Antarctica.  Remember, one of the “fingerprints” of global warming due to the Enhanced Greenhouse Effect is greater warming towards the Poles.

USA 49 States:

USA aug

Australia:

aust aug

There has been no warming in the atmosphere above Australia for almost the whole lives of the current cohort of 1st Year Uni students. Just for comparison, the Australian ACORN-SAT surface data show a pause since February 2002- since they were in Preschool.

aust acorn

The Pause continues.

The Carbon Tax We Still Pay

September 7, 2015

Under Prime Minister Julia Gillard a Carbon Tax was introduced into Australia, set at $23 per tonne of CO2 equivalent, with numerous exclusions and a compensation package.  Apparently fulfilling his promise to get rid of the Carbon Tax, Prime Minister Tony Abbott’s coalition government succeeded in repealing it on 17 July 2014.

Unfortunately we still have a myriad of green schemes, solar bonus schemes, and of course our Renewable Energy Target.  How much does this cost us?  The following is based on regional Queensland, but applies Australia wide.

Ergon Energy provides electricity to all of Queensland outside the south-east corner.  With my last bill was included Ergon’s latest pamphlet for residential consumers, Issue #5 of “The Bright Side”.  Half of this issue was devoted to changes to electricity pricing and how it will affect consumers.  Ergon, and the Queensland government, have been claiming that after a couple of years of steep rises, 2015-16 will actually see a small drop in prices.  I read the information with interest, and as well checked with the Queensland Competition Authority (which sets prices).

Ergon summarises the changes to the typical Tariff 11 bill over a full year with this supposedly helpful graphic:

Ergon price changes

You will note that the cost to the average consumer of the solar bonus scheme and the Renewable Energy Target will rise by $23.  So what, you say, the average bill will reduce by $7.  Actually, it’s not so simple.   Ergon gives five scenarios of how it will affect consumers.  The QCA provides more detailed information, with percentages of the total cost.

qca elect prices

This is based on an annual Tariff 11 consumption of 4,053 kWh, which is the average for residential customers. From this, it is possible to calculate exactly the changes and how much of this goes towards solar and RET schemes.  As well, using an estimate of 0.86 Tonnes of CO2 per Megawatt-hour (0.84 – 0.88) for black coal power stations, it is possible to estimate how much CO2 the average consumer is directly responsible for.  Of course, the 0.86 is for the generation of electricity, not consumption, and consumption is about 83% of electricity generated.  This has been incorporated in my estimates.

In 2014-15, the direct additional cost to the average consumer of the solar and RET schemes was $146.63, rising in 2015-16 to $169.63.

This represents a direct additional cost to the consumer of approximately $34.90 per Tonne of CO2 emitted, rising to $40.40.

A direct additional cost imposed through government policy is a tax.  Applied to residential consumers it is a nasty regressive tax, as it applies to all regardless of income or capacity to pay.  The Solar Bonus Scheme portion is particularly cruel, as low income consumers are subsidising those who could afford and took advantage of this scheme, which will keep paying 44c a kWh feed in tariff for original systems until 2028, now reduced to 6.348c for new systems.  This cost the average consumer $106.64 last year, and the $20 extra is an increase of 18.75%.

Not only that, the Joe Hockey argument does not apply.  Poor people who do use less than 3,800 kWh will see an increase in their bill, while those who use more than 3,800 kWh will see a decrease, and proportionately less the more they consume.

This is robbing the poor to pay the rich.  It is set to continue with the proposed increases in the RET, so the poor will be subsidising inefficient green projects well into the future.  A scheme too good to be true certainly is.  Years ago I knew the Solar Bonus Scheme was an unsustainable scam and immoral.  Now, more than ever, both the Solar Bonus Scheme and Renewable Energy Targets should be completely abolished, with compensation for Solar Bonus users limited to initial cost of installation less subsequent feed in revenue.  Poor people have better things to spend their money on.


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