Archive for the ‘temperature’ Category

The Australian Temperature Record- A Quick Update

August 23, 2014

This morning (Saturday 23 August) the Weekend Australian published articles by Graham Lloyd, their Environment Editor, on homogenisation practices at the Bureau of Meteorology as questioned by Jennifer Marohasy.  As I had a small part to play in bringing this to public light, here is a brief post to bring readers up to date.

The last paragraph in the second article reads:

“And the bureau says an extensive study has found homogeneity adjustments have little impact on national trends and changes in temperature extremes.”

This is laughable.  Here is a graph of the national means of Raw and Homogenised minima data from 83 sites (out of 104) that I was able to compare directly. (I also analysed the remaining sites, finding 47% bias, but because large slabs of data had to be left out this is not reliable.)

Fig. 1: Australian mean minimum temperature anomalies 1910-2012

Tmin comp

 The ‘raw’ trend is +0.63C per 100 years.  The adjusted trend is +1.05C.  The effect of the homogenisation adjustments is an increase in the national trend of +0.42C or 66.6%.  So much for “little impact.”

The article referred mainly to adjustments at Amberley and Rutherglen.

Fig. 2:  Amberley minima

amberley tmin

According to the BOM, the major adjustment was due to a pronounced discontinuity around 1980, that is, Amberley’s drop in temperature is not reflected in those of neighbouring sites, as is evidently correct.

Fig. 3:  Amberley compared with the mean of 5 Acorn neighbours

amb raw v reg mean raw

 However, the nearest Acorn site only 50km away, Brisbane Aero, also has a pronounced cooling trend, and a local cooling cannot be discounted.

An adjustment to the raw data before 1980 may be warranted, however, the size of the adjustment is questionable to say the least.  The resulting trend at Amberley has now become greater than the trend of adjusted data at every one of the Acorn neighbours, and more than +0.86C greater than their mean.

Fig. 4: Amberley’s adjusted (Acorn) data vs mean of adjusted data at 5 closest Acorn sitesamb acorn v reg acorn

Rutherglen in Victoria again shows cooling turned into warming.

Fig.5: Rutherglen minima

rutherglen tmin

And again, the Acorn adjustments make Rutherglen’s trend greater than every one of its neighbours’ adjusted trends, as well as their mean:

Fig. 6: Rutherglen Acorn vs neighbours’ Acorn (mean)

rutherglen acorn v reg acorn


The BOM is defending its territory, but this latest media exposure will mean increasing and critical scrutiny.

Click for further examples and background.




Adjustments vs CO2

August 3, 2014

Steven Goddard has posted about the remarkable correlation between USHCN adjustments and atmospheric carbon dioxide concentrations:

goddard co2

Here’s my plot of Australian adjustments to minima, Acorn minus raw vs CO2 data (downloaded from NASA GISS at ):

acorn vs co2

R2= 0.777 not as impressive as 0.988, so not proof of anything except past cooling adjustments which we already knew.  Interesting all the same.

Tarcoola- A Cooling Outlier

July 28, 2014

In a previous post I looked at the warming outliers in the Acorn network- those sites that had homogenisation adjustments that created a difference of more than +2 degrees Celsius between the Acorn trend and the raw trend in minima.  In all of these six examples, the adjustments had created trends that were not just greater than the raw trends at each site, not just the mean of their Acorn neighbours raw data trends, but greater than the Acorn trends of their neighbours, and in all but one, greater than each of the individual Acorn trends of their neighbours.

In this post I consider the opposite scenario.  I look at one cooling outlier, Tarcoola in South Australia, where the cooling adjustments have created a difference in trend of -2.81C per 100 years.  There is one other, Forrest in W.A., with an enormous cooling adjustment of around -2.14C, but I have little faith in the accuracy of the data there.  Greg Geegman suggested in a comment that if a site that is adjusted downwards is cooled relative to the neighbour group, this may indicate the Percentile Matching algorithm operates iteratively, although Technical Report No. 49 does not mention this.  An alternative explanation might be that the algorithm is too sensitive and exaggerates necessary adjustments.

All data may be downloaded from the BOM website: Site networks and Climate Data Online.

Tarcoola is in the centre of South Australia:Tarcoola map

As before, I compare Tarcoola with its neighbours in the Acorn network, using anomalies from the 1961-1990 mean.

Fig. 1:  Tarcoola Acorn vs ‘Raw’ minimaTarcoola tmin

Cooler trend than raw.  Note the spurious data pre-1930.

Fig. 2: Tarcoola raw vs mean of Acorn neighbours’ rawtarcoola raw v reg mean raw

Tarcoola appears to need cooling.

Fig. 3: Tarcoola Acorn vs mean of neighbours’ meantarcoola acorn v reg raw

Cooler trend than neighbours’ raw

Fig. 4: Tarcoola Acorn vs mean of neighbours’ Acorntarcoola acorn v reg acorn

Cooler trend than neighbours Acorn

Tarcoola Acorn trend is also cooler than each of the neighbours’ individual Acorn trends.

So which neighbours were used to make the Tarcoola adjustments?


Both warming and cooling outliers show Acorn adjustments outperforming those of the neighbours. This suggests that the algorithm exaggerates adjustments, both warming and cooling, and needs serious re-examination.

Carnarvon- a closer look

July 24, 2014

A lot of interest was generated by my last post on the Acorn outliers, especially the dependence on very distant sites for homogenisation adjustments.  In this post I will compare Carnarvon’s closer neighbours – excluding Wittenoom, Meekatharra, and Morowa- to show how  a better understanding of Carnarvon’s minimum temperature record may be derived, and how reliance on more distant sites with different climate regimes can distort adjustments.

Carnarvon Airport 6011 has been recording temperatures since 1945.  Before that, Carnarvon Post Office 6062 has data from 1885 to 1950 so there is useful overlap.

Fig. 1: Carnarvon PO and Carnarvon Airport (raw minima)carnarvon raw

These records can be spliced by reducing the Post Office data for 1946-1948 by 0.4C (as the Post Office recorded increasingly warmer minima than the Airport in these years as shown by the monthly temperatures for 1949-1950) but making no changes to PO data before this, to produce a long composite record.  I commence at 1910 to compare with the official BOM figures.

Fig. 2: Carnarvon splice vs Carnarvon Acorncarnarvon tmin

Note how Acorn reduces minima from 1974.  Note the size of these adjustments.

Fig. 3: AdjustmentsCarnarvon adjustments

So at 1910 the Acorn record shows minima 1.8C less than the raw data.

Now lets look at how Carnarvon’s neighbours compare.  To do this we need to convert to anomalies from the 1961-1990 mean.  Neighbours are listed at Climate Data Online, ranging from 19 km  to 296 km away, but most are too short or have too much missing data to be useful.

The neighbours that I have used for comparison are:  Hamelin Pool (174.4 km away), Winning (211.3 km), Emu Creek (248.5 km), and Learmonth (296.3 km).  Learmonth is almost due north of Carnarvon, and like Carnarvon right on the coast, while the others are inland sites.

Fig. 4: Carnarvon and closest useful neighboursCarnarvon neighbours

We can see that there is close agreement for most of the time.  There are minor periods of disagreement in the 1970s and 1990s, but the major disagreement is 1926-1950.  Which is a more accurate reflection of Carnarvon minima- Carnarvon PO or Hamelin Pool?  To find which is the odd one out, we need to look at other sites with data for this period.

Fig. 5: Carnarvon vs Winning- anomalies from 1910-1939 meanCarnarvon v Winning

Winning, although confirming the approximate agreement 1910-1920,  has very little data for 1926-1950, so we have to look further afield… all the way to Geraldton, which like Carnarvon is on the coast, but 447 km south.

Fig. 6:  All neighbours including GeraldtonCarnarvon inc Geraldton

Hamelin Pool is clearly the outlier, so we can accept Carnarvon raw temperatures as reasonably accurate from 1910 to 1970.  There is a short period in the 1970s of disagreement, but little difference after that… and Acorn does not adjust after 1974 anyway.

How does this compare with Acorn?

Fig. 7: All anomalies including AcornCarnarvon acorn vs all

Can you pick the outlier?

We can only presume that the Acorn homogenisation depends on data fed into the algorithm from much, much further away.

I can see no justification for any major adjustment to the raw record at Carnarvon.


The Australian Temperature Record Revisited: Part 4- Outliers

July 16, 2014

In my previous posts I showed how the Acorn adjustments to the ‘raw’ minimum temperature data have the effect of enormously increasing the apparent trend across the whole network, and very differently in different regions.  In this post I am looking more closely at the six locations where the adjustments cause a change in trend of greater than +2 degrees Celsius.  These are:  Brisbane Airport, Amberley RAAF, Dubbo, Rutherglen, Rabbit Flat, and Carnarvon.

And I am mystified.

The purpose of homogenisation adjustments is to remove discontinuities in data, which show up as differences between the ‘candidate’ site’s record and those of its neighbours, the ‘reference’ sites.  The Acorn method of detecting discontinuities uses pairwise comparison with up to 40 neighbouring sites, and this includes sites many hundreds of kilometres distant.  Adjustments are made with a Percentile Matching algorithm which compares with up to 10 neighbouring sites.

I use my own method to compare sites with neighbours.  When comparing any sites, anomalies from a common base period (1961-1990) are used.  Only sites with data (at least 15 years) in this period can be used.  Sites also need long data records.  While in closely settled areas there will be a selection of observation sites, very few meet these requirements.  Therefore I compare the data of each of these six locations with those of their nearest surrounding Acorn sites’ ‘raw’ data, (adjusted by me only when necessary to create a long combined series), individually and with their mean.

Even with only five neighbours, for Carnarvon and Rabbit Flat these can be over 500km away.

I then repeat this using Acorn (adjusted) data for the neighbours.

The results are surprising.

Here are the six outliers and their surrounding Acorn neighbours:Outliers map

Note the remoteness of Rabbit Flat and Carnarvon.


Brisbane Air

Fig. 1a: Brisbane ‘raw’ spliced vs Acorn minimabris raw v acorn

The neighbours are: Amberley, Cape Moreton Lighthouse, Bundaberg, Gayndah, Miles, and Yamba Pilot Station.

Fig. 1b: Brisbane raw vs mean of neighbours (‘raw’ data)bris raw v reg mean raw

Fig. 1c: Brisbane Acorn vs neighbours’ raw meanbris acorn v reg raw

Note the adjusted trend (+1.95C per 100 years) is greater than the mean of the neighbours (+1.06) by nearly +0.9C.

Fig. 1d:  Brisbane Acorn vs mean of neighbours (Acorn, adjusted data)bris acorn v reg acorn

As you would expect, the data are now very similar, and the trend for Brisbane is thus 0.23C per 100 years less than the trend for the mean of the neighbours’ Acorn data.  (This is the only outlier site where this happens, as you will see.)


Neighbours are the same as Brisbane’s, including Brisbane, 50km away.

Fig. 2a:  Raw vs Acornamberley tmin

Fig. 2b: Amberley and mean of neighbours (raw).amb raw v reg mean raw

Fig.2c: Amberley Acorn vs neighbours mean (raw)amb acorn v reg raw

Note the trend is more than one degree steeper than the trend of the neighbouring Acorn sites’ raw data.

Fig.  2d: Amberley Acorn vs neighbours’ mean (Acorn)amb acorn v reg acorn

Amberley’s adjusted trend is +0.87C greater than that of the mean of its neighbours’ adjusted data.


Neighbours are: Gunnedah, Scone, Bathurst, Cobar, Wyalong

Fig. 3a:  Raw vs Acorndubbo tmin

Fig. 3b: Dubbo and mean of neighbours.dubbo raw v reg mean raw

Fig.3c: Dubbo Acorn vs neighbours mean (raw)dubbo acorn v reg raw

+1.47C difference.

Fig.  3d: Dubbo Acorn vs neighbours’ mean (Acorn)

dubbo acorn v reg acorn

Now only +1.29C per 100 years greater than the neighbours.


Neighbours are: Deniliquin, Wagga Wagga, Sale, Kerang, Cabramurra

Fig. 4a:  Raw vs Acornrutherglen tmin

Fig. 4b: Rutherglen raw and mean of neighbours (raw).rutherglen raw v reg mean raw

Note that Rutherglen’s cooling trend is only 0.3C different from that of its neighbours.

Fig.4c: Rutherglen Acorn vs neighbours mean (raw)rutherglen acorn v reg raw

Fig.  4d: Rutherglen Acorn vs neighbours’ mean (Acorn)rutherglen acorn v reg acorn

+0.51C per 100 years greater than the neighbours.

Rabbit Flat

Rabbit Flat is a roadhouse in the Tanami Desert on the track between Alice Springs and Halls Creek.  Climate Data Online shows the current Rabbit Flat site 015666 as being 71km away from the old closed site 015548, though the Acorn Station Catalogue says it’s only 200 metres.  This in itself is peculiar.

The nearest non-Acorn site is Balgo Hills 211 km  away.

Acorn neighbours are:  Giles (567km), Halls Creek (328km), Victoria River Downs (433km), Tennant Creek (440km), and Alice Springs (568km).

Fig. 5a:  Raw vs Acornrabbit flat tmin

Fig. 5b: Rabbit Flat and mean of neighbours (raw).rbt flt raw v reg mean raw

Fig.5c: Rabbit Flat Acorn vs neighbours mean (raw)rbt flt acorn v reg raw

+1.17C more warming than neighbours.

Fig.  5d: Rabbit Flat Acorn vs neighbours’ mean (Acorn)rbt flt acorn v reg acorn

Rabbit Flat adjustments give it a trend +0.75C more than the neighbours’.


Carnarvon’s Acorn neighbours are Learmonth (298km), Wittenoom (560km) , Meekatharra (524km), Geraldton (447km), and Morawa (538km).  The only non-Acorn site with continuous data for the early part of last century is Hamelin Pool 6025 (174km away).

Fig. 6a:  Raw vs Acorncarnarvon tmin

Fig. 6b: Carnarvon and mean of neighbours (raw).carnarvon raw v reg mean raw inc morawa

Now note the effect of just one of the neighbours- Morawa.

Fig. 6c:   Carnarvon raw vs neighbours’ mean excluding Morawacarnarvon raw v reg mean raw excl morawa

Note the much closer comparison.

Fig.6d: Carnarvon Acorn vs neighbours mean (raw) (including Morawa)carnarvon acorn v reg raw

Note the trend is +1.58C per 100 years more.

Fig.  6e: Carnarvon Acorn vs neighbours’ mean (Acorn)carnarvon acorn v reg acorn

The difference is +1.49C.

The Acorn trend at Carnarvon is also greater than the Acorn trends at each of the neighbours separately.

Conclusion: -

The Acorn adjustment algorithm creates homogenised data by comparing with up to 10 neighbouring sites.  I have shown that the adjustments have made the Acorn trends greater than, not only the raw data trends for each site, not only the raw data trends of the closest neighbours in the Acorn dataset, but in every case but one, greater even than the trends of Acorn homogenised data from the same neighbouring locations.   The adjustments created thus appear to be spurious and the algorithm faulty.


The Australian Temperature Record Revisited Part 3: Remaining Sites

June 15, 2014

In Part 1 of this series I showed a 66.6% increase in warming trend of Australian annual minimum temperatures caused by adjustments to the ‘raw’ data.  This was based on analysis of 83 of the 104 Acorn sites, as I restricted my study to only those sites with at least 24 months overlap between old and new stations within 30 kilometres.  I now turn to the remaining sites.

These remaining sites all have records less than the full 103 years, as I only use the longest available record from a single site, with no splicing to form a composite record.  I truncated Acorn and ‘raw’ annual data to exactly the same start and end dates.  Trends calculated over these shorter periods are therefore exaggerated.  As well, some of the records show enormous gaps.  Trends calculated for these sites showed much less warming bias than the 83 I first analysed: the mean difference in trend was +0.26 C, or 26.7% increase.  This is not a meaningful metric, however.  The crucial measure is the effect of the adjustments across the whole network.  To do this, temperature data must be converted to anomalies from the 1961-1990 mean.  This meant the loss of Tennant Creek PO, which has insufficient data in this time period.

Here, then, is the result for 103 of the 104 Acorn sites.  Figure 1 shows the straight mean of minima anomalies for the 103 sites for which data can be compared, ‘raw’ vs Acorn.

Fig.1:103 chart

The adjustments to the ‘raw’ data have the effect of increasing the trend in minimum temperatures from +0.7C per 100 years to +1.03C, or 47%.  Going by this plot, the increase is by nearly half rather than two-thirds- still embarrassingly large.  However, large slabs of data are missing or unaccounted for.  I have zero confidence that the trend in minima is +0.63, +0.7, +1.0, or +1.03, or any other figure, and an average trend for Australia is meaningless given the wide differences in different parts.  By the way, with no stations missing, the warming bias in Victoria is still +350%.

In my next post I will look at some of the ‘outlier’ sites with very large differences in trend.

The Australian Temperature Record Revisited Part 2: Regional Effects

June 1, 2014

In my last post I showed how a numerical near-balance of adjustments to the ‘raw’ minimum temperatures at 83 out of 104 Acorn sites resulted in a 66.6% increase in warming trend across the nation.

I now turn to the effect on state and regional temperatures, which is enormously varied.

Figure 1 shows the official BOM trend map of trends in minima from 1910 to 2013:Trend map min

Note the little “bulls eyes” in various places, indicating where the local trend at individual sites is out of sync with the wider trend.  I’m sure you can identify Tibooburra in north western NSW, Richmond in northern inland Qld, Rutherglen in Victoria, Marree in northern SA, and Carnarvon on the WA coast.

Figure 2 shows the median position of all 104 sites, the four unequal area quadrants, and the number of sites I analysed in each with the increased warming resulting from adjustments.
Median network position map adj results

The concentration of Acorn sites in the south east of Australia, and the concentration of warming adjustment there as well, is plainly obvious.

Now I shall show each quadrant in turn, showing the trend difference at each site.

Figure 3:  South west Quadrant sites:
Bar graph SW Quad

Figure 4: SW Quadrant minimum temperature trends:SW quad chart

Figure 5:  North west Quadrant sites:Bar graph NW Quad

Figure 6:  NW Quadrant minimum temperature trends:NW quad chart

Figure 7:  North east Quadrant sites:Bar graph NE Quad

Figure 8:  NE Quadrant minimum temperature trends:NE quad chart

Figure  9:  South east Quadrant sites:Bar graph SE Quad

Figure  10: SE Quadrant minimum temperature trends:SE quad chart

In the next section I look at how the adjustments affect the mean minima in each state.  First I’ll look at the Northern Territory, which is atypical and based on only three sites (Alice Springs, Victoria River Downs, and Rabbit Flat), the two last with less than 50 years of observations.

Figure  11:  Northern Territory- cooling reversedNT Chart

Figure 12:  South Australia- adjustments result in less warmingSA chart

Figure 13:  Tasmania- adjustments result in less warmingTas chart

Figure 14: Western Australia- 23.7% increased warming.WA chart

Figure 15:  Queensland- 37% extra warmingQld chart

So far, every state has seen an increase in warming much less than the national mean of 66.6%, so much depends on the final two states.

Figure  16:  New South Wales- 245% extra warming!NSW chart

That is pretty amazing, but the result for Victoria is even more astounding.

Figure 17: VictoriaVic chart

The implications for the trend map in Figure 1 are obvious.  One hopes that those adjustments are well and truly justified!

In the next post I will discuss the remaining 21 sites which I am unable to compare directly, and later, the trend outliers.

The Australian Temperature Record Revisited: A Question of Balance

May 16, 2014

The effect of adjustments made to create the official Australian temperature record is an increase in warming trend of 66.6% for minima, and 13% for maxima.

Given that minimum temperatures are particularly sensitive to Urban Heat Island (UHI) effects and also enhanced greenhouse warming (where greater night time than day time warming can be attributed to higher concentrations of greenhouse gases), this result is extraordinary.  Any analysis of UHI or greenhouse warming signal is rendered impossible as the true signal is distorted by the adjustments.


Included in the supporting papers for the Australian Climate Observations Reference Network- Surface Air Temperatures (ACORN-SAT or Acorn) is this statement concerning the balance of adjustments:

“There is an approximate balance between positive and negative adjustments for maximum temperature but a weak tendency towards a predominance of negative adjustments (54% compared with 46% positive) for minimum temperature.”

Acorn adj table

(Techniques involved in developing the Australian Climate Observations Reference Network – Surface Air Temperature (ACORN-SAT) dataset. (CAWCR Technical Report No. 049 ), Blair Trewin , March 2012,  p.62)

Four years ago in 2010 I posted my analysis of Australia’s so-called High Quality (HQ) temperature dataset.  This was a temperature record which the highest officers of the Bureau of Meteorology (BOM) assured me had adjustments that were on average neutral across the whole network.  My analysis of the mean HQ temperatures compared with the data available at Climate Data Online showed a warming bias of over 40%.

HQ was quietly abandoned with the introduction with some fanfare in March 2012 of the Australian Climate Observations Reference Network- Surface Air Temperatures (ACORN-SAT or Acorn).  This is a homogenised dataset based on daily temperatures, from 112 observing stations around Australia.  104 of these are used for climate analysis, being non-urban.  Acorn data is readily available as daily, monthly, and annual data.

Figure 1:  The Acorn network Acorn network

The 8 yellow dots are locations dubbed “urban” and are not used in temperature analyses.  These stations are:  Townsville, Rockhampton, Sydney, Richmond NSW, Melbourne, Laverton RAAF, Adelaide, Hobart.

The median position of all 104 Acorn stations is about 200km north-east of Broken Hill in NSW.

I studied a sample of 10 Acorn sites in May 2012, which convinced me that the Acorn dataset has many defects.  However, I have now decided to study the whole network, comparing Acorn data with the station data from which it is derived, to discover whether the approximate balance between positive and negative adjustments has any influence on long term trends of the original data.  I did not concern myself with the individual adjustments themselves, but with the effect these adjustments have had on the temperature record.


I downloaded annual minima and maxima data for each Acorn site from Site Networks at the BOM’s Climate Change page, and also downloaded corresponding data for the same and preceding sites from Climate Data Online (CDO).  I am therefore comparing the effect on trends of publicly available data from two sections of the BOM website.

When an observing station closes and is superseded by a new station, the two temperature series may be combined or spliced to form a continuous series that is in theory homogeneous- that is, the series shows a smooth transition with no discontinuities or spurious jumps or drops from the old to the new.  The previous data, all else being equal, may be compared with the later data, and temperature trends derived.  I do this only for stations with at least 24 months of overlap and no more than 30km apart, by adjusting previous data up or down by the mean difference from the new data for from 24 months to the first five years during the overlap period.  The Acorn series are constructed from sites sometimes with much less than 24 months, and sometimes no overlap, and occasionally by combining records many kilometres apart, by comparing with up to 40 sites to detect discontinuities, and up to 10 sites to adjust the record.  (See CAWCR Technical Report No. 049 for full details.)  A check on the validity of my adjustment for station change was the difference between my values and those of Acorn for the same period at ransition.  In all but a couple of cases, my values matched identically, or occasionally with a difference of +/- 0.1 degree.  Instances of mismatch were due to additional homogenisation by Acorn after or during the transition not related to the overlap adjustment.

I made no other adjustments.  Because of this major difference caused by my conservative decision not to combine records with less than 24 months of overlap, I was unable to compare trends at a number of sites.  Further, at several sites, Acorn shows data for many years for which there is no corresponding CDO data, so comparison was not possible.  Of the 104 possible sites, I compared temperature trends for 83 minima sites and 84 maxima sites.

Another complication was different length series, in particular different start and end points.  To ensure trends were accurately compared, I removed up to three additional years at the start to ensure both Acorn and minimally adjusted series had the same start date.  As well, Acorn displays annual data for 2013 for nearly all stations, even though CDO data has not yet been quality assured and therefore not published.  I removed all 2013 Acorn data unless CDO also provides it.  Doing this decreases the warming in Acorn, making it closer to the ‘raw’ warming.  I did not remove intermediate data that did not match.  For example, Acorn removes several spurious annual means in the 1930s for Palmerville (Qld) which remain in CDO- the effect is slightly less warming in Acorn than CDO.  My object was to compare Acorn with minimally adjusted data for the same length of period, so intermediate data adjustments, replacements, and deletions, are relevant.

For each Acorn site for which comparison could be made, I calculated trend in degrees Celsius per 100 years for Acorn and ‘raw’ data (CDO data, only adjusted for overlapping data) for the period 1910 to 2013.

I also calculated annual anomalies from 1961 -1990 means for each site, and calculated annual means for the whole network from these.


Mean of the trends of the ‘raw’ data at the 83 minima sites was +0.82 degree Celsius per 100 years.  The Acorn mean trend was +1.18 C.

The mean difference in trends (Acorn minus raw) of the 83 minima sites was +0.37 degree Celsius.  That is a 44.8 % warming bias.

Figure 2 shows the range in the difference in trend caused by Acorn adjustments (from greatest cooling to greatest warming) across the 83 sites I was able to compare.

Figure. 2: (click to enlarge)Bar chart tmin

However, the practical effect of the Acorn adjustments is on the long term annual temperature trend.  Here is BOM’s official graph of minimum temperatures (calculated as anomalies) 1910 – 2013, based on 104 Acorn sites.

Figure 3:BOM ann tmin anom graph

BOM calculates a trend of +0.1C per decade (or 1 degree per 100 years).

Figure 4 shows my plot of annual mean minima for the same period (calculated as a straight average- the BOM graph is area averaged) for 83 sites ‘raw’ compared with Acorn.

Figure 4:Tmin comp

Note: the ‘raw’ trend is +0.63C per 100 years.  The adjusted trend is +1.05C.  The difference of +0.42C represents an increase of 66.6 %.

Figure 5 is a plot of the annual average difference in temperatures.

Figure 5:Avg adjustments

Note there is only one year (1959) before 1971 that has on average greater positive adjustments, and there are no years after 1971 of average adjustments below zero.  In other words, the record before 1971 is cooled and after 1971 is warmed.

Is my comparison robust?   In particular, how do trend values calculated from a straight mean of annual data for 83 sites compare with those for 104 sites, and how does a straight mean compare with area averaged data?  Figure 6 is a comparison of the straight average of Acorn data at my 83 sites, with my calculation of the straight average of official Acorn minima for the whole 104 sites (excluding 2013), and the official area averaged data for 104 sites.

Figure 6:Acorn tmin 104

The comparison is made clearer by plotting the difference between straight averaged 104 and 83 site series, and the difference between area averaged and straight averaged 104 site series.

Figure 7:area vs straight avg

Note that area averaging appears to increase extremes, but the trend is almost the same.  The straight average trend of 104 sites is +1.03C per 100 years, almost identical to that of the 83 sites.  Therefore, my comparison is valid, and area averaging which decreases the trend in Acorn by 0.03C  to +1 C should apply proportionately to the ‘raw’ trend for exactly the same sites.

What about mean temperatures?

If the mean is calculated as (Tmin + Tmax) / 2, then unless maxima are massively cooled, the result will be a significant increase in the mean trend.

Figure 8:    Annual mean maxima for 84 sites (‘raw’ vs Acorn)Tmax comp

Acorn adjustments have increased the maxima warming trend by +0.09C, or 13 %.

Therefore, the minima adjustments are not balanced by the maxima, and mean temperatures are also artificially warmed.


While the number of positive and negative adjustments made by the creators of Acorn may be balanced or nearly so, their effect on the minimum temperature record is enormous.  Analysis of a not insignificant sample of 83 of 104 Acorn sites shows a warming bias in adjustments to minima of 45 %, which has the effect of increasing the network-wide temperature trend by 66.6 %.  The adjustments have predominantly cooled pre-1971 temperatures and warmed post-1971 temperatures. For maxima, the increase in trend is 13 %.  This result casts doubt on the veracity of the Acorn temperature record, and its usefulness for climate analysis.

Please note: I make no judgement about the justification or lack of it for the individual adjustments.  Nor am I claiming that my calculation of +0.63C per 100 years is the true trend in minima for Australia.  Far from it: that figure is based on only 83 stations, not evenly distributed, many of which have much less than 100 years of data and/or many years of missing data.  I’m saying no one knows for sure, but that the adjustments to the ‘raw’ data at CDO, in order to create the Acorn dataset, result in a massive and unexplained difference.

I welcome any comments or arguments that can show how I may remove errors from this finding, or how I may improve my analysis.


1. Stations excluded from comparison:

Eddystone Point, Mt Gambier, Port Lincoln, Tarcoola, Marree, Darwin, Tennant Creek, Eucla, Forrest, Meekatharra, Port Hedland, Horn Island, Weipa, Normanton, Charters Towers, Bundaberg, St. George, Bourke, Nowra, Walgett, Moree.

Mt Gambier is included in maxima comparison only.

2. Some readers have expressed interest in Rutherglen, Victoria.  Here is the comparison for Rutherglen.

Figure 9:rutherglen tmin

But Rutherglen is not the worst example.  That title belongs to Amberley in Queensland:

Figure 10:
amberley tmin

3. ‘Raw’ data is not raw.  Data at Climate Data Online is as close as we are likely to get, but has been observed, scribbled down, transcribed to monthly reporting sheets/ registers, transmitted, quality assured, converted from Fahrenheit to Celsius (pre-1972 data), and digitised.  Much can go wrong.

CRUTEM vs ACORN: Tasmania

February 8, 2014

Australia has done it again- we have beaten the Poms at their own game (and I don’t mean cricket).

The English climate scientists say the temperature trend for the island state of Tasmania is +0.48C per 100 years.

We’ve beaten that: we say it’s +0.81C per 100 years- better by 69%!

Today I looked at data now available as an interface with Google Earth.

I quote firstly directly from WattsUpWithThat:

Climate researchers at the University of East Anglia have made the world’s temperature records available via Google Earth.

The Climatic Research Unit Temperature Version 4 (CRUTEM4) land-surface air temperature dataset is one of the most widely used records of the climate system.

The new Google Earth format allows users to scroll around the world, zoom in on 6,000 weather stations, and view monthly, seasonal and annual temperature data more easily than ever before.

Users can drill down to see some 20,000 graphs – some of which show temperature records dating back to 1850.

This new initiative is described in a new research paper published on February 4 in the journal Earth System Science Data (Osborn T.J. and Jones P.D., 2014: The CRUTEM4 land-surface air temperature dataset: construction, previous versions and dissemination via Google Earth).

For instructions about accessing and using the CRUTEM Google Earth interface (and to find out more about the project) visit To view the new Google Earth interface download Google Earth, then click here CRUTEM4-2013-03_gridboxes.kml.

I immediately downloaded the new interface, and can report that it is indeed useful and fascinating.  Click anywhere and you can get mean temperature data and trend for that precise region, and individual weather stations as well.  It allows easy comparison between the temperature record as shown by the Bureau of Meteorology (BOM) and one of the world’s leading datasets produced by the renowned Climatic Research Unit in England.

Three things to note:

1.  CRUTEM4 uses data from back to the 1850s- BOM says it only uses data from 1910 as data previous to this may be unreliable.

2.  CRUTEM4 uses data from many more than the 104 ACORN sites used by BOM.  Some may be of doubtful quality.

3.  The results are vastly different.

I have downloaded data from CRUTEM4 and from BOM for Tasmania, as that appears to be the easiest region to compare records.  As you can see from the Google Earth image below, Tasmania fits fairly neatly into one 5 degree by 5 degree grid cell.

google earth tassie

First I show the annual data for both datasets:Tassie means

Note CRUTEM4 has annual data from 1883.  BOM has this as well but declares it to be unreliable.  Note also that the trends are vastly different- CRUTEM4 trend is +0.48C per 100 years, while BOM has it as +0.81C.  (+0.8C on their Time Series graph.)

(The difference is +0.33c- that’s an improvement of 69%! And they did it with only 5 sites, vs CRUTEM4’s 21.)

How was this done?  Apart from not including pre-1910 data, BOM also made some small adjustments to the raw data:bom-crutem

And that comparison is not with raw data, but with CRUTEM4.

So, what is the correct temperature trend for Tasmania?  The world acclaimed CRUTEM 4, or “world’s best practice” ACORN-SAT? Or neither?

It’s anyone’s guess.


Jones P.D., Lister D.H., Osborn T.J., Harpham C., Salmon M. and Morice C.P., 2012: Hemispheric and large-scale land surface air temperature variations: an extensive revision and an update to 2010. Journal of Geophysical Research 117, D05127. doi: 10.1029/2011JD017139.

Osborn T.J. and Jones P.D., 2013: The CRUTEM4 land-surface air temperature dataset: construction, previous versions and dissemination via Google Earth. Earth System Science Data Discussion 6, 597-619. doi: 10.5194/essdd-6-597-2013

The Rhythm of Life has a Powerful Beat

January 30, 2014

Here’s a fresh look at global temperatures as calculated by the University of Alabama, Huntsville- the UAH dataset- from satellite measurements of the Temperature of the Lower Troposphere (TLT).

Warwick Hughes suggests that there has been a drift in the measurements since about 2005, such that calculated temperatures are too high, and we await a proposed correction.  However, we can live with that.

Here are plots of TLT for various regions of the globe.

Fig.1:  12 month running means of Global anomalies and Tropical anomalies (the region of the Earth between 20 degrees North and 20 South, which gets the majority of the solar radiation striking the Earth).Glob - Tropics

The two sets move in lock step, with a much larger variation in the Tropics than the world as a whole.

What causes these large variations?

Fig. 2: Global and Tropical anomalies with the SOI inverted, and scaled by a factor of 30.Glob - Tropics v SOI

SOI is the acronym for the Southern Oscillation Index, calculated from pressure differences between Tahiti and Darwin, and is a reasonably good indicator of El Nino or La Nina conditions.  The ENSO cycle (El Nino Southern Oscillation) originates in the tropical Pacific.  El Nino brings warmer temperatures to the world; La Nina is associated with cooler temperatures.  I have inverted the SOI to show this relationship, and scaled it down by 30 to fit on the graph.

Note how the 12 month mean of SOI precedes the temperature data.  Here’s a plot with the SOI advanced 5 months.

Fig.3:  SOI advancedGlob - Tropics v SOI adv'd

While the peaks (El Ninos) match very closely, I have marked periods following the major eruptions of El Chichon and Mt Pinatubo, which cooled temperatures for several years.  I also suggest that the atmospheric dust and cooler surfaces upset the ENSO cycle as traced by the SOI.  Note also that temperatures in the 2010-2011 La Nina appear higher than expected.

Fig.4: SOI advanced with Tropic and Australian land TLT.Australia

Note how Australian temperatures appear to fluctuate about as much as the Tropics (we’re one third north of 20S after all).  Australian temperatures are influenced by events in the Indian Ocean and Southern Ocean as well as the Pacific, so the match isn’t exact.

I will look at Australian data specifically in another post.

Finally, here’s a way to check on that other “finger print” of the enhanced greenhouse effect, as espoused by Dr Karl Braganza: land areas are expected to warm faster than oceans, supposedly showing that greenhouse gases, not ocean currents, drive global warming.

Fig. 5: Global Land and Ocean v oceans

Well of course that proves it- land areas are indeed warming faster than oceans.

However, have a closer look at the timing of the switches between warming and cooling.  If well mixed greenhouse gases are warming both land and oceans, it would be expected that oceans, with higher specific heat and enormous thermal inertia, would take longer to warm.  The land response would be almost immediate.  Oceans would not be expected to warm before the land, and if anything might show a slight lag.

Fig.6: close up of the 1998 Super El v oceans 1997-99

The oceans change phase about one month before the land.  They definitely do not lag behind.

And what causes these rapid changes?

Fig.7: Land, ocean, and the SOI advanced 5 v oceans v soi


The world’s temperatures respond to the powerful beat of ENSO events- as well as large explosive volcanic





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