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.

IPCC Dud Rainfall Predictions for the Murray-Darling Basin

April 4, 2014

The IPCC’s recently released 5th Assessment Report (AR5) dedicated Chapter 25 to impacts of climate change on Australasia. There was wide media reporting of these impacts, including that of decreasing rainfall- more droughts and floods. The relevant part of Chapter 25 outlines eight regional key risks, including:

For some impacts, severity depends on changes in climate variables that span a particularly large range, even for a given global temperature change. The most severe changes would present major challenges if realized:

……. significant reduction in agricultural production in the Murray-Darling Basin and far south-eastern and south-western Australia if scenarios of severe drying are realised; more efficient water use, allocation and trading would increase the resilience of systems in the near term but cannot prevent significant reductions in agricultural production and severe consequences for ecosystems and some rural communities at the dry end of the projected changes.

Section 25.2, Observed and Projected Climate Change, gives the details:

This pattern of projected rainfall change is reflected in annual average CMIP5 model results (Figure 25-1), but with important additional dimensions relating to seasonal changes and spread across models (seealso WGI Atlas, AI.70-71). Examples of the magnitude of projected annual change from 1990 to 2090 (percent model mean change +/- intermodel standard deviation) under RCP8.5 from CMIP5 are -20±13% in south-western Australia, -2±21% in the Murray Darling Basin, and -5±22% in southeast Queensland (Irving et al., 2012). Projected changes during winter and spring are more pronounced and/or consistent across models than the annual changes, e.g. drying in southwestern Australia (-32±11%, June to August), the Murray Darling Basin (-16±22%, June to August), and southeast Queensland (-15±26%, September to November), whereas there are increases of 15% or more in the west and south of the South Island of New Zealand (Irving et al., 2012). Downscaled CMIP3 model projections for New Zealand indicate a stronger drying pattern in the south-east of the South Island and eastern and northern regions of the North Island in winter and spring (Reisinger et al., 2010) than seen in the raw CMIP5 data; based on similar broader scale changes this pattern is expected to hold once CMIP5 data are also downscaled (Irving et al., 2012).

As the Murray-Darling Basin (MDB) is the nation’s major food bowl, contributing a very large proportion of our agricultural production, a Reality Check on these claims is in order.

The Murray-Darling Basin is the largest catchment in Australia, and is one of the Bureau of Meteorology’s climate regions:

Fig.1: MDBRegions

First, annual rainfall. The IPCC projects an annual change of -2% +/-16% from 1990 to 2090. Here are the rainfall anomalies for the MDB straight from the Bureau’s Climate Change page:

Fig.2: MDB Annual Rainfall Anomalies, 1900-2013:MDB annual anoms

Linear trends have limited use in such a manifestly non-linear dataset as rainfall, however I put one in just in case someone says rainfall is decreasing. Even with 2010 deleted the trend is still positive. Let’s now look at the 10 year means:

Fig.3: MDB Annual Decadal Means:MDB annual anoms 10yrs

Note that for the entire period before the 1950s, the 10 year mean was below the 1961-1990 mean, and in 1946 was 94mm below. While in 2009 the 10 year average was 69mm below the mean, this being the first time in six decades it had been below -60mm, for most of the 1940s it was more than 60mm below the mean. It is entirely possible that rainfall will be below average in the MDB for several more decades, and this would be completely normal.

I shall now project this historical trend through to 2090, with a 2090 rainfall of 512.35mm, 2% below that of 1990 (522.81mm).

Fig.4: MDB Annual Rain to 2090:MDB annual rain to 2090

So that’s what a decrease in rainfall looks like! Note the uncertainty range- well within historical norms, and the low figure (404.76mm) is in the below average (lowest 30% of years) rainfall category by less than 4mm.

Next, winter rainfall (-16% +/-22%, June to August). From BOM Climate Change,

Fig.5: MDB Winter Anomalies 1900-2013MDB winter anoms
There you can see the declining trend (BOM says -0.57mm per decade)- but note the size of the trend compared with the variability.

Interestingly, consider the same data for the last 54 years.

Fig.6: MDB Winter Anomalies 1960-2013MDB winter anoms 1960-2013
But of course, the authors have detected the drying trend since the 1990s!

Now, decadal means:

Fig.7: MDB winter decadal means:MDB winter anoms 10yrs

Note the 10 year mean about -10mm in past decade, but -15mm in the 1970s and -19mm in the 1940s. Note also that the 10 year average was below zero for the better part of two decades, twice, in the past. Below average winter rain for the next few years would be completely normal, if the past is anything to go by.

Here is a chart showing the number of dry winters per 10 year period in the MDB.

Fig.8: 10 year count of below average winters.MDB winter anoms  under30%10yrs

Below average winters were more frequent in the past.

Projecting the winter anomalies into the future, with a decrease of -16±22%, June to August, we get:

Fig.9: MDB Winter Rain to 2090:MDB winter rain to 2090

109.74mm is almost exactly the 1961-1990 mean (111.1mm). The low end of the uncertainties, 85.6mm, is in the below average range but well outside the severe deficiency or even serious deficiency level. Yet this will cause “significant reductions in agricultural production and severe consequences for ecosystems and some rural communities”?

Note: these projections are based on continued warming by up to 2 degrees. Consider that we have already seen warming in the MDB of about +0.8 C since 1910 (according to BOM analysis based on ACORN-SAT).

It appears that the IPCC can’t be wrong, whether rainfall is higher, lower, or stays the same. They’re having two bob each way.

In discussing agricultural production, I would have been less underwhelmed if rainfall in other seasons had been considered. If winter rain is down (marginally), but annual rain is up, when did it fall?
Briefly, autumn, like winter, is almost flat (-0.59mm per decade), spring is up by 1.61mm per decade, but summer rain has increased 3.86mm per decade. If heavy rain falls before the wheat harvest is off, the crop is seriously downgraded, so late spring- early summer rainfall increasing would be of concern.

Fig.10: MDB Summer Rain AnomaliesMDB summer anoms

Fig.11: MDB Decadal Summer RainMDB summer anoms 10yrs

Note that summer rain increase is all since 1950. For 60 years farmers have been contending with this. It’s nothing new. Farmers adapt farming methods to changing conditions and with new technology. Moreover, the recent decadal peak is about the same as the 1960s and 1990s. Note also that the low decadal mean of the Millennium Drought is nowhere near the levels of past dry periods.

The warming to now has ‘resulted’ in increased annual rain, made up mostly of stronger summer rains since 1950, and marginally less winter and autumn rain which is less variable than in the early decades of last century.  The IPCC’s projections are thus the result of climate models and not historic observation, are subject to large uncertainty, and not greatly different from patterns of the past 114 years.

The AR5 prediction of dire consequences for the Murray-Darling Basin, based on rainfall projections that are essentially no different from historical observations, is nonsense. It is beyond parody, beyond ridicule. It treats the citizens and farmers of Australia with contempt.

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




North Australian Temperatures

January 24, 2014

For those of you think- “Gee it’s been hot with all these heat waves lately- it must be even worse up north”.

Here’s a plot of maximum temperatures across Northern Australia (the area north of 26 degrees south)  since 1985- the 29 years to the end of the hottest year on record.

tmax n aust 85-13

That trend is actually (very slightly) negative.

And yes of course it’s cherry picked- but 2014 will have to be a hotter than average year to make the 30 year trend positive- a 2014 anomaly of +0.35C gives a 30 year trend of: zero.  (The  mean of 1985-2013 maxima is +0.28C, the median is +0.34C.)

I guess the BOM is not hoping for a La Nina.

Data from Acorn.

Australian DTR – the Regional Context

January 12, 2014

I’ve been banging on about DTR in Australia for a while, showing that as an indicator of greenhouse warming, decreasing DTR trend has been lacking from Australian records for some time, such that the trend is flat since 1947.


DTR is Diurnal Temperature Range, the difference between Minimum and Maximum temperature daily.  Several previous posts discuss this.  Greenhouse gases slow back radiation, and thus night time temperatures are expected to be warmer than normal, and minima are expected to increase faster than maxima, so DTR should decrease.

Fig.1: Australian DTR anomalies, 1947 – 2013dtr1947-2013

I’ll now show what is happening on a regional basis.  This map shows the main meteorological regions of Australia.

Fig. 2: The regions.summer1213  regions

The main difference is between Northern Australia and Southern Australia.

Fig.3:  Northern Australian DTR anomalies, 1971 – 2013dtr nth oz 71-2013

43 years of flat trend in DTR!

Fig.4: Southern Australian DTR anomalies, 1938 – 2013dtr sth oz

76 years!

Fig. 5:  South-Western Australian DTR anomalies, 1941 – 2013dtr sw aus

73 years.  But the real eye opener is South Eastern Australia:

Fig. 6: South-Eastern Australian DTR anomalies, 1934 – 2013dtr se aus

That’s right, in South-East Australia, the DTR trend has been flat for 80 years!

Decreasing DTR as a “fingerprint” of greenhouse warming was championed by the 2004 paper by Dr Karl Braganza,

“Diurnal temperature range as an index of global climate change during the twentieth century” Karl Braganza, School of Mathematical Sciences, Monash University, Clayton, Victoria, Australia; David J. Karoly, School of Meteorology, University of Oklahoma, Norman, Oklahoma, USA; J. M. Arblaster, National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA

Braganza et. al. analysed global DTR from 1951 to 2000, finding a significant decline of ~0.4 degrees C.  If we compare Australian data for the same period we find this is corroborated.

Fig. 7:  Australian DTR anomalies 1951 – 2000dtr oz 51-2000

The observed decrease over this period is ~0.35  – 0.4 C.

With the benefit of an extra 13 years of data, we can check whether this continues to be the case.

Fig. 8:  Australian DTR anomalies 1951 – 2013dtr oz 51-2013

What a difference a few years make.

Open Letter from Jennifer Marohasy

January 10, 2014

Jennifer Marohasy has written to Dr David Jones, head of climate monitoring and predictions at the Bureau of Meteorology, which she has posted as an Open Letter at her blog.

She asked me to review her draft and I made a few small suggestions.

I wish her good luck with Dr Jones.  I am persona non grata with him apparently and I had to write to the Minister before getting a very unsatisfactory reply, many months later, from BOM- Jones refused to reply.  I had to follow up with the Minister again, with a copy to Greg Hunt as Opposition spokesman, before getting some requested information, and an apology.  I analysed this information here.  Promised Journal articles did not arrive at all, and after writing again to the Minister, I received a completely irrelevant paper on ACORN-SAT.  Another letter brought another reply from the next Minister, but still no substantive information I had requested.  This was in August 2012.  My first request for a response from Dr Jones was in July 2010, and my first letter to the Director of Meteorology was in October 2010.  I gave up after this.

I hope Dr Marohasy has more success than I did.


Here is her letter:

Open Letter Requesting Verification of 2013 Temperature Record

Posted by jennifer, January 9th, 2014 – under Information.

Dr David Jones
Manager of Climate Monitoring and Predictions
Australian Bureau of Meteorology

Dear Dr Jones

Re: Request Verification of 2013 Temperature Record

I am writing to request information be made publicly available to myself and others so we may have the opportunity to verify the claim made by you on behalf of the Australian Bureau of Meteorology that 2013 was the hottest year on record in Australia. In particular it is claimed that the average temperature was 1.20°C above the long-term average of 21.8°C, breaking the previous record set in 2005 by 0.17°C.

This claim is being extensively quoted, including in a report authored by Professor Will Steffen of the Climate Council, where he calls for the Australian government to commit to further deep reductions in greenhouse gas emissions because of this “record-breaking year”. Accurate climate records are not only of political interest, but are also of importance to those of us who rely on historical temperature data for research purposes. For example, the skill of the medium-term rainfall forecasts detailed in my recent peer-reviewed publications with John Abbot, have been influenced by the reliability of the historical temperature data that we inputted. From a very practical perspective, businesses will adjust their plans and operations based on climate data, and ordinary Australians worry and plan for the future based on anticipated climate trends.

Further, I note that you said in a radio interview on January 3, 2014, following your “hottest year on record” press release that, “We know every place across Australia is getting hotter, and very similarly almost every place on this planet. So, you know, we know it is getting hotter and we know it will continue to get hotter. It’s a reality, and something we will be living with for the rest of this century.”

The Australian Bureau of Meteorology is the custodian of an extensive data network and over a long period now, questions have been asked about the legitimacy of the methodology used to make adjustments to the raw data in the development of the Australian Climate Observations Reference Network – Surface Air Temperatures (ACORN-SAT). Furthermore, questions have been asked about why particular stations that are subject to bias through the Urban Heat Island (UHI) effect continue to be included in ACORN-SAT. In particular why is ‘Melbourne Regional Office’, a station at the corner of Victoria Parade and Latrobe Street (Melbourne CBD) still included in the ACORN-SAT network when this station is known to have become sheltered from previously cooling southerly winds following construction of office towers.

I understand ACORN-SAT was used to calculate the statistics indicating 2013 was the hottest year on record, but it is unclear specifically which stations from this network were used and how data may have been further adjusted in the development of the record breaking temperature anomaly.

Rockhampton-based blogger Ken Stewart, for example, has suggested that in the calculation of the annual average temperature for Australia, the eight sites acknowledged as having anomalous warming due to the UHI would not have been included. Is this the case? I had assumed that the Bureau used all 112 ACORN-SAT locations, and thus that the record hot temperature anomaly announced by you, actually includes a UHI bias.

Radio presenter Michael Smith has given some publicity to claims made by blogger Samuel Gordon-Stewart that the Bureau has overestimated the average Australian temperature by about 4 degrees. Mr Gordon-Stewart calculated average temperatures and temperature anomalies from data from all the weather stations listed by Weatherzone.

Furthermore, given many ACORN-SAT stations have continuous temperature records extending back to the mid-late 1800s and many stations were fitted with Stevenson screens by 1900, why does the Bureau only use data after 1909, all the while claiming that 2013 is the hottest year on record? Indeed it is well documented that the 1890s and early 1900s, years corresponding to the Federation drought, were exceptionally hot.

In summary, given the importance of the historical temperature record, and the claim that 2013 is the hottest year on record, could you please provide details concerning:
1. The specific stations used to calculate this statistic;
2. The specific databases and time intervals used for each of these stations;
3. The history of the use of Stevenson screens at each of these station;
4. How the yearly average temperature is defined; and
5. Clarify what if any interpolation, area weighting, and/or adjustments for UHI bias, may have been applied to the data in the calculation of the annual mean values.

Kind regards

Dr Jennifer Marohasy

No Excess Winter Warming for 103 Years!

January 9, 2014

Greenhouse Myth Buster No. 2

Another key indicator of greenhouse warming, a pattern of temperature change “uniquely associated with the enhanced greenhouse effect” according to Dr Braganza, is greater warming in winter compared with summer.

Not in Australia.

This is a graph of summer annual means minus winter annual means for the years 1910 – 2012, straight from BOM’s time series data.


No winter increase over summer in 103 years.  This summer- we find out in early March- will have to be less than +0.7 C above average to make  the trend ever so slightly negative (to 5 decimal places).

But then how will we get another “Angry Summer”?

No Evidence of Greenhouse Warming for 67 Years!

January 8, 2014

The release of 2013 data by the BOM has provided me with plenty to work on.  Various commentators are busily alarming people by claiming that the hottest year on record is an indication that global warming due to the enhanced greenhouse effect is already impacting Australia.  What is most disappointing is that the BOM has done nothing to report the truth: that while Australia has definitely been warming, and breaking records, the data show no evidence of greenhouse warming.

One of the key indicators of warming uniquely associated with the enhanced greenhouse effect is night time temperatures (minima) increasing faster than daytime temperatures (maxima).  The difference between the two is called the Diurnal Temperature Range, or DTR.  So, decreasing DTR would be evidence of greenhouse warming.

Here is Australian DTR since 1947:dtr1947-2013

That’s dead flat or slightly rising for 67 years!

I couldn’t believe it either, and double checked.  There’s no mistake- DTR shows no evidence of greenhouse warming in Australia, with a flat trend for 67 years.


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