Archive for October, 2014

Adjustments Grossly Exaggerate Monthly and Seasonal Warming

October 4, 2014

The Bureau of Meteorology has reportedly claimed “an extensive study has found homogeneity adjustments have little impact on national trends and changes in temperature extremes.”  (Weekend Australian, August 23-24).

I have always said that the true test of the homogenisation process is its effect on national trends.  Problems at individual stations like Rutherglen are merely symptoms of a system wide malady.

If the adjustments really do have “little impact on national trends” then the Acorn dataset is a reliable indicator of broad temperature change in Australia.

If not, the Bureau has a problem.

So, how do we define “little impact”?

The Bureau has known since March 2012 that mean annual temperature increase from 1911 to 2010 in adjusted data (+0.94C) is 36% greater than in unadjusted data (+0.69C).  This information is publicly available in Table 1 on page 14 of On the sensitivity of Australian temperature trends and variability to analysis methods and observation networks  (CAWCR Technical Report No. 050), R.J.B. Fawcett, B.C. Trewin, K. Braganza, R.J Smalley, B. Jovanovic and D.A. Jones , March 2012 (hereafter CTR-050).  In this paper the authors claim that the rise in unadjusted data is “somewhat smaller”.  If this is so, then what increase in trend over unadjusted data may be considered to be beyond small or “little impact”? 50%? More than 50%?

What about 200%?

The Bureau has this graphic on their new Adjustments tab, which presumably is meant to support the claim of “little impact”:

Fig. 1: Official comparison (click graphics to enlarge)

BOM graphic

How big is that increase?  The devil is in the detail- monthly and seasonal trends, which the Bureau is yet to analyse.

According to the Bureau, AWAP (Australian Water Availability Project) represents unadjusted data. (It’s not, CTR-050 even calls it “partially homogenised”, and there are major issues with it, but that’s another story to be discussed later.  For now, let’s play along with calling it “unadjusted”).  Using this same “unadjusted” data, and the same method as the Bureau, here are results for the 1911 – 2013 period.  (See the Appendix below for full details.)

These tables summarize the results.  Highlighted cells show large ( > 50%) difference.

Fig. 2:  Summary Table: Percentage Increases to Unadjusted Data- Seasons

summary table seasons

The major effect is on summer trend:  increase in Mean trend 64%, Maxima 200%.

Fig .3:  Summary Table: Percentage Increases to Unadjusted Data- Months

summary table months

In Maxima trends, of the hot months, November, December and January have had large increases, and February and March have had cooling trends reversed.

June and November Mean, Minima, and Maxima trends have been massively increased.

One month (August) has had a warming trend reduced.

May, July, August, and September are largely unchanged.


Compared with ‘unadjusted’ data, for the period 1911 – 2013 Acorn shows obvious changes in monthly and seasonal data.  Exploration of the reasons for this needs to be included in the terms of reference of the forthcoming “independent review”.

The difference between AWAP and Acorn, especially in summer maxima, is of particular concern for anyone wishing to analyse national data.  For example: What was the national summer maximum in 1926?  AWAP says 35.87C.  Acorn says 33.53C.  Which dataset is to be believed?

The Bureau has a problem.

The Acorn dataset is NOT a reliable indicator of broad temperature change in Australia.

Appendix: Background, Charts, Methods, and Analysis

CTR-050 analyses data for the 1911-2010 period, comparing Acorn with several other datasets, including AWAP.  All trends are determined by quadratic fit, rather than linear, to better show the temperature trends across the period: cooling then warming.  The authors also use anomalies from 1981-2010 means.

This table shows the change in temperature over the period, which represents trend per 100 years, (and I am annoyed at myself for not reading this more closely two years ago.)

Fig.4:  Table 1 from CTR 050:

BOM table 1 comps

The authors explain (pp. 41-46) that the difference between AWAP and Acorn is mainly between 1911 and 1955 and is largely due to the large impact on national temperature of very few remote sites in the earlier years of last century, and station moves to cooler sites around 1930 and the 1940s.  That may certainly be true, but the large discrepancy calls for closer analysis.

My methods

Monthly and annual AWAP data (minima, maxima, and mean) 1911 – 2013 obtained from the Bureau allows analysis of the impact the adjustments.  I use 1961 – 1990 as the reference period for anomalies.  I also use quadratic trends and calculate temperature change per 100 years by (last quadratic trendline point – first point) X 100/103.  (These first and last points are accurately determined to 0.01C by zooming in on Excel charts- see Figures 22 and 23 below.)  I calculate percentage change in 100 year trend as {(Acorn trend – AWAP trend)/AWAP trend} x 100.

For example:  Annual means.

Quadratic first point (1911)   Quadratic last point (2013)    Change

AWAP:   -0.13                          +0.56                            +0.69

Acorn:   -0.34                           +0.58                            +0.92

AWAP Quadratic trend per 100 years =  0.69 X 100/103 = 0.67

Acorn Quadratic trend per 100 years =   0.92 X 100/103 = 0.89

Percentage change in trend = {(0.89 – 0.67) / 0.67} X 100 = 32.8%.

While my analysis largely confirms the figures in the Figure 4 above, the devil is in the detail.

Firstly, here are charts for comparison of mean temperatures, showing linear and quadratic trends to 2013:

Fig. 5: Linear

mean linear

Fig. 6: Quadratic

mean quadratic

Linear analysis produces a trend value of 31%, a little less than quadratic .  Acorn adjustments produce a quadratic trend about 32.8% greater than AWAP- not as great as 1911-2010, but still substantial.  Quadratic trend lines produce a better fit than linear and clearly show the earlier cooling.

Fig.7:  Annual Minima

min quadratic

Over 25% increase.

Fig. 8: Annual Maxima

max quadratic

36.7% increase.

Seasonal and Monthly Means:

Fig. 9:  Table of Seasonal Differences for Means.

mean table seasons

Note summer mean trend has been increased by 64%.  Graphs may make the comparison starker.

Fig. 10:  Comparison of 100 year trends in unadjusted and adjusted seasonal data.

mean trends diff seasons

Fig. 11: Percentage Difference in Trends

mean trends diff % seasons

Fig. 12: Comparison of 100 year trends in unadjusted and adjusted monthly data.

mean trends comp

Fig. 13:  Percentage Difference in Trends

mean trends diff % months

February trend doubled, March, June, and November are increased by about 80%.


Fig. 14:  Table of Seasonal Differences for Minima.

min table seasons

Fig. 15:  Comparison of 100 year trends in unadjusted and adjusted seasonal data.

min trends comp seasons

Fig. 16:  Percentage Difference in Trends

min trends diff % seasons

Fig. 17: Comparison of 100 year trends in unadjusted and adjusted monthly data.

min trends comp

Fig. 18:  Percentage Difference in Trends

min trends diff %

Note the doubling of the June minima trend, and October and November increased by 50%.


Fig. 19:  Table of Seasonal Differences for Maxima.

tmax table seasons

Fig. 20:  Comparison of 100 year trends in unadjusted and adjusted seasonal data.

max  trends seasons

Fig. 21:  Percentage Difference in Trends- we need to rescale the y-axis!

max trends diff % seasons

Don’t believe the 200% figure?  Here are close ups of the graph.

Fig. 22:  Summer maxima detail

max summer quadratic bottom

Fig. 23:

max summer quadratic top

Fig. 24: Comparison of 100 year trends in unadjusted and adjusted monthly data.

max trends comp

Note cooling trends in February and March reversed., August reduced.

Fig. 25:  Percentage Difference in Trends

max trends diff % months

Strong August warming slightly reduced.  No calculation for February and March.  January, June, December greatly warmed.  November massively warmed.

Why the huge discrepancies between unadjusted and adjusted data?

Acorn data freely available at

AWAP data available at a cost on request from