Archive for the ‘temperature’ Category

Rain, clouds, and temperature

November 19, 2014

Looking at the continent of Australia as a whole, and using 12 month running means to smooth the very noisy data, we can see some intriguing patterns.

Firstly, here is a comparison of tropospheric temperatures above Australia from the University of Alabama- Huntsville (UAH), with surface air temperatures from the Bureau of Meteorology’s ACORN-SAT database.   To be comparable, both datasets are in anomalies from their 1981 – 2010 means.  The data are monthly since December 1978, with a 12 month running mean.

Fig. 1

uah v mean

Both datasets show concurrent rises and falls and are very similar (though not always).  Note how Acorn means were very much cooler in 2011 -2012 and much hotter in 2013.  Note also that 2014 has Buckley’s of being the hottest year on record.

Mean equals the average of maximum and minimum, so let’s look at maxima and minima.

Fig. 2

uah v max & min

Note that UAH usually tracks Acorn maxima, except when it doesn’t- shown above by the Xes.

Perhaps it has something to do with rainfall, or lack of it.  In the next plot, rainfall is inverted, so dry is at the top, wet at the bottom.

Fig. 3

uah v rain inv

Incidentally, the Bureau also has 9 a.m. and 3 p.m. cloud data available.  Note how closely both cloud datasets match, and how rainfall largely corresponds.

Fig. 4

rain v cloud

And the Southern Oscillation Index runs in close partnership with rainfall- sometimes SOI leads rain, sometimes rain leads SOI.

Fig. 5

rain v soi

Which is why I don’t take a lot of notice of predictions based on SOI.

Now see what happens when we plot inverted rainfall (dry at the top, wet at the bottom) and maxima.

Fig. 6

rain v max

Only once does 12 month mean maximum temperature precede 12 month rainfall (1991-1992).  At all other times, rainfall peaks or troughs occur before maxima (or at most, simultaneously).

With minima, the lead is even more obvious, however there are apparent exceptions in 1982 and 1994-1995, although these may be further examples of rain leading minima by more than a year (marked with “?”).

Fig. 7

rain v min

When we compare maxima with minima, the pattern is clear.

Fig. 8

max v min

Only in the summer of 1994-1995 do the records diverge.

Generalisations (and farmers have known about these rules of thumb for years):

  1. Climate is cyclical.  Rain and temperature rise and fall in roughly two or three year cycles.
  2. It always rains after a drought.
  3. Dry years are followed by spikes in maximum and minimum temperatures, from one to several months later.
  4. Wet years, with heavy cloud and rain, cause sharp drops in minimum and maximum temperatures, from one to several months later.
  5. Maximum temperatures lead minimum temperatures by several months in wet years, and by a shorter period in dry years.
  6. There are exceptions to all of the above.

Next step: Australia is a large continent with several distinct climatic regions.  I will next look at smaller regions to see if the above generalisations hold true and indeed may be modified or enhanced.

More Bizarre Adjustments

November 5, 2014

In September, the Bureau of Meteorology added two extra tabs to its ACORN-SAT webpage, in response to media and public pressure.  The first tab (“Adjustments”) included a link to a list of temperature adjustments for each of its 112 Acorn stations.  (This had been promised two and a half years earlier.)

Soon after, and probably in response to continued interest in adjustments at Amberley, Rutherglen, and Deniliquin (amongst others), six links to PDF files were added at the bottom of the adjustment page, which gave further explanations and summaries of adjustments at six individual sites- Amberley, Deniliquin, Mackay, Orbost, Rutherglen, and Thargomindah. (Click to enlarge.)

station summaries

Two days ago I posted about the bizarre case of Mackay 33119, listing differing adjustments from the two sources, extra neighbours found, and finding that the set of adjustments in the individual summary did not match the end result (the Acorn record for Mackay).

I thought this must be just a freak problem with Mackay.  Surely the other examples couldn’t all be wrong.

Not so.

Here is a table summarising the adjustments listed by the Bureau in the 28  page Station adjustment summary list, compared with the individual station summaries (click to enlarge).

adj comp table

Only one station (Deniliquin) has matching pairs of adjustments- but none are the same.

Out of 25 pairs of matching adjustments, only one pair has the same adjustment.

Most of the adjustments differ by only a few hundredths of a degree, but some are hugely different (over 1 degree in the case of Mackay).

There are a total of 60 adjustments, but 10 of these do not have a matching adjustment.  Seven of the extras are in the individual station summaries, three are from those in the original 28 page list.

Note that these station summaries are “indicative of the sorts of adjustments made across the 112 ACORN-SAT sites”. As a result, we can have no confidence in the accuracy of the Bureau’s adjustments, and we are left wondering what the Bureau would have us believe are the real temperatures at any site.

An old school teacher’s response to such sloppy work?

Fail.  Check your work and repeat.  Stay in at lunch time until you get it right.

The Bizarre Case of Mackay 33119

November 3, 2014

What has the Bureau of Meteorology done to Mackay’s temperatures?

Mackay’s temperature records from the old Post Office, Te Kowai, and the Met. Office have been combined into one, and this has been “homogenised” by reference to Mackay’s neighbours.

But in attempting to justify their actions the Bureau has provided TWO lists of neighbour stations and TWO lists of adjustments at their website.

First, the list of “neighbour” stations used at Mackay (from the 28 page Adjustments document at http://www.bom.gov.au/climate/change/acorn-sat/documents/ACORN-SAT-Station-adjustment-summary.pdf ).

33119    Mackay Met. Office (Mt Bassett) 1960-2014 is the official Acorn site.

33046    Mackay Post Office  1910-1949  (4 km away)

33047    Te Kowai  1910-2011  (10 km)

33058    Pine Islet Lighthouse  (70 km)

39023    Cape Capricorn Lighthouse (335 km)

33013    Collinsville Post Office  (154 km)

39083    Rockhampton Aero  (283 km)

32005    Cape Cleveland Lighthouse (290 km)

33077    Pacific Heights (Yeppoon- 273 km)

32078    Ingham  (421 km)

39122    Heron Island  (380 km)

32037    South Johnstone Experiment Station (Tully- 517 km)

34002    Charters Towers PO  (329 km)

33001    Burdekin Shire Council (Ayr- 255 km)

33007    Bowen PO  (160 km)

39069    Walterhall (Mt Morgan- 300 km)

35019    Clermont PO  (246 km)

However, they provide a different list in the explanation for Mackay’s adjustments given at http://www.bom.gov.au/climate/change/acorn-sat/documents/station-adjustment-summary-Mackay.pdf at a link from the site above, and the two explanations are quite different.

Here is the second list, including four extras:

mackay explanation list

You can imagine the reaction of Mackay residents on finding that Mackay’s temperatures have been homogenised using Tully, Heron Island, Townsville, Mount Morgan, Charters Towers, Clermont, and their old rival, Rockhampton.  None of these places has a climate anything like Mackay’s.

Further, the Bureau claims that Townsville and Rockhampton are excluded from climate analyses because they are both affected by Urban Heat Island (UHI) warming, but here they have been included in the climate analysis of Mackay.

Now the lists of adjustments:

mackay adjustments comp

The matching adjustments are completely different, and there are three extra breakpoints detected by statistical means, with adjustments, including two extra for maxima.  So which set of adjustments was actually used?

Here is a chart of Mackay’s annual maximum temperature records. Suffice to say that the Mackay record is a mess, and good luck to anyone trying to homogenise it.

Fig. 1:

mackay max chart

These are the results from applying the two lists of adjustments to the raw Mackay temperatures, to see which matches the Acorn records.

Fig.  2:   Calculated maximum temperatures (raw temperature with listed adjustments applied) minus Acorn temperatures.  Zero difference equals a perfect match.

mky replic

Fig. 3:   Minima:

mky replic min

The original list given in the 28 page list of adjustments appears to be the one used for both maxima and minima.  Mackay Acorn maxima cannot be replicated with the Station temperature adjustment summary list, which has two adjustments clearly not used, and is moreover confusing and does not follow the protocol for 1939-1940.  Similarly, there is an additional adjustment for minima which does not match the Acorn record.

The summary list appears to have been put together in a hurry in an attempt to head off criticism about lack of transparency.  But why the different adjustments?

And were the actual adjustments justified?  A simple test is to find the differences between the station being homogenised and its neighbours.  If Mackay has been properly homogenised, the average difference after homogenisation should have a trend close to zero.  Here are the results:

Fig. 4:  Average differences in anomalies of the 10 listed neighbours for the period around the “statistical” breakpoint at 01/01/1971.

mky 1971 raw adj diff comp

The adjustment of about -0.3C for all years up to 1970 makes the differences worse.  Interestingly, when the two most distant sites to the north and south are excluded (South Johnstone and Heron Island), the trend in raw difference is almost zero.  The raw Mackay MO record is similar to the neighbours, without any adjustment.

Fig. 5: As for Fig. 4, but excluding 2 distant sites:

mky 1971 raw adj diff excl 2 sites

The adjustments to the Post Office for 01/01/1941 and 01/01/1948 cause the following differences:

Fig. 6:

mky PO raw adj diff comp

Once again, there is a major difference between the Acorn record and the average of the neighbours, as shown by the steep trend- not much better than the raw difference.

To conclude,

  1. the Bureau has made an embarrassing mistake in publishing two different lists of adjustments and neighbours for Mackay
  2. the adjustments listed in the Mackay station adjustment summary are not those actually made
  3. adjustments are based on “neighbours” up to 500 km away, including two with UHI effect
  4. very few of these neighbours have climates similar to Mackay’s
  5. differencing shows that homogenising makes Mackay Met Office maxima LESS like the neighbours, and Post Office maxima not much closer.

If the adjustments at Mackay are, as the Bureau claims, “indicative of the sorts of adjustments made across the 112 ACORN-SAT sites”, then we can look forward to finding many more problems.

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.

Conclusion

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%.

Minima:

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%.

Maxima:

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 http://www.bom.gov.au/climate/change/index.shtml#tabs=Tracker&tracker=timeseries

AWAP data available at a cost on request from http://www.bom.gov.au/climate/data-services/

A Check on ACORN-SAT Adjustments: Part 1

September 18, 2014

I have commenced the long and tedious task of checking the Acorn adjustments of minimum temperatures at various stations by comparing with the lists of “highly correlated” neighbouring stations that the Bureau of Meteorology has kindly but so belatedly provided.   Up to 10 stations are listed for each adjustment date, and presumably are the sites used in the Percentile Matching process.

It is assumed by the Bureau that any climate shifts will show up in all stations in the same (though undefined) region.  Therefore, by finding the differences between the target or candidate station’s data and its neighbours, we can test for ‘inhomogeneities’ in the candidate site’s data, as explained in CTR-049, pp. 44-47.  Any inhomogeneities will show up as breakpoints when data appears to suddenly rise or fall compared with neighbours.  Importantly, we can use this method to test both the raw and adjusted data.

Ideally, a perfect station with perfect neighbours will show zero differences: the average of their differences will be a straight line at zero.  Importantly, even if the differences fluctuate, there should be zero trend.  Any trend indicates past temperatures appear to be either relatively too warm or too cool at the station being studied.  It is not my purpose here to evaluate whether or not individual adjustments are justified, but to check whether the adjusted Acorn dataset compares with neighbours more closely.   If so, the trend in differences should be close to zero.

In all cases I used differences in annual minima anomalies from the 1961-1990 mean, or if the overlap was shorter than this period, anomalies from the actual period of overlap.  Where I am unable to calculate differences for an Acorn merge or recent adjustment due to absence of suitable overlapping data (e.g. Amberley 1997 and Bourke 1999, 1994), as a further test I have assumed these adjustments are correct and applied them to the raw data.

I have completed analyses for Rutherglen, Amberley, Bourke, Deniliquin, and Williamtown.

The results are startling.

In every case, the average difference between the Acorn adjusted data and the neighbouring comparison stations shows a strongly positive trend, indicating Acorn does not accurately reflect regional climate.

Even when later adjustments are assumed to be correct the same effect is seen.

Interim Conclusion:

Based on differencing Raw and Adjusted data from listed comparison stations at five of the sites that have been discussed by Jennifer Marohasy, Jo Nova, or myself recently, Acorn adjustments to minima have a distinct warming bias.  It remains to be seen whether this is a widespread phenomenon.

I will continue analysing using this method for other Acorn sites, including those that are strongly cooled.  At those sites I expect to find the opposite: that the differences show a negative trend.

Scroll down for graphs showing the results.

Rutherglen

rutherglen

(Note the Rutherglen raw minus neighbours trend is flat, indicating good regional comparison.  Adjustments for discontinuities should maintain this relationship.)

Amberley (a)

amberley

(Note that the 1980 discontinuity is plainly obvious but may have been over-corrected.)

Amberley (b): 1997 merge (-0.44) assumed correct

 amberley inc 1997

Treating the 1997 adjustment as correct has no effect on the trend in differences.

Bourke (a)

bourke

Bourke (b):  1999 and 1994 merges assumed correct.

bourke inc merges

No change in trend of differences.

Deniliquin

deni

(Note the adjusted differences still show a strong positive trend, but less than the other examples.)

Williamtown

williamtown

(Applying an adjustment to all years before 1969 produces a strong positive trend in differences.)

Better Late Than Never- BOM Releases Adjustment Details

September 11, 2014

On Monday, quietly and without any announcement, a new tab appeared on the Bureau’s ACORN-SAT webpage.

adj tab

This “Adjustments” tab opens to a page explaining why homogenisation is necessary, supposedly showing how the adjustments don’t make much difference to the mean temperatures, and how Australia really is warming because everyone agrees.  More on this later.  So how do we get to see the actual adjustments for each site?  Tucked away under the first graph is a tiny link:

adj tab link

Click on that and a 27 page PDF file opens, listing every Acorn station, dates and reasons for adjustments, and most importantly, a list of reference stations used for comparison.  (You have to go to Climate Data Online to find the station names, their distance away, site details, and their raw data.)

Finally it will be possible to check the methods and results using the correct comparison stations- until now we could only guess.

Back in September, 2011 the Independent Peer Review Panel made a series of recommendations, including that

“C1. A list of adjustments made as a result of the process of homogenisation should be assembled, maintained and made publicly available, along with the adjusted temperature series. Such a list will need to include the rationale for each adjustment.”

The Bureau responded on 15 February 2012, just before the release of Acorn:

“Agreed. The Bureau will provide information for all station adjustments (as transfer functions in tabular format), cumulative adjustments at the station level, the date of detected inhomogeneities and all supporting metadata that is practical. This will be provided in digital form. Summaries of the adjustments will be prepared and made available to the public.”

That was two and a half years ago.  What took so long?  Why was it not publicly available from the start?  Perhaps it is just a co-incidence that the long awaited information was released shortly after a series of articles by Graham Lloyd appeared in The Australian, pointing out some of the apparent discrepancies between raw and adjusted data.  Graham Lloyd deserves our heartfelt thanks.

The Bureau of Meteorology has been dragged kicking and screaming into the 21st Century.  The Bureau is having trouble coming to terms with this new era of transparency and accountability, an era in which decisions are held up to public scrutiny and need to be defensible.

I trust we won’t have to wait another two and a half years for the other information promised, such as “sufficient station metadata to allow independent replication of homogeneity analyses” and “computer codes… algorithms… and protocols”,  “the statistical uncertainty values associated with calculating Australian national temperature trends” and “error bounds or confidence intervals along the time series”

The final recommendation of the Review Panel, and undertaking by the Bureau:

“E6. The Review Panel recommends that the Bureau assembles and maintains for publication a thorough list of initiatives it has taken to improve transparency, public accessibility and comprehensibility of the ACORN-SAT data-set.

Agreed. The Bureau will provide such information on the Bureau website by March 2012.”

I must have missed that.

 

 

 

Homogenisation: A Test for Validity

September 8, 2014

This follows on from my last post where I showed a quick comparison of Rutherglen raw data and adjusted data, from 1951 to 1980, with the 17 stations listed by the Bureau as the ones they used for comparison when detecting discontinuities. 

Here is an alternate and relatively painless way to check the validity of the Bureau’s homogenisation methods at Rutherglen, based on their own discontinuity checks.  According to the “Manual” (CAWCR Technical Report No. 49), they performed pair-wise comparisons with each of the 17 neighbours to detect discontinuities.  An abbreviated version of this can be used for before and after comparisons.  For each of the 17 stations, I calculated annual anomalies from the 1961-1990 means for both Rutherglen and the comparison site, then subtracted the comparison data from Rutherglen’s.  I did the same with Rutherglen’s adjusted Acorn data.

A discontinuity is indicated by a sudden jump or drop in the output.  The ideal, if all sites were measuring accurately and there are no discontinuities, would be a steady line at zero: a zero value indicates temperatures are rising or falling at the same rate as neighbours.  In practice no two sites will ever have the same responses to weather and climate events, however, timing and sign should be the same.  Therefore pairwise differencing will indicate whether and when discontinuities should be investigated for possible adjustment.

Similarly, pairwise differencing is a valid test of the success of the homogenisation process.  Successful homogenisation will result in differences closer to zero, with zero trend in the differences over time.  The Bureau has told the media that adjustments are justified by discontinuities in 1966 and 1974.  Let’s see.

Fig. 1:  Rutherglen Raw minus each of 17 neighbours

pairwise diffs Rutherglen Raw

Note: there is a discernible drop in 1974, to 1977.  There is a very pronounced downwards spike in 1967 (ALL differences below zero, indicating Rutherglen data were definitely too low.)  There also a step up in the 1950s, and another spike upwards in 1920.  Rutherglen is also lower than most neighbours in the early 1930s.  Also note several difference lines are obviously much higher or lower than the others, needing further investigation, but the great majority cluster together.  Their differences from Rutherglen are fairly consistent, in the range +/- 1 degree Celsius.

Now let’s look at the differences AFTER homogenisation adjustments:

Fig. 2:  Rutherglen Acorn minus the neighbours: The Test

pairwise diffs Rutherglen Acorn

The contrast is obvious.  The 1920 and 1967 spikes remain.  Differences from adjusted data are NOT closer to zero, most of the differences before 1958 are now between 0 and -2 degrees Celsius, and there is now an apparent large and artificial discontinuity in the late 1950s.  This would indicate the need for Rutherglen Acorn data to be homogenised!

Compare the before and after average of the differences:

Fig. 3:

pairwise diffs Rutherglen Raw v Acorn average

There is now a large positive trend in the differences when the trend should be close to zero.

There are only two possible explanations for this:

(A)  The Bureau used a different set of comparison stations.  If so, the Bureau released false and misleading information. 

(B)   As this surely can’t be true, then if these 17 stations were the ones used, this is direct and clear evidence that the Bureau’s Percentile Matching algorithm for making homogenisation adjustments did not produce correct, successful, or useful results, and further, that no meaningful quality assurance occurred.

If homogenising did not work for Rutherglen minima, it may not have worked at the other 111 stations. 

While I am sure to be accused of “cherry picking”, this analysis is of 100% of the sites for which the identities of comparison stations have been released.  When the Bureau releases the lists of comparison stations for the other 111 sites we can continue the process.

A complete audit of the whole network is urgently needed.

Rutherglen: Spot the Outlier

September 2, 2014

In today’s Australian there was another article by Graham Lloyd, “Climate scientists defend data changes”. The Bureau of Meteorology is quoted as claiming that “statistical analysis of minimum temperatures at Rutherglen indicated jumps in the data in 1966 and 1974….. These changes were determined through comparison with 17 nearby sites”.

 Two and a half years after being asked to explain the reasons for the myriads of changes to the data, the Bureau has finally given up some of the information it should have released in 2012.  I have been given the names of these 17 sites.  They are:

74034 Corowa, 82053 Wangaratta, 82002 Benalla, 72097 Albury Pumping Station, 82100 Bonegilla

74106 Tocumwal, 81049 Tatura, 81084 Lemnos, 72023 Hume Reservoir, 82001 Beechworth

72150 Wagga Wagga, 74114 Wagga Research Centre, 80015 Echuca, 74039 Deniliquin (Falkiner Memorial)

74062 Leeton, 74128 Deniliquin, and 75032 Hillston.

 This at last allows me to understand how they went about turning a cooling trend of -0.33C per 100 years into a warming trend of +1.74C. 

 Fig. 1: Rutherglen unadjusted data vs adjusted, 1913 – 2013

rutherglen tmin

  I checked the monthly unadjusted minimum data for Rutherglen, the adjusted data for Rutherglen, and the unadjusted data at all 17 of the listed neighbours, in the period 1951 – 1980, which according to the Bureau is the critical period containing the 1966 and 1974 break points.  30 years is a suitably long period for analysis.  For the technically minded, I calculated monthly anomalies from the 1951-1980 means for each record, then 12 month averages.  This should allow us to see the problems around 1966 and 1974.

 Here is a chart of the results.  Can you spot the outlier?

 Fig. 2:  Rutherglen raw (unadjusted), the 17 neighbours’ raw data, and Rutherglen Acorn (adjusted)

 rutherglen v Acorn v neighbours all

You won’t be able to pick out the light blue line of Rutherglen raw data in the spaghetti lines of the neighbours, but you should be able to see the dark red of the adjusted data peeping above and below the others.

 For a clearer picture, here is the same information, but with the 17 neighbours averaged to a single orange line.

 Fig. 3: Rutherglen unadjusted (blue), average of the 17 neighbours (orange), and Acorn- the homogenised version of Rutherglen (dark red).

 rutherglen v Acorn v neighbours avg

Forgive me, but I thought the idea of “homogenising” was to adjust the data so that it is not so different from the neighbours.  That happens in1966.  They got that right, but not in 1974, where the adjustments have increased the difference, and have produced warming.  Odd things also happen in 1952, 1954, 1957, 1969, and 1975-80.

 It is clear that the changes to the temperatures at Rutherglen do not “homogenise” them.  They make the differences from the neighbours greater, and change a cooling trend into a warming one.

 This is not unique to Rutherglen- adjustments warm the temperature trends at 66 of the 104 Australian sites, and warm the national mean temperature trend by around 47%.

 But what would I know- I’m just an amateur according to Professor Karoly.

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

http://data.giss.nasa.gov/modelforce/ghgases/Fig1A.ext.txt ):

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.


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