# Perth 1940 Max Min Daily Temps

Previous posts have introduced the work that Chris Gillham is doing in spot auditing the accuracy of the Bureau of Meteorology’s temperature records. He has now re-recorded the daily max and min temperatures from one Australian weather station for one year, Perth 9034 in 1940, using original sources in The West Australian newspaper.

Below is an initial look at the historic data (in red) compared to the BoM’s “unadjusted” or “raw” records (grey) for the station.

Its fairly clear that there are a lot of errors. The minimum temperatures, however, are shockers. Each of the red lines seen on the lower series above is an error in the daily minimum — mostly down.

Mean of the max differences = +0.20C
Mean of the min differences = -1.18C
Average max all differences = +0.04C
Average min all differences = -0.33C

While the average error of the max temperatures is up 0.2C, the average magnitude of the errors in the min temperatures is a whopping 1.18C! Over the whole year that changes the annual minimum temperature by -0.33C.

The diurnal range is increased by an average of 0.4C. While these errors are only in one year in one station, it is noteworthy that the magnitude of these errors is similar to the change in the diurnal range attributed to global warming.

The data file is here – perth-1940-actual-raw. You need to open it in excel and save as a CVS file.

The code below should run on the datafile.

```P1940=ts(read.csv("perth-1940-actual-raw.csv"),start=1940,freq=365) l=2 plot(P1940[,3],col=2,ylim=c(0,45),main="Perth Regional Office 9034",ylab="Temperature C",lwd=l) lines(P1940[,4],col="gray",lwd=l) lines(P1940[,7],col=2,lwd=l) lines(P1940[,8],col="gray",lwd=l) maxErrs=P1940[P1940[,3]!=P1940[,4],] print(mean(maxErrs[,4]-maxErrs[,3])) minErrs=P1940[P1940[,7]!=P1940[,8],] print(mean(minErrs[,8]-minErrs[,7])) print(mean(P1940[,4]-P1940[,3])) print(mean(P1940[,8]-P1940[,7])) ```

# Perth 1940 Jan-Dec – Errors

Chris Gillham has completed re-digitizing one years worth of the daily temperature records for Perth in 1940 (perth-1940-actual-raw). These are digitised for all of 1940 at Perth Regional Office 9034 from temperatures published in The West Australian newspaper.

While the majority of the temperatures agree with contemporary BoM data, up to a third of the temperatures in some months disagreed, sometimes by over 1C! This is a very strange pattern of errors, and difficult to explain.

I will be doing more detailed analysis, but Chris reports that overall, the annual average of actual daily Perth max temperatures in 1940, as published in the newspaper, was the same as the BoM raw daily max. The annual average of newspaper daily min temperatures was .3C warmer than in the BoM raw daily min. ACORN max interpreted 1940 as 1.3C warmer than both actual newspaper max and BoM raw max, with ACORN min 1.5C cooler than actual newspaper min and 1.2C cooler than BoM raw min.

Anything above a .1C newspaper/raw difference is highlighted.

Chris notes:

It took a couple of days wading through about 310 newspapers to find all the weather reports and although it would be great to have all years from all locations (those with decimalised F newspaper sources) to confirm the Perth 1940 results, it’s a huge task. It would certainly be easier if the BoM just provided the temps from the old logbooks.

# Rewriting the Temperature Records – Adelaide 1912

Record temperature always make the news, with climate alarmists trumpeting any record hot day. But what if the historic record temperatures recorded by BoM were adjusted down, and recent records were not records at all? More detective work using old newspapers by Chis Gillham in Adelaide this time.

The BoM claims the hottest ever Feb max at West Terrace was 43.4C on 1 February 1912. They got the date sort of right except the Adelaide Advertiser below shows Feb 1 at 112.5F (44.7C) and Feb 2 at 112.8F (44.9C). The BoM cut Feb 2 to 43.3C in raw.

# Perth 1940 Jan-Mar Historic Comparisons

Continuing the comparison of historic sources of temperature and contemporary records, Chris Gillham has compiled a list of maximum and minimum daily temperatures for Perth for the months of January, February and March 1940 and uncovered some strange discrepancies (highlighted – all months at perth-newspapers-mar-qtr-1940).

Chris notes that while BoM’s contemporary temperatures largely agree with temperatures reported in newspapers of the day, a couple of temperatures in each month disagree by up to a degree C!

File attached comparing the March quarter 1940 daily newspaper and BoM raw data for Perth Regional Office 9034 (Perth Observatory atop Mt Eliza at the time), plus an ACORN average for each month.

Combining all days in the March 1940 quarter, average max in The West Australian newspaper was 29.51C and average BoM raw max was 29.56C. Average min in the newspaper was 17.38C and average BoM raw min was 17.15C. Rounded, max up .1C and min down .3C in BoM raw compared to what was reported in 1940. There seems a tendency for just two or three temps each month to be adjusted in raw, sometimes up but obviously with a downward bias in min.

ACORN-SAT judged the three months to have an average max of 31.32C and an average min of 16.17C. So max has been pushed up about 1.8C and min has been pushed down about 1.2C or 1C, depending on your point of view :-).

It always pays to go back to the source data.

# Comparison of historic temperature with BoMs record

Siliggy has pulled a simple list of maximum temperatures for Adelaide from old newspapers early last century then compared to the BOM records for that time. In most cases the BoM records are 1.5C cooler that the current record. What a clear demonstration of how much they have been adjusted down — equal to the putative warming trend!

JoNova posts on ACORN.

# Should the ABS take over the BoM?

He has a refreshing, mature attitude to his job.

‘I want people to challenge our data – that’s a good thing, it helps us pick things up,’ he says.

Big contrast to the attitude of Climate Scientists. Examples that they believe they cannot be challenged are legion, from meetings to peer review. For example, emails expressing disagreement with the science are treated as threatening, as shown by the text of eleven emails released under ‘roo shooter’ FOI by the Climate Institute at Australian National University.

Australia’s Chief statistician is also egalitarian. In response to a complaint by the interviewer about employment figures, he responds:

He says he doesn’t believe there is a problem, but gives every indication he’ll put my concerns to his staff, giving them as much weight as if they came from the Treasurer.

This is a far cry from the stated policy of the CSIRO/BoM (Bureau of Meteorology) to only respond to peer-reviewed publications. Even when one does publish statistical audits identifying problems with datasets, as I have done, you are likely to get a curt review stating that “this paper should be thrown out because its only purpose is criticism”. It takes a certain type of editor to proceed with publication under those circumstances.

When the Federal Government changes this time, as appears inevitable, one initiative they might consider is a greater role for the ABS in overseeing the BoM responsibilities. Although the BoM is tasked with the collection of weather and water data by Acts of Parliament, it would benefit from an audit and ongoing supervision by the ABS, IMHO.

# Dynamical vs Statistical Models Battle Over ENSO

There is a battle brewing between dynamical and statistical models. The winner will be determined when the current neural ENSO conditions resolve into an El Nino or not in the current months.

The International Research Institute for Climate and Society compares the predictions of ensembles of each type of model here.

Although most of the set of dynamical and statistical model predictions issued during late April and early May 2012 predict continuation of neutral ENSO conditions through the middle of northern summer (i.e., June-August), slightly more than half of the models predict development of El Nino conditions around the July-September season, continuing through the remainder of 2012. Still, a sizable 40-45% of the models predict a continuation of ENSO-neutral conditions throughout 2012. Most of the models predicting El Nino development are dynamical, while most of those predicting persistence of neutral conditions are statistical.

The figure above shows forecasts of dynamical (solid) and statistical (hollow) models for sea surface temperature (SST) in the Nino 3.4 region for nine overlapping 3-month periods. While differences among the forecasts of the models reflect both differences in model design, and actual uncertainty in the forecast of the possible future SST scenario, the divergence between dynamical and statistical models is clear.

This question fascinates me so much, I studied it for three years “Machine learning and the problem of prediction and explanation in ecological modelling” (1992). Why is there a distinction between dynamical and statistical models? What does it mean for prediction? What does it mean if one set of models are wrong?

For example, what if ENSO remains in a neutral or even La Nina state, thus ‘disproving’ the dynamical models. These models are based in the current understanding of physics (with a number of necessary approximations). Clearly this would say that something about the understanding of the climate system is wrong.

Alternatively, what if the currently neutral ENSO resolves into an El Nino, ‘disproving’ the statistical models. These models are based in past correlative relationships between variables. It would mean that some important physical feature of the system that is missing from the correlative variable has suddenly come into play.

Why should there be a distinction between dynamical and physical models at all? I have always argued that good, robust prediction requires no distinction. More precisely , the set of predictive models is at the intersection of statistical and dynamical models.

To achieve this intersection, from a starting point of a statistical model, each of the parameters and their relationships should by physically measurable. That is, if you use a simple linear regression model, each of the coefficients need to be physically measurable and the physical relationships between them additive.

From a starting point of a dynamical model, the gross, robust features of the systems should be properly described, and if necessary statistically parameterized. This usually entails a first or second order differential equation as the model.

This dynamical/statistical model is then positioned to incorporate both meaningful physical structure, and accurate correlative relationships.

It amazes me that most research models are developed along either dynamical or statistical lines, while ignoring the other.