Category Archives: Weather station

Climate at Townsville

Dr. Bill Johnston

Dr. Bill Johnston’s scientific interests include agronomy, soil science, hydrology and climatology. With colleagues, he undertook daily weather observations from 1971 to 1979.

Abstract

Main points

  • Aerial photographs and Royal Australian Air Force plans and documents held by the National Library and National Archives of Australia show the Stevenson screen at Townsville airport moved at least three, possibly four times before 1969 while it was on the eastern side of the main runway; and probably twice between when it moved to a mound on the western side in January 1970 and to the current automatic weather station site in December 1994.
  • Of those site changes, a site move in 1953/54 and another in 1970 resulted in step-changes in maximum temperature data that were unrelated to the climate. A step-change in minima in 1968 appeared to be due to nearby disturbances associated with building an extension to the met-office. Importantly, except in the Bureau’s Garbutt instruments file, which is online at the National Archives (Barcode 12879364), none of the relocations or nearby changes are listed or described in site-summary metadata.   
  • By ignoring prior changes and smoothing the 1994 transition to the automatic weather station and small (60-litre) Stevenson screen, homogenisation created trends in maximum and minimum temperature that had nothing to do with the climate. 
  • Accounting simultaneously for site-related changes and covariates (rainfall for Tmax and Tmax for Tmin) leaves no residual trend, change or cycles attributable to the climate. Thus there is no evidence that the climate has warmed or changed.

Background

Like many of Australia’s ACORN-SAT weather stations[1], the site at Townsville airport was set-up in 1939 as an Aeradio office for monitoring air-traffic and to provide advice of inclement weather along the east coast route between Melbourne and Port Moresby.

Changes in facilities, instruments and functions caused the site to move irregularly; however, moves and changes prior to December 1994 were not detailed in ACORN-SAT or site-summary metadata. Despite repeated assurances in peer-reviewed publications written by Bureau climate scientists and others, that the history of ACORN-SAT sites had been exhaustively researched and appropriate adjustments had been made for the effect of site changes on data, it was not the case at Cairns and neither is it true for Townsville.

As there is no measurable change or warming in temperature data for Townsville Airport, claims of catastrophic consequences for the Great Barrier Reef are unfounded in the temperature data and, as a consequence, are grossly overstated.

An important link – find out more

The page you have just read is the basic cover story for the full paper. If you are stimulated to find out more, please link through to the full paper – a scientific Report in downloadable pdf format. This Report contains far more detail including photographs, diagrams, graphs and data and will make compelling reading for those truly interested in the issue.

Click here to download the full paper including photographs and tables of data used.

Note: Line numbers are provided in the linked Report for the convenience of fact checkers and others wishing to provide comment. If these comments are of a highly technical nature, relating to precise Bomwatch protocols and statistical procedures, it is requested that you email Dr Bill Johnston directly at scientist@bomwatch.com.au referring to the line number relevant to your comment.   


[1] http://www.bom.gov.au/climate/change/acorn-sat/documents/ACORN-SAT-Station-Catalogue-2012-WEB.pdf

Methods: Gladstone Qld

Dr Bill Johnston [1]

Dr. Bill Johnston’s scientific interests include agronomy, soil science, hydrology and climatology. With colleagues, he undertook daily weather observations from 1971 to 1979.

Abstract

Main Points

  • The weather station at Gladstone Radar marks the approximate southern extremity of the Great Barrier Reef.
  • Temperature and rainfall data are used to case study an objective method of analysing trend and changes in temperature data.
  • The 3-stage approach combines covariance and step-change analysis to resolve site change and covariable effects simultaneously and is widely applicable across Australia’s climate-monitoring network.
  • Accounting for site and instrument changes leaves no residual trend or change in Gladstone’s climate.

Background

In Part 1 of this series, temperature and rainfall data for Gladstone Radar (Bureau of Meteorology (BoM) site 39326) are used to case-study a covariate approach to analysing temperature data that does not rely on comparisons with neighbouring sites whose data may be faulty.

Advantages of the method are:

  • The approach is based on physical principles and is transparent, objective and reproducible across sites.
  • Temperature data are not analysed as time-series in the first instance, which side steps the problem of confounding between serial site changes and the signal of interest.
  • Changes in data that are unrelated to the causal covariate are identified statistically and cross-referenced where possible to independent sources such as aerial photographs and archived plans and documents. Thus the process can’t be manipulated to achieve per-determined trends.
  • The effect of site-changes and other inhomogeneties are verified statistically in the covariate domain. Thus the approach is objective and reproducible.
  • Covariate-adjusted data are tested for trend and other systematic signals in the time-domain.

Further, statistical parameters such as significance of the overall fit (Preg), variation explained R2adj and significances of coefficients provide an independent overview of data quality.

An important link – find out more

The page you have just read is the basic cover story for the full paper. If you are stimulated to find out more, please link through to the full paper – a scientific Report in downloadable pdf format. This Report contains far more detail including photographs, diagrams, graphs and data and will make compelling reading for those truly interested in the issue.

click here to download the full case study including photographs and tables of data used.

Note: Line numbers are provided in the linked Report for the convenience of fact checkers and others wishing to provide comment. If these comments are of a highly technical nature, relating to precise Bomwatch protocols and statistical procedures, it is requested that you email Dr Bill Johnston directly at scientist@bomwatch.com.au referring to the line number relevant to your comment.   

[1] Dr. Bill Johnston’s scientific interests include agronomy, soil science, hydrology and climatology. With colleagues, he undertook daily weather observations from 1971 to 1979. 

About

Welcome to BomWatch.com.au a site dedicated to examining Australia’s Bureau of Meteorology, climate science and the climate of Australia. The site presents a straight-down-the-line understanding of climate (and sea level) data and objective and dispassionate analysis of claims and counter-claims about trend and change.

BomWatch delves deeply into the way in which data has been collected, the equipment that has been used, the standard of site maintenance and the effect of site changes and moves.

Dr. Bill Johnston is a former senior research scientist with the NSW Department of Natural Resources (abolished in April 2007); which in previous guises included the Soil Conservation Service of NSW; the NSW Water Conservation and Irrigation Commission; NSW Department of Planning and Department of Lands. Like other NSW natural resource agencies that conducted research as a core activity including NSW Agriculture and the National Parks and Wildlife Service, research services were mostly disbanded or dispersed to the university sector from about 2005.

BomWatch.com.au is dedicated to analysing climate statistics to the highest standard of statistical analysis

Daily weather observations undertaken by staff at the Soil Conservation Service’s six research centres at Wagga Wagga, Cowra, Wellington, Scone, Gunnedah and Inverell were reported to the Bureau of Meteorology. Bill’s main fields of interest have been agronomy, soil science, hydrology (catchment processes) and descriptive climatology and he has maintained a keen interest in the history of weather stations and climate data. Bill gained a Batchelor of Science in Agriculture from the University of New England in 1971, Master of Science from Macquarie University in 1985 and Doctor of Philosophy from the University of Western Sydney in 2002 and he is a member of the Australian Meteorological and Oceanographic Society (AMOS).

Bill receives no grants or financial support or incentives from any source.

BomWatch accesses raw data from archives in Australia so that the most authentic original source-information can be used in our analysis.

How BomWatch operates

BomWatch is not intended to be a blog per se, but rather a repository for analyses and downloadable reports relating to specific datasets or issues, which will be posted irregularly so they are available in the public domain and can be referenced to the site. Issues of clarification, suggestions or additional insights will be welcome.   

The areas of greatest concern are:

  • Questions about data quality and data homogenisation (is data fit for purpose?)
  • Issues related to metadata (is metadata accurate?)
  • Whether stories about datasets consistent and justified (are previous claims and analyses replicable?)

Some basic principles

Much is said about the so-called scientific method of acquiring knowledge by experimentation, deduction and testing hypothesis using empirical data. According to Wikipedia the scientific method involves careful observation, rigorous scepticism about what is observed … formulating hypothesis … testing and refinement etc. (see https://en.wikipedia.org/wiki/Scientific_method).

The problem for climate scientists is that data were not collected at the outset for measuring trends and changes, but rather to satisfy other needs and interests of the time. For instance, temperature, rainfall and relative humidity were initially observed to describe and classify local weather. The state of the tide was important for avoiding in-port hazards and risks and for navigation – ships would leave port on a falling tide for example. Surface air-pressure forecasted wind strength and direction and warned of atmospheric disturbances; while at airports, temperature and relative humidity critically affected aircraft performance on takeoff and landing.

Commencing in the early 1990s the ‘experiment’, which aimed to detect trends and changes in the climate, has been bolted-on to datasets that may not be fit for purpose. Further, many scientists have no first-hand experience of how data were observed and other nuances that might affect their interpretation. Also since about 2015, various data arrive every 10 or 30 minutes on spreadsheets, to newsrooms and television feeds largely without human intervention – there is no backup paper record and no way to certify those numbers accurately portray what is going-on.

For historic datasets, present-day climate scientists had no input into the design of the experiment from which their data are drawn and in most cases information about the state of the instruments and conditions that affected observations are obscure.

Finally, climate time-series represent a special class of data for which usual statistical routines may not be valid. For instance, if data are not free of effects such as site and instrument changes, naïvely determined trend might be spuriously attributed to the climate when in fact it results from inadequate control of the data-generating process: the site may have deteriorated for example or ‘trend’ may be due to construction of a road or building nearby. It is a significant problem that site-change impacts are confounded with the variable of interest (i.e. there are potentially two signals, one overlaid on the other).

What is an investigation and what constitutes proof?

 The objective approach to investigating a problem is to challenge the straw-horse argument that there is NO change, NO link between variables, NO trend; everything is the same. In other words, test the hypothesis that data consist of random numbers or as is the case in a court of law, the person in the dock is unrelated to the crime. The task of an investigator is to open-handedly test that case. Statistically called a NULL hypothesis, the question is evaluated using probability theory, essentially: what is the probability that the NULL hypothesis is true?

In law a person is innocent until proven guilty and a jury holding a majority view of the available evidence decides ‘proof’. However, as evidence may be incomplete, contaminated or contested the person is not necessarily totally innocent –he or she is simply not guilty.

In a similar vein, statistical proof is based on the probability that data don’t fit a mathematical construct that would be the case if the NULL hypothesis were true. As a rule-of-thumb if there is less than (<) a 5% probability (stated as P < 0.05) that that a NULL hypothesis is supported, it is rejected in favour of the alternative. Where the NULL is rejected the alternative is referred to as significant. Thus in most cases ‘significant’ refers to a low P level. For example, if the test for zero-slope finds P is less than 0.05, the NULL is rejected at that probability level, and trend is ‘significant’. In contrast if P >0.05, trend is not different to zero-trend; inferring there is less than 1 in 20 chance that trend (which measures the association between variables) is not due to chance.

Combined with an independent investigative approach BomWatch relies on statistical inference to draw conclusions about data. Thus the concepts briefly outlined above are an important part of the overall theme. 

Using the air photo archives available in Australia, Dr Bill Johnston has carried out accurate and revealing information about how site changes have been made and how these have affected the integrity of the data record.