Guest opinion: Dr. Tim Ball

Al Gore even made some hardened liberal journalists sit up and question when in 2007 he told a joint session of the House Energy Committee and The Senate Environment Committee that the climate debate was over, “the science was settled”. The journalists knew, as any moderately informed person does, that science is never settled. But, what does “settled” mean in this context? The most reasonable definition is linked directly to a simple definition of science, namely the ability to predict. If you can’t predict then your science is wrong, as Feynman and others made clear. Failed predictions prove that the science isn’t settled. Gore and the supporters of the Intergovernmental Panel on Climate Change (IPCC) version of anthropogenic global warming (AGW) claim the science is settled, but their climate predictions (projections) are consistently wrong. The problem is wider because the weather predictions of national weather agencies who are, through the World Meteorological Organization (WMO) the IPCC, don’t work either.

The climate is the average weather, which raises the question; when does weather become climate? Since climate is an average of the weather, the average temperature for a 24-hour period is the climate of the day. If the science is settled, then the weather forecasts should also be accurate, but they are still increasingly unreliable beyond 48 hours. One use of the millions of weather data points created for my doctoral thesis was by a statistician, Alexander Basilevsky. He was working on Markov Chains defined as follows:

A Markov chain is collection of random variables (where the index runs through 0, 1, …) having the property that, given the present, the future is conditionally independent of the past.

He needed a continuous long-run data set derived from nature. He wanted to address the issue of probabilities and accuracy of predictions in nature, particularly weather predictions. I never spoke with him about his results but assume, since the work was done 20-years ago, they achieved nothing applicable because prediction accuracies didn’t improve.

In fact, weather forecasting accuracy has not significantly increased since it began officially in 1904. In that year, Vilhelm Bjerknes (1862-1951) introduced the idea of numerical weather predictions by solving mathematical equations. This assumes you have adequate and appropriate data to put into the equation, but that is still not the case, and that is the root of the problem.

Prediction failures are a situation obvious to those who make empirical observations because their lives and livelihood depend on the weather. Robin Page, farmer/author in his book “Weather Forecasting: The Country Way,” wrote,

“Yet it is strange to record that as the weather forecasting service has grown in size and expense, so it’s predictions seem to have become more inaccurate.”

New Scientist reports that Tim Palmer, a leading climate modeler at the European Centre for Medium-Range Weather Forecasts in Reading England said:

I don’t want to undermine the IPCC, but the forecasts, especially for regional climate change, are immensely uncertain.

Then in an apparent attempt to claim some benefit we’re told:

…he does not doubt that the Intergovernmental Panel on Climate Change (IPCC) has done a good job alerting the world to the problem of global climate change. But he and his fellow climate scientists are acutely aware that the IPCC’s predictions of how the global change will affect local climates are little more than guesswork.

Roger Harrabin, BBC Reporter, made a comment about a climate conference in Reading:

So far modellers have failed to narrow the total bands of uncertainties since the first report of the Intergovernmental Panel on Climate Change (IPCC) in 1990.

Koutsoyiannis et al., confirmed this in April 2008 where in an article they found:

The GCM (General Circulation Models) outputs of AR4 (FAR) as compared to those of TAR, are a regression in terms of the elements of falsifiability they provide…

Is there a common denominator here? Weather predictions don’t work, especially if you consider their accuracy for severe weather, and climate predictions don’t work either. The common denominators for the failure are the lack of spatial and temporal data and little understanding of the mechanisms. It is assumed that if we knew those, then accurate predictions would occur.

The IPCC were the first official group to make climate predictions that caught world attention and they were wrong from the start. Because their objective was political, they deliberately chose to separate claims about the accuracy of their forecasts. The Summary for Policymakers (SPM) deliberately misleads and as Figure 1 by Roy Spencer shows they increased the misdirection as the gap between their claims and reality widened.

Figure 1

The IPCC Physical Science Basis Reports of Working Group I all identify the problems and severe limitations of the data and knowledge of the mechanisms.

The only thing predictable is that as their forecasts fail the claims of success are magnified and amplified.

In the 1990s, one segment of the climate debate involved the US and western nations support for Chaos Theory. The other segment promoted by the Soviet Union, China and Eastern nations supported the cyclical explanation of climate change. Many, especially the western media saw the division as a Cold War ideological difference. In fact, it was a legitimate scientific difference and debate. It was fuelled by the establishment of translation services of Soviet science by Jewish scientists who escaped to Israel.

In the Third Assessment, Science Report the IPCC wrote,

In climate research and modeling, we should recognize that we are dealing with a coupled non-linear chaotic system, and therefore that the long-term prediction of future climate states is not possible.

Essex and McKitrick identified the chaos portion in their book Taken By Storm.

“Fluids are governed by nonlinear, as opposed to linear equations… … these represent major distinctions in times of great importance. However, the misleading averages can yield exact average equations of quantities describing fluids that are linear! This is especially remarkable because the correct differential equations for fluids are some of the most notorious examples of nonlinear differential equations there are. In a non-mathematical world, a differential equation being notorious seems hard to imagine, but some really are. They aren’t to be found in People magazine, but nonlinear equations have notoriety among those who know about them, because we cannot solve most of them. We are left to rely on computer approximations of solutions. Furthermore, unlike linear equations, they can and do exhibit a kind of peculiar unpredictability in their solutions, not unlike randomness, known as chaos.” The most obvious difference between them (Kinetic theory and Navier-Stokes) is that we have no guide in the larger climate world to any key structures and relationships. There is no one living on climate scales to observe structures, do experiments, or establish physically meaningful structure for us. Without a climate structure analogous to Navier-Stokes to act as a beacon to climb toward in our averaging schemes, we are little better than bacteria in a test tube trying to deduce from first principles what the laboratory ought to be like.

I tease chaos theory supporters that their only hope is that chaos theory is correct so, when they are finally asked about their failed predictions by the mainstream media, they can then explain why their predictions consistently fail.

So, according to Essex and McKitrick, the theoretical approach is not possible because of internal mathematical problems. Actually, the problem is more basic. We don’t have the data on which to perform our “averaging schemes.” From the start, the data was completely inadequate. Lamb knew what was going to happen as he recorded in his autobiography (1997). He created the Climatic Research Unit (CRU) because

“…it was clear that the first and greatest need was to establish the facts of the past record of the natural climate in times before any side effects of human activities could well be important.”

Lamb told me that he determined the need for better historical records because of the failure of the weather forecasts he gave Royal Air Force pilots flying over Europe in WWII. He thought that a better understanding of past weather patterns could provide a base for improved forecasts. Unfortunately, the objective did not last long.

“My immediate successor, Professor Tom Wigley, was chiefly interested in the prospects of world climates being changed as result of human activities, primarily through the burning up of wood, coal, oil and gas reserves…” “After only a few years almost all the work on historical reconstruction of past climate and weather situations, which first made the Unit well known, was abandoned.”

As we know from the leaked emails, it was all downhill from there.

The cyclical approach is similarly limited by lack of data. How long and accurate a record is required to determine the existence of cyclical events? Apparently the mathematical answer is partly provided by the length required for spectral analysis, but that doesn’t address the quality and spatial resolution of the record. Cyclical analysis has a better chance of producing reasonably accurate general forecasts because it is based on empirical data that is somewhat independent of the small scale mathematical and physical problems Essex and McKitrick and others identify.

All the manipulation, corruption, and deception carried out in climate science were possible because of the use of mathematics and statistics with inadequate data. As Prime Minister Benjamin Disraeli said, “There are three types of lies; lies, damn lies, and statistics.” When the data was inadequate, the AGW proponents compounded the problems by making it up. The extent of the data fiasco was acutely displayed in Bob Tisdale’s recent article and reinforced by Werner Brozek’s article asking if two data sets, presumably from the same original data source, can both be right.

The 2001 IPCC Report, using data prepared by Phil Jones, Director of the CRU said the global temperature average, reportedly using the best modern instrumental database over the longest period of data available, rose 0.6°C over 100+ years. The problem is the error factor was ±0.2°C or ±33.3%. So, the modern instrumental temperature record, which is supposedly many times more accurate than any paleoclimate temperature record, is useless. Compare the Jones number of temperature change in a 100+ record with the difference between GISS and HadCRUT in any given year. If for the sake of argument, the difference is 0.1°C then it is one-sixth of the difference for the total change in 100+ years.

The science of climate and weather predictions may be settled, but only in the sense that they are not possible? If you pursue either of the current practices, the climate physics of the IPCC and most skeptics or the cyclical approach favoured by most Russians and others, the data is inadequate. Despite my respect for the work of H. H. Lamb and his reconstruction of historical records, it is not possible to reconstruct weather records with the degree of accuracy claimed necessary for the IPCC or WMO approach to climate and weather predictions. It is why there was a tendency to leave out error bars in much early work. They underlined the severe limitations, if not the impossibility, of their work.

This brings us back to the cyclical approach that might allow for the educated speculations that climate change will continue, and the global temperature may go up or down. Right now, my more specific speculation based on historic records is that it is more likely to go down. Based on the evidence, I clearly have a better probability of being correct than the AGW and IPCC speculators.

There is no more common error than to assume that, because prolonged and accurate mathematical calculations have been made, the application of the result to some fact of nature is absolutely certain. – A. N. Whitehead (1861 – 1947) Mathematician and Philosopher

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