Posts Tagged ‘climate science’

How climate science models are validated

August 20, 2014

Mathematical and computer models are wonderful tools. Once they can be validated they are powerful methods of interpolation. They are useful methods of improving the understanding embodied in the models by extrapolation. The divergence between extrapolated model results and real data can then be used for improving the models to better account for real data. If model results do not fit real data it is time to change the model.

Extrapolated model results are never evidence. They are just indicators of what may come to pass provided that the model – in spite of all its simplifications – does truly apply.

And when climate model results are fudged to move towards climate data which, in turn, has to be fudged to move towards the model results, one wonders whether there is any scientific method left in “climate science”.

This is reported by WUWT from a recent paper from ETH Zurich.

If the model data is corrected downwards, as suggested by the ETH researchers, and

the measurement data is corrected upwards, as suggested by the British and Canadian researchers,

then the model and actual observations are very similar.

Shameless is one adjective that comes to mind.

Fraudulent is another.




On constraining the quality of climate science

July 3, 2013

I wrote this – following the words of the Bard – about something else

The quality of intelligence is not strain’d,
It may not be shaped or created or invented
to suit a man’s convenience. It is twice cursed:
It curses him who invents and curses the fool who believes.

and then realised it could easily be adapted to fit “hockey stick” climate science

The quality of climate science is not strain’d,
It may not be shaped or tricked into a hockey stick
to suit a Mann’s convenience. It is twice cursed:
It curses him who tricks and curses the fools who believe.

Book Burning to promote climate science!

May 3, 2013

Dr. Craig Clements and Dr. Alison Bridger demonstrating their methods for the advancement of science.


Drs. Bridger and Clements of San Jose State University burning a book to advance their cause


Climate science on “negative watch”

March 30, 2013

Graphic: The Economist

The almost 20 year pause in global warming while emissions of carbon dioxide have continued to increase can no longer be ignored. Following The Economist’s article earlier this week, more of the main steam media are beginning to question if climate science is as “settled” as some would like us to believe. I would go a little further than The Australian and say that “climate science” and not just “climate sensitivity”  is now on “negative watch” if not as yet “downgraded”. While it is encouraging that some sanity may be returning to the debate as evidenced by the greater interest from the main stream media to question global warming orthodoxy (Die Welt, Jyllands Posten, Der Spiegel, The Telegraph, Daily Mail), they are already a little late. “Climate Science” has actually been at “junk” levels since Copenhagen and Climategate and is only just beginning to creep up from there!

The Australian:

DEBATE about the reality of a two-decade pause in global warming and what it means has made its way from the sceptical fringe to the mainstream.

In a lengthy article this week, The Economist magazine said if climate scientists were credit-rating agencies, then climate sensitivity – the way climate reacts to changes in carbon-dioxide levels – would be on negative watch but not yet downgraded.

Another paper published by leading climate scientist James Hansen, the head of NASA’s Goddard Institute for Space Studies, says the lower than expected temperature rise between 2000 and the present could be explained by increased emissions from burning coal.

For Hansen the pause is a fact, but it’s good news that probably won’t last.

International Panel on Climate Change chairman Rajendra Pachauri recently told The Weekend Australian the hiatus would have to last 30 to 40 years “at least” to break the long-term warming trend. 

But the fact that global surface temperatures have not followed the expected global warming pattern is now widely accepted.

Research by Ed Hawkins of University of Reading shows surface temperatures since 2005 are already at the low end of the range projections derived from 20 climate models and if they remain flat, they will fall outside the models’ range within a few years.

“The global temperature standstill shows that climate models are diverging from observations,” says David Whitehouse of the Global Warming Policy Foundation.

“If we have not passed it already, we are on the threshold of global observations becoming incompatible with the consensus theory of climate change,” he says.

Whitehouse argues that whatever has happened to make temperatures remain constant requires an explanation because the pause in temperature rise has occurred despite a sharp increase in global carbon emissions. ….. 


We learn about climate only when the models are wrong!

March 29, 2013

When a forecast based on a mathematical model is correct, we learn nothing.

A mathematical model is merely a theory, a simplification of reality or an approximation to the real world. By definition a mathematical model is a hypothesis.  When forecasts are incorrect, we can return to our model and improve it and make a new hypothesis. A forecast is then a test of the model but in just one particular set of circumstances. Being correct does not prove the theory behind the model. It does of course add to the body of evidence that the model may be a satisfactory representation of reality and it does allow further forecasts to be made without tweaking the model. For learning to take place the mathematical model must be the falsifiable hypothesis of the scientific method.

It seems to me that Solar Science has a much healthier (scientifically) attitude to models and forecasts than “Climate Science”. When observations don’t match a climate forecast, the observations are impugned rather than the models being improved. This is, I think, because the forecast climate results have been used to establish huge revenue flows in the political arena (whether as taxes or carbon credits or just as research funding). There has been a vested interest in denying the observations and calling the science “settled”. Once the science is “settled”  the climate forecast and its underlying model become sacrosanct and take on the certainty of prophecy. Instead of being falsifiable hypotheses, climate model forecasts have taken on the character of unfalsifiable prophecies!

No scientist would presume to claim that we know or understand all solar effects. Or that we know and understand the role of the oceans or of the water vapour and dust and aerosols in the atmosphere. “Climate” is contained in the thin, chaotic layer of atmosphere which surrounds us. Yet “Climate Science” makes the arrogant assumption that the effect of trace amounts of carbon dioxide on climate is known definitively. Filling a real greenhouse with higher concentrations of carbon dioxide does not make that greenhouse any warmer than one filled with normal air – but the plants do grow faster with access to the additional CO2!! But – claim the climate priesthood –  in the real atmosphere, carbon dioxide causes other forcings (clouds? aerosols? precipitation effects?) which maximise warming which means that our model is still valid. Why not just admit that we don’t know what we don’t know?

The behavioural issue of course is whether it is worth trying to control something as poorly understood as climate rather than ensuring that we have the wherewithal to adapt to whatever changes may come. Another ice age will surely come whether in 10 years or a 100 years or 2,000. It will then be our ability to harness all available energy sources around us which will determine our capacity to adapt.

Learning from forecasts when they are wrong – not just in science but also in business and project management and technology development – has long been a hobby-horse of mine and is why forecasts need to be wrong.

When there is no difference there is no learning.

  • I take prophecies to be a promise about the future  based primarily on faith and made by prophets , witchdoctors, soothsayers and politicians such as ”You will be doomed to eternal damnation if you don’t do as I say”,
  • I take “forecasts” to be an estimate of future conditions based on known data with the use of calculations, logic, judgement, some intuition and even some faith. They are extrapolations of historical conditions to anticipate – and thereby plan for -future conditions.

……. Over the last 30 years I have spent of a lot of time conducting and participating in reviews. Reviews of research projects, of construction projects, of organisations and processes, of designs, of strategies and action plans, of businesses and of companies. The common features  in all these different reviews, that I have found the most penetrating, have been the comparisons not only between forecast values  and actual values, (which may be any values indicating performance and capable of being extrapolated), but also between past forecasts and current forecasts.

Whether considering construction progress or costs or sales figures or cash flow or profit or number of patents applied for, it is the differences between forecast and actual values, or values forecast before and values forecast later which have led to learning. In all these fields we are in the area of the behaviour of complex systems; and where people and their behaviour is involved any system is inevitably a complex system.

When a forecast is fulfilled there is usually an air of congratulation, satisfaction and self-adulation and this leads to a deadly complacency that everything is “settled science” and well understood. In any enterprise of any kind, that kind of complacency is the kiss of death. It is the differences which lead to questioning, to proper scientific scepticism, to further investigation and ultimately to an increase of understanding and – perhaps – a better forecast. (Of course, ignoring all such differences  and to merely “continue as before” can be equally fatal).

Which brings me to climate (which is not a science by any stretch of the imagination) and solar cycles. They are both in the realm not only of where “what we know is a great deal less than what we don’t know” but they are also both in the region where “we don’t even know what we don’t know”. We do not even know all the questions to be asked. They are both complex systems where – by definition – the complexity lies in the multitude of the processes involved and their interactions.

When climate – which is contained in the 100 m of ocean and 20 or so km thick, turbulent and chaotic atmospheric layer (and which is dimensionally miniscule in relation to the 140 million km of the earth-to-sun system) – is so complacently considered to be “settled science” then we have shifted into the area of faith and soothsaying and prophecies. When climate modellers are smug enough to believe they have understood the climate system and believe that their models are complete, then the models produce outputs which are not forecasts but prophecies. (No doubt soothsayers and shamans have sometimes made accurate prophecies but I still would not buy a used car from one of them)! Weather is in the realm of forecast (though you could argue that the most accurate forecast is still that “the weather tomorrow will be like today”) but climate is not yet there.

This kind of “arrogance” which pervades some of the climate “scientists” is not so prevalent when it comes to the study of Solar Cycles. There is a clear understanding that “we don’t know what we don’t know”. In addition to the 11 year and 22 year cycles, other cycles are hypothesised for 87 years, 210 years, 2300 years (or maybe 2241 or 2500 years) and 6000 years. We have no idea what causes these cycles. Even the 11 year cycle which has been most studied produces  surprises every day but is properly in the area of “forecast” (and hopefully never again will be in the area of prophecy). ….

…… We seem to be in a solar minimum. We may be seeing a 210 year cycle – or maybe not. There are changes to the forecasts not only regarding the maximum level of sunspot activity but also about when it will occur and what the length of cycle 24 might be. There is speculation as to what effect the length of the solar cycle may have on climate – but we haven’t a clue as to what mechanisms may be involved.  This is not to say that there isn’t much speculation and hypothesising. There is a great deal of comment about the effect these changing forecasts may have on global warming or cooling or climate disruption.  In some quarters there is much glee that the forecasts have been “wrong”. Some comments question the intelligence of the forecasters.

But of course the forecasts themselves say nothing about how the behaviour of the sun may impact our climate. They do not pretend to be prophecies or to be statements of inevitable outcomes. All they do say is that we don’t know very much – yet – about the sun. But we do know enough to make some tentative forecasts.

But I am very glad that people continue to be brave enough to make forecasts and I am quite relieved that the forecasts are not spot on. That at least ensures we will continue learning.

Social psychology falls from grace

July 3, 2012

It is not only scientists in social psychology who indulge in fraud.  Anthropology for example has had its share of frauds. While corporations – such as Glaxo Smith Kline– can be held liable and sanctioned for fraud, it is very rare for individual academics who fake data in pursuit of their own agendas to be held liable. Why cannot a concept of tort or “product liability”apply to scientists? The members of the medical profession who aided and abetted GSK are unlikely to face any sanctions. But the recent scandals of social psychologists faking data to show statistical correlations between sets of propositions and then inferring causal relationships have demonstrated two things which I think apply in many more so-called “scientific” disciplines  than just social psychology. :

  1. The ease with which sampled data can be faked or cherry picked by workers from reputed institutions to show apparent correlations can then be provided a stamp of authority through the publication of “peer-reviewed” papers, and
  2. that there is a need to return to the scientific method of focusing on propositions that are falsifiable and to avoid the temptation of concluding that any positive statistical correlation provides proof of a causal relationship.


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