A significant amount of evidence indicates that the global temperature did increase during the 20th century. For example, direct thermometer measurements indicate that the temperature increased by perhaps 0.8°C.
…
In summary, there is no direct evidence showing that CO2 caused the 20th century warming, or as a matter of fact, any warming. The question to ask is therefore can we point to some other culprit? If humans are not the only ones responsible for climate change, what else is responsible?
…
Moreover, when studying directly the total ocean heat content, it is possible to see that the amount of heat going into the oceans is at least 5 times larger than can be expected from just the changes in the total solar irradiance (e.g., see this blog entry and references therein). Thus, one can conclude that there must be at least one mechanism amplifying the link between solar activity and climate.
…
The fact that the sun plays a decisive role in climate change has important implications to the understanding of the causes of 20th century global warming and the expected temperature change in the coming century. The increased solar activity over the 20th century can be translated into a radiative forcing contribution. Since the solar/climate link was already quantified, it is possible to estimate the solar contribution, which turns out to be about half of the measured warming.
…
Thus, the warming component left to be explained by humans is much smaller than is often claimed by the proponents of the anthropogenic warming. However, if we are to predict the temperature change over the 21st century, we have to know what is the expected human contribution to the radiative budget, but equally important, also the climate sensitivity to these changes in the energy budget.
…
As we have seen above, the answer to the second question is that the sensitivity is most likely small. In fact, this sensitivity is about 1 degree increase per doubling of CO2.
…
The evidence shows therefore that even if we continue with “business as usual”, we will not cause a climate catastrophe. It is also possible to estimate the sea level increase, which will be of order 10 cm over the coming century, much less than the meters talked about in Gore’s movie.

The High Quality data does NOT give an accurate record of Australian temperatures over the last 100 years.

BOM has produced a climate record that can only be described as a guess.

The best we can say about Australian temperature trends over the last 100 years is “Temperatures have gone down and up where we have good enough records, but we don’t know enough.”

If Anthropogenic Global Warming is so certain, why the need to exaggerate?

It is most urgent and important that we have a full scientific investigation, completely independent of BOM, CSIRO, or the Department of Climate Change, into the official climate record of Australia.

I will ask Dr Jones for his response.

My wife does not provide the best example of demonization, but it is current, motivational and recent and on my mind. Please excuse me if I become too personal. The important part is not the personal part. It is the insidious danger of demonization.

I am a chemist, with a B.Sc. and part-time honours, chemistry major, from the University of Queensland, Australia. I also did a couple of years of aero engineering before a car crash curtailed further flying in the Air Force. My career covered many aspects of science, some engineering and much politics at the end. In retirement I can draw on a diverse set of experiences, but can also appreciate points of the philosophy of science. Mostly, that only comes with age and experience.

“Demonisation” is not my term. Maybe it originated with alcohol, the “demon drink”. It is now in fairly wide and expanding use. It was used a few years ago in climate circles to describe CO2 as a troublesome compound for all people, probably before the facts were complete. More recently, it has been used in a people context, as in the demonization of denialists.

Scientists to whom the demon label is attached can have a harder time than they should. In the case of Colleen, I was demonised by the general physician in charge and by senior nursing staff because I consistently told them that they should look beyond the easy diagnosis they had made. When you know a person for nearly 50 years, you can detect changes that are not so obvious to the casual observer, but then the doctor has the training and his word should be accepted. As it turned out, Colleen had a diagnosis of post-operative constipation following a repair of a broken hip – not an uncommon happening. What had really happened was that she had had an earlier colonoscopy where a cut had been made and marked with methylene blue die in case follow-up was need. This had weakened the bowel wall. Several days of large doses of morphine had dehydrated her and the combination resulted in a blocked and perforated bowel, the hole about an inch in diameter, with a couple of pounds of faecal material mingled with her other abdominal organs.

It was necessary for me to be quite forceful of expression for several days before the correct tests were done and the problem – perhaps then 6 days old – was rectified by lengthy surgery. This demonised me in the eyes of some of the medical profession involved. A lay person cannot diagnose, only a doctor can do that. But, unless I had persisted I would now no longer have a wife. Experience, observation, open mind, thinking about the evidence, reinterpretation, explaining every little observation, insisting on more data – these were the life-saving ingredients.

In my career I have met demonization several times. We inherited a site where lead batteries had been recycled and there was residual lead in the soil. We were told “authoritatively” that lead poising affects the IQ of young children. A “Team” has worked for years to prove this alarming hypothesis – but they have demonised the authors and the possibility of a simple and strong reverse causation explanation. See

http://www.mja.com.au/public/bookroom/2001/rathus/rathus.html

http://dnacih.com/SILVA.htm

Two comments follow about lead and children. First, in the mid 1980’s, when some intensive work was done, published estimates of the weight of daily soil ingestion by children differed by well over an order of magnitude. So the models had uncertainty – as do today’s global climate models. Second, this question is relevant to climate change because of the discontinuation of leaded petrol and the resultant increase of fuel use by cars and trucks. This affected the so-called GHG load on the atmosphere.

Another case from Australia, again medical, resulted in 2 Nobel Laureates, Barry Marshall and J. Robin Warren. See

http://nobelprize.org/nobel_prizes/medicine/laureates/2005/index.html

http://www.guardian.co.uk/world/2005/oct/04/science.health

Barry Marshall in particular was demonised for his astounding proposition that ulcers were caused by bacteria and could be cured by antibiotics. He even self-administered a dangerous concoction to drive home his point. Personally, I feel that the “Team” opposition to their work should be documented better and spread more widely, so that those who demonise will know that they can be shamed in public when shown wrong.

Getting closer to climate matters, there was widespread demonization of the peaceful use of uranium for large scale electricity generation from the late 1960s onwards. From 1972 I consulted to and then joined the company which had discovered the immense Ranger uranium deposits in 1969. It was soon apparent that there were considerable learning curves for science sub-sets like radioactive decay, assaying, ore resource calculation and particularly radiation health measures. Well, the mines are still operating today and no person appears to have been harmed by them (though we did lose one employee to a crocodile attack).

The second steep learning curve for nuclear was to counter the demonization of radioactivity. You probably know that there are still many people today who have absolute belief that nuclear power generation wastes have to be managed for 250,000 years. This is simple psycobabble as can be shown in a few minutes. Of course, the future of nuclear electricity interacts with fossil fuel use and projections for atmospheric composition of GHG.

Almost by necessity, I lowered my involvement in geochemistry and increasingly went political about late 1980s to counter the anti-nuclear protest. Slowly but surely, the arguments put up in opposition were demolished by demonstration and logic, until no realistic viable case exists to further hold back on nuclear expansion. Demonisation has however resulted in Australia still having no nuclear power generators, despite a large output of mined uranium.

Demonisation has distorted the science, in a way costly to power users and the environment.

Demonisation is resurgent in relation to the Climategate emails and inquiries. Those who have submitted words critical of the science are not all extremists. There is a weight of experience and intelligence in these contra submissions, but those who make them are being ignored or derided. This is not a civil way to act. The inquirers have done shoddy work (with the exception of Graham Stringer MP from Britain; and now the penny is dropping for the Commons Inquiry leader Phil Willis.)

Wherever you look, it is not hard to find evidence of demonization in science. The anti vaccination movement is an example. There is widespread chemophobia, for example in framing where “organic” or “natural” methods are suddenly trendy, despite the certainty that many people would die of starvation if global organic farming was made compulsory. Ditto for genetic modification of crops, a human extension of a process of Nature. The demonisation of science is an insult to the truly professional scientist. I worked several years in the synthetic fertilizer industry in a very large new plant and mixed with people whose skill and dedication was too prominent to be insulted.

Recently was saw the terrible example of the Proceedings of the National Academy of Sciences publishing lists of names of good people and demons with respect to attitudes, as they interpreted them, towards man-made global warming. This is ugly. . PNAS should take a long look at its charter, purpose and methods.

Deductions.

1. We frequent bloggers need to check if we are falling into the demonization trap ourselves. I am guilty, especially a few times when blogging was new to me.
2. Demonisation is antithetic to good science. Good science equates to open minds.
3. There are more effective ways to make an example than by demonising. Shaming is an acceptable alternative sometimes, but most effective of all is the dispassionate analysis that is done by people like Steve McIntyre and several others whom you know too well to need mentioning here. Lead by example.
4. Emotion and politics interact with science, but in an ideal world they are separate from it. It is harder to set out to do good science when you have made up your mind on the outcome.
5. Try to avoid making a mellow generalisation from data. In a surprising number of cases (well, surprising to me) the solution is in the exception to intuition. Data points that are averaged out of existence often take with little Rosetta stones.

It was the devil in the detail that led to intervention in the case of my wife, at the start of this essay. It was not helpful to be demonised for pointing out that misdiagnosis was possible.

There is more that can be written on this theme, more in the context of climate. Should our host feel that responses below warrant it, I would be honoured to write more.

Lubos makes me laugh:

Well, let me make it clear that there’s nothing controversial about negative feedbacks. In this battle between negative feedbacks and Rasmus Benestad, it is the latter who is an utterly controversial crackpot. The existence of crackpots may make basic concepts of science controversial among crackpots – and the remaining readers of Real Climate, if there are any – but it can’t make it controversial in the real science.

]]> http://landshape.org/enm/monthly-roundup/feed/ 3 http://landshape.org/enm/upper-atmosphere-inflow-of-moisture/ http://landshape.org/enm/upper-atmosphere-inflow-of-moisture/#comments Wed, 30 Jun 2010 22:55:26 +0000 David Stockwell http://landshape.org/enm/?p=4470 For those interested in the theory that upper atmosphere inflow of moisture from the Indian Ocean is a major determinant of rain in Australia, check out the satellite loop for the last 4 hours right now.

Here is the development of the inflow over the last few days.

Note this is in the presence of a high pressure system with an upper atmosphere ridge and trough, as can be seen by the slight deformation of the isobars over Queensland.

Like many people, he thought that the analysis of climate change in Australia, and information given to the public and the government, was based on the raw temperature data. He was wrong. He averaged maxima and minima for all stations at each site, then compared the result with the High Quality means. By these calculations (averaging the trend at each site in Victoria) the raw trend is 0.35 degrees C per 100 years, and the High Quality state trend is 0.83C. That’s a warming bias of 133%!

CRITIQUE OF DROUGHT MODELS IN THE AUSTRALIAN DROUGHT EXCEPTIONAL CIRCUMSTANCES REPORT (DECR)

ABSTRACT
This paper evaluates the reliability of modeling in the Drought Exceptional Circumstances Report (DECR) where global circulation (or climate) simulations were used to forecast future extremes of temperatures, rainfall and soil moisture. The DECR provided the Australian government with an assessment of the likely future change in the extent and frequency of drought resulting from anthropogenic global warming. Three specific and different statistical techniques show that the simulation of the occurrence of extreme high temperatures last century was adequate, but the simulation of the occurrence of extreme low rainfall was unacceptably poor. In particular, the simulations indicate that the measure of hydrological drought increased significantly last century, while the observations indicate a significant decrease. The main conclusion and purpose of the paper is to provide a case study showing the need for more rigorous and explicit validation of climate models if they are to advise government policy.

Meanwhile, scientists are finding new ways to communicate worthless forecasts to decision makers.

These models have been the basis of climate information issued for national and seasonal forecasting and have been used extensively by Australian industries and governments. The results of global climate models are complex, and constantly being refined. Scientists are trialling different ways of presenting climate information to make it more useful for a range of people.

Conducting professional validation assessment of models would be a start, followed by admitting they are so uncertain they should be ignored.

The talks at Emerald that I organized went quite well, considering this is a small regional town. About 80-100 people attended an teaser session during the Property Rights Australia meeting during the day, and around 40 attended at night. We got a standing ovation during the day — the first time for me! The crowd was a mixture of ages and sexes and I think messages of bureaucratic sloth and opportunism resonated with them. Central Queensland turned on one of its trademark sunsets for Anthony:

It was good to spend a bit of time with Anthony and catch up on the goss — well not really gossip, but about bloggers and the people behind the curtain. You know how it is, you tend to get a certain view of the people involved, but when you learn more about them, it turns out they are just regular people who put their hand up for something they believe in.

Anthony has done an exception job of standing up to a crushing schedule. All the 18 venues were organized by volunteers, people who put one hand up and the other hand in their own pockets to make this happen. Kudos to all involved.

I talked in Newcastle, Noosa as well, to an older crowd (except for a couple of spies who sat quietly and disappeared early). Its a pity as it would have been a worthwhile contribution to field questions from people in the trade. My slides are here.

We failed to get any real national coverage of the Tour, but the response from the local press was been fantastic. I was interviewed 4 times, and even once by the ABC which was a very peculiar interview, eg.

ABC: Most people think that global warming is a serious problem, why don’t you?
ABC: How do we know you (the climate sceptics party) in not an extreme right wing group?

I think presenting a contrary view is confusing people. I was trying to explain that we are the centerists, and if CSIRO only did a professional job of model validations instead of producing glossy pamphlets promulgating vague fears I would have nothing to complain about.

As the local press coverage has been good all over, it has no doubt had a greater effect than immediately apparent. Plenty of people who didn’t go to the talks have come up to me, so the message has got out there than not everyone believes global warming is an imminent catastrophe. At least the Greens are not going take over over without a fight.

The ‘strongest male’ is itself a highly variable component.

How to formalise this as a niche? Preamble. All we have, really, are observations. To put niches into a statistical framework, we only have the expected distributions of those observations (both singly and jointly). Selection (either natural or through our study design) changes the distribution of features, and we observe those changes.

For example, if the sample of breeding males is generally taller than the population of breeding males, then we could presume there is selective pressure on this feature — an important item of information. This could be detected statistical significant (e.g. the distribution differ in a Chi–squared test).

If the distribution of the sample of breeding males is the same as the population then we presume height is not important. If breeding males are significantly taller in some cases and smaller in other cases the feature ‘height’ could still be important. As a persistent feature of this species then you could say that height selection is generally important to this species’ survival, is a determining factor, etc.

Compare this approach with a typical characterization of a niche (i.e. Hutchinsonian). Here you would record observations that characterise all the breeding males, such as that the height of the breeding males ranges from X to Y. You might describe other aspects of breeding males (e.g. hats, RV’s, flip-flops). You might say that males in this height range with these characters represent ‘core habitat’ for the species.

But there is no guarantee that males with hats, RV’s and flip-flops are breeding because males in the non-breeding group may also have these features. But to a large degree, the information about breeding males’ hats, RV’s and flip-flops is just non-significant junk.

The only reason you would be interested in this information is if there was a prior expectation that it was important information. You might find some important characteristics of non-breeding males (e.g. males wearing cowboy hats are non-breeding). This is the case for temperature and rainfall; it is only the limits of the range of temperature and rainfall, i.e. those features of non-suitable habitat.

If the discriminating feature of the sample of breeding males is height (i.e. All tall males are breeding males), this would be useful information; it could be applied to predict in other situations, it could be the basis of theories, conservation practises, and so on. If you want to select people for a mission to Mars you could just select the tall guys.

This would, however, leave out some small males that are also breeding. That is just bad luck (depending on whether the mission is successful of course). You could look for an even stronger bijective relationship (e.g. All tall males are breeding males, AND all breeding males are tall). Chances are, however, that this would be a different feature again that optimizes this two-way relationship.

In the same way there are different logical relationships within data sets, and these may be optimised by different variables and have different uses.

1. Characterization: recording the envelope range of temperature, rainfall, etc that a species lives within and other junk information with no statistical basis.
2. Prediction: recording those features that when present are high probability indicators of the presence of the species.
3. Correlation: recording those features that vary together with the density of the species.

So you can see at least three different ways of describing the nature of the habitat of a species, with three different logical structures (right-if, left-if and iff). These three different logical structures usually result in three different features being identified, and they have three different uses. Moreover, to the extent that any particular analytical method is more like one or the other logical structures, it will identify different features and give different results (eg regression, decision trees, BIOCLIM, etc.).

Lack of appreciation of the logical distinction leads to many errors in ecology. Typically only the characterization model is used for all sorts of things it is the least equipped to do. For example, people who make living out of promulgating vague fears like the Climate Adaptation Flagship who state:

Many species are at risk because they are restricted in geographical and climatic range.

Wrong. Because species are found in a narrow climatic range does not mean they are restricted to a narrow climate range. It is more likely the narrow range is due to resource limitation or accidents of history. And so a moderate shift in climate is unlikely to be negative, and may be neutral or beneficial, as has actually been observed.

I’m not even sure what is meant by an optimal environment for a species/genus/whatever.

while Andrew said that:

it wouldn’t surprise me if a lot of species tend to live at the margins of their “ideal” habitat.

We need a bit of abstraction to address these questions. In a laboratory, a plant would be expected to show a humped response to the main variables of temperature and water availability. The parameterisation of this function can be termed the ‘fundamental niche’ of the species, and may be equated with a physiochemical optimum unaffected by competition.

In the wild there will be gaps in this function. This can be called the ‘realized niche’, and may be due to interspecific competition, but also affected by chance dislocation of the species’ distributions that are unrelated to any physiological capacity to occupy that space. If this were not the case, then no species could ever ‘invade’ another region.

Now we can imagine a ‘niche model’ of a species as consisting of an estimate of the humped function that represents it fundamental or realized niche. That function — to a first approximation the range of temperature and rainfall where the species has been observed — is the basis of original ‘envelope’ methods such as BIOCLIM.

But consider the logic of this model, calling the range model R and the sightings of the species S. If a species occurs at a point, then that implies it lies within the range R. However, we know from basic logic that:

  S → R ≠ R → S

But R → S is what we need for predicting the distribution of the species. We need to know that if the location has certain characteristics M then the species occurs, i.e. M → S. Thought of another way, the ‘gaps’ in the fundamental niche cause inaccuracy when we try to use it to predict.

To discover the optimal description M such that M → S we need to do a different kind of search that optimises the accuracy of prediction of the model. It has been shown conclusively that methods based on the correct logical form of implication are more robust predictors of the occurrence of a species, than an envelope model composed of climatic variables temperature and rainfall.

I found that in practise, a single variable such as monthly rainfall (ie seasonality) was much better predictor of the distribution of a species than the range of temperature and rainfall. In some cases, non-climatic factors such as soil types were optimal. The notion of an optimal environment only makes sense in the lab. In the wild, it really only makes sense to talk about the optimal determinant of a species.

Why this should be the case is kind of conjectural, but it would make sense from an entropy or information viewpoint for species to ‘spread out’ over possible environmental determinants, providing they stay with their fundamental niche. This would include ‘marginal’ locations.

In this view, the effects of climate change on species would be very mixed. Some species with a determinant that is not primarily climatic would be very unaffected. Other species would be affected over only part of their range, depending on the determining factor. This has been seen, as the primary response of species to climate change has been range expansion to the north in the northern hemisphere, due to a temperature determinant up there. Some unlucky species with highly seasonal determinants, or isolated non-climatic determinants, may be severely affected.