Whilst RealClimate don’t have a seperate thread, their refutation is pretty entertaining – they seem to struggle to work out who the Institure of Physics are to start with, then accuse them of being industry led (actually, they seem to be a predominantly academic organisation, and have printed many many articles in Physics World lamenting the fact that the planet is doomed). RealClimate site responses (from the ‘real climate scientists’) are all on message, supporting the ‘standard practice’ of not releasing code.

By 2nd March (the day after the oral hearings, which recieved a good amount of media coverage), the IoP issues a clarification stating that their submission does not address the question of the consensus, and the Institute does not doubt that. Quite what has caused this is not clear, but there is certainly a severe mis-understanding regarding the principle of repeatability.

The view from RealClimate is that it is OK to keep the data secret, if it gets into the wrong hands it could be mis-used in a dangerous way, and anyway the process has been replicated independantely and shown to be good. Since people like Gavin speak from a modeling point of view, I can see some logic in saying that making the models open wouldn’t be much of an advancment of the science. (The code is probably ugly, there are several pseudo-independant models, and the models are not particularly precise anyway)

Frank O’Dwyer keeps repeating that open-access should apply to all, sceptics included, yet fails to accept that several people have been attempting to verify the methods used in several studies in order to check the methods. The accusation of (unintentional) selection bias in datasets, inapropriate adjustments to data series, manipulation of the peer review process, etc. are not issues which the skeptical community need to answer. Much of the interest is in series which to date show a fairly small degree of warming, so small errors are significant. Even if most users of the data only use it to confirm correlation with (e.g. hurricane disaster losses) there is a need to enable the reconstruction itself to be challenged. The various tree ring cronology questions which have been asked in the past show that if the reasons for selecting a specific series are secret, it rapidly becomes impossible to determine how robust a method is to being fed bad data.

Some commentors have argued that the IoP’s submission justify doing nothing, and that the precautionary principle is not applicable to the climate change scenario. it may be this reasoning which prompted the clarification. The precautionary principle theory goes along the lines of ‘even if it’s not proven for sure, we should react just in case it is important to start now’. All that has been expressed by the IoP is that the scientific method has not been sufficiently open. Extrapolating from that to say that the data we have should be thrown away, and we need not worry about anything is pure flat-earth territory. We have a number of facts, which justify a level of concern. Some of those facts have not been tested as well as an independent scientist might have assumed, but that is still the best case analysis to date. Some policies are still sensible, regardless of the need/desire to restrict CO2.

The IoP has said it is good to question the consensus, and to encourage critical analysis of the reconstructions. It should be enough for now to agree that more open access to the data and methods is a priority – a point which is still contested by many of the alarmist proponents. questions like UHI adjustments and divergence must be brought back to the table at the scientific/statistical level. This is a straightforward view which requires no judgment of value or personal priority, it is basic scientific process.

From a policy point of view, there are many things changing. The datasets are slowly growing, agreement with model projections is decreasing, trends are changing, the independence of the consensus position is coming under question. The validity of some of the data has a very small influence on this today. It is likely that people will become less prepared to support expensive measures in the short term, but it is best to assume that nothing will emerge to prove that the science is completely wrong.

As many people seem to be grasping, weather and climate are not quite the same thing. Roger Pielke Sr has a blog post comparing the surface anomalies in December against 2009. Very different. Yet the Met Office (who claim to have a good understanding of these matters) have stated that snow will become rare in the UK (March 2000), and repeatedly made (presumably) politically motivated seasonal forecasts. The UK is unusual in that our weather seems to be the residuals of several systems. Sadly, one of the best instrumental temperature records is therefore mostly noise. Following in the theme that localised temperature measurements tell us almost nothing about global climatic trends, how about those poor twisted scrub trees in Siberia which tell us how warm it was there 1000 years ago? On a more technical front, some analysis here shows that Over-fitting is likely to show a historical dip and then a recovery to a mostly unchanging prediction for past temperatures (see figure 4).
Regardless of the meaning of the global average temperature, comparing the trend from 1950, ’60, ’70, ’00, ’01 to November 2009 against the accepted model means shows a statistically significant difference (and remember that the models were designed around the perid of better agreement). Temperatures averaged everywhere are not continuing to rise as they seemed to be in 1980-2000, and yet it was this rapid rise which could not be otherwise explained that lead to the demise of the coal mining industry in the UK. A plausible explanation for this could be the dramatic number of instrumental stations used to contribute to the instrumental records. This peaked at 6000 in 1970, and has now dropped to less than 2000. Consider how difficult it is to ensure an accuracy sufficient to identify a 0.1 degree trend over 20 years when thermometers move and their local environment changes. Satellite readings have their own problems, but comparing the reagonal differences in trends does show that surface temperature measurements in some places give a high result.

The last decade may well have been the warmest on record (in the context of our chosen measurement scenario) but even without significant volcanic activity, the upward trend has definitely stopped. Regardless of that, weather is not becoming more extreme. The current weather in Europe is not particularly significant on a global scale, other than to emphasise that the impacts of global warming are not observable today, and should definitely not be relied upon when making forecasts or policy decisions.

Prof. Qing-Bin Lu published a paper (DOI: 10.1103/PhysRevLett.102.118501) in Physical Review Letters suggesting a correlation between Cosmic Rays and Ozone Depletion, using data up to 2007. There are predictions on the paper of large Ozone losses in 2008-2009, and certainly Cosmic Rays have peaked in 2009 as a result of the prolonged minimum in solar activity. (Solar activity tends to shield the earth from cosmic rays).

This article has a demonstration of the depletion and recovery of Ozone, which does seem to correlate with F10.7 solar flux.

Lu’s paper says little about climate. Ozone seems to have an indeterminate effect as a greenhouse gas. What is clear is that Ozone reached a minimum around 1997, and has been recovering slowly since. The solar minimum could be expected to slow that recovery. As it stands, Ozone seems to be inversely correlated with surface temperature, which has a recent peak around 2000, and a more recent stabilisation. Actually, Ozone may well be driven by temperature so the whole argument seems a little confused.

Now CFCs are strong greenhouse gases, so at least that part makes sense. CFC production started roughly at the point where temperatures started to rise, and cosmic rays could impact the lifetime and concentration. Nothing proven yet, just more potential mechanisms.

A 2007 presentation (pdf) has more detail on modelling the recovery of ozone, and the presence of CFCs.

Now, this is important because carbon neutral energy is not necessarily soot free. Burning bio-fuels is maybe just as bad or worse. Diesel may be worse than petrol since the nature of the soot is different. This is a perfect example of a mechanism where fixing the wrong problem might actually make the problem worse.
Of course, from a policy point of view, this is bad news. Particulate emission is easy to manage, without the need to tax or ration energy use. Providing any electricity (clean or not) to third world countries could be a massive improvement over insisting that they can only have access to modern energy technology if it is 100% clean (otherwise they can burn dung, because that is natural).

Even if not proven, this shows how large the uncertainties are in our current understanding of climate science. If CO2 is only half the problem, maybe we should look to identify the other half as well, since fixing half of a critical issue is probably a waste of effort.

First, to save the people at RealClimate (climate propaganda by the people who deny that there is any thing to worry about in the stolen private emails, which incidentally discuss the use of that very website to discredit alternative opinion) the trouble, here is some background on me and what some people are claiming is a very important issue.

I’m not a climate scientist, so I don’t understand the nuances of the argument

True, I don’t work in academia – I work in the real world. I don’t feel under much pressure to ensure that my public views conform to the consensus view. I do have a physics degree, and knowing a small amount about particle physics, I appreciate that much of science today is based on indirect measurement, inference and models. As a radio design engineer (often viewed as a black art) I am familiar with the need for a model to have a good understanding of the important parameters and a way of proving the applicability of a model for a specific task. I do understand the strengths and limitations of statistical methods, and can easily tell when a paper

• does not use a full or (a-priori selected) data set
• does not consider alternative hypotheses
• does not present the error bounds on a conclusion

I haven’t published in the peer reviewed journals

Actually, I have no real desire to. I do not work in an environment where published papers are any sort of metric of skill. Patents maybe? They are ‘peer reviewed’ in a sense. It is becoming clearer that the peer review process is not as open to contrary views as one might wish. Searching the emails might lead one to suppose that the the ‘we won’t read it until it’s published in a the peer reviewed literature’ mantra and the discussions about friends and influence on editorial boards of journals.

Look at the glaciers, they prove it

(substitute any other proxy which supports your argument) Well, neglecting the fact that your glacier proxy comes with some questions:

• Not all glaciers are retreating
• Glacier growth also relies on rainfall
• Glaciers seem to have been higher in Roman times
• Some glaciers (such as Greenland) are not stable in the current climate, and are self sustaining only due to their own micro-climate, since the last cool period.

Having one good proxy does not justify extrapolating the validity of that one point to another data point which you have a strong believe must obviously be related. It is nonsense to say ‘the stalactite records are a bit inconclusive, but since the glaciers show the last 20 years were warm, the stalactites want to tell us that too. Indeed, they would if they hadn’t had such a tough life’. Each point in the argument has to stand alone, and it’s weaknesses understood – unless you have a desire to build a house from a pack of cards.

Even if we’re not sure, we must do something

Well, wouldn’t it be good to know what it is you’re supposed to be doing something about? Must it be our fault, and we thing we truly understand the whole picture? Even on CO2, we don’t know why the situation isn’t worse than we thought it should be. Even assuming that the temperature is not varying as part of a natural cycle, there is a high chance that CO2 is only half of the explanation for what we observe. Intensive farming has had a pretty significant impact on the water cycle. Transforming forest into desert does much more than remove a carbon sink. Clean air regulation is thought to have had an impact on the thermal energy balance. The most serious risk with taking action blindly to solve an imaginary problem is that we generate a different problem which might be equally hard to solve. Imagine pilling up sandbags in the face of an oncoming tidal wave. Every little helps? No. Too little action is just as bad as taking the wrong action and wasting time and resources in the process. This is why a clear open understanding of the science is important.

So many people seem to forget that the planet is dynamic. The continents move (although claim this 60 years ago, and you would have been laughed at), massive natural disasters occur (and are not on the increase, despite what the ‘talking points’ readers will say). We shouldn’t be trying to preserve it intact (preventing natural forest fires, as an example), we have to be prepared to adapt to the way it changes. From the past changes which we are now aware of, it is clear that the climate is to some extent self regulating, with some feedbacks that tend to lead to stabilisation (such as increased CO2 in the atmosphere leading to an increase in shell formation). If this was not the case, we wouldn’t be here now. This natural variation and stability lends weight to the suggestion that an urgent need to respond to a tipping point is a false claim.

What was your agenda exactly?

I was introduced to the idea of skepticism about 25 years ago at the Centre for Alternative Technology, Machynlleth. The scientific support presented along with their exhibits was incomplete or inaccurate, their personal ideologies were clearly apparent. Green and anti-globalisation views are very interesting, but they are not science. We’ve been subjected to scare stories about over population ever since I can remember, always supported by global population projections which have repeatedly been proven false. These seem to the the latest recruits to the ‘it’s all our fault and we must fix it before we’re too late’ game. Almost always these people will deny a technological improvement on the basis that it is still not 100% sustainable, see the refusal to support nuclear as a short term alternative to coal – it’s not about emissions it’s about energy rationing. It would be a shame if this got in the way of our solving the important issue which we haven’t noticed yet.

Steve McIntyre has a detailed analysis of what has been described in the recent news as ‘a trick‘ to present temperature data in a certain way. His analysis is probably too detailed for many people to understand, so I have tried to provide a simple synopsys.

In 1999  the IPCC was preparing it’s Third Assessment Report on climate. Some of the climategate emails discuss a graph which was provided for publication in this report. From the evidence collated by Steve, it seems that the IPCC authors were concerned that the Report should provide a very clear view of unprecedented warming. Rather than simply collating evidence, they seem to have been selecting evidence which supported the message which they wished to convey to policy makers.

An early draft of this graph is shown here.

Of key significance is the Biffra reconstruction (yellow line) which seems to diverge from the measured temperature records about 50 years ago. At the time, there was a degree of concern amongst the scientists that this was an important problem with the reconstruction, and a reluctance to adjust the data so this was not such a visible problem.

They seemed keen to provide a graph which would be acceptable to the IPCC, and some of their discussions are now available and are discussed in Steve’s post. In conclusion, they appear to have truncated the problematic series, and spliced or grafted it in some way to the instrumental series. There is a subtle side effect here, the whole series moves down because the overlap with instrumental temperatures is now different.

This version seems to have been acceptable to the IPPC, and is now part of history.

I also show below an overlay of the two versions, showing how in a short period of time the expression of our knowledge of historical temperatures has been changed. Since these data are only shown in printed graphs, it is impossible to cleanly reconstruct the comparison – I have had to manually emphasise the green line which represents the modified Biffra series (clearly there has been some re-claibration of this series, it is not even of the same shape.

The panel consisted of Michael Buerk as chairs, with Michael Portillo, Matthew Taylor, Claire Fox and Clifford Longley. Now I assume these are moderately intelligent people who ought to have a fair understanding of science, and scientific method. I wasn’t expecting a particularly effective investigation into the issues surrounding this weeks discussions in Copenhagen, but I was actually stunned by the perception of science which they portrayed.

The questions which they were looking at were to do with how much influence scientists could and should have on policy making. Is it good for policy and politics to be supported by science, or should the thinkers be free to follow their own personal ideologies? As a generalisation, a liberal might wish to blank out and ignore science because it prevents a politician from having a free choice – they would do this in the belief that their philosophy is correct, even if the facts are in disagreement and they would believe the facts.

Should a scientist take steps to ensure that what is reported in the news about their research is fair, honest, safe, and should they even go so far as to distort their presentation in order to ensure that the correct message is passed out to the people by the media?

It seems from this programme that the non-scientists are of the view that science is an absolute truth, which cannot be debated, and probably can’t even be fully comprehended by mere mortals. When they do interact with scientific opinion, they do not focus on the substance of a claim, they tend to focus on the semantics of the argument, and treat it as a literary exercise. Scientists seem to be regarded as extremely rare people who work in small closed communities. Science is regarded as above corruption, and capable of providing absolute guarantees of certainty. Despite all of this, there was some acceptance that scientists have been wrong in the past, and it is maybe the job of the policy makers to play the role of the skeptic and keep the scientists honest. (Clearly, politicians are also above corruption).

I had initially intended to address a specific issue of evidence, but I think some background is more relevant first.

I wasn’t able to easily determine the proportion of the population who have a science degree, and even that definition is maybe not very useful, but even if it is only 1 in 20 people, or 1 in 50, there are still a good number of us. We don’t all have the same education or background, but we probably tend to share the same Myers-Briggs personality types. We are inquisitive, expect answers to be accurate, and probably aren’t too interested in asking if you’ve had a good day. We can give a convincing answer to all manner of complicated question, and you probably assume we’re reciting some fact that we learnt, rather than relying on a combination of knowledge and guesswork.

Despite the fact that scientists may train and specialise in very detailed fields, much of what they learn is to do with the process of science rather than the facts. Many scientists are perfectly able to read papers in a field which they are not familiar with, and still understand if the work is consistent and well argued. It is not necessary to be an expert in a particular field to be able to determine if a set of results is convincing proof, or likely to be just a coincidence.

The nature of science is not to prove things, it is to give us a better understanding. Generally it helps us to engineer things better, building bridges that are just strong enough, making laptops lighter, making artificial joints which last longer, etc. More fundamentally, it provides a framework for us to take observations, make assumptions which may explain those observations, develop a strategy to test and break the assumptions, and finally make predictions about new and different scenarios.

Scientists tend to like a consensus. Speaking out against the experts in a field is not a good way of making friends (and even scientists are aware of this). The idea that the continents (Africa and South America for example) move over geological timescales, is well accepted today. We can even measure the movements from year to year now with milimetre precision. Just by looking at the coastlines, it seems quite plausible, and the distribution of different types of rock is also good evidence. Consider then that the theory of continental drift was proposed in 1912, but ridiculed for 53 years, and only finally accepted in 1965. This natural human behaviour suggests that maybe reaching a decision by a simple approach such as a literature review may not give an honest picture of the state of science at any point in time. Rather, it is instructive to compare the opposing view points and compare the merits of the arguments rather than the weights. The approach taken in a court of law, deciding based on who can argue best, is not likely to lead to an improved conclusion.

Even though many people seem to view the word of a scientist in the same way as they would have listened to an oracle, they should remember that scientists can make mistakes, and are not even above corruption. It seems that the mistakes are not remembered too well. The Piltdown Man, and more recently Hwang Woo-suk’s claim to have succeeded in creating human embryonic stem cells by cloning are rapidly becoming distant memories even amongst people who were aware of the stories. Can anyone now believe that lobotomy was a common medical procedure in the 1940′s?

A frequent problem with scientific research is it’s relevance to new scenarios. it is very easy to overlook the un-intentional consequences of making a change to an established system based on measurements made in a different environment. Assessment of the survivability and treatment of hypothermia victims based on careful laboratory experiments neglected the effect of removing a submerged body in a vertical harness (sudden loss of blood pressure, usually fatal). Analysis of the health risks associated with taking illegal drugs makes assumptions (when extrapolated to policy) that de-criminalisation would only have a limited effect on behaviour, and conversely is unable to counter the concern that it would resuit (as was claimed for pop music) in widespread breakdown in society.

Very few people, even amongst those with a technical background, seem to have a good grasp of statistics. Bizarrely, as a society we seem to be comforted by statistics. The most generous explanation for the recent MMR vaccine scare would be that a small number of unusual cases occurred by chance, and that in the hope of finding more proof to support a genuine concern, the likelyhood of the results being a fluke were not given sufficient consideration. How do we weigh the risks between something we think is likely, and something which is really serious? Is it safer to risk getting Swine Flu, or risk having a reaction to the virus – and what about your chance of some other common need to be in hospital if 30% of the population became incapacitated?

When making statistical comparisons, it is important to check that the data is consistent. Is there a control group, and is the data really as complete as it seems? Much of the historical data on hypothermia fails to record body temperature more than a few degrees below ‘normal’ because of the medical thermometers in common use. Clearly there is not much laboratory data covering the precise point of survivability. A scientist might even worry that measuring a person’s core temperature in such an extreme state is quite complex, and subtle changes in technique over time could untroduce an unwelcome spread in the data.

I had intended to make this post about the claims that extreme weather results have been increasing – but I’ll leave it with this link and the points that it’s easy to infer a trend when looking at noise – as well as our ability to observe, measure and categorise hurricanes has improved dramatically since we invented satellites, so using the historical data is educated guesswork.

Science is not definitive, it is useful in guiding decisions, but it is very dangerous to assume that scientific theories are always correct.