Tag Archives: climate science

What is a climate scientist? Indeed, are there any?

We hear the term frequently, but what qualifies some people and not others to be classed as climate scientists?  You might think it is just someone who studies things that affect the climate. But very many people do that, not just those who call themselves climate scientists. The term actually seems to refer solely to a group who have commandeered the term for themselves and share a particular viewpoint, with partly overlapping skills in a subset of the relevant disciplines. In recent times,it seems that to be an official ‘climate scientist’ you must believe that the main thing that counts is human interference and in particular, CO2. All other factors must be processed from this particular bias.

To me, the climate looks like it is affected by a great many influences. Climate models produced by ‘climate scientists’ have been extremely poor at predicting changes so far, and one reason for this is that they exclude many of the relevant factors.

I am struggling to think of any scientific discipline that doesn’t have something to say about some influence on climate. Many branches of chemistry and physics are important in understanding how the atmosphere works, and the oceans, and glaciers, and soil. We have some understanding of some natural cycles, but far from all, and far from complete. We need biologists and chemists and physicists to tell us about soil, and forests, and ocean life, and how species and entire ecosystems react and adapt to changing circumstances, with migrations or adaptation or evolution for example. We need to understand how draining bogs or chopping trees to make room for biofuels affects the climate. How using bio-waste for fuel instead of ploughing it into the ground affects soil structure, plant growth, and carbon interchange. We need to understand how cosmic rays interact with the earth’s magnetic field, how this is affected by solar activity, how sunspots form, and even gravitational interactions with the planets that affect solar cycles. We need to understand glacial melting, how glaciers move differently as temperature changes, how black carbon from diesel engines affects their heat absorption, how clouds form, how they act to warm or cool the earth according to circumstances. We need to understand ocean cycles much better, as well as gas and heat interchange between layers, how this is affected by weather and so on. I could go on, endlessly. We need to understand the many different ways we could make energy in the future, the many options for capture and containment of emissions or pollutants, or positive effects some might have on plant growth and animal food chains.

But it doesn’t stop with science, not be a long way. We also need people skilled in anthropology and demography and sociology and human psychology, who understand how people react when faced with choices of lifestyle when presented in many different ways with different spins, or faced with intimidation or eviction because of environmental policies.  And how groups or tribes or countries will interact and distribute burdens and costs and rewards, or fight, or flee. And religious leaders who understand well the impacts of religious pressures on people’s attitudes and behaviours, even if they don’t subscribe to any organised religion. Clearly environmental behaviour has a strong religious motivation for many people, even if that is just as a crude religion substitute.

We even need people who understand animal psychology, how small mammals react to wind turbine flicker for example, and how this affects the food chain, ecosystem balance and eventual interchange with the atmosphere and the rest of the environment.

And politicians, they understand how to influence people, and marketers, and estate agents. They can help predict behaviours and adaptation and how entire countries may or will interact according to changes in climate, real or imagined.

And we need economists to look at the many alternatives and compare costs and benefits, preferably without ideological and political bias. We need to compare strategies for adaptation and mitigation and avoidance. Honestly and objectively. And we need ethicists to help evaluate the same from human perspectives.

And we need loads of mathematicians, especially statisticians. Climate science is very complicated, and a lot of measurements and trend analyses need in-depth statistical skills, apparently lacking in official climate science, as evidenced by the infamous hockey stick graph. But we also need some to model things like traffic flows so we can predict emissions from different policies.

And we need lots of engineers too, to assess likely costs and timescales for development of alternatives for energy, transport, entertainment and business IT. We need a lot of engineers!

And don’t forget architects, who influence energy balance via choices of shapes, materials and colour schemes as well as how buildings maintain a pleasant environment for the inhabitants.

Ah yes, and futurists. Many futurists are systems thinkers with an understanding of how things link together and how they may develop. You need a few of them too.

I have probably forgotten lots of others. The point is that there are very many factors that need to be included. No-one, and I mean no-one, can possibly have a good grasp of all of them. You can know a bit about a lot of things or a lot about a few things, but you can’t know a lot about everything. I would say that there are no people at all who know about all the things that affect climate in any depth, and therefore no group deserves a monopoly on that title.

So, if you only look in any depth at a few interaction in the oceans and atmosphere and ignore many of the rest of the factors affecting climate, as ‘climate scientists’ seem to, it is hard to see a good reason to continue to hold the title any more than anyone with another label like astrophysicist, or politician. ‘Climate scientists’ as we currently classify them, know a bit about some things that affect climate. So do many other groups. Having skills in a few of the relevant areas doesn’t give any right to dismiss others with skills in a different few. And if they consistently get it wrong, as they do, then there is even less reason to trust their particular viewpoints. And that’s before we even start considering whether they are even honest about the stuff they do talk about. And as Donna Lamframboise has pointed out recently, they don’t deserve to be trusted.


Computer models are not reality

I spent the first decade of my working life in mathematical modelling, using computers. I simulated all kinds of things to design better ones. I started on aircraft hydraulic braking systems, moved on to missile heat shields, springs, zoom lenses, guidance systems, electromagnetic brakes, atmospheric behaviour, lightning strikes, computer networks, telecomms systems, markets, evolution….

I wrote my last computer model soon after I became a futurologist, 21 years ago now. Why? Because they don’t work, in anything other than tiny closed systems. Any insight about the future worth mentioning usually requires thinking about highly complex interactions, many of which are subjective. Humans are very good at deductions based on very woolly input data, vague guesses and intuition, but it is not easy or even possible to explain all you take into account to a computer, and even if you could, it would take far more than a lifetime to write a model to do what your brain does routinely in seconds. Models are virtually useless in futurology. They only really work in closed systems where all the interactions are known, quantifiable, and can be explained to the computer. Basically, the research and engineering lab then.

Computer models all work the same way, they expect a human to describe in perfect detail how the system  works. When you are simulating a heat shield, whether for a missile of a space shuttle, it is a long but essentially very simple process because only very simple known laws of physics are involved. A few partial differential equations, some finite difference techniques and you’re there. The same goes for material science or biotech. Different equations, but essentially a reasonably well-known closed system that just needs the addition of some number crunching. When a closed system is accurately modelled, you can get some useful data. But the model isn’t reality, it is just an approximation of those bits of reality that the modeller has bothered to model, and done so correctly.

People often cite computer models now as evidence, especially in environmental disciplines. Today’s papers talk of David Attenborough and his arguments with Lawson over Polar Bears. I have no knowledge about polar bears whatsoever, either may be right, but I can read. The cited report http://pbsg.npolar.no/en/status/population-map.html uses mostly guesses and computer-generated estimates, not  actual bear counts. I’d be worried if the number of bears was known and was actually falling. Looking at the data, I still don’t have a clue how many bears there are or whether they are falling or growing in number. The researchers say they are declining. So what? That isn’t evidence. They have an axe to grind so are likely to be misleading me. I want hard evidence, not guesses and model outputs.

I discovered early on that not all models are what they appear. I went to a summer school studying environmental engineering. We had to use a computer model to simulate an energy policy we designed, within a specific brief. As a fresh mathematician, I found the brief trivially easy and jumped straight to the optimal solution – it was such a simple brief that there was one. I typed the parameters in to the model and it created an output that was simply wrong. I challenged the lecturer who had written it, and he admitted that his model was biased. Faced with my inputs, it would simply over-rule them for ethical reasons and use a totally different policy instead. Stuff that!

It was a rude awakening to potential dishonesty in models, and I have rarely trusted anyone’s models since. My boss at the time explained it: crap in, crap out. Models reflect reality, but only as far as the modeller allows. Lack of knowledge, omissions, errors and quite deliberate bias are all common factors that make models a less than accurate representation of reality.

Since that was my first experience in someone deliberately biasing their models to produce the answer they want, I have always distrusted environmental models. Much of the evidence since has confirmed bias to be a good presumption. As well as ignorance. The environment is an extremely complex system, and humanity is a very long way from understanding all the interactions well enough to model it all. Even a small sub-field such as atmospheric modelling has been shown (last year by CERN’s CLOUD experiment) to be full of bits we don’t know yet. And yet the atmosphere interacts with the ground, with space, with oceans, with countless human activities in many ways that are still in debate, and almost certainly in many ways we don’t even know exist yet. Without knowing all the interactions, and certainly without knowing all the appropriate equations and factors, we don’t have a snowflake’s chance in a supernova of making a model of the atmosphere that works yet, let alone the entire environment. And yet we see regular pronouncements on the far future of the environment based on computer models that only look at a fraction of the system and don’t even do that well.

Climate models suffer from all of these problems.

First, there is a lack of basic knowledge, even disagreement on what is missing and what is important. Even in the areas agreed to be important, there is strong disagreement on the many equations and coefficients.

Secondly, there are many omissions. In any engineering department, people will be well familiar with the problem of ‘not invented here’. Something invented by a competing team is often resented, rather than embraced. The same applies in science too. So models can feature in great detail interactions discovered by the team, though they may be highly reluctant to model things discovered by other scientists. Some scientific knowledge is therefore often missing from models, or tweaked, or discounted, or misunderstood and mis-modelled.

Thirdly, there is strong bias. If a researcher wants their work to further some particular point of view, it is extremely easy to leave things out of change equations or coefficients to produce the output desired. There are very many factors causing the bias now. Climategates 1 and 2 are enough to convince any right-thinking person that the field is corrupt beyond repair.

Finally, there are errors. There always are. Errors in data, algorithm, programming, interpretation and presentation.

Models can be useful, but they are far too open to human failings to ever consider computer model outputs as evidence where there is any debate whatsoever about the science or data. There is huge debate in climate science and researchers are frequently accused of bias, error, omission and lack of knowledge. But quite simply, these model outputs fail by the ‘crap in, crap out’ rule. Their output cannot be considered evidence, however much it may be spun that way by the researchers.

Let’s put it another way. One of the simplest programs most programmers write is to write ‘X is a genius’ again and again on the screen. But that doesn’t make it true, however often or large it is printed. The same goes for models. The output is only as honest as the researcher, only as accurate as their completeness and representation of the entire system. Using a long winded program to print ‘we’re all doomed’ doesn’t make it any more true. I don’t trust the researchers, I know the tricks, I don’t trust their models, and I don’t trust their output.