[Arragonis]

Originally Posted by NeilBlanchard

97% of all climate scientists agree on what is happening. Temperature is rising overall right now - about 0.8C/1.4F over the last 100 years and most of that in the last 35 years, and there are reasons for why it is not linear - the sun's output varies, we have volcanic eruptions which put up ash which temporarily cools things, and as I have said, there is a 30-40 year lag because it is the accumulation of more heat over time. There is a lot of "momentum" in the system.

There have been many climate changes in the past...

The 97% figure is often put forward, it sometimes comes down to 77 out of 79 responses, or 75 out of 77 in the case of the AGU survey. Self selecting surveys with responses under 100 don't make good percentages - unless you are after a good headline. And a lot of the questions match my statement a few pages ago, even I would be in the "97/98%".

The obvious question moving forward though is where has the heat gone because surface temps are not showing it. The seas ? Where is the evidence for that ?

I'm glad you like to find out about things, did you see the recent discussion about climate sensitivity ? If only I understood all those hard sums...

Sorry to go on about it, but this prior thing this is an important issue. So here are my 7 reasons for why climate scientists should *never* use uniform priors for climate sensitivity, and why the IPCC report shouldn’t cite studies that use them.

It pains me a little to be so critical, especially as I know some of authors listed in Nic Lewis’s post, but better to say this now, and give the IPCC authors some opportunity to think about it, than after the IPCC report is published.

1) *The results from uniform priors are arbitrary and hence non-scientific*

If the authors that Nic Lewis lists above had chosen different coordinate systems, they would have got different results. For instance, if they had used 1/S, or log S, as their coordinates, instead of S, the climate sensitivity distributions would change. Scientific results should not depend on the choice of coordinate system.

2) *If you use a uniform prior for S, someone might accuse you of choosing the prior to give high rates of climate change*

It just so happens that using S gives higher values for climate sensitivity than using 1/S or log S.

3) *The results may well be nonsense mathematically*

When you apply a statistical method to a complex model, you’d want to first check that the method gives sensible results on simple models. But flat priors often given nonsense when applied to simple models. A good example is if you try and fit a normal distribution to 10 data values using a flat prior for the variance…the final variance estimate you get is higher than anything that any of the standard methods will give you, and is really just nonsense: it’s extremely biased, and the resulting predictions of the normal are much too wide. If flat priors fail on such a simple example, we can’t trust them on more complex examples.

4) *You risk criticism from more or less the entire statistics community*

The problems with flat priors have been well understood by statisticians for decades. I don’t think there is a single statistician in the world who would argue that flat priors are a good way to represent lack of knowledge, or who would say that they should be used as a convention (except for location parameters…but climate sensitivity isn’t a location parameter).

5) *You risk criticism from scientists in many other disciplines too*

In many other scientific disciplines these issues are well understood, and in many disciplines it would be impossible to publish a paper using a flat prior. (Even worse, pensioners from the UK and mathematicians from the insurance industry may criticize you too ).

6) *If your paper is cited in the IPCC report, IPCC may end up losing credibility*

These are much worse problems than getting the date of melting glaciers wrong. Uniform priors are a fundamentally unjustifiable methodology that gives invalid quantitative results. If these papers are cited in the IPCC, the risk is that critics will (quite rightly) heap criticism on the IPCC for relying on such stuff, and the credibility of IPCC and climate science will suffer as a result.

7) *There is a perfectly good alternative, that solves all these problems*

Harold Jeffreys grappled with the problem of uniform priors in the 1930s, came up with the Jeffreys’ prior (well, I guess he didn’t call it that), and wrote a book about it. It fixes all the above problems: it gives results which are coordinate independent and so not arbitrary in that sense, it gives sensible results that agree with other methods when applied to simple models, and it’s used in statistics and many other fields.

In Nic Lewis’s email (number 89 above), Nic describes a further refinement of the Jeffreys’ Prior, known as reference priors. Whether the 1946 version of Jeffreys’ Prior, or a reference prior, is the better choice, is a good topic for debate (although it’s a pretty technical question). But that debate does muddy the waters of this current discussion a little: the main point is that both of them are vastly preferable to uniform priors (and they are very similar anyway). If reference priors are too confusing, just use Jeffreys’ 1946 Prior. If you want to use the fanciest statistical technology, use reference priors.

ps: if you go to your local statistics department, 50% of the statisticians will agree with what I’ve written above. The other 50% will agree that uniform priors are rubbish, but will say that JP is rubbish too, and that you should give up trying to use any kind of noninformative prior. This second 50% are the subjective Bayesians, who say that probability is just a measure of personal beliefs. They will tell you to make up your own prior according to your prior beliefs. To my mind this is a non-starter in climate research, and maybe in science in general, since it removes all objectivity. That’s another debate that climate scientists need to get ready to be having over the next few years.

Take a walk on the other side for a while

Originally Posted by jamesqf

Sorry yourself :-) The problem here is that you don't bother to understand the fairly simple physics at work in that feedback cycle.

I understand the "simple physics" the problem is that the world doesn't seem to be acting like a simple experiment says it should. If there was a super positive feedback and CO2 lasted so long, why aren't the levels much higher ? World emissions haven't declined although some countries (like the US) have managed to do so.