Nate Silver is a math pundit who founded the fivethirtyeight blog now over at the NY Times. That blog was all about the presidential election and over there he used a series of polls to predict (very successfully) the results of both the 2008 and 2012 US elections. An integral part of Nate’s approach is to use Bayesian probability thinking to keep reviewing the data as it comes in regardless of whether that data is from baseball, a poker game, the US elections or climate change.
Silver’s book — The Signal and the Noise: The Art and Science of Prediction [Fishpond, Book Depository] should be required reading for anyone who needs to review increasingly large tranches of data. Chapter 12 of his book is devoted to the climate change numbers — called “A Climate of Healthy Skepticism”. Part of Silver’s thesis is that many of us can’t sort the noise from the signal.
We have known about the greenhouse effect for a very long time. As he notes politics and other factors have undermined the search for scientific truth in this debate. Back in 1990 the IPCC made two main findings.
“There is a greenhouse effect that keeps the Earth warmer than it would otherwise be”
Notes to the effect that as concentration of greenhouse gases increases that global temperatures will also increase along with them…
He notes that the switch from referring to the issue as “the greenhouse effect” instead swapping it for global warming or climate change is a subtle one but significant as it moves the debate from discussion of “the causes of change” into the predictive implications and that causes predictably misinformed debate.
He further notes that temperature data (by itself) is predictably noisy.
” A warming trend might validate the greenhouse hypothesis or it might be caused by cyclical factors. A cessation in warming could undermine the theory or it might represent a case where the noise in the data had obscured the signal.”
You could see straight away the problem. We have today in the media a number of self interested groups arguing about whether or not a particular temperature change is moving up or down. That is actually a red herring of sorts since it does not take into account the real issue of greenhouse warming.
To summarise the problem. There would be almost no scientists who dispute that the greenhouse effect is the cause of global warming. There are – however, many commentators who get very excited about every temperature change movement as if these fluctuations discount the central problem.
In my view an analogy would be (this is not in the book) the planet is like a giant slow motion car smash. We all know it is happening but some of us are arguing about whether it is better to crash a red car than a blue one. Temperature changes are an expected outcome but we need to take into account a wider range of factors than just temperature change. And that relationship is not necessarily simple.
Disappointedly much of the debate is about the effects of global warming despite almost no one disagreeing with the cause.
Nate went to the Copenhagen summit in 2009 and was able to talk with some of the delegates there. He also noted that in 2011 alone the fossil fuel industry spent more than $300m on lobbying activities.
So to the computer modelling and the data – what did Nate find?
What matters most, as always is how well the predictions do in the real world. He notes the 3 different types of uncertainty in building a climate forecast model. These are initial condition uncertainty, scenario uncertainty and structural uncertainty.
All of this means that on a snapshot of any less than 25 years at a time the level of noise may outweigh the overall signal. The problem then becomes if the IPCC (as they did) makes forecasts on shorter time scales then they will be wrong because these structural uncertainties can not be eliminated.
Silver goes on to show various graphs of temperature changes and SO2 (sulfur emissions) since 1850 and 1900 respectively.
Global warming does not progress at a steady pace. Silver makes the point that temperatures are going to fluctuate up and down but with an overall trend of long term increase. I encourage you to read the book as there are more graphs and calculations in there that I can’t easily reproduce here. (indicative graph below.)
For example (page 407):
“Under Bayes theorem a no-net-warming decade would cause you to revise downward your estimate of the global warming hypothesis’s likelihood to 85% from 95%.
The extreme mix of politics and science over causality in these forecasts means that forecasts are going to continue to be wrong but that does not negate the overall thesis of greenhouse warming that is causing long term damage to all of our life systems.
“In science, one rarely sees all the data point to one precise conclusion. Real data is noisy- even if the theory is perfect, the strength of the signal will vary. And under Bayes’s theorem, no theory is perfect. Rather, it is a work in progress, always subject to further refinement and testing. This is what scientific skepticism is all about.”
At the end of the chapter on climate change he makes the point that much of the argument is to do with the black and white nature of politics. Politics is very much about beliefs and ideology and just can not cope with the intrinsic uncertainty in the forecast models.
In short if we make the argument about temperature changes alone then we risk discounting the real complexity of global warming and unnecessarily confusing that with the very real consequences (of which one is temperature change.) The The Signal and the Noise: The Art and Science of Prediction book is about much more that just this one chapter but it did give me a useful perspective on the underlying complexity of predictions.
At the very real risk of over simplifying a chapter that is 50+ pages – predicting the outcomes over the short term is quite uncertain and if you use Bayes theorem you just keep re-running the numbers to account for that uncertainty. The real problem for most is that politicians and the public discount global warming because of what they see as failed predictions on the temperature line.