We are talking about the Dangerous Climate Change Hypothesis, which I define as meaning:
An hypothesis that human carbon emissions are the predominant cause of a dangerous shift in climate to a higher energy state, resulting in, among other things, higher average global temperatures.
There, I have made it easy by highlighting the key points and they are not open to dispute. They are axiomatic on this blog.
Also, some rule of engagement.
If by some extravagant exhibition of the laws of probability someone actual a) finds this blog, b) stays long enough to read this blog, c) reads this or another post on this topic, d) even considers it worthwhile responding, they should not accuse me of "not understanding the science". Although only a humble economist by training, I have surprised many people by the way I have mastered the dark art of reading. Moreover, there are areas in the field of econometrics and statistics and in particular the nature and behaviour of large jointly determined and highly endogenous models of natural systems I am well qualified to understand some extremely important issues.
Grand so. Buckle the seat belt and off we go.
This entre to the minefield of climate science will start on familiar ground for me - forecasting. I know the IPCC and climate modellers hate using that word (and I know why they hate it), but any extrapolation of a time series into a period that extends beyond the availability of data is a forecast. Yes, they may be conditional on certain assumed states of the world, but they are still forecasts.
And forecasts are important and this is why. Testing the ability of any model to predict future outcomes is the only real test of its efficacy. Every time someone tells you how well climate models track past temperature trends or variations as "proof" of the robustness of the hypothesis, call bullshit on them. For a surprisingly large number of reasons this is simply not true and any trained statistician or econometrician will verify that fact.
So when I want to prove to myself (unfortunately I rarely take peoples' word for anything) that the Dangerous Climate Change (DCC) hypothesis might be true, I look to the ultimate test of our understanding of climate processes - forecasts. And this, surprisingly, is very hard to do. I say surprisingly, because for something that could tell us so much about the state of our knowledge on such an important issue, very few people involved in climate research bother to do it. Instead they seem to spend their $BILLIONS (yes it is that much) increasing the complexity of their models and churning out more and more forecasts of different types, simply leaving the older work behind like yesterday's newspapers.
So let do some of this work for them. First let's be realistic about the challenge here and some valid points that climate modellers would raise.
- Climate change is about decadal trends. A year or two means nothing, you really need to look at maybe 20 or even 30 years to begin to get enough information to draw any sort of conclusions.
- Given, as I point out earlier, we will be dealing with "conditional" forecasts, we need to consider the role of underlying assumptions (poor forecasts due to erroneous assumptions can still indicate that the underlying understanding of the process is sound).
- We are dealing with "stochastic" processes. That means random. In statistics you need to be simply close enough, not exactly correct to 2 decimal places.
That will do for now. Note I will deal with these points qualitatively where I see the need. I don't think this is the place for number crunching confidence intervals and the like (because I say so OK!?!).
Boy, that turned into a longer preamble than planned, so let's get down to brass tacks. Firstly we need to find a forecast we can have a look at. It needs to have some hard numbers in it and it has to be long enough in the tooth for us to have had time (ideally decades) to collect data to test it. Given that the field of DCC is very youthful in scientific terms, we aren't spoiled for choice here, but there is one excellent candidate. In 1988 James Hansen, considered by some as the Godfather of DCC produced a famous paper and in it he produced some forecasts. Here is the chart contained in that paper showing these forecasts:
This chart nicely summarises the data we are interested in. There are four lines. The first runs to 1988 and is temperature anomaly data available at the time of publication. The other three are conditional forecasts. The different "conditions" are assumptions about how much more CO2 (and other greenhouse gases - "GHGs") humans will pump into the air. The only important point to know about these three forecasts is that none of them assumed that there would be less GHG pumped into the atmosphere than actually occur ed from 1988 to present.
Now we simply take some temperature data and carefully overlay to see ho good these forecasts were:
OUCH! Actual global temperature over the 20 years since the forecast was made have risen far less than forecast. And don't fool yourself. Hansen himself described the lowest forecast (the best outcome for mankind) as being based on an assumption that "drastic cuts" would be made to GHG emissions. Don't come here arguing that the world has made "drastic cuts" to GHGs.
Now some will complain that I am being an evil, oil industry funded, creationist supporting, conspiracy nutter, right wing neocon sceptic about all this. They would say that I should be using Hansen's own data for global temperature (yes, for those uninitiated in the concept of science as it is practiced in climate circles, the most vocal, qualified campaigner manages his own measure for the existence of this DCC - the GISS surface temperature series). But I am nothing if not fair, so lets repeat with Hansen approved temperature measures:
Double OUCH! Hansen manages to keep those temps up compared with satellite derived measures (the UAH in the previous chart), but still the trend over 20 years is well below what Hansen himself forecast it would be had we made "drastic cuts" to our emissions.
Well, all I can say is thank crikey we weren't so stupid to have done that.