Fake Predictions for Fake Skeptics

Let’s make a fake prediction.

Chip’s Cherries

After the record-breaking global temperatures noted in 2010, Chip Knappenberger treated us to his Cherry-Picker’s Guide to Temperature Trends. It was a follow-up to his earlier Cherry-Picker’s Guide to Temperature Trends.

Best Case Scenario

Chip Knappenberger has posted about some papers, including Santer et al. (2011, Separating Signal and Noise in Atmospheric Temperature Changes: The Importance of Timescale. Journal of Geophysical Research, doi:10.1029/2011JD016263) which proves one thing: that Knappenberger doesn’t get it. In more than one way.

Step 3

One of the most important lessons to learn about statistics, and not forget, is that just because your model is statistically significant, that doesn’t mean it’s right. In fact, just about all models are wrong. And some models, even some which strongly pass statistical significance, aren’t even useful.

After investigating some claims about temperate records showing a “step change” rather than continuous trend, and finding those claims faulty, a reader mentioned yet another such claim. Specifically, it discusses a paper by Stockwell and Cox, submitted to International Journal of Forecasting. The essence of all these claims is that the temperature trend is discontinuous, that there aren’t just sudden jumps in temperature caused by noise, but the trend itself is a discontinuous function of time.

2011 Temperature Roundup

Now that 2011 is complete, most of the major global temperature estimates have updated their data to include the complete calendar year. The only one which hasn’t yet is HadCRU, for which data are available through November of 2011 but December’s estimate is not yet online.

Step 2

Pursuant to the subject of step change and telling the difference from linear increase, a reader pointed to a post on Roger Pielke’s site which claims to prove a step change (rather than linear increase) in two time series, of warm and cold nights over South America from 1960 to 2000. There’s a good bit of hand-waving about visual inspection of graphs (which really amounts to “it sure looks like a step change”), but the essence of the “proof” comes from modelling the data as two different straight lines, one fit to data before the purported step change, the other fit to data after the purported step change. The stated conclusion is:

… the slopes before and after the change points are not statistically significant (P > 0.05) and thus not significantly different from zero. In each data series, therefore, μ1 ≠μ2 and β1 = β2 = 0, proving, beyond doubt, the presence of flat step changes in the two data series.

Permit me to doubt.

Steps

A reader recently asked about a post on WUWT which, to everyone’s shocking surprise, claims to contradict man-made global warming. It claims that temperature records (at individual stations) follow step changes rather than linear increase, so the theory of anthropogenic global warming (AGW) is wrong.

Trend and Cycle Together

Many climate signals show both trend and cycle (usually an annual cycle) together. A typical example is the concentration of carbon dioxide (CO₂) in the atmosphere. If you look at the data (say, from the Mauna Loa atmospheric obsevatory) both the trend and the cycle are obvious.

Open Thread

The last thread is intended only to enable anyone to express an opinion about the probability of disastrous climate change under business-as-usual.

But it’s natural that folks want to discuss related issues, and even argue with each other about their stated estimates.

So — here’s an open thread. Please use the “What is epsilon?” thread ONLY to state your estimate, use this one to discuss at length. Thanks.

What is epsilon?

I recently posted about the unfortunate failure to examine data properly which contributed to the disastrous loss of life with space shuttle mission 51-L (the Challenger disaster). One of the members of the commission which investigated the disaster was physicist Richard Feynman.