Having forsworn blogging activity for several months in favour of actual dissertation work, I thought I'd mark a return to Stickman's Corral in time for the holidays. Our topic for discussion today is a poster (study?) by Cato Institute researchers, Patrick Michaels and "Chip" Knappenberger.
Michaels & Knappenberger (M&K) argue that climate models predicted more warming than we have observed in the global temperature data. This is not a particularly new claim and I'll have more to say about it generally in a future post. However, M&K go further in trying to quantify the mismatch in a regression framework. In so doing, they argue that it is incumbent upon the scientific community to reject current climate models in favour of less "alarmist" ones. (Shots fired!) Let's take closer look at their analysis, shall we?
In essence, M&K have implemented a simple linear regression of temperature on a time trend,
\begin{equation}
Temp_t = \alpha_0 + \beta_1 Trend + \epsilon_t.
\end{equation}
This is done recursively, starting from 2014 and incrementing backwards one year at a time until the sample extends until the middle of the 20th century. The key figure in their study is the one below, which compares the estimated trend coefficient, $\hat{\beta_1}$, from a bunch of climate models (the CMIP5 ensemble) with that obtained from observed climate data (global temperatures as measured by the Hadley Centre's HadCRUT4 series).
Since the observed warming trend consistently falls below that predicted by the suite of climate models, M&K conclude: "[A]t the global scale, this suite of climate models has failed. Treating them as mathematical hypotheses, which they are, means that it is the duty of scientists to reject their predictions in lieu of those with a lower climate sensitivity."
Bold words. However, not so bold on substance. M&K's analysis is incomplete and their claims begin to unravel under further scrutiny. I discuss some of these shortcomings below the fold.
