Bias in a widely used climate sensitivity estimation method

 Ross McKitrick points out that a widely used method for estimating climate sensitivity is biased upward. This is yet another example of why climate forecasts are unreliable and why you cannot trust what you hear from the climate alarmists.

Here is the link.

Here are some excerpts.

-----------------------------------------------

• Optimal fingerprinting is a statistical method that estimates the effect of greenhouse gases (GHGs) on the climate in the form of a regression slope coefficient.
• The larger the coefficient associated with GHGs, the bigger the implied effect on the climate system.
• In 2003 Myles Allen and Simon Tett published an influential paper in Climate Dynamics recommending the use of a method called Total Least Squares in optimal fingerprinting regression to correct a potential downward bias associated with Ordinary Least Squares
• The problem is that in most cases TLS replaces the downward bias in OLS with an upward bias that can be as large or larger
• Under special conditions TLS will yield unbiased estimates, but you can’t test if they hold
• Econometricians never use TLS because another method (Instrumental Variables) is a better solution to the problem

The method of “optimal fingerprinting” works by regressing a vector of climate observations on a set of climate model-generated analogues (called “signals”) which selectively include or exclude GHG forcing. According to the theory behind the methodology, the coefficient associated with the GHG signal indicates the size of the effect of GHGs on the real climate. If the coefficient is greater than zero then the signal is “detected”. The larger the coefficient value, the larger is the implied effect on the real climate.

The seminal method of optimal fingerprinting was presented in a 1999 Climate Dynamics paper by Myles Allen and Simon Tett. With some modifications it has been widely used by climate scientists ever since. Last year I published a paper in Climate Dynamics showing that the basis for believing the method yields unbiased and significant findings was flawed. This website provides links to my paper, as well as to the Allen and Tett (1999) paper I critiqued, a non-technical summary of my argument, Myles Allen’s reply and my response, and a comment by Richard Tol.

One of the arguments Allen made in response was that the issue is now moot because the method he co-authored has been replaced by newer ones (emphasis added):

The original framework of AT99 was superseded by the Total Least Squares approach of Allen and Stott (2003), and that in turn has been largely superseded by the regularised regression or likelihood-maximising approaches, developed entirely independently. To be a little light-hearted, it feels a bit like someone suggesting we should all stop driving because a new issue has been identified with the Model-T Ford.

Ha ha, Model T Ford; we all drive Teslas now, aka Total Least Squares. But in 20 years of usage did any climate scientists check if TLS actually solves the problem? A few statisticians looked at it over the years and have expressed significant doubts about TLS. But once it was adopted by climatologists that was that; with few exceptions no one asked any questions.

I have just published a new paper in Climate Dynamics critiquing the use of TLS in fingerprinting applications. TLS was intended to correct a potential downward bias in OLS coefficient estimates which could understate the influence of GHG’s on the climate. While there is a legitimate argument that OLS can be biased downward, the problem is that in typical usage TLS is biased upwards, in other words it overstates the influence of GHGs. There is a special case in which TLS gives unbiased results, but a user cannot know if a data set matches those conditions. Moreover, TLS is specifically unsuitable for testing the null hypothesis in signal detection and its results ought to be confirmed using OLS.