Link: https://julesandjames.blogspot.com/2017/11/watts-up-with-pat-frank.html

Posted 19 February 2018, but did not appear on the blog.

I was glad to find your post today, James. We can have a conversation about it.

You wrote that you found a “glaring error,” in my manuscript but neglected to tell anyone what it is or what you wrote in review.

Let's amend that. Here’s your entire review for all to read; [sentence numbers] inserted: 

“<em><strong>Topical Editor Initial Decision: Reject</strong> (07 Nov 2017) by James Annan
Comments to the Author:
[1] This manuscript is silly and I'd be embarrassed to waste the time of reputable scientists by sending it out for review. [2] The trivial error of the author is the assumption that the ~4W/m^2 error in cloud forcing is compounded on an annual basis. [3] Nowhere in the manuscript it is explained why the annual time scale is used as opposed to hourly, daily or centennially, which would make a huge difference to the results. [4] The ~4W/m^2 error is in fact essentially time-invariant and thus if one is determined to pursue this approach, the correct time scale is actually infinite. [5] Of course this is what underpins the use of anomalies for estimating change, versus using the absolute temperatures. [6] I am confident that the author has already had this pointed out to them on numerous occasions (see refs below) and repeating this process in GMD will serve no useful purpose.</em>”

Here we go, sentence-by-sentence.

[1]: Analytically empty, prejudicial in tone. Further comment is unnecessary.

[2.1]: What you described as “<em>~4 W/m^2 error</em>” is not +4 W/m^2 and is not error.

The entry is ±4 W/m^2 and is an uncertainty statistic. You’ve mistaken an uncertainty statistic for physical error. 

The notion of statistical uncertainty was introduced in the Abstract, was discussed in Section 2.1.4 in some detail, and was repeated throughout the manuscript.  You apparently missed that.

Manuscript Section 3 referred you to Section 10.1 of the Supporting Information, where the distinction between error and uncertainty is extensively discussed. You apparently missed all of that, too.

That mistake alone renders your decision critically vacant.

[2.2] Manuscript Section 2.3 and especially 2.3.1 showed that cloud error is systematic and inherent within climate models. Model-inherent error is necessarily injected into every single step of a climate projection. The resultant uncertainty is likewise, and is necessarily compounded forward. 

[3]: “<em>the annual time scale</em>” is prominently introduced in manuscript Section 2.4.1.  Lauer and Hamilton’s <strong>20-year annual mean</strong> cloud rms error statistic is thoroughly described. The dimension of an <strong>annual</strong> mean is <strong><em>per-year</em></strong>.

Specifically, manuscript line 570: “<em>CMIP model error was derived as the differences in modeled (xmod) and observed (xobs) <strong>20-yr means.</strong></em>”

Line 583: “<em>This calibration error statistic is <strong>the average uncertainty in simulated cloud cover across any given projection year</strong>, and is representative of any given CMIP model.</em>”

It is apparent, in contrast to your [3], that “<em>[somewhere] in the manuscript it is explained why the annual time scale is used …</em>.”

Your Dr. Didier Roche had similarly poor reading skills.

[4]: The ±4 W/m^2 in Lauer and Hamilton is the rmse of 27 CMIP5 models. It represents the global spatial rms uncertainty in tropospheric thermal energy flux across a particular model calibration interval (1986-2005). It is applicable to any temperature projection made using any CMIP5 model, over any duration between one year and arbitrary multiple years. Time invariance doesn’t pertain at all. You've presented an analytical non-sequitur.

[5]: You’re supposing ±4 W/m^2 is identical to +4 W/m^2, which then subtracts away in an anomaly. It is not, and it does not.

You also presume that subtracting away the calibration error produced by a physically incomplete theory reduces predictive uncertainty. It does not. The reason being that the underlying physics used predictively remains poorly specified. 

Uncertainty conditions the state of physical knowledge, not a particular identity of magnitude. 

In short, getting the “right answer” by subtracting away calibration error does not remove predictive uncertainty.

[6] You are correct that your mistaken views have been widely shared among my journal reviewers. Their continual appearance seems to reflect some uniform lack of training.