The most important outcome from my career has been to discover how much hard work is required to get initial ideas and concepts ( those initial EWAGs ) straightened out and finally resting on firm theoretical foundations.

And what Richard Hamming said, “The objective of computing is insight, not numbers.”

http://www-history.mcs.st-and.ac.uk/Quotations/Hamming.html

GCM software is being used in support of potentially high-consequence decisions (and the timing of those decisions). The software engineering consensus is that high-consequence software must undergo rigorous IV&V. The climate modelers seem to deny this consensus. Dan has been drawing attention to this fact for some time. He has even been providing suggestions and insight into how rigorous IV&V could be applied to climate science software. As a person knowledgeable about high-consequence technical software quality assurance in the nuclear field, I believe his efforts to be useful and worthy of serious consideration.

However, at RealClimate, I have seen where Dan’s comments, observations and suggestions have been misunderstood and less than, shall we say, cordially received.

Now we have evidence that the dismissive attitude taken may not have even been sincere. And in a comment above I make the observation that, in any event, the attitude is clearly not scientific — Feynman-wise.

Do you have any thoughts about the actual subject of Dan’s post? Do you think it actually better to be more confident about the reliability of the GCMs in public than within the climate clique? Rather than the Feynman-like reverse? To avoid rigorous IV&V in order to appear more confident? Do you think the GCMs reliable? Has the reliability been properly verified, validated, and accredited already?

I think I know how Dan would answer these questions.

George

No one here said fraud.

That is your word, not anyone else has used it.

If you begin to deteriorate into your usual MO on this blog, you will be banned.

Tom, with respect to the difference between the models and the data, the fundamental issue on short time scales is the magnitude of the internal variability. Using the full CMIP3 ensemble at least has multipl individual realisations of that internal variability and so is much more suited to a comparison with a short period of observations

For instance, do you understand what this means?

And how it pertains to this:

The kind of things we are hearing “no model showed a cooling”, the “data is outside the range of the models” need to be addressed directly.

Nothing mysterious or shady at all here. Makes perfect sense.

No One Peer-Reviews Scientific Software

Scientists Are Not Software Engineers

Along with

Climate Science and the Inner Ring

I wish these scientists were in the habit of making these kinds of statements in public as well as private. You certainly won’t find them uttering anything remotely resembling this stuff on the RealClimate blog.

Richard Feynman once said, in his famous “Cargo Cult Science” address: “I would like to add something that’s not essential to the science, but something I kind of believe, which is that you should not fool the layman when you’re talking as a scientist. … I’m talking about a specific, extra type of integrity that is not lying, but bending over backwards to show how you’re maybe wrong, that you ought to have when acting as a scientist. And this is our responsibility as scientists, certainly to other scientists, and I think to laymen.”

Do you think Feynman would feel differently now? I don’t.