Climate science is hardly alone in having incentives working against confirmation and replication of research. Here is a good article on lack of replication of data and studies in economics:

Replication, replication, replication

Replication rarely leads to career success. “Ideas” people — those exciting scholars generating new insights into how society functions — are the stars of the profession. Those who do the grindingly difficult work of checking whether the stars’ insights are actually true rarely get recognized. Who can name an economist who achieved fame through replication?

Editors of academic journals prefer to give what scarce space they have to exciting new ideas, rather than rehashing old debates. They commonly ignore even clear evidence of errors. In one case, according to the economist Mark Thoma, the flagship American Economic Review declined to correct a mistake in a paper written by Ben S. Bernanke and Alan Blinder, even though the authors acknowledged the error.

For a scholar, replication offers an unappealing bet. Heads, you discover that the findings of an original study are largely correct, and no journal will publish your paper because there’s no interest in learning that something is still true. Tails, you find a serious flaw, but your results still probably won’t be published and you’ve earned enemies who may try to land some reputational punches against you.

I am also returning to many of the comments above on this thread, since there is a lot of good commentary in response to a couple of angry apologists for Mann et al above.

Perhaps someone who knows these matters thoroughly could assist Steve (who has so much to keep him busy) and all with an updated article on data, disclosure, transparency, and best practices for climate sciences.

I approach this climate debate from two other different perspectives: medical, where confidence levels are calculated quite accurately, and risk management.

My question is, can someone identify the flaw in the following simplistic analysis:

Anthropogenic global warming requires three assumptions or ifs to be valid:
1. there is continued global warming, not just a recent upward part of a cycle;
2. This is due to increasing CO2 levels;
3. This in turn is due to human activities.

My risk management expertise suggests that for AGW to be valid requires a multiplication of the three risks to result in a high value, eg IPCC puts the first at 100 percent and the second two combined at greater than 90 percent, giving a ‘very likely’ conclusion.

However my medical expertise suggests that while calculating confidence levels for a series of points on a time graph is relatively easy, similar calculations for correlation factors is not, particularly when there are many inter-related effects and sometimes positive or negative feedback links between these factors.

How then can anyone give a greater than 90 percent confidence level for the second and third ‘if’s combined?

My simplistic guess is that with the recent stabilisation of the global temperature the first is likely to be a lot less than 100 percent. The second and third assumptions are widely disputed by many geologists and some climate scientists so would have confidence levels well below 80 percent.

The combined risk is therefore likely to be less than 50 percent. In which case what then?

I would appreciate an explanation of the flaw in my reasoning. Lord Monckton simply said my mathematics is unsound but did not say why.

If you’re going to do good science, release the computer code too
Programs do more and more scientific work – but you need to be able to check them as well as the original data, as the recent row over climate change documentation shows.