Stephen McIntyre isn't a working climate scientist, but his criticism of Mann's statistical errors (which aren't necessarily relevant to the main arguments for AGW) have been acknowledged as essentially correct. I also took a reasonably detailed look at the specifics of the argument
Did you have a look at these responses? Or at Mann's book on the subject?
There are a number of points here, but the most compelling is that the statistical criticisms were simply irrelevant. Contrary to McIntyre and McKitrick's claims, the differences in principal component methodology make no difference to the proxy reconstructions. And the hockey stick graph has since been replicated dozens of times using multiple different proxies and methods, by multiple authors.
As part of a review of forecasting, I've been looking at weather and climate forecasting (I wrote one post on weather forecasting and another on the different time horizons for weather and climate forecasting).
Climate forecasting is turning out to be a fairly tricky topic to look into, partly because of the inherent complexity of the task, and partly because of the politicization surrounding Anthropogenic Global Warming (AGW).
Due to the complexity and the potential for bias, I decided to disclose what materials I've read and my potential sources of bias.
Why am I looking at climate forecasting?
Climate forecasting, and the debate surrounding what'll happen to the climate and how human choices today can shape it, is one of the biggest examples of a long-range forecasting effort that has attracted widespread attention, both in terms of the science and the policy and political implications. Understanding how it was done can give insights into the ability of humans to make forecasts about the long-run future (on the decadal or centennial timescale) in the face of considerable uncertainty, and use those forecasts to drive decisions today. This would be relevant for other long-range forecasting problems, such as (possibly) friendly AI. Note though that my focus isn't driven by finding parallels with any other specific forecasting problem, such as friendly AI.
The sorts of questions I hope to answer by the end of this inquiry
The following are questions to which I hope to state relatively clear answers by the end:
The following are questions to which I may not obtain clear answers, but I'll be looking for and reporting information on them because they influence the answers to the preceding questions:
Sources
Courses or full-fledged reviews
Books about climate change aimed at a popular audience
Books about specific controversies surrounding climate change
Book chapters
IPCC reports
Blogs and websites
I reference here only the blogs and websites I've identified as places to check out, rather than ones where I chanced upon an isolated blog post by link-traipsing or searching the web.
Papers
I read many papers from a diverse array of sources. I arrived at most papers either by clicking links on one of the blogs or websites mentioned above, or using Google or Google Scholar searches for specific topics. Any paper that I use as input to my opinion in a specific post will be explicitly linked in that post.
Potential for bias and inaccuracy
Looking for suggestions
As always, I'm happy to hear suggestions. In particular, I am interested in suggestions on these fronts: