The Science & Environmental Policy Project is an outstanding resource for those unwilling to bury their heads in the sand and blindly accept the notion that human-caused catastrophic global warming is settled science and must be the highest priority in allocating the world’s limited economic resources.
Its April 1, 2017 issue of “The Week That Was” leads with the point that “government-funded Climate Studies have largely turned from empirical science to dogma – a belief system unsubstantiated by physical evidence.” Each week’s TWTW is chock full of commentary and links describing the latest science and other developments that challenge the climate change orthodoxy. This issue highlights the written testimony of John Christy, distinguished professor of atmospheric science, Alabama’s state climatologist, and director of the Earth System Science Center at the University of Alabama in Huntsville, at the March 29 hearing titled “Climate Science: Assumptions, Policy Implications, and the Scientific Method,” held by the U.S. House of Representatives Committee on Science, Space, and Technology.
Professor Christy’s summary of his written testimony, supported by evidence in the full statement, gives rise to serious questions about those who think the subject of catastrophic global warming is no longer open to further scientific inquiry and debate.
“Science” is not a set of facts but a process or method that sets out a way for us to discover information and which attempts to determine the level of confidence we might have in that information. In the method, a “claim” or “hypothesis” is stated such that rigorous tests might be employed to test the claim to determine its credibility. If the claim fails a test, the claim is rejected or modified then tested again. When the “scientific method” is applied to the output from climate models of the IPCC AR5, specifically the bulk atmospheric temperature trends since 1979 (a key variable with a strong and obvious theoretical response to increasing GHGs in this period), I demonstrate that the consensus of the models fails the test to match the real-world observations by a significant margin[.] …