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Originally Posted by fw102807
Bingo. That is the reason that lots of "studies" for many issues are not totally "fact" and need to be looked at with a little skepticism.
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Agreed. I find this same thing in my industry. You have to get beyond the ideology and/or marketing of these issues/products if you really want to get to the facts.
Quote:
Originally Posted by tuccillo
Your two statements highlighted in RED are not true. There have been published papers regarding the impact of the missing solar forcing (part of which is the impact on cloud nucleation from solar effects) as a cause of the over sensitivity of climate models. Essentially, the impact of the missing solar forcing can be up to a watt per square meter - more than enough to explain the over predictions. Climate models have consistently over predicted temperature trends by a factor of two when used in a hindcasting format. This is directly from the IPCC reports when compared with actual measured temperatures. This has been well documented by Christy and Spencer. This should not really be a surprise as it is very difficult to get the clouds correct in the model and if you don't get that right then you have no chance. Also, when you are missing important forcing then you also have no chance. In addition, when the only thing you are looking at is anthropogenic causes then that is what you will find because your funding will dictate that as the result. I have seen this. As I previously stated, in my opinion, climate models are not ready as a tool for setting public policy as they are still in the R&D stage. Climate dynamics are not well understood. The inability to diagnose how much of the recent warming is due to climate cycles and how much is anthropogenically driven is evidence of this. Numerical models make a large number of assumptions due to lack of understanding of physical processes, omission of important physical processes, lack of computer power, and the individual biases, as to what is important, of the developers. I know this because I have been there. In addition, there are a number of parameters that can be tuned in a model to achieve the desired results. The reason for these parameter is a lack of understanding of physical processes and as a way to compensate for errors you cannot explain (often because of incorrect assumptions). Also, your analogs have no applicability to climate science and your graph is hopelessly out of date.
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It is a privilege to have someone with your expertise on this forum. This is without a doubt an important issue that would benefit everyone if decisions and policy were based on factual data examined without bias. Just want to thank you for your informative posts on this thread and others in the past.