Wayne2
<b>Buggy:</b> All models are buggy - we appear to have little idea by how much. Thus the need for independent verification & validation.
<b>Chaotic:</b> Weather and climate have <b>chaotic uncertainty </b>- most models are run with few replications - we don't know how much of the difference between models and data is chaotic, how much is poor understanding of weather/climate and how much is climatic trends - or how much of the trends are natural vs anthropogenic – despite IPCC's claimed > 90% confidence. e.g., See Fred Singer <a href="http://www.sepp.org/science_papers/ICCC_Booklet_2011_FINAL.pdf" rel="nofollow">NIPCC vs. IPCC Addressing the Disparity between Climate Models and Observations: Testing the Hypothesis of Anthropogenic Global Warming (AGW)</a>
<blockquote>(2) Climate models are known to be chaotic. None of current models have a sufficient number of runs to overcome chaotic uncertainty and therefore cannot be validated against observations. . . .
Attribution of observed warming trends to GH-gas increases is based largely on claimed agreement between observed (tropical) tropospheric trends and modeled ones [Santer et al., IJC 2008, Fig 6]. We show that the claimed consistency is spurious.
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<b>Uncertain data</b>. The data has major uncertainties - there are major issues trying to evaluate by how much. To validate models we need to compare models against climate data. e.g., See Nigel Fox of the National Physics Lab:
<blockquote>Dr Nigel Fox, head of Earth Observation and Climate at NPL, says: "Nowhere are we measuring with uncertainties anywhere close to what we need to understand climate change and allow us to constrain and test the models. Our current best measurement capabilities would require >30 yrs before we have any possibility of identifying which model matches observations and is most likely to be correct in its forecast of consequential potentially devastating impacts. The uncertainties needed to reduce this are more challenging than anything else we have to deal with in any other industrial application, by close to an order of magnitude. </blockquote>
<a href="http://www.eurekalert.org/pub_releases/2011-09/npl-ucm091911.php" rel="nofollow">Uncertain climate models impair long-term climate strategies</a>
See Fox's presentation: <a href="http://www.npl.co.uk/science-lectures/resolving-uncertainty-in-climate-change-data" rel="nofollow">Resolving uncertainty in climate change data</a>
Climate science is still wandering in a wasteland of uncertainty. We are trying to make policy issues to spend billions per bug – while having wide variations between models for unknown reasons.
Better to get the bugs out first, reduce the uncertainty in the data to be able to test models, understand the physics of climate especially the clouds, and then compare to see if there is a serious issue – so we can evaluate the pros/cons of what to do about it.
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