One of the biggest, if not the biggest issues of climate science skepticism is the criticism of over-reliance on computer model projections to suggest future outcomes. In this paper, climate models were hindcast tested against actual surface observations, and found to be seriously lacking. Just have a look at Figure 12 (mean temperature -vs- models for the USA) from the paper, shown below:
Fig. 12. Various temperature time series spatially integrated over the USA (mean annual), at annual and 30-year scales. Click image for the complete graph
The graph above shows temperature in the blue lines, and model runs in other colors. Not only are there no curve shape matches, temperature offsets are significant as well. In the study, they also looked at precipitation, which fared even worse in correlation. The bottom line: if the models do a poor job of hindcasting, why would they do any better in forecasting? This from the conclusion sums it up pretty well:
…we think that the most important question is not whether GCMs can produce credible estimates of future climate, but whether climate is at all predictable in deterministic terms.
Selected sections of the entire paper, from the Hydrological Sciences Journal is available online here as HTML, and as PDF ~1.3MB are given below:
A comparison of local and aggregated climate model outputs with observed data
Anagnostopoulos, G. G. , Koutsoyiannis, D. , Christofides, A. , Efstratiadis, A. and Mamassis, N. ‘A comparison of local and aggregated climate model outputs with observed data’, Hydrological Sciences Journal, 55:7, 1094 – 1110
Abstract
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