From the Financial Post, an editorial by Ross McKitrick of the University of Guelph. He is an expert reviewer for the UN’s Intergovernmental Panel on Climate Change. (H/T ECM)
[I]n 2008 and 2010, a team of hydrologists at the National Technical University of Athens published a pair of studies comparing long-term (100-year) temperature and precipitation trends in a total of 55 locations around the world to model projections. The models performed quite poorly at the annual level, which was not surprising. What was more surprising was that they also did poorly even when averaged up to the 30-year scale, which is typically assumed to be the level they work best at. They also did no better over larger and larger regional scales. The authors concluded that there is no basis for the claim that climate models are well-suited for long-term predictions over large regions.
A 2011 study in the Journal of Forecasting took the same data set and compared model predictions against a “random walk” alternative, consisting simply of using the last period’s value in each location as the forecast for the next period’s value in that location. The test measures the sum of errors relative to the random walk. A perfect model gets a score of zero, meaning it made no errors. A model that does no better than a random walk gets a score of 1. A model receiving a score above 1 did worse than uninformed guesses. Simple statistical forecast models that have no climatology or physics in them typically got scores between 0.8 and 1, indicating slight improvements on the random walk, though in some cases their scores went as high as 1.8.
The climate models, by contrast, got scores ranging from 2.4 to 3.7, indicating a total failure to provide valid forecast information at the regional level, even on long time scales. The authors commented: “This implies that the current [climate] models are ill-suited to localized decadal predictions, even though they are used as inputs for policymaking.”
Indeed. Nor is the problem confined just to a few models. In a 2010 paper, a co-author and I looked at how well an average formed from all 23 climate models used for the 2007 IPCC report did at explaining the spatial pattern of temperature trends on land after 1979, compared with a rival model that all the experts keep telling me should have no explanatory power at all: the regional pattern of socioeconomic growth. Any effects from those factors, I have been told many times, are removed from the climate data before it is published. And yet I keep finding the socioeconomic patterns do a very good job of explaining the patterns of temperature trends over land. In our 2010 paper we showed that the climate models, averaged together, do very poorly, while the socioeconomic data does quite well.
The computer models have to be able to predict changes in specific regions, otherwise we have no reason to trust that they are accurate. We have to be able to evaluate whether the models work by testing them. When we can test them to predict climate change in specific regions, they fail.