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 localised decadal predictions, even though they are used as inputs for policy making.”
Indeed. Nor is the problem confined just to a few models. In a 2010 paper I and a coauthor10 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 to 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.
I accept that there’s been some warming. I’m not convinced that it’s due to anthropogenic factors (and the long lull since 1998 has cast doubt on that for a lot of people). All of the scary scenarios are based on computer models, which aren’t science.
More to the point, all of the solutions to what we should do about the scary scenarios are public policy, which sure as heck isn’t science. Even if the scientists could tell us what was going to happen, which is dubious to begin with, they couldn’t tell us what we should do about it.
What to do about it gets into questions of values. Should we stop development, quit building roads, limit how much electricity people can use? Those are loaded political questions. Scientists are no better equipped to answer those questions than anyone else. Science can inform public policy, but it can’t control it.