Two new papers by independent teams have been published in peer-reviewed journals showing that the latest generation of climate models overstates atmospheric warming in response to greenhouse gases. Not just some of the models mind you, every single one of them, in most cases by a lot. The papers are reviewed at Judith Curry’s blog by Ross McKitrick, who coauthored one of them. The other paper, from a British team, singled out the Canadian model for special mention: it projected seven times too much warming compared to satellite observations since 1979. McKitrick notes that this is the model the Canadian government relies on for “science-based quantitative information to inform climate change adaptation and mitigation in Canada and internationally.” Which explains a lot… and not in a good way.
What is striking about these studies is that they look at what should be an easy test for models--their ability to reproduce the climate of the past 40 years when the modelers can observe the inputs (solar fluctuations, greenhouse gases, ozone depletion, etc.) and the outputs, namely air temperatures as measured by satellites and weather balloons. Both studies reviewed a long list of models (38 in one case and 48 in the other--why so many climate models if the science is settled?) and found that every single model made the same mistake, namely too much warming. Not one of them produced too little warming. And they drilled down into the details enough to show that the problem is excessively high greenhouse-gas sensitivity or ECS (see our video for an explanation of this term). Even the low-ECS models warm too much.
McKitrick concludes: “I get it that modeling the climate is incredibly difficult, and no one faults the scientific community for finding it a tough problem to solve. But we are all living with the consequences of climate modelers stubbornly using generation after generation of models that exhibit too much surface and tropospheric warming, in addition to running grossly exaggerated forcing scenarios (e.g. RCP8.5). [It’s] bias, not uncertainty, and until the modeling community finds a way to fix it, the economics and policy making communities are justified in assuming future warming projections are overstated, potentially by a great deal depending on the model.”
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