Use of ‘too hot’ climate models exaggerates impacts of global warming – ‘Threatens to undermine the credibility of climate science’
BY PAUL VOOSEN
One study suggests Arctic rainfall will become dominant in the 2060s, decades earlier than expected. Another claims air pollution from forest fires in the western United States could triple by 2100. A third says a mass ocean extinction could arrive in just a few centuries.
All three studies, published in the past year, rely on projections of the future produced by some of the world’s next-generation climate models. But even the modelmakers acknowledge that many of these models have a glaring problem: predicting a future that gets too hot too fast. Although modelmakers are adapting to this reality, researchers who use the model projections to gauge the impacts of climate change have yet to follow suit. That has resulted in a parade of “faster than expected” results that threatens to undermine the credibility of climate science, some researchers fear.
Scientists need to get much choosier in how they use model results, a group of climate scientists argues in a commentary published today in Nature. Researchers should no longer simply use the average of all the climate model projections, which can result in global temperatures by 2100 up to 0.7°C warmer than an estimate from the Intergovernmental Panel on Climate Change (IPCC). “We need to use a slightly different approach,” says Zeke Hausfather, climate research lead at payment services company Stripe and lead author of the commentary. “We must move away from the naïve idea of model democracy.” Instead, he and his colleagues call for a model meritocracy, prioritizing, at times, results from models known to have more realistic warming rates. …
“A large number of our colleagues had no idea that the IPCC did this,” he says. And since then, dozens of published studies have used projections based on the raw average of all CMIP6 models. The outcomes, they note, are often “worse” than the IPCC projections—and that has drawn attention from those unaware of the underlying problems with the models.