The fallibility of using computer models arises again, whether it be climate change as in this case, or the covid forecast computer models thats driven the covid response since 2020.
Climate Change.
“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”.
Original Article available here: Science.org – https://www.science.org/content/article/use-too-hot-climate-models-exaggerates-impacts-global-warming?fbclid=IwAR1Bs87aYi5JxiSjTeZD3oPq-Px0NCmk6IkQmjl34QQqzUvGow8HkgrC-Z4
Covid-19.
“When Neil Ferguson visited the heart of British government in London’s Downing Street, he was much closer to the COVID-19 pandemic than he realized. Ferguson, a mathematical epidemiologist at Imperial College London, briefed officials in mid-March on the latest results of his team’s computer models, which simulated the rapid spread of the coronavirus SARS-CoV-2 through the UK population. Less than 36 hours later, he announced on Twitter that he had a fever and a cough. A positive test followed. The disease-tracking scientist had become a data point in his own project.
Ferguson is one of the highest-profile faces in the effort to use mathematical models that predict the spread of the virus — and that show how government actions could alter the course of the outbreak. “It’s been an immensely intensive and exhausting few months,” says Ferguson, who kept working throughout his relatively mild symptoms of COVID-19. “I haven’t really had a day off since mid-January.”
“Research does not get much more policy-relevant than this. When updated data in the Imperial team’s model1 indicated that the United Kingdom’s health service would soon be overwhelmed with severe cases of COVID-19, and might face more than 500,000 deaths if the government took no action, Prime Minister Boris Johnson almost immediately announced stringent new restrictions on people’s movements. The same model suggested that, with no action, the United States might face 2.2 million deaths; it was shared with the White House and new guidance on social distancing quickly followed (see ‘Simulation shock’)”.
Original article available here: Nature – https://www.nature.com/articles/d41586-020-01003-6
Featured Image: Neuvelles – Computer modelling can give a window into how the virus works in the body and help identify drugs to target it. Credit: Getty.
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