Model Failures: Inflated Pandemic Estimates Weaken Climate Forecasts
The Global Warming Policy Forum (GWPF)/by bennypeiser/
It’s remarkable we put so much faith in expert models, given their history of failure.
Tony Abbott’s suspicion that climate change modelling was “absolute crap” soon will resonate more broadly — so spectacularly bad was expert modelling of the spread and lethality of the coronavirus, faith in all modelling must surely suffer.
Why trust the experts to forecast the climate decades into the future when they were so wrong about a disease related to the common cold?
The pandemic has damaged the credibility of “experts” and highlighted the limits of “the science” and the misplaced hubris of the political class.
On whatever measure you choose — deaths, infections, rate of transmission — the epidemiological models that convinced governments to take a sledgehammer to their economies, now mired in unrest, have proved scandalously pessimistic and out by orders of magnitude.
We were told the virus’s spread would be “exponential”. It wasn’t; transmission was falling before mandatory lockdowns scared the daylights out of people.
The raw numbers speak for themselves. The death toll globally is on track to be smaller than the flu pandemics of the 1950s and 60s, when the world’s population was less than half that today.
Indeed, if you put the number of global deaths last year, this year, and next year (about 60 million each year) on a simple column chart you’ll struggle to see the impact of COVID-19.
Future historians will be shocked at the disproportionate response. At least they will chuckle at SAGE, the acronym for the Scientific Advisory Group for Emergencies, the expert panel advising the British government, which has presided over the worst performance of any country.
But it wasn’t only the British. Experts in Sweden warned 100,000 would die by June if it didn’t lock down as the rest of Europe had, yet fewer than 5000 were lost. Experts said 420,000 might die in Japan without a hard lockdown. Fewer than 950 did.
Our own experts at the Doherty Institute said 5000 intensive care unit beds would be required, even with strict isolation and social distancing; fewer than 50 were needed. For anyone here who is worried about a second wave of COVID-19, we’re still waiting on the first one.
Climate modelling was struggling even before the pandemic, given the planet has warmed about half as much as forecast by the first Intergovernmental Panel on Climate Change report in back 1990.
Almost the entire alarm about global warming is based on model predictions. If you just look at the last 30 to 40 years of data, nothing spectacular has happened, there’s no sign temperature increase is accelerating,” says Benny Peiser, founder of the Global Warming Policy Foundation in London.
It’s remarkable we put so much faith in expert models, given their history of failure. The Club of Rome in 1972 notoriously forecast that growth would collapse as the world’s resources ran out, ignoring human ingenuity and the shale revolution.
Financial models failed to account for — indeed they probably facilitated — the global financial crisis. And as almost every utterance by a central bank governor since has reminded us, economists struggle to know what happened last month, let alone forecast the impact of a policy change tomorrow.
In the late 1990s, models suggested the entire Great Barrier Reef would bleach every year by 2020, but in the last 15 years parts of the reef have bleached on only three occasions, with each event affecting only one-third of the reef,” says physicist Peter Ridd, a former professor of James Cook University.
It’s remarkable we put so much faith in climate modelling, given it is a far more complex task. “Climate sensitivity” — the size and speed of the response of global temperatures to a doubling of carbon dioxide in the atmosphere — is harder to predict than a spreading virus. Even if we knew it, drawing implications about future economic growth is even more heroic.
The big mistake that’s clearly been made is the failure to systematically appraise the models that underpin policy with actual data,” notes Gordon Hughes, a former professor of economics at the University of Edinburgh, speaking on a panel about the pitfalls of mathematical modelling last month.