Scientists warn about misuse of climate models in financial markets

LONDON, Feb 8 (Reuters) – Misuse of climate models could pose a growing risk to financial markets by giving investors a false sense of certainty about how the physical impacts of climate change will develop, according to the authors of a document released Monday.

With heat waves, wildfires, massive storms and sea level rise intensifying as the planet warms, companies are under increasing pressure to reveal how the disruption could affect their business.

But the authors of a peer-reviewed article here in Nature Climate Change warned that the drive to integrate global warming into financial decision-making blew up the models used to simulate climate for “at least a decade.” .

“Just as a Formula 1 Grand Prix car is not what you would use to go to the supermarket, climate models were never developed to provide adequate information for financial risk,” said Andy Pitman, a scientist at climate at the University of New South Wales and co-author of the paper.

Improper use of climate models could lead to unintended consequences, such as “washing green” some investments while minimizing risks, or affecting companies ’ability to increase debt by exaggerating others, according to the authors.

The problem is that existing climate models have been developed to predict temperature changes over many decades, on a global or continental scale, while investors often need location-specific analyzes in much shorter periods of time.

Nor are climate models designed to simulate extreme weather events, such as storms, that can lead to sudden financial losses.

To bridge the gap, the authors called for the development of new forms of climate projection to support the financial sector, backed by qualified “climate translators” to help regulators, investors and companies make better use of science.

“Companies like to use models, because the numbers give them a sense of security,” said Tanya Fiedler, a professor at the University of Sydney and lead author of the paper. “It doesn’t necessarily mean the numbers are reliable.” (Report by Matthew Green; edited by Hugh Lawson)

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