This paper examines limitations in the modelling approach used by the Network for Greening the Financial System (NGFS) and other institutions to analyse physical climate risks.
Written by a team of climate scientists and finance academics, it finds that top-down methods beginning with global mean temperature (GMT) predictions offer little insight into how acute risks might impact the financial sector, due to uncertainties involved in determining local and granular effects.
The study employs data from the Coupled Model Intercomparison Project, a meta ensemble of climate modelling results used in IPCC predictions of climatic effects under various emission scenarios. Historical simulations for 1950–2004 were developed from this data and compared against temperature, rainfall and other meteorological records from four cities: London, New York, Beijing and Mumbai.
Although applicable to a variety of approaches for examining physical climate risks, the paper focuses mainly on the example of modelling used by the NGFS to develop its climate scenarios. NGFS scenarios are widely used by central banks and financial regulators to assess climate risks to financial institutions and the financial system.
The analysis finds that GMT provides little insight into how acute physical climate risks that are likely to be material to the financial sector will change at a city-scale. While the authors emphasise that the methods and models used by the NGFS to create large ensembles of GMT and assess the impacts of climate change on a large scale are not in question, they are critical of the assumption that these methodologies can be used to inform acute climate risk at spatial scales “well below the sub-regional scale”.
“The issue is the link implied within the NGFS methodology that translates GMT to a granular level of physical climate risk which, in reality, is generated through climate-induced weather-scales and weather-related extremes,” they argue.
Concluding that top-down approaches are likely to be flawed when applied at a granular scale, the paper ends with a call for a review of existing top-down approaches before they develop into de facto standards. It also notes that catastrophe modelling, storylines and other bottom-up approaches are more likely to enable a robust assessment of material physical climate risk.
This page was last updated September 1, 2022
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