Acute climate risks in the financial system: examining the utility of climate model projections

Abstract Efforts to assess risks to the financial system associated with climate change are growing. These commonly combine the use of Integrated Assessment Models to obtain possible changes in global mean temperature and then use coupled climate models to map those changes onto finer spatial scales to estimate changes in other variables. Other methods use data mined from “ensembles of opportunity” such as the Coupled Model Intercomparison Project. Several challenges with current approaches have been identified. Here, we focus on demonstrating the issues inherent in applying global ‘top-down’ climate scenarios to explore financial risks at geographical scales of relevance to financial institutions (e.g. city-scale). We use data mined from the Coupled Model Intercomparison Project to determine the degree to which estimates of global mean temperature can be used to estimate changes in the annual extremes of temperature and rainfall, two compound events (heatwaves and drought, and extreme rain and strong winds), and whether the emission scenario provides insights into the change in the 20-, 50- and 100-year return values for temperature and rainfall. We show that global mean temperature provides little insight on how acute risks likely material to the financial sector (“material extremes”) will change at a city-scale. We conclude that “top-down” approaches are likely to be flawed when applied at a granular scale, and that there are risks in employing the approaches used by, for example, the Network of Central Banks and Supervisors for Greening the Financial System. Most fundamental, uncertainty associated with projections of future climate extremes must be propagated through to estimating risk. We strongly encourage a review of existing top-down approaches before they develop into de facto standards and note that existing approaches that use a “bottom-up” strategy (e.g., catastrophe modelling and storylines) are more likely to enable a robust assessment of material risk.