Estimating near- and long-term climate risks to inform risk management policies

What is it about?

Designing and investing in climate risk management strategies requires evaluating climate risk over the lifetime of each project considered. The climate science literature highlights that different physical mechanisms control the predictability and uncertainty of climate risk at different time signals. For example, historical records emphasize the role of natural modes of climate variability that oscillate at time scales of years to decades. At the same time, anthropogenic climate change has altered and will further alter climate risk into the future. This paper presents stylized experiments to explore the uncertainty associated with estimating climate risks over different future periods. These experiments consider both the amount of informational uncertainty in the system (for example, the length of an N-year data set) and also, crucially, the amount of time into the future that the projection is extrapolated, M. We find that the relative importance of estimating short- or long-term risk depends on the investment life (M). Shorter design lives are preferred for situations where interannual to decadal variability can be successfully identified and predicted, highlighting the importance of sequential investment strategies for adaptation.

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Photo by Chris Gallagher on Unsplash

Photo by Chris Gallagher on Unsplash

Why is it important?

The climate risk management literature often seems to suggest that societies need to make investments today to prepare for worst-case future scenarios, which are highly uncertain (largely due to questions about how much climate change humans will cause.) It's absolutely true that climate management policies need to be implemented immediately, and updated regularly. However, preparing for a too-optimistic vision of the future can lead to exacerbated future losses (perceived safety leads to higher investment and thus greater losses when unanticipated extremes eventually occur) while preparing for a too-pessimistic vision of the future can limit the funds available for more needed projects. This work emphasizes the importance of sequential planning processes, in which future conditions are monitored to decide which actions make sense as the future evolves. As with all modeling experiments, policy-makers should use their own judgement when applying these findings to their unique contexts.

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