Near-Term Predictability Lowers Long-Term Adaptation Costs
Abstract
Managing climate risks involves making decisions in the face of deep and dynamic uncertainties. One challenge this poses is that static instruments (e.g., physical infrastructure) are likely to be over- or under-designed relative to future observed conditions, presenting risks of maladaptation. As an alternative, dynamic or adaptive plans can be formulated that identify conditions under which specific actions are needed, enabling decision makers to respond to changing conditions as new information emerges. While many studies have demonstrated that dynamic plans can considerably outperform static ones, most studies have thus far been silent on the question of how dynamic plans should simultaneously monitor potentially predictable modes of climate variability (which often dominate risks on interannual to decadal scales) and deeply uncertain long-term trends. We postulate that the substantial and improving skill of seasonal to decadal (S2D) forecasts can be used to improve the design and management of infrastructure systems. More specifically, we hypothesize that as the S2D predictability of the climate system increases, soft adaptation policies with short planning periods can be more precisely designed, lowering their cost and thereby favoring policy portfolios with relatively more soft instruments. Using a didactic case study of heightening coastal infrastructure to mitigate flooding under sea level rise, we show that soft adaptation strategies are robust to different model structures and assumptions while hard instruments perform poorly under conditions for which they were not designed, and that increasing the hypothetical predictability of near-term climate extremes substantially lowers long-term adaptation costs.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.H35F..07D