I'm giving a talk this morning at the American Geophysical Union 2018 Fall Meeting in the session titled H52F: Toward Better Water Planning and Management in an Uncertain World. You can view a PDF of my slides here.
Here's my abstract
Evaluating and optimizing investments in climate adaptation requires projecting future climate risk over the operational life of each proposed investment. While many studies have considered that different climate change scenarios may emerge over the course of this M-year future period, adaptation policies remain vulnerable to the temporal and spatial clustering of climate risk which dominates much of the observational record. Large-scale, low-frequency climate variability can induce spatial shocks by favoring simultaneous extremes around the world, and can also cause a historical record to be a misleading indicator of future risk. In this work we consider whether the limited information in an \(N\)-year observational record permits the identification and projection of quasi-periodic climate variability and secular change, and what the resulting bias and uncertainty portends for risk mitigation instruments with a service life ranging from a few years to several decades. We present a set of stylized experiments to assess how well one can learn and predict the two kinds of risk for the design life (\(M\) years) and the probability of over- or under-design of a climate adaptation strategy based on these projections. We consider different temporal structures for the underlying risk which encompass quasi-periodic, regime-like, and secular variability, as well as statistical models for estimating this risk from an \(N\)-year historical record. The relative importance of estimating the short- or long-term risk associated with these extremes depends on the design life \(M\), but the potential to understand and predict these different types of variability depends on the informational uncertainty in the \(N\)-year historical record. Though we use floods as an example, the framework also applies to other forms of climate extremes.