Uncertainty in climate model projections, sea level rise in particular, can lead to suboptimal, ineffective, and - at worst - outright dangerous policy decisions. To avoid this, we must use the information we have available make the best possible policy decisions. This requires accounting for not only varying forms of uncertainty in model parameters and projections, but deep uncertainty - uncertainty in the uncertainty in model structure and parameters. Statistical calibration approaches allow us to constrain these models and characterize the uncertainties inherent in both the model and data, and are a critical part of any modeling effort. In particular, I am interested in future projections of sea-level rise and storm surges, and their impacts on coastal defense decision-making.
Bayesian statistics, storm surge, sea-level rise, coastal adaptation, model calibration and simulation, deep uncertainty, climate risk management