In many coastal communities, the risks driven by storm surges are motivating substantial investments in flood risk management. The design of adaptive risk management strategies, however, hinges on the ability to detect future changes in storm surge statistics. Previous studies have used observations to identify changes in past storm surge statistics. Here, we focus on the simple and decision-relevant question: How fast can we learn from past and potential future storm surge observations about changes in future statistics?

Albedo modification (AM) is sometimes characterized as a potential means of avoiding climate threshold responses, including large-scale ice sheet mass loss. Previous work has investigated the effects of AM on total sea-level rise over the present century, as well as AM's ability to reduce long-term (Gt103 yr) contributions to sea-level rise from the Greenland Ice Sheet (GIS). These studies have broken new ground, but neglect important feedbacks in the GIS system, or are silent on AM's effectiveness over the short time scales that may be most relevant for decision-making (<103 yr).