Do We Really Understand What a Meter of Sea-level Rise by 2100 Means for the Coast?

Abstract:

Many projections of sea-level rise (SLR) by 2100 require rise rates of >7 mm/yr after about 2050. Thus, in the latter half of this century, the global average SLR rate may be higher than it has been since about 7000 years ago. That includes a prolonged period of relatively slow sea-level rise over the past 2000-4000 years, during which many coastal landforms and their associated ecosystems (e.g., barrier islands, estuaries, wetlands) matured and extended across the landscape, building rich sedimentary archives. The understanding of coastal evolution in these settings is typically based on both recent geologic history and modern process observations. The recent geologic history can be sampled from relatively accessible sedimentary records, as opposed to earlier records deposited more than ~5000 years ago, which are scarce on the transgressed continental margin. In addition, few to no modern analogs exist for many coastal settings that will experience high SLR rates and where there is significant human infrastructure and development. These settings provide valuable resources and ecosystem services on which society depends (e.g., sandy recreational beaches and fisheries), where useful projections are most needed. Thus, traditional sources of insight – the stratigraphic record and modern analogs – are lacking and limit our ability, and confidence, to predict the form, magnitude, and spatial extent of future changes to the coastal landscape. If sea-level is indeed heading towards ~1 m SLR by 2100, we have a limited timeframe to understand coastal change at higher SLR rates, communicate information to decision makers, and allow sufficient time for action. This is a moderate probability, but extraordinarily high risk, high impact scenario. It is a region of the coastal evolution parameter space that warrants focused exploration to identify critical knowledge gaps, conduct research to fill these gaps, and build our understanding. We also need to apply current knowledge to this problem; we know how to make skillful, yet imperfect models. Are we ready to set them against reality and evaluate their utility for a rapidly-changing world? We present some examples of current landscape-change predictive capability and discuss their application with and without updated understanding of higher-end SLR scenarios.