Introduction to the problem: Changes in sea levels have been studied on many spatiotemporal levels, from the local to the global, and short-term to long-term, as well as secular trends. One of the key drivers in seasonal fluctuations in coastal sea levels are ambient atmospheric conditions. Thus, the ability to predict anomalous sea levels should be viewed within the context of the ability to predict the modes of atmospheric variability that affect these seasonal anomalies. The improvement of mid-range to seasonal forecasts of atmospheric conditions has long been a priority of the weather/climate modeling world, and the North American Multi-Model Ensemble (NMME) experiment has been designed to help overcome a number of uncertainties in climate predictions. The ability of models to forecast anomalous sea levels can thus be examined in light of their ability to predict atmospheric circulation. Rationale and objectives: We focus on two main objectives. First, we will assess the relationship between short-term to seasonal-term atmospheric circulation patterns and anomalous coastal sea-level values for all oceanic tidal gauges in the conterminous United States from 1982-2016. Our hypothesis is that the occurrence of extreme atmospheric circulation patterns, as well as the anomalous frequency of these patterns, can be associated with anomalous sea levels locally and regionally on multiple timescales. Second, we will assess the ability of the NMME to successfully simulate both the arrays of atmospheric circulation patterns that are identified, in terms of their overall frequency, persistence, and seasonality, as well as anomalous sea levels using the relationships that were developed. Summary of the work to be completed: We will obtain tidal gauge data for the conterminous US, and classify circulation patterns (CP) using multiple variables, via self-organizing maps. The relationship between CPs and anomalous sea levels will be analyzed by examining the short-term and seasonal-term relationships between anomalous sea-level values and individual CPs, and then modeling the time series with non-linear autoregressive models with exogenous input (NARX models). The output of the NARX model will not evaluate the relationship between atmospheric circulation and anomalous sea level, but also the role of individual drivers. Once this is complete, forecast data from the NMME will be used to evaluate the ability of the model to reproduce observed synoptic circulation patterns, as well as modeled sea-level anomalies.
Climate Risk Area: Coastal Inundation