SPATIO-TEMPORAL EMPIRICAL BAYESIAN HIERARCHICAL MODELING USED TO ESTIMATE GLOBAL SEA LEVEL OVER THE LATE HOLOCENE (Invited Presentation)

Many global, regional and local-scale processes affect relative sea-level (RSL) changes. Constraining the magnitude of these contributions and their variability during the late Holocene (last 4000 years) can provide insights into future sea-level rise. We present an estimate of global sea-level (GSL) change over the late Holocene that is based upon the statistical synthesis of a global database of local sea-level reconstructions. The increasing availability and geographical coverage of standardized, high-resolution RSL reconstructions, with full consideration of their uncertainties, provides a new opportunity to formally estimate GSL in relation to climate and quantify the contributions to RSL change over last ∼4,000 years.

Using the hierarchical model of Kemp et al., 2018 (K18), we assess the sensitivity of GSL model results and variability over the late Holocene through various combinations of data from a global atlas of late Holocene sea-level index points from near, intermediate and far field sites. The atlas consists of over 6,000 sea-level index points, which show RSL varied between > 50 m and < -8 m over the past 4000 years.

K18 deconstructs RSL, quantifying contributions from global, regional, and local-scale processes using a spatio-temporal empirical Bayesian hierarchical framework with prior distributions defined by various glacial isostatic adjustment (GIA) models. We assess the sensitivity of the model to these priors as well as to the information gain from potential new data at various times and spatial locations. We produce a GSL estimate for the last 4000 years and place the current rates of GSL rise in the context of the recent geologic past.