GGrantIndex
← Search

Collaborative Research: Understanding Multidecadal Changes in the Instrumental Mean Sea Level Record

$302,548FY2016GEONSF

Atmospheric And Environmental Research Inc, Lexington MA

Investigators

Abstract

Global sea levels have risen steadily over the last century and there is concern that sea level rise will accelerate over the next century. Early detection of sea level acceleration, necessary for adaptation efforts, depends on an improved understanding of multidecadal sea level changes. This study consists of a detailed investigation of multidecadal sea level changes using sea level data, ocean and climate models, atmospheric reanalyses, vertical land motion, and a hierarchical Bayesian data-assimilation approach. An outcome of this study will be an enhanced, value-added sea level dataset based on the hierarchical Bayesian model approach, which will complement the available sea level estimates from altimetry, tide gauges, and ocean circulation models. These sea level fields from the Bayesian model solution will be made freely and publicly accessible and available. With their estimates of uncertainty, these fields would be suited for use in state estimation efforts on century time scales. This project is a partnership between academia and industry and would also support an early career scientist. The main objectives of this project include: reconstructing regional maps and global time series of sea level going back two centuries; elucidating roles of internal versus external forcing of global mean multidecadal sea level change; diagnosing the impacts of static and dynamic processes on multidecadal regional sea level changes; and evaluating the veracity of climate models. The outcomes will fill basic knowledge gaps, improving understanding of ocean circulation and climate change. To increase basic knowledge of ocean circulation and climate change, and to cope with difficulties related to the sparseness of sea level data in space and time, a hierarchical Bayesian model will be brought to bear on extant datasets (altimetry, tide gauge, vertical land motion), giving observation-based constraints on sea level behavior over the last two centuries. Exploration of the underlying ocean dynamics based on hierarchical Bayesian model solutions will elucidate the physical drivers and space-time scales of multidecadal sea level changes and provide a basis to evaluate climate models.

View original record on NSF Award Search →