Speaker: Dr Maeve Upton
Title: Bayesian generalised additive models for quantifying sea-level change.
Abstract:
The 2021 Intergovernmental Panel on Climate Change report highlighted how rates of sea level rise are the fastest in at least the last 3,000 years. As a result, understanding historical sea level trends globally and locally is important to comprehend the dynamics and impacts of sea level change. The influence of different sea level drivers, for example thermal expansion, ocean dynamics and glacial – isostatic adjustment (GIA), has changed throughout time and space. Therefore, a useful statistical model requires both flexibility in time and space and have the capability to examine these separate drivers, whilst taking account of uncertainty.
In this talk, I will discuss the statistical models we developed to examine historic relative sea level changes, employing sea-level proxy and tide gauge data and the noisy input uncertainty method to account for uncertainty. Our approach uses Generalised Additive Models (GAMs) within a Bayesian framework which enables separate modelling of sea level components, smooth calculation of rates and the ability to incorporate external prior information guiding the evolution of sea level change over time and space. Our findings reveal that current sea levels along North America's Atlantic coast are the highest in at least the past 15 centuries. GAMs demonstrate the different drivers of relative sea level change, indicating that GIA dominated until the 20th century when a sharp rise in sea level change rates occurred.