Exploring the non-stationarity of coastal sea level probability distributions
Fabrizio Falasca, Andrew Brettin, Laure Zanna, Stephen M. Griffies,, Jianjun Yin, Ming Zhao

TL;DR
This paper introduces a new framework to quantify changes in the probability distributions of coastal sea level data, revealing that observed changes are mainly in the mean, while models show additional shifts in distribution shape with climate change.
Contribution
A novel, theoretically grounded framework for quantifying distributional shape changes in climate data, applied to sea level observations and models to distinguish different drivers of change.
Findings
Observed sea level trends driven mainly by mean shifts.
Modeled sea levels show changes in higher moments with CO2 increase.
Ocean circulation and pressure fluctuations influence sea level distribution shapes.
Abstract
Studies agree on a significant global mean sea level rise in the 20th century and its recent 21st century acceleration in the satellite record. At regional scale, the evolution of sea level probability distributions is often assumed to be dominated by changes in the mean. However, a quantification of changes in distributional shapes in a changing climate is currently missing. To this end, we propose a novel framework quantifying significant changes in probability distributions from time series data. The framework first quantifies linear trends in quantiles through quantile regression. Quantile slopes are then projected onto a set of four polynomials quantifying how such changes can be explained by shifts in the first four statistical moments. The framework proposed is theoretically founded, general and can be applied to any climate observable with…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGeophysics and Gravity Measurements · Climate variability and models · Oceanographic and Atmospheric Processes
