Exploring the nonstationarity of coastal sea level probability distributions

Details

• Journal Title:
  Environmental Data Science
• Personal Author:
  Falasca, Fabrizio ; Brettin, Andrew ; Zanna, Laure ; Griffies, Stephen M. ; Yin, Jianjun ; Zhao, Ming
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research)
• Description:
  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 orthogonal polynomials quantifying how such changes can be explained by independent shifts in the first four statistical moments. The framework proposed is theoretically founded, general, and can be applied to any climate observable with close-to-linear changes in distributions. We focus on observations and a coupled climate model (GFDL-CM4). In the historical period, trends in coastal daily sea level have been driven mainly by changes in the mean and can therefore be explained by a shift of the distribution with no change in shape. In the modeled world, robust changes in higher order moments emerge with increasing $ {\mathrm{CO}}_2 $ concentration. Such changes are driven in part by ocean circulation alone and get amplified by sea level pressure fluctuations, with possible consequences for sea level extremes attribution studies.
• Content Notes:
  Github FabriFalasca Quantiles-and-Moments: This repository includes the code for the paper “Exploring the nonstationarity of coastal sea level probability distributions”.
  
  Data availability statement: The model’s output considered in this study are available upon request to the authors. ZENODO RELEASE: https://doi.org/10.5281/zenodo.7796135
  
  
• Source:
  Environmental Data Science, 2
• DOI:
  https://doi.org/10.1017/eds.2023.10
• ISSN:
  2634-4602
• Format:
  PDF
• Publisher:
  Cambridge University Press (CUP)
• Document Type:
  Journal Article
• Funding:
  Grant no. NOAA-OAR-CPO-2019-2005530 ; Grant no. OAR-CPO-2019-2005530
• License:
  https://creativecommons.org/licenses/by/4.0/
• Rights Information:
  CC BY
• Compliance:
  Library
• Main Document Checksum:
  urn:sha-512:b430e723291a5f67fe9bc7cae7d68f4b8571537dbb2838fda55f25d5259bbfaadf6e6e78fc8d5935c9b7c31f7456a481c60d1201e3c2dea917eeb9c3ac36a72f
• Download URL:
  https://repository.library.noaa.gov/view/noaa/58469/noaa_58469_DS1.pdf
• File Type:
  [PDF - 5.37 MB ]