Statistical Prediction of Global Sea Level From Global Temperature
Artikel i vetenskaplig tidskrift, 2015

Sea level rise is a threat to many coastal communities, and projection of future sea level for different climate change scenarios is an important societal task In this paper, we first construct a time series regression model to predict global sea level from global temperature. The model is fitted to two sea level data sets (with and without corrections for reservoir storage of water) and three temperature data sets. The effect of smoothing before regression is also studied. Finally, we apply a novel methodology to develop confidence bands for the projected sea level, simultaneously for 2000-2100, under different scenarios, using temperature projections from the latest climate modeling experiment. The main finding is that different methods for sea level projection, which appear to disagree, have confidence intervals that overlap, when taking into account the different sources of variability in the analyses.

climate projections

singular spectrum smoothing

ARMA time series models


David Bolin

Göteborgs universitet

Chalmers, Matematiska vetenskaper, matematisk statistik

P. Guttorp

University of Washington

A. Januzzi

Seattle Public Schools

D. Jones

House of Representatives

M. Novak

Seattle Office of Sustainability and Environment

H. Podschwit

University of Washington

L. Richardson

Carnegie Mellon University

Aila Särkkä

Göteborgs universitet

Chalmers, Matematiska vetenskaper, matematisk statistik

C. Sowder

University of Washington

A. Zimmerman

University of Washington

Statistica Sinica

1017-0405 (ISSN)

Vol. 25 351-367


Sannolikhetsteori och statistik