Statistical Prediction of Global Sea Level From Global Temperature
Journal article, 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.
ARMA time series models
climate projections
singular spectrum smoothing
Author
David Bolin
University of Gothenburg
Chalmers, Mathematical Sciences, Mathematical Statistics
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 (CMU)
Aila Särkkä
Chalmers, Mathematical Sciences, Mathematical Statistics
University of Gothenburg
C. Sowder
University of Washington
A. Zimmerman
University of Washington
Statistica Sinica
1017-0405 (ISSN)
Vol. 25 1 351-367Subject Categories
Probability Theory and Statistics
DOI
10.5705/ss.2013.222w