Statistical prediction of global sea level from global temperature

David Bolin, Peter Guttorp, Alex Januzzi, Daniel Jones, Marie Novak, Harry Podschwit, Lee Richardson, Aila Särkkä, Colin Sowder, Aaron Zimmerman

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)351-367
Number of pages17
JournalStatistica Sinica
Issue number1
StatePublished - Jan 1 2015
Externally publishedYes


Dive into the research topics of 'Statistical prediction of global sea level from global temperature'. Together they form a unique fingerprint.

Cite this