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Statistical Prediction of Global Sea Level From Global Temperature

David Bolin (Institutionen för matematiska vetenskaper, matematisk statistik) ; P. Guttorp ; A. Januzzi ; D. Jones ; M. Novak ; H. Podschwit ; L. Richardson ; Aila Särkkä (Institutionen för matematiska vetenskaper, matematisk statistik) ; C. Sowder ; A. Zimmerman
Statistica Sinica (1017-0405). Vol. 25 (2015), 1, p. 351-367.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: ARMA time series models, climate projections, singular spectrum smoothing

Denna post skapades 2015-03-10. Senast ändrad 2015-03-12.
CPL Pubid: 213584


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Institutioner (Chalmers)

Institutionen för matematiska vetenskaper, matematisk statistik (2005-2016)



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