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Assessing the Uncertainty in Projecting Local Mean Sea Level from Global Temperature

P. Guttorp ; A. Januzzi ; M. Novak ; H. Podschwit ; L. Richardson ; C. D. Sowder ; A. Zimmerman ; David Bolin (Institutionen för matematiska vetenskaper, matematisk statistik) ; Aila Särkkä (Institutionen för matematiska vetenskaper, matematisk statistik)
Journal of Applied Meteorology and Climatology (1558-8424). Vol. 53 (2014), 9, p. 2163-2170.
[Artikel, refereegranskad vetenskaplig]

The process of moving from an ensemble of global climate model temperature projections to local sea level projections requires several steps. Sea level was estimated in Olympia, Washington (a city that is very concerned with sea level rise because parts of downtown are barely above mean highest high tide), by relating global mean temperature to global sea level; relating global sea level to sea levels at Seattle, Washington; and finally relating Seattle to Olympia. There has long been a realization that accurate assessment of the precision of projections is needed for science-based policy decisions. When a string of statistical and/or deterministic models is connected, the uncertainty of each individual model needs to be accounted for. Here the uncertainty is quantified for each model in the described system and the total uncertainty is assessed in a cascading effect throughout the system. The projected sea level rise over time and its total estimated uncertainty are visualized simultaneously for the years 2000-2100, the increased uncertainty due to each of the component models at a particular projection year is identified, and estimates of the time at which a certain sea level rise will first be reached are made.


Denna post skapades 2014-10-21. Senast ändrad 2015-02-26.
CPL Pubid: 204662


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Institutioner (Chalmers)

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



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