The statistical relation of sea-level and temperature revisited |
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| Institution: | 1. University of Kent, School of Economics, Canterbury, UK;2. Center for Research in Econometric Analysis of Time Series (CREATES), Department of Economics and Business, Aarhus University, Denmark;3. Division of Economics, Center for Research and Teaching in Economics (CIDE), Mexico City, Mexico;1. State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200062, China;2. Department of Resource Management and Geography, The University of Melbourne, VIC 3010, Australia;3. Department of Geography, East China Normal University, Shanghai 200062, China;1. Eindhoven University of Technology, Mechanics of Materials, Department of Mechanical Engineering, PO Box 513, 5600 MB Eindhoven, The Netherlands;2. Eindhoven University of Technology, Polymer Technology, Department of Mechanical Engineering, PO Box 513, 5600 MB Eindhoven, The Netherlands;1. Space Research and Technology Institute, Bulgarian Academy of Sciences, Stara Zagora Department, P.O. Box 73, 6000 Stara Zagora, Bulgaria;2. Space Research and Technology Institute, Bulgarian Academy of Sciences, Acad. Gerogy Bonchev Str. Bl. 1, 1113 Sofia, Bulgaria |
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| Abstract: | We propose a semi-empirical model for the relation between global mean surface temperature and global sea-levels. In contradistinction to earlier approaches to this problem, the model allows for valid statistical inference and joint estimation of trend components and interaction term of temperature and sea-level. Estimation of the model on the data set used in Rahmstorf (2007) yields a proportionality coefficient of 4.6 mm/year per °C at a one-sided significance level of 7.6 percent or higher. Long-term simulations of the model result in a two-sided 90-percent confidence interval for the sea-level rise in the year 2100 of 15 cm, 150 cm] above the 1990 level. This is a wider margin of error than was reported in the previous literature, and it reflects the substantial uncertainty in relating two trending time series. |
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| Keywords: | Sea-level Temperature Semi-empirical models Long-term forecasts State-space models |
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