Forecasting the Global Mean Sea Level, a Continuous-Time State-Space Approach

Lorenzo Boldrini ()
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Lorenzo Boldrini: Aarhus University and CREATES, Postal: Department of Economics and Business, Fuglesangs Allé 4, 8210 Aarhus V, Denmark

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: In this paper we propose a continuous-time, Gaussian, linear, state-space system to model the relation between global mean sea level (GMSL) and the global mean temperature (GMT), with the aim of making long-term projections for the GMSL. We provide a justification for the model specification based on popular semi-empirical methods present in the literature and on zero-dimensional energy balance models. We show that some of the models developed in the literature on semi-empirical models can be analysed within this framework. We use the sea-level data reconstruction developed in Church and White (2011) and the temperature reconstruction from Hansen et al. (2010). We compare the forecasting performance of the proposed specification to the procedures developed in Rahmstorf (2007b) and Vermeer and Rahmstorf (2009). Finally, we compute projections for the sea-level rise conditional on the 21st century SRES temperature scenarios of the IPCC fourth assessment report. Furthermore, we propose a bootstrap procedure to compute confidence intervals for the projections, based on the method introduced in Rodriguez and Ruiz (2009).

Keywords: energy balance model; semi-empirical model; state-space system; Kalman filter; forecasting; temperature; sea level; bootstrap JEL classification: C32 (search for similar items in EconPapers)
Pages: 44
Date: 2015-08-24
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ore
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