G43B-1047
A New Approach to Detection and Attribution of Ocean Thermal Expansion, using Realistic Values of Climate Internal Variability

Thursday, 17 December 2015
Poster Hall (Moscone South)
Elodie Charles, CNES French National Center for Space Studies, Toulouse Cedex 09, France
Abstract:
Tide gauges and satellite-based radar altimeter measurements provide evidence that the global mean sea level (GMSL) has been rising during the last two centuries and that this rate has been accelerating since the early 1900's, reaching 2.0±0.3 mm/year over 1971-2010 and 3.2±0.4 mm/year over 1993-2012 (Church et al. 2013). Ocean thermal expansion was identified as one of the main contributors, accounting for about 40% of the GMSL rise over 1971-2010 and 30% over 1993-2012 (Church et al. 2013).

The influence of external forcings and of climate internal variability (CIV) on the climate system and more specifically on the global mean thermosteric sea level (GMTSL) remains unclear while its understanding is essential to project sea level rise. Idealized simulations of the climate system, with none or a chosen combination of forcings, provide some information on the pattern and amplitude of the GMTSL response. However, climate models significantly underestimate the CIV and therefore its effects.

This study aims at assessing each forcing and CIV contributions to GMTSL changes, using a new detection and attribution method based on additive decomposition. Developed by Ribes et al. (2015), this approach dismisses the usual linear regression and proposes a symmetric treatment of the magnitude and pattern of the climate response to each forcing. Besides, more realistic estimates of the CIV than those provided by climate models are tested to gain more confidence in the results. We use CMIP5 large ensemble of forced and unforced simulations to estimate the GMTSL response to natural, anthropogenic, greenhouse gas and other forcings. Observational datasets are then used to constrain this first estimate to a more accurate result. Uncertainties may be further reduced by applying this method to a bivariate case. In this latter approach, we investigate the spatial and temporal distribution of thermosteric sea level changes and will present some results at a regional scale.