Some remarks on Glacial Isostatic Adjustment modelling uncertainties

Abstract

Glacial Isostatic Adjustment (GIA) modelling has recently seen a significant development, stimulated by the need of understanding past, current and future sea level variations and geodetic signals associated with climate change. Our main motivation is that albeit its importance is well recognized within the climate science community, the problem of classifying and quantifying GIA modelling uncertainties has so far received little attention. Here, we consider two possible ways of defining and evaluating these uncertainties. The first is associated with limited knowledge of input model parameters (e.g. the viscosity profile of the Earth's mantle or the deglaciation history), once it is assumed that the ice margins are known and a unique set of relative sea level (RSL) data are used to constrain the model. We also discuss a second and more problematic source of uncertainty, associated with structural differences in GIA models, stemming from distinct eustatic curves and ice margins geometries, different RSL constraints, non-identical input parameters and different numerical solution schemes. By analysing the present-day 'GIA fingerprints' of relative and absolute sea level change, and exploring the GIA contribution to secular sea level rise and to the time-variations of the Earth's gravity field, here we evaluate the two types of uncertainty showing that they are (i) of significant amplitude and (ii) of comparable importance.

SELEN<sup>4</sup> (SELEN version 4.0): a Fortran program for solving the gravitationally and topographically self-consistent Sea Level Equation in Glacial Isostatic Adjustment modeling

“…This has been recently confirmed by Melini and Spada (2019). The post-processing phase of SELEN 4 can be configured to handle any properly formatted input dataset with coordinates of geodetic points of interest, where all the variables considered in Figure 8 can be evaluated.…”

“…Tushingham and Peltier (1993) (hereafter referred to as TP93). In view of its historical importance in the development of GIA studies (e.g., Peltier, 1991, 1992;Melini and Spada, 2019), the TP93 database is available 25 with the SELEN 4 package; however, there are no restrictions on the use of other datasets, or simply individual RSL records, if available to the user. The black curves in Figure 4 have been obtained using the GIA model described in Section 3.1, characterized by the settings R44/L128/I3.…”

“…Plag and Jüettner (2001), these are often referred to as GIA fingerprints; their spatial variability reflects the effects of deformation, gravitational attraction, and rotation within the system composed by the solid Earth, the oceans and the ice sheets (Clark et al, 1978;Mitrovica and Milne, 2002). In view of their importance on the interpretation of ground-based (King et al, 2010) or satellite geodetic observations (Peltier, 2004) and of tide-gauge secular trends (e.g., Wöppelmann and Marcos, 2016), their properties have been the 20 subject of various investigations during last decade (see e.g., Tamisiea, 2011;Spada, 2017;Husson et al, 2018;Melini and Spada, 2019).…”

“…Since the late 1980s, with the awareness of global warming and the availability of numerical solutions of the SLE (Peltier and Tushingham, 1989), GIA corrections to the observed trends of sea level have been routinely applied (for 10 a review, see Wöppelmann and Marcos, 2016). However, since GIA models are progressively improved to provide a better description of reality, corrections at tide gauges are not given once and for all (Kendall et al, 2006;Tamisiea, 2011;Melini and Spada, 2019). Furthermore, new constraints from past sea level or modern geodetic observations have permitted gradual refinements (either by formal inverse methods or simply by trial and error) of the two basic ingredients of GIA modeling, i.e., the Earth rheological profile and the history of deglaciation since the LGM.…”

“…Furthermore, new constraints from past sea level or modern geodetic observations have permitted gradual refinements (either by formal inverse methods or simply by trial and error) of the two basic ingredients of GIA modeling, i.e., the Earth rheological profile and the history of deglaciation since the LGM. 15 Uncertainties in modeling are significant (Melini and Spada, 2019), which constitutes an additional motivation to improve the approach to GIA.…”