Advances in glacial isostatic adjustment modeling

2019 Past Global Change Magazine  
We overview two PALSEA-relevant applications of glacial isostatic adjustment modeling and highlight recent advances. These include the consideration of models with lateral Earth structure and the development of methods to determine optimal parameters and model uncertainty. The primary aim of the PALSEA (PALeo constraints on SEA level rise) working group is to promote and improve the use of constraints from observations and modeling of past sea-level changes and ice-sheet extent to better inform
more » ... projections of future sea-level change. Glacial isostatic adjustment (GIA) -the deformational, gravitational and rotational response of the Earth to past ice-sheet evolution -plays an important role in reaching this objective in several respects (see Editorial, this issue). Here we review recent advances in two key PALSEA-relevant GIA model applications -estimating global ice volume during past warm periods and the contribution of GIA to future sea-level change -and consider recent developments towards improving uncertainty estimation in GIA model output which is central to these applications. Estimating global ice volume during past warm periods A core aim of PALSEA is to estimate the peak in global mean sea level (GMSL), from which global ice volume can be inferred, during past warm periods when GMSL was greater than at present. There are three periods in relatively recent Earth history for which observations indicate that GMSL was above the present value: the Mid-Pliocene warm period (~3 Myr BP), Marine Isotope Stage 11 (~420-370 kyr BP), and Marine Isotope Stage 5e (~129-116 kyr BP; the Last Interglacial). Estimating GMSL from a sparse distribution of local relative sea level (RSL) indicators is non-trivial due to under-sampling and the fact that local sea level can depart significantly from the global mean value. GIA is one of a number of processes (e.g. dynamic topography and sediment loading; see contributions on these topics in this issue) that should be considered when estimating GMSL from RSL records. A small number of studies have demonstrated that the GIA "overprint" can significantly bias estimates of GMSL for each of the three warm periods mentioned above (e.g. Raymo et al. 2011; Raymo and Mitrovica 2012; Kopp et al. 2009; Dutton and Lambeck 2012; Dendy et al. 2017) . Specifically, they show that the GIA contribution to RSL can range from order 1-10 m depending on the data location and the choice of model inputs (parameters). Regarding model inputs, the ice-loading history and Earth viscosity structure are the most important. There is considerable uncertainty in both of these, so it is necessary to perform model-sensitivity analyses to map out which parametric uncertainty dominates at the specific data locations. The analysis of Dendy et al. (2017) is the most thorough in this respect. In addition to uncertainty in model parameters, limitations in the model itself, due to, for example, missing processes or simplifications in the geometry, can lead to considerable error or bias in the output (formally known as model structural error). A recent advancement in this area is the development of coupled models that account for feedbacks between GIA-related processes and ice dynamics (see Whitehouse 2018). One limitation in all of the GIA studies noted above is the use of spherically symmetric Earth models in which parameters vary only with depth. GIA models that include a 3D Earth structure have been applied in some studies that consider post-Last Glacial Maximum sea levels or geodetic observations (Whitehouse 2018) and the impact has been shown to be non-negligible. However, the computational expense of these models currently prohibits their use for earlier times such as those mentioned above due to the longer time integrations required.
doi:10.22498/pages.27.1.16 fatcat:o7vtfv3n5rbfpazlmegcpqgbqa