Session: Warm Climates (Mid-Holocene, Last interglacial, Deep-time, Pliocene)
Author: Jorge Bernales / email@example.com / GFZ Potsdam, Germany
Co-author: Irina Rogozhina, MARUM Center for Marine Environmental Sciences, Bremen, Germany;
Matthias Prange, MARUM Center for Marine Environmental Sciences, Bremen, Germany;
the MAGIC-DML team, ;
Repeated build-up and retreat events across different sectors of the Antarctic ice sheet (AIS) were tightly linked to global climate variations over glacial-interglacial cycles. However, the long-term changes in the volume of the East Antarctic ice sheet (EAIS) and its contribution to the sea-level variations remain poorly understood. The international research project MAGIC-DML aims to constrain the past vertical extents and volumes of the EAIS and shed light on the regional long-term climate evolution using a combination of direct evidence from field-based reconstructions and numerical ice-sheet and climate simulations. As part of this, we will focus on the ice sheet history since the mid-Pliocene warm period, while zooming in on the past warm interglacial intervals to provide insights into the responses of the EAIS to warmer-than-present climate and ocean conditions. In these numerical reconstructions we will employ climate fields from our in-house simulations with the Community Climate/Earth System Model and outputs of general circulation models provided by the PMIP 2-4 to drive the Antarctic ice sheet simulations. We will build upon the approach presented in our recent study where the performance of several present-day climate data sets from CMIP5 has been evaluated during the initialization of an AIS model. This initialization utilizes the observed ice elevation and thickness to derive heterogeneities in the basal sliding parameters for the grounded ice sheet sectors and melting and freezing rates under ice shelves. We have evaluated different sets of the inferred sub-glacial parameters and compared the resulting dynamical states of the AIS with observations to demonstrate that this approach can be used to identify biases in the climate model outputs across the Antarctic continent. Our results indicate that similar methods can be adopted to evaluate the skill of paleoclimate model reconstructions of the periods for which only fragmentary data on ice geometry are available.