Investigating the climate predictability in the Southern Ocean using global and regional coupled models

Abstract:

PARAMOUR (Decadal Predictability and vAriability of polar climate: the Role of AtMosphere-Ocean-cryosphere mUltiscale inteRactions) is a new project funded in the framework of the Belgian program EOS - The excellence of Science. It aims at revealing fundamental drivers of climate variability and assessing the predictability in high-latitudes by using coupled regional climate models in both hemispheres. Here, we present ongoing efforts by the Earth and Life Institute in Louvain-la-Neuve (ELI, Belgium) and the Barcelona Supercomputing Center (BSC, Spain). ELI and BSC efforts center around two main objectives. The first one seeks improving our understanding of key processes controlling variability in the ice–ocean–atmosphere system at decadal timescales, first focusing on interactions between these climate components at a regional scale, and later on links at larger spatial scales. The second objective aims to determine how those interactions can provide some predictability at decadal timescales in the full ice–ocean–atmosphere system or in any particular components alone. Achieving these two objectives will require the development of coupled regional models including atmosphere, ocean, sea ice, and ice sheet models, and all driven by boundary conditions generated with a global coupled climate models. Three model configurations are proposed in PARAMOUR, covering 1/ Greenland, the Arctic, and the North Atlantic, 2/ Antarctica, and the Southern Ocean, and 3/ The Totten Glacier region. Here we focus on the latter two configurations, over Antarctica and the Southern Ocean. Retrospective (1980-2015) and prospective (2015-2045) climate simulations at high resolution are planned to be conducted to evaluate the respective role the initial conditions, specific physical processes, teleconnections, and couplings in recent trends, and to investigate potential fluctuations of key climate indicators within the next few decades. A specific aspect will also be to determine the added-value of regional compared to global modeling.