National Modelling Hub

Regional Climate Modeller: Alexandra Gossart

Funded by: Antarctic Science Platform (aligned to Project 1)

Project Scope

Near-surface climate provides an essential boundary condition for most physical and biological systems, and its accurate simulation is fundamental to future projections of Antarctic environments. This fellowship brings skills in atmospheric modelling at high spatial and temporal resolutions to provide inputs to ice sheet and ocean models, terrestrial geo-statistical models, glacial surface energy balance models and terrestrial hydrological routing models.

Process-scale ice shelf cavity modeller: Alena Malyarenko

Funded by: Antarctic Science Platform (aligned to Project 2)

Project Scope

Ocean processes drive significant changes in ice shelf evolution over hourly (tidal) to decadal or longer timescales. Process understanding is fundamental to making accurate future projections. This project uses simulations of ice-ocean interactions at ice shelf cavity scales. The ocean-ice modelling project seeks to capture details of cavity circulation and explore the roles of

• under ice basal processes (parameterisations, role of basal topography),
• internal processes (double diffusion, internal waves at high latitude), and
• non-hydrostatic processes (i.e. grounding-line, ice shelf front).

The project works towards coupling the modelling with glaciological and Global Climate Model modelling required to explore ice sheet evolution arising from interactions with a changing ocean.

Data Scientist: Mario Krapp

Funded by: Antarctic Science Platform (Cross-Project)

Project Scope

Much of the observational and numerical simulation work undertaken through the research strands of the New Zealand Antarctic Science Platform as a whole will generate large datasets. Novel insights can be gleaned from ‘Big Data’ when statistical approaches are applied. This project provides a data-driven multidisciplinary perspective on the data generated from the platform. Our aspiration is to utilise techniques such as Bayesian networks, statistical emulation, or machine learning to provide additional approaches for the generation of policy-relevant information. This project could also contribute specifically to more statistically- (rather than process-) based research questions, such as future projections of species distribution and population change.