Climate Projections from the Narclim Project: Model Biases and Significance of Projected Changes

Friday, 19 December 2014
Roman Olson1, Jason Peter Evans2,3, Daniel Argueso2,3 and Alejandro Di Luca2,3, (1)University of New South Wales, Climate Change Research Centre, Sydney, NSW, Australia, (2)University of New South Wales, Climate Change Research Centre, Sydney, Australia, (3)University of New South Wales, ARC Centre of Excellence for Climate System Science, Sydney, Australia
NARCliM (NSW/ACT Regional Climate Modelling Project) is a regional climate project for Australia and surrounding area. It uses dynamical downscaling to provide climate projections for the CORDEX-AustralAsia region at 50 km resolution, and for South-East Australia at 10 km resolution. The project is the first of its kind in the level of sophistication of model selection. Specifically, the selection process for General Circulation Models (GCMs) included (i) evaluating model performance based on literature review, (ii) calculating model independence, and (iii) analyzing future changes in temperature and precipitation. Regional Climate Models (RCMs) for downscaling the GCMs were identified based on model independence, as well as on their performance for several precipitation events over South-East Australia.

We first present seasonal and annual mean changes for key climate metrics (e.g., temperature, precipitation, etc.) for both regions for periods 2020-2039 and 2060-2079. We then use the seasonal and annual mean output from NARCliM to address several key questions: (1) How independent are the members of the NARCliM ensemble? (2) Do the RCMs span sufficient range of uncertainty in the driving GCM projections? (3) Are the RCMs significantly different from observations? and (4) Over which areas do we expect a significant change in surface atmospheric temperature and precipitation in periods 2020-2039 and 2060-2079? More broadly, we share strategies on overcoming common computational challenges faced during the project with those intending to undertake a similar Regional Climate Modeling effort.