Improved resilience to extreme winds through the world can be helped by correcting the bias in climate projections. This project would test and apply a novel bias correction method (existing Python code) to make climate projections (CMIP6 simulations) consistent with observations-based data (ERA5 reanalysis data). This work will be useful for examining near-surface winds important for bio-physical processes (e.g., extreme values of 10-minute average winds are relevant for wildfire risk) and for wind gusts at various levels of the atmosphere important for damage to structures (extreme values of 3-second average winds are used for design of buildings, wind turbines for renewable energy, etc.). These results would also provide new insight on the storm types that cause extreme winds in regions through world.

Supervisor: Andrew Dowdy

Location: University of Melbourne