How complex should a land surface model be to accurately predict extremes?

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We live in a data-rich world, yet the representations of the land surface in climate models were largely conceived in the absence of observations. Comparisons against observations identifies model weaknesses; this then fuels a drive towards increased model complexity. How much of this added complexity is warranted? This project aims to build the simplest model of the terrestrial biosphere that the data can support. The project will combine a data-driven approach with the principles of optimality theory. By delivering a simpler, data- and theory-driven model we will unlock new understanding about climate model behaviour to improve predictions of climate extremes.

We are looking for expressions of interest from outstanding graduates with a strong academic record including Honours Class I or equivalent. Graduates with a strong background in mathematics, physics, atmospheric science, engineering or a similar quantitative science are particularly encouraged to apply. Programming experience with fortran 90, Python or R is highly desirable.

Supervisory team
Martin
De Kauwe

Science
Centre of Excellence for Climate Extremes
Lisa
Alexander

Science
Centre of Excellence for Climate Extremes
Andy
Pitman

Science
Centre of Excellence for Climate Extremes
Register to Apply
Non-UNSW staff/students must Register to Apply
m.dekauwe@unsw.edu.au