Martin De Kauwe

Centre of Excellence for Climate Extremes
Climate Change Research Centre
UNSW Sydney
Australia

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Research

My research focuses on improving our understanding of land-atmosphere interactions, with an emphasis on forest ecosystems and their responses to global change (increasing CO2, temperature and changing water availability). I am particularly interested in ways to utilise experimental and satellite data to develop more evidence-based models.

A core theme of my research has been the use of multi-scale observations to constrain model predictions. Recently, my research has leaned heavily on expermental (e.g. Free-Air CO2 Enrichment experiments, FACE) and eddy covariance measurements (FLUXNET). Prior to this, I utlised satellite-based estimates to constrain models. During my PhD, I implemented a data assimilation framework to combine satellite derived observations, with a simple carbon balance ecosystem model (DALEC) to estimate (with uncertainty) global carbon stocks and fluxes. Whilst employed at Centre for Ecology and Hydrology (CEH), I applied spectral analysis to satellite derived land surface temperature, to characterise the timescales that the land surface responds to rainfall events in the Sahel. This analysis framework was then used to evaluate model predictions from JULES (the Joint UK Land Environment Simulator).

To understand how plants will respond to global change, I use a range of models of varying complexity, from simple (GDAY), to the more complex: stand (MAESPA), land surface (CABLE), dynamic vegetation (SDGVM) and coupled climate (ACCESS) models. I am also a member of the management committee for the CABLE land surface model.

Feel free to drop me an email if any of that sounds interesting! In particular, potential PhD students that have an interest in exploring research questions related to the carbon and water cycles and their responses to global change.

"The method of science depends on our attempts to describe the world with simple theories: theories that are complex may become untestable, even if they happen to be true. Science may be described as the art of systematic over-simplification-the art of discerning what we may with advantage omit" - Karl Popper, The Open Universe: An Argument for Indeterminism, pg. 44.

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