The Drought program has been strongly focused on evaluating and improving climate models, and developing a drought database for documenting drought and for benchmarking model performance.
The MAAT framework can be used to systematically run multiple model simulations to explore how different underlying model assumptions, hypotheses and parameters lead to predicted model behaviour and isolate the causes of model divergence.
This project will use satellite and flux tower observations to characterise the response of Australian ecosystems to water stress. These data will then be used to evaluate how well the Australian climate model predicts droughts. The successful candidate will obtain skills in programming and analysis of spatial datasets and model outputs.
A new study by CLEX researchers using observations from FLUXNET sites identifies regions of high and low predictability and will likely help improve land surface model evaluation.
New research clearly demonstrates the potential to predict long-term LAI using simple ecohydrological theory. This approach could potentially be incorporated into existing terrestrial biosphere models and help improve predictions of LAI.
This research suggests some trees and in particular, Australian trees, may be more resilient than expected to future warming and extreme events. These findings have implications for planning around which species to plant in “green cities” to help mitigate future climate extremes.
The application of a simple carbon balance model, combined with a data assimilation approach, has the potential to improve the process understanding embedded in models, which is used to predict responses of the carbon cycle to climate change.
Presentations from the ARCCSS / CLEX Winter School 2018
This study explored the key sources of uncertainty when scaling leaf-level understanding of water-use efficiency to ecosystem scales. The results provide key insights into interpreting (ecosystem-scale) eddy-covariance derived water-use efficiency in an ecophysiological context.