Researchers develop a novel algorithm that should enable improved prediction of the function of global ecosystems in a warming climate.
By employing an atmosphere-only version of ACCESS, CLEX researchers generated multiple sea surface temperature patterns of the same El Nino and La Nina events, and assessed how this influenced heatwaves over various Australian regions.
New research in Nature Climate Change suggests droughts may not increase as a result of climate change. This finding resulted from researchers investigating an apparent climate model contradiction that saw climate change projections of the 21st Century produce increased droughts along with more run-off and a greening of the landscape
Urban Climate and Energy Model (UCLEM) is a new climate model focused on Australian conditions that integrates energy use and human behaviour to generate accurate representations of urban climates.
This paper shows that many models overestimate the interaction between hot and dry conditions in wet regions and therefore overamplify heat extremes. The study points to necessary model improvement to increase confidence in future projections of heat extremes.
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.
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.