Select Publications
Preprints
2024, An ensemble estimate of Australian soil organic carbon using machine learning and process-based modelling, http://dx.doi.org/10.5194/egusphere-2023-3016
,2024, On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results, http://dx.doi.org/10.5194/egusphere-2023-3084
,2023, Australia’s Tinderbox Drought: an extreme natural event likely worsened by human-caused climate change, http://dx.doi.org/10.31223/x53q2b
,2022, Opening Pandora's box: How to constrain regional projections of the carbon cycle, http://dx.doi.org/10.5194/egusphere-2022-623
,2021, Examining the Role of Environmental Memory in the Predictability of Carbon and Water Fluxes Across Australian Ecosystems, http://dx.doi.org/10.5194/bg-2021-254
,2020, Robust historical evapotranspiration trends across climate regimes, http://dx.doi.org/10.5194/hess-2020-595
,2018, Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing, http://dx.doi.org/10.5194/esd-2018-51
,2017, Selecting a climate model subset to optimise key ensemble properties, http://dx.doi.org/10.5194/esd-2017-28
,2014, Response of microbial decomposition to spin-up explains CMIP5 soil carbon range until 2100, http://dx.doi.org/10.5194/gmdd-7-3481-2014
,2014, Disentangling residence time and temperature sensitivity of microbial decomposition in a global soil carbon model, http://dx.doi.org/10.5194/bgd-11-4995-2014
,2012, Towards a public, standardized, diagnostic benchmarking system for land surface models, http://dx.doi.org/10.5194/gmdd-5-549-2012
,2012, A framework of benchmarking land models, http://dx.doi.org/10.5194/bgd-9-1899-2012
,2011, The CSIRO Mk3L climate system model v1.0 coupled to the CABLE land surface scheme v1.4b: evaluation of the control climatology, http://dx.doi.org/10.5194/gmdd-4-1611-2011
,What is the Probability that a Drought Will Break in Australia?, http://dx.doi.org/10.2139/ssrn.4251061
,A Cmip6-Based Multi-Model Downscaling Ensemble to Underpin Climate Change Services in Australia, http://dx.doi.org/10.2139/ssrn.4210919
,Other
2024, Future drought changes in Australia from multiple projections, http://dx.doi.org/10.5194/egusphere-egu24-1920
,2024, Supplementary material to "On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results", http://dx.doi.org/10.5194/egusphere-2023-3084-supplement
,2023, Using Machine Learning to Reveal the Relationships Between Plant Functional Traits and Flux Regimes at Eddy-Covariance Towers, http://dx.doi.org/10.5194/egusphere-egu23-10049
,2021, Supplementary material to "Examining the Role of Environmental Memory in the Predictability of Carbon and Water Fluxes Across Australian Ecosystems", http://dx.doi.org/10.5194/bg-2021-254-supplement
,2021, Supplementary material to "A flux tower dataset tailored for land model evaluation", http://dx.doi.org/10.5194/essd-2021-181-supplement
,2019, Supplementary material to "A daily/25 km short-latency rainfall product for data scarce regions based on the integration of the GPM IMERG Early Run with multiple satellite soil moisture products", http://dx.doi.org/10.5194/hess-2019-387-supplement
,2019, Supplementary material to "How representative are FLUXNET measurements of surface fluxes during temperature extremes?", http://dx.doi.org/10.5194/bg-2018-502-supplement
,2015, Supplementary material to "Modelling evapotranspiration during precipitation deficits: identifying critical processes in a land surface model", http://dx.doi.org/10.5194/hessd-12-10789-2015-supplement
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