ORCID as entered in ROS

Select Publications
2005, 'What are the limits to statistical error correction in land surface schemes when projecting the future', Geophysical Research Letters, 32, pp. L14403, http://dx.doi.org/10.1029/2005GL023158
,2015, 'Multi-model ensemble projections of future extreme temperature change using a statistical downscaling method in eastern Australia', in Proceedings 21st International Congress on Modelling and Simulation Modsim 2015, pp. 1565 - 1571
,2021, 'Robust Future Changes in Meteorological Drought in CMIP6 Projections Despite Uncertainty in Precipitation', http://dx.doi.org/10.5194/egusphere-egu21-1889
,2013, The Community Atmosphere Biosphere Land Exchange (CABLE) model Roadmap for 2012-2017, The Centre for Australian Weather and Climate Research, Melbourne, 057, http://www.cawcr.gov.au/publications/technicalreports
,2006, The CSIRO Atmosphere Biosphere Land Exchange (CABLE) model for use in climate models and as an offline model, CSIRO, Research Technical Paper 013
,2010, Good Practice Guidance Paper on Assessing and Combining Multi Model Climate Projections, University of Bern, Switzerland, http://dx.doi.org
,2024, On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates, http://dx.doi.org/10.48550/arxiv.2409.13934
,2024, A Reliable Generative Adversarial Network Approach for Climate Downscaling and Weather Generation, http://dx.doi.org/10.22541/essoar.171352077.78968815/v2
,2024, A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation, http://dx.doi.org/10.22541/essoar.171352077.78968815/v1
,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
,2015, A model inter-comparison study to examine limiting factors in modelling Australian tropical savannas, http://dx.doi.org/10.5194/bgd-12-18999-2015
,2014, A test of an optimal stomatal conductance scheme within the CABLE Land Surface Model, http://dx.doi.org/10.5194/gmdd-7-6845-2014
,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
,2025, 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
,2020, Supplementary material to "Robust historical evapotranspiration trends across climate regimes", http://dx.doi.org/10.5194/hess-2020-595-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
,2018, Response to Anonymous Referee #1, Copernicus GmbH, http://dx.doi.org/10.5194/esd-2018-51-ac1
,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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