Journal article
Approximating uncertainty of annual runoff and reservoir yield using stochastic replicates of global climate model data
MC Peel, R Srikanthan, TA McMahon, DJ Karoly
Hydrology and Earth System Sciences | Published : 2015
Abstract
Two key sources of uncertainty in projections of future runoff for climate change impact assessments are uncertainty between global climate models (GCMs) and within a GCM. Within-GCM uncertainty is the variability in GCM output that occurs when running a scenario multiple times but each run has slightly different, but equally plausible, initial conditions. The limited number of runs available for each GCM and scenario combination within the Coupled Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) data sets, limits the assessment of within-GCM uncertainty. In this second of two companion papers, the primary aim is to present a proof-of-concept approximation of within-GCM unce..
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Grants
Awarded by Australian Research Council
Funding Acknowledgements
This research was financially supported by Australian Research Council grants LP100100756 and FT120100130, Melbourne Water and the Australian Bureau of Meteorology. Lionel Siriwardena, Sugata Narsey and Ian Smith assisted with extraction and analysis of CMIP3 GCM data. We acknowledge the modelling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP's Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model data set. Support of this data set is provided by the Office of Science, US Department of Energy. We also acknowledge the contribution of two anonymous reviewers whose comments and suggestions improved the paper.