Evaluating synoptic systems in the CMIP5 climate models over the Australian region
Climate models are our principal tool for generating the projections used to inform climate change policy. Our confidence in projections depends, in part, on how realistically they simulate present day climate and associated variability over a range of time scales. Traditionally, climate models are less commonly assessed at time scales relevant to daily weather systems. Here we explore the utility of a self-organizing maps (SOMs) procedure for evaluating the frequency, persistence and transitions of daily synoptic systems in the Australian region simulated by state-of-the-art global climate models. In terms of skill in simulating the climatological frequency of synoptic systems, large spread was observed between models. A positive association between all metrics was found, implying that relative skill in simulating the persistence and transitions of systems is related to skill in simulating the climatological frequency. Considering all models and metrics collectively, model performance was found to be related to model horizontal resolution but unrelated to vertical resolution or representation of the stratosphere. In terms of the SOM procedure, the timespan over which evaluation was performed had some influence on model performance skill measures, as did the number of circulation types examined. These findings have implications for selecting models most useful for future projections over the Australian region, particularly for projections related to synoptic scale processes and phenomena. More broadly, this study has demonstrated the utility of the SOMs procedure in providing a process-based evaluation of climate models.
KeywordsSelf-organizing maps Weather typing Frequency Persistence Transitions
This work was supported by the Australian Research Council Centre of Excellence for Climate System Science Grant CE110001028. Author P.G. was supported by an Australian Postgraduate Award and author S.P. was supported by an Australian Research Council Discovery Early Career Researcher Award (DE140100952). We thank the NCI National Facility at the Australian National University for providing data storage and computational facilities. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We acknowledge NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, for the provision of the 20CRv2 and NCEP2 data, ECMWF for providing the ERA-Interim data and the JMA for providing the JRA-55 data used in this study.
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