Abstract
The Atlantic subpolar gyre (SPG) is one of the main drivers of decadal climate variability in the North Atlantic. Here we analyze its dynamics in pre-industrial control simulations of 19 different comprehensive coupled climate models. The analysis is based on a recently proposed description of the SPG dynamics that found the circulation to be potentially bistable due to a positive feedback mechanism including salt transport and enhanced deep convection in the SPG center. We employ a statistical method to identify multiple equilibria in time series that are subject to strong noise and analyze composite fields to assess whether the bistability results from the hypothesized feedback mechanism. Because noise dominates the time series in most models, multiple circulation modes can unambiguously be detected in only six models. Four of these six models confirm that the intensification is caused by the positive feedback mechanism.
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Acknowledgments
We acknowledge the modeling 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 dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy. We are grateful to Dr. Johann Jungclaus and to Dr. Jonathan Gregory for providing data of MPI-ESM and HadCM3, respectively. Simulations with CCSM4 were carried out on the ’bluefire’ system of the Computational and Informations Systems Laboratory (CISL). Simulations with IPSL CM4 hires were funded by the EC-FP6 project ‘ENSEMBLES’. Additional compute time was provided by the Swiss National Supercomputing Centre (CSCS). A. B. is supported by the European Commission under the Marie Curie Intra-European Fellowship ECLIPS (PIEF-GA-2011-300544) and the ‘National Centre for Excellence in Research: Climate’ of the Swiss National Science Foundation.
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Born, A., Stocker, T.F., Raible, C.C. et al. Is the Atlantic subpolar gyre bistable in comprehensive coupled climate models?. Clim Dyn 40, 2993–3007 (2013). https://doi.org/10.1007/s00382-012-1525-7
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DOI: https://doi.org/10.1007/s00382-012-1525-7