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On the robustness of near term climate predictability regarding initial state uncertainties

Abstract

A set of four ensemble simulations has been designed to assess the relative importance of atmospheric, oceanic, and deep ocean initial state uncertainties, as represented by spatial white noise perturbations, on seasonal to decadal prediction skills in a perfect model framework. It is found that a perturbation mimicking random oceanic uncertainties have the same impact as an atmospheric-only perturbation on the future evolution of the ensemble after the first 3 months, even if they are initially only located in the deep ocean. This is due to the fast (1 month) perturbation of the atmospheric component regardless of the initial ensemble generation strategy. The divergence of the ensemble upper-ocean characteristics is then mainly induced by ocean–atmosphere interactions. While the seasonally varying mixed layer depth allows the penetration of the different signals in the thermocline in the mid-high latitudes, the rapid adjustment of the thermocline to wind anomalies followed by Kelvin and Rossby waves adjustment dominates the growth of the ensemble spread in the tropics. These mechanisms result in similar ensemble distribution characteristics for the four ensembles design strategy at the interannual timescale.

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Acknowledgments

The data used in this study are freely available: the authors can send them upon request. This work has been funded by the European community 7th framework programme (FP7) through the SPECS (Seasonal-to-decadal climate Prediction for the improvement of Climate Service) project under Grant agreement 308378 and by the Natural and Environmental Research Council UK (MESO-CLIP, NE/K005154/1 and SMURPHS, NE/N005767/1). We also thank the TGCC for computing resources and the IPSL model pole. The authors would like to thank Pablo Ortega and Javier Garcia-Serrano for helpful discussions and two reviewers for there helpful comment on the manuscript. They also thank J. Annan and W. Connolley for interesting exchanges about their experiment with HadCAM3. A.G. also wishes to thank the University of Southampton and the National Oceanography Centre Southampton and especially the PO and MSM teams for their welcome and the facilities they provided in order to help collaboration.

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Germe, A., Sévellec, F., Mignot, J. et al. On the robustness of near term climate predictability regarding initial state uncertainties. Clim Dyn 48, 353–366 (2017). https://doi.org/10.1007/s00382-016-3078-7

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Keywords

  • Climate predictability
  • Uncertainties
  • Ensemble spread
  • Initial condition perturbation
  • Prediction reliability
  • Ensemble generation