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Decadal predictability and prediction skill of sea surface temperatures in the South Pacific region

Abstract

The South Pacific Ocean is a key driver of climate variability within the Southern Hemisphere at different time scales. Previous studies have characterized the main mode of interannual sea surface temperature (SST) variability in that region as a dipolar pattern of SST anomalies that cover subtropical and extratropical latitudes (the South Pacific Ocean Dipole, or SPOD), which is related to precipitation and temperature anomalies over several regions throughout the Southern Hemisphere. Using that relationship and the reported low predictive skill of precipitation anomalies over the Southern Hemisphere, this work explores the predictability and prediction skill of the SPOD in near-term climate hindcasts using a set of state-of-the-art forecast systems. Results show that predictability greatly benefits from initializing the hindcasts beyond the prescribed radiative forcing, and is modulated by known modes of climate variability, namely El Niño-Southern Oscillation and the Interdecadal Pacific Oscillation. Furthermore, the models are capable of simulating the spatial pattern of the observed SPOD even without initialization, which suggests that the key dynamical processes are properly represented. However, the hindcast of the actual phase of the mode is only achieved when the forecast systems are initialized, pointing at SPOD variability to not be radiatively forced but probably internally generated. The comparison with the performance of an empirical prediction based on persistence suggests that initialization may provide skillful information for SST anomalies, outperforming damping processes, up to 2–3 years into the future.

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Acknowledgements

The authors would like to thank the two anonymous reviewers, whose comments and suggestions led to a significant improvement of the manuscript. This study also benefited from discussion with Fabian Lienert. It was supported by the University of Buenos Aires through Grant UBACYT-20020100100803, UBACYT-2002170100428BA, CONICET through PIP2009-00444, ANPCyT through Grants PICT07-00400 and PICT2016-1898, the EUCP (GA 776613) and PRIMAVERA (GA 641727) EU-funded projects, and the CLIMAX (Belmont Forum/ANR-15-JCL/-0002-01) Project. J.G-S was funded by the Spanish “Ramón y Cajal” programme (RYC-2016-21181). The authors acknowledge the Red Española de Supercomputación (RES) and PRACE for awarding access to Marenostrum 3 at the Barcelona Supercomputing Center through the HiResClim project, as well as the World Climate Research Programme’s Working Group on Coupled Modelling and the diverse climate modeling groups for producing and making available the model outputs used in this paper. The support given by Oriol Mula-Valls and Virginie Guémas at early stages of this research is particularly appreciated.

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Correspondence to Ramiro I. Saurral.

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Appendix

Appendix

Leading mode of SST variability in the SP region in the forecast systems

Figure 9 shows the spatial pattern associated with the leading mode of interannual SST variability in the SP region in forecast year 1 of the (left) initialized and (right) uninitialized hindcasts. The resemblance of the Init vs. NoInit patterns in each forecast system indicates that the SPOD mode is internally generated by the models. Additional discussion on this issue can be found in the main text.

Fig. 9
figure 9

Leading mode of SST interannual variability in the SP region in (from top to bottom) DePreSys, EC-Earth, GFDL, HadCM3 and MIROC5, considering Init (left column) and NoInit (right column) experiments. The numbers in the sub-titles denote the fraction of variance explained by the mode in each case. The smaller numbers within each sub-figure indicate the spatial correlation between the simulated mode in the hindcast and the observed field. The computation of the leading mode was done taking, for each forecast system, all the ensemble members together

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Saurral, R.I., García-Serrano, J., Doblas-Reyes, F.J. et al. Decadal predictability and prediction skill of sea surface temperatures in the South Pacific region. Clim Dyn 54, 3945–3958 (2020). https://doi.org/10.1007/s00382-020-05208-3

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Keywords

  • Near-term climate predictions
  • IPO
  • ENSO
  • SST
  • South Pacific