Evaluation of the CFSv2 CMIP5 decadal predictions

Abstract

Retrospective decadal forecasts were undertaken using the Climate Forecast System version 2 (CFSv2) as part of Coupled Model Intercomparison Project 5. Decadal forecasts were performed separately by the National Center for Environmental Prediction (NCEP) and by the Center for Ocean-Land-Atmosphere Studies (COLA), with the centers using two different analyses for the ocean initial conditions the NCEP Climate Forecast System Reanalysis (CFSR) and the NEMOVAR-COMBINE analysis. COLA also examined the sensitivity to the inclusion of forcing by specified volcanic aerosols. Biases in the CFSv2 for both sets of initial conditions include cold midlatitude sea surface temperatures, and rapid melting of sea ice associated with warm polar oceans. Forecasts from the NEMOVAR-COMBINE analysis showed strong weakening of the Atlantic Meridional Overturning Circulation (AMOC), eventually approaching the weaker AMOC associated with CFSR. The decadal forecasts showed high predictive skill over the Indian, the western Pacific, and the Atlantic Oceans and low skill over the central and eastern Pacific. The volcanic forcing shows only small regional differences in predictability of surface temperature at 2m (T2m) in comparison to forecasts without volcanic forcing, especially over the Indian Ocean. An ocean heat content (OHC) budget analysis showed that the OHC has substantial memory, indicating potential for the decadal predictability of T2m; however, the model has a systematic drift in global mean OHC. The results suggest that the reduction of model biases may be the most productive path towards improving the model’s decadal forecasts.

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References

  1. Balmaseda MA, Mogensen K, Molteni F, Weaver AT (2010) The NEMOVAR-COMBINE ocean reanalysis. COMBINE technical report no. 1, 11 p. (Available at http://www.combinep)

  2. Balmaseda MA, Trenberth KE, Källén E (2013) Distinctive climate signals in reanalysis of global ocean heat content. Geophys Res Lett 40:1754–1759. doi:10.1002/grl.50382

    Article  Google Scholar 

  3. Bellucci A, Gualdi S, Masina S et al (2012) Decadal climate predictions with a coupled OAGCM initialized with oceanic reanalyses. Clim Dyn 40:1483–1497. doi:10.1007/s00382-012-1468-z

    Article  Google Scholar 

  4. Bellucci A, Haarsma R, Gualdi S et al (2014) An assessment of a multi-model ensemble of decadal climate predictions. Clim Dyn. doi:10.1007/s00382-014-2164-y

    Google Scholar 

  5. Doblas-Reyes FJ, Andreu-Burillo I, Chikamoto Y et al (2013) Initialized near-term regional climate change prediction. Nat Commun 4:1715. doi:10.1038/ncomms2704

    Article  Google Scholar 

  6. Ek MB, Mitchell KE, Lin Y et al (2003) Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J Geophys Res 108:8851. doi:10.1029/2002JD003296

    Article  Google Scholar 

  7. García-Serrano J, Doblas-Reyes FJ (2012) On the assessment of near-surface global temperature and North Atlantic multi-decadal variability in the ENSEMBLES decadal hindcast. Clim Dyn 39:2025–2040. doi:10.1007/s00382-012-1413-1

    Article  Google Scholar 

  8. Goddard L, Kumar A, Solomon A et al (2012) A verification framework for interannual-to-decadal predictions experiments. Clim Dyn. doi:10.1007/s00382-012-1481-2

    Google Scholar 

  9. Griffies SM, Harrison MJ, Pacanowski RC, Rosati A (2004) A technical guide to MOM4. GFDL Ocean group technical report no. 5, p 281

  10. Guemas V, Doblas-Reyes FJ, Lienert F et al (2012) Identifying the causes of the poor decadal climate prediction skill over the North Pacific. J Geophys Res Atmos 117:D20111. doi:10.1029/2012JD018004

    Article  Google Scholar 

  11. Guemas V, Corti S, García-Serrano J et al (2013) The Indian Ocean: the region of highest skill worldwide in decadal climate prediction*. J Clim 26:726–739. doi:10.1175/JCLI-D-12-00049.1

    Article  Google Scholar 

  12. Huang B, Hu Z-Z, Schneider EK et al (2011) Influences of tropical–extratropical interaction on the multidecadal AMOC variability in the NCEP climate forecast system. Clim Dyn 39:531–555. doi:10.1007/s00382-011-1258-z

    Article  Google Scholar 

  13. Huang B, Zhu J, Marx L et al (2014) Climate drift of AMOC, North Atlantic salinity and Arctic sea ice in CFSv2 decadal predictions. Clim Dyn (submitted)

  14. Kalnay E, Kanamitsu M, Kistler R et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–471. doi:10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2

    Article  Google Scholar 

  15. Keenlyside NS, Latif M, Jungclaus J et al (2008) Advancing decadal-scale climate prediction in the North Atlantic sector. Nature 453:84–88. doi:10.1038/nature06921

    Article  Google Scholar 

  16. Kim H-M, Webster PJ, Curry JA (2012) Evaluation of short-term climate change prediction in multi-model CMIP5 decadal hindcasts. Geophys Res Lett 39. doi:10.1029/2012GL051644

  17. Kirtman B, Power SB, Adedoyin JA et al (2013) Near-term climate change: projections and predictability. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of Working Group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK

  18. Krishnamurthy L, Krishnamurthy V (2013) Decadal scale oscillations and trend in the Indian monsoon rainfall. Clim Dyn 43:319–331. doi:10.1007/s00382-013-1870-1

    Article  Google Scholar 

  19. Kumar A, Chen M, Zhang L et al (2012) An Analysis of the Nonstationarity in the bias of sea surface temperature forecasts for the NCEP Climate Forecast System (CFS) version 2. Mon Weather Rev 140:3003–3016. doi:10.1175/MWR-D-11-00335.1

    Article  Google Scholar 

  20. Meehl GA, Goddard L, Boer G et al (2014) Decadal climate prediction: an update from the trenches. Bull Am Meteorol Soc 95:243–267. doi:10.1175/BAMS-D-12-00241.1

    Article  Google Scholar 

  21. Murphy AH (1988) Skill scores based on the mean squared error and their relationships to the correlation coefficient. Mon Weather Rev 116:2417–2424

    Article  Google Scholar 

  22. Pohlmann H, Jungclaus JH, Köhl A et al (2009) Initializing decadal climate predictions with the GECCO oceanic synthesis: effects on the North Atlantic. J Clim 22:3926–3938. doi:10.1175/2009JCLI2535.1

    Article  Google Scholar 

  23. Robock A (2000) Volcanic eruptions and climate. Rev Geophys 38:191–219. doi:10.1029/1998RG000054

    Article  Google Scholar 

  24. Saha S, Moorthi S, Pan H-L et al (2010) The NCEP climate forecast system reanalysis. Bull Am Meteorol Soc 91:1015–1057. doi:10.1175/2010BAMS3001.1

    Article  Google Scholar 

  25. Saha S, Moorthi S, Wu X et al (2014) The NCEP climate forecast system version 2. J Clim. doi:10.1175/JCLI-D-12-00823.1

    Google Scholar 

  26. Smith DM, Cusack S, Colman AW et al (2007) Improved surface temperature prediction for the coming decade from a global climate model. Science 317:796–799. doi:10.1126/science.1139540

    Article  Google Scholar 

  27. Smith DM, Eade R, Pohlmann H (2013) A comparison of full-field and anomaly initialization for seasonal to decadal climate prediction. Clim Dyn 41:3325–3338. doi:10.1007/s00382-013-1683-2

    Article  Google Scholar 

  28. Swingedouw D, Mignot J, Labetoulle S et al (2012) Initialisation and predictability of the AMOC over the last 50 years in a climate model. Clim Dyn 40:2381–2399. doi:10.1007/s00382-012-1516-8

    Article  Google Scholar 

  29. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498. doi:10.1175/BAMS-D-11-00094.1

    Article  Google Scholar 

  30. Trenberth KE (2002) Evolution of El Niño–southern oscillation and global atmospheric surface temperatures. J Geophys Res 107:4065. doi:10.1029/2000JD000298

    Article  Google Scholar 

  31. Van Oldenborgh GJ, Doblas-Reyes FJ, Wouters B, Hazeleger W (2012) Decadal prediction skill in a multi-model ensemble. Clim Dyn 38:1263–1280. doi:10.1007/s00382-012-1313-4

    Article  Google Scholar 

  32. Weisheimer A, Doblas-Reyes FJ, Palmer TN et al (2009) ENSEMBLES: a new multi-model ensemble for seasonal-to-annual predictions—skill and progress beyond DEMETER in forecasting tropical Pacific SSTs. Geophys Res Lett 36:L21711. doi:10.1029/2009GL040896

    Article  Google Scholar 

  33. Xue Y, Huang B, Hu Z-Z et al (2010) An assessment of oceanic variability in the NCEP climate forecast system reanalysis. Clim Dyn 37:2511–2539. doi:10.1007/s00382-010-0954-4

    Article  Google Scholar 

  34. Xue Y, Chen M, Kumar A et al (2013) Prediction skill and bias of tropical Pacific sea surface temperatures in the NCEP climate forecast system version 2. J Clim 26:5358–5378. doi:10.1175/JCLI-D-12-00600.1

    Article  Google Scholar 

  35. Yeager S, Karspeck A, Danabasoglu G et al (2012) A decadal prediction case study: late twentieth-century North Atlantic Ocean heat content. J Clim 25:5173–5189. doi:10.1175/JCLI-D-11-00595.1

    Article  Google Scholar 

  36. Zhu J, Huang B, Balmaseda MA (2012a) An ensemble estimation of the variability of upper-ocean heat content over the tropical Atlantic Ocean with multi-ocean reanalysis products. Clim Dyn 39:1001–1020. doi:10.1007/s00382-011-1189-8

    Article  Google Scholar 

  37. Zhu J, Huang B, Marx L et al (2012b) Ensemble ENSO hindcasts initialized from multiple ocean analyses. Geophys Res Lett 39:L09602. doi:10.1029/2012GL051503

    Google Scholar 

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Acknowledgments

The authors thank the support from the National Science Foundation (NSF 0830068), the National Oceanic and Atmospheric Administration (NOAA NA09OAR4310058), and the National Aeronautics and Space Administration (NASA NNX09AN50G). We thank Dr. Magdalena A. Balmaseda, Dr. Chris Bretherton, and the two anonymous reviewers for their suggestions for the improvement of this manuscript. We also thank Dr. Magdalena A. Balmaseda for providing the COMBINE-NV initial conditions for the CFS_v2. We thank the ECMWF for providing the COMBINE-NV reanalysis data.

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Correspondence to Rodrigo J. Bombardi.

Additional information

This paper is a contribution to the Topical Collection on Climate Forecast System Version 2 (CFSv2). CFSv2 is a coupled global climate model and was implemented by National Centers for Environmental Prediction (NCEP) in seasonal forecasting operations in March 2011. This Topical Collection is coordinated by Jin Huang, Arun Kumar, Jim Kinter and Annarita Mariotti.

Appendix: An issue with the CFSR coupled and its influence

Appendix: An issue with the CFSR coupled and its influence

In the course of the investigating the causes of the model biases discussed in Sect. 4, COLA scientists discovered a coding error in the coupler associated with the sea ice mask and the atmosphere–ocean coupling. This error is present in the versions of CFSv2 used by both COLA and NCEP in the results described here, as well as in the version of CFSv2 used for the CFSR data assimilation and CFS reforecasts (Saha et al. 2010). The result of this code error is that the fluxes provided by the atmosphere to the ocean in some regions are substantially different from the fluxes seen by the ocean. The locations of the inconsistency in the fluxes are dependent on domain decomposition and the number of processors used. Figure 13 shows the inconsistencies in net surface heat fluxes in the COLA runs and in a run made by COLA where the code error was corrected. The differences are computed from monthly mean atmospheric model and ocean model outputs, and points with nonzero sea ice are excluded.

Fig. 13
figure13

The annual mean difference (W m−2) between the diagnostic of the net surface heat flux supplied by the atmospheric model to the ocean model and the net surface heat flux received by the ocean model in CFSv2 for a the model run by COLA and b a run by COLA with the code error corrected. Points with nonzero sea ice are excluded

Considering the COLA version in Fig. 13a, there is inconsistency in the net surface heat flux in the annual mean in the eastern North Atlantic near 0°E 60°N, with the ocean net cooling calculated by the atmosphere on the order of 100 W m−2. This negative inconsistency extends southward to the coast along Spain as well as westward into the North Atlantic and northeastward to 60°E. There is also a region of weaker negative inconsistency near the Bering Strait and in the northwestern North Atlantic. Similar features are also found in the NCEP version (not shown). When the code error is fixed (Fig. 13b), the regions of negative inconsistency disappear. The other surface fluxes (surface wind stress and fresh water flux) also show inconsistencies coincident with the negative heat flux inconsistencies in Fig. 13a, and these also disappear when the code error is fixed. Our explanation of the negative inconsistencies that are removed by fixing the code error is that the coupler is erroneously insulating the ocean from the atmosphere in these regions as if there were sea ice.

We might expect that the wind stress and heat flux inconsistencies would have seriously distorted the ocean circulation in the North Atlantic, including the AMOC and the subpolar gyre. Given that the AMOC variability has been postulated to be an important mechanism for decadal climate variability and predictability in the North Atlantic (e.g. Huang et al. 2011; Swingedouw et al. 2012), that mechanism is probably not realistically represented in the results reported here.

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Bombardi, R.J., Zhu, J., Marx, L. et al. Evaluation of the CFSv2 CMIP5 decadal predictions. Clim Dyn 44, 543–557 (2015). https://doi.org/10.1007/s00382-014-2360-9

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Keywords

  • Decadal forecast and prediction
  • Skill
  • Biases
  • CFSv2
  • CMIP5
  • Volcanic forcing