Advertisement

Climate Dynamics

, Volume 44, Issue 3–4, pp 907–923 | Cite as

An evaluation of experimental decadal predictions using CCSM4

  • A. KarspeckEmail author
  • S. Yeager
  • G. Danabasoglu
  • H. Teng
Article

Abstract

This study assesses retrospective decadal prediction skill of sea surface temperature (SST) variability in initialized climate prediction experiments with the Community Climate System Model version 4 (CCSM4). Ensemble forecasts initialized with two different historical ocean and sea-ice states are evaluated and compared to an ensemble of uninitialized coupled simulations. Both experiments are subject to identical twentieth century historical radiative forcings. Each forecast consists of a 10-member ensemble integrated over a 10-year period. One set of historical ocean and sea-ice conditions used for initialization comes from a forced ocean-ice simulation driven by the Coordinated Ocean-ice Reference Experiments interannually varying atmospheric dataset. Following the Coordinated Model Intercomparison Project version 5 (CMIP5) protocol, these forecasts are initialized every 5 years from 1961 to 1996, and every year from 2000 to 2006. A second set of initial conditions comes from historical ocean state estimates obtained through the assimilation of in-situ temperature and salinity data into the CCSM4 ocean model. These forecasts are only available over a limited subset of the CMIP5 recommended start dates. Both methods result in retrospective SST prediction skill over broad regions of the Indian Ocean, western Pacific Ocean and North Atlantic Ocean that are significantly better than reference skill levels from a spatio-temporal auto-regressive statistical model of SST. However the subpolar gyre region of the North Atlantic stands out as the only region where the CCSM4 initialized predictions outperform uninitialized simulations. Some features of the ocean state estimates used for initialization and their impact on the forecasts are discussed.

Keywords

Decadal prediction Verification CCSM4 Initialization Ocean data assimilation Probabilistic skill score 

Notes

Acknowledgments

We acknowledge the hard work and dedication of all the scientists and software engineers who contributed to the development of the CCSM4. Special thanks are extended to Jeffrey Anderson, Tim Hoar, Nancy Collins, and Kevin Raeder of the National Center for Atmospheric Research (NCAR) Data Assimilation Research Section, who developed DART and provided ongoing support for the ocean assimilation system. Thanks also to Marianna Vertenstein for her guidance in developing the CCSM-DART interface and to Joe Tribbia, Peter Gent, Jerry Meehl and Grant Branstator for useful discussions. We are grateful to Tony Rosati for providing generous support during the initial phase of decadal prediction efforts at NCAR. This work was funded in part by the NOAA Climate Program Office under the Climate Variability and Predictability Program grants NA09OAR4310163 and NA13OAR4310138, and by the NSF Collaborative Research EaSM2 grant OCE-1243015. NCAR is sponsored by the National Science Foundation (NSF) and the CCSM project is supported by NSF and the Office of Science Biological and Environmental Research of the U.S. Department of Energy (DOE). Computing resources were provided by the Climate Simulation Laboratory at NCAR’s Computational and Informational Systems Laboratory, sponsored by the NSF and other agencies, and by the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. DOE under contract DE-AC05-00OR22725.

References

  1. Anderson J (2001) An ensemble adjustment Kalman filter for data assimilation. Mon Wea Rev 129:2884–2902CrossRefGoogle Scholar
  2. Anderson J, Hoar T, Raeder K, Liu H, Collins N, Torn R, Avellano A (2009) The data assimilation research testbed. Bull Am Meteorol Soc 90:1283–1296CrossRefGoogle Scholar
  3. Bowman A, Azzalini A (1997) Applied smoothing techniques for data analysis. Oxford University Press, New YorkGoogle Scholar
  4. Branstator G, Teng H (2010) Two limits of initial-value decadal predictability in a CGCM. J Clim 23:6292–6311CrossRefGoogle Scholar
  5. Cane M (2010) Decadal predictions in demand. Nature Geosci 3:231–232CrossRefGoogle Scholar
  6. Chikamoto Y et al (2013) An overview of decadal climate predictability in a multimodel ensemble by climate model MIROC. Clim Dyn 40:1201–1222CrossRefGoogle Scholar
  7. CLIVAR (2011) Data and bias correction for decadal climate predictions. CLIVAR publication series 150, International CLIVAR Project OfficeGoogle Scholar
  8. Danabasoglu G et al (2014) North Atlantic simulations in coordinated ocean-ice reference experiments phase II (CORE-II). Part I: mean states. Ocean Model 73:76–107CrossRefGoogle Scholar
  9. Danabasoglu G, Bates S, Briegleb B, Jayne SR, Jochum M, Large W, Peacock S, Yeager S (2012) The CCSM4 ocean component. J Clim 25:1361–1389CrossRefGoogle Scholar
  10. Deser C, Knutti R, Solomon S, Phillips A (2012a) Communication of the role of natural variability in future North American climate. Nature Clim Change 2:775–779CrossRefGoogle Scholar
  11. Deser C, Phillips A, Bourdette V, Teng H (2012b) Uncertainty in climate change projections: the role of internal variability. Clim Dyn 38:527–546CrossRefGoogle Scholar
  12. Doblas-Reyes F et al (2013) Initialized near-term regional climate change prediction. Nature Commun 1–9. doi: 10.1038/ncomms2704
  13. Efron B, Tibshirani R (1993) An introduction to the bootstrap. Chapman & Hall, LondonCrossRefGoogle Scholar
  14. Gangsto R, Weigel A, Liniger M, Appenzeller C (2013) Methodological aspects of the validation decadal predictions. Clim Res 55:181–2000CrossRefGoogle Scholar
  15. Garcia-Serrano J, Doblas-Reyes F (2012) On the assessment of near-surface global temperature and North Atlantic multi-decadal variability in the ENSEMBLES decadal hind cast. Clim Dyn 39:2025–2040CrossRefGoogle Scholar
  16. Gent P et al (2011) The community climate system model version 4. J Clim 24:4973–4991CrossRefGoogle Scholar
  17. Goddard L et al (2013) A verification framework for interannual-to-decadal predictions experiments. Clim Dyn 40:245–272Google Scholar
  18. Hawkins E, Sutton R (2009) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteorol Soc 90:1095–1107CrossRefGoogle Scholar
  19. Hazeleger W, Wouters B, van Oldenborgh G, Corti S, Palmer T, Smith D, Dunstone N, Kroger J, Pohlmann H, von Storch J (2013) Predicting multiyear North Atlantic Ocean variability. J Geophys Res Oceans 118:1087–1098CrossRefGoogle Scholar
  20. Jin E et al (2008) Current status of ENSO prediction skill in coupled ocean-atmosphere models. Clim Dyn 31:647–664CrossRefGoogle Scholar
  21. Karspeck A et al (2013) An ensemble adjustment Kalman Filter for the CCSM4 ocean component. J Clim 26:7392–7413CrossRefGoogle Scholar
  22. Keenlyside N, Latif M, Kornblueh L, Roeckner E (2008) Advancing decadal-scale climate prediction in the North Atlantic sector. Nature 453:84–88CrossRefGoogle Scholar
  23. Kirtman B, Shaklee J, Balmaseda M, Graham N, Penland C, Xue Y, Zebiak X (2001) Current status of ENSO forecast skill: a report to the CLIVAR working group on seasonal to interannual prediction. CLIVAR publication series 56, International CLIVAR Project OfficeGoogle Scholar
  24. Knight J, Fold C, Scaife A (2006) Climate impacts of the Atlantic multidecadal oscillation. Geophys Res Lett 33(L17):706Google Scholar
  25. Kröger J, Müller W, vonStorch J (2012) Impact of different ocean reanalyses on decadal climate prediction. Clim Dyn 39:795–810CrossRefGoogle Scholar
  26. Large W, Yeager S (2009) The global climatology of an interannually varying air-sea flux data set. Clim Dyn 33:341–364. doi: 10.1007/s00382-008-0441-3 CrossRefGoogle Scholar
  27. Liu Z (2012) Dynamics of interdecadal climate variabiltiy: a historical perspective. J Clim 25:1963–1995CrossRefGoogle Scholar
  28. Mantua N, Hare S, Wallace YZJ (1997) A Pacific interdecadal climate oscillation with impact on salmon production. Bull Am Meteorol Soc 78:1069–1079CrossRefGoogle Scholar
  29. Matei D, Pohnmann H, Jungclas J, Müller W, Haak H, Marotzke J (2012) Two tales of initializing decadal climate prediction experiments wit the echam5/mpi-om model. J Clim 25:8502–8522CrossRefGoogle Scholar
  30. McCabe G, Palecki M, Betancourt J (2004) Pacific and Atlantic Ocean influences on multidecadal drought frequency in the United States. Proc Natl Acad Sci 101(12):4136–4141CrossRefGoogle Scholar
  31. Meehl G, Teng H (2012) Case studies for the initialized decadal hindcasts and predictions for the pacific regions. Geophys Res Lett 39(L22):705. doi: 10.1029/2012GL053,423 Google Scholar
  32. Meehl G, Hu A, Arblaster J, Fasullo J, Trenberth K (2013) Externally forced and internally generated decadal climate variability associated with the interdecadal Pacific oscillation. J Clim. doi: 10.1175/JCLI-D-12-00,548.1
  33. Mochizuki T, Chikamoto Y, Kimoto M, Ishii M, Tatebe H, Komuro Y, Sakamoto TT, Watanabe M, Mori M (2012) Decadal prediction using a recent series of MIROC global climate models. J Meteorol Soc Jpn 90A:373–383Google Scholar
  34. Moss R et al (2010) The next generation of scenarios for climate change research and assessment. Nature 463:747–756CrossRefGoogle Scholar
  35. Munoz E, Kirtman B, Weijer W (2011) Varied representation of the atlantic meridional overturning across multidecadal ocean reanalysis. Deep Sea Res II 58:1848–1857CrossRefGoogle Scholar
  36. Neale R, Richter J, Park S, Lauritzen R, Vavrus S, Rasch P, Zhang M (2013) The mean climate of the Community Atmosphere Model (CAM4) in forced SST and fully coupled experiments. J Clim 26:5150–5168CrossRefGoogle Scholar
  37. Newman M (2013) An emperical benchmark for decadal forecasts of global surface temperature anomalies. J Clim 26:5260–5269CrossRefGoogle Scholar
  38. Penland C, Sardeshmukh P (1995) The optimal growth of tropical sea surface temperature anomalies. J Clim 8:1999–2024CrossRefGoogle Scholar
  39. Pohlmann H, Jungclaus J, Köho A, Stammer D, Marotzke J (2009) Initializing decadal climate predictions with the GECCO oceanic synthesis: effects on the North Atlantic. J Clim 22:3926–3938CrossRefGoogle Scholar
  40. Pulwarty R (2003) Climate and water in the West: science, information, and decision making. Water Resour 124:4–12Google Scholar
  41. Rayner N, Parker D, Horton E, Folland C, Alexander L, Rowell D, Kent E, Kaplan A (2003) Globally complete analyses of sea surface temperature, sea ice and night marine air temperature, 1871–2000. J Geophys Res 108(D14):4407. doi: 10.1029/2002JD002670 CrossRefGoogle Scholar
  42. Robson J, Sutton R, Lohmann K, Smith D (2012) Causes of the rapid warming of the North Atlantic Ocean in the mid-1990’s. J Clim 25:4116–4134CrossRefGoogle Scholar
  43. Ross S (2007) Introduction to probability theory. Elsevier, AmsterdamGoogle Scholar
  44. Seager R, Kushnir Y, Herweijer C, Naik N, Velez J (2005) Modeling of tropical forcing of persistent droughts and pluvials over western North America:1856–2000. J Clim 18:4065–4088CrossRefGoogle Scholar
  45. Smith D, Cusack S, Colman A, Holland C, Harris G, Murphy J (2007) Improved surface temperature prediction for the coming decade from a global climate model. Science 317:796–799CrossRefGoogle Scholar
  46. Smith D, Eade R, Dunstone N, Fereday D, Murphy J, Pohlmann H, Scaife A (2010) Skilful multi-year predictions of Atlantic hurricane frequency. Nature Geosci 3:846–849CrossRefGoogle Scholar
  47. Smith T, Reynolds R, Peterson T, Lawrimore J (2008) Improvements to NOAA’s historical merged land-ocean surface temperature analysis (1880–2006). J Clim 21:1601–1612Google Scholar
  48. Stan C, Kirtman B (2008) The influence of atmospheric noise and uncertainty in ocean initial conditions on the limit of predictability. J Clim 21:3487–3503CrossRefGoogle Scholar
  49. Sutton R, Hodson D (2005) Atlantic Ocean forcing of North American and European summer climate. Science 309:115–118CrossRefGoogle Scholar
  50. Taylor K, Stouffer R, Meehl G (2012) An overview of the CMIP5 and the experiment design. Bull Am Meteorol Soc 93. doi: 10.1175/BAMS-D-11-00,094.1
  51. Ting M, Kushnir Y, Seager R, Li C (2009) Forced and internal twentieth-century sst trends in the north Atlantic. J Clim 22:1469–1481CrossRefGoogle Scholar
  52. Troccoli A, Palmer T (2007) Ensemble decadal predictions from analyzed initial conditions. Phil Trans R Soc 365A:2179–2191CrossRefGoogle Scholar
  53. van Oldenborgh G, Doblas-Reyes F, Wouters B, Hazeleger W (2012) Skill in the trend and internal variability in a multi-model decadal prediction ensemble. Clim Dyn 38(7):1263–1280CrossRefGoogle Scholar
  54. Yasunaka S, Ishii M, Kimoto M, Mochizuki T, Shiogama H (2011) Influence of XBT temperature bias on decadal climate prediction with a coupled climate model. J Clim 24:5303–5308CrossRefGoogle Scholar
  55. Yeager S, Karspeck A, Danabasoglu G, Tribbia J, Teng H (2012) A decadal prediction case study: late 20th century North Atlantic Ocean heat content. J Clim 25:5173–5189CrossRefGoogle Scholar
  56. Zhang R, Delworth T (2006) Impact of Atlantic multidecadal oscillations on India/Sahel rainfall and Atlantic hurricanes. Geophys Res Lett 33(L17):712Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • A. Karspeck
    • 1
    Email author
  • S. Yeager
    • 1
  • G. Danabasoglu
    • 1
  • H. Teng
    • 1
  1. 1.National Center for Atmospheric ResearchBoulderUSA

Personalised recommendations