Advertisement

Climate Dynamics

, Volume 37, Issue 11–12, pp 2495–2509 | Cite as

Evaluating the potential for statistical decadal predictions of sea surface temperatures with a perfect model approach

  • Ed HawkinsEmail author
  • Jon Robson
  • Rowan Sutton
  • Doug Smith
  • Noel Keenlyside
Article

Abstract

We explore the potential for making statistical decadal predictions of sea surface temperatures (SSTs) in a perfect model analysis, with a focus on the Atlantic basin. Various statistical methods (Lagged correlations, Linear Inverse Modelling and Constructed Analogue) are found to have significant skill in predicting the internal variability of Atlantic SSTs for up to a decade ahead in control integrations of two different global climate models (GCMs), namely HadCM3 and HadGEM1. Statistical methods which consider non-local information tend to perform best, but which is the most successful statistical method depends on the region considered, GCM data used and prediction lead time. However, the Constructed Analogue method tends to have the highest skill at longer lead times. Importantly, the regions of greatest prediction skill can be very different to regions identified as potentially predictable from variance explained arguments. This finding suggests that significant local decadal variability is not necessarily a prerequisite for skillful decadal predictions, and that the statistical methods are capturing some of the dynamics of low-frequency SST evolution. In particular, using data from HadGEM1, significant skill at lead times of 6–10 years is found in the tropical North Atlantic, a region with relatively little decadal variability compared to interannual variability. This skill appears to come from reconstructing the SSTs in the far north Atlantic, suggesting that the more northern latitudes are optimal for SST observations to improve predictions. We additionally explore whether adding sub-surface temperature data improves these decadal statistical predictions, and find that, again, it depends on the region, prediction lead time and GCM data used. Overall, we argue that the estimated prediction skill motivates the further development of statistical decadal predictions of SSTs as a benchmark for current and future GCM-based decadal climate predictions.

Keywords

Decadal prediction Atlantic SSTs Statistical methods Predictability 

Notes

Acknowledgments

We thank Chun-Kit Ho, Fiona Underwood, Len Shaffrey, Dan Hodson and Manoj Joshi for useful suggestions. The two anonymous reviewers provided valuable comments which helped improve the paper. The authors are supported by NCAS-Climate (EH, JR, RS) and the NERC VALOR project (JR). The research leading to these results has received funding (EH, NK) from the European Community’s 7th framework programme (FP7/2007–2013) under grant agreement No. GA212643 (THOR: ‘Thermohaline Overturning—at Risk’, 2008–2012).

Supplementary material

382_2011_1023_MOESM1_ESM.pdf (4.8 mb)
PDF (4931 KB)

References

  1. Arnell NW, Delaney EK (2006) Adapting to climate change: public water supply in England and Wales. Clim Change 78:227–255. doi: 10.1007/s10584-006-9067-9 CrossRefGoogle Scholar
  2. Barnston AG, van den Dool HM, Rodenhuis DR, Ropelewski CR, Kousky VE, O’Lenic EA, Livezey RE, Zebiak SE, Cane MA, Barnett TP, Graham NE, Ji M, Leetmaa A (1994) Long-lead seasonal forecasts? Where do we stand? Bull Am Meteorol Soc 75:2097–2114CrossRefGoogle Scholar
  3. Biastoch A, Boning CW, Lutjeharms JRE (2008) Agulhas leakage dynamics affects decadal variability in Atlantic overturning circulation. Nature 456:489–492. doi: 10.1038/nature07426 Google Scholar
  4. Boer GJ (2004) Long time-scale potential predictability in an ensemble of coupled climate models. Clim Dyn 23:29–44. doi: 10.1007/s00382-004-0419-8 CrossRefGoogle Scholar
  5. Boer GJ, Lambert SJ (2008) Multi-model decadal potential predictability of precipitation and temperature. Geophys Res Lett 35:L05706. doi: 10.1029/2008GL033234 CrossRefGoogle Scholar
  6. Collins M, Botzet M, Carril AF, Drange H, Jouzeau A, Latif M, Masina S, Otteraa OH, Pohlmann H, Sorteberg A, Sutton R, Terray L (2006) Interannual to decadal climate predictability in the North Atlantic: a multimodel-ensemble study. J Clim 19:1195–1202. doi: 10.1175/JCLI3654.1 CrossRefGoogle Scholar
  7. Collins M, Sinha B (2003) Predictability of decadal variations in the thermohaline circulation and climate. Geophys Res Lett 30:1306. doi: 10.1029/2002GL016504 CrossRefGoogle Scholar
  8. Colman A, Davey M (1999) Prediction of summer temperature, rainfall and pressure in Europe from preceding winter North Atlantic Ocean temperature. Int J Clim 19:513–536CrossRefGoogle Scholar
  9. Colman A, Davey M (2003) Statistical prediction of global sea-surface temperature anomalies. Int J Clim 23:1677–1697. doi: 10.1002/joc.956 CrossRefGoogle Scholar
  10. Delworth TL, Mann ME (2000) Observed and simulated multi-decadal variability in the Northern Hemisphere. Clim Dyn 16:661–676. doi: 10.1007/s003820000075 CrossRefGoogle Scholar
  11. Dong B, Sutton RT (2005) Mechanism of interdecadal thermohaline circulation variability in a coupled ocean-atmosphere GCM. J Clim 18:1117–1135CrossRefGoogle Scholar
  12. Dunstone NJ, Smith DM (2010) Impact of atmosphere and sub-surface ocean data on decadal climate prediction. Geophys Res Lett 37:L02709. doi: 10.1029/2009GL041609 CrossRefGoogle Scholar
  13. Emanuel K (2005) Increasing destructiveness of tropical cyclones over the past 30 years. Nature 436:686–688. doi: 10.1038/nature03906 Google Scholar
  14. Folland CK, Palmer TN, Parker DE (1986) Sahel rainfall and worldwide sea temperatures, 1901–85. Nature 320:602–607. doi: 10.1038/320602a0 Google Scholar
  15. Goldenberg SB, Landsea CW, Mestas-Nuñez AM, Gray WM (2001) The recent increase in Atlantic hurricane activity: causes and implications. Science 293:474–479. doi: 10.1126/science.1060040 Google Scholar
  16. Gordon C, Cooper C, Senior CA, Banks H, Gregory JM, Johns TC, Mitchell JFB, Wood RA (2000) The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Clim Dyn 16:147–168CrossRefGoogle Scholar
  17. Griffies SM, Bryan K (1997) A predictability study of simulated North Atlantic multidecadal variability. Clim Dyn 13:459–487CrossRefGoogle Scholar
  18. Haarsma RJ, Campos EJD, Drijfhout S, Hazeleger W, Severijns C (2011) Impacts of interruption of the Agulhas leakage on the tropical Atlantic in coupled ocean-atmosphere simulations. Clim Dyn (in press). doi: 10.1007/s00382-009-0692-7
  19. Hawkins E, Sutton R (2007) Variability of the Atlantic thermohaline circulation described by three-dimensional empirical orthogonal functions. Clim Dyn 29:745–762. doi: 10.1007/s00382-007-0263-8 CrossRefGoogle Scholar
  20. Hawkins E, Sutton R (2009a) Decadal predictability of the Atlantic Ocean in a coupled GCM: estimation of optimal perturbations using linear inverse modelling. J Clim 22:3960–3978. doi: 10.1175/2009JCLI2720.1 CrossRefGoogle Scholar
  21. Hawkins E, Sutton R (2009b) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteorol Soc 90:1095–1107. doi: 10.1175/2009BAMS2607.1 CrossRefGoogle Scholar
  22. Hodson DLR, Sutton RT (2011) The impact of model resolution on MOC adjustment in a coupled climate model. Clim Dyn (in prep)Google Scholar
  23. Jewson S, Hawkins E (2009) Improving the expected accuracy of forecasts of future climate using a simple bias-variance tradeoff available from http://arxiv.org/abs/0911.1904
  24. Johns TC, Durman CF, Banks HT, Roberts MJ, McLaren AJ, Ridley JK, Senior CA, Williams KD, Jones A, Rickard GJ, Cusack S, Ingram WJ, Crucifix M, Sexton DMH, Joshi MM, Dong BW, Spencer H, Hill RSR, Gregory JM, Keen AB, Pardaens AK, Lowe JA, Bodas-Salcedo A, Stark S, Searl Y (2006) The New Hadley centre climate model (HadGEM1): evaluation of coupled simulations. J Clim 19:1327–1353. doi: 10.1175/JCLI3712.1 CrossRefGoogle Scholar
  25. Johnson HL, Marshall DP (2002) A theory for the surface Atlantic response to thermohaline variability. J Phys Ocean 32:1121–1132CrossRefGoogle Scholar
  26. Keenlyside NS, Latif M, Jungclaus J, Kornblueh L, Roeckner E (2008) Advancing decadal-scale climate prediction in the North Atlantic sector. Nature 453:84–88. doi: 10.1038/nature06921 Google Scholar
  27. Laepple T, Jewson S, Coughlin K (2008) Interannual temperature predictions using the CMIP3 multi-model ensemble mean. Geophys Res Lett 35:L10701. doi: 10.1029/2008GL033576 CrossRefGoogle Scholar
  28. Lean JL, Rind DH (2009) How will Earth’s surface temperature change in future decades? Geophys Res Lett 36:L15708. doi: 10.1029/2009GL038932 CrossRefGoogle Scholar
  29. Lee TCK, Zwiers FW, Zhang X, Tsao M (2006) Evidence of decadal climate prediction skill resulting from changes in anthropogenic forcing. J Clim 19:5305–5318. doi: 10.1175/JCLI3912.1 CrossRefGoogle Scholar
  30. Liu G, Matrosova LE, Penland C, Gledhill DK, Eakin C, Webb RS, Christensen TRL, Heron SF, Morgan JA, Skirving WJ, Strong AE (2009) NOAA coral reef watch coral bleaching outlook system in proceedings of the 11th international coral reef symposium, Ft. Lauderdale, Florida, pp 951–955Google Scholar
  31. Lorenz EN (1973) On the existence of extended range predictability. J Atmos Sci 12:543–546Google Scholar
  32. Meehl GA, Goddard L, Murphy J, Stouffer RJ, Boer G, Danabasoglu G, Dixon K, Giorgetta MA, Greene AM, Hawkins E, Hegerl G, Karoly D, Keenlyside N, Kimoto M, Kirtman B, Navarra A, Pulwarty R, Smith D, Stammer D, Stockdale T (2009) Decadal prediction: can it be skillful. Bull Am Meteorol Soc 90:1467–1485. doi: 10.1175/2009BAMS2607.1 CrossRefGoogle Scholar
  33. Meehl GA, Stocker TF, Collins W, Friedlingstein P, Gaye AT, Gregory JM, Kitoh A, Knutti R, Murphy JM, Noda A, Raper SCB, Watterson IG, Weaver AJ, Zhao ZC (2007) Global climate projections. In: Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  34. Michaels A, Close A, Malmquist D, Knap A (1997) Climate science and insurance risk. Nature 389:225–227. doi: 10.1038/38378 Google Scholar
  35. Minobe S, Maeda A (2005) A 1° monthly gridded sea-surface temperature dataset compiled from ICOADS from 1850 to 2002 and Northern Hemisphere frontal variability. Int J Climatol 25:881–894. doi: 10.1002/joc.1170 CrossRefGoogle Scholar
  36. Newman M, Alexander MA, Scott JD (2010) An empirical model of tropical ocean dynamics. Clim Dyn (submitted)Google Scholar
  37. Penland C, Magorian T (1993) Prediction of Niño 3 sea surface temperatures using linear inverse modelling. J Clim 6:1067–1076CrossRefGoogle Scholar
  38. Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res 108:4407. doi: 10.1029/2002JD002670 CrossRefGoogle Scholar
  39. Robson JI (2010) Understanding the performance of a decadal prediction system. Ph.D. thesis, University of Reading, UKGoogle Scholar
  40. Saha S, Nadiga S, Thiaw C, Wang J, Wang W, Zhang Q, Vanden Dool HM, Pan HL, Moorthi S, Behringer D, Stokes D, Peña M, Lord S, White G, Ebisuzaki W, Peng P, Xie P (2006) The NCEP climate forecast system. J Clim 19(15):3483–3517. doi: 10.1175/JCLI3812.1 CrossRefGoogle Scholar
  41. Scaife A, Kucharski F, Folland C, Kinter J, Bronnimann S, Fereday D, Fischer A, Grainger S, Jin E, Kang I, Knight J, Kusunoki S, Lau N, Nath M, Nakaegawa T, Pegion P, Schubert S, Sporyshev P, Syktus J, Yoon J, Zeng N, Zhou T (2009) The CLIVAR C20C project: selected twentieth century climate events. Clim Dyn 33:603–614. doi: 10.1007/s00382-008-0451-1 CrossRefGoogle Scholar
  42. Shaffrey LC, Stevens I, Norton WA, Roberts MJ, Vidale PL, Harle JD, Jrrar A, Stevens DP, Woodage MJ, Demory ME, Donners J, Clark DB, Clayton A, Cole JW, Wilson SS, Connolley WM, Davies TM, Iwi AM, Johns TC, King JC, New AL, Slingo JM, Slingo A, Steenman-Clark L, Martin GM (2009) U.K. HiGEM: the new U.K. high-resolution global environment model: model description and basic evaluation. J Clim 22:1861–1896. doi: 10.1175/2008JCLI2508.1 CrossRefGoogle Scholar
  43. Smith DM, Cusack S, Colman AW, Folland CK, Harris GR, Murphy JM (2007) Improved surface temperature prediction for the coming decade from a global climate model. Science 317:796–799. doi: 10.1126/science.1139540 Google Scholar
  44. Smith DM, Eade R, Dunstone NJ, Fereday D, Murphy JM, Pohlmann H, Scaife A (2010) Skilful multi-year predictions of Atlantic hurricane frequency. Nature Geosci 3:846–849. doi: 10.1038/ngeo1004 Google Scholar
  45. Sutton RT, Hodson DLR (2005) Atlantic Ocean forcing of North American and European summer climate. Science 309:115–118. doi: 10.1126/science.1109496 Google Scholar
  46. Tziperman E, Zanna L, Penland C (2008) Non-normal thermohaline circulation dynamics in a coupled ocean-atmosphere GCM. J Phys Ocean 38:588–604. doi: 10.1175/2007JPO3769.1 CrossRefGoogle Scholar
  47. van den Dool H (1994) Searching for analogues, how long must we wait. Tellus 46A:314–324Google Scholar
  48. van den Dool H (2007) Empirical methods in short-term climate prediction. Oxford University Press, OxfordGoogle Scholar
  49. van den Dool H, Huang J, Fan Y (2003) Performance and analysis of the constructed analogue method applied to U.S. soil moisture over 1981–2001. J Geophys Res 108:8617. doi: 10.1029/2002JD003114 CrossRefGoogle Scholar
  50. Vecchi GA, Swanson KL, Soden BJ (2008) Whither hurricane activity. Science 322:687–689. doi: 10.1126/science.1164396 Google Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Ed Hawkins
    • 1
  • Jon Robson
    • 1
  • Rowan Sutton
    • 1
  • Doug Smith
    • 2
  • Noel Keenlyside
    • 3
  1. 1.NCAS-Climate, Department of MeteorologyUniversity of ReadingReadingUK
  2. 2.Met Office Hadley CentreExeterUK
  3. 3.IFM-GEOMARKielGermany

Personalised recommendations