An evaluation of decadal probability forecasts from state-of-the-art climate models

Journal of Climate

Volumn 26, Issue 23, 2013, Pages 9334-9347

  Suckling, Emma B ^a   Smith, Leonard A ^a  

^a LONDON SCHOOL OF ECONOMICS   (United Kingdom)

Author keywords

Ensembles; Forecast verification skill; Hindcasts; Probability forecasts models distribution; Statistical forecasting; Time series

Indexed keywords

ENSEMBLES; FORECAST VERIFICATION/SKILL; HINDCASTS; PROBABILITY FORECASTS/MODELS/DISTRIBUTION; STATISTICAL FORECASTING;

CLIMATE CHANGE; COMPUTER SIMULATION; DECISION SUPPORT SYSTEMS; DIELECTRIC PROPERTIES; EARTH (PLANET); FORECASTING; MODELS; TIME SERIES;

CLIMATE MODELS;

CLIMATE MODELING; CLIMATOLOGY; DECADAL VARIATION; ENSEMBLE FORECASTING; HINDCASTING; PROBABILITY; TIME SERIES ANALYSIS;

EID: 84888049213     PISSN: 08948755     EISSN: None     Source Type: Journal    
DOI: 10.1175/JCLI-D-12-00485.1     Document Type: Article

References (51)

1. Alessandri, A., A. Borrelli, A. Navarra, A. Arribas, M. Deque, P. Rogel, and A. Weisheimer, 2011: Evaluation of probabilistic quality and value of the ensembles multimodel seasonal forecasts: Comparison with DEMETER. Mon. Wea. Rev., 139, 581-607.
2. Anderson, J., H. Van den Dool, A. Barnston, W. Chen, W. Stern, and J. Ploshay, 1999: Present-day capabilities of numerical and statistical models for atmospheric extratropical seasonal simulation and prediction. Bull. Amer. Meteor. Soc., 80, 1349-1361.
3. Barnston, A. G., and Coauthors, 1994: Long-lead seasonal forecasts-Where do we stand? Bull. Amer. Meteor. Soc., 75, 2097-2114.
4. Binter, R., 2012: Applied probabilistic forecasting. Ph.D. thesis, London School of Economics and Political Science, 276 pp.
5. Bröcker, J., and L. A. Smith, 2006: Scoring probabilistic forecasts: The importance of being proper. Wea. Forecasting, 22, 382-388.
6. Bröcker, J., and L. A. Smith, 2008: From ensemble forecasts to predictive distributions. Tellus, 60A, 663-678.
7. Colman, A., and M. Davey, 2003: Statistical prediction of global sea-surface temperature anomalies. Int. J. Climatol., 23, 1677-1697.
8. Doblas-Reyes, F. J., A. Weisheimer, T. N. Palmer, J. M. Murphy, and D. Smith, 2010: Forecast quality assessment of the ensembles seasonal-to-decadal stream 2 hindcasts. ECMWF Tech. Memo. 621, 45 pp.
9. Doblas-Reyes, F. J., M. A. Balmaseda, A. Weisheimer, and T. N. Palmer, 2011: Decadal climate prediction with the European Centre for Medium-Range Weather Forecasts coupled forecast system: Impact of ocean observations. J. Geophys. Res., 116, D19111, doi:10.1029/2010JD015394.
10. Fildes, R., and N. Kourentzes, 2011: Validation and forecasting accuracy in models of climate change. Int. J. Forecasting, 27, 968-995.
11. Giorgi, F., 2002: Variability and trends of sub-continental scale surface climate in the twentieth century. Part I: Observations. Climate Dyn., 18, 675-691.
12. Good, I. J., 1952: Rational decisions. J. Roy. Stat. Soc., 14A, 107-114.
13. Hagedorn, R., and L. A. Smith, 2009: Communicating the value of probabilistic forecasts with weather roulette. Meteor. Appl., 16, 143-155.
14. Hagedorn, R., F. J. Doblas-Reyes, and T. N. Palmer, 2005: The rationale behind the success of multi-model ensembles in seasonal forecasting. Part I: Basic concept. Tellus, 57A, 219-233.
15. Hastie, T. J., R. Tibshirani, and J. Friedman, 2001: The Elements of Statistical Learning. Springer, 533 pp.
16. Hawkins, E., J. Robson, R. Sutton, D. Smith, and N. Keenlyside, 2011: Evaluating the potential for statistical decadal predictions of sea surface temperature with a perfect model approach. Climate Dyn., 37, 2495-2509.
17. Hewitt, C. D., and D. J. Griggs, 2004: Ensembles-based predictions of climate and their impacts. Eos, Trans. Amer. Geophys. Union, 85, 566, doi:10.1029/2004EO520005.
18. Hoeting, J. A., D. Madigan, A. E. Raftery, and C. T. Volinsky, 1999: Bayesian model averaging: A tutorial. Stat. Sci., 14, 382-417.
19. Jolliffe, I. T., and D. B. Stephenson, 2003: Forecast Verification: A Practitioner's Guide in Atmospheric Science. John Wiley and Sons, 254 pp.
20. Keenlyide, N. S., M. Latif, M. Botzet, J. Jungclaus, and U. Schulzweida, 2005: A coupled method for initializing El Niño Southern Oscillation forecasts using sea surface temperature. Tellus, 57A, 340-356.
21. Keenlyide, N. S., M. Latif, J. Jungclaus, L. Kornblueh, and E. Roeckner, 2008: Advancing decadal-scale climate prediction in the North Atlantic sector. Nature, 453, 84-88.
22. Kim, H.-M., P. J. Webster, and J. A. Curry, 2012: Evaluation of short-term climate change prediction in multi-model CMIP5 decadal hindcasts. Geophys. Res. Lett., 39, L10701, doi:10.1029/2012GL051644.
23. Krueger, O., and J.-S. von Storch, 2011: A simple empirical model for decadal prediction. J. Climate, 24, 1276-1283.
24. Laepple, T., S. Jewson, and K. Coughlin, 2008: Interannual temperature predictions using the CMIP3 multi-model ensemble mean. Geophys. Res. Lett., 35, L10701, doi:10.1029/2008GL033576.
25. Lee, T. C. K., F. W. Zwiers, X. Zhang, and M. Tsao, 2006: Evidence of decadal climate prediction skill resulting from changes in anthropogenic forcing. J. Climate, 19, 5305-5318.
26. Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20, 130-141.
27. Meehl, G. A., and Coauthors, 2009: Decadal prediction: Can it be skillful? Bull. Amer. Meteor. Soc., 90, 1467-1485.
28. Met Office, 2011: 3-month outlook for UK contingency planning. Met Office Rep., 12 pp. [Available online at http://www.metoffice.gov.uk/media/pdf/g/o/3-month_Outlook_user_guidance-150.pdf.]
29. Paparella, F., A. Provenzale, L. A. Smith, C. Taricco, and R. Vio, 1997: Local random analogue prediction of nonlinear processes. Phys. Lett., 235A, 233-240.
30. Pierce, D. W., T. P. Barnett, R. Tokmakian, A. Semtner, M. Maltrud, J. A. Lysne, and A. Craig, 2004: The ACPI project, element 1: Initialising a coupled climate model from observed conditions. Climatic Change, 62, 13-28.
31. Pohlmann, H., J. H. Köhl, D. Stammer, and J. Marotzke, 2009: Initializing decadal climate predictions with GECCO oceanic synthesis: Effects on the North Atlantic. J. Climate, 22, 3926-3938.
32. Reifen, C., and R. Toumi, 2009: Climate projections: Past performance no guarantee of future skill? Geophys. Res. Lett., 36, L13704, doi:10.1029/2009GL038082.
33. Smith, D. M., S. Cusack, A. W. Colman, C. K. Folland, G. R. Harris, and J. M. Murphy, 2007: Improved surface temperature prediction for the coming decade from a global climate model. Science, 317, 796-799.
34. Smith, D. M., R. Eade, N. J. Dunstone, D. Fereday, J. M. Murphy, H. Pohlmann, and A. A. Scaife, 2010: Skilful multi-year predictions of Atlantic hurricane frequency. Nat. Geosci., 3, 846-849.
35. Smith, L. A., 1992: Identification and prediction of low-dimensional dynamics. Physica D, 58 (1-4), 50-76.
36. Smith, L. A., 1994: Local optimal prediction: Exploiting strangeness and the variation of sensitivity to initial condition. Philos. Trans. Roy. Soc., 348A, 371-381.
37. Smith, L. A., 1997: The maintenance of uncertainty. Proc. 133rd Int. School of Physics "Enrico Fermi" Course, Varenna, Italy, Società Italiana di Fisica, 177-246.
38. Smith, L. A., 2002: What might we learn from climate forecasts? Proc. Natl. Acad. Sci. USA, 4, 2487-2492.
39. Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. Averyt, M. Tignor, and H. L. Miller Jr., Eds., 2007: Climate Change 2007: The Physical Science Basis. Cambridge University Press, 996 pp.
40. Stockdale, T. N., 1997: Coupled ocean-atmosphere forecasts in the presence of climate drift. Mon. Wea. Rev., 125, 809-818.
41. Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2009: A summary of the CMIP5 experimental design. CMIP5 Rep., 33 pp. [Available online at http://cmip-pcmdi.llnl.gov/cmip5/docs/Taylor_CMIP5_design.pdf.]
42. Tennekes, H., A. P. M. Baede, and J. D. Opsteegh, 1987: Forecasting forecast skill. Proc. Workshop on Predictability, Reading, United Kingdom, ECMWF, 277-302.
43. Troccoli, A., and T. N. Palmer, 2007: Ensemble decadal predictions from analysed initial conditions. Philos. Trans. Roy. Soc., 365A, 2179-2191.
44. Unger, D., H. Van den Dool, E. O'Lenic, and D. Collins, 2009: Ensemble regression. Mon. Wea. Rev., 137, 2365-2379.
45. Van den Dool, H. M., 1994: Long-range weather forecasts through numerical and empirical methods. Dyn. Atmos. Oceans, 20, 247-270.
46. Van den Dool, H. M., 2007: Empirical Methods in Short-Term Climate Prediction. Oxford University Press, 240 pp.
47. van Oldenborgh, G. J., M. Balmaseda, L. Ferranti, T. Stockdale, and D. Anderson, 2005: Evaluation of atmospheric fields from the ECMWF seasonal forecasts over a 15-year period. J. Climate, 18, 3250-3269.
48. van Oldenborgh, G. J., F. J. Doblas-Reyes, B. Wouters, and W. Hazeleger, 2012: Decadal prediction skill in a multi-model ensemble. Climate Dyn., 38 (7-8), 1263-1280.
49. Weigela, A. P., and N. E. Bowlerb, 2009: Can multi-model combination really enhance prediction skill of probabilistic ensemble forecasts? Quart. J. Roy. Meteor. Soc., 135, 535-539.
50. Weisheimer, A., and Coauthors, 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.
51. Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. Academic Press, 704 pp.