Advances in Atmospheric Sciences

, Volume 30, Issue 4, pp 1129–1142 | Cite as

Probabilistic multimodel ensemble prediction of decadal variability of East Asian surface air temperature based on IPCC-AR5 near-term climate simulations

  • Jia Wang (王 佳)
  • Xiefei Zhi (智协飞)
  • Yuwen Chen (陈钰文)
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Abstract

Based on near-term climate simulations for IPCC-AR5 (The Fifth Assessment Report), probabilistic multimodel ensemble prediction (PMME) of decadal variability of surface air temperature in East Asia (20°–50°N, 100°–145°E) was conducted using the multivariate Gaussian ensemble kernel dressing (GED) methodology. The ensemble system exhibited high performance in hindcasting the decadal (1981–2010) mean and trend of temperature anomalies with respect to 1961–90, with a RPS of 0.94 and 0.88 respectively. The interpretation of PMME for future decades (2006–35) over East Asia was made on the basis of the bivariate probability density of the mean and trend. The results showed that, under the RCP4.5 (Representative Concentration Pathway 4.5 W m−2) scenario, the annual mean temperature increases on average by about 1.1–1.2 K and the temperature trend reaches 0.6–0.7 K (30 yr)−1. The pattern for both quantities was found to be that the temperature increase will be less intense in the south. While the temperature increase in terms of the 30-yr mean was found to be virtually certain, the results for the 30-yr trend showed an almost 25% chance of a negative value. This indicated that, using a multimodel ensemble system, even if a longer-term warming exists for 2006–35 over East Asia, the trend for temperature may produce a negative value. Temperature was found to be more affected by seasonal variability, with the increase in temperature over East Asia more intense in autumn (mainly), faster in summer to the west of 115°E, and faster still in autumn to the east of 115°E.

Key words

decadal climate prediction PMME GED surface air temperature East Asia 
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References

  1. Doblas-Reyes, F. J., M. Deque, and J.-P. Piedelievre, 2000: Multimodel spread and probabilistic seasonal forecasts in PROVOST. Quart. J. Roy. Meteor. Soc., 126, 2069–2087.CrossRefGoogle Scholar
  2. Du, H., F. J. Doblas-Reyes, J. García-Serrano, V. Guemas, Y. Soufflet, and B. Wouters, 2012: Sensitivity of decadal predictions to the initial atmospheric and oceanic perturbations. Climate Dyn., 39(7–8), 2013–2023, doi: 10.1007/s00382-011-1285-9.CrossRefGoogle Scholar
  3. Frame, D. J., N. E. Faull, M. M. Joshi, and M. R. Allen, 2007: Probabilistic climate forecasts and inductive problems. Philosophical Transactions of the Royal Society, 365(1857), 1971–1992, doi: 10.1098/rsta.2007.2069.CrossRefGoogle Scholar
  4. Fritsch, J. M., J. Hilliker, J. Ross, and R. L. Vislocky, 2000: Model consensus. Wea. Forecasting, 15, 571–582.CrossRefGoogle Scholar
  5. Furtado, J. C., E. D. Lorenzo, N. Schneider, and N. A. Bond, 2011: North Pacific decadal variability and climate change in the IPCC AR4 models. J. Climate, 24, 3049–3067.CrossRefGoogle Scholar
  6. Greene, A. M., L. Goddard, and U. Lall, 2006: Robabilistic multimodel regional temperature change projections. J. Climate, 19, 4326–4343.CrossRefGoogle Scholar
  7. Hawkins, E., and R. Sutton, 2009: The potential to narrow uncertainty in regional climate predictions. Bull. Amer. Meteor. Soc., 90, 1095–1107, doi: http://dx.doi.org/10.1175/2009BAMS2607.1.CrossRefGoogle Scholar
  8. Hoerling, M., and Coauthors, 2011: On North American decadal climate for 2011–20. J. Climate, 24, 4519–4528, doi: http://dx.doi.org/10.1175/2011JCLI4137.1.CrossRefGoogle Scholar
  9. Honerkamp, J., 2002: Statistical physics: An Advanced Approach with Applications. 2nd ed. Springer, Berlin, 536.Google Scholar
  10. Hurrell, J., G. Meehl, D. Bader, T. Delworth, B. Kirtman, and B. Wielicki, 2009: A unified modeling approach to climate system prediction. Bull. Amer. Meteor. Soc., 90, 1819–1832.CrossRefGoogle Scholar
  11. Keenlyside, N., M. Latif, J. Jungclaus, E. Roeckner, V. Semenov, and W. Parket, 2008: Advancing decadalscale climate prediction in the North Atlantic sector. Nature, 453, 84–88.CrossRefGoogle Scholar
  12. Kharin, V. V., and F. W. Zwiers, 2002: Climate predictions with multimodel ensembles. J. Climate, 15, 793–799.CrossRefGoogle Scholar
  13. Krzysztofowicz, R., 1983: Why should a forecaster and a decision maker use Bayes theorem. Water Resour. Res., 19, 327–336.CrossRefGoogle Scholar
  14. Latif, M., and Coauthors, 2009: Dynamics of decadal climate variability and implication for its prediction. [Available on line at http://www.oceanobs09.net/blog/?p=104.]Google Scholar
  15. Meehl, G. A., and Coauthors, 2009: Decadal prediction: can it be skillful? Bull. Amer. Meteor. Soc., 90, 1467–1484.CrossRefGoogle Scholar
  16. Meehl, G. A., A. Hu, and C. Tebaldi, 2010: Decadal prediction in the Pacific region. J. Climate, 23, 2959–2973, doi: http://dx.doi.org/10.1175/2010JCLI3296.1.CrossRefGoogle Scholar
  17. Min, Y. M., V. N. Kryjov, and C. K. Park, 2009: A probabilistic multimodel ensemble approach to seasonal prediction.Wea. Forecasting, 24, 812–828, doi: http://dx.doi.org/10.1175/2008WAF2222140.1.CrossRefGoogle Scholar
  18. Mochizuki, and Coauthors, 2010: Pacific decadal oscillation hindcasts relevant to near-term climate prediction. Proc. the National Academy of Sciences, 107, 1833–1837.CrossRefGoogle Scholar
  19. Murphy, A. H., 1973: A new vector partition of the probability score. J. Appl. Meteor., 12, 595–600.CrossRefGoogle Scholar
  20. Palmer, T. N., and Coauthors, 2004: Development of a European Multimodel Ensemble System for Seasonal to Interannual prediction (DEMETER). Bull. Amer. Meteor. Soc., 85, 853–872.CrossRefGoogle Scholar
  21. Peng, P., A. Kumar, H. van den Dool, and A. G. Barnston, 2002: An analysis of multimodel ensemble predictions for seasonal climate anomalies. J. Geophys. Res., 107, 4710, doi: 10.1029/2002JD002712.CrossRefGoogle Scholar
  22. Raftery, A. E., T. Gneiting, F. Balabdaoui, and M. Polakowski, 2005: Using Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev., 133(5), 1155–1174.CrossRefGoogle Scholar
  23. Räisänen, J., and L. Ruokolainen, 2006: Probabilistic forecasts of near-term climate change based on a resampling ensemble technique. Tellus, 58A, 461–472.Google Scholar
  24. Silverman, B. W., 1986: Density Estimation for Statistics and Data Analysis. Chapman and Hall/CRC, London/Boca Raton, 75–93.Google Scholar
  25. Smith, D., S. Cusack, A. Colman, C. Folland, G. Harris, and J. Murphy, 2007: Improved surface temperature prediction for the coming decade from a global circulation model. Science, 317, 796–799.CrossRefGoogle Scholar
  26. Stephenson, D. B., and F. J. Doblas-Reyes, 2000: Statistical methods for interpreting Monte Carlo ensemble forecasts. Tellus A, 52, 300–322.CrossRefGoogle Scholar
  27. Sugiura, N., T. Awaji, S. Masuda, T. Toyoda, H. Igarashi, Y. Ishikawa, M. Ishii, and M. Kimoto, 2009: Potential for decadal predictability in the North Pacific region. Geophys. Res. Lett., 36, L20701, doi: 10.1029/2009GL039787.CrossRefGoogle Scholar
  28. Taylor, K. E., R. J. Stouffer, and G. A. Meehl, cited 2009: A Summary of the CMIP5 Experiment Design. [Available on line at http://cmip-pcmdi.llnl.gov/cmip5/docs/TaylorCMIP5 design.pdf.]Google Scholar
  29. Thompson, J. C., 1962: Economic gains from scientific advances and operational improvements in meteorological prediction. J. Appl. Meteor., 1, 13–17.CrossRefGoogle Scholar
  30. Tippett, M. K., and A. G. Barnston, 2008: Skill of Multimodel ENSO Probability Forecasts. Mon. Wea. Rev., 136(10), 3933–3946.CrossRefGoogle Scholar
  31. Wu, B., and T. J. Zhou, 2011: Prediction of decadal variability of sea surface temperature by a coupled global climate model FGOALS gl developed in LASG/IAP. Chinese Science Bulletin, 57 (19), 2453–2459, doi: 10.1007/s11434-012-5134-y.Google Scholar
  32. Yoshimitsu, C., and Coauthors, 2012: An overview of decadal climate predictability in a multi-model ensemble by climate model MIROC. Climate Dyn., 23, 1–22, doi: 10.1007/s00382-012-1351-y.Google Scholar
  33. Zhi, X. F., Q. Wu, Y. Q. Bai, and H. X. Qi, 2010: The multimodel superensemble prediction of the surface temperature using the IPCC AR4 scenario runs. Scientia Meteorologica Sinica, 30(5), 708–714. (in Chinese)Google Scholar

Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jia Wang (王 佳)
    • 1
    • 2
  • Xiefei Zhi (智协飞)
    • 1
  • Yuwen Chen (陈钰文)
    • 2
  1. 1.Key Laboratory of Meteorological Disaster of Ministry of EducationNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Climate Center of Jiangsu ProvinceNanjingChina

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