We are grateful to the institutions participating in the APCC multimodel ensemble operational system for providing the hindcast experiment data. Our special thanks are addressed to three anonymous reviewers whose very useful comments and recommendations helped us to considerably improve the paper. We are very thankful to Dr. Peter Mayes of the New Jersey Department of Environmental Protection for his kind help in editing of the manuscript. This research has been supported by the Korea Meteorological Administration (KMA).
Atger, F., 2003: Spatial and interannual variability of the reliability of ensemble-based probabilistic forecasts: Consequences for calibration. Mon. Wea. Rev., 131 , 1509–1523.
Atger, F., 2004: Estimation of the reliability of ensemble based probabilistic forecasts. Quart. J. Roy. Meteor. Soc., 130 , 627–646.
Barnston, A. G., , Mason S. , , Goddard L. , , DeWitt D. G. , , and Zebiak S. E. , 2003: Increased automation and use of multimodel ensembling in seasonal climate forecasting at the IRI. Bull. Amer. Meteor. Soc., 84 , 1783–1796.
Doblas-Reyes, F. J., , Déqué M. , , and Piedeliérem J-P. , 2000: Multi-model spread and probabilistic seasonal forecasts in PROVOST. Quart. J. Roy. Meteor. Soc., 126 , 2069–2088.
Doblas-Reyes, F. J., , Hagedorn R. , , and Palmer T. N. , 2005: The rationale behind the success of multi-model ensembles in seasonal forecasting —II. Calibration and combination. Tellus, 57A , 234–252.
Fritsch, J. M., , Hilliker J. , , Ross J. , , and Vislocky R. L. , 2000: Model consensus. Wea. Forecasting, 15 , 571–582.
Hagedorn, R., , Doblas-Reyes F. J. , , and Palmer T. N. , 2005: The rationale behind the success of multi-model ensembles in seasonal forecasting—I. Basic concept. Tellus, 57A , 219–233.
Kanamitsu, M., , Ebisuzaki W. , , Woollen J. , , Yang S. K. , , Hnilo J. J. , , Fkorino M. , , and Potter G. , 2002: NCEP–DOC AMIP-II reanalysis. Bull. Amer. Meteor. Soc., 83 , 1631–1643.
Kharin, V. V., , and Zwiers F. W. , 2001: Skill as function of time scale in ensemble of seasonal hindcast. Climate Dyn., 17 , 127–141.
Kharin, V. V., , and Zwiers F. W. , 2002: Climate predictions with multimodel ensembles. J. Climate, 15 , 793–799.
Kharin, V. V., , and Zwiers F. W. , 2003: Improved seasonal probability forecast. J. Climate, 16 , 1684–1701.
Krzysztofowicz, R., 1983: Why should a forecaster and a decision maker use Bayes theorem. Water Resour. Res., 19 , 327–336.
Leith, C. E., 1973: The standard error of time-average estimates of climatic means. J. Appl. Meteor., 12 , 1066–1069.
Mason, S. J., , and Graham N. E. , 1999: Conditional probabilities, relative operating characteristics, and relative operating levels. Wea. Forecasting, 14 , 713–725.
Mason, S. J., , Goddard L. , , Graham N. E. , , Yulaeva E. , , Sun L. , , and Arkin P. A. , 1999: The IRI Seasonal Climate Prediction System and the 1997/98 El Niño event. Bull. Amer. Meteor. Soc., 80 , 1853–1873.
Murphy, A. H., 1977: The value of climatological, categorical, and probabilistic forecasts in the cost–loss ratio situation. Mon. Wea. Rev., 105 , 803–816.
Murphy, A. H., , and Winkler R. L. , 1977: Reliability of subjective probability forecasts of precipitation and temperature. Appl. Stat., 26 , 61–78.
Murphy, A. H., , and Winkler R. L. , 1987: A general framework for forecast verification. Mon. Wea. Rev., 115 , 1330–1338.
Palmer, T. N., , Brankovic C. , , and Richardson D. S. , 2000: A probability and decision-model analysis of PROVOST seasonal multi-model ensemble integrations. Quart. J. Roy. Meteor. Soc., 126 , 2013–2034.
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.
Peng, P., , Kumar A. , , Barnston A. G. , , and Goddard L. , 2000: Simulation skills of the SST-forced global climate variability of the NCEP–MRF9 and Scripps–MPI ECHAM3 models. J. Climate, 13 , 3657–3679.
Peng, P., , Kumar A. , , Van den Dool A. H. , , and Barnston A. G. , 2002: An analysis of multimodel ensemble predictions for seasonal climate anomalies. J. Geophys. Res., 107 , 4710. doi:10.1029/2002JD002712.
Robertson, A. W., , Lall U. , , Zebiak S. E. , , and Goddard L. , 2004: Improved combination of multiple atmospheric GCM ensembles for seasonal prediction. Mon. Wea. Rev., 132 , 2732–2744.
Stephenson, D. B., , and Doblas-Reyes F. J. , 2000: Statistical methods for interpreting Monte Carlo ensemble forecasts. Tellus, 52A , 300–322.
Taylor, J. R., 1997: An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements. 2nd ed. University Science Books, 327 pp.
Thompson, J. C., 1962: Economic gains from scientific advances and operational improvement sin meteorological prediction. J. Appl. Meteor., 1 , 13–17.
Tracton, M. S., , and Kalnay E. , 1993: Operational ensemble prediction at the National Meteorological Center: Practical aspects. Wea. Forecasting, 8 , 379–398.
Van den Dool, H., , and Toth Z. , 1991: Why do forecasts for “near normal” often fail? Wea. Forecasting, 6 , 76–85.
Vislocky, R. L., , and Fritsch J. M. , 1995: Improved model output statistics forecasts through model consensus. Bull. Amer. Meteor. Soc., 76 , 1157–1164.
Weigel, A. P., , Liniger M. A. , , and Appenzeller C. , 2008: Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts? Quart. J. Roy. Meteor. Soc., 134 , 241–260.
WMO, 2002: Standardised Verification System (SVS) for Long-Range Forecasts (LRF). New attachment II-9 to the manual on the GDPS. Vol. 1. WMO-No. 485, 24 pp.
WMO, cited. 2007: Report of WMO/KMA Workshop of Global Producing Centres on Lead Centre for Long-Range Forecast Multi-Model Ensemble Prediction. [Available online at http://www.wmo.int/pages/prog/www/DPFS/Reports/Wshop-LCLRFMME_Busan.doc].
WMO, cited. 2008: Meeting of the Expert Team on Extended and Long-Range Forecasting. [Available online at http://www.wmo.int/pages/prog/www/DPFS/Reports/ET-ELRF_Beijing2008.doc].
Xie, P., , and Arkin P. A. , 1997: Global precipitation: A 17-year monthly analysis based on gauge observation, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78 , 2539–2558.
Zwiers, F. W., 1996: Interannual variability and predictability in an ensemble of AMIP climate simulations conducted with the CCC GCM2. Climate Dyn., 12 , 825–848.