Evaluation of Probabilistic Quality and Value of the ENSEMBLES Multimodel Seasonal Forecasts: Comparison with DEMETER

Abstract The performance of the new multimodel seasonal prediction system developed in the framework of the European Commission FP7 project called ENSEMBLE-based predictions of climate changes and their impacts (ENSEMBLES) is compared with the results from the previous project [i.e., Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER)]. The comparison is carried out over the five seasonal prediction systems (SPSs) that participated in both projects. Since DEMETER, the contributing SPSs have improved in all aspects with the main advancements including the increase in resolution, the better representation of subgrid physical processes, land, sea ice, and greenhouse gas boundary forcing, and the more widespread use of assimilation for ocean initialization. The ENSEMBLES results show an overall enhancement for the prediction of anomalous surface temperature conditions. However, the improvement is quite small and with considerable space–time variations. In the ...

[1]  D. Richardson.  Skill and relative economic value of the ECMWF ensemble prediction system , 2000 .

[2]  David L. T. Anderson,et al.  Sensitivity of dynamical seasonal forecasts to ocean initial conditions , 2004 .

[3]  Valérie Dulière,et al.  On the representation of high latitude processes in the ORCA-LIM global coupled sea ice–ocean model , 2005 .

[4]  I. Jolliffe,et al.  Forecast verification : a practitioner's guide in atmospheric science , 2011 .

[5]  David S. Richardson,et al.  Predictability of Weather and Climate: Predictability and economic value , 2006 .

[6]  T. Palmer,et al.  ENSEMBLES: A new multi‐model ensemble for seasonal‐to‐annual predictions—Skill and progress beyond DEMETER in forecasting tropical Pacific SSTs , 2009 .

[7]  Andrea Alessandri,et al.  The INGV-CMCC Seasonal Prediction System: improved ocean initial conditions , 2010 .

[8]  Lifeng Luo,et al.  Contribution of land surface initialization to subseasonal forecast skill: First results from a multi‐model experiment , 2010 .

[9]  W. Briggs.  Statistical Methods in the Atmospheric Sciences , 2007 .

[10]  Bin Wang,et al.  Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004) , 2009 .

[11]  M. Balmaseda,et al.  Impact of Argo on analyses of the global ocean , 2007 .

[12]  Renate Hagedorn,et al.  The rationale behind the success of multi-model ensembles in seasonal forecasting — I. Basic concept , 2005 .

[13]  In-Sik Kang,et al.  Examination of multi-model ensemble seasonal prediction methods using a simple climate system , 2006 .

[14]  Mark A. Liniger,et al.  Can multi‐model combination really enhance the prediction skill of probabilistic ensemble forecasts? , 2007 .

[15]  Roberto Buizza,et al.  TIGGE: Preliminary results on comparing and combining ensembles , 2008 .

[16]  Anthony Rosati,et al.  The Impact of Ocean Initial Conditions on ENSO Forecasting with a Coupled Model , 1997 .

[17]  M. Huddleston,et al.  Quality control of ocean temperature and salinity profiles — Historical and real-time data , 2007 .

[18]  Renate Hagedorn,et al.  The rationale behind the success of multi-model ensembles in seasonal forecasting — II. Calibration and combination , 2005 .

[19]  Renate Hagedorn,et al.  The rationale behind the success of multi-model ensembles in seasonal forecasting-II , 2005 .

[20]  松山 洋.  「Statistical Methods in the Atmospheric Sciences(2nd edition), International Geophysics Series 91」, Daniel S. Wilks著, Academic Press, 2005年11月, 648頁, $94.95, ISBN978-0-12-751966-1(本だな) , 2010 .

[21]  Olivier Talagrand,et al.  The attributes of forecast systems: a general framework for the evaluation and calibration of weather forecasts , 2006 .

[22]  Andrew P. Morse,et al.  DEVELOPMENT OF A EUROPEAN MULTIMODEL ENSEMBLE SYSTEM FOR SEASONAL-TO-INTERANNUAL PREDICTION (DEMETER) , 2004 .

[23]  A. Alessandri,et al.  On the coupling between vegetation and rainfall inter‐annual anomalies: Possible contributions to seasonal rainfall predictability over land areas , 2008 .

[24]  David S. Richardson,et al.  Economic Value and Skill , 2012 .

[25]  A. H. Murphy,et al.  What Is a Good Forecast? An Essay on the Nature of Goodness in Weather Forecasting , 1993 .

[26]  Balaji Rajagopalan,et al.  Categorical Climate Forecasts through Regularization and Optimal Combination of Multiple GCM Ensembles , 2002 .

[27]  A. Sterl,et al.  The ERA‐40 re‐analysis , 2005 .

[28]  Renate Hagedorn,et al.  Strategies: Revolution in Climate Prediction is Both Necessary and Possible: A Declaration at the World Modelling Summit for Climate Prediction , 2009 .

[29]  Bin Wang,et al.  How are seasonal prediction skills related to models’ performance on mean state and annual cycle? , 2010 .

[30]  T. N. Krishnamurti,et al.  Improved Weather and Seasonal Climate Forecasts from Multimodel Superensemble. , 1999, Science.