The Subseasonal Experiment (SubX): A Multimodel Subseasonal Prediction Experiment

The Subseasonal Experiment (SubX) is a multimodel subseasonal prediction experiment designed around operational requirements with the goal of improving subseasonal forecasts. Seven global models have produced 17 years of retrospective (re)forecasts and more than a year of weekly real-time forecasts. The reforecasts and forecasts are archived at the Data Library of the International Research Institute for Climate and Society, Columbia University, providing a comprehensive database for research on subseasonal to seasonal predictability and predictions. The SubX models show skill for temperature and precipitation 3 weeks ahead of time in specific regions. The SubX multimodel ensemble mean is more skillful than any individual model overall. Skill in simulating the Madden–Julian oscillation (MJO) and the North Atlantic Oscillation (NAO), two sources of subseasonal predictability, is also evaluated, with skillful predictions of the MJO 4 weeks in advance and of the NAO 2 weeks in advance. SubX is also able to make useful contributions to operational forecast guidance at the Climate Prediction Center. Additionally, SubX provides information on the potential for extreme precipitation associated with tropical cyclones, which can help emergency management and aid organizations to plan for disasters.

[1]  E. Lorenz.  A study of the predictability of a 28-variable atmospheric model , 1965 .

[2]  E. Lorenz,et al.  The predictability of a flow which possesses many scales of motion , 1969 .

[3]  P. R. Julian,et al.  Detection of a 40–50 Day Oscillation in the Zonal Wind in the Tropical Pacific , 1971 .

[4]  P. R. Julian,et al.  Description of Global-Scale Circulation Cells in the Tropics with a 40–50 Day Period , 1972 .

[5]  Ross N. Hoffman,et al.  Lagged average forecasting, an alternative to Monte Carlo forecasting , 1983 .

[6]  A. Dalcher,et al.  Forecasting forecast skill , 1987 .

[7]  A. H. Murphy,et al.  Skill Scores and Correlation Coefficients in Model Verification , 1989 .

[8]  T. Barnett,et al.  Long-Lead Seasonal ForecastsWhere Do We Stand? , 1994 .

[9]  B. Liebmann,et al.  Description of a complete (interpolated) outgoing longwave radiation dataset , 1996 .

[10]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

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

[12]  Praveen Kumar,et al.  A catchment‐based approach to modeling land surface processes in a general circulation model: 1. Model structure , 2000 .

[13]  C. M. Kishtawal,et al.  Multimodel Ensemble Forecasts for Weather and Seasonal Climate , 2000 .

[14]  C. Jones,et al.  Prediction skill of the Madden and Julian Oscillation in dynamical extended range forecasts , 2000 .

[15]  M. Wheeler,et al.  An All-Season Real-Time Multivariate MJO Index: Development of an Index for Monitoring and Prediction , 2004 .

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

[17]  S. Saha,et al.  The NCEP Climate Forecast System , 2006 .

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

[19]  P. Xie,et al.  A Gauge-Based Analysis of Daily Precipitation over East Asia , 2007 .

[20]  Å. Johansson.  Prediction Skill of the NAO and PNA from Daily to Seasonal Time Scales , 2007 .

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

[22]  Christophe Cassou,et al.  Intraseasonal interaction between the Madden–Julian Oscillation and the North Atlantic Oscillation , 2008, Nature.

[23]  Ricardo Todling,et al.  The GEOS-5 Data Assimilation System-Documentation of Versions 5.0.1, 5.1.0, and 5.2.0 , 2008 .

[24]  H. V. D. Dool,et al.  A global monthly land surface air temperature analysis for 1948-present , 2008 .

[25]  V. Kousky,et al.  Assessing objective techniques for gauge‐based analyses of global daily precipitation , 2008 .

[26]  Gilbert Brunet,et al.  An Observed Connection between the North Atlantic Oscillation and the Madden-Julian Oscillation , 2009 .

[27]  Guillem Candille,et al.  The Multiensemble Approach: The NAEFS Example , 2009 .

[28]  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 .

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

[30]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[31]  Guillem Candille,et al.  Bias Correction and Multiensemble in the NAEFS Context or How to Get a “Free Calibration” through a Multiensemble Approach , 2010 .

[32]  Timothy DelSole,et al.  Model Fidelity versus Skill in Seasonal Forecasting , 2010 .

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

[34]  A. Lorenc,et al.  Collaboration of the Weather and Climate Communities to Advance Subseasonal-to-Seasonal Prediction , 2010 .

[35]  P. Sardeshmukh,et al.  Prospects for Improving Subseasonal Predictions , 2011 .

[36]  Harry H. Hendon,et al.  Prediction of the Madden–Julian oscillation with the POAMA dynamical prediction system , 2011 .

[37]  Lifeng Luo,et al.  The Second Phase of the Global Land–Atmosphere Coupling Experiment: Soil Moisture Contributions to Subseasonal Forecast Skill , 2011 .

[38]  Peter J. Webster,et al.  Seasonal prediction skill of ECMWF System 4 and NCEP CFSv2 retrospective forecast for the Northern Hemisphere Winter , 2012, Climate Dynamics.

[39]  E. Fetzer,et al.  Does the Madden–Julian Oscillation Influence Wintertime Atmospheric Rivers and Snowpack in the Sierra Nevada? , 2012 .

[40]  Andrea Molod,et al.  The GEOS-5 Atmospheric General Circulation Model: Mean Climate and Development from MERRA to Fortuna , 2012 .

[41]  J. Hurrell,et al.  An overview of the North Atlantic Oscillation , 2013 .

[42]  J. Whitaker,et al.  NOAA's Second-Generation Global Medium-Range Ensemble Reforecast Dataset , 2013 .

[43]  Chidong Zhang.  Madden–Julian Oscillation: Bridging Weather and Climate , 2013 .

[44]  Michael K. Tippett,et al.  Constructed Analogs and Linear Regression , 2013 .

[45]  Xiouhua Fu,et al.  MJO prediction in the NCEP Climate Forecast System version 2 , 2014, Climate Dynamics.

[46]  Qin Zhang,et al.  Predictability and Forecast Skill in NMME , 2014 .

[47]  Rolf H. Reichle,et al.  Observation-Corrected Precipitation Estimates in GEOS-5 , 2014 .

[48]  Maria Flatau,et al.  The Navy Global Environmental Model , 2014 .

[49]  A. Barnston,et al.  The North American multimodel ensemble: Phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction , 2014 .

[50]  Adam A. Scaife,et al.  Skillful long‐range prediction of European and North American winters , 2014 .

[51]  M. Iredell,et al.  The NCEP Climate Forecast System Version 2 , 2014 .

[52]  P. Posey,et al.  US Navy Operational Global Ocean and Arctic Ice Prediction Systems , 2014 .

[53]  A. Sobel,et al.  Seamless precipitation prediction skill in the tropics and extratropics from a global model , 2014 .

[54]  C. Appenzeller,et al.  Estimating daily climatologies for climate indices derived from climate model data and observations , 2015, Journal of geophysical research. Atmospheres : JGR.

[55]  A. Robertson,et al.  Evaluation of Submonthly Precipitation Forecast Skill from Global Ensemble Prediction Systems , 2015 .

[56]  Arun Kumar,et al.  Improving and Promoting Subseasonal to Seasonal Prediction , 2015 .

[57]  Yuejian Zhu,et al.  A Comparison of Perturbations from an Ensemble Transform and an Ensemble Kalman Filter for the NCEP Global Ensemble Forecast System , 2016 .

[58]  B. Kirtman,et al.  Prediction and predictability of land and atmosphere initialized CCSM4 climate forecasts over North America , 2016 .

[59]  Michael K. Tippett,et al.  Forecast Comparison Based on Random Walks , 2016 .

[60]  Hai Lin,et al.  GEPS-Based Monthly Prediction at the Canadian Meteorological Centre , 2016 .

[61]  H. V. D. Dool,et al.  Probabilistic Seasonal Forecasts in the North American Multimodel Ensemble: A Baseline Skill Assessment , 2016 .

[62]  Florian Pappenberger,et al.  The TIGGE Project and Its Achievements , 2016 .

[63]  Adam A. Scaife,et al.  Skilful predictions of the winter North Atlantic Oscillation one year ahead , 2016 .

[64]  Adam A. Scaife,et al.  The Climate-System Historical Forecast Project: Providing Open Access to Seasonal Forecast Ensembles from Centers around the Globe , 2017 .

[65]  Michael K. Tippett,et al.  Multimodel Ensembling of Subseasonal Precipitation Forecasts over North America , 2017 .

[66]  Yuejian Zhu,et al.  Performance of the New NCEP Global Ensemble Forecast System in a Parallel Experiment , 2017 .

[67]  Andrew P. Morse,et al.  Potential applications of subseasonal‐to‐seasonal (S2S) predictions , 2017 .

[68]  M. Tippett,et al.  Subseasonal Predictability of Boreal Summer Monsoon Rainfall from Ensemble Forecasts , 2017, Front. Environ. Sci..

[69]  Jorgen S. Frederiksen,et al.  Review of Tropical‐Extratropical Teleconnections on Intraseasonal Time Scales , 2017 .

[70]  Hyemi Kim.  The impact of the mean moisture bias on the key physics of MJO propagation in the ECMWF reforecast , 2017 .

[71]  M. L’Heureux,et al.  The Predictors and Forecast Skill of Northern Hemisphere Teleconnection Patterns for Lead Times of 3–4 Weeks , 2017 .

[72]  Michel Rixen,et al.  The Subseasonal to Seasonal (S2S) Prediction Project Database , 2017 .

[73]  M. Tippett,et al.  Predictability of Week-3–4 Average Temperature and Precipitation over the Contiguous United States , 2017 .

[74]  The Subseasonal Experiment (SubX) , 2017 .

[75]  Frederic Vitart,et al.  Madden—Julian Oscillation prediction and teleconnections in the S2S database , 2017 .

[76]  Sources of Bias in the Monthly CFSv2 Forecast Climatology , 2018 .

[77]  Benjamin W. Green,et al.  Subseasonal Forecasting with an Icosahedral, Vertically Quasi-Lagrangian Coupled Model. Part II: Probabilistic and Deterministic Forecast Skill , 2018 .

[78]  Walter C. Kolczynski,et al.  Toward the Improvement of Subseasonal Prediction in the National Centers for Environmental Prediction Global Ensemble Forecast System , 2018, Journal of Geophysical Research: Atmospheres.

[79]  F. Vitart,et al.  Prediction of the Madden–Julian Oscillation: A Review , 2018, Journal of Climate.

[80]  E. Barnes,et al.  Skillful empirical subseasonal prediction of landfalling atmospheric river activity using the Madden–Julian oscillation and quasi-biennial oscillation , 2018, npj Climate and Atmospheric Science.

[81]  Niti B. Mishra,et al.  Multi-model skill assessment of seasonal temperature and precipitation forecasts over Europe , 2018, Climate Dynamics.

[82]  A. Mariotti,et al.  Progress in subseasonal to seasonal prediction through a joint weather and climate community effort , 2018, npj Climate and Atmospheric Science.

[83]  Monthly ENSO Forecast Skill and Lagged Ensemble Size , 2018, Journal of advances in modeling earth systems.

[84]  Benjamin W. Green,et al.  Subseasonal Forecasting with an Icosahedral, Vertically Quasi-Lagrangian Coupled Model. Part I: Model Overview and Evaluation of Systematic Errors , 2018 .