Seasonal precipitation forecast skill over Iran

This paper examines the skill of seasonal precipitation forecasts over Iran using one two-tiered model, three National Multi-Model Ensemble (NMME) models, and two coupled ocean–atmosphere or one-tiered models. These models are, respectively, the ECHAM4.5 atmospheric model that is forced with sea surface temperature (SST) anomalies forecasted using constructed analogue SSTs (ECHAM4.5-SSTCA); the IRI-ECHAM4.5-DirectCoupled, the NASA-GMAO-062012 and the NCEP-CFSv2; and the ECHAM4.5 Modular Ocean Model version 3 (ECHAM4.5-MOM3-DC2) and the ECHAM4.5-GML-NCEP Coupled Forecast System (CFSSST). The precipitation and 850 hPa geopotential height fields of the forecast models are statistically downscaling to the 0.5° × 0.5° spatial resolution of the Global Precipitation Climatology Centre (GPCC) Version 6 gridded precipitation data, using model output statistics (MOS) developed through the canonical correlation analysis (CCA) option of the Climate Predictability Tool (CPT). Retroactive validations for lead times of up to 3 months are performed using the relative operating characteristic (ROC) and reliability diagrams, which are evaluated for above- and below-normal categories and defined by the upper and lower 75th and 25th percentiles of the data record over the 15-year test period of 1995/1996 to 2009/2010. The forecast models' skills are also compared with skills obtained by (a) downscaling simulations produced by forcing the ECHAM4.5 with simultaneously observed SST, and (b) the 850 hPa geopotential height NCEP-NCAR (National Centers for Environmental Prediction-National Center for Atmospheric Research) reanalysis data. Downscaling forecasts from most models generally produce the highest skill forecast at lead times of up to 3 months for autumn precipitation – the October-November-December (OND) season. For most seasons, a high skill is obtained from ECHAM4.5-MOM3-DC2 forecasts at a 1-month lead time when the models' 850 hPa geopotential height fields are used as the predictor fields. For this model and lead time, the Pearson correlation between the area-averaged of the observed and forecasts over the study area for the OND, November-December-January (NDJ), December-January-February (DJF) and January-February-March (JFM) seasons were 0.68, 0.62, 0.42 and 0.43, respectively.

[1]  David L. T. Anderson,et al.  Impact of initialization strategies and observations on seasonal forecast skill , 2009 .

[2]  T. Raziei,et al.  Spatial patterns and regimes of daily precipitation in Iran in relation to large‐scale atmospheric circulation , 2012 .

[3]  R. Tibshirani,et al.  An introduction to the bootstrap , 1993 .

[4]  U. Schneider,et al.  GPCC Full Data Reanalysis Version 7.0: Monthly Land-Surface Precipitation from Rain Gauges built on GTS based and Historic Data , 2016 .

[5]  T. Barnett,et al.  Origins and Levels of Monthly and Seasonal Forecast Skill for United States Surface Air Temperatures Determined by Canonical Correlation Analysis , 1987 .

[6]  A. Barnston,et al.  Prediction of ENSO Episodes Using Canonical Correlation Analysis , 1992 .

[7]  T. Yamagata,et al.  The interannual precipitation variability in the southern part of Iran as linked to large-scale climate modes , 2012, Climate Dynamics.

[8]  W. Landman How the International Research Institute for Climate and Society has contributed towards seasonal climate forecast modelling and operations in South Africa , 2014 .

[9]  A. Ghasemi,et al.  Quantifying the ENSO-Related Shifts in the Intensity and Probability of Drought and Wet Periods in Iran , 2004 .

[10]  D. A. Sachindra,et al.  Statistical downscaling of general circulation model outputs to precipitation—part 2: bias‐correction and future projections , 2014 .

[11]  L. Goddard,et al.  El Niño: Catastrophe or Opportunity , 2005 .

[12]  Dong Eun Lee,et al.  Seasonal Rainfall Prediction Skill over South Africa: One- versus Two-Tiered Forecasting Systems , 2012 .

[13]  A. Barnston,et al.  Statistical–Dynamical Seasonal Forecasts of Central-Southwest Asian Winter Precipitation , 2005 .

[14]  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 .

[15]  M. Claussen,et al.  The atmospheric general circulation model ECHAM-4: Model description and simulation of present-day climate , 1996 .

[16]  D. Dewitt Retrospective Forecasts of Interannual Sea Surface Temperature Anomalies from 1982 to Present Using a Directly Coupled Atmosphere–Ocean General Circulation Model , 2005 .

[17]  W. Landman,et al.  Multi‐model forecast skill for mid‐summer rainfall over southern Africa , 2012 .

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

[19]  B. Lyon,et al.  Drought in Central and Southwest Asia: La Niña, the Warm Pool, and Indian Ocean Precipitation. , 2002 .

[20]  H. Glahn,et al.  The Use of Model Output Statistics (MOS) in Objective Weather Forecasting , 1972 .

[21]  A. Barnston,et al.  Evaluation of IRI’s Seasonal Climate Forecasts for the Extreme 15% Tails , 2011 .

[22]  Thomas M. Hamill,et al.  Reliability Diagrams for Multicategory Probabilistic Forecasts , 1997 .

[23]  C. F. Ropelewski,et al.  Precipitation Patterns Associated with the High Index Phase of the Southern Oscillation , 1989 .

[24]  Zoltan Toth,et al.  Why Do Forecasts for “Near Normal” Often Fail? , 1991 .

[25]  A. Barnston,et al.  Multimodel Ensembling in Seasonal Climate Forecasting at IRI , 2003 .

[26]  B. Lyon,et al.  Statistical correction of central Southwest Asia winter precipitation simulations , 2003 .

[27]  H. V. D. Dool,et al.  Empirical Methods in Short-Term Climate Prediction , 2006 .

[28]  W. Landman,et al.  Recalibration of general circulation model output to austral summer rainfall over southern Africa , 2003 .

[29]  Majid Habibi-Nokhandan,et al.  Spring rainfall prediction based on remote linkage controlling using adaptive neuro-fuzzy inference system (ANFIS) , 2010 .

[30]  Ian Cordery,et al.  On the relationships between ENSO and autumn rainfall in Iran. , 2000 .

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

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

[33]  David G. DeWitt,et al.  Verification of the First 11 Years of IRI’s Seasonal Climate Forecasts , 2010 .

[34]  D. A. Sachindra,et al.  Statistical downscaling of general circulation model outputs to precipitation—part 1: calibration and validation , 2014 .

[35]  T. Yamagata,et al.  Erratum to: The interannual precipitation variability in the southern part of Iran as linked to large-scale climate modes , 2012, Climate Dynamics.

[36]  M. Haylock,et al.  The Predictability of Interdecadal Changes in ENSO Activity and ENSO Teleconnections , 2006 .

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

[38]  David L. T. Anderson,et al.  Seasonal Climate: Forecasting and Managing Risk , 2008 .

[39]  Francisco J. Doblas-Reyes,et al.  Seasonal climate predictability and forecasting: status and prospects , 2013 .

[40]  C. Marzban The ROC Curve and the Area under It as Performance Measures , 2004 .

[41]  C. Ropelewski,et al.  Global and Regional Scale Precipitation Patterns Associated with the El Niño/Southern Oscillation , 1987 .