S2S Researches at IPRC/SOEST University of Hawaii Joshua Xiouhua Fu, Bin Wang, June-Yi Lee, and Baoqiang Xiang 1 S2S Workshop, DC, Feb.10-13, 2014 Outline ►S2S Research Highlights at IPRC/SOEST/UH. ►Development of S2S Forecasting Systems. ►Experimental S2S Forecasting. ►Summary and Future Study. 2 S2S Workshop, DC, Feb.10-13, 2014 Impacts of ENSO, BSISO, and MJO 3 S2S Workshop, DC, Feb.10-13, 2014 PNA ENSO=>EASM Wang, Wu and Fu, 2000 4 S2S Workshop, DC, Feb.10-13, 2014 H H H L H L H L L L H L H H L L H Moon et al. 2013; Ding and Wang 2007 5 S2S Workshop, DC, Feb.10-13, 2014 MJO and the Record-Breaking East Coast Snowstorms in 2009/2010 L H L L L H H H Bar: Eastern US snow Line: Central Pacific MJO Moon et al. 2012 6 S2S Workshop, DC, Feb.10-13, 2014 S2S Forecasting Systems 7 S2S Workshop, DC, Feb.10-13, 2014 UH Hybrid Coupled GCM (UH) Atmospheric component: ECHAM-4 T30 (vers_1) &T106 (vers_2) L19 AGCM (Roeckner et al. 1996) Ocean component: Wang-Li-Fu 2-1/2-layer upper ocean model (0.5ox0.5o) (Fu and Wang 2001) Wang, Li, and Chang (1995): upper-ocean thermodynamics (2-1/2 ocean model) McCreary and Yu (1992): upper-ocean dynamics (2-1/2 ocean model) Jin (1997) : mean and ENSO (intermediate fully coupled model) Zebiak and Cane (1987): ENSO (intermediate anomaly coupled model) Fully coupling without heat flux correction Coupling region: Tropical Indian and Pacific Oceans (30oS-30oN) Coupling interval: once per day 8 S2S Workshop, DC, Feb.10-13, 2014 Madden-Julian Oscillation 9 S2S Workshop, DC, Feb.10-13, 2014 Climatology of Tropical Cyclones 10 S2S Workshop, DC, Feb.10-13, 2014 Two Versions of New Coupled Model POEM1 (T42) & POEM2 (T159) Structure of the new POEM2 POEM (POP/CICE-OASIS-ECHAM) model ECHAM5.3 (T159) Atmosphere and Land OASIS3-MCT Coupler POP2.01 CICE4.1 o o (1 lon x 0.5 lat) (1o lon x 0.5o lat) Ocean Sea Ice 11 S2S Workshop, DC, Feb.10-13, 2014 ENSOENSO inin POEM1POEM1 andand POEM2POEM2 12 S2S Workshop, DC, Feb.10-13, 2014 Sea Ice Climatology – Annual Mean Sea Ice Concentration Observation Hadley Center POEM2 13 S2S Workshop, DC, Feb.10-13, 2014 A Multi-Model Subseasonal-to-Seasonal Forecast System Other (e.g., NMME, NCEP NCEP/CPC UH-HCM CLIPAS, NICAM) CFS Forecast Statistical Forecast Forecast Forecasts Formula are developed MME Forecast from long-term reforecasts over Asian-Pacific with three models Region Downscaling MME Forecast to Specific Regions or Individual Islands 14 S2S Workshop, DC, Feb.10-13, 2014 Experimental S2S Forecasting 15 S2S Workshop, DC, Feb.10-13, 2014 UH Multi-Model Seasonal Forecast Skill (Prec.) 16 S2S Workshop, DC, Feb.10-13, 2014 Statistical-Dynamical Ensemble Forecasting Skill of Southeast Asian Monsoon ISO in 2008 Rainfall U850 Individual Statistical or Statistical-Dynamical Dynamical Models Ensemble Fu et al. (2013) 17 S2S Workshop, DC, Feb.10-13, 2014 Extended-range Forecasting of TC “Nargis” (2008) Initial Date: Fu and Hsu (2011) 18 April 10, 2008 S2S Workshop, DC, Feb.10-13, 2014 MJO Skills in Three GCMs during DYNAMO/CINDY (Wheeler-Hendon Index) (Sep 2011- Mar 2012) CFSv2&UH: 25/25 days GFS: 14 days CFSv2&UH MME: 37 days Fu et al. (2013) 19 S2S Workshop, DC, Feb.10-13, 2014 Numerical Experiments with Different SST Settings Names of Experiments SST Settings CPL Atmosphere-ocean coupled forecasts. Fcst_SST (or fsst) Atmosphere-only forecasts driven by daily SST derived from the ‘cpl’ forecasts. Pers_SST (or psst) Atmosphere-only forecasts driven by persistent SST. TMI_SST (or osst) Atmosphere-only forecasts driven by observed daily TMI SST. 20 S2S Workshop, DC, Feb.10-13, 2014 SST-Feedback Significantly Extends MJO Forecast Skill Potential CPL Persistent SST Observed Daily SST Forecasted Daily SST 21 S2S Workshop, DC, Feb.10-13, 2014 Summary and Future Study ► Combination of Multiple Dynamical and Statistical Model Forecasts is a Practical Approach to Improve S2S Forecasting Skill. ►Using Daily SST Forecasted from Good Coupled Models as Boundary Conditions is Expected to Improve the S2S Skill of High-resolution AGCMs (e.g., TIGGE Models). ► Researches are Needed to Better Understand the Sources of S2S Predictability of High-impact Weather and Climate (or Extreme) Events, Such as Tropical Cyclones, Heat Waves, and Flooding et al. ►Further Develop and Improve Dynamical and Statistical S2S Models. ►Explore the Ways to Advance S2S Forecast Skills (e.g., MME) and to Efficiently Utilize Available S2S Products for Societal Applications. 22 S2S Workshop, DC, Feb.10-13, 2014 ThankThank YouYou VeryVery Much!Much! 23 S2S Workshop, DC, Feb.10-13, 2014 24.