S2S Researches at IPRC/SOEST University of Hawaii
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1 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
2 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
3 Impacts of ENSO, BSISO, and MJO 3 S2S Workshop, DC, Feb.10-13, 2014
4 ENSO=>EASM PNA Wang, Wu and Fu, S2S Workshop, DC, Feb.10-13, 2014
5 H L H L H H L L H L L H H L H L H Moon et al. 2013; Ding and Wang S2S Workshop, DC, Feb.10-13, 2014
6 MJO and the Record-Breaking East Coast Snowstorms in 2009/2010 L H L H L H H L Bar: Eastern US snow Line: Central Pacific MJO Moon et al S2S Workshop, DC, Feb.10-13, 2014
7 S2S Forecasting Systems 7 S2S Workshop, DC, Feb.10-13, 2014
8 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 (0.5 o x0.5 o ) (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 (30 o S-30 o N) Coupling interval: once per day 8 S2S Workshop, DC, Feb.10-13, 2014
9 Madden-Julian Oscillation 9 S2S Workshop, DC, Feb.10-13, 2014
10 Climatology of Tropical Cyclones 10 S2S Workshop, DC, Feb.10-13, 2014
11 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 (1 o lon x 0.5 o lat) Ocean CICE4.1 (1 o lon x 0.5 o lat) Sea Ice 11 S2S Workshop, DC, Feb.10-13, 2014
12 ENSO in POEM1 and POEM2 12 S2S Workshop, DC, Feb.10-13, 2014
13 Sea Ice Climatology Annual Mean Sea Ice Concentration Observation Hadley Center POEM2 13 S2S Workshop, DC, Feb.10-13, 2014
14 A Multi-Model Subseasonal-to-Seasonal Forecast System NCEP CFS Forecast NCEP/CPC Statistical Forecast UH-HCM Forecast Other (e.g., NMME, CLIPAS, NICAM) Forecasts Formula are developed from long-term reforecasts with three models MME Forecast over Asian-Pacific Region Downscaling MME Forecast to Specific Regions or Individual Islands 14 S2S Workshop, DC, Feb.10-13, 2014
15 Experimental S2S Forecasting 15 S2S Workshop, DC, Feb.10-13, 2014
16 UH Multi-Model Seasonal Forecast Skill (Prec.) 16 S2S Workshop, DC, Feb.10-13, 2014
17 Statistical-Dynamical Ensemble Forecasting Skill of Southeast Asian Monsoon ISO in 2008 Rainfall U850 Individual Statistical or Dynamical Models Statistical-Dynamical Ensemble Fu et al. (2013) 17 S2S Workshop, DC, Feb.10-13, 2014
18 Extended-range Forecasting of TC Nargis (2008) Initial Date: April 10, 2008 Fu and Hsu (2011) 18 S2S Workshop, DC, Feb.10-13, 2014
19 MJO Skills in Three GCMs during DYNAMO/CINDY (Wheeler-Hendon Index) (Sep Mar 2012) GFS: 14 days Fu et al. (2013) CFSv2&UH: 25/25 days CFSv2&UH MME: 37 days 19 S2S Workshop, DC, Feb.10-13, 2014
20 Numerical Experiments with Different SST Settings Names of Experiments SST Settings CPL Fcst_SST (or fsst) Atmosphere-ocean coupled forecasts. 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
21 SST-Feedback Significantly Extends MJO Forecast Skill Potential Persistent SST Forecasted Daily SST CPL Observed Daily SST 21 S2S Workshop, DC, Feb.10-13, 2014
22 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
23 Thank You Very Much! 23 S2S Workshop, DC, Feb.10-13, 2014
24 24
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