S2S Researches at IPRC/SOEST University of...
Transcript of S2S Researches at IPRC/SOEST University of...
Joshua Xiouhua Fu, Bin Wang, June-Yi Lee, and Baoqiang Xiang
S2S Workshop, DC, Feb.10-13, 2014
S2S Researches at IPRC/SOESTUniversity of Hawaii
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►S2S Research Highlights at IPRC/SOEST/UH.
►Development of S2S Forecasting Systems.
►Experimental S2S Forecasting.
►Summary and Future Study.
Outline
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Impacts of ENSO,
BSISO, and MJO
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ENSO=>EASM
Wang, Wu and Fu, 20004 S2S Workshop, DC, Feb.10-13, 2014
PNA
L
L
H HL L
LHH
H
H H H HLL
Moon et al. 2013; Ding and Wang 2007
L
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H H H
H
L
L
L
L
MJO and the Record-Breaking East Coast Snowstorms in 2009/2010
Bar: Eastern US snowLine: Central Pacific MJO
Moon et al. 2012 6 S2S Workshop, DC, Feb.10-13, 2014
S2S Forecasting
Systems
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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
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Madden-Julian Oscillation
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Climatology of Tropical Cyclones
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Two Versions of New Coupled Model Two Versions of New Coupled Model POEM1 (T42) & POEM2 (T159)POEM1 (T42) & POEM2 (T159)
POP2.01(1o lon x 0.5o lat)
Ocean
CICE4.1(1o lon x 0.5o lat)
Sea Ice
ECHAM5.3(T159)
Atmosphere and Land
OASIS3-MCTCoupler
Structure of the new POEM2
POEM (POP/CICE-OASIS-ECHAM) model
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ENSO in POEM1 and POEM2ENSO in POEM1 and POEM2
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Sea Ice Climatology Sea Ice Climatology –– Annual Mean Sea Ice ConcentrationAnnual Mean Sea Ice Concentration
Observation Hadley Center
POEM2
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A Multi-Model Subseasonal-to-Seasonal Forecast System
NCEPCFS Forecast
NCEP/CPCStatistical Forecast
UH-HCMForecast
MME Forecastover Asian-Pacific
Region
Formula are developedfrom long-term reforecastswith three models
Downscaling MME Forecast to Specific Regions or
Individual Islands
Other (e.g., NMME, CLIPAS, NICAM)
Forecasts
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ExperimentalS2S
Forecasting
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UH Multi-Model Seasonal Forecast Skill (Prec.)
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Statistical-Dynamical Ensemble Forecasting Skillof Southeast Asian Monsoon ISO in 2008
Fu et al. (2013)
Individual Statistical orDynamical Models
Rainfall U850
Statistical-Dynamical Ensemble
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Extended-range Forecasting of TC “Nargis” (2008)
Initial Date:April 10, 2008
Fu and Hsu (2011) 18 S2S Workshop, DC, Feb.10-13, 2014
GFS: 14 daysCFSv2&UH: 25/25 days
CFSv2&UH MME: 37 daysFu et al. (2013)
(Wheeler-Hendon Index)MJO Skills in Three GCMs during DYNAMO/CINDY
(Sep 2011- Mar 2012)
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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.
Numerical Experiments with Different SST Settings
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SST-Feedback Significantly Extends MJO Forecast Skill
Persistent SST CPL
Forecasted Daily SSTObserved Daily SST
Potential
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► Combination of Multiple Dynamical and Statistical ModelForecasts is a Practical Approach to Improve S2S ForecastingSkill.
►Using Daily SST Forecasted from Good Coupled Models asBoundary Conditions is Expected to Improve the S2S Skill ofHigh-resolution AGCMs (e.g., TIGGE Models).
► Researches are Needed to Better Understand the Sources ofS2S Predictability of High-impact Weather and Climate (orExtreme) Events, Such as Tropical Cyclones, Heat Waves,and Flooding et al.
►Further Develop and Improve Dynamical and Statistical S2SModels.
►Explore the Ways to Advance S2S Forecast Skills (e.g., MME)and to Efficiently Utilize Available S2S Products for SocietalApplications.
Summary and Future Study
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Thank You Very Much!Thank You Very Much!
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