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

2 S2S Workshop, DC, Feb.10-13, 2014

Impacts of ENSO,

BSISO, and MJO

3 S2S Workshop, DC, Feb.10-13, 2014

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

5 S2S Workshop, DC, Feb.10-13, 2014

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

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

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Climatology of Tropical Cyclones

S2S Workshop, DC, Feb.10-13, 2014

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

S2S Workshop, DC, Feb.10-13, 2014

Sea Ice Climatology Sea Ice Climatology –– Annual Mean Sea Ice ConcentrationAnnual Mean Sea Ice Concentration

Observation Hadley Center

POEM2

13 S2S Workshop, DC, Feb.10-13, 2014

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

S2S Workshop, DC, Feb.10-13, 201414

ExperimentalS2S

Forecasting

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UH Multi-Model Seasonal Forecast Skill (Prec.)

S2S Workshop, DC, Feb.10-13, 2014

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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

S2S Workshop, DC, Feb.10-13, 2014

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)

19 S2S Workshop, DC, Feb.10-13, 2014

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

20 S2S Workshop, DC, Feb.10-13, 2014

SST-Feedback Significantly Extends MJO Forecast Skill

Persistent SST CPL

Forecasted Daily SSTObserved Daily SST

Potential

21 S2S Workshop, DC, Feb.10-13, 2014

► 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

22 S2S Workshop, DC, Feb.10-13, 2014

Thank You Very Much!Thank You Very Much!

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