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					                    CLIVAR/ISVHE
               Intraseasonal Variability Hindcast Experiment


          Design and Preliminary results
ISVHE was initiated by an ad hoc group:
B. Wang, D. Waliser, H. Hendon, K. Sperber, I-S. Kang

Research Coordinator: June-Yi Lee

Preliminary results were prepared by June-Yi Lee and Bin Wang


  Supporters
                       CLIVAR/ISVHE
                Intraseasonal Variability Hindcast Experiment
The ISVHE is a coordinated multi-institutional ISV hindcast experiment supported by APCC,
NOAA CTB, CLIVAR/AAMP & MJO WG, and AMY.


           ECMWF                                                 EC
                                         SNU
                                PNU                                      NCEP
                CMCC
                                             JMA
                                 CWB                              GFDL      NASA

                                                   UH IPRC


                                      ABOM




                                               http://iprc.soest.hawaii.edu/users/jylee/clipas.htm
   Supporters
ISVHE Participations
                    Current Participating Groups
  Institution         Participants
  ABOM, Australia    Harry Hendon, Oscar Alves
  CMCC, Italy        Antonio Navarra, Annalisa Cherichi, Andrea Alessandri
  CWB, Taiwan        Mong-Ming Lu
  ECMWF, EU          Franco Molteni, Frederic Vitart
  GFDL, USA          Bill Stern
  JMA, Japan         Kiyotoshi Takahashi
  MRD/EC, Canada     Gilbert Brunet, Hai Lin
  NASA/GMAO, USA     S. Schubert
  NCEP/CPC           Arun Kumar, Jae-Kyung E. Schemm
  PNU, Korea         Kyung-Hwan Seo
  SNU, Korea         In-Sik Kang
  UH/IPRC, USA       Bin Wang, Xiouhua Fu, June-Yi Lee
Motivation of the ISVHE
     The Madden-Julian Oscillation (MJO, Madden-Julian 1971, 1994)
       interacts with, and influences, a wide range of weather and climate phenomena
           (e.g., monsoons, ENSO, tropical storms, mid-latitude weather), and
       represents an important, and as yet unexploited, source of predictability at
           the subseasonal time scale (Lau and Waliser, 2005).

     The Boreal Summer Monsoon Intraseasonal Oscillation (MISO)
       is one of the dominant short-term climate variability in global monsoon system
            (Webster et al. 1998, Wang 2006).
       The wet and dry spells of the MISO strongly influence extreme hydro-meteorological
           events, which composed of about 80% of natural disaster, thus
           the socio-economic activities in the World's most populous monsoon region.

Need for a Coordinated Multi-Model ISO Hindcast Experiment
  The development of an MME is the intrinsic need for lead-dependent model climatologies
  (i.e. multi-decade hindcast datasets) to properly quantify and combine the independent skill
  of each model as a function of lead-time and season.
  There are still great uncertainties regarding the level of predictability that can be ascribed to
  the MJO, other subseasonal phenomena and the weather/climate components that they
  interact with and influence. The development and analysis of a multi-model hindcast
  experiment is needed to address the above questions and challenges.
Numerical Designs and Objectives

      Control Run                         ISV Hindcast EXP                       YOTC EXP

  Free coupled runs with            ISV hindcast initiated every 10           Additional    ISO
  AOGCMs or AGCM                    days on 1st, 11th, and 21st of each       hindcast EXP from
  simulation with specified         calendar month for at least 45            May 2008 to Sep
  boundary forcing for at           days with more than 6 ensemble            2009
  least 20 years                    members from 1989 to 2008

  Daily or 6-hourly output          Daily or 6-hourly output                  6-hourly output



                                       Three experimental
                                      Designs for aiming to


    Better understand the physical basis for ISV prediction and determine potential and practical
     predictability of ISV in a multi-model frame work.
    Develop optimal strategies for multi-model ensemble ISV prediction system
    Identify model deficiencies in predicting ISV and find ways to improve models’ convective
     and other physical parameterization
    Determine ISV’s modulation of extreme hydrological events and its contribution to seasonal
     and interannual climate variation.
Output Request

  I. Atmospheric 2D Field          II. Atmospheric 3D Field        III. Upper Ocean 3D Field


total precipitation rate, OLR,     17 standard pressure levels:    temperature, salinity,
surface (2m) air temperature,      humidity,        temperature,   ocean currents (U and V),
SST, mean sea level pressure,      horizontal wind and vertical    and vertical motion from
surface heat fluxes (latent,       pressure velocity (Pa/s), and   surface to 300m
sensible, solar and longwave       each of the components of
radiation), surface wind stress,   the diabatic heating rates
and geopotential, horizontal       (e.g., shortwave, longwave,
wind fields (u and v) at three     stratiform cloud, deep and
specific levels: 850, 500, and     shallow convection).
200 mb



   The requested outputs are as follows.
       1. Output requested from the control simulations: 6-hour values of
          items I, II and III.
       2. Output from the hindcasts initiated from January 1989 up to May
          2008: Daily mean values of items I and III.
       3. Output from the hindcasts initiated during the YOTC Period (May
          2008 – October 2009): 6-hour values of items I, II and III.
Description of Models and Experiments

 One-Tier System
                                   Control                            ISO Hindcast
             Model
                                   Run               Period       Ens No Initial Condition
             POAMA 1.5 & 2.4
  ABOM                             CMIP (100yrs)     1980-2006    10       The first day of every month
             (ACOM2+BAM3)
             CMCC
  CMCC                             CMIP (20yrs)      1989-2008    5        Every 10 days
             (ECHAM5+OPA8.2)
  ECMWF      ECMWF (IFS+HOPE)      CMIP(11yrs)       1989-2008    15       Every 15 days
  GFDL       CM2 (AM2/LM2+MOM4) CMIP (50yrs)         1982-2008    10       The first day of every month
  JMA        JMA CGCM              CMIP (20yrs)      1989-2008    6        Every 15 days
             CFS v1 (GFS+MOM3) &
  NCEP/CPC                         CMIP 100yrs       1981-2008    5        Every 10 days
             v2
  PNU        CFS with RAS scheme   CMIP (13yrs)      1981-2008    3        The first day of each month
             SNU CM
  SNU                              CMIP (20yrs)      1989-2008    1        Every 10 days
             (SNUAGCM+MOM3)
  UH/IPRC    UH HCM                CMIP (20yrs)      1994-2008    6        Every 10 days

Two-Tier System
                                Control                               ISO Hindcast
             Model
                                Run                Period        Ens No Initial Condition
  CWB        CWB AGCM           AMIP (25yrs)       1981-2005     10      Every 10 days
  MRD/EC     GEM                AMIP (21yrs)       1985-2008     10      Every 10 days
Evaluation on Control Runs
        Variance of 20-100-day Bandpass Filtered Precipitation
          in Observation and Control Simulations (NDJFMA)
Evaluation on Control Runs

       Pattern Correlation Coefficient and Normalized Root Mean Square Error for
                      Mean Precipitation and 20-100-day Variance




  The PCC and NRMSE between the observed and simulated seasonal mean precipitation
  (mean) and variance of 20-100-day bandpass filtered precipitation (variance) for individual
  control simulations.
Evaluation on Control Runs
            20-100-Day U850, U200 and OLR
              along the equator (15oS-15oN)




                                          The first two multivariate EOF
                                          modes of 20-100-day 850- and
                                          200-hPa zonal wind and OLR
                                          along the equator (15oS-15oN)
                                          obtained from observation and
                                          control      simulations.  The
                                          percentage variance explained
                                          by each mode is shown in the
                                          lower left of each panel.
 ISV Forecast Skills/ ONDJFM
                 Temporal Correlation Coefficient Skill for U850
                 ABOM                       JMA                          NCEP
           ISO          ISO+IAV      ISO          ISO+IAV          ISO          ISO+IAV


1 Pentad
  Lead




2 Pentad
  Lead



3 Pentad
  Lead



4 Pentad
  Lead
The MME and Individual Model Skills for MJO
 Can the MME approach improve MJO forecast?




    Common Period: 1989-2008
    Initial Condition: 1st day of each month from Oct to March
    MME1: Simple composite with all models
    MMEB2: Simple composite using the best two models
    MMEB3: Simple composite using the best three models
The MME and Individual Model Skills for MJO

               Normalized RMSE
ENSO Dependency
    Total anomaly vs ISO component & ENSO Dependency




 Taking into account IAV anomaly, the practical TCC skill for the RMM1 and 2 extends about 5 to 10
  days depends on model. In particular, the skill improvement is remarkable for the RMM1 verifying the
  interference of the ENSO phase onto the MJO phase.
 It is noted that the skill in the La Nina years is better than El Nino years in most models.
ENSO Dependency
    The dependence of the forecast skill on the initial phase of the MJO




 NCEP model has less skill for the MJO with phase 3 and 4 initial condition than other phases.
  CMCC model has better skill when the MJO initially locates in the eastern Indian Ocean and
  west of the Maritime continent. Other models have less sensitive to the initial phase of MJO.
                                    Summary
 The ISVHE has been coordinated to better understand the physical basis for prediction
  and determine predictability of ISO.

 12 climate models’ hindcast for ISO have been collected from research institutions in
  North America, Europe, Asia, and Australia.

 Using the best three models’ MME, ACC skill for RMM1 and RMM2 reaches 0.5 at 27-day
  forecast lead.

 An enhanced nudging of divergence field is shown to significantly improve the initial
  conditions, resulting in an extension of the skillful rainfall prediction by 2-4 days and U850
  prediction by 5-10 days. This suggests that improvement of initial conditions are a very
  important aspect of the ISO prediction (Fu et al. 2011).

 It is noted that the skill in the La Nina years is better than El Nino years in most models.
  This may be related to the previous findings indicating the presence of weaker MJO activity
  in El Nino conditions and stronger activity in La Nino conditions (Seo 2009).

 Each model’s forecast skill has different sensitivity to the initial phase of MJO. NCEP
  model has less skill for the MJO with phase 3 and 4 initial condition than other phases.
  CMCC model has better skill when the MJO initially locates in the eastern Indian Ocean
  and west of the Maritime continent. Other models have less sensitive to the initial phase of
  MJO.
Thank You!
Individual Model Skills for MJO




 •   Evaluation of the temporal correlation coefficient (TCC) skill for the RMM1 and RMM2 using
     available hindcast data
 •   Validation dataset: NOAA OLR, U850 and U200 from NCEP Reanalysis II (NCEPII)
 •   Each model has different initial condition and forecast period.
MV EOF Modes for BS MISO




  Major Northward Propagating MISO mode   Grand Onset mode (LinHo and Wang 2002)
Hindcast Skill for the MISO index




  ISVHE from ABOM, CMCC, ECMWF, and JMA
  Period: MJJAS from 1989 to 2008
Example for the MISO forecast




                                    Jun 15, 2006


                     Jun 30, 2006

      Jun 15, 2006



                                     Jun 30, 2006

				
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