CLIVAR/ISVHEIntraseasonal Variability Hindcast Experiment

Supporters

Design and Preliminary resultsDesign 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

ECMWF

JMACWB

ABOM

EC

NCEP

CLIVAR/ISVHEIntraseasonal Variability Hindcast Experiment

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

UH IPRC

Supporters

SNUPNU

GFDL NASA

CMCC

http://iprc.soest.hawaii.edu/users/jylee/clipas.htm

ISVHE Participations

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

Current Participating Groups

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). 

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.

The Boreal Summer Monsoon Intraseasonal Oscillation (MISO)

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

Free coupled runs with AOGCMs or AGCM simulation with specified boundary forcing for at least 20 years

Daily or 6-hourly output

Control Run

ISV hindcast initiated every 10 days on 1st, 11th, and 21st of each calendar month for at least 45 days with more than 6 ensemble members from 1989 to 2008

Daily or 6-hourly output

ISV Hindcast EXP

Additional ISO hindcast EXP from May 2008 to Sep 2009

6-hourly output

YOTC EXP

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

temperature, salinity, ocean currents (U and V), and vertical motion from surface to 300m

III. Upper Ocean 3D Field

total precipitation rate, OLR, surface (2m) air temperature, SST, mean sea level pressure, surface heat fluxes (latent, sensible, solar and longwave radiation), surface wind stress, and geopotential, horizontal wind fields (u and v) at three specific levels: 850, 500, and 200 mb

I. Atmospheric 2D Field

17 standard pressure levels: humidity, temperature, horizontal wind and vertical pressure velocity (Pa/s), and each of the components of the diabatic heating rates (e.g., shortwave, longwave, stratiform cloud, deep and shallow convection).

II. Atmospheric 3D Field

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

  ModelControl Run

ISO Hindcast

Period Ens No Initial Condition

ABOMPOAMA 1.5 & 2.4(ACOM2+BAM3)

CMIP (100yrs) 1980-2006 10 The first day of every month

CMCCCMCC (ECHAM5+OPA8.2)

CMIP (20yrs) 1989-2008 5 Every 10 days

ECMWF ECMWF (IFS+HOPE) CMIP(11yrs) 1989-2008 15 Every 15 days

GFDLCM2 (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

NCEP/CPCCFS v1 (GFS+MOM3) & v2

CMIP 100yrs 1981-2008 5 Every 10 days

PNU CFS with RAS scheme CMIP (13yrs) 1981-2008 3 The first day of each month

SNUSNU CM(SNUAGCM+MOM3)

CMIP (20yrs) 1989-2008 1 Every 10 days

UH/IPRC UH HCM CMIP (20yrs) 1994-2008 6 Every 10 days

One-Tier System

  ModelControlRun

ISO Hindcast

Period Ens No Initial Condition

CWB CWB AGCM AMIP (25yrs) 1981-2005 10 Every 10 daysMRD/EC GEM AMIP (21yrs) 1985-2008 10 Every 10 days

Two-Tier System

Evaluation on Control Runs

Variance of 20-100-day Bandpass Filtered Precipitationin Observation and Control Simulations (NDJFMA)

Evaluation on Control Runs

Pattern Correlation Coefficient and Normalized Root Mean Square Error forMean 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

1 PentadLead

2 PentadLead

3 PentadLead

4 PentadLead

Temporal Correlation Coefficient Skill for U850

ABOM JMA NCEPISO ISO+IAV ISO ISO+IAV ISO ISO+IAV

Can the MME approach improve MJO forecast?

The MME and Individual Model Skills for MJO

Common Period: 1989-2008Initial Condition: 1st day of each month from Oct to MarchMME1: Simple composite with all modelsMMEB2: Simple composite using the best two modelsMMEB3: 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 JMAPeriod: MJJAS from 1989 to 2008

Jun 15, 2006

Jun 30, 2006

Jun 15, 2006

Jun 30, 2006

Example for the MISO forecast