Intraseasonal Variability Hindcast Experiment (ISVHE)

Intraseasonal Variability Hindcast Experiment (ISVHE) June-Yi Lee 1, Bin Wang 1, D. Waliser 2, H. Hendon 3, I.-S. Kang 4, K. Sperber 5 and ISVHE Team members 1 International Pacific Research Center, University of Hawaii, USA 2 JIFRESSE, University of California, Los Angeles, USA 3 CAWCR, Bureau of Meteorology, Melbourne, Australia 4 Seoul National University, Seoul Korea 5 PCMDI, LLNL, Livermore, USA

Content 1 Description of the ISVHE and Relevant Projects 2 The MJO and BSISO Indices Lee, June-Yi, Bin Wang, Matthew C. Wheeler, Xiouhua Fu, Duane E. Waliser, and In-Sik Kang, 2012: Real-time multivariate indices for the boreal summer intraseasonal oscillation over the Asian summer monsoon region. Climate Dynamics, 40:493-509 3 Multi-model Ensemble Prediction for the MJO and BSISO using the ISVHE dataset

Relevant Projects Website: http://iprc.soest.hawaii.edu/users/jylee/clipas

The CliPAS Project Climate Prediction and its Application to Society IAP SNU NASA GFDL FRCGC COLA FSU NCEP ABOM UH IPRC Wang, Bin, June-Yi Lee, J. Shukla, I.-S. Kang, C.-K. Park et al., 2009: Advance and prospectus of seasonal prediction: Assessment of APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980-2004). Clim. Dyn. 33, 93-117 Lee, June-Yi, B. Wang, I.-S. Kang, J. Shukla et al., 2010: How are seasonal prediction skills related to models performance on mean state and annual cycle? Clim. Dyn. 35, 267-283 Wang, B., B. Xiang, and J.-Y. Lee, 2012: Subtropical high predictability establishes a promising way for monsoon and tropical storm prediction. PNAS in press 36 Papers published since 2008

CliPAS: Climate Prediction and Its Application to Society The international project, the CliPAS, in support of APCC is aimed at establishing well-validated multi-model model ensemble (MME) prediction systems for climate prediction and developing economic and societal applications. CliPAS/APCC Investigators BMRC: : O. Alves CES/SNU: : I.-S. Kang, J.-S. Kug COLA/GMU: : J. Shukla, B. Kirtman, J. Kinter, K. Jin FSU: : T. Krishnamurti, S. Cocke, FRCGC/JAMSTEC: : J. Luo, T. Yamagata (UT) IAP/CAS: : T. Zhou, B. Wang KMA: : W.-T. Yun NASA/GSFC: : M. Suarez, S. Schubert, W. Lau NOAA/GFDL: : N.-C. Lau, T. Rosati, W. Stern NOAA/NCEP: : J. Schemm, A. Kumar UH/IPRC/ICCS: : B. Wang, J.-Y. Lee, P. Liu, L. X. Fu APCC/CliPAS

CliPAS Models Hindcast System (1980-2004) APCC/CliPAS Tier-1 Models Institute AGCM Resolution OGCM Resolution Ensemble Member Reference BMRC BAM v3.0d T47L17 ACOM2 0.5-1.5 o latx 2 o lon L25 10 Zhong et al., 2005 FRCGC ECHAM4 T106 L19 OPA 8.2 2 o cos(lat)x2 o lon L31 9 Luo et al. (2005) GFDL R30 R30L14 R30 R30 L18 10 Delworth et al. (2002) NASA NSIPP1 2 o lat x 2.5 o lon L34 Poseidon V4 1/3 o lat x 5/8 o lon L27 3 Vintzileos et al. (2005) NCEP GFS T62 L64 MOM3 1/3 o lat x 1 o lon L40 15 Saha et al. (2005) SNU SNU T42 L21 MOM2.2 1/3 o lat x 1 o lon L32 6 Kug et al. (2005) UH ECHAM4 T31 L19 UH Ocean 1 o lat x 2 o lon L2 10 Fu and Wang (2001) APCC/CliPAS Tier-2 Models Institute AGCM Resolution Ensemble Member SST BC FSU FSUGCM T63 L27 10 SNU SST forecast Reference Cocke, S. and T.E. LaRow (2000) GFDL AM2 2 o lat x 2.5 o lon L24 10 SNU SST forecast Anderson et al. (2004) IAP LASG 2.8 o lat x 2.8 o lon L26 6 SNU SST forecast Wang et al. (2004) NCEP GFS T62 L64 15 CFS SST forecast Kanamitsu et al. (2002) SNU/KMA GCPS T63 L21 6 SNU SST forecast Kang et al. (2004) UH CAM2 T42 L26 10 SNU SST forecast Liu et al. (2005) UH ECHAM4 T31 L19 10 SNU SST forecast Roeckner et al. (1996) CliPAS

Semi Operational 6-Month 6 Prediction (since 2006) APCC launched its six-month-lead seasonal climate prediction using four ocean atmosphere land coupled models, which is issued four times a year. All coupled model hindcast experiments cover the 23-year period of 1983 2005, and their real-time forecasts for the six-year period of 2006 present are also considered. Institute (model name) APCC (APCC-CCSM3) BoM (POAMA) FRCGC (SINTEX-F) NCEP (NCEP_CFS) SNU (SNU) AGCM (resolution) CAM3 (T85L26) BAMv3.0d (T47L17) ECHAM4 (T106L19) GFS (T62L64) SNU (T42L21) OGCM (resolution) POP1.3 (Gx1v3 L40) ACOM2 (0.5-1.5 lat x 2 lon L25) OPA8.2 (2 cos(lat) x 2 lon L31) MOM3 ( 1/3 lat x 1 lon L40) MOM2.2 ( 1/3 lat x 1 lon L32) Ensemble member 5 10 9 15 6 Reference Jeong et al. (2008) Zhao and Hendon (2009) Luo et al. (2005) Saha et al. (2006) Ham and Kang (2010) CliPAS

The ISVHE Project Intraseasonal Variability Hindcast Experiment The ISVHE is a coordinated multi-institutional ISV hindcast experiment supported by APCC, NOAA CTB, CLIVAR/AAMP, YOTC/MJO TF, and AMY. ECMWF PNU SNU EC NCEP CMCC CWB JMA GFDL NASA JAMSTEC UH IPRC ABOM Supporters Website: http://iprc.soest.hawaii.edu/users/jylee/clipas

ISVHE Participations Institution Participants Current Participating Groups ABOM, Australia CMCC, Italy CWB, Taiwan ECMWF, EU GFDL, USA JAMSTEC, Japan JMA, Japan MRD/EC, Canada NASA/GMAO, USA NCEP/CPC PNU, Korea SNU, Korea UH/IPRC, USA Harry Hendon, Oscar Alves Antonio Navarra, Annalisa Cherichi, Andrea Alessandri Mong-Ming Lu Franco Molteni, Frederic Vitart Bill Stern T. Yamagata, J.-J. Luo Kiyotoshi Takahashi Gilbert Brunet, Hai Lin S. Schubert Arun Kumar, Jae-Kyung E. Schemm Kyung-Hwan Seo In-Sik Kang 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 (BSISO) 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 Multi-model Hindcast 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 AOGCMs or AGCM simulation with specified boundary forcing for at least 20 years Daily or 6-hourly output ISV hindcast initiated every 10 days on 1 st, 11 th, and 21 st of each calendar month for at least 45 days with more than 6 ensemble members from 1989 to 2008 Daily or 6-hourly output Additional ISO hindcast EXP from May 2008 to Sep 2009 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.

Description of Models and Experiments One-Tier System Model Control Run ISO Hindcast Period Ens No Initial Condition ABOM POAMA 1.5 & 2.4 (ACOM2+BAM3) CMIP (100yrs) 1980-2008 10 The first day of every month CMCC CMCC (ECHAM5+OPA8.2) CMIP (20yrs) 1989-2008 5 Every 10 days 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 JAMSTEC SINTEX-F CMIP (20yrs) 1989-2008 9 The first day of every month NCEP/CPC CFS 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 SNU SNU CM (SNUAGCM+MOM3) CMIP (20yrs) 1989-2008 1 Every 10 days UH/IPRC UH HCM CMIP (20yrs) 1994-2008 6 Every 10 days Two-Tier System Model Control ISO Hindcast 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

On-going researches 1. Overview of MJO and BSISO prediction in ISVHE June-Yi Lee and Bin Wang lead 2. Intrinsic modes of MJO and BSISO in ISVHE coupled models June-Yi Lee and Bin Wang lead 3. MJO predictability Duane E. Waliser and Neena Joseph lead 4. ISO prediction over the eastern Pacific Duane E. Waliser and Neena Joseph lead 5. Prediction of MJO teleconnection Net Johnson leads 6. Prediction of BSISO teleconnection Ja-Yeon Moon leads Data server: apcc1.soest.hawaii.edu, (PRCP, OLR, U850, U200, V850, V200, Z500, MSLP, TSFC)

Interannual vs Intraseasonal OLR Variability MJJASO NDJFMA Intraseasonal : 20-90 Interannual : > 90

Bimodal Representation of the Tropical ISO Boreal Winter Rainfall anomalies propagate in a eastward fashion and mainly affect the Tropical eastern hemisphere Boreal Summer Rainfall anomalies propagate in a northeast fashion and mainly affect the Asian summer monsoon region Spatial temporal pattern of OLR anomaly associated with the intraseasonal oscillation during (a) boreal winter (DJF, referred to as Madden Julian Oscillation (MJO) mode) and (b) boreal summer (JJA, referred to as BSISO mode) by means of the extended EOF (EEOF) analysis. Kikuch et al. (2011, Clim Dyn)

The RMM Index for the MJO As a measure of the strength of the MJO, Wheeler and Hendon (2004) Realtime Multivariate MJO (RMM) index used the first two leading multivariate EOF modes of the equatorial mean (between 15S and 15N) OLR, and zonal winds at 850 and 200 hpa. This index captures equatorial eastward propagating mode, the MJO, very well and has been applied all year around to depict MJO activity. Wheeler an Hendon (2004) RMM index has limitation to explain ISO over offequatorial monsoon domain during boreal summer.

Definition of the BSISO index Lee, June-Yi, Bin Wang, Matthew C. Wheeler, Xiouhua Fu, Duane E. Waliser, and In-Sik Kang, 2013: Real-time multivariate indices for the boreal summer intraseasonal oscillation over the Asian summer monsoon region. Climate Dynamics, 40:493-509 Data Process Variables : daily OLR and U850 Data Period: MJJASO 1981-2010 Removal of the first 3 harmonics in climatological annual cycle Removal of the effect of ENSO signal through subtracting last 120 day mean Normalization of each of two fields by area averaged temporal standard deviation (The ASM standard deviation is 33.04 W m -2 for OLR and 4.01 m s -1 for U850) BSISO index: The first four leading multivariate EOF modes of daily OLR and U850 over the ASM region (10 o S-40 o N, 40 o -150 o E) Filtering is not applied to define BSISO index for monitor and forecast purpose Criterion for Determining the BSISO Index 1. Fractional variance explained by the reconstructed field from the BSISO index 2. Ability to capture the northward propagating ISO

BSISO1: Canonical Northward Propagating BSISO Mode BSISO1, consisting of EOF1 and EOF2, represents the canonical northward and northeastward propagating ISO over the ASM region during the entire warm season from May to October with quasi-oscillating periods of 30-60 days in conjunction with the eastward propagating MJO. Spatial Characteristics Seasonal cycle of variance Coherence and lead-lag relationship PC1 tends to lead PC2 by about 13 days with a maximum correlation of 0.34 for non-filtered data. PC1 (PC2) is more significantly correlated with RMM2 (RMM1). The greatest coherence in the 30- to 60-day range with a 90 o phase difference

BSISO1: Canonical Northward Propagating BSISO Mode BSISO1, consisting of EOF1 and EOF2, represents the canonical northward and northeastward propagating ISO over the ASM region during the entire warm season from May to October with quasi-oscillating periods of 30-60 days in conjunction with the eastward propagating MJO. Spatial Characteristics Seasonal cycle of variance Given the strong lead-lag behavior of PC1 and PC2, it is convenient to diagnose the state of BSISO1 as a point in the two-dimensional phase space. Coherence and lead-lag relationship Phase space composite curves

BSISO1: Canonical Northward Propagating BSISO Mode BSISO1, consisting of EOF1 and EOF2, represents the canonical northward and northeastward propagating ISO over the ASM region during the entire warm season from May to October with quasi-oscillating periods of 30-60 days in conjunction with the eastward propagating MJO. Life cycle composite of OLR (shading) and 850-hPa wind anomalies Spatial Characteristics Seasonal cycle of variance Coherence and lead-lag relationship Phase space composite curves Composite life cycle

BSISO2: The ASM pre-monsoon and onset mode BSISO2, consisting of EOF3 and EOF4, captures the northward/northwestward propagating variability with periods of 10-30 days during primarily the pre-monsoon and monsoon-onset season. Spatial Characteristics Seasonal cycle of variance Maximum variance from late May to early July, corresponding to the pre-monsoon and onset period

BSISO2: The ASM pre-monsoon and onset mode BSISO2, consisting of EOF3 and EOF4, captures the northward/northwestward propagating variability with periods of 10-30 days during primarily the pre-monsoon and monsoon-onset season. Spatial Characteristics Seasonal cycle of variance Coherence and lead-lag relationship PC3 tends to lead PC4 by about 3-4 days for 10-20 period and 7-8 days for 30 day period. BSISO2 is not correlated with MJO High coherence in the 10-20 day range and at ~30 days, with 90 o phase difference

BSISO2: The ASM pre-monsoon and onset mode BSISO2, consisting of EOF3 and EOF4, captures the northward/northwestward propagating variability with periods of 10-30 days during primarily the pre-monsoon and monsoon-onset season. Spatial Characteristics PC3 and PC4 phase space composite curves Seasonal cycle of variance Coherence and lead-lag relationship Phase space composite curves

BSISO2: The ASM pre-monsoon and onset mode BSISO2, consisting of EOF3 and EOF4, captures the northward/northwestward propagating variability with periods of 10-30 days during primarily the pre-monsoon and monsoon-onset season. Spatial Characteristics Life cycle composite of OLR (shading) and 850-hPa wind anomalies Seasonal cycle of variance Coherence and lead-lag relationship Phase space composite curves Composite life cycle

BSISO Real-time Monitoring Website: http://iprc.soest.hawaii.edu/users/jylee/bsiso Paper: Real-time multivariate indices for the BSISO over the ASM region

Fractional Variance Explained by the MJO and BSISO Indices By the RMM Index By the BSISO Indices

The MME and Individual Model Skills for MJO Anomaly Correlation Coefficients (1989-2008, NDJFMA) Common Period: 1989-2008 Initial Condition: 1 st day of each month from November to April MME: Simple composite with eight coupled models

The MME and Individual Models Skill for BSISO Anomaly Correlation Coefficients (1989-2008, MJJASO) BSISO1 Common Period: 1989-2008 Initial Condition: 1 st day of each month from May to October MME: Simple composite with seven models BSISO2 Using the MME, forecast skill for BSISO1 reaches 0.5 at 15 to 20-day forecast lead and for BSISO2 at 10- to 15-day forecast lead.

Phase Dependency of MJO Forecast Skills

Phase Dependency of BSISO Forecast Skills Life cycle composite of OLR (shading) and 850-hPa wind anomalies

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