ISO Hindcast Experiment ( ): A joint effort by APCC, AAMP, YOTC, AMY

Transcription

1 Climate Prediction and its Association to Society ISO Hindcast Experiment ( ): A joint effort by APCC, AAMP, YOTC, AMY Bin Wang, J.-Y. Lee, H. Hendon, D. Waliser, I.-S. Kang, J. Shukla, K. Sperber and ISO Hindcast Team APCC/CliPAS

2 APCC/CliPAS Background Forecast of MJO and MISO is one of the major concerns of APCC, YOTC, CLIVAR/AAMP and AMY( ). It is also a central theme for WCRP cross-cutting monsoon research. Determination of ISO prediction skill and estimate ISO predictability in current AOGCMs is a pressing scientific need for developing 2-6 week subseasonal prediction. Launching a coordinated ISO hindcast experiment was recommended at the Nov 2007 CLIVAR MJO Workshop, endorsed and supported by APCC, CLIVAR/AAMP, and the SSC of AMY ( ), and echoed by THORPEX.

3 Need for a Coordinated ISO Hindcast Exp. APCC/CliPAS 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. Development of an MME is intrinsic need for leaddependent 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.

4 Objectives Better understand physical basis for ISO prediction. Estimate potential and practical predictability of ISO with multi-model. Developing optimal strategies for MME ISO prediction. Identify model deficiencies in predicting ISO and suggest ways to improve models through development of model process diagnostics. Revealing new physical mechanisms associated with ISV that cannot be obtained from analyses of a single model. Study ISO s modulation of extreme hydrological events and its contribution to S-I climate variation.

5 Experimental Design EXP1: Control Simulation Free runs with coupled OGCMs or forced AGCM simulation with specified boundary conditions are requested for at least 20 years. The period for the forced AGCM run should be consistent with the hindcast period. The long-term simulation allows us to better understand the dependence of the prediction on initial conditions and better define metrics that measure the "drift" of the model toward their intrinsic MJO/MISV modes EXP2: 21-year (January Oct ) ISO Hindcast Re Forecast Period 20 years from 1989 to 2008 Initial Date The Length of Integration Ensemble Member Initial condition Every 10 days on 1 st, 11 th, and 21 st of each calendar month At least 45 days At least 5 members Initial conditions may use 12-hour lags No uniform specification regarding model resolution and initialization procedures. (for AGCM experiments, the ERA, NCEP 2 were recommend for initial conditions) No information from future is used, for AGCM experiments, SST must be forecasted. APCC/CliPAS

6 Requested output data and information APCC/CliPAS The data requested permit a basic understanding of (a) the models ability to spontaneously generate MJO s/miso s in the control simulation, (b) the model predictability and prediction skill of ISO and its seasonal, interannual and MJO life- cycle phase dependencies, (c) the models weakness and (d) To reveal new physical mechanisms. Requested data and information Model description Output from control and hindcast experiments I. Atmospheric 2D fields: II. Atmosphere 3-D fields at 17 standard pressure levels III. Upper Ocean 3D fields (for coupled models) from surface to 300m. Outputs for different experiments (1) Control simulations: 6-hour values of items I, II and III. (2) The 21-year hindcasts : Daily mean values of items I and III. (3) The YOTC Period: 6-hour values of items I, II and III.

7 Current Participating Group Institution ABOM, Australia BCC/CMA, China CMCC/Italy COLA/GMU, USA CWB, Taiwan ECMWF, EU GFDL, USA IAP/LASG, China IITM, India JAMSTEC/APL, Japan JMA, Japan MRD/EC, Canada NASA/GMAO, USA NCEP/CPC NCMRWF, India PNU, Korea SNU, Korea UH/IPRC, USA UM, USA Participants Harry Hendon, Oscar Alves Zhang Peigun, Chen Lijuan Tony Navarra, Annalisa Cherichi, Andrea Alessandri Emilia K. Jin, J. Kinter, J. Shukla Mong-Ming Lu Franco Molteni, Frederic Vitart Bill Stern T. Zhou, B. Wang, Y. Q. Yu A. K. Sahai T. Yamagata, J.-J. Luo Kiyotoshi Takahashi Gilbert Brunet, Hai Lin S. Schubert Arun Kumar, Jae-Kyung E. Schemm Ashwini Bohara Kyung-Hwan Seo, Joong-Bae An In-Sik Kang Bin Wang, Xiouhua Fu, June-Yi Lee Ben Kirtman APCC/CliPAS

8 APCC/CliPAS Current Status of the Experimental reuslts Institution Participants Model Current Status ABOM Harry Hendon POAMA year integration initiated the first day of every month Collected CGCM with 10 ensemble simulations ( ) CMCC Tony Navarra CMCC A. Alessandri CGCM 20-year integration initiated every 10 days ( ) Collected CWB Mong-Ming Lu CWB AGCM 25-year integration initiated every 10 day ( ) Collected ECMWF F. Molteni, ECMWF 20-year integration initiated the 15 th of every month Collected Frederic Vitart CGCM ( ) GFDL W. Stern CM year integration initiated the first day of every month Collected CGCM ( ) JMA K. Takahashi JMA AGCM 20-year integration initiated every month ( ) NASA/ S. Schubert GMAO 20-year integration initiated every day ( ) GMAO NCEP/ CPC SNU UH/IPRC MRD/EC P. Pegion A. Kumar J.K.E. Schemm I.-S. Kang X. Fu J.-Y. Lee Gilbert Brunet Hai Lin AGCM CFS CGCM SNU CGCM UH CGCM 26-year integration initiated every 10 days ( ) 21-year integration initiated every five days during NDJFM season ( ) 20-year integration initiated every 5 day during MJJAS ( ) Collected Collected Collected MRD AGCM 24-year integration initiated every 10 days ( ) Collected

9 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, NOAA CTB, and AMY. ECMWF PNU SNU EC NCEP CMCC CWB JMA GFDL NASA UH IPRC ABOM Supporters

10 Description of Model and Experimental Design One-Tier System ABOM Model POAMA 1.5 (ACOM2+BAM3) Control Run ISO Hindcast Period Ens No Initial Condition CMIP The first day of every month CMCC INGV (ECHAM4+OPA8.1) CMIP (20yrs) Every 10 days ECMWF ECMWF (IFS+HOPE) CMIP(11yrs) The 15 th day of every month GFDL CM2 (AM2/LM2+MOM4) CMIP The first day of every month NCEP/CPC CFS (GFS+MOM3) CMIP (100yrs) Every 10 days SNU SNU CM (SNUAGCM+MOM3) CMIP (20yrs) Every 10 days UH/IPRC UH HCM CMIP Every 10 days during MJJAS Two-Tier System Model Control ISO Hindcast Run Period Ens No Initial Condition CWB CWB AGCM AMIP (25yrs) Every 10 days JMA (not collected) JMA AGCM AMIP The first day of every month MRD/EC CCCma AMIP (21yrs) Every 10 days NASA/GMAO (not collected) NSIPP AMIP Every day APCC/CliPAS

11 APCC/CliPAS Contact Information If you have any questions regarding the experiment design and data submission, please contact coordinator Dr. June-Yi Lee Detailed information are posted on the website ( iso.html)

12 Data policy The experimental dataset will be immediately available to participating groups once the results are collected and passed quality check. Users should utilize the hindcast dataset for research purpose only and shall not distribute the hindcast datasets to any third parties. The source of the datasets shall be duly acknowledged in scientific or technical papers, publications, press releases, or any other communications regarding the datasets. For those users who used JMA dataset shall provide JMA with a copy of their scientific or technical papers, publications, press releases or any other communications regarding the JMA datasets. APCC/CliPAS

13 Fig. 1 (a) Variance of pentad mean OLR anomaly (W 2 m -4 ) after removing climatological annual cycle and interannual variability during November to April (NDJFMA) and May to October (MJJASO), respectively. (b) Fractional variance (%) of 5-day mean OLR anomaly accounted by the two-component RMM index. Blue dashed line in right-hand side of (a) indicates the Asian summer monsoon (ASM) domain. Red contour in (b) represents OLR variability center with variance larger than 800 W 2 m -4 shown in (a).

14 Fig. 2 Spatial structure (a, b) and PC time series (c) of the first two leading MV-EOF modes of daily OLR (shading) and zonal wind at 850 hpa (U850) anomalies normalized by their area averaged temporal standard deviation over the ASM region (33.04 W m -2 for OLR and 4.01 m s -1 for U850). To display the full horizontal wind vector, the associated meridional wind at 850 hpa (V850) was obtained by regressing V850 anomaly, normalize by its area averaged standard deviation (3.14 m s -1 ), against each PC. The MV-EOF modes were obtained during MJJASO for the 30 years of

15 Fig.3 Same as Fig. 2 except for the 3 rd and 4 th modes

16 Fig. 9 The life cycle composite of OLR (shading) and 850-hPa wind (vector) anomaly reconstructed based on PC1 and PC2 of BSISO1 in 8 phases.

17 Fig. 10 Same as Fig. 9 except for PC3 and PC4 of BSISO2.

18 Fig. 11 Spatial distribution of fractional variance of pentad (a) OLR and (b) U850 anomaly that are accounted for by the first two PCs (BSISO1 only in upper panels), the first four PCs (BSISO1 and BSISO2 in middle panels) and the two-component RMM index (lower panels).

19 TCC Skill for RMM Index/ ONDJFM Can the MME approach improve MJO forecast? Common Period: Initial Condition: 1 st 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 MME_MLRM: MME with weighting ft. Independent forecast ( ) skill using MME_MLRM is not better than the simple MME skill.

20 TCC Skill for the MISO Index/ MJJAS

21 APCC/CliPAS Forecast Skills of Coupled Models/ Pentad Mean Anomaly Pattern Correlation Coefficients (30 o S-30 o N, 40 o -160 o E) ONDJFM AMJJAS

22 Forecast Skills of Coupled Models/ ONDJFM Temporal Correlation Coefficient Skill for U850 ABOM every month CMCC GFDL NCEP SNU every 10 days every month every 10 days every 5 days 1 Pentad 2 Pentad 3 Pentad 4 Pentad APCC/CliPAS

23 APCC/CliPAS Forecast Skills of Coupled Models/ ONDJFM Temporal Correlation Coefficient Skill for OLR ABOM every month CMCC every 10 days GFDL every month NCEP every 10 days SNU every 5 days 1 Pentad 2 Pentad 3 Pentad 4 Pentad

24 Forecast Skills of Coupled Models/ AMJJAS Temporal Correlation Coefficient Skill for U850 ABOM every month CMCC GFDL NCEP UH every 10 days every month every 10 days every 10 days 1 Pentad 2 Pentad 3 Pentad 4 Pentad APCC/CliPAS

25 Forecast Skills of Coupled Models/ AMJJAS Temporal Correlation Coefficient Skill for OLR ABOM every month CMCC GFDL NCEP UH every 10 days every month every 10 days every 10 days 1 Pentad 2 Pentad 3 Pentad 4 Pentad APCC/CliPAS

26 APCC/CliPAS Forecast Skills for the RMM Index Temporal Correlation Skill for the RMM1 and RMM2 ONDJFM AMJJAS

27 MISO Index: The First Four PCs of ASM EOF of U850 and OLR APCC/CliPAS Eigen value for each mode with uncertainty Power Spectra Correlation Coefficients against Each Mode -lag Correlation Between Modes

28 Forecast Skills for the MISO Index APCC/CliPAS