To What Extent can Coupled Climate Models Predict Major Modes of A-AM Variability? Bin Wang1, June-Yi Lee1, In-Sik Kang2, J. Shukla3, J.-S. Kug2, A. Kumar4, J. Schemm4, J.-J. Luo5, T. Yamagata5, C.-K. Park6 1. University of Hawaii 2. Seoul National University 3. George Mason University and COLA 4. National center for Environmental prediction 5. University of Tokyo and FRCGC 6. APEC Climate Center

Acknowledge DEMETER and Contributions from the APCC/CliPAS Team members COLA/GMU: B. Kirtman, J. Kinter, K. Jin FSU: T. Krishnamurti, S. Cocke, FRCGC/JAMSTEC: T. Yamagata, J. Luo, NOAA/GFDL: N.-C. Lau, T. Rosati, W. Stern NASA/GSFC: S. Schubert, M. Suarez, W. Lau NOAA/NCEP: A. Kumar , J. Schemm IPRC/UH: P. Liu, X. Fu CES/SNU: J.-S. Kug IAP/CAS: T. Zhou, Y. Yu KMA: W.-T. Yun

APCC/CliPAS Research Team SNU COLA IPRC NCEP IAP FSU FRCGC GFDL NASA BMRC

ACC Skill of Precipitation AAM (40-160E): moderate skill; land monsoon region: little skill

Physical Basis for Monsoon Prediction Why coupled models? Physical Basis for Monsoon Prediction a. 5-AGCM ensemble hindcast skill (20 years) AGCMs forced by observed SST failed monsoon rainfall prediction. Models yield positive SST-rainfall correlations that are at odds with observation. Treating monsoon as a slave to prescribed SST results in the models’ failure. b. OBS SST-rainfall correlation c. Model SST-rainfall correlation Wang, et al. 2005

GloSea OGCM based on HadCM3 Description of the 10 Coupled Models Institute AGCM Resolution OGCM Ensemble Member Reference FRCGC ECHAM4 T106 L19 OPA 8.2 2ocos(lat) x 2o lon L31 9 Luo et al. (2005) NCEP GFS T62 L64 MOM3 1/3olat x 5/8olon L27 15 Vintzileos et al. (2005) SNU T42 L21 MOM2.2 1/3olat x 1olon L40 6 Kug et al. (2005) CERFACS ARPEGE T63 L31 OPA8.2 2.0o x 2.0o L31 Deque (2001) Delecluse and Madec (1999) ECMWF IFS T95 L 40 HOPE-E 1.4x0.3-1.4 L29 Gregory et al. (2000) Wolff et al. (1997) INGV ECHAM-4 T42 L19 OPA 8.1 2.0x0.5-1.5 L29 Roeckner (1996) Madec et al. (1998) LODYC T95 L40 2.0x2.0 Meteo-France OPA 8.0 182GPx152GP Madec et al. (1997) MPI ECHAM-5 MPI-OM1 2.5x0.5-2.5 L23 Pope et al. (2000) Gordon et al. (2000) UK Met Office HadAM3 2.5x3.75 L19 GloSea OGCM based on HadCM3 1.25x0.3-125 L40 Marsland et al. (2003) What we have here is one-tier systems from DEMETER several models. AGCM, AGCM resolution, OGCM, OGCM resolution… All those seven models are one-tier coupled ocean-atmosphere models but independent ocean models are probably only four and independent atmospheric model are also four. So there are many model share common components . Table 1 Description of 10 coupled atmosphere-ocean models.

Why care the major modes The first two distinguished major modes of interannual AAM variability capture ~45% of the total variance in seasonal precipitation variation. They have clear physical meaning and determined by slow coupled dynamics, providing a meaningful measure for A-AM predictability. Models’ capability in capturing these leading modes reflect the models’ seasonal prediction skills.

Questions 1. What are the essential features of the major modes of IAV of A-AM? 2. How well do the ten coupled models and their MME capture these leading modes? 3. How does the forecast performance decay with lead time? (NCEP/CFS analysis)

How to determine the major modes? Physical consideration Season-Reliant EOF (Wang and An 2005, GRL) Physical consideration Anomalous climate (ENSO, monsoon) is regulated by the seasonal march of the solar radiation forcing (annual cycles). Season-reliant EOF (S-EOF) analysis detects seasonal evolving major modes of climate variability. Procedure Constructing a covariance matrix by treating the given set of seasonal sequence as one time step (year). The obtained spatial patterns for each mode describes season-evolving anomalies in a given “monsoon year”.

What are the essential features of the major modes of IAV of A-AM?

S-EOF1: Left :500 omega& 850 winds; right Rain & SST JJA(0) SON(0) DJF(0/1) MAM(1)

S-EOF2: Left: 500 Omega & 850 winds; right: Rain & SST JJA(0) SON(0) C DJF(0/1) C MAM(1)

0.83 0.13 -0.29 0.73 Relationship with ENSO 56-04 S-EOF1(0) S-EOF2(0) NINO3.4 SSTA (0) 0.83 0.13 SSTA (1) -0.29 0.73 S-EOF1 exhibits a prominent biennial tendency and concurs with the turnabout of ENSO S-EOF2 leads ENSO by one year and provides a precursor for ENSO

2) The local monsoon-warm ocean interaction is important What determine the leading mode of variability? 1) The remote El Nino/La Nina forcing is a primary factor, but not a full story. 2) The local monsoon-warm ocean interaction is important Rossby wave – SST dipole feedback 3) Annual cycle 1) controls the nature of the monsoon-ocean feedback 2) modifies the atmospheric response to remote El Niño forcing

(Websetr et al 1999. Saji et al 1999) Monsoon-warm pool interaction mechanism a) Equatorial Walker cell-SST feedback through upwelling and thermocline displacement (Websetr et al 1999. Saji et al 1999) b) Off-equatorial moist Rossby wave – ocean mixed layer feedback (Wang et al. 2000) c) Monsoon-induced negative feedback: Ekman transport (Webster et al. 2002); Surface heat flux (Lau and Nath 2000) Indian Ocean Coupled Mode (IOCM) tends to maintain TBO (Li et al. 2006) (Wang et al 2003) (Wang et al. 2000)

How well do the ten coupled models and their MME capture these leading modes for the 1981-2001 hindcast period?

Fractional variances of the S-EOFs (precipitation) Observed first two modes are statistically distinguished. (North et al. 1982) The MME predict distinguished modes well But MME tends to exaggerate the fractional variance of the first mode. .(less noisy)

Comparison of Spatial Patterns

MME capture ENSO-MNS relation PC time series MMEs are highly correlated with CMAP PC Spectra: MMEs underestimate QB Peak and total variances S-EOF1 concurs with ENSO SEOF2 leads ENSO by 1 year

Comparison of MME forecast with Reanalyses The MME beats two re-analyses in capturing both the spatial patterns and temporal evolutions of the two leading modes

How does seasonal prediction skill depend on lead time How does seasonal prediction skill depend on lead time? NCEP/CFS analysis

ACC skill as function of lead time PCC of eigenvector (Blue curves) and temporal correlation of principle component (Red curves). AAC skill for the first PC remains exceeds 0.9 up to six months, Spatial pattern APCC has a drop around 3-month lead.

Why there is a drop of skill in spataiala pattern?

Fractional variance Percentage variances accounted for by the first two major modes as functions of the forecast lead time in CFS hindcast experiment. Long-lead forecast tends to overstress the first two leading modes while undervaluing the contributions from the higher modes. ENSO is the dominant source that can provide long-lead (eight-month) A-AM predictability

Power spectrum density of CFS seasonal forecast As lead time increases, the spectrum of PC 1 loses biennial peak quickly, and the power of PC2 is reduced substantially. The biennial component of the A-AM is more difficult to predict than the low-frequency component.

Conclusion (1) The CMAP anomalies show a Quasi-Biennial (QB) and Low-frequency (LF) Compt, which accounts for ~ 31% and 13% of the total variance. The QB mode concurs with ENSO, but LF mode leads ENSO by ~one year. The MME captures the first two leading modes with high fidelity in terms of spatial patterns, temporal variations, and the relations with ENSO. The MME has potential to capture the precursors of ENSO in the A-AM domain about four seasons prior to the maturation of a strong El Niño.

Conclusion (2) MME underestimates the total variances of the two modes and the biennial tendency of the first mode. The models have difficulties in capturing precipitation over the maritime continent and the Walker-type teleconnection in the decaying phase of ENSO, which contributes in part to a monsoon “spring prediction barrier”.

Conclusion (3) As the lead time increases, the fractional variance of the first mode increases, suggesting that the long-lead predictability of A-AM rainfall comes primarily from ENSO. Monsoon prediction has a “spring predictability” barrier, which is associated with ENSO SPB.

Surprising findings The coupled models’ MME beats two Reanalyses in capturing both the spatial patterns and temporal evolutions of the two leading modes Reason: Treating the atmosphere as a slave may be inherently unable to assimulate summer monsoon rainfall variations in the heavily precipitating regions (Wang et al. 2005). NCEP/CFS ensemble hindcast beats MME in capturing the biennial tendency and the amplitude of the anomalies. Implication: Improved skill of MME is at the expense of overestimating the fractional variance of the leading mode.

Recommendation Future reanalysis should be carried out with coupled atmosphere and ocean models.

Remaining Issues What give rise to the second important A-AM mode? What are additional source of predictability for A-AM rainfall prediction besides ENSO? What limit the predictability of the A-AM? Are skill-less monsoon predictions over the land related to models’ deficiencies in initial condition and atmosphere-land interaction?

Any Questions and comments? Thanks

CliPAS Climate Prediction and Its Application to Society Objectives A Joint International Research Project in Support of APCC Objectives Investigate a set of key scientific problems on multi-model ensemble (MME) climate prediction Establish well-validated MME prediction systems for intraseasonal and seasonal prediction Develop economic and societal application models.