1. 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

2. 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

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

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

5. 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

6. 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
1.4x L29
Gregory et al. (2000)
Wolff et al. (1997)
INGV
ECHAM-4
T42 L19
OPA 8.1
2.0x
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
2.5x
L23
Pope et al. (2000)
Gordon et al. (2000)
UK Met Office
HadAM3
2.5x3.75 L19
GloSea OGCM based on HadCM3
1.25x
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.

7. 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.

8. 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)

9. 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”.

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

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

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

13. 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

15. 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

16. (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 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)

17. How well do the ten coupled models and their MME capture these leading modes for the hindcast period?

18. 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)

19. Comparison of Spatial Patterns

20. 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

21. 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

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

23. 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.

24. Why there is a drop of skill in spataiala pattern?

25. 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

26. 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.

27. 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.

28. 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”.

29. 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.

30. 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.

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

32. 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?

33. Any Questions and comments?
Thanks

34. 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.