Prediction of East Asian Summer Monsoon Intensity

Bin WangUniversity of Hawaii

FORCRAII 2011April 6-8, 2011 Beijing

JA-JF 925 hPa winds and precipitation rate (mm/day)

Fig. 1

Asian-Australian Monsoon System

Circulation systems differ between Indian and EA sectors

Indian sector EA-WNP sector

Seasonal Distribution of rainfall

Wang, Clemens and Liu 2003

WNPMIM

EAM

Fig. 2

An eastward shift of convection centers from Indian (in June-July) to the WNP (in August) during boreal summer . Peak and retreat dates differ.WNP is the largest heat source during NH summer.

Wang, B., Z. Wu, J. Li, J. Liu, C.-P. Chang, Y. Ding, and G.-X. Wu, 2008: How to Measure the Strength of the East Asian Summer Monsoon? J. Climate, 21, 4449-4463.

• WF shear vorticity index reflects the variations in both the WNP monsoon trough and Subtropical High, two key sub-systems.

• Conveniently monitor the variety of time scales ranging from daily to season.(NOAA monsoon website-Song Yang)

Measure of EASM Strength: the WF index

Wang and Fan 1999: Choice of South Asian Summer Monsoon Indices. BAMS

Figure 1. Regressed PREC/L precipitation (color shadings) to the EASM index (EASMI) [Wang et al., 2008c] for summer (JJA).

How well does the WF index represent the EA-WNP SM variation?

• Leading MV-PC 1 of the EASM system (r=-0.97 for 1979-2006).

• Rainfall variation over the WNP monsoon region (10-20N, 110-140E) (r=0.80 for 1979-2006).

• Leading EOF of JJA precipitation variations over a large subtropical and extratropical region (20-50N, 100-180E) (r=0.71 1979-2004) (Lee and Jhun 2005).

• Leading PC of the 850 hPa wind anomalies in a large domain (5-45N, 100-170E) (r=0.88 for a 50-year period1948-1997).

• Describing the year-to-year variation of the SCS summer monsoon onset (Wang et al 2004).

Prediction of the Intensity of EASM (Meiyu-Baiu)

Wu, ZW, B. Wang, and J. Li, and F.F. Jin,2009:An empirical seasonal prediction model of the East Asian summer monsoon using ENSO and NAO, J. Geophys. Res. VOL. 114, D18120.

EASMI = - WFI

EASMI = – 0.3 ENSOdecay+ 0.25ENSOdevelop+0.65 NAOI +7.52

ENSOdecay Niño3.4 index (December-February) (-0.62) ENSOdevelop, Niño3.4 index (April-May minus Feb-March) (0.58) NAOI : NAO index for April-May (Li and Wang 2003). (-0.56)

Correlation skill for EASMI is 0.79 for the period 1979-2006.

Empirical Prediction ofEASM Strength (EASMI or -WFI)

The EASM hindcast made by the conceptual model and the ensemble mean of the 14 state-of-the-art models (DEMETER and CliPAS). Cross validation: leaving-7-out. 0.69; 0.50

(0.50)

0.69

Hindcast for the 1979−2006 period and real forecast for the 2007−2010 period.

HINDCAST and REAL FORECAST TEST

Physical basis of the empirical prediction model

Liu et al. (2007)

The leading mode of EA-WNP SM variability (1979-2007)

Wang et al. 2008

MV-EOF 2 mode

How does ENSO affect Asian-Australian monsoon

Wang, B., R. Wu, and T. Li, 2003:Atmosphere-Warm Ocean interaction and its impact on Asian-Australian Monsoon variation. J. Climate, 16, 1195-1211.

17

S-SVD mode of 850 hPa winds and SST anomalies(1957–2001)

Wang, Wu, Li 2003

El Nino evolution

AAM Interannual Variation: Season-evolving dominant pattern

The evolution of SIO and WNP anticyclone are not in phase with El Nino “forcing”

Why ENSO has a delayedimpacts on EA summer

monsoon

•PSAC Maintenance mechanism•Coupled model Experiments

Wang, B., R. Wu, and X. Fu, 2000: Pacific-East Asia teleconnection: How does ENSO affect East Asian climate? J. Climate, 13, 1517-1536.

Schematic of the RW-ocean interaction in the western North Pacific in the winter and spring of the mature and decaying El Niño

Rossby wave-SST interaction mechanism

Experimental design (Lau et al. 2004)

CTRL: Climatology SST outside DTEPMLM: Coupled GCM-Mixed layer Ocean (Alexander et. Al. 2000)

GFDL R-30 L-14Ensemble runs: MLM16; CTRL8

Consequence of AO interaction: MLM-CTRL

2xSST-CLIMONC mm day-1

4 m s-1

Lau et al. 2004

Figure 3. Time series of the EASMI and spring NAO index (NAOI) [Li and Wang, 2003] for the 1979-2006 period. For comparison, the sign of NAOI is reversed.

Figure 5. Correlation patterns between sea surface temperature (SST) in North Atlantic and EASMI (NAOI) time series in Fig. 3 from preceding spring through following summer. The averaged SST in the three red boxes is used to quantify the tri-pole SST pattern in North Atlantic during boreal summer by a tri-pole SST index (TSSTI) defined as the difference between the sum of averaged SST in two positive correlation boxes and averaged SST in the negative correlation box.

Figure 9. SST anomaly as a high TSSTI forcing in the experiment with S-GCM (a low TSSTI forcing is just opposite). Note that the sign of a vertical profile is reversed for a cooling situation.

Figure 10. The observed 200 hPa geopotential height for (a) the high and (b) the low TSSTI summers. The equilibrium S-GCM 150 hPa geopotential height responses to the imposed TSST forcing in Fig.9 associated with (c) the high and (b) the low TSSTI summers.

End

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