The East Asian Winter Monsoon: Re-amplification in the Mid-2000s - - PowerPoint PPT Presentation

The East Asian Winter Monsoon: Re-amplification in the Mid-2000s

Lin Wang & Wen Chen

Institute of Atmospheric Physics, Chinese Academy of Sciences Email: wanglin@mail.iap.ac.cn

HKM25@Hong Kong, 2-4 November, 2013

Outline

• Introduction
• A new intensity index for the EAWM
• Re-amplification of the EAWM in the mid-

2000s and its possible causes

• Conclusion

• 1. Introduction

DJF mean SLP and 10-m wind

The East Asian winter monsoon (EAWM)

2008/01 Nanyue, China 2013/01/14 Tokyo, Japan 2006/01/07 Tsunan, Japan 2013/02/17 Nanjing, China

instrument shelter ~3m

Severe winters in recent years

Does strong EAWM epoch come back again after the weakening around 1986?

Climatology of DJF mean SLP

Siberian High (SH) Siberian High (SH) Aleutian Low (AL) Aleutian Low (AL) Maritime Continent Low (MCL) Maritime Continent Low (MCL)

• The thermal (hence SLP) contrast between Asian continent

and adjacent oceans is a primary nature of the EAWM.

• East-west & North-south SLP gradient are both important!

– Strong EAWM-> high SLP over SH, low SLP over MC due to more convection-> enhanced north-south SLP gradient

• Advantages of defining EAWM with SLP:

– Reflect the nature of monsoon – Reliable to investigate the historical EAWM variations

• 2. The new EAWM index

Climatology Interannual standard deviation

Siberian High (SH) Siberian High (SH) Aleutian Low (AL) Aleutian Low (AL) Maritime Continent Low (MCL) Maritime Continent Low (MCL)

1 2 3

*: normalization *: normalization

Selected strong/weak winters for composite (+/- 0.5 sigma). El Nino La Nina

(16) (16)

ERA-40 dataset 1957/58-2001/02

Composite of circulation anomalies strong-weak EAWM winters

SLP 850 hPa wind U200 H500

PREC 160 China stations Rainfall Ts ERA40 160 China stations

Composite of Ts & rainfall anomalies strong-weak EAWM winters

X-axis：EAWM index Y-axis：East Asian SAT

• No. of warm

winters in weak EAWM years

• No. of warm

winters in weak EAWM years

• No. of cold

winters in strong EAWM year

• No. of cold

winters in strong EAWM year

• Corr. Coeff.

New index ranks No. 2 in correlation coefficient (0.77 vs 0.78), and No. 1 in describing the extreme

• winters. Besides, it has

better predictability, as will be shown later. New index

ERA40 data

• No. of warm

winters in weak EAWM years

• No. of warm

winters in weak EAWM years

• No. of cold

winters in strong EAWM year

• No. of cold

winters in strong EAWM year

• Corr. Coeff.

New index X-axis：EAWM index Y-axis：160-station SAT For station data, we can get similar conclusion regarding correlation coefficient (No. 2, 0.67 vs 0.71) and extreme years (No. 1, 9/10 vs 9/8, especially for strong EAWM).

Station data

The new index has good ability to describe the EAWM-related circulation and temperature/rainfall features

SST anomalies strong-weak EAWM winters

• SON

+JJA +MAM DJF

** 99.9%, * 99%

The tropical Indian Ocean SST seems to exert more impacts on the EAWM than ENSO does, especially when SST leads EAWM by one and zero month. The tropical Indian Ocean SST seems to exert more impacts on the EAWM than ENSO does, especially when SST leads EAWM by one and zero month.

Lag correlation with oceanic indices

Tropical Indian Ocean may provide additional prediction potential for the EAWM besides ENSO

SCWF (ECMWF) CNRM (Météo-France) UKMO (UK Met Office) New index

• 0.07

0.52** 0.22

Predictability of the index in DEMETER CGCMs

• Three models were used.
• Nine runs in each model.
• Prediction started from November (1 month lead)
• Data periods: 1958/59-2001/02
• French model did a good job, consistent with Li

and Wang (2012).

• Possible reason of success (failure): The EAWM-

tropical ocean relationship was (was not) reasonably reproduced in the model.

**99.9%

SCWF (ECMWF) CNRM (Météo-France) UKMO (UK Met Office) New index

• 0.07

0.52** 0.22 Guo (1994) 0.00 0.31

• 0.27

Shi (1996) 0.00 0.36 0.02 Wu & Wang (2002)

• 0.04

0.31

• 0.11

Chan & Li (2004)

• 0.06

0.22

• 0.28

Wang et al. (2009b)

• 0.04

0.16

• 0.19

Chen et al. (2000) 0.61** 0.67** 0.58** Hu et al. (2000) 0.49** 0.65** 0.50** Zhu (2008)

• 0.09

0.36 0.00 Li & Yang (2010 0.21 0.39* 0.15 Cui & Sun (1999) 0.09 0.46* 0.29 Sun & Li (1997)

• 0.01

0.48* 0.29 Wang et al. (2009a) 0.18

• 0.16
• 0.21

Predictability of all the EAWM indices in DEMETER CGCMs Low-level v-wind indices are best predicted Our index ranks No.3

* 99% **99.9%

SLP contrast indices Upper u-wind shear indices Mid-tropospheric trough indices

• 3. Re-amplification of the EAWM

EAWM index for DJF

• f 1957/58-2012/13

EAWM index for DJF

• f 1957/58-2012/13

7 out of 9 winters feature strong EAWM for 2004/05-2012/13

The index indicates a decadal amplification of the EAWM

Area-averaged Ts over East Asia 7 out of 9 winters feature cold Ts for 2004/05-2012/13

Ts confirms that the EAWM entered strong epoch since 2004

• --Possible cause?

Previous strong EAWM epoch 1976-1987 Previous strong EAWM epoch 1976-1987 Recent strong EAWM epoch 2004-2012 Recent strong EAWM epoch 2004-2012

Ts SLP H500 Ts SLP H500

AO-like pattern is responsible AO-like pattern is responsible Ural blocking is clear all the time Ural blocking is clear all the time Somewhat AO-like, but not typical Somewhat AO-like, but not typical

Blocking frequency over Ural (50-80E) region Longitudinal distribution of blocking frequency

Enhanced Ural blocking is responsible for the recent amplification of the EAWM

Weak Strong Strong

External forcing? Maybe diminished Arctic sea ice Less autumn Arctic sea ice

• >More wintertime blocking

The physical process needs be studied in the future.

• 4. Summary
• A new EAWM index was proposed by taking

into account the north-south SLP gradient. The performance of the new index is good.

• The EAWM re-amplified since the mid-2000s.
• The Enhanced Ural blocking and diminished

Arctic sea ice are responsible for this re- amplification.

• For details:

– Wang and Chen (2013a, J. Climate, minor revision) – Wang and Chen (2013b, Chinese Sci. Bull., in press)

SON mean snow depth

DJF mean snow depth