Monsoon: Its Annual Cycle, Natural

Variability, Trend and Future Change

Tim Li

University of Hawaii

1. Classic monsoon definition

2. Monsoon onset and seasonal progression

3. Intra-seasonal (sub-seasonal) oscillation

4. Inter-annual (year-to-year) variation: relationship with ENSO

5. Monsoon inter-decadal variation

6. Trend and future change under global warming

1. Monsoon basics

Original meaning of monsoon:

derived from the Arabic word

for season

Character: Seasonal reversal

of the wind direction

In JJA, heated land low

pressure cyclonic flow

(due to Earths rotation),

northward cross-equatorial

flow (due to land-ocean

thermal contrast)

In DJF, cold land mass

high pressure

What is Monsoon?

JA-JF

Seasonal differential precipitation and winds (JA-JF)

IM

EAM

WNPM

WNPM

IM

EAM

Wang, Clemens and Liu 2003

Top: domains for

three sub-monsoon

systems

IM: westerly, north-

south T gradient

EAM: southerly, east-

west T gradient

WNPM: hemispheric

asymmetric SST

gradient

Bottom: area-averaged

rainfall evolution

WNPM strongest, with a peak

phase lag to IM and EAM.

Why Monsoon is important: Precipitation and its social relevance

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The month of maximum precipitation

The precipitation during JJA

From Gadgil (2003)Monsoon

zone

5/21

5/11

5/01 6/15

7/20 8/10

6/21

7/01

9/15

6/01

6/11

6/21

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6/11

Wang and LinHo 2002(Climatology 1979-2001)

7

Seasonal march of East Asian summer monsoon:

- a stepwise northward advance

During the period from early May to mid May, the South China Sea monsoon onsets.

The monsoon rain then progresses northward to the Yangtze River valley in early to mid June,

and finally penetrates northern China (3441N) in mid July.

The rainy season in northern China generally lasts for one month and ends in the early or middle part of August.

From the end of August to early September, the monsoon rain belt rapidly moves back to southern China.

Li, K. et al. 2013, Clim. Dyn.

Time-latitude sections of

7-day running mean OLR

(shaded, W m-2, with

values lower than 220 are

plotted) and surface wind

(vector, m s-1) fields

along 85E-95E.

Time-latitude section of composite OLR (shaded, W m-2), rainfall (contour,

mm day-1), and surface wind (vector, m s-1) fields along 85-95E.

The first-branch northward-propagating ISO leads to the monsoon onset!

Composite evolution of first-branch northward-propagating ISO

Distribution of the background

convective instability field during

the FNISO phase (from day -10

to day +10)

winter vs. summer

Background convective instability distribution

(a) Time-latitude section of the background convective instability field (shaded, K) and the OLR

perturbation associated with FNISO (contour, Wm-2) along 80-100E; (b) Time evolution of

and its partial contributions due to the temperature (red) and moisture (green) changes. se

The warmest SST occurs in late April over BoB !

Monthly TC number

during 1981-2009 in

BoB

Composite map of the FNISO over the BoB. Purple dots denote the time (relative to the monsoon onset time) and

latitude of intense TC (Category 4 or 5) when it reached its maximum intensity. Green dots denote the genesis time and

latitude of these super cyclones. (The OLR and wind fields are averaged over 85E-95E. Y-axis is latitude and x-axis is

relative time to monsoon onset in BoB.

Contrast of the monsoon rainfall over India and China:

Indian:

1. Steady rainfall zone along the west coast due to the topography

effect.

2. Strong inter-annual variations over the monsoon zone, due to the

synoptic/supersynoptic disturbance from the northern BoB

China:

1. Quasi-Steady wave propagation from the south to north, zonal

band structure

2. Much larger amplitude of the inter-annual variations along three

bands

16

Asian summer monsoon components

(Figure from Yihui Ding)

2. Monsoon natural variability

I: intra-seasonal oscillation

Monsoon Intraseasonal Oscillation In Boreal Summer

Time series of daily precipitation rate

estimates averaged over 1015N, 7580E

for JunSep 1987 (mm day-1).

Timelatitude section of daily precipitation rate

estimates along 7580E for JunSep 1987 and

1988. Contour interval is 5 mm/day with the

first contour at 5 mm/day.

From Lawrence and Webster (2001): Interannual Variations of the Intraseasonal

Oscillation in the South Asian Summer Monsoon Region. J. Climate

Classic Madden-Julian Oscillation (MJO)

0 day

5 day

11 day

16 day

22 day

28 day

34 day

40 day

Eastward

Madden and Julian, 1972

Observed

Horizontal

Structure of

MJO:

Kelvin-

Rossby wave

couplet with

BL friction

leading

convection

Hendon

and Salby

1994

C

C

A

A

Sperber and Slingo 2003

Observed Vertical Structure of MJO:

BL Convergence leads Convection

The Life Cycle of the Madden Julian Oscillation (Northern Winter)

Regression maps are calculated with 25-80-day band-pass filtered OLR and 850-mb wind fields against filtered OLR time series at the reference box. Only anomalies that are statistically significant are plotted.

Boreal summer ISO eastward and northward propagation

Real-time Multi-variable MJO (RMM) index

(Wheeler and Hendon 2004)

EOF1variance: 12.8%

EOF2variance12.1%

OLRsolidu850dashu200dot

OLRsolidu850dashu200dot

EOF1Maximum MJO convection over maritime continent, first-baroclinic zonal wind structure

EOF2maximum convection over western Pacific

2001

-4

-3

-2

-1

0

1

2

3

4

-4 -3 -2 -1 0 1 2 3 4

RMM1

RMM2

1

2 3

4

5

67

8

W. Pacific

W. HemisphereMaritime

cont.

Indian Ocean

Impact of MJO on summer

circulation ad rainfall in East Asia

WWB

Asian Monsoon

Australian

MonsoonIOD

Global Impacts of MJO

West Africa

Monsoon

North America

Goswami et al (2003)

Maloney and Hartmann (2000)

McPhaden (1999)

dry Kelvin WavesEl Nino

Propagation tendency

(vector) and variance

(shaded) of summer OLR

(MJJASO)

The length of vectors

represents the magnitude

of the lagged correlation

coefficients

10-20 days

20-70 days

Quasi-biweekly oscillation (QBWO) vs. MJO

South China Sea (110o-120oE,10o-20oN)

Lagged correlation maps (10-20 days)

South China Sea (110o-120oE,10o-20oN)

Lagged correlation maps (20-70 days)

South China Sea

(110o-120oE,10o-20oN)

Lagged correlation maps (10-20 days)

Day -7

Day -5

Day -3

Day 0

South China Sea (110o-120oE,10o-20oN)

Lagged correlation maps (20-70 days)

Day -25

Day -20

Day -15

Day -10

Day -5

Day 0

Bay of Bengal

(85o-95oE,10o-20oN)

Lagged correlation maps (10-20 days)

Day -6

Day -4

Day -2

Day 0

Bay of Bengal (85o-95oE,10o-20oN)

Lagged correlation maps (20-70 days)

Day -25

Day -20

Day -15

Day -10

Day -5

Day 0

100%

50%

Normalized Useful Predictability (%)

Wea

ther

ISV

EN

SO

PD

O

AC

C

WCRP/WWRP Seamless Prediction

Courtesy of Duane Waliser

Issue on monthly forecast products

November monthly mean

rainfall anomaly is near zero.

Is the zero rainfall forecast

useful and benefit to

public/society?

Two predictability sources for

monthly forecast:

Boundary forcing (e.g., SST)

Initial value problem (MJO)

For latter, pended mean

forecast is needed at 5-30-

day lead?

350525mm1525mm150mm55

=2 +1.5 1 0.2 0.04 +0.01

A Spatial-Temporal Projection Model for rainfall prediction in Fujian

Non-filtering method for real time use

For real-time forecast, time filtering is not applicable to extract ISO signal.

A simple non-filtering method was developed:

(1) Remove mean and annual cycle by subtracting 90-day low-pass filtered

climatological data.

(2) Remove the effects of interannual, decadal variability and trend (signals

with the period larger than 60 days) by subtracting the mean of the last 30 days.

(3) Remove the effect of synoptic-scale disturbances (signals with the period

smaller than 10 days) by taking the mean of the last 5 days.

AC

X ' X X

30d

X '' X ' X '

5

, is the ISO variability with the period around 10-60 dayd

* *X X '' X

Non-filtering method to extract 10-60-day signal

Raw heavy rainfall index in Fujian

Extracted data

The non-filtering method could reasonably extract the 10-60-day intraseasonal

component.

filtered (shad) vs. non-filtered (cont) DJF MJO_U850

1990-2009 Pattern corr. coef. = 0.81

RMSE = 1.6 m/s

Predictand Y (t)Normalized heavy rainfall index

Each predictor X (i, j, n, t)Normalized large-scale fields

Spatial-Temporal Coupled Pattern

t

COV(i, j, n)= Y(t) X(i, j, n, t)

S T Coupled Pattern Projection

P

i, j, n

X (t)= COV(i, j, n) X(i, j, n, t)

Transfer FunctionF PY (t) = X (t) +

Fitting

Procedure

Rainfall Forecast YF (tp)

ForecastProcedure at tp

X (i, j, n, tp)

Xp(tp)

Six predictors:

OLR, U850, U200,

H850, H500, H200

STPM Procedure

1. Normalize predictand Y and predictor field X.

2. Construct spatial-temporal coupled co-variance patterns for the region where Y and X are significantly correlated.

3. The coupled pattern projection is obtained by multiplying the co-variance field with each predictor.

4. Transfer function is constructed with a linear regression method.

5. Heavy rainfall forecast is performed based on the coupled pattern projection and transfer function.

i, j : spatial gridn: preceding n pentads(n=6 in this study)

Spatial-Temporal Projection Method (STPM)

Independent forecast skill for 2008-2012based on TCC (models were built based on 1996-2007 training period,

total forecast:90points, 95%sig=0.21)

lead+5d lead+10d lead+15d lead+20d lead+25d lead+30d

STPM_OLR 0.26 0.3 0.25 0.22 0.17 0.19

STPM_U850 0.2 0.25 0.21 0.16 0.11 0.15

STPM_U200 0.25 0.27 0.27 0.26 0.18 0.2

STPM_H850 0.28 0.35 0.34 0.3 0.22 0.25

STPM_H500 0.24 0.29 0.3 0.3 0.18 0.21

STPM_H200 0.22 0.28 0.2 0.23 0.14 0.17

SVD_OLR 0.3 0.32 0.28 0.28 0.2 0.21

SVD_U850 0.31 0.32 0.29 0.27 0.2 0.21

SVD_U200 0.29 0.32 0.25 0.3 0.21 0.19

SVD_H850 0.26 0.33 0.27 0.26 0.15 0.18

SVD_H500 0.31 0.32 0.26 0.3 0.15 0.18

SVD_H200 0.21 0.25 0.17 0.24 0.14 0.15

SVD_index 0.44 0.47 0.32 0.27 -0.03 0.14

MVR 0.23 0.33 0.29 0.27 0.26 0.27

ens_STPM 0.29 0.34 0.31 0.29 0.19 0.23

ens_SVD 0.36 0.4 0.33 0.33 0.17 0.22

2013

3. Monsoon natural variability

II: inter-annual variation

Webster et al. 1998

Observed negative correlation between Indian monsoon and El Nino

El Nio

Observed seasonal

evolution of interannual

SST anomaly in the

equatorial eastern

Pacific

SST anomaly pattern

Normal condition

El Nino condition

J. Bjerknes (1969) first

termed the equatorial

atmospheric overturning

circulation as Walker

circulation.

Reversed Walker

circulation anomaly, with

negative convective

heating anomaly over the

maritime continent.

Q1: How does El Nino

remotely affect Indian

monsoon?

Q2: By the summer after

peak El Nino, the eastern

Pacific SST becomes

normal. How can El Nino

has a delayed impact on

EAM?

Monsoon - ENSO RelationshipObservational fact:

1.Indian monsoon is drier during El Nino developing summer

2.East Asian monsoon (EAM) meiyu-rainfall significantly increases 6 months after

the peak of El Nino (EAM becomes wetter during El Nino decaying summer).

El Nino developing year El Nino decay year

Response of circulation and rainfall anomalies to El Nino

Suppressed convective heating in maritime continent

Atmospheric Rossby wave response drought over India

El Nino composite in JJA(0), vector: 850-hPa wind, shaded: prec. anomaly

Gill modelFig. 1 Solutions for heating symmetric

about the equator in the region |x|

Anomaly dry AGCM experiment:

- 3D summer mean flow and anomalous

heating in MC are specified.

How does the El Nino remotely

impact the Asian monsoon?

Large-scale east-west overturning ?

Equatorial asymmetric response to a

symmetric El Nino forcing, why?

Wang, Wu and Li, 2003, J. Climate

Rainfall anomaly over China in summer of 1998, 6 months

after a peak El Nino in winter 1997

80 90 100 110 120 130

20

30

40

50 200

150

100

50

-50

-100

-150

-200

1998

Leading mode of Monsoon Interannual Variation

850 hPa winds and local SST

anomalies (19572001)

Biennial tendency associated with

ENSO turnabout

Distinct evolutions of anticyclonic

anomalies over SIO and WNP

Wang, Wu, Li 2003, J. Climate

Wang and Zhang

2002, JC

A positive thermodynamic air-sea feedback mechanismWang et al. 2000

El Nino heating atmospheric Rossby wave response

cold SSTA in WNP anomalous AC EAM

UV850OMEGA500 SST 200hPa velocity potential

Q3: As the local SSTA dissipates quickly in JJA(1), what maintains the anomalous

anticyclone in WNP ? How does the IO SSTA affect the WNPM? (Wu et al.

2009, JC; Xie et al. 2009, JC)

Q4: What is the relative role of the remote IO forcing vs. local SSTA in affecting

circulation anomaly in WNP? (Wu et al. 2010, JC)

12 El Nino composite (1950-2006)

How does the basin-wide IO warming in JJA (1) impact the WNPM?

IO equatorial heating Kelvin wave response Anticyclonic shear

of the Kelvin wave easterly Ekman pumping induced PBL

divergence Suppressed WNP monsoon heating Anomalous

anticyclone

Wu, Zhou and Li

2009, J. Climate

Relative contribution to WNPAC by

IO SSTA and WNP SSTA

Vorticity anomaly over 10-35N, 115-160E

Wu, Li, Zhou, J. Climate, 2010

Inter-monsoon Relationships among IM, AM and WNPM

Wang, et al. 2005

Lagged correlations among WNPM, IM and AM for 1979-2005 using CMAP and NCEP2 data

Lagged correlations among WNPM, IM and AM for 1979-2005 using GPCP2 and JRA data

FromIndia

JJA(0)

Australia

DJF(0)

SCS/WNP

JJA(0)

Australia

DJF(0)

SCS/WNP

JJA(0)

ToAustralia

DJF(1)

India

JJA(0)

Australia

DJF(1)

SCS/WNP

JJA(0)

India

JJA(0)

Correlation

coefficient0.29 -0.28 -0.41 0.37 -0.64

FromIndia

JJA(0)

Australia

DJF(0)

SCS/WNP

JJA(0)

Australia

DJF(0)

SCS/WNP

JJA(0)

ToAustralia

DJF(1)

India

JJA(0)

Australia

DJF(1)

SCS/WNP

JJA(0)

India

JJA(0)

Correlation

coefficient0.35 -0.26 -0.32 0.32 -0.70

Red highlighted number indicates that the correlation is statistically significant.

Gu, Li, et al. 2010, J. Climate

Schematic for AM-WNPM in-phase relation ENSO impact: remote vs. local process

Schematic for out-of-phase relation between

WNPM and AM

El Nino developing

phase

El Nino decaying/La

Nina developing

Schematic for out-of-phase WNPM-IM relation

4. Monsoon interdecadal

variation

Global mean surface

temperature shows a

rising trend in recent

decades. Removing this

trend, one may find a

nature oscillation mode

on decadal timescale.

The PDO index was high

after 1976/77 (regime

shift) and stayed pretty

high till the late 1990s.

Associated with the high

PDO index (or warm

phase PDO) is a

strong Aleutian Low

anomaly.

Pacific Decadal Oscillation (PDO)

Decadal change of JJA rainfall

Temperatureshading) and meridional circulation (vector)

E. Asian regional average (105-122 E)

Why did the temperature have a cooling trend

in North China in past 40 years?

Li, C., T. Li, J. Liang, D. Gu, A. Lin, and B. Zheng, 2010: Interdecadal variations of meridional

winds in the South China Sea and their relationship with summer climate in China. Journal of

Climate, 23, 825-841.

High-latitude (NAO) impact (Yu and Zhou 2004)

Tropical forcing (Li et al. 2010)

The difference (Phase II minus Phase I) of wind (vector, units: ms-1,

dark vectors denote that the difference exceeds the 95% significance

level), geopotential height (contour, units: gpm,) and temperature

(shaded, units: C) fields at 850 hPa.. NCEP reanalysis data (1958-

2005) were used.

Vertical profiles of

temperature (a, units: C)

and geopotential height

(b, units: gpm)

anomalies averaged over

(100~110E35~45N) and

meridional wind

anomalies (c, units: m.s-1)

averaged over

(110~120E20~30N) at Phase I

(solid line) and Phase II

(dashed line)

Fig. 5 Meridional-vertical section of

difference (Phase II minus Phase I) fields

for a) temperature (C), b) geopotential

height (gpm), c) zonal wind (ms-1), d) p-

vertical velocity (Pa s-1) and e) meridional

wind (ms-1) averaged between 100~110E.

Vertical profiles of a)

anomalous specific

humidity (unit: gkg-1),

b) apparent heat source

Q1 (unit: Ks-1) and c)

apparent water vapor

sink Q2 (unit: Ks-1 )

averaged over

(100~11035~45N)at Phase I (solid line)

and Phase II (dashed

line).

The difference (Phase II minus Phase I) fields for a) NCEP OLR (units: Wm-2lessthan -2 Wm-2 is shaded; symbol * denotes the OLR difference exceeding the 95%

significance level ) and b) meridional-vertical streamfunction averaged over 105-130E.

Enhanced convection over southern SCS (105-120E0-10N) in association with the tropical ocean warming

Anomalous descending motion over

midlatitude East Asia (Hadley circulation)

Decrease in humidity and

increase in outgoing

longwave radiation into

space in the midlatitude

East Asia

Decrease in local

tropospheric

temperature and

thickness

Local negative

(positive)

geopotential height

anomaly at upper

(lower) levels

Local convergent

(divergent) flows at

upper (lower) levels

Weakening in land

ocean thermal

contrast

Weakening of the

LLMW over SCS

A tropical SST forcing hypothesis

Prior to 1978, YRV

and SEC are both

wet after a peak El

Nino.

After 1978, YRV is

wet while SEC is

dry after a peak El

Nino.

Chang, Zhang, Li, 2000 JC

79

Contour lines for 5870 gpm of 500 hPa geo-potential height for each summer

1980-99

1958-77

NCEP/NCAR ERA40

5. Monsoon trends in past 30 years

and future change under global

warming

Trends of the global monsoon

precipitation during 1979-2008

Tim Li, Pang-chi Hsu and Bin Wang

International Pacific Research Center,

University of Hawaii, Honolulu, Hawaii

81

Hsu, P.-C., T. Li, and B. Wang, 2011: Trends in Global Monsoon Area and Precipitation

over the Past 30 Years. Geophys. Res. Lett., 38, L08701, doi:10.1029/2011GL046893

The GPCP data shows that the global monsoon shows an increasingtrend since 1980. (Wang and Ding 2006)

BUT, the CMAP shows an decreasing trend in the global monsoon rainfall. (Zhou et al. 2008)

Questions:

Why does the global monsoon trend show an inconsistence between

GPCP and CMAP? Is it due to the uncertainty of data or the analysis

methodology?

Note that the global monsoon area is defined in the past 30 years

with a fixed region for each year. Does the global monsoon area

change annually?

Motivation

82

Observed monthly precipitation 1979-2008

1) Global Precipitation Climatology Project (GPCP)

2) CPC Merged Analysis of Precipitation (CMAP)

interpolated onto a 1 longitude by 1 latitude grid

Definitions of global monsoon

Global Monsoon Area (GMA)

Regions in which (1) annual range precipitation > 2mm/day

[MJJAS-NDJFM]

(2) local summer precipitation > 55 % annual rainfall

[NH: MJJAS SH: NDJFM]

Global Monsoon Precipitation (GMP)

total summer monsoon rainfall falling in the monsoon domain

[NH: JJA SH: DJF]

Data & Definitions of global monsoon index

83

Trends of GMP (defined based on a fixed GMA)

This result is consistent with Wang and Ding (2006) and Zhou et al. (2008).

Linear trend (29yr)-1 GPCP (%) CMAP (%)

GMP in

climatological

GMA

glb 28.46 (0.06) -19.92 (-0.04)

glb_lnd 5.44 (0.02) 33.40** (0.16)

glb_ocn 23.02+ (0.10) -53.32 (-0.18)

Man-Kendall test

+ 80%

* 90%

** 95%

*** 99%

increasing trend decreasing

trend

84

Trends of GMP (defined based on a varying GMA)

increasing trend increasing trend

The GMP based on a varying GMA show increasing trends in both the GPCP

and CMAP.

Linear trend (29yr)-1 GPCP (%) CMAP (%)

GMP in

yearly varying

GMA

glb 127.65 (0.25) 23.02 (0.04)

glb_lnd 31.58 (0.13) 70.80** (0.34)

glb_ocn 96.07+ (0.34) -47.79 (-0.14)

85

Trends of GMA

Trends of global monsoon area are both increased in the GPCP and CMAP.

increasing trend increasing trend

contour:

clim_GMA

shading:

blue

extension

orange

shrink

86

Trends of global monsoon intensity

The GMP and GMA are both increased over time, how about the GMI?

decreasing

trend

decreasing

trend

Since the rate of increased GMA is larger than it of GMP, the GMI shows

decreasing trends in GPCP and CMAP.

Global Monsoon Intensity (GMI) rainfall amount per unit area

87

Summary

The trend of global monsoon precipitation (GMP) in a fixed global

monsoon area (GMA), is increased in the GPCP but it is decreased in the

CMAP. It reveals the inconsistent trends between GPCP and CMAP.

Calculated based on a varying GMA, the trends of GMP in GPCP and

CMAP are consistent, both of which are increasing during 1979-2008.

The GMA shows an increasing trend in both the GPCP and CMAP,

which is associated with the increased GMP.

Since the increased rate of GMA is larger than it of GMP, the global

monsoon intensity (GMI), which is defined as rainfall amount per unit

area, shows a decreasing trend in both datasets.

The decreased GMI in the GPCP is contributed mainly by the land

monsoon while it is contributed by the oceanic monsoon in the CMAP.88

Tim Li1, Pang-chi Hsu1, Jing-Jia Luo2,

Hiroyuki Murakami3, Akio Kitoh3 and Ming Zhao4

1International Pacific Research Center, University of Hawaii, USA2Research Institute for Climate Change, JAMSTEC, Japan

3Meteorological Research Institute, Tsukuba, Ibaraki, Japan4Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA

Changes in global monsoon precipitation

under global warming

Hsu, P.-C., T. Li, J.-J. Luo, H. Murakami, A. Kitoh, and M. Zhao, 2012: Increase of global

monsoon area and precipitation under global warming: A robust signal? Geophys. Res. Lett., in

press.

Seeking for consistent and robust global monsoon changes indifferent high-resolution AGCMs forced by

different SST warming patterns

Model Control Runs Global Warming Experiments

MRI

ECHAM5

T106

(~ 1.125)

T106_pd

AMIP-type

run with

observed

SST

T106_mw

Globally uniform SST

warming (2.24C) derived by

ECHAM5/MPI-OM simulated

SST anomaly between A1B

and 20C3M

T106_sw

Spatially-varying SST

warming derived by

ECHAM5/MPI-OM simulated

SST anomaly between A1B

and 20C3M

MRI

ECHAM5

T319

(~40km)

T319_pd

ECHAM5/

MPI-OM

20C3M

SST

T319_sw

Model is forced by

ECHAM5/MPI-OM simulated

SST in A1B scenario

Japan

MRI-JMA

T959

(~20km)

MRI_pd Historical

HadISST MRI_sw.e

18-model ensemble mean of

future SST warming in CMIP3

plus the present-day

interannual variations

US GFDL

HiRAM

C180

(~50km)

GFDL_pd Hsitorical

HadISST GFDL_sw.e

18-model ensemble mean of

future SST warming in CMIP3

Robust global monsoon changes under global warming

Global Monsoon Precipitation (GMP)Regions in which (1) annual range

precipitation > 2mm/day (2) local

summer rainfall > 55 % annual

rainfall

Global Monsoon Precipitation (GMP) total summer monsoon rainfall in GMA

Global Monsoon Intensity (GMI)global monsoon precipitation amount

per unit area0

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16

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GMA GMP GMI(%)

Change rates in global monsoon

index_avg

composite

Change rate (%) EXP_mw EXP_sw T319_sw MRI_sw.e GFDL_sw.e

GMA 7.75 4.58 8.66 7.15 6.59

GMP 19.80 10.35 15.18 9.85 7.36

GMI 11.22 5.61 7.14 2.51 0.88

GMA, GMP and GMI increase consistently among individual models

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GMA GMP GMI

(%)

K-1

CMIP5_index_avg CMIP5_MME_prep

CMIP3_index_avg CMIP3_MME_prep

Normalized change rates in global monsoon

CMIP3 and CMIP5 Results

GMA changes under global warming

High-resolution AGCMs perform well in capturing the major GMA in the

present-day simulations (red contour)

Significant GMA expansions occur in the oceanic monsoon and the South

American and African land monsoon regions (blue shaded areas).

Rainfall changes under global warming

Contour: Climatology of

difference in precipitation

between global warming

and present-day.

Dark shading: Consistent

change in 5 models

(100% consistency)

Light shading: Consistent

change in 4 models

(80% consistency)

Rainfall increases

over the ITCZ and

SPCZ where are the

wettest regions while it

shows decrease in the

tropical western Pacific

and Indian Oceans. Not

always rich-get-richer.

Moisture diagnosis of enhanced GMP

GMP = < q>

Relative contributions of thermodynamic and dynamic effect

The thermodynamic component via increasing water vapor content plays the

major role in strengthening GMA while global SST warming.

The dynamic effect associated with weakening monsoon circulation and surface

wind speed contributes negatively to the GMP.

-20

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0

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40

q*D q*D q*D

Decompositions of moisture convergence

T106_mw T106_sw T319_sw MRI_sw.e GFDL_sw.e

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q*V q*V q*V

Decompositions of evaporation

T106_mw T106_sw T319_sw MRI_sw.e GFDL_sw.e

pd pd

< q * D > =

< q * D > < q D > < q D >

dynamic thermodynamic nonlinear

E s a

E s a pd s a s apd

E = [ L C (q -q )]

= L C [ *(q -q ) + * (q -q )+ * (q -q )]

V

V V V

dynamic thermodynamic nonlinear

Summary of future GM change

By comparing the ECHAM5/MRI/GFDL model simulations driven

by present-day observed SST and future warming scenarios, we

note an increasing trend for both the global monsoon area (GMA)

and overall global monsoon precipitation (GMP) amount. The

global monsoon intensity (GMI) also shows an increasing trend.

The signal appears robust across different model physics and

different future SST warming patterns.

A moisture budget analysis shows that the enhanced global

monsoon precipitation is attributed to both the increased

evaporation and moisture flux divergence under global warming.

The increase of moisture is primarily responsible for the increase

of moisture flux convergence in the future warming scenario.

ThanksDiamond Head

http://www.tonyandkitty.com/gallery/album01/Diamond_Head?full=1

Impact of MJO on winter

circulation ad rainfall in East Asia