Other issues

1
Lecture 3, Monsoon Institute, Honolulu, 2008.1.9
Other issues Akio KITOH Meteorological
Research Institute, Japan Meteorological Agency
1 Aerosols 2 Orography 3 Vegetation 4
ENSO-monsoon relationship 5 Super-high-resolution
modeling
2
Aerosols
3
Aerosol
Atmospheric aerosol loading affects regional
climate and its future changes. If the direct
effect of the aerosol increase is considered,
surface temperatures will not get as warm because
the aerosols reflect solar radiation. For this
reason, land-sea temperature contrast becomes
smaller than in the case without the direct
aerosol effect, and the summer monsoon becomes
weaker.
IPCC WGI AR4 Ch10
4
Aerosol
Model simulations of the Asian monsoon project
that the sulphate aerosols direct effect reduces
the magnitude of precipitation change compared
with the case of only greenhouse gas increases
(Emori et al., 1999 Roeckner et al., 1999 Lal
and Singh, 2001). However, the relative cooling
effect of sulphate aerosols is dominated by the
effects of increasing greenhouse gases by the end
of the 21st century in the SRES marker scenarios,
leading to the increased monsoon precipitation at
the end of the 21st century in these scenarios.
IPCC WGI AR4 Ch10
5
Aerosol
Furthermore, it is suggested that aerosols with
high absorptivity such as black carbon absorb
solar radiation in the lower atmosphere, cool the
surface, stabilise the atmosphere and reduce
precipitation (Ramanathan et al., 2001). The
solar radiation reaching the surface decreases as
much as 50 locally, which could reduce the
surface warming by greenhouse gases (Ramanathan
et al., 2005). These atmospheric brown clouds
could cause precipitation to increase over the
Indian Ocean in winter and decrease in the
surrounding Indonesia region and the western
Pacific Ocean (Chung et al., 2002), and could
reduce the summer monsoon precipitation in South
and East Asia (Menon et al., 2002 Ramanathan et
al., 2005). However, the total influence on
monsoon precipitation of temporally varying
direct and indirect effects of various aerosol
species is still not resolved and the subject of
active research.
IPCC WGI AR4 Ch10
6
Climate effects of black carbon aerosols in China
and India
absorbing aerosols
white aerosols
Absorbing aerosols heat the air, alter regional
atmospheric stability and vertical motions, and
affect the large-scale circulation and hydrologic
cycle with significant regional climate effects
Menon et al. (2002)
7
Effects of radiative forcing of sulfate aerosol
on large scale circulation and rainfall in East
Asia during boreal spring
Radiative forcing of sulfate aerosol leads to
cooling of the land surface and reduction in
rainfall over central East Asia. The maximum
reduction in precipitation is shifted northward
relative to the maximum aerosol loading region as
a result of dynamical feedback.
Kim et al. (2007) GRL
8
Atmospheric brown clouds Impacts on South Asian
climate and hydrologic cycle
Absorbing aerosols in atmospheric brown clouds
may have played a major role in the observed
regional climate and hydrological cycle changes
and have masked as much as 50 of the surface
warming due to the global increase in greenhouse
gases. If current trends in emissions continue,
the subcontinent may experience a doubling of the
drought frequency in the coming decades.
Ramanathan et al. (2005) PNAS
9
Effect of orography
Kitoh (2004) J.Climate, 17, 783-802 Kitoh (2005)
J.Geol.Soc.Japan, 111, 654-667 Kitoh (2007)
Clim.Dyn., 28, 781-796
10
precip
Annual Precipitation
Precipitation increases in South Asia and East
Asia, and decreases over Eurasia
11
Monsoon intensity index
NM
M
M-NM
Monsoon intensity is larger in M over the
tropical Indian Ocean, western Arabian Sea and NE
Asia, but smaller in S Asia, SE Asia and the
western Pacific
Index is defined from wind speed and direction
difference between summer and winter
12
Summer (JJA) Precipitation
0
60
120
20
80
140
Precipitation area moves inland by mountain
uplift Baiu appears with more than 60
orography ? Tibetan Plateau is important for East
Asian climate
100
OBS
40
13
Koppen climate Asia
Köppen climate
No M
60
120
20
80
140
Note the difference in arid climate (desert BW,
steppe BS)
M (100)
40
Kitoh (2005) JGSJ
14
Sea surface temperature
Surface winds
When mountain is low, a warm water pool is
located over the central Pacific it shifts
westward with uplift SST gradient reverses over
the Indian Ocean
Pacific trade winds become stronger associated
with strengthened subtropical high with mountain
uplift
uplift
Kitoh (2007) CD
15
El Nino Modulation
SST/SOI time series SST pattern power spectrum
7 yr
large amp
In M0, the SST pattern is nearly symmetric about
the equator The spatial pattern (e.g. meridional
width) changes with uplift
In M0, large amplitude and regular El Nino El
Nino becomes weaker, shorter period and less
periodic with mountain uplift
uplift
small amp
4 yr
Kitoh (2007) CD
16
Vegetation
17
Some studies on land use and vegetation
land-use and land-cover change Wei and Fu
(1998) grass to desert in northern China Fu
(2003) potential vegetation to present
vegetation both resulted in weakened monsoon
circulation and reduced precip vegetation
feedback Douville et al. (2000) inclusion of
vegetation feedback will lead to different
regional scale climate change Chen et al.
(2004) largest changes and feedbacks between
vegetation and climate occur in northern China
orography vs vegetation Yasunari et al. (2006)
land surface and the Tibetan Plateau
contribute to increase precipitation in Asian
monsoon
18
Vegetation
Thick solid vegetated surface Dashed
non-vegetated surface (wet soil) Dotted
non-vegetated surface Thin flat and
non-vegetated surface Thin dash-dotted CMAP
observation
In the Asian monsoon region, both land surface
effect and the Tibetan Plateau effect contribute
equally to increase precipitation
Yasunari et al. (2006) JHM
19
Land-Use distribution for 2000 2050
CRIEPI (2004)
Land-Use 2000
Land-Use 2050
1-4???(??????????????)?51???52????????????53??
???????????57???58????59???????61???????62???
????63???????64???????65?????69????????71????
??????73?????????
20
Annual Precipitation Change withFixed Variable
Land-Use
CRIEPI (2004)
2050 minus 2000
Land-Use change
21
ENSO-Monsoon Relationship
22
Monsoon ENSO Connections
Three major circulations associated with the
boreal and austral summer monsoons. The three
components of the monsoon system are tied to the
heated land, the tropical warm pools and the cold
winter ocean and land areas. All of these
conspire to produce the strongest heating
gradient on the planet.
Webster (1997)
23
ENSO-Monsoon relationship
Drought conditions over India accompany warm ENSO
events and vice versa
Lagged correlations between the Indian monsoon
rainfall anomaly and the SST anomaly in the
western Pacific Ocean (0-8N, 130-150E solid
curve) and the eastern Pacific Ocean (0-8N,
170-150W dashed curve). Y(0) denotes the
reference year, and Y(-1) and Y(1) refer to the
year before and after the reference year. From
Yasunari (1990, Meteor. Atmos. Phys., 44, 29-41).
From CLIVAR homepage
24
ENSO-Monsoon relationship
Recent collapse? decadal variability Kripalani
and Kulkarni 1997 change in seasonality of ENSO
cycle Kawamura et al. 2003 chaotic nature of
monsoon Webster and Palmer 1997 Indian Ocean
Dipole mode Ashok et al. 2001 Atlantic Ocean
effect Chang et al. 2001 global warming
Krishna Kumar et al. 1999 Ashrit et al. 2001
- continental warming - Walker circulation
change Again strengthening? e.g. 2002 and 2004
From CLIVAR homepage
25
ENSO-Monsoon relationship in GCMs
Modeling natural variability (MPI, CNRM
Ashrit et al. 2001, 2002) global warming (MRI
Ashrit et al. 2004) Models ability in
representing basic state Sperber and Palmer
(1996), Sperber (1999) Lau and Nath
(2000) Annamalai et al. (2007)
Lead-lag correlations between all-india-rainfall
and Nino3.4 SST anomalies
good basic state models
poor basic state models
Annamalai et al. (2007)
26
ENSO-Monsoon relationship in GCMs
IPCC AR4 AOGCMs (Annamalai et al. 2007) Six of
the 18 models have a reasonably realistic monsoon
precipitation climatology. Four out of these six
exhibit a robust ENSO-monsoon relationship. At
CO2 doubling in these models, the ENSO-monsoon
correlation is very similar to that in the 20th
century runs (i.e. no weakening).
GFDL_CM2.0, GFDL_CM2.1, MRI_CGCM2.3, MPI_ECHAM5
27
This study investigate ENSO-monsoon relationship
and its long-term variability in the 1000-year
integration of the MRI-CGCM2.3.4

• AGCM
• MRI/JMA98
• T42 (2.8x2.8), L30 (top at 0.4 hPa 55km)
• Longwave radiation - Shibata and Aoki (1989)
• Shortwave radiation - Shibata and Uchiyama (1992)
• Cumulus - Prognostic Arakawa-Schubert type
• PBL - Mellor and Yamada level 2 (1974)
• Land Surface - L3SiB or MRI/JMA_SiB
• OGCM
• Resolution 2.5x(0.5-2.0), 23layers
• Eddy mixing Isopycnal mixing, GM
• Sea ice Mellor and Kantha (1989)
• Integration
• 1000 years with flux adjustment
• 2000 years without flux adjustment (use last 1000
  yrs)
• Also use 2xCO2 and LGM simulation results

28
JJA Precipitation 850 wind
FA
29
EOF1 of 1000-yr SST(JJA) and its regressions
FA
Model ENSO corresponds very well to the observed
one
30
IMR NINO3.4 Regression on SST_EOF1
FA
Tsa
cc 0.61
Tsa
Precip
NINO3.4 SST correlation
IMR
Precip
IMR and Niño3.4 SST shows large negative
simultaneous lagged correlation
Year(1) Year(0) Year(1)
31
Regression to IMR Regression to NINO3.4
FA
in 1000 years
Tsa
Tsa
Precip
Precip
Regressions are robust both with IMR and Niño3.4
SST
32
Full Partial correlation
FA
IMR
NINO34
full
partial
Indian Ocean-Western Pacific Ocean are decoupled
with Central/Eastern Pacific
33
31-year sliding window correlations between IMR
and Niño3.4 SST anomalies (JJA)
FA
bad cc periods (120 yrs) vs good cc
periods (120 yrs)
95
negative correlation strong ENSO-monsoon
relationship
c.c. varies inter-decadally between 0.82 and
-0.28-gt compare good cc periods and bad cc
periods
34
Precip Moisture flux Regressions on IMR /
Nino3.4
FA
IMR
NINO34
good cc years
Decoupling in moisture flux is large in the
western-to-central tropical Pacific in bad
regression years
bad cc years
35
Correlation between ENSO-monsoon relationship and
other indices
FA
Strong ENSO-monsoon relationship during a period
when IMR variability is large This is robust for
the both 500 year period
Positive (negative) correlation weak (strong)
ENSO-monsoon relationship
IMR 31yr_r.m.
IMR 31yr_s.d.
0.47(-0.56,-0.38)
0.17(0.15,0.16)
Also when mean IMR itself is large relation is
weak,thus monsoon varies by itself
NINO3.4 SST 31yr_r.m.
NINO3.4 SST 31yr_s.d.
-0.09(-0.07,-0.09)
0.08(-0.18,0.06)
36
X-Z cross-section of moisture flux stream function
mean
regression on NINO3.4
bluegtclockwise redgtanticlockwise
good relation years
good relation years
Difference in location
Difference in strength
bad relation years
bad relation years
bad years good years
bad years good years
Relation depends on the location of the mean
Walker circulation
37
Projection of the change in future weather
extremes
38
MRI/JMA Atmospheric GCM

• JMA Operational global NWP model since Nov 2007
• MRI Next generation climate model (MRI-CGCM3)
• Based on operational JMA-GSM
• Resolution TL959(20km) with 60 layers
• Time integration Semi-Lagrangian Scheme
  (Yoshimura, 2004)
• 2 days/1 year integration with DT6 min and
  
• 30 nodes of Earth Simulator (ES has total 640
  nodes)
• Physics
• SW radiation Shibata Uchiyama (1992)
• LW radiation Shibata Aoki (1989)
• Cumulus convection Prognostic Arakawa-Schubert
  (Randall and Pan, 1993)
• Land hydrology MJ-SiB SiB with 4 soil-layers
  and 3 snow-layers
• Clouds large-scale condensation, Cumulus,
  stratocumulus
• PBL Mellor Yamada (1974,1982) level-2 closure
  model
• Gravity wave drag Iwasaki et al. (1989)
  Rayleigh friction

39
Frequency of strong precipitation events
GPCP
20km
60km
120km
180km
40
Tropical cyclones
MEXT Kyo-sei Project (FY2002-FY2006) using the
Earth Simulator by MRI/JMA/AESTO group
It is likely that future tropical cyclones will
become more intense, with larger peak wind speeds
and more heavy precipitation associated with
ongoing increases of tropical sea surface
temperatures. There is less confidence in
projections of a global decrease in numbers of
tropical cyclones. IPCC AR4
41
Typhoon track and intensity 60-km vs 20-km
60-km model forecasts shallower central pressures
and weaker maximum winds. 20-km model represents
typhoon development closer to the observations.
42
Extreme event projection with super-high-resolutio
n atmospheric models
MRI / JMA / AESTO
Atmosphere-ocean coupled model
Super-high-resolution global atmospheric model
Regional cloud resolving model by nesting
5km 1km mesh
20km mesh
180km mesh
Atmosphere
Atmosphere
Boundary condition
SST
SST
Ocean
Boundary condition
Future
Near Future
Present
100-50km mesh
SST
42
Year
1979-2003
2075-2099
2015-2039
43
Assessment of the impact of climate-change on
flood disaster risk and its reduction measures
over the globe and specific vulnerable areas

(ICHARM/PWRI)
Global 20km- or 30km-mesh GCM data from JMA-MRI
and Univ.Tokyo-CCSR (presentnear future21C end)
Evaluation of uncertainty
Twelve UNESCO Centres WMO, IFI, WWAP, ISDR

• Approaches, methodologies, tools for ICHARM
  study
• Relation between GCM outputs in-situ
  precipitation
• Hydrologic model for large-scale poorly-gauged
  basins
• i.e. IFAS-BTOPMC
• Flood inundation evaluation model
• Development of indices to evaluate flood risk
  benefit
• Cost-benefit evaluation model to build
  countermeasures

Reality of flood disaster mitigation measures

• Research outcomes
• Evaluation of uncertainty of extreme rainfall
  prediction in GCM
• 20-40km- (global) or 1km- (specific local) mesh
  flood risk map
• Indices to evaluate flood risk benefit
• Scenarios of flood risk reduction measures on a
  global scale
• Local case-studies on flood risk reduction in
  specific areas

World wide information network through ICHARM,
ex) cooperative organizations, JICA experts, etc.
43
3rd World Water Development Report, 5th IPCC
report
44
Integrated assessment of climate change impacts
on watersheds in a disaster environment
DPRI / Kyoto-Univ.
Mountains
Slope
River
Habitable Area
Coastal Area
Precipitation, temperature, water vapor, wind
velocity, radiation and air pressure (30-years
time series (20km) and ensemble predictions
(60km) for current, near future and century end)
Output from GCM
Regional climate model (RCM_5km, RCM_1km)
Stochastic typhoon model
Surface hydrological model
Interpreta-tion of output
Probability density function of extreme value
(depending on spacio-temporal scales) Stochastic
precipitation model (time series depending on
spacio-temporal scales)
Soil production
Various Models (with long-term run)
Reservoir operation
Building damage by strong wind
Storm surge
Inundation including underground shopping center
Sedimentation and transportation of soil
Soil runoff
Rainfall runoff
River channel flow
Decreasing of safety against landslide, debris
flow, flood, drought, storm surge and strong
wind. Assessment of current protection system
and proposal of alternatives.
Evaluation
45
Innovative Program of Climate Change Projection
for the 21st century(KAKUSHIN Program)
FY2007-FY2011
Participating groups and their studies

• Long-term global environmental projection
• with an earth system model
• - Frontier Research Center for Global Change
  (FRCGC) et al.
• Near-term climate prediction
• with a high-resolution coupled
  ocean-atmosphere GCM
• - Center for Climate System Research (CCSR) of
  the University of Tokyo et al.
• Projection of changes in extremes in the future
• with super-high-resolution atmospheric
  models
• - Meteorological Research Institute (MRI) et
  al.