Transcript Large-scale orography and monsoon Akio KITOH Meteorological Research Institute, Japan Meteorological Agency 1: Introduction 2: Surface temperature change 3: Asian monsoon 4: El Niño/Southern Oscillation (ENSO)

Large-scale orography and
monsoon
Akio KITOH
Meteorological Research Institute, Japan Meteorological Agency
1: Introduction
2: Surface temperature change
3: Asian monsoon
4: El Niño/Southern Oscillation (ENSO)
Effects of
mountains
on climate
Kutzbach et al.
(1993) J.Geology
Ruddiman and
Kutzbach
(1989) JGR
Arid and Semiarid Climate
mountain
no mountain
observed
Broccoli and Manabe (1992)
soil moisture
precipitation
M
Eurasia is drier in
M than in NM
NM
Broccoli and
Manabe (1992)
transient eddy
moisture flux
M
Larger eddy
activity and larger
moisture flux over
Northern Eurasia
in NM
NM
Broccoli and
Manabe (1992)
Tibetan Plateau uplift
Ramstein et al. (1997) Nature
4 types of largescale forcing or
b.c. for the South
Asian monsoon
the monsoon is
most sensitive to
the elevation and
radiation (orbital)
changes
CCM1+50m mixed-layer
Kutzbach et al.
(1993)
J.Geology
GCM Study on mountain and monsoon
#AGCM perpetual July
Hahn and Manabe 1975: Jul GFDL 270km L11
Kutzbach et al. 1989: Jan/Jul CCM R15 L9
#AGCM seasonal cycle
Broccoli and Manabe 1992: GFDL R30 L9
NH midlatitude dry climates
An et al. 2001: NCAR CCM3
4 stage Himalayan uplift
Liu and Yin 2002: COLA AGCM
11cases: 0%, 10%, …, 100%
#AGCM + slab ocean
Kutzbach et al. 1993: CCM1 R15 L12 + 50m slab ocean
Kitoh 1997: MRI-II 4x5 L15 + 50m slab ocean
#AOGCM
Kitoh 2002, Abe et al. 2003: MRI-CGCM1 (4x5) Effect of SST change
Kitoh 2004: MRI-CGCM2 (T42) 0% to 140%
Effect of Large-Scale Mountains
on Surface Climate
・Exp-M
control
・CGCM
coupled GCM
・Exp-NM
no mountain
・SGCM
slab-ocean
・AGCM
Model topography in the control run
Stationary eddies at 500 hPa in January
Variance northward of 20N are 3,800 (M), 1,600 (NM) and 2,200 m2 (M-NM).
Thus, the land-sea distribution effect (NM) explains about 40%, and the
mountain effect (M-NM) explains about 60% of the total variance.
200 hPa Winds
January: The Asian subtropical jet M is 15 m s-1 stronger. But zonal mean zonal wind at 30N is the same.
July: The subtropical jet in NM stays at 30N.
Surface Winds
Note the difference in trade winds both in Jan and Jul, and different wind direction over the Arabian Sea in July.
Precipitation
An overall precipitation pattern is similar. > land-sea configuration and SST distribution are the main factors.
NM summertime Asian precipitation elongates along 10N belt. M has less precipitation over Eurasia. Shape of ITCZ.
Sea-level pressure
January: Shape of the Siberan high.
July: strong Pacific subtropical anticyclone in M
Annual mean surface air temperature difference
non-adjusted
Large negative
temperature
change over
mountains.
< elevation effect
SST also changes.
adjusted for 6.5 K/km
+ inland area
- coastal area / ocean
South Asia and Eastern Asia: precipitation-soil moisture-evaporation, precipitation-cloudiness-insolation
Continental interior: precipitable water and moisture flux convergence are less, dry ground, less cloud
Summary (Land surface temperature)
Orography induces a warmer continental
interior and colder coastal area over land. The
land surface temperature drops due to the
lapse-rate effect. When this effect is eliminated,
the continent interior becomes warmer with a
mountain uplift, because clouds become fewer
and the surface drier due to a decreased
moisture transport. On the other hand, South
Asia becomes cooler because the summer
monsoon is stronger, and heavier precipitation
makes the land surface wetter and increases the
clouds.
Summary (SST)
The SST decreases due to orography particularly
over the subtropical eastern oceans. This occurs
because less solar radiation reaches the surface
due to more low-level clouds that are induced by
a strong subtropical anticyclone.
Changes of Asian monsoon by uplift
Experiments
M14 (140%)
M12 (120%)
M10 (control)
M8 (80%)
M6 (60%)
M4 (40%)
M2 (20%)
M0 (no mountain)
0
10
20
30
year
40
50
All mountains are varied uniformly between 0% and 140%.
Land-sea distribution is the same for all experiments.
MRI-CGCM2. No flux adjustment.
MRI CGCM2
•AGCM
–MRI/JMA98
–T42 (2.8x2.8), L30 (top at 0.4 hPa)
–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
–Seaice : Mellor and Kantha (1989)
•Coupling
–Time interval : 24hours
–Flux adjustment: “without” in this experiment
120E-140E
pentad precipitation
50N
obs
10S
Numbers indicate
spatial cc with obs
M0
M2
0.71
0.74
M4
M6
0.75
0.79
M8
M10
0.81
0.79
M12
M14
0.74
0.66
0%
20
40
60
120
80
140
100%
OBS
Taylor’s diagram
Note the difference in
the Pacific warm pool.
Over the Indian Ocean,
SST gradient reverses.
What is the merit of using CGCM?
AGCM: only dynamical/thermodynamical effect of mountain
CGCM: air-sea interaction, effect of SST change
Additional AGCM experiments were performed with the same
experimental design
A0, A2, A4, A6, A8, A10, A12, A14
Comparison between CGCM and AGCM experiments
Precipitation
CGCM
AGCM
C-A
Precipitable water
Rainfall Index
IMR: India, land
CGCM
10N-30N, 60E-100E
AGCM
SEAM: Southeast Asia
AGCM
5N-25N, 100E-130E
CGCM
CGCM
EAM: East Asia
AGCM
25N-35N, 120E-140E
Koppen climate: Asia
Koppen climate: India
0%
100%
• “BW”  “BS”  “Aw” as precip
increases
• “BS” in the interior part of southern
peninsular India does not appear in
the model due to coarse resolution
OBS
Koppen climate: China
0%
100%
• “BW” “BS” dominates in 0%〜40%
cases; too dry
• “Cw” “Cf” appears from 60% case
as precip increases
• “Cs” appears in 80%〜120% cases
due to larger winter precip
OBS
Summary (Monsoon)
• Systematic changes in precipitation pattern
and circulation fields as well as SST appeared
with progressive mountain uplift.
• In the summertime, precipitation area moved
inland of Asian continent with mountain uplift,
while the Pacific subtropical anticyclone and
associated trade winds became stronger.
• The model has reproduced a reasonable Baiu
rain band at the 60% case and higher.
• CGCM results were different from AGCM’s:
CGCM showed a larger sensitivity to mountain
uplift than AGCM.
Changes of ENSO by mountain uplift
Control run: global SST EOF1 and regressions
No-mountain run: global SST EOF1 and regressions
NINO3.4 SST and SOI
m0
m6
m12
m2
m8
m14
m4
m10
→ lower mountain
cases have larger
amplitude
In M0, the SST pattern
is nearly symmetric
about the equator.
The spatial pattern (e.g.,
meridional width)
changes with uplift.
Power spectra of each leading mode of SST EOF
33.6%
29.5%
17.5%
25.6%
18.1%
16.9%
25.6%
18.5%
6 4 2 yr
In M0, frequency peak
is at 7 yr. When
mountain becomes
higher, it shifts toward
high frequency, and
explained variance
smaller.
Pacific trade winds
become stronger
associated with
strengthened
subtropical high with
mountain uplift
Change in Mean Climate: Trade Winds
low mountain
high mountain
→ Easterlies in lower mountain cases are strong in
the eastern Pacific, but weak in the western Pacific
Change in Mean Climate:
Upper Ocean Heat Content and its zonal gradient
low mountain
high mountain
→ lower mountain cases have larger OHC gradient
Summary (ENSO)
Systematic changes in SST and ENSO as well as precipitation
pattern and circulation fields appeared with progressive mountain
uplift.
When the mountain height is low, a warm pool is located over the
central Pacific; it shifts westward with mountain uplift.
Model El Nino is strong, frequency is long and most periodic in the
no mountain run. They become weaker, shorter and less periodic
when the mountain height increases.
As mountain height increases, the trade winds intensify and the
location of the maximum SST variability shifts westwards.
Smaller amplitude of El Nino with high mountain cases may be
related to smaller SST/OHC gradient in the central Pacific.
Short return period of El Nino may be associated with a westward
displacement of most variable SST longitude and a decrease in the
meridional width.