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)

Kutzbach et al. (1993) J.Geology Effects of mountains on climate

Ruddiman and Kutzbach (1989) JGR

Broccoli and Manabe (1992) Arid and Semiarid Climate mountain observed no mountain

Broccoli and Manabe (1992) soil moistureprecipitation M NM Eurasia is drier in M than in NM

Broccoli and Manabe (1992) M NM transient eddymoisture flux Larger eddy activity and larger moisture flux over Northern Eurasia in NM

Tibetan Plateau uplift

Ramstein et al. (1997) Nature

Kutzbach et al. (1993) J.Geology 4 types of large- scale 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

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 L m slab ocean Kitoh 1997: MRI-II 4x5 L m slab ocean #AOGCM Kitoh 2002, Abe et al. 2003: MRI-CGCM1 (4x5) Effect of SST change Kitoh 2004: MRI-CGCM2 (T42)0% to 140%

・ Exp-Mcontrol ・ CGCMcoupled GCM ・ Exp-NMno mountain ・ SGCMslab-ocean ・ AGCM Model topography in the control run Effect of Large-Scale Mountains on Surface Climate

Model Climate (CGCM1)

CGCM was integrated for 30 years, and the last 10 years of data were used. SGCM was integrated for 12 years, and the last 5 years of data were used.

Variance northward of 20N are 3,800 (M), 1,600 (NM) and 2,200 m 2 (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. Stationary eddies at 500 hPa in January

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

non-adjusted adjusted for 6.5 K/km Annual mean surface air temperature difference + inland area - coastal area / ocean Large negative temperature change over mountains. < elevation effect SST also changes.

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

Shortwave radiation and latent heat flux maintain cold SST in M-NM over the subtropical eastern Pacific

Coupled GCM Slab-ocean Surface Air Temperature Difference (M-NM)

Ocean Stream function

AGCM SGCM CGCM

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

All mountains are varied uniformly between 0% and 140%. Land-sea distribution is the same for all experiments. MRI-CGCM2. No flux adjustment year M14 (140%) M12 (120%) M10 (control) M8 (80%) M6 (60%) M4 (40%) M2 (20%) M0 (no mountain) Experiments

MRI CGCM2 AGCM 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 OGCM –Resolution : 2.5x( ), 23layers –Eddy mixing : Isopycnal mixing, GM –Seaice : Mellor and Kantha (1989) Coupling Coupling –Time interval : 24hours –Flux adjustment: “without” in this experiment

120E-140E pentad precipitation obs M M M M M M M M Numbers indicate spatial cc with obs 50N 10S

100%OBS 0%

Taylor’s diagram

100% 0%

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

PrecipitationPrecipitable water CGCM AGCM C-A

Moisture flux monsoon-ocean interaction Wang et al SST CGCM AGCM C-A

CGCM AGCM U850 (5N-15N) 140% 0%

Rainfall Index IMR: India, land 10N-30N, 60E-100E SEAM: Southeast Asia 5N-25N, 100E-130E EAM: East Asia 25N-35N, 120E-140E CGCM AGCM CGCM AGCM CGCM AGCM

Koppen climate: Asia

Koppen climate: India “ 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 0%100% OBS

Koppen climate: China “ 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 100%0%

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 → lower mountain cases have larger amplitude m0 m14 m12 m10 m8 m6 m4 m2

In M0, the SST pattern is nearly symmetric about the equator. The spatial pattern (e.g., meridional width) changes with uplift.

In M0, frequency peak is at 7 yr. When mountain becomes higher, it shifts toward high frequency, and explained variance smaller. Power spectra of each leading mode of SST EOF 33.6% 16.9% 17.5% 18.5% 18.1% 29.5% 25.6% yr

Pacific trade winds become stronger associated with strengthened subtropical high with mountain uplift

Change in Mean Climate: Trade Winds → Easterlies in lower mountain cases are strong in the eastern Pacific, but weak in the western Pacific low mountain high mountain

Change in Mean Climate: Upper Ocean Heat Content and its zonal gradient → lower mountain cases have larger OHC gradient low mountain high mountain

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.