1. The NTU-GCM'S AMIP Simulation of the Precipitation over Taiwan Area Wen-Shung Kau 1, Yu-Jen Sue 1 and Chih-Hua Tsou 2 1 Department of Atmospheric Sciences National Taiwan University 2 Department of Earth Sciences National Taiwan Normal University

2. Outline （ A ） NTU － GCM （ B ） AMIP Simulation 1. Climatology 2. Interannual Variation 3. Mei-Yu and Summer Seasons （ C ） Low Frequency Oscillation, Cloud / Radiation Precipitation （ D ） Conclusions

3. Abstract Based on the NMC global spectral model, with significant modifications to virtually all of the physical parameterizations, we have developed the NTU-GCM (T42L13). The NTU-GCM was used to perform an AMIP integration from 1 January 1979 to 31 December 2002. For the JJA climatology, the simulated stream function at 850 hPa captures most of the major features in the observed climatological-mean fields. e.g., the subtropical high over the ocean, the cyclonic circulation over South Asia and the eddy straddling the equator in the Indian Ocean, as well as the Tibetan high, simulated in the 200 hPa stream function. The East Asian Summer Monsoon (EASM) rainfall distribution is the result of complex interactions between the atmosphere, the earth's surface and the tropical and extra tropical systems. The EASM rainfall distribution involves a wide range of spatial and temporal scales from the mesoscale to the planetary scale. The NTU- GCM is able to simulate the large-scale features of the EASM and the sudden change of the monsoon rainfall, which is associated with abrupt changes in large- scale atmospheric circulation. In this study, we will focus on the rainfall simulations over Taiwan area by NTUGCM. The climatological heavy precipitation events in Taiwan dramatically increased after the 30-60 day convection over the South China Sea (SCS) reached maximum value and started to propagate northward. The interannual variability of ISO in SCS is positively correlated with the interannual variability of heavy rainfall in Taiwan. Monitoring the structure and propagation of 30-60 day convective oscillation in SCS, as well as its evolution simulated by GCM, may be helpful to the medium range forecast of heavy rainfall in Taiwan.

4. 1. Cloud / Radiation parameterization ： Ou & Liou （ 1988 ）. Broadband. 2. Cloud Scheme ： Slingo & Slingo （ 1991 ）. 3. Convection : Kuo (1974), with modification of ‘ b ’ value. 4. 6 integrations from 1979/01/01 ~2002/12/31. Δt = 20 min. 5. Taiwan Area = 9 Gaussian Grid Points 20.9 ~ 26.5 N ; 120.9 ~ 126.6 E 6. Intel Pentium 3, 1KGHz, 1(min / day) NTU-GCM Model

5. AMIP Simulation : ModelCMAP ≥ 7.5(mm/day)~onset

6. Climatology Seasonal

8. Monthly

9. Interannual

10. >0.75

11. ENSO

13. 93 VS 94 ModelCMAP

14. 97 VS 98 CMAPModel

15. 79 VS 80 ModelCMAP

16. 79 80 81 82 83 84 85 86 86 87 88 89 5/18 5/13 5/28 5/23 5/28 5/18 5/31 5/23 5/20 5/18 ≥ 7.5(mm/day)~onset ONSET AMIP

17. 91 92 93 94 95 96 97 98 99 00 01 02 6/6 5/18 5/28 5/20 5/16 5/18 5/20 ≥ 7.5(mm/day)~onset ONSET

18. The interannual variability of ISO in South China Sea is positively correlated with the interannual variability of heavy rainfall in Taiwan. When ISO in South China Sea has positive anomalies, the occurrence of heavy rainfall in Taiwan is above normal. The interannual variability of ISO in South China Sea is also dominated by 30-60 day oscillation. The distribution of the interannual variability of 30-60 day oscillation, while the heavy rainfall in Taiwan is less correlated with 10-20 day oscillation. Tsou et al. (2002) :

19. Wang et al. （ 2002 ） examined the precipitation rate averaged over the IM and WNPN regions during APSM period from 11 AGCMs.They pointed out that over the IM region nearly all models overestimated the summer precipitation rate. As a consequence, all-model ensemble mean has a systematic positive bias in summer rainfall from May to September,which is particularly large in June and July. The spreading among the model is moderate. On the other hand, over the WNPN region, the all-model ensemble mean annual cycle matches the smoothed CMAP climayology to some extent, but none of the models were close to the all-models mean. Thus the spreading among the models are nearly triple that over the IM region. This deficiency is consistent with the result of Kang et al. （ 2001 ） in that the monsoon trough over the WNP were misrepresented in neatly all models. The AGCMs exhibit great difficulty in reproducing correct climatology in WNP than over the Indian region.

20. OLR & LFO Model Analysis

22. After the onset of the summer monsoon （ about mid-May ）, there is a lower OLR, and great cloud amounts with heavy and persistent rainfall in BB throughout the whole summer. While SCS and WNP are different from BB, a brief recess of deep convection occurs in mid- July. This causes the magnitude of precipitation, cloud amount, optical depth and CRF for the entire summer average in SCS and WNP to be less than in BB. However, the precipitation, optical depth and low OLR strength are stronger after second monsoon outbreak （ about late-July ）. For example, the optical depth of deep-convection can reach to 70in the WNP.

25. OLR analysis AMIP

26. Model Analysis

27. 1.NTU-GCM ’ s AMIP simulation can capture the observed annual rainfall rate over Taiwan area. However, there have some discrepancies among inter- annual variations. 2.The inter-annual variability of ISO in SCS is positively correlated with the heavy rainfall in Taiwan during Mei-Yu season. (a) For the active ISO year in SCS (1979,1984,1986,1987 and 1997), NTU-GCM can simulate the positive rainfall anomaly as well as the observations. (b) For the weak ISO year (1982,1983,1990,1993,1994), the rainfall anomalies are more random among observations,same as the model's simulations. 3.The analysis of ISO is based on OLR data and the magnitude of such data is strongly correlated with cloud structures in the atmosphere. Therefore,to simulate correct cloud formations and radiative properties of clouds, are vital to the climate model. 4 … Conclusions