颱風季節預報可以事先幫助沿海附近的居民降低經濟損失及人員傷亡，進一步減少熱帶氣旋登陸時所造成的災害。因此對於亞洲太平洋地區，建立良好的颱風季節預報系統是非常迫切的事務。不過近年來利用動力模式從事這方面的研究大多在大西洋地區，而以低解析度的全球模式為主要的工具，模擬出來的熱帶氣旋具有水平尺度的環流系統以及暖心結構，不過中心強度並不是非常理想。雖然模式本身會有一些偏差，但仍然有能力去模擬出近似於氣候平均和年際變化之現象，甚至還可以模擬出與觀測位置、時間大致皆相同的熱帶氣旋。
　　本次研究目標是將高解析度的全球模式做為動力預報系統的主軸，針對1979~2009年，每年1~12月的時間找尋西北太平洋上屬於模式之熱帶氣旋進行分析。這種模式內部的方程組是以流體力學、熱力學和物理過程為基礎，並不是加入自己所設想的方法和理想定則。在本文中，模式對於熱帶氣旋在數量及生成位置的氣候尺度上有不錯的表現；但個數之年際變化掌握並不是很理想，不過卻在累積氣旋能量(Accumulated cyclone energy,ACE)、平均生存時間以及熱帶氣旋主要活動區域有不錯的表現；另外在El Niño and Southern Oscillation(ENSO)期間，熱帶氣旋生成位置東西變化之現象也有出現在模式結果。在季節尺度上，雖然模式可以模擬出很好的季節變化，但非活躍季時高估熱帶氣旋生成數量，而低估在活躍季的個數。
　　受到解析度之關係，模式熱帶氣旋無法與實際觀測熱帶氣旋強度相比，而且中心最大風速有一定之極限，因此對於往後強度之模擬還需要靠動力降尺度或是透過區域氣候模式協助，但總而言之，藉由全球模式進行西北太平洋的颱風氣候模擬在某些指數以及因子上都有不錯之表現，因此利用這些因子可以運用在熱帶氣旋之季節預報上。

The western North Pacific is the ocean basin where the typhoons are most active in the whole world. In view of the catastrophic damage on the environment and lives by tropical cyclones landfall, it is very important to builds the seasonal forecast system of the tropical cyclones. This prediction tool can help the preparedness of coastal populations for an upcoming typhoon season and reduce economical and human losses.
Most previous studies on seasonal tropical cyclone have used comparatively coarse resolutions and over Atlantic basin. In this study, we use high resolution (T106) global climate models about European Center Hamburg Atmospheric Model version 4.6 (ECHAM4) and version 5.4 (ECHAM5) to study the feasibility of dynamical seasonal forecast on tropical cyclone (TC) activities. The simulated TCs tend to have a larger horizontal scale and warm cores, but the intense inner core is not well simulated. Although the models have biases, they are able to reconstruct some aspects of the observed climatology and interannual variability of typhoons. Results indicate that the models display good performance in 31-yr (1979-2009) interannual variability of accumulated cyclone energy (ACE) and typhoon lifetime. In addition, there is a signal of a response to El Nino-Southern Oscillation (ENSO) with different genesis location and seasonal variability of typhoons in agreement with previous studies. Despite the models have a good correlation in seasonal variability of tropical cyclone counts, the ensembles tend to overestimate the numbers of typhoons during October to May period and underestimate the numbers during the peak typhoon season (June-September).
However, the landfalling statistics still represents a major challenge for dynamical model and real usefulness of TC seasonal forecast. Further dynamical downscaling with regional model is possible and can provide better typhoon intensity simulation.

Bengtsson, L., H. Böttger, and M. Kanamitsu, 1982: Simulation of hurricane-type vortices in a general circulation model. Tellus, 34, 440-457.
Bengtsson, L., M. Botzet, and M. Esch, 1995: Hurricane-type vortices in a general circulation model. Tellus 47A: 175-196
Bengtsson, L., K. I. Hodges, and M. Esch, 2007: Tropical cyclones in a T159 resolution global climate model: Comparison with observations and reanalysis. Tellus, 59A, 396416.
Broccoli, A., and S. Manabe, 1990: Can existing climate models be used to study anthropogenic changes in tropical cyclone climate? Geophys. Res Lett., 17, 1917-1920.
Camargo, S. J., A. G. Barnston, and S. E. Zebiak, 2005: A statistical assessment of tropical cyclone activity in atmospheric general circulation models. Tellus, 57A: 589-604.
Camargo, S. J., H. Li, L. Sun, 2007b: Feasibility study for downscaling seasonal tropical cyclone activity using the NCEP regional spectral model. Int. J. Climate, 27, 311-325.
Chia, H.-H. and C. F. Ropelewski, 2002: The interannual variability in the genesis location of tropical cyclones in the northwest Pacific. J. Climate, 15, 2934-2944.
Chen, J.-H. and Lin, S.-J., 2011: The remarkable predictability of inter-annual variability of Atlantic hurricanes during the past decade. Geophysical Research Letters, 38, L11804, doi:10.1029/2011GL047629
Gray, W. M., 1968: Global view of the origin of tropical disturbances and storms. Mon. Wea. Rev., 96, 669-700.
Haarsma, R.J., J.F.B. Mitchell and C.A. Senior, 1993: Tropical disturbances in a GCM. Clim. Dyn., 8, 247-257.
Hsu, P.-C., C.-H. Tsou, and, C.–T. Chen 2007: Simulations of the Interannual Variations of Intraseasonal Oscillation during Typhoon Season over the Western Pacific. Atmospheric Sciences, 35, 189-218. (In Chinese with English abstract)
Kanamitsu, M., W. Ebisuzaki, J Woollen, S.-K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP-DOE AMIP-II Reanalysis (R-2), Bull. Amer. Meteor. Soc., 83, 1631-1643.
Knutson, T. R., J. J. Sirutis, S. T. Garner, I. M. Held, and R. E. Tuleya, 2007: Simulation of the recent multidecadal increase of Atlantic Hurricane activity using an 18km Grid Regional Model. Bull. Amer. Meteor. Soc., 88, 15491565.
LaRow, T. E., Y.K. Lim, D. W. Shin, E. P. Chassignet, and S. Cocke, 2008: Atlantic basin seasonal hurricane simulations, J. Climate, 21, 13, 3191-3206.
Lin X., and R. H. Johnson, 1996: Kinematic and thermodynamic characteristics of the flow over the western Pacific warm pool during TOGA COARE. J. Atmos. Sci., 53, 695–715.
Louis, J. 1979: A parametric model of vertical eddy fluxes in the atmosphere., Bound.-Layer Meteor., 17, 187-202.
Manabe, S., J., 1970: Tropical circulation in a time-integration of a
global model of the atmosphere. J. Atmos. Sci., 27, 580–613.
Morcrette, J.-J., Smith, L. and Fouquart, Y., 1986: Pressure and temperature dependence of the absorption in longwave radiation parametrizations. Beitr. Phys. Atmos., 59, 455-469.
Nordeng, T. E., 1994: Extended versions of the convective parameterization scheme at ECMWF and their impact on the mean and transient activity of the model in the tropics. Technical Memorandum 206, ECMWF, Reading, UK.
Oouchi, K., J. Yoshimura, H. Yoshimura, R. Mizuta, S. Kusunoki, and A. Noda, 2006: Tropical cyclone climatology in a global-warming climate as simulated in a 20km-mesh global atmospheric model: Frequency and wind intensity analysis. J. Meteor. Soc. Japan, 84, 259-276.
Pauluis, O., and S. T. Garner, 2006: Sensitivity of radiative-convective equilibrium simulations to horizontal resolution. J. Atmos. Sci., 63, 1910–1923.
Roeckner E., Coauthors, 1996: The atmospheric general circulation model ECHAM4: Model description and simulation of present day climate. Max Planck Institute for Meteorology Rep., 218.
Roeckner, E., R. Brokopf, M. Esch, M. Giorgetta, S. Hagemann, L. Kornblueh, E. Manzini, U. Schlese, and U. Schulzweida,2006: Sensitivity of simulated climate to horizontal and vertical resolution in the ECHAM5 atmosphere model, J. Climate, 19, 3771-3791.
Smith, T.M., and R.W. Reynolds, 2004: Improved Extended Reconstruction of SST (1854-1997). J. Climate, 17, 2466-2477.
Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 1779-1800.
Tompkins A. M., 2002: A prognostic parameterization for the subgrid-scale variability of water vapor and clouds in large-scale models and its use to diagnose cloud cover. J. Atmos. Sci., 59, 1917–1942.
Vitart, F., J. L. Anderson, and W. F. Stern, 1997: Simulation of interannual variability of tropical storm frequency in an ensemble of GCM integrations. J. Climate, 10, 745-760.
Vitart, F., J. L. Anderson, and W. F. Stern, 1999: Impact of large-scale circulation on tropical storm frequency, intensity and location, simulated by an ensemble of GCM integrations. J. Climate, 12, 3237–3254.
Vitart F. D. and T. N. Stockdale, 2001: Seasonal forecasting of tropical storms using coupled GCM integrations. Mon. Wea. Rev., 129, 2521–2537.
Vitart, F., 2006: Seasonal forecasting of tropical storm frequency using a multi-model ensemble. Quart. J. Roy. Meteor. Soc., 132, 647-666.
Vitart, F., M.R. Huddleston, M. Déqué, D. Peake, T.N. Palmer, T.N. Stockdale, M.K. Davey, S. Inenson, A. Weisheimer, 2007: Dynamically-based seasonal forecasts of Atlantic tropical storm activity issued in June by EUROSIP. Geophys. Res. Lett., 34, L16815, doi:10.1029/2007GL030740.
Walsh, K. J., M. Fiorino, C. W. Landsea, and K. L. McInnes, 2007: Objectively determined resolutiondependent threshold criteria for the detection of tropical cyclones in climate model and reanalyses. J. Climate, 20, 2307-2314.
Wang Bin and Chan C. L.,2002: How Strong ENSO Events Affect Tropical Storm
Activity over the Western North Pacific, J. Climate, 15, 1643-1658
Wang Y., O. L. Sen, and B. Wang, 2003: A highly resolved regional climate model and its simulation of the 1998 severe precipitation events over China. Part I: Model description and verification of simulation. J. Climate, 16, 1721-1738.
Xie, S.P., K.-M. Hu, J. Hafner, H. Tokinaga, Y. Du, G. Huang, and T. Sampe, 2009: Indian capacitor effect on Indo-western Pacific climate during the summer following El Niño. J. Climate, 22, 730-747.
Zhao, M., Held, I., Lin, S.-J., and Vecchi, G. A., 2009: Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50 km resolution GCM. J. Climate., 22, 6653-6678
Zhao M., and I. M. Held 2010: An Analysis of the Effect of Global Warming on the Intensity of Atlantic Hurricanes Using a GCM with Statistical Refinement. J. Climate, 23, 6382-6393.
Zhan, R., Wang Y., and Lei, X., 2011: Contributions of enso and east indian ocean ssta to the interannual variability of northwest pacific tropical cyclone frequency. J. Climate, 24, 509-521.