以機率密度函數探討台灣及鄰近地區未來氣候變遷特性:IPCC全球海氣耦合模式資料之分析研究

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翁禎佑(Jhen-you Wong) 查詢紙本館藏

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大氣物理研究所

論文名稱

以機率密度函數探討台灣及鄰近地區未來氣候變遷特性:IPCC全球海氣耦合模式資料之分析研究
(Discussion on the future Climate Change Characteristics around Taiwan from IPCC AR4 AOGCMs Using Probability Density Functions)

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摘要(中)

IPCC AR4指出20世紀全球平均表面溫度上升0.74 ℃ ± 0.18 ℃，並出現加速增溫的現象；而未來21世紀，無論人類是否積極進行溫室氣體減量，溫度都將會持續上升。IPCC利用設定數種「情境假設」(未來溫室氣體可能的排放濃度假設)針對未來全球氣候變遷做模擬。但IPCC中所討論的均為全球性的氣候變化，若想進一步了解在台灣及鄰近地區的區域氣候變化，會因氣候模式空間尺度的問題，無法直接針對區域的氣候變遷做出詳細的描述。而本研究則試圖在已有的IPCC全球海氣耦合模式模擬的資料下，以探討整體模擬結果的統計特徵作為重點，選擇使用「機率密度函數」來做為分析的方法，期望可藉由機率密度函數的分布來了解台灣及鄰近地區在氣候變遷的統計特性。
本研究選擇IPCC中的模式(日本的MIROC3.2(medres)，簡稱為NIES；德國的ECJAM5_MOI-OM，簡稱為MPI)在A1B情境假設下所模擬的資料進行分析。在單日最高溫中，計算當溫度大於或等於30 ℃的高溫發生機率，以NIES來重建1980~1999年間之氣候資料顯示，高溫的發生機率不到1 ％，但在A1B情境下以NIES模式所做的模擬結果顯示，在2046~2065年時期此高溫發生機率增加到約15 ％，而到了2081~2100年更增加為約36 ％。而在MPI模式中，重塑的20世紀末期資料顯示高溫發生機率不到9 ％，而在A1B情境下的模擬21世紀末期結果顯示，其發生機率變為將近47 ％。
在單日最低溫的部份，計算當溫度小於或等於16 ℃時所對應的溫度，以NIES模式重塑在1980~1999年間的資料顯示，小於或等於16 ℃的低溫發生機率約為4 ％，而在模擬21世紀中期的結果顯示此發生機率就降至不到1 ％，而到了21世紀末期時此機率又略為往下降了一些。在MPI模式中，重建的20世紀末氣候資料顯示，在小於或等於16 ℃的低溫發生機率僅只有2 ％，而模擬的21世紀末期結果顯示此發生機率已變為0 ％。
而降雨方面，兩模式的機率密度函數圖沒有太顯著的改變。在小雨（小於或等於5 mm day-1）的部份兩模式均沒有顯著改變的趨勢；而在大雨（大於或等於40 mm day-1）的部份，兩模式均呈現增加的趨勢。此外，在年平均總雨量的模擬結果兩模式是呈現相反的趨勢。
在分析此兩模式的未來模擬結果時，需將在重塑過去氣候與觀測資料間的誤差考慮進去，以機率密度函數的角度來看，兩模式在三個參數中的技術得分均在0.8左右，表示模式在模擬未來的機率密度函數結果有一定的可信度。而本研究的結果也能說明雖然氣候模式的模擬有其不確定性，但仍可經由機率密度函數等統計方法做出定量的分析。

摘要(英)

In the Fourth Assessment Report (AR4), the Intergovernmental Panel on Climate Change (IPCC) concluded that the global mean surface temperature have risen by 0.74 ℃ ± 0.18 ℃ over the 20th century, and the warming trend is accelerating. And in the 21st century the surface temperature is expected to rise continuously. IPCC adopts several types of “Special Report on Emissions Scenarios” (SRES) to simulate the future global climate change. The SRES are constructed based on potential greenhouse gas emission strength in the future.
The spatial scale of climate change is global in the IPCC. We can’t directly describe in detail how the regional climate change by using Atmosphere-Ocean General Circulation Models (AOGCMs). If we want to understand the regional climate projections, the questions are then we have to use the dynamic downscaling. In this study, the statistical features of the whole simulated result from IPCC are focal point. So we used “probability density function” method and hope we can examine the statistical features of the climate change characteristics around Taiwan by means of the probability density function.
Two models within IPCC are selected for this study. They are MIROC3.2(medres) (abbreviated as NIES) from Japan and ECJAM5_MOI-OM (abbreviated as MPI) from Germany respectively. The simulated data come from the models with the SRES A1B. For daily maximum temperature, we calculate the probability of the temperature which is greater than or equal to 30 Celsius. The probability value in NIES model indicates that 1980-1999 data don’t reach 1 ％. But in the SRES A1B, the simulated probability in 2046-2065 is about 15 ％. In 2081-2100, the probability is 36 ％. For MPI model, the probability from 1980-1999 data don’t reach 9 ％. And in the SRES A1B, the probability from simulated 2081-2100 data is 47 ％.
For daily minimum temperature, we calculate the probability of the temperature which is smaller than or equal to 16 Celsius. The probability value in NIES model during 1980-1999 period is about 4 ％. But in the SRES A1B, the simulated probability in 2046-2065 is less than 1 ％. In 2081-2100, the probability is even smaller decreasing. For MPI model, the probability from reproduced 1980-1999 period is only 2 ％. And in the SRES A1B, the probability from simulated 2081-2100 period almost is nonexistent.
For precipitation, the PDF of two models don’t change much. In light rain (the rainfall is smaller than or equal to 5 mm day-1), the change of two models are not obvious. And during the heavy rain (the rainfall are greater than or equal to 40 mm day-1), the increasing trends embedded in two models. However for annual mean rainfall, the trends of two models are opposite.
When we use the simulated result of two models, we should consider the difference between the data of model and observation. By PDF, the skill score of two models are around 0.8 based on the analysis from three variables. The meaning is the simulated PDF in the future is a reasonable methodology. In this study, we can show even though the simulations of the climate models are uncertain, we can still adopt the probability density functions method and come out quantitative analysis that can be useful in understanding the regional climate change characteristics.

關鍵字(中)

★ 機率密度函數
★ IPCC AR4

關鍵字(英)

★ IPCC AR4
★ probability density function

論文目次

目錄
頁次
中文摘要 …………………………………………………… i
英文摘要 …………………………………………………… iii
誌謝 …………………………………………………… v
目錄 …………………………………………………… vi
附圖說明 …………………………………………………… vii
附表說明 …………………………………………………… xiii
一、前言 …………………………………………………… 1
二、資料來源與分析方法 ………………………………… 7
三、模式模擬能力之評估 ………………………………… 10
四、A1B情境下之機率密度函數分布特徵 ………………… 14
五、累積分布函數的變化特徵 …………………………… 25
六、環流場的變化特徵 …………………………………… 33
七、結論與未來展望 ……………………………………… 35
參考文獻 …………………………………………………… 38
附件一 …………………………………………………… 44
附圖 …………………………………………………… 45
附表 …………………………………………………… 92

參考文獻

柳中明、吳明進、林淑華、陳盈蓁、楊胤庭、林瑋翔、曾于恆、陳正達， 2008：台灣地區未來氣候變遷預估。台大全球變遷研究中心。30pp。
Alexander, L. V., and Coauthors, 2006: Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res., 111, D05109, doi:10.1029/2005JD006290.
Angell, J. D., and J. Korshover, 1978: Global temperature variation, surface-100 mb: An update into 1977. Mon. Wea. Rev., 106, 755-760.
Angell, J. K., 1986: Annual and seasonal global temperature change in the troposphere and low stratosphere, 1960-85. Mon. Weather Rev. 114, 1922-1930.
Annamalai, H., K. Hamilton, and K. R. Sperber, 2007: South Asian summer monsoon and its relationship with ENSO in the IPCC AR4 simulations. J. Climate, 20, 1071-1092.
Boer, G. J., and S. J. Lambert, 2001: Second-order space time climate difference statistics. Climate Dyn., 17, 213-218.
Chan, 2000: Tropical cyclone activity over the western North Pacific associated with El Niño and La Niña events. J. Climate, 13, 2960-2972.
――, and K. S. Liu, 2004: Global warming and western North Pacific typhoon activity from an observational perspective. J. Climate, 17, 4590-4602.
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.
Christensen, J. H., and O. B. Christensen, 2003: Severe summertime flooding in Europe. Nature, 421, 805-806.
Chu, P.-S., 2004: ENSO and tropical cyclone activity, in Hurricanes and Typhoons: Past, Present, and Potential, edited by R. J. Murnane and K. B. Liu, pp297-332, Columbia University Press, New York.
Collins, M., 2000: The El Niño-Southern Oscillation in the second Hadley Centre coupled model and its response to greenhouse warming. J. Climate, 13, 1299-1312.
Collins, W. D., and Coauthors, 2006: The Community Climate System Model Version 3 (CCSM3). J. Climate, 19, 2122-2143.
Colombo, A., D. Etkin, and B. Karney, 1999: Climate variability and the frequency of extreme temperature events for nine sites across Canada: Implications for power usage. J. Climate, 12, 2490-2502.
Delworth, T. L., and Coauthors, 2006: GFDL’s CM2 Global Coupled Climate Models. PartⅠ: Formulation and simulation characteristics. J. Climate, 19, 643-674.
Dessai, S., X. Lu, and M. Hulme, 2005: Limited sensitivity analysis of regional climate change probabilities for the 21st century. J. Geophys. Res., 110, D19108, doi:10.1029/2005JD005919.
Easterling, D. R., G. A. Meehl, C. Parmesan, S. A. Changnon, T. R. Karl, and L. O. Mearns, 2000: Climate extremes: Observations, modeling, and impacts. Science, 289, 2068-2074.
Emanuel, K. A., 2005: Increasing destructiveness of tropical cyclones over the past 30years. Nature, 436, 686-688.
Flügel, M., P. Chang, and C. Penland, 2004: The role of stochastic forcing in modulating ENSO predictability. J. Climate, 17, 3125-3140.
Frich, P., L. V. Alexander, P. Della-Marta, B. Gleason, M. Haylock, A. M. G. Klein Tank, and T. Peterson, 2002: Observed coherent changes in climate extremes during the second half of the twentieth century. Climate Res., 19, 193-212.
Ho, C.-H., J.-H. Kim, J.-H. Jeong, H. S. Kim, and D. Chen, 2006: Variations of tropical cyclone activity in the south Indian Ocean: ENSO and MJO effects. J. Geophys. Res., 111, D22101, doi:10.1029/2006JD007289.
Houghton, J. T., Y. Ding, D. J. Griggs, M. Noguer, P. J. van der Linden, and D. Xiaosu, Eds., 2001: Climate Change 2001: The Scientific Basis. Cambridge University Press, 881 pp.
Hoyos, C. D., P. A. Agudelo, P. J. Webster, and J. A. Curry, 2006: Deconvolution of the factors contributing to the increasing global hurricane intensity. Science, 312, 94-97.
Hu, Z.-Z., M. Latif, E. Roeckner, and L. Bengtsson, 2000: Intensified Asian summer monsoon and its variability in a coupled model forced by increasing greenhouse gas concentrations. Geophys. Res. Lett., 27, 2681-2684.
Johns, T. C., and Coauthors, 2006: The New Hadley Centre Climate Mpdel (HadGEM1): Evaluation of coupled simulations. J. Climate, 19, 1327-1353.
Jones, P. D., S. C. B. Raper, and T. M. L. Wigley, 1986a: Southern Hemisphere surface air temperature variations: 1851-1984. J. Climate Appl. Meteor., 25, 1213-1230.
――, R. S. Bradley, H. F. Diaz, P. M. Kelly, and T. M. L. Wigley, 1986b: Northern Hemisphere surface air temperature variations: 1851-1984. J. Climate Appl. Meteor., 25, 161-179.
Katz, R., and B. Brown, 1992: Extreme events in a changing climate: Variability is more important than averages. Climatic Change, 21, 289-302.
Kharin, V., and F. Zwiers, 2000: Changes in the extremes in an ensemble of transient climate simulations with a coupled atmosphere-ocean GCM. J. Climate, 13, 3760-3788.
――, ――, X. Zhang, 2005: Intercomparison of near surface temperature and precipitation extremes in AMIP-2 simulations. J. Climate, 18, 5201-5223.
Kirtman, B. P., K. Pegion, and S. Kinter, 2005: Internal atmospheric dynamics and climate variability. J. Atmos. Sci., 62, 2220-2233.
Kiktev, D., D. M. H. Sexton, L. Alexander, and C. K. Folland, 2003: Comparison of modeled and observed trends in indices of daily climate extremes. J. Climate, 16, 3560-3571.
Klotzbach, P. J., 2006: Trends in global tropical cyclone activity over the past twenty years (1986-2005). Geophys. Res. Lett., 33, L10805, doi:10.1029/2006GL025881.
Knutson, T. R., and S. Manabe, 1995: Time-mean response over the tropical Pacific to increased CO2 in a coupled ocean-atmosphere model. J. Climate, 8, 2181-2199.
――, ――, and D. Gu, 1997: Simulated ENSO in a global coupled ocean-atmosphere model: Multidecadal amplitude modulation and CO2 sensitivity. J. Climate, 10, 138-161.
Knutti, R., G. A. Meehl, M. R. Allen, and D. A. Stainforth, 2006: Constraining climate sensitivity from the seasonal cycle in surface temperature. J. Climate, 19, 4224-4233.
Landsea, C. W., B. A. Harper, K. Hoarau, and J. A. Knaff, 2006: Can we detect trends in extreme tropical cyclones? Science, 313, 452-454.
Luo, Q., R. N. Jones, M. Williams, B. Bryan, and W. Bellotti, 2005: Probabilistic distributions of regional climate change and their application in risk analysis of wheat production. Climate Res., 29, 41-52.
Manabe, S., and R. T. Wetherald, 1975: The effects of doubling the CO2 Concentration on the climate of a general circulation model. J. Atmos. Sci., 32, 3-15.
Matsuura, T., M. Yumoto, and S. Iizuka, 2003: A mechanism of interdecadal variability of tropical cyclone activity over the western North Pacific. Climate Dyn., 21, 105-117.
McAvaney, B. J., and Coauthors, 2001: Model evaluation. Climate Change 2001: The Scientific Basis, J. T. Houghton et al., Eds., Cambridge University Press, 471-524.
Meehl, G. A., and W. M. Washington, 1993:South-Asian summer monsoon variability in a model with doubled atmospheric carbon dioxide concentration. Science, 260, 1101-1104.
――, F. Zwiers, J. Evans, T. Knutson, L. Mearns, and P. Whetton, 2000: Trends in extreme weather and climate events: Issues related to modeling extremes in projections of future climate change. Bull. Amer. Meteor. Soc., 81, 427-436.
Perkins, S. E., A. J. Pitman, N. J. Holbrook, J. McAneney, 2007: Evaluation of the AR4 climate models’ simulated daily maximum temperature, minimum temperature, and precipitation over Australia using probability density functions. J. Climate, 20, 4356-4376.
Piani, C., D. J. Frame, D. A. Stainforth, and M. R. Allen, 2005: Constraints on climate change from a multi-thousand member ensemble of simulations. Geophys. Res. Lett., 32, L23925, doi:10.1029/2005GL024452.
Ramanathan, V., and J. A. Coakley, Jr., 1978: Climate modeling through radiative-convective models. Rev. Geophys. Space Phys., 16, 465-689.
Rupa Kumar, K., and R. G. Ashrit, 2001: Regional aspects of global climate changes simulations: Validation and assessment of climate response over Indian monsoon region to transient increase of greenhouse gases and sulfate aerosols. Mausam, 52, 229-244.
Schaeffer, M., F. M. Selten, and J.D. Opsteegh, 2005: Shifts in means are not a proxy for changes in extreme winter temperatures in climate projections. Climate Dyn., 25, 51-63.
Schneider, S. H., 1975: On the carbon dioxide-climate confusion. J. Atmos. Scl., 32, 2060-2066.
Shukla, J., T. DelSole, M. Fennessy, J. Kinter, and D. Paolino, 2006: Climate model fidelity and projections of climate change. Geophys. Res. Lett., 33, L07702, doi:10.1029/2005GL025579.
Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106 (D7), 7183-7192.
Timmermann, A., J. Oberhuber, A. Bacher, M. Esch, M. Latif, and E. Roeckner, 1999: Increased El Niño frequency in a climate model forced by future greenhouse warming. Nature, 393, 694-697.
Trenberth, K. E., 1976: Fluctuations and trends in indices of the Southern Hemispheric circulation. Quart. J. Roy. Meteor. Soc., 102, 65-76.
Trigo, R. M., R. García-Herrera, J. Díaz, and I. F. Trigo, 2005: How exceptional was the early August 2003 heatwave in France? Geophys. Res. Lett., 32, L10701, doi:10.1029/2005GL022410.
van Loon, H., and J. Williams, 1977: The connection between trends of mean temperature and circulation at the surface. Part Ⅳ: Comparison of the surface changes in the Northern Hemisphere with the upper air and with the antarctic in winter. Mon. Wea. Rev., 105, 636-647.
Wang, B., and J. C. L. Chan, 2002: How ENSO regulates tropical storm activity over the western North Pacidic. J. Climate, 15, 1643-1658.
Watterson, I. G., 1996: Non-dimensional measures of climate model performance. Int. J. Climatol., 16, 379-391.
Webster, P. J., G. J. Holland, J. A. Curry, and H.-R. Chang, 2005:Changes in tropical cyclone number, duration and intensity in a warm environment. Science, 309, 1844-1846.
Yeh, S.-W., and B. P. Kirtman, 2005: Pacific decadal variability and decadal ENSO amplitude modulation. Geophys. Res. Lett., 32, L05703, doi:10.1029/2004GL021731.
Zwiers, F., and X. Zhang, 2003: Towards regional-scale climate change detection. J. Climate, 16, 793-797.

指導教授

王作台(Jough-tai Wang)

審核日期

2009-7-22

推文