碩士 / 國立中央大學 / 大氣物理研究所 / 97 / 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.
| Identifer | oai:union.ndltd.org:TW/097NCU05021017 |
| Date | January 2009 |
| Creators | Jhen-you Wong, 翁禎佑 |
| Contributors | Jough-tai Wang, 王作台 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 113 |
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