Using series of daily average temperature observations over the period of 1961-1999 of 701 meteorological stations in China, and simulated results of 20 global climate models （such as BCCR_BCM2.0, CGCM3T47） during t...Using series of daily average temperature observations over the period of 1961-1999 of 701 meteorological stations in China, and simulated results of 20 global climate models （such as BCCR_BCM2.0, CGCM3T47） during the same period as the observation, we validate and analyze the simulated results of the models by using three factor statistical method, achieve the results of mul- ti-model ensemble, test and verify the results of multi-model ensemble by using the observation data during the period of 1991-1999. Finally, we analyze changes of the annual mean temperature result of multi-mode ensemble prediction for the period of 2011-2040 under the emission scenarios A2, A1B and B 1. Analyzed results show that： （1） Global climate models can repro- duce Chinese regional spatial distribution of annual mean temperature, especially in low latitudes and eastern China. （2） With the factor of the trend of annual mean temperature changes in reference period, there is an obvious bias between the model and the observation. （3） Testing the result of multi-model ensemble during the period of 1991-1999, we can simulate the trend of temper- ature increase. Compared to observation, the result of different weighing multi-model ensemble prediction is better than the same weighing ensemble. （4） For the period of 20ll-2040, the growth of the annual mean temperature in China, which results from multi-mode ensemble prediction, is above 1℃. In the spatial distribution of annual mean temperature, under the emission scenarios of A2, A1B and B 1, the trend of growth in South China region is the smallest, the increment is less than or equals to 0.8℃; the trends in the northwestern region and south of the Qinghai-Tibet Plateau are the largest, the increment is more than 1℃.展开更多
对系统边际电价的概率分布研究是电力市场定量分析研究工作的基础。该文通过概率坐标图、峰度和偏度分析,对系统边际电价的分布特性进行定性和定量分析,并通过J-B检验严格验证系统边际电价分布的非正态性。根据概率坐标图发现的电价具...对系统边际电价的概率分布研究是电力市场定量分析研究工作的基础。该文通过概率坐标图、峰度和偏度分析,对系统边际电价的分布特性进行定性和定量分析,并通过J-B检验严格验证系统边际电价分布的非正态性。根据概率坐标图发现的电价具有分段正态分布的特点,提出用加权双高斯分布模型来刻画系统边际电价概率密度分布,美国PJM(Pennsylvania-new Jersey-Maryland)和澳大利亚NSW(New South Wales)电力市场的实际数据表明,该模型比传统的高斯分布和超高斯分布更接近实际的电价分布。展开更多
基金supported by Adapting Climate Change in China (ACCC) Project:Climate Science (Project No.ACCC/003)
文摘Using series of daily average temperature observations over the period of 1961-1999 of 701 meteorological stations in China, and simulated results of 20 global climate models （such as BCCR_BCM2.0, CGCM3T47） during the same period as the observation, we validate and analyze the simulated results of the models by using three factor statistical method, achieve the results of mul- ti-model ensemble, test and verify the results of multi-model ensemble by using the observation data during the period of 1991-1999. Finally, we analyze changes of the annual mean temperature result of multi-mode ensemble prediction for the period of 2011-2040 under the emission scenarios A2, A1B and B 1. Analyzed results show that： （1） Global climate models can repro- duce Chinese regional spatial distribution of annual mean temperature, especially in low latitudes and eastern China. （2） With the factor of the trend of annual mean temperature changes in reference period, there is an obvious bias between the model and the observation. （3） Testing the result of multi-model ensemble during the period of 1991-1999, we can simulate the trend of temper- ature increase. Compared to observation, the result of different weighing multi-model ensemble prediction is better than the same weighing ensemble. （4） For the period of 20ll-2040, the growth of the annual mean temperature in China, which results from multi-mode ensemble prediction, is above 1℃. In the spatial distribution of annual mean temperature, under the emission scenarios of A2, A1B and B 1, the trend of growth in South China region is the smallest, the increment is less than or equals to 0.8℃; the trends in the northwestern region and south of the Qinghai-Tibet Plateau are the largest, the increment is more than 1℃.
文摘对系统边际电价的概率分布研究是电力市场定量分析研究工作的基础。该文通过概率坐标图、峰度和偏度分析,对系统边际电价的分布特性进行定性和定量分析,并通过J-B检验严格验证系统边际电价分布的非正态性。根据概率坐标图发现的电价具有分段正态分布的特点,提出用加权双高斯分布模型来刻画系统边际电价概率密度分布,美国PJM(Pennsylvania-new Jersey-Maryland)和澳大利亚NSW(New South Wales)电力市场的实际数据表明,该模型比传统的高斯分布和超高斯分布更接近实际的电价分布。
