【作者】 陈威霖；

【导师】 江志红； 李肇新；

【作者基本信息】 南京信息工程大学 ， 气候系统与全球变化， 2012， 博士

【摘要】 本文利用28个全球耦合模式的模拟结果和中国区域台站、CRU观测资料,从温度和降水的空间型、年际年代际变率等方面,对各模式在中国区域的模拟能力进行评估,并据此对模式加权,得到变暖背景下21世纪中国区域温度、降水变化的概率预估结果；此外,通过LMDZ动力模式和统计模型SDSM,分别在中国东南区域进行动力和统计降尺度,对该区21世纪的气候变化进行了高分辨率预估研究。得到以下主要结论：1、对中国区域温度、降水模拟能力最优的前5个模式分别为INGV ECHAM4、UKMO HADCM3、CSIRO MK 3.5、NCAR CCSM3.0和MIROC3.2 (hires),且都优于多模式集合(MME)；基于模式表现的秩加权方法能有效改善概率预估。A1B情形下21世纪中期和末期温度显著增加,尤以冬季和北方为甚；末期降水也将显著增加。2、年代际时间尺度上,融入观测海洋同化的年代际模式和CMIP3模式都能较好地模拟出中国区域20世纪后期尤其是北方的增暖信号；年代际气候预测模式则能成功地模拟江淮流域和华南沿海的旱涝演变。但CMIP3模式对20世纪后期中国东部降水的旱涝结构演变的模拟与观测相反。3、ERA40资料驱动的动力降尺度模式LMDZ能较真实的模拟出中国东南区域各个季节大部分极端气候指数的空间分布,但对平均气候的模拟好于极端气候指数,对温度的模拟好于降水；对年际变化的模拟也较好。故可用该模式进行气候预估研究。4、利用LMDZ进行SRES-A2排放情形下的预估结果表明,未来东南区域所有季节平均温度、最高最低温度显著增加,导致霜冻日数将大幅减少,热浪天数大幅增加；极端降水事件增多,强度增大。5、统计降尺度方法模拟的极端温度与观测值有很好的一致性,能有效纠正耦合模式的“冷偏差”；对于极端降水则能显著纠正耦合模式模拟的降水强度偏低的问题,说明降尺度模型SDSM的确有“增加值”的作用。未来SRES A2情景下,江淮地区的极端温度和降水事件都将增加。更多还原

【Abstract】 Based on the University of East Anglia’s Climate Research Unit time series (CRU TS2.1) temperature and precipitation data sets as well as the results of 28 AOGCMs, performance of the 28 models over China is evaluated in terms of mean squared error (MSE) and interannual variability. Different weights were then given to the models according to their performances in present-day climate, with the purpose to obtain the probabilistic projection of climate change over China. Furthermore, two downscaling techniques, dynamical downscaling named LMDZ and empirical statistical one named SDSM, have been used over Southeast China as to produce high-resolution climate change information under gobal warming. The main results are as follows:(1) Results of the evaluation for the current climate show that five models that have relatively higher resolutions, namely INGV_ECHAM4, UKMO_HADCM3, CSIRO_MK3.5, NCAR_CCSM3.0 and MIROC3.2 (hires), perform better than others over China. Rank-based weighting of model results can improve probabilistic projections of climate change. Under the A1B scenario, surface air temperature is projected to increase significantly for both middle and end of the 21st century, with larger magnitude over the north and in winter. There are also significant increases in rainfall in 21st century under the A1B scenario, especially for the period 2070-2099.(2) The coupled models which have the assimilation of observational data into decadal prediction outperform the CMIP3/IPCC AR4 GCMs with no initialization. Both of the four decadal prediction models and the CMIP3 MME can simulate warming signal in the late 20th century over China, especially in the northern part. The CMIP3 MME can not reproduce the pattern of "wet South and dry North" in the eastern part of China in recent 20 years. In contrast, the four decadal prediction models show better agreement with observations in simulating the pattern of "wet South" in China in recent 20 years, although they still can not reproduce the pattern of "dry North"(3) LMDZ, which is forced by ERA-40 reanalysis data, can realistically simulate the climate distribution of the surface air temperature and precipitaion, as well as climate extremes that are expressed in terms of extreme indices, although the model tends to overestimate the extreme precipitation. The inter-annual variability can also be well simulated for most of the climate indices. All of those indicate that LMDZ can be used to simulate the projected climate change over this region under global warming.(4) Results with greenhouse gas forcing from the SRES-A2 emission scenario show that there is a significant increase for mean, daily-maximum and minimum temperature in the entire region, associated with a decrease in the number of frost days and an increase in the heat wave duration. The annual frost days are projected to significantly decrease by 12-19 days while the heat wave duration to increase by about 7 days. A warming environment gives rise to changes in extreme precipitation events.(5) The evaluation of simulated extreme indices of temperature and precipitation for the current climate shows that the downscaled temperature-related indices match the observations well for all seasons, and SDSM can modify the systematic cold biases of the AOGCMs. For indices of precipitation extremes most AOGCMs intend to underestimate the intensity, but SDSM improves this significantly. Scenario results using A2 emissions show that in all seasons there is a significantly increase for mean daily-maximum and minimum temperature in the 29 meteorological stations, associated with a decrease in the number of frost days and with an increase in the heat wave duration. Precipitation extremes are projected to increase over most of the 29 meteorological stations.更多还原