| 统计降尺度法对未来区域气候变化情景预估的研究进展 |
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| 引用本文： | 范丽军,符淙斌,陈德亮.统计降尺度法对未来区域气候变化情景预估的研究进展[J].地球科学进展,2005,20(3):320-329. |
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| 作者姓名： | 范丽军 符淙斌 陈德亮 |
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| 作者单位： | 1. 中国科学院大气物理研究所东亚区域气候-环境重点实验室,北京,100029
2. Earth Sciences Centre, G(o)teborg University, Sweden 40530;中国气象局国家气候中心,气候研究开放实验室,北京,100081 |
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| 基金项目： | 国家重点基础研究发展计划(973计划);中国科学院引进国外杰出人才基金;科技部科研项目;瑞典STINT基金 |
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| 摘 要： | 由于迄今为止大部分的海气耦合气候模式（AOGCM）的空间分辨率还较低,很难对区域尺度的气候变化情景做合理的预测,降尺度法已广泛用于弥补AOGCM在这方面的不足。简要介绍了3种常用的降尺度法：动力降尺度法、统计降尺度法和统计与动力相结合的降尺度法;系统论述了统计降尺度方法的理论和应用的研究进展,其中包括：统计降尺度法的基本假设,统计降尺度法的优缺点，以及常用的3种统计降尺度法；还论述了用统计降尺度法预估未来气候情景的一般步骤，以及方差放大技术在统计降尺度中的应用；同时还强调了统计降尺度方法和动力降尺度方法比较研究在统计降尺度研究中的重要性；最后指出统计与动力相结合的降尺度方法将成为降尺度技术的重要发展方向。
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| 关 键 词： | 统计降尺度法 动力降尺度法 统计与动力相结合的降尺度法 海气耦合气候模式(AOGCM) 未来区域气候变化情景 |
| 文章编号： | 1001-8166(2005)03-0320-10 |
| 收稿时间： | 2003-10-24 |
| 修稿时间： | 2003年10月24 |
REVIEW ON CREATING FUTURE CLIMATE CHANGE SCENARIOS BY STATISTICAL DOWNSCALING TECHNIQUES |
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| FAN Li-jun,FU Cong-Bin,CHEN De-liang.REVIEW ON CREATING FUTURE CLIMATE CHANGE SCENARIOS BY STATISTICAL DOWNSCALING TECHNIQUES[J].Advance in Earth Sciences,2005,20(3):320-329. |
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| Authors: | FAN Li-jun FU Cong-Bin CHEN De-liang |
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| Institution: | 1.Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,China;
2. Earth Sciences Centre, Göteborg University,Sweden 40530;
3.National Climate Center，China Meteorological Administration,Beijing 100081,China |
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| Abstract: | Coupled General Circulation models (AOGCMs) are widely used as an important tool of projecting global climate change. However, their resolution is too coarse to provide the regional scale information required for regional impact assessments. Therefore, downscaling methods for extracting regional scale information from output of AOGCMs have been developed. Regional climate models nested in AOGCMs, statistical downscaling, and dynamical-statistical downscaling are usually used for downscaling. In this review paper, focus is placed on statistical downscaling techniques. These methods can be used to predict regional scale climate from AOGCM output using statistical relationship between the large-scale climate and the regional-scale climate, which offers the advantages of being computationally inexpensive. The principle and assumptions of three categories of statistical downscaling are introduced. Important issues in using statistical downscaling to create future climate change scenario is also discussed. At the same time, dynamical downscaling is briefly compared with statistical downscaling in terms of their advantages and disadvantages. Finally, prospects of developing new downscaling techniques by combining statistical and dynamical downscaling techniques are pointed out. |
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| Keywords: | Statistical downscaling Dynamical downscaling Statistical-dynamical downscaling Coupled general circulation models(AOGCMs) Future climate change scenario |
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