统计降尺度法对未来区域气候变化情景预估的研究进展

REVIEW ON CREATING FUTURE CLIMATE CHANGE SCENARIOS BY STATISTICAL DOWNSCALING TECHNIQUES

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。

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.