中国区域气候极值重现水平的非平稳模型及趋势分析

应用基于GEV(Generalized Extreme Value)分布的平稳/非平稳极值概率模型,拟合中国区域489站自建站至2013年极端最高、最低温度和日最大降水量的年极值序列,并导出极值的重现水平及其变率随重现期和时间变化的一般表达式。着重分析了气候极值的"常态"(重现期为2年)与"极端态"(重现期为50年)的变化趋势及其线性变率的空间格局。详细探讨了极值的常态与极端态变化趋势相反的原因以及可能的影响。结果表明,极端最高温度在东部季风区普遍适用平稳模型;在其他地区更适用非平稳模型,其常态和极端态都以增温为主。极端最低温度在全国范围内普遍适用非平稳模型,其常态和极端态也都以增温为主,但在东北局部地区极端态呈现与常态相反的降温趋势。日最大降水量则在全国范围内普遍适用平稳模型。当GEV分布的尺度参数随时间变化时,与极值的常态相比,极端态的变率范围要大得多,并可能导致两者的变率异号的情形;尤其是当气候极值的常态日趋平缓而极端态却日益极端时,可能导致更为剧烈的灾害性天气。

Non-Stationary Modeling and Trend Analysis of Return Levels of Climate Extremes in China

Non-stationary probability models of extreme values following generalized extreme value (GEV) distributions were fitted to annual climate extremes of maximum temperature, minimum temperature, and maximum daily precipitation from 489 observational stations in China spanning from their own initial times to 2013. The non-stationary return level of extremes and its derivative with respect to time as functions of return period and time were also derived. Trends and spatial patterns of linear changing rates of "ordinary state" (2-year-period return level, denoted as z_0.5) and "extreme state" (50-year-period return level, denoted as z_0.02) of climate extremes were analyzed. A special case of opposite tendencies for z_0.5 and z_0.02 was particularly investigated for its origin and possible impact. The results show that, for maximum temperature, stationary models generally fit for the monsoon area of East China, while non-stationary models dominate for other regions in China and most of them indicate increasing trends for both z_0.5 and z_0.02. For minimum temperature, non-stationary models fit nationwide in China and also indicate increasing trends for both z_0.5 and z_0.02 in general, except for a part of Northeast China where z_0.02 shows a decreasing trend opposite to z_0.5. For maximum daily precipitation, stationary models fit overwhelmingly in China. When the scale parameter of GEV distribution changes with time, the variability of z_0.02 is much greater than that of z_0.5 and, consequently, opposite tendencies may happen for them. In particular, when z_0.5 becomes mild as z_0.02 becomes more extreme, much more intense disastrous weather may occur.