摘要
气候变暖背景下,极端气候事件越来越频繁,气象灾害越来越严重,社会环境受到的破坏越来越大。气候极值作为发生极端事件的必要条件,其时空分布规律是预测气象灾害的重要依据。这种规律包括极值变率与破纪录率在空间尺度上的区域性,在时间尺度上的周期振荡性。极值向极端事件的转变一般通过时空群发性来实现,其气候背景与大气环流运动有关,并最终可能由海气耦合系统所主导,如ENSO循环等。海气系统的作用以各向异性的方式向不同区域传递,并导致极值出现时空依赖现象。这是极端事件预测理论的动力或统计基础。
实际应用表明广义帕累托分布(GPD)模型是在中国现有气候资料基础上研究极值规律的理想方法,其空间参数化方案能充分模拟极值的区域特征,实现了“点”过程到“场”过程的拓展。计算结果揭示了中国降水极值和温度极值的空间分布特征主要以季风区与非季风区为界限的空间差异,时间尺度上表现为夏季与其它季节有明显区别。它们对海气系统响应的关键区也体现了与气候区密切相关的空间依赖性。(一)对于降水极值,在季风区的中国南部的变率大于北部,夏季大于秋季。破纪录率在非季风区最大,在季风区最小。时间尺度上表现为秋冬季节比夏季更容易发生破纪录事件。降水极值的变率对海气系统(SO)的响应也与季风过渡区有关,响应区位于干、湿过渡带,即在青藏高原及其东部的两河流域之间。破纪录率在大部分地区对海气系统都有响应,夏、秋季响应面积最大,春、冬季最小。(二)对于温度极值,无论高值还是低值,其变率的空间特征与降水基本相反,高纬度非季风区大于低纬度的季风区。其中低温极值的变率对海气系统(NAO)响应的面积要比降水大得多,并以东部为主；高温极值的变率对NAO的响应面积比低温极值小得多,仅在东北北部有所体现。温度极值的破纪录率也有类似的特征。(三)气候变暖对温度极值的破纪录率和变率的强迫特征基本相同,但高温极值与低温极值响应的空间格局基本相反。在面积上高温小于低温,低温极值除了在青藏高原和东北地区无明显响应外,在其它地区均响应强烈；而高温极值的情形几乎相反,对气候变暖响应的关键区主要位于季风过渡带上,从青藏高原经华北至东北结束,所表现的空间分布格局基本与低温极值相互弥补。(四)降水极值与温度极值具有相似的空间依赖性。以江苏省为例,面积越大,极值的群发几率越小；极值强度越大,面积就越小。(五)概率分析表明,极值具有可预测性,以高温为例,未来的破纪录强度在有些地区可能呈现上升态势,在部分地区则有下降的可能。Monte Carlo模拟显示,不同强迫条件下的破纪录情景体现了有趣的现象。如在当前气候变暖速率下,破纪录高温强度不会有明显变化。虽然其概率可能缓慢上升,但其值最终收敛于一个常数——增暖速率。
Under the background of global warming, climate extremes, meteorological disasters and related social and economical losses increased rapidly. As a necessary precondition of climate extremes, the spatial-temporal characteristics of extreme events provides fundamental scientific basis for severe weather prediction. Such laws include the variability of extremes, the regional coverage of record-breaking probability and the periodic oscillation in the time scale. Under most circumstance, the spatial-temporal cluster of extreme values mark a transition to extreme events, the climatic background is closely related to general circulation of atmosphere and might be dominated by air-sea coupled system, e.g. ENSO. The main contribution of air-sea coupled system is that their interactions transport to different regions by an anisotropic way, and resulted in a phenomenon of extremes'spatial-temporal dependence. This is statistical and dynamical basis of the theory of extreme events prediction.
Results indicate that the generalized Pareto distribution (GPD) model is an effective tool for the study of climate extremes based on existing observations whose scheme of spatial parameterization can simulate the regional features of extreme value by extend its definition from 'points'to'fields'. The results reveal the differences of spatial distribution of extreme temperature and precipitation between the monsoon zone and non-monsoon region, and the temporal differences between summer and other seasons over China. The responses of them to air-sea system also reflect different key areas closely relating to climatic zones. (1)For precipitation, variability of extremes is comparatively bigger in the monsoon region of southern China than that of north of China, and larger in summer than that in autumn. Record-breaking probability is bigger in the non-monsoon and smaller in monsoon area. The frequency of Record-breaking events shows a lower frequent in summer than that in other seasons. Variability of the precipitation extremes responding to air-sea system (e.g. SO) is related to monsoon transition region, whose response area locates at the junction between dry region and wet region, e.g. between the Tibet Plateau and the eastern area between the Yellow River and Yangtze River. In most parts of China, record-breaking probability response to sea-air systems have larger region in summer and autumn than that of spring and winter. (2) No matter high and low, the spatial characteristics of temperature extremes variability is opposite to precipitation, e.g. the area of variability of extreme low temperature is larger in the high-latitude (non-monsoon region) than that in the low latitude (monsoon region). The area with variability of the low temperature extreme response to the ocean-atmosphere system (e.g.) is much larger than that of precipitation, and mainly at the east China; The spatial coverage of response region of extremes variability of high temperature response to NAO is much smaller than that of low temperature, only a minor response area locating at the north of northeast. The record-breaking probability also shows similar distribution. (3) The impact of global warming on record-breaking probability of temperature extremes is in a good accordance with its variability, while the response patterns of extreme high and low is opposite. The response area of high temperature is smaller than that of low temperature, the latter have a large strong response area in China except Qinghai-Tibet Plateau and Northeast China. The characteristics of extreme high temperatures is on the contrary, the key region response to global warming locates on the junction region between monsoon and non-monsoon belt, e.g. the area from the Qinghai-Tibet Plateau pass the North to the end of Northeast. Its spatial patterns of distribution are complementary with that of low-temperature. (4) Temperature Extreme shows similar spatial dependence to precipitation extreme, and take Jiangsu Province as an example, the coverage of extremes varies inversely with its probability and amplitude. (5) Probability analysis shows that climate extremes are predictable. Take temperature as an example, the record-breaking temperatures of China are different from region to region, whose levels of record-breaking temperature in future will rise in some regions and decline in others. Monte Carlo simulation indicates that record-breaking high temperature shows under different climate forcing scenarios. For example, under the background of current warming probability, the level of record-breaking high temperature would not change significantly. While the probability may rise slowly, it would eventually converge to a constant value, e.g. the warming probability.
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