Simulation On Spatial And Temporal Distribution Of Precipitation And Temperature Extremes In China

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.