Projection And Evaluation Of The Extreme Temperature Events Simulation Over China

This paper has evaluated the simulation ability of seven coupled General Circulation Models (GCM) supplied by the Intergovernmental Panel on Climate Change's 4th Assessment Report (IPCC-AR4) which are applied into climate simulations for the extreme temperature indices in China, followed by forecast on the changes in extreme events in the 21st century on different emissions scenarios. Then variations in the extreme temperature events over mid-east China during 1961-2006 are investigated by the extreme temperature indices therein. Last, SDSM and LMDZ down scaling methods are used in the simulation and evaluation of extreme temperature over mid-east China. As the LMDZ method has better results, it is utilized to forecast the variations of the extreme temperature indices over mid-east China in the 21st century.The main results are as follows:1. Based on the 1961～2000 extreme temperature observations in China, we have evaluated the simulations and predictions of products such as frost days (FD), growing season length (GSL), extreme temperature range (ETR), warm nights (TN90), and heat wave duration index (HWDI) from seven coupled General Circulation Models (GCM) supplied by the Intergovernmental Panel on Climate Change's 4th Assessment Report (IPCC-AR4). Results indicate that all the seven models are able to simulate the extreme temperature indices of China in a certain degree, while their ensemble acts better than any single one. For each index, most of the models can present linear trends of the same positive/negative signs as the observations but for weaker intensities. The simulation effects are different on a nationwide basis, with 110°N as the division, east (west) of which the effects are better (worse) and the worst over the Qinghai -Tibetan plateau. The predictions for the 21st century on emissions scenarios show that except decreases in the FD and ETR, other indices display significant increasing trend, especially for the indices of notable extreme climate such as HWDI and TN90, indicating that the temperature-related climate is moving towards the extreme. Under different kinds of emissions scenarios as SRES A2, SRES A1B, and SRES B1, the extreme temperature indices are always increasing (decreasing) consistently in the 21st century, with decrease (increase) in the FD and ETR (GSL, HWDI and TN90) . In the high emissions scenarios (A2), trends of the indexes are most significant, with the least in the low emissions scenarios (B1). Among the five indices, the largest changing ranges are in the HWDI and TN90, FD and GSL as the second, and least in the ETR. Except the GSL, the distributions of other four indices' trends are almost the same throughout China, with the variability rising gradually from north to south, and greater changes in northwest than northeast for indices of FD, ETR and HWDI, as different from the TN90 whose trends are more significant in southwest and south China.2. In the recent 46 years, the summer days (SU), yearly maximum and minimum temperature, warm nights (TN90), warm days (TX90) and heat wave duration index are increasing, as opposed to the FD, cold nights (TN10) and cold days (TX10), in which the yearly minimum temperature and TN90 have the largest rising trends. As respect to the inter-decadal variations, the extremely cold events are reduced significantly, while the extremely warm events are increased but with smaller ranges. The change of extreme temperature events are more distinct in the winter and summer rather than in the spring and autumn. As for the spatial distributions, the temperature indices have the positive or negative tendencies all over the study area, with national increasing (decreasing) trends in the SU, yearly minimum temperature , TN90 and HWDI (the FD , TN10 and TX10).3. SDSM-had simulations are better than the General Circulation Model Hadcm3 not only for the number of modeling products, but also for the simulated precision of the interannual variations (indicated by the connection coefficient between the temporal series) and the spatially distributions (indicated by the spatially connection coefficients). The SDSM-had can simulate the normal distribution of daily maximum and minimum temperatures for the stations, but with smaller frequency of the extreme values than the observations. The simulation effects of SDSM-had for the inter-decadal variations are better in the minimum temperature of winter than in the maximum temperatures of summer for the typical stations. The SDSM-had method are able to simulate the spatial distributions of extremely maximum/minimum temperatures over mid-east China and display the primary distribution characteristics of the real stations, but with significant systematic error, while the simulation effects are better for the minimum temperature in winter than maximum temperatures in summer. Extreme temperature return value evaluations indicate that the simulation capability of SDSM-had method are weak for the extreme minimum temperature.4. The simulations of LMDZ regional model are better than the GCM. The zoomed regional model can improve the simulating precision in the inter-decadal variations and spatial distributions of regional maximum and minimum temperatures. The zoomed climate model LMDZ-era40 and LMDZ-reg have the simulating capability for the maximum and minimum temperatures in mid-east China to some extent. Modeled results of typical stations show that the frequencies of the minimum temperature in winter and the maximum temperatures in summer are almost the same between the LMDZ-era40 and LMDZ-reg simulations, but better for the minimum temperature in winter than the maximum temperature in the summer. Mean square deviation, spatial/temporal connection coefficients indicate that the simulations of LMDZ-era40 are better than the LMDZ-reg. Evaluation of spatial distributions show that both the tests can simulate the main spatial distributions of the minimum temperature in winter and the maximum temperature in the summer with similar characteristics. Both are better over Jiangsu, Anhui, and Hubei provinces, while LMDZ-reg is less close to the observations than the LMDZ-era40. And both have the stable effects for extreme temperature return value evaluations, while the effects of minimum temperature return value evaluations are more stable than the maximum one.5. The LMDZ-reg simulations of the extreme temperature of mid-east China in the mid- 21st century under SRES A2 show that the monthly maximum and minimum temperatures may rise consistently but with different changing ranges. The maximum (minimum) temperatures increase greatest in the winter and autumn (winter and summer), least in the summer (spring). Generally, the frequency of extreme warm (cold) events is increasing (decreasing) notably. Variations in the GEV probability density functions are also show the significant warming of the extremely maximum and minimum temperatures, while the later increases more than the former. For annual and seasonal distributions (except the summer), the tendency of extremely minimum temperature are decreased from north to south in mid-east China, with the largest (smallest) change in the north Yangtze-Huaihe river valleys (Guangdong and Fujian). The annual and seasonal distributions of the extremely maximum temperature's trends are most significant in the mid-lower Yangtze River valleys, and least in the coastal regions in Fujian. The TN90 indicates the largest changing range among the study indices; the FD is the second, and the HWDI as the least.