地理学报 ›› 1996, Vol. 51 ›› Issue (s1): 50-57.doi: 10.11821/xb1996S1005

• 论文 • 上一篇    下一篇

气候变化对日极值气温及日雨量的影响

邓慧平1, 吴正方2   

  1. 1. 东北师范大学草地研究所,长春130024;
    2. 东北师范大学城市与环境科学学院,长春130024
  • 收稿日期:1996-05-01 修回日期:1996-11-01 出版日期:1996-12-15 发布日期:1996-12-15
  • 基金资助:
    国家科委全球气候变化国家研究项目

A STUDY OF IMPACTS OF CLIMATE CHANGES IN DAILY MAXIMUM, MINIMUN TEMPERATURE AND DAILY PRECIPITATION

Deng Huiping1, Wu Zhengfang2   

  1. 1. Institute of Grassland. Northeast China Teachers’ University Chongchun 130024;
    School of Urban and Environmental Sciences, Northeast China Teochers’ University Changchun 130024
  • Received:1996-05-01 Revised:1996-11-01 Online:1996-12-15 Published:1996-12-15

摘要: 在未来二氧化碳加倍环境条件下,日最高气温、最低气温及日雨量的变化是评估气候变化对各类极端事件影响工作的基础。本文应用随机模拟技术及GCMs预测的未来气候情景,随机模拟了中国七个台站1×CO2和2×CO2条件下逐日最高气温、最低气温及日雨量。结果表明:在未来CO2加倍条件下,日最高气温和最低气温一般增加5℃~10℃左右。相应的极端高温事件将明显增加而低温事件将大大减少。日雨量则变化不大。

关键词: 气候变化, 日最高气温, 日最低气温, 日雨量, 随机模拟

Abstract: The changes of daily maximum, minimum temperature and daily precipitation are the basis of evaluating the climate change impacts on various extreme events in a CO2 doubling environment. In this paper, two stochasticsimulation models are developed for daily maximum and minimum temperature and daily precipitationrespectively.Based on present correlative equations between monthly averages of mean temperature and maximum or minimum temperature and scenario predicted by UKMO, monthly averages of maximum and minimum temperature are calculated in CO2 doubling situation. After that, A new set of annual cycle parameters are estimated while other two component's parameters are unchanged in CO2 doubling condition.As to daily precipitation simulation, monthly one-order Markovian chain transition matrices for wet and dry transition probabilities are derived from historical daily precipitation records. Then, precipitation values aregenerated by daily precipitation distributions. To reflect the climate change scenario, mean monthly precipitationis changed according to UKMO GCM outputs in daily precipitation distributions.The 100 year daily maximum and minimum temperatures can be decomposed three components. They areseasonal cycle, a sequence of several-day-time scale waves and random fluctuation components. Seasonal cyclecan be derived with a Fourier series. When higher harmonics are excluded, seasonal cycle is represented by theannual cycle. When the seasonal cycle is removed from the temperature data, short-time scale waves are apparent.A wave is approximated by a linear rising limb and a falling limb. After the annual cycle and short-time scalewaves are removed from temperature data, the residual value is random component. Each component can begenerated stochastically by producing various new parameter according to their probability distributions whichobtained from daily maximum or minimum temperature records of many years. After three components aresummed. the daily temperatures are produced. At present, GCMs only predict with some certainty a change in themonthly mean temperatures.100 year daily maximum, minimum temperature and precipitation values of seven stations in China aregenerated stochastically in present and CO2 doubling situations. The results have showed in CO2 doublingenvironment daily maximum and minimum temperature will increase 5 ℃~10℃ in seven places. The extremehigh temperature events will increase greatly while extreme low temperature events decrease obviously. Dailyprecipitation will change a little.

Key words: climate change, daily maximum temperature, daily minimum temperature, daily precipitation, stochastic simulation

中图分类号: 

  • P461