气候变化研究进展 ›› 2016, Vol. 12 ›› Issue (5): 396-406.doi: 10.12006/j.issn.1673-1719.2016.010

• 气候系统变化 • 上一篇    下一篇

CMIP5模式集合对中国区域性低温事件的模拟与预估

胡浩林1,任福民2   

  1. 1 中国人民解放军93176部队,大连 116023;
    2 中国气象科学研究院灾害天气国家重点实验室,北京 100081
  • 收稿日期:2016-01-18 修回日期:2016-07-09 出版日期:2016-09-30 发布日期:2016-09-30
  • 通讯作者: 任福民 E-mail:fmren@163.com
  • 基金资助:

    国家自然科学基金;全球变化重大科学研究计划

Simulation and Projection for China’s Regional Low Temperature Events with CMIP5 Multi-Model Ensembles

Hu Haolin1, Ren Fumin2   

  1. 1 Troop 93176, People’s Liberation Army, Dalian 116023, China
    2 State key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
  • Received:2016-01-18 Revised:2016-07-09 Online:2016-09-30 Published:2016-09-30
  • Contact: Ren Fumin E-mail:fmren@163.com

摘要:

基于CMIP5逐日最低气温的模拟和预估数据,对中国区域性低温事件进行了研究。通过对中国区域性低温事件的历史模拟显示,模式集合的结果低估了中国区域性低温事件的变化趋势,但能够反映出与观测结果相同的减弱趋势,且比单个模式的结果更稳定,其空间分布与观测结果相似度也较高。在此基础上,采用模式集合方案对不同排放情景下(RCP2.6, RCP4.5, RCP8.5)的中国区域性低温事件进行了预估。结果显示,在RCP2.6排放情景下,中国区域性低温事件的减弱趋势较为缓和;在RCP4.5排放情景下,中国区域性低温事件呈现出显著的减弱趋势;在RCP8.5排放情景下,中国区域性低温事件的减弱趋势更明显。温室气体的排放可能主要影响中国区域性低温事件的强度和发生频次,对其空间分布影响较小。

关键词: 中国区域性低温事件, 模式集合, 模拟及预估, 时空变化

Abstract:

China’s regional low temperature events were studied with multi-model ensembles based on CMIP5 daily minimum temperature data. China’s regional low temperature events were recognized with historical data. Results show that multi-model ensembles undervalue the change tendency of China’s regional low temperature events, but the temporal change with multi-model ensembles shows same weaken tendency as the observation, the result is also more stable than any single model, and the spatial distribution of China’s regional low temperature events is very similar as the observation. Furthermore, China’s regional low temperature events under different GHG emission scenarios (RCP2.6, RCP4.5, RCP8.5) were projected. Results show that all indices are decreasing slowly under RCP2.6 scenario. Under RCP4.5 and RCP8.5 scenarios, all indices have marked drop trends from early stage to late stage, but the drop trends of all indices under RCP8.5 is more sharply than that under RCP4.5. GHG emissions may play an important role on intensity and frequency of China’s regional low temperature events, but have little influence on the spatial distribution.

Key words: China’s regional low temperature events, multi-model ensemble, simulation and projection, temporal and spatial change

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