中国农业气象 ›› 2022, Vol. 43 ›› Issue (08): 597-611.doi: 10.3969/j.issn.1000-6362.2022.08.001

• 农业气候资源与气候变化栏目 •    下一篇

CMIP6模式对中国西南地区气温的模拟与预估

晋程绣,姜超,张曦月   

  1. 北京林业大学生态与自然保护学院, 北京 100083
  • 收稿日期:2021-11-09 出版日期:2022-08-20 发布日期:2022-08-16
  • 通讯作者: 姜超,副教授,主要从事全球变化生态学研究。 E-mail:jiangchao@bjfu.edu.cn
  • 作者简介:晋程绣,E-mail:2904936412@qq.com
  • 基金资助:
    国家自然科学基金(42175170)

Evaluation and Projection of Temperature in Southwestern China by CMIP6 Models

JIN Cheng-xiu, JIANG Chao, ZHANG Xi-yue   

  1. School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China
  • Received:2021-11-09 Online:2022-08-20 Published:2022-08-16

摘要: 应用1961−2014年CN05.1月平均气温观测数据集,以及国际耦合模式比较计划第六阶段(CMIP6)的19个全球气候模式数据,基于泰勒图、泰勒指数和年际变化技巧评分,系统评估了CMIP6模式对中国西南地区气温的气候态空间分布以及年际变化的模拟能力,并预估该地区未来气温在SSP1−2.6、SSP2−4.5、SSP3−7.0和SSP5−8.5情景下的变化特点。结果表明:(1)与其他季节相比,大多数CMIP6模式对研究区1961−2014年秋季气温气候态空间分布的模拟表现最好;CMIP6模式模拟四季和年平均气温年际变化的结果整体偏低。19个模式中对西南地区气温模拟较好的模式有ACCESS−CM2、CMCC−CM2−SR5和CMCC−ESM5。(2)3个较优模式的等权重集合,在模拟气温的气候态空间分布和年际变化方面优于19个模式的等权重集合。(3)与1961−2014年同期观测结果的多年平均气温相比,未来西南地区四季及年平均气温在4种情景下均呈升高趋势,四季和年平均气温升高0.94~3.48℃。4种气候情景下均表现为夏季升温最多(2.17~3.48℃),且夏季平均气温的年际波动幅度最小;冬季升温最少(0.94~2.24℃),其年际波动幅度最大。(4)在21世纪初,4种情景间季节和年平均气温的升高趋势差异不大,随着时间的推移,到21世纪中期,高辐射强迫情景下气温的升高趋势逐渐高于低辐射强迫情景。(5)在4种情景下,21世纪初期(2015−2034年)、中期(2045−2064年)及末期(2081−2100年)的多年平均气温与历史(1961−2014年)观测气温的距平值均呈现西北大于东南、高纬度高海拔地区大于低纬度低海拔地区的空间分布特点。随着时间推移,在21世纪末期,同一地区高辐射强迫情景的气温距平值明显高于低辐射强迫情景。

关键词: CMIP6模式, 西南地区, 气温变化, 评估, 预估

Abstract: Using on the CN05.1 monthly average temperature observation data set from 1961 to 2014 and the output data from 19 global climate models from Coupled Model Intercomparison Project Phase 6 (CMIP6), the simulation ability of CMIP6 models on the climatology spatial distribution and interannual variability of temperature in Southwestern China was systematically evaluated by means of Taylor diagram, Taylor index and interannual variability skill score. The variation characteristics of future temperature in this area were predicted under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios. The results showed that: (1) compared with other seasons, most CMIP6 models had the best performance in simulating the spatial distribution of autumn temperature climatology during 1961-2014; and CMIP6 models underestimated the interannual variability of seasonal and annual average temperature. Among the 19 models, the best models simulated the temperature in Southwestern China were ACCESS-CM2, CMCC-CM2-SR5 and CMCC-ESM5. (2) The multi-model ensemble mean(MME) of 3 best-fit models simulated the climatology spatial distribution and interannual variability of average temperature better than the MME of 19 models. (3) Compared with the multi-year average temperature observed in the same period during 1961−2014, the seasonal and annual average temperature in Southwestern China showed an upward trend in the future under the four climatic scenarios, seasonal and annual average temeprature increased by 0.94−3.48℃. Under the four scenarios, the increase of average temperature in summer was the largest(2.17−3.48℃) and the interannual fluctuation range was the smallest, the increase of temperature in winter was the smallest(0.94−2.24℃) and the interannual fluctuation range was the largest. (4) In the early of 21st century, there was little difference in the increase of seasonal and annual average temperature under 4 scenarios. During the middle of the 21st century, the upward trend of seasonal and annual average temperature in high radiation forcing scenarios was gradually larger than that in low radiation forcing scenarios. (5) Under the four scenarios, the anomaly values of multi-year average temperature at the early (2015−2034), middle (2045−2064) and end (2081−2100) period of 21st century and the historical(1961−2014) observed temperature showed the spatial distribution characteristics that the northwest was greater than southeast of this region, and the high latitude and high altitude areas were greater than the low latitude and low altitude areas. With the passage of time, at the end of 21st century, the temperature anomaly in the same region was significantly higher under high forcing scenarios than that in low forcing scenarios.

Key words: CMIP6 models, Southwestern China, Surface air temperature change, Evaluation, Projection