doi:  10.3878/j.issn.1006-9895.1810.18225

Temperature and precipitation change over China under a 1.5 °C global warming based on CMIP5 models
摘要点击 906  全文点击 164  投稿时间:2018-09-06  修订日期:2018-10-04
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中文关键词:  1.5℃全球变暖、气候变化、信噪比、预估、中国
英文关键词:  1.5 ℃ global warming, Climate change, Signal-to-noise ratio, Projection, China
王晓欣Wang Xiao Xin中国科学院大气物理研究所
姜大膀Jiang Da Bang中国科学院大气物理研究所
Citation:Wang Xiao Xin,Jiang Da Bang.2019.Temperature and precipitation change over China under a 1.5 °C global warming based on CMIP5 models[J].Chinese Journal of Atmospheric Sciences (in Chinese)
      To get a better understanding of climate changes over China associated with a 1.5 ℃ global warming world relative to the pre-industrial levels, here we present an analysis based on the numerical experiments undertaken by 39 Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models. Results indicate that a global warming of 1.5 ℃ will occur in the median year 2034 for the Representative Concentration Pathways (RCP) 2.6, 2033 for RCP4.5, and 2029 for RCP8.5, respectively. Under a 1.5 ℃ global warming world, annual and seasonal temperatures are projected to increase by an average of 1.8 ℃ and 1.6–2.1 ℃, respectively, with the strongest warming occurring in winter. Generally, the warming strengthens from south to north, and an amplification occurs in the Tibetan Plateau. Change signals of annual and seasonal temperatures exceed the local natural internal variability over the entire country, and the corresponding signal-to-noise ratios average 3.4 and 1.6–2.7, respectively. Both annual and seasonal precipitation is expected to increase in northern China and to decrease in southern China. Averaged over the country, annual and seasonal precipitation increase by 1.4% and 0.1%–5.1%, respectively, with the greatest increase occurring in winter. On the whole, changes in annual and seasonal precipitation do not go outside the local natural internal variability, and the signal-to-noise ratios averaged over the country are 0.1 and 0.01–0.2, respectively. Greater inter-model uncertainty occurs in the projection of changes in precipitation than that of temperature, and the same holds for the seasonal projections.
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