1. 1. 中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室,北京 100029;
2. 中国科学院大学,北京 100049
• 收稿日期:2016-11-07 修回日期:2017-02-28 出版日期:2017-04-20
• 基金资助:

公益性行业(气象)科研专项项目“基于FGOALS-s、CMA和CESM气候系统模式的年代际集合预测系统的建立与研究”(编号:GYHY201506012); “气候变化”江苏高校协同创新中心资助

Surface Air Temperature Projection Under 1.5 ℃ Warming Threshold Based on Corrected Pattern Scaling Technique

Chen Xiaolong 1, Zhou Tianjun 1, 2

1. 1.LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,China;
2.University of Chinese Academy of Sciences, Beijing 100049, China
• Received:2016-11-07 Revised:2017-02-28 Online:2017-04-20 Published:2017-04-20
• About author:First author: Chen Xiaolong (1988-), male, Pucheng County, Shaanxi Province, Post doctor. Research areas include monsoon variability and climate change.E-mail:chenxl@lasg.iap.ac.cn
• Supported by:

Project supported by the R&D Special Fund for Public Welfare Industry (Meteorology) “Development and research of ensemble decadal climate prediction system based on global climate models FGOALS-s, CMA and CESM” (No.GYHY201506012); The Jiangsu Collaborative Innovation Center for Climate Change

The global mean temperature during the recent decade (2007-2016) has increased above 1 ℃ relative to the pre-industrial period (1861-1890). The climate change and impact under 1.5 ℃ warming in the future have become a great concern in global society. Temperature projections, especially in regional scale, show great uncertainty depending on used climate models. Taking advantage of pattern scaling technique and observed temperature changes during 1951-2005, we tried to project the temperature changes globally under 1.5 ℃ threshold relative to current climate state, i.e. about 1 ℃ warming around 2007-2016. The projections of 21 climate models from the Coupled Model Intercomparison Project - Phase 5 under four Representative Concentration Pathways (RCP2.6, RC4.5, RCP6.0 and RCP8.5) were used to correct the assumptions in pattern scaling. Results showed that the geographical distribution and warming amplitude of surface air temperature changes under 1.5 ℃ threshold are similar in the four scenarios. Warming over most of the land would be above 0.6 ℃, 0.3 ℃ warmer than ocean. The Northern Hemisphere would be 0.2 ℃ warmer than the Southern Hemisphere. The temperature over China region will increase by 0.7 ℃. The warming in the Northern and Central China under RCP2.6 was obviously higher than that in the other scenarios. Ignoring the impact of correction method, uncertainty in temperature projection based on pattern scaling was much smaller than that in climate models, both in global and regional scales.

 [1] Tollefson J. 2015 breaks heat record[J]. Nature , 2016, 529: 450. [2] Schleussner C F, Lissner T K, Fischer E M, et al . Differential climate impacts for policy-relevant limits to global warming:The case of 1.5 ℃ and 2 ℃[J]. Earth System Dynamics , 2016, 7:327-451. [3] Koutroulis A G, Grillakis M G, Tsanis I K, et al . Evaluation of precipitation and temperature simulation performance of the CMIP3 and CMIP5 historical experiments[J]. Climate Dynamics , 2016, 47(5): 1 881-1 898. [4] Chen X L, Zhou T J. Uncertainty in the 2℃ warming threshold related to climate sensitivity and climate feedback[J]. Journal of Meteorological Research , 2015, 29(6): 884-895, doi:10.1007/s13351-015-5036-4. [5] Santer B D, Wigley T M L, Schlesinger M E, et al . Developing Climate Scenarios from Equilibrium GCM Results[R]. Report No. 47, Max-Planck-Institut für Meteorologie, Hamburg, Germany,1990. [6] Herger N, Sanderson B M, Knutti R. Improved pattern scaling approaches for the use in climate impact studies[J]. Geophysical Research Letters , 2015, 42: 3 486-3 494, doi:10.1002/2015GL063569. [7] Mitchell T D. Pattern scaling—An examination of the accuracy of the technique for describing future climates[J]. Climatic Change , 2003, 60(3): 217-242. [8] Tebaldi C, Arblaster J M. Pattern scaling: Its strengths and limitations, and an update on the latest model simulations[J]. Climatic Change , 2014, 122(3): 459-471. [9] Bichet A, Kushner P J, Mudryk L. Estimating the continental response to global warming using pattern-scaled sea surface temperatures and sea ice[J]. Journal of Climate , 2016, 29: 9 125-9 139, doi:10.1175/JCLI-D-16-0032.1. [10] Collins M, Knutti R, Arblaster J, et al . Long-term climate change: Projections, commitments and irreversibility[M]∥Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA:Cambridge University Press, 2013. [11] Morice C P, Kennedy J J, Rayner N A, et al . Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 dataset[J]. Journal of Geophysical Research , 2012, 117, D08101, doi:10.1029/2011JD017187. [12] Taylor K E, Stouffer R J, Meehl G A. An overview of CMIP5 and the experiment design[J]. Bulletin of the American Meteorological Society , 2012,93(4): 485-498, doi:10.1175/BAMS-D-11-00094.1.
 [1] 李东欢, 邹立维, 周天军. 全球1.5 ℃温升背景下中国极端事件变化的区域模式预估[J]. 地球科学进展, 2017, 32(4): 446-457. [2] 王澄海,靳双龙,吴忠元,崔洋. 估算冻结（融化）深度方法的比较及在中国地区的修正和应用[J]. 地球科学进展, 2009, 24(2): 132-140.