1 Department of Earth System Science, Tsinghua University, Beijing 100084, China 2 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;
Model internal variability has been recognized as an important source of uncertainties of climate simulation results. However, the impact of model internal variability on the uncertainties in the simulation of 1.5℃ and 2℃ warming threshold-crossing time has not been explored to date. In this paper, such impact and the corresponding sensitivity to different future emission scenarios are investigated based on the outputs of Coupled Model Intercomparison Project Phase5 (CMIP5) models. The results show that the effect of internal variability on uncertainties in the simulation of threshold-crossing time is equivalent to that of external forcing. The difference between the threshold-crossing time of model members reaching 1.5℃ or 2℃ global warming is 2-12 years. The influence of internal variability has a clear spatial distribution. Maximum uncertainties are observed at the ocean northern of Eurasia, the area around the Bering Strait, the northeastern North America and the ocean between it and Greenland, and the high latitudes in the Southern Hemisphere. Model internal variability causes greater uncertainties in the low emission scenario than the high emission scenario.
季涤非,刘利,李立娟,孙超,于馨竹,李锐喆,张诚,王斌. 模式内部变率引起的1.5℃和2℃升温阈值出现时间模拟的不确定性研究[J]. 气候变化研究进展, 2019, 15(4): 343-351.
Di-Fei JI,Li LIU,Li-Juan LI,Chao SUN,Xin-Zhu YU,Rui-Zhe LI,Cheng ZHANG,Bin WANG. Uncertainties in the simulation of 1.5℃ and 2℃warming threshold-crossing time arising from model internal variability based on CMIP5 models. Climate Change Research, 2019, 15(4): 343-351.
IPCC. Climate change 2013: the physical science basis [M]. Cambridge: Cambridge University Press, 2013
UNFCCC. Communications received from parties in relation to the listing in the chapeau of the Copenhagen Accord [EB/OL]. 2010 [ 2012- 07- 05].
Randalls S . History of the 2℃ climate target[J]. Wiley Interdisciplinary Reviews Climate Change, 2010,1(4):598-605
Arora V K, Scinocca J F, Boer G J , et al. Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases[J]. Geophysical Research Letters, 2011,38(5):387-404
Baek H J, Lee J, Lee H S , et al. Climate change in the 21st century simulated by HadGEM2-AO under representative concentration pathways[J]. Asia-pacific Journal of Atmospheric Sciences, 2013,49(5):603-618
Meehl G A, Washington W M, Arblaster J M , et al. Climate change projections in CESM1 (CAM5) compared to CCSM4[J]. Journal of Climate, 2013,26(17):6287-6308
Wang Z, Lin L, Zhang X , et al. Scenario dependence of future changes in climate extremes under 1.5 ℃ and 2 ℃ global warming[J]. Scientific Reports, 2017,7:46432
Hawkins E, Sutton R . The potential to narrow uncertainty in projections of regional precipitation change[J]. Climate Dynamics, 2011,37(1-2):407-418
Deser C, Phillips A, Bourdette V , et al. Uncertainty in climate change projections: the role of internal variability[J]. Climate Dynamics, 2012,38(3-4):527-546
Dai A, Bloecker C E . Impacts of internal variability on temperature and precipitation trends in large ensemble simulations by two climate models[J]. Climate Dynamics, 2018 ( 365):1-18
Wittenberg A T . Are historical records sufficient to constrain ENSO simulations?[J]. Geophysical Research Letters, 2009,36(12):L12702
Deser C, Knutti R, Solomon S , et al. Communication of the role of natural variability in future North American climate[J]. Nature Climate Change, 2012,2(11):775-779
Deser C, Phillips A S, Alexander M A , et al. Projecting North American climate over the next 50 years: uncertainty due to internal variability[J]. Journal of Climate, 2013,27(6):2271-2296
Kang S M, Deser C, Polvani L M . Uncertainty in climate change projections of the hadley circulation: the role of internal variability[J]. Journal of Climate, 2013,26(19):7541-7554
Shepherd T G . Atmospheric circulation as a source of uncertainty in climate change projections[J]. Nature Geoscience, 2014,7(10):703-708
Schindler A, Toreti A, Scoccimarro E , et al. On the internal variability of simulated daily precipitation[J]. Journal of Climate, 2015,28(9):3264-3630
Woldemeskel F M, Sharma A, Sivakumar B , et al. Quantification of precipitation and temperature uncertainties simulated by CMIP3 and CMIP5 models[J]. Journal of Geophysical Research Atmospheres, 2016,121(1):3-17
Zheng X T, Hui C, Yeh S W . Response of ENSO amplitude to global warming in CESM large ensemble: uncertainty due to internal variability[J]. Climate Dynamics, 2018,50:1-17
Sun C, Liu L, Li L , et al. Uncertainties in simulated El Niño-Southern Oscillation arising from internal climate variability[J]. Atmospheric Science Letters, 2018,19:805
Hawkins E, Sutton R . The potential to narrow uncertainty in regional climate predictions[J]. Bulletin of the American Meteorological Society, 2009,90(8):333-337
Zhang L, Ding Y H, Wu T W , et al. The 21st century annual mean surface air temperature change and the 2℃warming threshold over the globe and China as projected by the CMIP5 models[J]. Acta Meteorologica Sinica, 2013,71(6):1047-1060 (in Chinese)
Chen X, Zhou T . Uncertainty in crossing time of 2℃warming threshold over China[J]. Science Bulletin, 2016 (18):1451-1459
Jiang D, Sui Y, Lang X . Timing and associated climate change of a 2℃ global warming[J]. International Journal of Climatology, 2016,36(14):4512-4522
Zhou M Z, Zhou G S, Lyu X M , et al. CMIP5-based threshold-crossing times of 1.5℃ and 2℃ global warming above pre-industrial levels[J]. Advances in Climate Change Research, 2018,14(3):221-227 (in Chinese)
Wang X, Jiang D, Lang X . Climate change of 4℃ global warming above pre-industrial levels[J]. Advances in Atmospheric Sciences, 2018,35(7):757-770
Zhao L, Xu J, Powell A M , et al. Uncertainties of the global-to-regional temperature and precipitation simulations in CMIP5 models for past and future 100 years[J]. Theoretical & Applied Climatology, 2015,122(1-2):259-270
Kay J E, Deser C, Phillips A , et al. The Community Earth System Model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability[J]. Bulletin of the American Meteorological Society, 2015,96(8):1333-1349