Theoretical and Applied Climatology

, Volume 132, Issue 3–4, pp 1073–1088 | Cite as

Narrowing the surface temperature range in CMIP5 simulations over the Arctic

  • Mingju Hao
  • Jianbin Huang
  • Yong Luo
  • Xin Chen
  • Yanluan Lin
  • Zongci Zhao
  • Ying Xu
Original Paper


Much uncertainty exists in reproducing Arctic temperature using different general circulation models (GCMs). Therefore, evaluating the performance of GCMs in reproducing Arctic temperature is critically important. In our study, 32 GCMs in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) during the period 1900–2005 are used, and several metrics, i.e., bias, correlation coefficient (R), and root mean square error (RMSE), are applied. The Cowtan data set is adopted as the reference data. The results suggest that the GCMs used can reasonably reproduce the Arctic warming trend during the period 1900–2005, as observed in the observational data, whereas a large variation of inter-model differences exists in modeling the Arctic warming magnitude. With respect to the reference data, most GCMs have large cold biases, whereas others have weak warm biases. Additionally, based on statistical thresholds, the models MIROC-ESM, CSIRO-Mk3-6-0, HadGEM2-AO, and MIROC-ESM-CHEM (bias ≤ ±0.10 °C, R ≥ 0.50, and RMSE ≤ 0.60 °C) are identified as well-performing GCMs. The ensemble of the four best-performing GCMs (ES4), with bias, R, and RMSE values of −0.03 °C, 0.72, and 0.39 °C, respectively, performs better than the ensemble with all 32 members, with bias, R, and RMSE values of −0.04 °C, 0.64, and 0.43 °C, respectively. Finally, ES4 is used to produce projections for the next century under the scenarios of RCP2.6, RCP4.5, and RCP8.0. The uncertainty in the projected temperature is greater in the higher emissions scenarios. Additionally, the projected temperature in the cold half year has larger variations than that in the warm half year.



We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP. We thank the climate groups (listed in Table 1) for providing the model output. We also thank the anonymous reviewer for the comments and suggestions that much improved the original manuscript. Special thanks also go to Dr. Xiangdong Zhang for his comments on this research. We also thank the “Explorer 100” cluster system of Tsinghua National Laboratory for Information Science and Technology for the computation support. This work was supported by the State Key Development Program for Basic Research of China (Grant No. 2013CBA01805), the National Science Foundation for Young Scientists of China (Grant No. 41305054), and the Tsinghua University Initiative Scientific Research Program (Grant No. 20131089356).


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Copyright information

© Springer-Verlag Wien 2017

Authors and Affiliations

  • Mingju Hao
    • 1
    • 2
  • Jianbin Huang
    • 1
    • 2
  • Yong Luo
    • 1
    • 2
    • 3
  • Xin Chen
    • 1
    • 2
  • Yanluan Lin
    • 1
    • 2
  • Zongci Zhao
    • 1
    • 2
  • Ying Xu
    • 4
  1. 1.Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System ScienceTsinghua UniversityBeijingChina
  2. 2.Joint Center for Global Change StudiesBeijingChina
  3. 3.State Key Laboratory of Cryosphere Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of SciencesLanzhouChina
  4. 4.National Climate CenterChina Meteorological AdministrationBeijingChina

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