Journal of Meteorological Research

, Volume 32, Issue 3, pp 337–350 | Cite as

Near-Term Projections of Global and Regional Land Mean Temperature Changes Considering Both the Secular Trend and Multidecadal Variability

  • Yajie Qi
  • Zhongwei Yan
  • Cheng QianEmail author
  • Ying Sun
Regular Articles


Near-term climate projections are needed by policymakers; however, these projections are difficult because internally generated climate variations need to be considered. In this study, temperature change scenarios in the near-term period 2017–35 are projected at global and regional scales based on a refined multi-model ensemble approach that considers both the secular trend (ST) and multidecadal variability (MDV) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations. The ST and MDV components are adaptively extracted from each model simulation by using the ensemble empirical mode decomposition (EEMD) filter, reconstructed via the Bayesian model averaging (BMA) method for the historical period 1901–2005, and validated for 2006–16. In the simulations of the “medium” representative concentration pathways scenario during 2017–35, the MDV-modulated temperature change projected via the refined approach displays an increase of 0.44°C (90% uncertainty range from 0.30 to 0.58°C) for global land, 0.48°C (90% uncertainty range from 0.29 to 0.67°C) for the Northern Hemispheric land (NL), and 0.29°C (90% uncertainty range from 0.23 to 0.35°C) for the Southern Hemispheric land (SL). These increases are smaller than those projected by the conventional arithmetic mean approach. The MDV enhances the ST in 13 of 21 regions across the world. The largest MDV-modulated warming effect (46%) exists in central America. In contrast, the MDV counteracts the ST in NL, SL, and eight other regions, with the largest cooling effect (220%) in Alaska.

Key words

near-term projection multidecadal variability multi-model ensemble method ensemble empirical mode decomposition (EEMD) Bayesian model averaging (BMA) 


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The authors thank the reviewers and the Editor for their comments and suggestions to help improve the manuscript. We also thank the Met Office Hadley Center and Climate Research Unit for providing the observed HadCRUT4 data used in this work and the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the model data.


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Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yajie Qi
    • 1
    • 2
  • Zhongwei Yan
    • 1
    • 3
  • Cheng Qian
    • 1
    • 3
    Email author
  • Ying Sun
    • 4
    • 5
  1. 1.CAS Key Laboratory of Regional Climate–Environment for Temperate East Asia, Institute of Atmospheric PhysicsChinese Academy of Sciences (CAS)BeijingChina
  2. 2.Institute of Urban MeteorologyChina Meteorological AdministrationBeijingChina
  3. 3.University of Chinese Academy of SciencesBeijingChina
  4. 4.Laboratory for Climate Studies, National Climate CenterChina Meteorological AdministrationBeijingChina
  5. 5.Joint Center for Global Change StudiesBeijing Normal UniversityBeijingChina

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