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Theoretical and Applied Climatology

, Volume 122, Issue 1–2, pp 259–270 | Cite as

Uncertainties of the global-to-regional temperature and precipitation simulations in CMIP5 models for past and future 100 years

  • Lilong Zhao
  • Jianjun XuEmail author
  • Alfred M. PowellJr.
  • Zhihong Jiang
Original Paper

Abstract

Global-to-regional surface temperature and precipitation trends are examined based on the CMIP5 model 100 years of historical simulations and another future 100 years following the Representative Concentration Pathway (RCP) emission scenario projection. Different from the ensemble mean approach in the previous studies, the probabilistic multimodal ensemble prediction with Gaussian fitting is used to generate probabilistic simulations. The results show that the averaged precipitation increases slightly with global warming, but the response is not globally uniform. Both historical model simulations and the RCP emission scenario projections suffer from large uncertainties in the selected models and the geographic distribution. The spatial distribution of spreads among the multimodal scenario projections is similar to that in the historical simulations, except the magnitude of spread sharply increases and the region expands equatorward and poleward in surface temperature and precipitation, respectively.

Keywords

Emission Scenario CMIP5 Model Representative Concentration Pathway Precipitation Trend Historical Simulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The surface temperature data for HadCRUT4 is created by the Met Office Hadley Centre and the Climatic Research Unit at the University of East Anglia and the new GISS analysis dataset is developed by the National Aeronautics and Space Administration’s (NASA) Goddard Institute for Space Studies (GISS). The Program for Climate Model Diagnosis and Intercomparison (PCMDI) collected and archived the model data. In addition, the first author was partially supported by a key program of the National Natural Science Foundation of China (Grant 41230528, 41305039) and the priority Academic program development (PAPD) of Jiangsu Higher Education. The authors would like to thank these agencies for providing the data and funding support.

This work was supported by the National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite, Data, and Information Service (NESDIS), Center for Satellite Applications and Research (STAR). The views, opinions, and findings contained in this publication are those of the authors and should not be considered an official NOAA or US Government position, policy, or decision.

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Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  • Lilong Zhao
    • 1
    • 2
  • Jianjun Xu
    • 2
    Email author
  • Alfred M. PowellJr.
    • 3
  • Zhihong Jiang
    • 1
  1. 1.Nanjing University of Information Science and TechnologyNanjingChina
  2. 2.Environmental Science and Technological Center, College of ScienceGeorge Mason UniversityFairfaxUSA
  3. 3.NOAA/NESDIS/STAR, College ParkMarylandUSA

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