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Theoretical and Applied Climatology

, Volume 132, Issue 3–4, pp 1057–1072 | Cite as

Multi-criterion model ensemble of CMIP5 surface air temperature over China

  • Tiantian YangEmail author
  • Yumeng Tao
  • Jingjing Li
  • Qian Zhu
  • Lu Su
  • Xiaojia He
  • Xiaoming Zhang
Original Paper

Abstract

The global circulation models (GCMs) are useful tools for simulating climate change, projecting future temperature changes, and therefore, supporting the preparation of national climate adaptation plans. However, different GCMs are not always in agreement with each other over various regions. The reason is that GCMs’ configurations, module characteristics, and dynamic forcings vary from one to another. Model ensemble techniques are extensively used to post-process the outputs from GCMs and improve the variability of model outputs. Root-mean-square error (RMSE), correlation coefficient (CC, or R) and uncertainty are commonly used statistics for evaluating the performances of GCMs. However, the simultaneous achievements of all satisfactory statistics cannot be guaranteed in using many model ensemble techniques. In this paper, we propose a multi-model ensemble framework, using a state-of-art evolutionary multi-objective optimization algorithm (termed MOSPD), to evaluate different characteristics of ensemble candidates and to provide comprehensive trade-off information for different model ensemble solutions. A case study of optimizing the surface air temperature (SAT) ensemble solutions over different geographical regions of China is carried out. The data covers from the period of 1900 to 2100, and the projections of SAT are analyzed with regard to three different statistical indices (i.e., RMSE, CC, and uncertainty). Among the derived ensemble solutions, the trade-off information is further analyzed with a robust Pareto front with respect to different statistics. The comparison results over historical period (1900–2005) show that the optimized solutions are superior over that obtained simple model average, as well as any single GCM output. The improvements of statistics are varying for different climatic regions over China. Future projection (2006–2100) with the proposed ensemble method identifies that the largest (smallest) temperature changes will happen in the South Central China (the Inner Mongolia), the North Eastern China (the South Central China), and the North Western China (the South Central China), under RCP 2.6, RCP 4.5, and RCP 8.5 scenarios, respectively.

Notes

Acknowledgements

This research was supported by the National Natural Science Foundation of China (No. 41622101), the NASA MIRO grant (NNX15AQ06A) program, and the DOE (Prime Award No. DE-IA0000018). The authors would like to thank anonymous reviewers for their valuable suggestions and comments.

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Copyright information

© Springer-Verlag Wien 2017

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringUniversity of CaliforniaIrvineUSA
  2. 2.Department of Geosciences and EnvironmentCalifornia State UniversityLos AngelesUSA
  3. 3.Institute of Hydrology and Water Resources, College of Civil Engineering and ArchitectureZhejiang UniversityHangzhouChina
  4. 4.College of Global Change and Earth System ScienceBeijing Normal UniversityBeijingChina
  5. 5.The Administrative Center for China’s Agenda21BeijingChina
  6. 6.State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, ChinaInstitute of Water Resources and Hydropower Research (IWHR)BeijingChina

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