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Theoretical and Applied Climatology

, Volume 130, Issue 1–2, pp 655–671 | Cite as

Can reanalysis datasets describe the persistent temperature and precipitation extremes over China?

  • Jian Zhu
  • Dan-Qing Huang
  • Pei-Wen Yan
  • Ying Huang
  • Xue-Yuan Kuang
Original Paper

Abstract

The persistent temperature and precipitation extremes may bring damage to the economy and human due to their intensity, duration and areal coverage. Understanding the quality of reanalysis datasets in descripting these extreme events is important for detection, attribution and model evaluation. In this study, the performances of two reanalysis datasets [the twentieth century reanalysis (20CR) and Interim ECMWF reanalysis (ERA-Interim)] in reproducing the persistent temperature and precipitation extremes in China are evaluated. For the persistent temperature extremes, the two datasets can better capture the intensity indices than the frequency indices. The increasing/decreasing trend of persistent warm/cold extremes has been reasonably detected by the two datasets, particularly in the northern part of China. The ERA-Interim better reproduces the climatology and tendency of persistent warm extremes, while the 20CR has better skill to depict the persistent cold extremes. For the persistent precipitation extremes, the two datasets have the ability to reproduce the maximum consecutive 5-day precipitation. The two datasets largely underestimate the maximum consecutive dry days over the northern part of China, while overestimate the maximum consecutive wet days over the southern part of China. For the response of the precipitation extremes against the temperature variations, the ERA-Interim has good ability to depict the relationship among persistent precipitation extremes, local persistent temperature extremes, and global temperature variations over specific regions.

Keywords

Precipitation Extreme Pattern Correlation Coefficient Reanalysis Dataset Extreme Index Occurrence Season 
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

This study is jointly sponsored by by the National Key Research and Development Program of China (Grant Nos. 2016YFA0600701 and 2016YFA0601504),the National Natural Science Foundation of China (Grant Nos. 41575071 and 51190091), Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology (KLME1502), and the Jiangsu Collaborative Innovation Center for Climate Change.

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Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  • Jian Zhu
    • 1
  • Dan-Qing Huang
    • 2
    • 3
  • Pei-Wen Yan
    • 2
  • Ying Huang
    • 2
  • Xue-Yuan Kuang
    • 2
  1. 1.State Key Laboratory of Hydrology-Water Resources and Hydraulic EngineeringHohai UniversityNanjingChina
  2. 2.CMA-NJU Joint Laboratory for Climate Prediction Studies, School of Atmospheric SciencesNanjing UniversityNanjingPeople’s Republic of China
  3. 3.Key Laboratory of Meteorological Disaster of Ministry of EducationNanjing University of Information Science and TechnologyNanjingChina

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