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Optimal average annual mean surface air temperature for East Asia since 1901

  • Wei Hua
  • Zouxin Lin
  • Qiuyue Wu
  • Yao Hu
  • Guangzhou Fan
  • Lihua Zhu
Original Paper

Abstract

In this paper, the authors use Climate Research Unit (CRU) land surface air temperature (SAT) and empirical orthogonal function (EOF)-based spectral optimal averaging (SOA) method to estimate the regional average annual mean SAT series for East Asia since 1901 with a comparison of arithmetic averaging (AA). The SOA method minimizes the first-M-mode mean square error (MSE) between the estimates and true SAT anomalies series. In the optimization process, the errors of CRU SAT dataset are used to test their contribution to the uncertainties of regional average annual mean SAT series estimated by SOA method. Furthermore, the MSE of SOA estimates is also calculated. Our results show that (i) the EOF-based SOA method can accurately estimate the regional average SAT anomalies series than AA method with smaller error, particularly when the sampling is poor, (ii) the SAT of East Asia has also experienced pronounced interdecadal cycles during last century, with the linear trend of being 0.91 °C(100a)−1, (iii) the errors of SOA estimates are large and close to 0.2 °C at the beginning of the twentieth century and then declining to 0.1 °C by 1960s, and (iv) the SOA estimates are non-sensitive to the values of error variance in the range of 0.001 to 1.0, but tends to zero as error variance tends infinity or extremely small values near or equal to zero.

Notes

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (41775072, 91537214, 41405069, 41705065, and 41605063), the Key Foundation of the Education Department of Sichuan Province (16ZA0203), and the Scientific Research Foundation of Chengdu University of Information Technology (KYTZ201517, J201516, and J201518). The anonymous reviewer’s excellent suggestions helped improve the paper’s presentation quality significantly.

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Atmospheric Sciences/Joint Laboratory of Climate and Environment Change/Plateau Atmosphere and Environment Key Laboratory of Sichuan ProvinceChengdu University of Information TechnologyChengduChina
  2. 2.Nansen-Zhu International Research CentreInstitute of Atmospheric Physics, Chinese Academy of SciencesBeijingChina

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