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Performance and uncertainty analysis of a short-term climate reconstruction based on multi-source data in the Tianshan Mountains region, China

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Abstract

Short-term climate reconstruction, i.e., the reproduction of short-term (several decades) historical climatic time series based on the relationship between observed data and available longer-term reference data in a certain area, can extend the length of climatic time series and offset the shortage of observations. This can be used to assess regional climate change over a much longer time scale. Based on monthly grid climate data from a Coupled Model Inter-comparison Project phase 5 (CMIP5) dataset for the period of 1850–2000, the Climatic Research Unit (CRU) dataset for the period of 1901–2000 and the observed data from 53 meteorological stations located in the Tianshan Mountains region (TMR) of China during the period of 1961–2011, we calibrated and validated monthly average temperature (MAT) and monthly accumulated precipitation (MAP) in the TMR using the delta, physical scaling (SP) and artificial neural network (ANN) methods. Performance and uncertainty during the calibration (1971–1999) and verification (1961–1970) periods were assessed and compared using traditional performance indices and a revised set pair analysis (RSPA) method. The calibration and verification processes were subjected to various sources of uncertainty due to the influence of different reconstructed variables, different data sources, and/or different methods used. According to traditional performance indices, both the CRU and CMIP5 datasets resulted in satisfactory calibrated and verified MAT time series at 53 meteorological stations and MAP time series at 20 meteorological stations using the delta and SP methods for the period of 1961–1999. However, the results differed from those obtained by the RSPA method. This showed that the CRU dataset produced a low degree of uncertainty (positive connection degree) during the calibration and verification of MAT using the delta and SP methods compared to the CMIP5 dataset. Overall, the calibrated and verified MAP had a high degree of uncertainty (negative connection degree) regardless of the dataset or reconstruction method used. Therefore, the reconstructed time series of MAT for the period of 1850 (or 1901)–1960 based on the CRU and CMIP5 datasets using the delta and SP methods could be used for further study. The results of this study will be useful for short-term (several decades) regional climate reconstruction and longer-term (100 a or more) assessments of regional climate change.

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (41401050, 41761014), the Foundation of A Hundred Youth Talents Training Program of Lanzhou Jiaotong University, and the Discovery Grant of Natural Sciences and Research Council of Canada. We acknowledge the Coupled Model Inter-comparison Project phase 5 (CMIP5) of the Program for Climate Model Diagnosis and Inter-comparison (PCMDI) and the World Climate Research Programme (WCRP), for collecting and archiving the model output and organizing the analysis of a multi-model dataset. We also acknowledge the help of Professor Slobodan P SIMONOVIC and the members of his research laboratory from the Facility for Intelligent Decision Support (FIDS) in Western University, Canada.

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Authors and Affiliations

  1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, 730070, China

    Xuemei Li & Qirui Qin

  2. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou, 730070, China

    Xuemei Li

  3. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou, 730070, China

    Xuemei Li & Qirui Qin

  4. Facility for Intelligent Decision Support, Department of Civil and Environmental Engineering, Western University, London, N6A 3K7, Canada

    Slobodan P. Simonovic

  5. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China

    Lanhai Li

  6. Qilian Alpine Ecology & Hydrology Research Station, Key Laboratory of Ecohydrology of Inland River Basin, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China

    Xueting Zhang

  7. University of Chinese Academy of Sciences, Beijing, 100049, China

    Xueting Zhang

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  1. Xuemei Li
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Correspondence to Xuemei Li.

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Li, X., Simonovic, S.P., Li, L. et al. Performance and uncertainty analysis of a short-term climate reconstruction based on multi-source data in the Tianshan Mountains region, China. J. Arid Land 12, 374–396 (2020). https://doi.org/10.1007/s40333-020-0065-y

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