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Evaluation of global climate model on performances of precipitation simulation and prediction in the Huaihe River basin

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Abstract

Using climate models with high performance to predict the future climate changes can increase the reliability of results. In this paper, six kinds of global climate models that selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under Representative Concentration Path (RCP) 4.5 scenarios were compared to the measured data during baseline period (1960–2000) and evaluate the simulation performance on precipitation. Since the results of single climate models are often biased and highly uncertain, we examine the back propagation (BP) neural network and arithmetic mean method in assembling the precipitation of multi models. The delta method was used to calibrate the result of single model and multimodel ensembles by arithmetic mean method (MME-AM) during the validation period (2001–2010) and the predicting period (2011–2100). We then use the single models and multimodel ensembles to predict the future precipitation process and spatial distribution. The result shows that BNU-ESM model has the highest simulation effect among all the single models. The multimodel assembled by BP neural network (MME-BP) has a good simulation performance on the annual average precipitation process and the deterministic coefficient during the validation period is 0.814. The simulation capability on spatial distribution of precipitation is: calibrated MME-AM > MME-BP > calibrated BNU-ESM. The future precipitation predicted by all models tends to increase as the time period increases. The order of average increase amplitude of each season is: winter > spring > summer > autumn. These findings can provide useful information for decision makers to make climate-related disaster mitigation plans.

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Acknowledgements

This study is supported by the National Natural Science Foundation of China (Grant No. 51579068/51609062), the Special Fund for Public Welfare Industry of the Ministry of Water Resources of China (Grant No. 201501007), and the National Key Technologies R&D Program of China (Grant No. 2016YFC0400909).

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Authors and Affiliations

  1. College of Hydrology and Water Resources, Hohai University, No.1 Xikang Road, Nanjing, 210098, China

    Yenan Wu, Ping-an Zhong, Bin Xu, Feilin Zhu & Jisi Fu

  2. National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, No.1, Xikang Road, Nanjing, 210098, China

    Ping-an Zhong

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  1. Yenan Wu
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  2. Ping-an Zhong
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  3. Bin Xu
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  4. Feilin Zhu
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  5. Jisi Fu
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Correspondence to Ping-an Zhong.

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Wu, Y., Zhong, Pa., Xu, B. et al. Evaluation of global climate model on performances of precipitation simulation and prediction in the Huaihe River basin. Theor Appl Climatol 133, 191–204 (2018). https://doi.org/10.1007/s00704-017-2185-7

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  • DOI: https://doi.org/10.1007/s00704-017-2185-7

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