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Assessment and prediction of regional climate based on a multimodel ensemble machine learning method

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Abstract

Accurate modeling of climate change at local scales is critical for climate applications. This study proposes a regional downscaling model (stacking-MME) based on the fusion of multiple machine learning models (stacking). The performance of the model was evaluated for simulating precipitation, solar radiation, maximum temperature and minimum temperature and predicted three future possible changes in climate variables over time (near-term (2031–2040), medium-term (2051–2060), and long-term (2081–2090)). After determining the optimal GCM(Global climate model) based on rating metric calculations, the parametric and structural uncertainties in the GCM simulation of CMIP6 (Sixth International Coupling Model Comparison Project) were reduced. Furthermore, the performance of MME (multimodel ensembles) was enhanced by integrating three machine learning algorithms. The results show that among the nine machine learning models, the Light Gradient Boosting Machine, Gradient Boosting Regressor and Random Forest have the best performances. These three models are also considered for the development of stacking model fusion. The Stacking-MME model can reliably reduce the systematic error of GCMs and has the potential to better predict climate. In the SSP245 and SSP585 situations, precipitation will increase by 23.79% and 29.26% at the end of the twenty-first century, respectively. Maximum and minimum temperatures will increase by 1.48 and 2.89 °C and 1.22 and 2.36 °C, respectively.

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Data availability

The original datasets analyzed during the current study are available in the repositories [National Center for Meteorological Sciences (data.cma.cn), The Earth System Grid Federation (esgf-node.llnl.gov/projects/cmip6 )].

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Funding

This work was supported by The Training Project for the Top Young Talents in Ningxia (Grant numbers 030103030008), The Natural Science Foundation of Ningxia (Grant numbers 2021AAC03043), and Ningxia Key Research and Development Program (Grant numbers 2019BEB04029).

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Authors and Affiliations

  1. School of Civil and Hydraulic Engineering, Ningxia University, Yinchuan, 750021, China

    Yinghao Fu, Haoran Zhuang, Xiaojing Shen & Wangcheng Li

  2. Engineering Research Center of Efficient Utilization of Modern Agricultural Water Resources in Arid Regions, Ministry of Education, Yinchuan, 750021, China

    Wangcheng Li

  3. State Key Laboratory of Land Degradation and Ecological Restoration in Northwest China, Yinchuan, 750021, China

    Wangcheng Li

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  1. Yinghao Fu
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  2. Haoran Zhuang
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Contributions

Material preparation, data collection, and analysis were performed by FY and SX. The first draft of the manuscript was written by FY. SX and LW performed supervision, and reviewed paper. ZH and LW mainly takes charge of revision work for paper revision. All authors read and approved the final manuscript.

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Correspondence to Xiaojing Shen.

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Fu, Y., Zhuang, H., Shen, X. et al. Assessment and prediction of regional climate based on a multimodel ensemble machine learning method. Clim Dyn 61, 4139–4158 (2023). https://doi.org/10.1007/s00382-023-06787-7

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