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<p>Choosing appropriate climate models is key for enhancing our understanding of Global Climate Models (GCMs) and mitigating their inherent weaknesses in climatic applications. This study fills a knowledge gap by exploring model selection and evaluation near the Tropic of Cancer, focusing on using multiple ensemble approaches to reduce uncertainties in Coupled Model Intercomparison Project 6 (CMIP6) GCMs. The goal is to guide climate researchers in making informed modeling decisions by comprehensively assessing various machine-learning (ML) models for predicting maximum temperature (T<sub>max</sub>) using historical India Meteorological Department data and 22 CMIP6 models. T<sub>max</sub> is crucial as it influences heat-related stress on agriculture, human health, and water resources, making its accurate prediction vital for climate adaptation strategies. This study evaluates the performance of 14 multi-model ensembles (MMEs) based on ML techniques, alongside multi-ML ensemble (MMLE) approaches through rigorous cross-validation and hyperparameter tuning. Key findings show that K-nearest neighbors (KNN) and stacking models outperform others, showcasing superior predictive accuracy within the MMLE framework. The ensemble approaches outperform individual models, with MMLE achieving R² of 0.934, significantly higher than individual ML models except for KNN (R² of 0.933). Detailed visualizations and error analyses validate results, highlighting the strengths and weaknesses of models. This research highlights the significance of model selection and tuning in temperature data analysis, offering a robust methodology for model evaluation. The findings improve climate predictions, guide adaptive strategies for climate change, and support efforts in climate resilience and policymaking.</p>

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A Comparative Approach to Understand the Performance of CMIP6 Models for Maximum Temperature near Tropic of Cancer Using Multiple Machine Learning Ensembles

  • 期刊论文

  • 发表日期：

    2025年2月11日

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  • Gaurav Patel,
  • Subhasish Das,
  • Rajib Das

摘要

Choosing appropriate climate models is key for enhancing our understanding of Global Climate Models (GCMs) and mitigating their inherent weaknesses in climatic applications. This study fills a knowledge gap by exploring model selection and evaluation near the Tropic of Cancer, focusing on using multiple ensemble approaches to reduce uncertainties in Coupled Model Intercomparison Project 6 (CMIP6) GCMs. The goal is to guide climate researchers in making informed modeling decisions by comprehensively assessing various machine-learning (ML) models for predicting maximum temperature (Tmax) using historical India Meteorological Department data and 22 CMIP6 models. Tmax is crucial as it influences heat-related stress on agriculture, human health, and water resources, making its accurate prediction vital for climate adaptation strategies. This study evaluates the performance of 14 multi-model ensembles (MMEs) based on ML techniques, alongside multi-ML ensemble (MMLE) approaches through rigorous cross-validation and hyperparameter tuning. Key findings show that K-nearest neighbors (KNN) and stacking models outperform others, showcasing superior predictive accuracy within the MMLE framework. The ensemble approaches outperform individual models, with MMLE achieving R² of 0.934, significantly higher than individual ML models except for KNN (R² of 0.933). Detailed visualizations and error analyses validate results, highlighting the strengths and weaknesses of models. This research highlights the significance of model selection and tuning in temperature data analysis, offering a robust methodology for model evaluation. The findings improve climate predictions, guide adaptive strategies for climate change, and support efforts in climate resilience and policymaking.