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<p>Global warming is rapidly posing serious threats to communities, ecosystems, and economies. These threats are resulting in increasingly severe impacts across various regions that must be addressed to ensure a secure future. Advancements in technological and computational efficiency have enabled the forecasting of future climate conditions. This is achieved through high-resolution simulations of climate variables, known as Global Climate Models (GCMs). Despite their ability to simulate the climatic variables, significant biases exist in GCM outputs when considered at regional scales. This study proposes a novel probabilistic and metaheuristic bias correction approach to improve the performance and predictive accuracy of GCMs. The significance of this approach lies in its ability to preserve the probabilistic structure of the simulated data and its past performance while ensuring optimal bias correction in terms of its statistical properties. For the implementation, 20 GCMs from Coupled Model Intercomparison Project Phase 6 (CMIP6) for monthly temperature simulations for Tibetan Plateau (TP) region in China were selected in this study. The results underscore that the proposed bias-corrected (BC) multi-model ensemble shows overall consistency and better performance when compared with other ensembles. Additionally, the Standardized Temperature Index (STI) has been developed by utilizing the K-component Gaussian mixture distribution. Using a Markov chain process, steady-state probabilities are computed to assess the long-term pattern of extreme temperature events in future projections. The findings reveal that the “near normal” climate will most likely persist, with average probabilities of 0.6769 and 0.5608 under SSP1-2.6 and SSP2-4.5 respectively. Whereas under the high emission scenario SSP5-8.5, the future climate may shift towards more frequent “hot” climate conditions.</p>

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A novel probabilistic and metaheuristic approach for evaluation of CMIP6 models and bias corrected multimodel ensemble for future projection of temperature characteristics

  • 期刊论文

  • 发表日期：

    2025年6月5日

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  • Rashida Khalil,
  • Zulfiqar Ali

摘要

Global warming is rapidly posing serious threats to communities, ecosystems, and economies. These threats are resulting in increasingly severe impacts across various regions that must be addressed to ensure a secure future. Advancements in technological and computational efficiency have enabled the forecasting of future climate conditions. This is achieved through high-resolution simulations of climate variables, known as Global Climate Models (GCMs). Despite their ability to simulate the climatic variables, significant biases exist in GCM outputs when considered at regional scales. This study proposes a novel probabilistic and metaheuristic bias correction approach to improve the performance and predictive accuracy of GCMs. The significance of this approach lies in its ability to preserve the probabilistic structure of the simulated data and its past performance while ensuring optimal bias correction in terms of its statistical properties. For the implementation, 20 GCMs from Coupled Model Intercomparison Project Phase 6 (CMIP6) for monthly temperature simulations for Tibetan Plateau (TP) region in China were selected in this study. The results underscore that the proposed bias-corrected (BC) multi-model ensemble shows overall consistency and better performance when compared with other ensembles. Additionally, the Standardized Temperature Index (STI) has been developed by utilizing the K-component Gaussian mixture distribution. Using a Markov chain process, steady-state probabilities are computed to assess the long-term pattern of extreme temperature events in future projections. The findings reveal that the “near normal” climate will most likely persist, with average probabilities of 0.6769 and 0.5608 under SSP1-2.6 and SSP2-4.5 respectively. Whereas under the high emission scenario SSP5-8.5, the future climate may shift towards more frequent “hot” climate conditions.