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<p>Climate change has received considerable attention from researchers due to its impact on the environment, ecological systems, hydrology, and agriculture. Monitoring the effects of climate change on water scarcity and risks such as floods and droughts is important for the future. General Circulation Models (GCMs) are pivotal tools for assessing climate change and drought conditions across various temporal and spatial scales. Recent studies indicate that combining multiple climate models improves correlation with observational data compared to individual GCMs. However, the challenge lies in determining the optimal method for ensemble averaging. This study proposes a new performance-based spatio-temporal weighting scheme for multimodel ensemble (MME) known as the Inter-rater Reliability Adaptive Weighting Ensemble (IRRAWE), which utilizes an inter-rater reliability measure using the linearly weighted kappa statistic and point-to-point divergence between simulated and observed data. As a case study, monthly precipitation simulations of eighteen GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were utilized to assess the implications of the proposed ensemble. The findings suggest that IRRAWE achieved a 1.6% higher correlation (0.6625 vs. 0.6518) and a 3.8% lower NRMSE (0.4798 vs. 0.4989) on average. These improvements exhibit consistent gains in both metrics to enhance predictive accuracy.</p>

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Inter-rater reliability adaptive weighting (IRRAWE) - a novel ensemble scheme for improved precipitation projections using CMIP6 climate models

  • 期刊论文

  • 发表日期：

    2025年12月4日

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  • Rashida Khalil,
  • Zulfiqar Ali

摘要

Climate change has received considerable attention from researchers due to its impact on the environment, ecological systems, hydrology, and agriculture. Monitoring the effects of climate change on water scarcity and risks such as floods and droughts is important for the future. General Circulation Models (GCMs) are pivotal tools for assessing climate change and drought conditions across various temporal and spatial scales. Recent studies indicate that combining multiple climate models improves correlation with observational data compared to individual GCMs. However, the challenge lies in determining the optimal method for ensemble averaging. This study proposes a new performance-based spatio-temporal weighting scheme for multimodel ensemble (MME) known as the Inter-rater Reliability Adaptive Weighting Ensemble (IRRAWE), which utilizes an inter-rater reliability measure using the linearly weighted kappa statistic and point-to-point divergence between simulated and observed data. As a case study, monthly precipitation simulations of eighteen GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were utilized to assess the implications of the proposed ensemble. The findings suggest that IRRAWE achieved a 1.6% higher correlation (0.6625 vs. 0.6518) and a 3.8% lower NRMSE (0.4798 vs. 0.4989) on average. These improvements exhibit consistent gains in both metrics to enhance predictive accuracy.