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<p>The inter-model spread in future West African monsoon rainfall under high-emission scenarios remains large due to climate model biases in large-scale circulation. Here, we develop a physics-guided artificial neural network (ANN) to constrain a subset of CMIP6 precipitation projections using sea-level pressure patterns from the Sahelian monsoon ocean-pressure index (SMOPI). Model-specific ANNs are trained to learn nonlinear SMOPI-related circulation patterns and precipitation relationships under an amplitude-preserving constraint. The physical realism of these learned teleconnections is evaluated against JRA-55 reanalysis to construct a performance-based weighted ensemble. This approach reduces end-of-century inter-model spread by 20% in high-skill models, 33% in lower-skill models, and 30% across the full ensemble. It shifts the spread budget toward models reproducing observed teleconnections by up-weighting physically consistent models and down-weighting less reliable ones without collapsing ensemble diversity. This physics-weighted deep-learning architecture delivers more coherent projections and offers computationally affordable pathways to robust climate information in data-limited regions.</p>

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Constraining high-emission future West African monsoon with physics-weighted deep learning ensembles

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月21日

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  • Alain T. Tamoffo,
  • Fernand L. Mouassom,
  • Torsten Weber,
  • Daniela Jacob

摘要

The inter-model spread in future West African monsoon rainfall under high-emission scenarios remains large due to climate model biases in large-scale circulation. Here, we develop a physics-guided artificial neural network (ANN) to constrain a subset of CMIP6 precipitation projections using sea-level pressure patterns from the Sahelian monsoon ocean-pressure index (SMOPI). Model-specific ANNs are trained to learn nonlinear SMOPI-related circulation patterns and precipitation relationships under an amplitude-preserving constraint. The physical realism of these learned teleconnections is evaluated against JRA-55 reanalysis to construct a performance-based weighted ensemble. This approach reduces end-of-century inter-model spread by 20% in high-skill models, 33% in lower-skill models, and 30% across the full ensemble. It shifts the spread budget toward models reproducing observed teleconnections by up-weighting physically consistent models and down-weighting less reliable ones without collapsing ensemble diversity. This physics-weighted deep-learning architecture delivers more coherent projections and offers computationally affordable pathways to robust climate information in data-limited regions.