We introduce conformal ensembling, a new approach to uncertainty quantification in climate projections based on conformal inference. Unlike traditional methods, conformal ensembling seamlessly integrates climate models and observational data across a range of scales to generate statistically rigorous, easy-to-interpret uncertainty estimates. It can be applied to any climatic variable using any ensemble analysis method and outperforms existing inter-model variability methods in uncertainty quantification across all time horizons and most spatial locations under SSP2-4.5. Conformal ensembling is also computationally efficient, requires minimal assumptions, and is highly robust to the conformity measure. Experiments show that it is effective when conditioning future projections on historical reanalysis data compared with standard ensemble averaging approaches, yielding more physically consistent projections.

翻译：我们提出了一种基于保形推理的气候预测不确定性量化新方法——保形集成。与传统方法不同，保形集成能够无缝整合不同尺度的气候模型和观测数据，生成统计严谨且易于解释的不确定性估计。该方法可应用于任何气候变量，并兼容任何集成分析方法；在SSP2-4.5情景下，其在所有时间尺度和大多数空间位置的不确定性量化性能均优于现有的模型间变异性方法。保形集成还具有计算效率高、假设条件少、对保形度量具有高度鲁棒性等优势。实验表明，相较于标准集成平均方法，该方法在基于历史再分析数据约束未来预测时效果显著，能够生成更具物理一致性的预测结果。