我困惑还是这令人困惑？：用于ENSO不确定性量化的大深度集成
Am I Confused or Is This Confusing?: Deep Ensembles for ENSO Uncertainty Quantification
AI总结：
本文利用深度集成分析ENSO预测性，揭示了在气候变暖背景下，epistemic不确定性与aleatoric不确定性的差异及影响。
摘要：
在高风险气候预测中，准确的不确定性量化（UQ）至关重要。深度集成，或概率神经网络的集成，是机器学习（ML）中UQ的前沿方法，日益应用于天气和气候预测。然而，对这些复杂问题设置中集成机制、优势和局限性的详细分析仍不足。本文通过在Community Earth System Model 2 Large Ensemble（CESM2-LE）中部署深度集成，分析了ENSO的可预测性。主要发现是，epistemic不确定性，通过集成分歧建模，稳健地信号与月度海表温度（SST）、海洋热量含量（OHC）和经向表面风应力（τ_x）异常分布变化相关的预测误差增长。相反，aleatoric不确定性，作为模型置信度的流行衡量标准，在气候变化引起的分布偏移下变得不可靠且行为反常。我们强调，由于集成性能相对于预期单个模型的提升与epistemic不确定性成正比，因此在气候变化引起的分布偏移下，集成改进会增加。本工作展示了深度集成在ML气候预测中建模aleatoric和epistemic不确定性的效用，以及在人为变暖下准确量化这两种不确定性形式的日益重要性。
英文摘要：
Faithful uncertainty quantification (UQ) is paramount in high stakes climate prediction. Deep ensembles, or ensembles of probabilistic neural networks, are state of the art for UQ in machine learning (ML) and are growing increasingly popular for weather and climate prediction. However, detailed analyses of the mechanisms, strengths, and limitations of ensembles in these complex problem settings are lacking. We take a step towards filling this gap by deploying deep ensembles for predictability analysis of the El-Niño Southern Oscillation (ENSO) in the Community Earth System Model 2 Large Ensemble (CESM2-LE). Principally, we show that epistemic uncertainty, modeled by ensemble disagreement, robustly signals predictive error growth associated with shifts in the distributions of monthly sea-surface temperature (SST), ocean heat content (OHC), and zonal surface wind stress ($τ_x$) anomalies under a climate change scenario. Conversely, we find that aleatoric uncertainty, which remains a popular measure of model confidence, becomes less reliable and behaves counterintuitively under climate-change-induced distributional shift. We highlight that, because ensemble performance improvement relative to the expected single model scales with epistemic uncertainty, ensemble improvement increases with distributional shift from climate change. This work demonstrates the utility of deep ensembles for modeling aleatoric and epistemic uncertainty in ML climate prediction, as well as the growing importance of robustly quantifying these two forms of uncertainty under anthropogenic warming.
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