用于干旱预测的概率深度学习：内部气候变率的作用

中文摘要 AI 辅助

预测干旱风险对于预判其对水资源、农业、生态系统及气候适应规划的影响至关重要。然而干旱预测仍存在不确定性，因为变率会显著改变区域降水和蒸发需求。将这种变率视为非结构化噪声，忽略了内部变率具有空间、季节和时间结构，因此包含可用于改进干旱预测的信息。我们提出一种基于深度学习的欧洲干旱预测框架，并扩展了一种感知不确定性的干旱边界，该边界明确纳入了大型气候模式集合的内部预测变率。此边界代表未来干旱状况的物理上合理的下尾轨迹，标记了在内部变率不利实现下干旱可能达到的严重程度，为适应规划提供保守、规避风险的参考。我们将提出的边界与仅由再分析数据得出的下边界进行比较，结果显示我们提出的集合信息边界在大多数地区和季节的校准效果更好，尤其在异常干旱条件下，仅历史再分析数据会低估下尾干旱风险。我们的结果表明，内部变率应被视为独立的预测量。更广泛而言，大型集合提供了一种将物理上合理的气候变率转化为机器学习干旱预测的实用方法，从而产生更具信息性的风险感知边界，用于气候条件变化下的干旱评估。

英文摘要

Predicting drought risk is essential for anticipating impacts on water resources, agriculture, ecosystems, and climate adaptation planning. Yet drought forecasts remain uncertain because variability can substantially alter regional precipitation and evaporative demand. Treating this variability as unstructured noise ignores the fact that internal variability has spatial, seasonal, and temporal structure and thus contains information that can be used to improve drought forecasting. We propose a deep-learning-based forecasting framework for European drought prediction and extend it with an uncertainty-aware drought bound that explicitly incorporates internal forecast variability from a large climate model ensemble. This bound represents a physically plausible lower-tail trajectory of future drought conditions and marks how severe drought could plausibly become under an unfavourable realisation of internal variability, giving adaptation planning a conservative, risk-averse reference. We compare the proposed bound with a lower bound derived from reanalysis data only and show that our proposed ensemble-informed bound is better calibrated across most regions and seasons. This is specifically true during anomalously dry conditions, when historical reanalysis alone underestimates lower-tail drought risk. Our results show that internal variability should be treated as a forecast quantity in its own right. More broadly, large ensembles provide a practical way to transfer physically plausible climate variability into machine-learning drought forecasts, yielding risk-aware bounds that are more informative for drought assessment under shifting climate conditions.

发表机构

• LMU Munich（慕尼黑大学）

机构由 AI 辅助整理，请以论文原文为准。