利用可解释的人工智能评估机会次季节预报的年代际变化

Marybeth Arcodia, Elizabeth A. Barnes, Kirsten J. Mayer, Ji-Woo Lee, A. Ordoñez, M. Ahn
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引用次数: 0

摘要

识别气候系统的可预测状态,可以通过更高的置信度和准确性,即所谓的机会预测,在通常低技能的亚季节时间尺度上提高预测技能。本研究采用神经网络方法探讨亚季节可预测性的年代际变化,特别是在机会预测期间。具体来说,这项工作量化了社区地球系统模式第2版(CESM2)大集合中热带地区提供的亚季节预测技能,并评估了这种技能在年代际时间尺度上的演变。利用网络的信心和可解释的人工智能,确定了与技能增强时期相关的物理上有意义的可预测性来源。利用这些网络,我们发现在机会预报期间,热带驱动的亚季节可预测性在年代际时间尺度上变化。此外,我们还研究了热带-温带遥相关低频调制的驱动因素,并讨论了其意义。分析扩展到ECMWF再分析v5数据,揭示了在CESM2-Large Ensemble中学习到的关系适用于现代再分析数据。这些结果表明,神经网络能够识别CESM2内气候系统的可预测年代际状态,这有助于在现实世界中做出自信、准确的亚季节降水预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing decadal variability of subseasonal forecasts of opportunity using explainable AI
Identifying predictable states of the climate system allows for enhanced prediction skill on the generally low-skill subseasonal timescale via forecasts with higher confidence and accuracy, known as forecasts of opportunity. This study takes a neural network approach to explore decadal variability of subseasonal predictability, particularly during forecasts of opportunity. Specifically, this work quantifies subseasonal prediction skill provided by the tropics within the Community Earth System Model Version 2 (CESM2) Large Ensemble and assesses how this skill evolves on decadal timescales. Utilizing the networks’ confidence and explainable artificial intelligence, physically meaningful sources of predictability associated with periods of enhanced skill are identified. Using these networks, we find that tropically-driven subseasonal predictability varies on decadal timescales during forecasts of opportunity. Further, we investigate the drivers of the low frequency modulation of the tropical-extratropical teleconnection and discuss the implications. Analysis is extended to ECMWF Reanalysis v5 data, revealing that the relationships learned within the CESM2-Large Ensemble holds in modern reanalysis data. These results indicate that the neural networks are capable of identifying predictable decadal states of the climate system within CESM2 that are useful for making confident, accurate subseasonal precipitation predictions in the real world.
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