美国夏威夷大学Jin, Fei-Fei研究团队取得一项新突破。他们的研究报道了基于气候模式相互作用的可解释厄尔尼诺可预报性。2024年6月26日出版的《自然》发表了这项成果。

据介绍，厄尔尼诺—南方涛动(ENSO)提供了大部分的全球季节气候预测技巧，然而，量化技巧预测的强度是一个长期的挑战。不同程度来源的可预测性影响ENSO演变，导致不同的全球效应。人工智能预测提供了有希望的进步，但将它们的技能与特定的物理过程联系起来尚不可能，这限制了人们对支持这些进步的动力的理解。

研究人员展示了一个扩展的非线性补给振荡器(XRO)模型，可以进行长达16-18各月娴熟的ENSO预测，比全球气候模型更好，可以与最熟练的人工智能预测相媲美。XRO结合了核心ENSO动力学和ENSO与全球海洋其他变率模式的季节性调制相互作用。ENSO长期预报能力的内在增强通过它们的记忆和与ENSO的相互作用，可追溯到其他气候模式的初始条件，并可以量化这些模式对ENSO振幅的贡献。

使用在气候模式输出上进行训练的XRO的重新预测表明，减少模式ENSO动力学和气候模式相互作用的偏差可以使ENSO预测更加熟练。XRO框架对ENSO全球多时间尺度相互作用的整体处理，突出了改善ENSO模拟和预测的有希望的目标。

附：英文原文

Title: Explainable El Nio predictability from climate mode interactions

Author: Zhao, Sen, Jin, Fei-Fei, Stuecker, Malte F., Thompson, Philip R., Kug, Jong-Seong, McPhaden, Michael J., Cane, Mark A., Wittenberg, Andrew T., Cai, Wenju

Issue&Volume: 2024-06-26

Abstract: The El Nino–Southern Oscillation (ENSO) provides most of the global seasonal climate forecast skill, yet, quantifying the sources of skilful predictions is a long-standing challenge. Different sources of predictability affect ENSO evolution, leading to distinct global effects. Artificial intelligence forecasts offer promising advancements but linking their skill to specific physical processes is not yet possible, limiting our understanding of the dynamics underpinning the advancements. Here we show that an extended nonlinear recharge oscillator (XRO) model shows skilful ENSO forecasts at lead times up to 16–18months, better than global climate models and comparable to the most skilful artificial intelligence forecasts. The XRO parsimoniously incorporates the core ENSO dynamics and ENSO’s seasonally modulated interactions with other modes of variability in the global oceans. The intrinsic enhancement of ENSO’s long-range forecast skill is traceable to the initial conditions of other climate modes by means of their memory and interactions with ENSO and is quantifiable in terms of these modes’ contributions to ENSO amplitude. Reforecasts using the XRO trained on climate model output show that reduced biases in both model ENSO dynamics and in climate mode interactions can lead to more skilful ENSO forecasts. The XRO framework’s holistic treatment of ENSO’s global multi-timescale interactions highlights promising targets for improving ENSO simulations and forecasts.

DOI: 10.1038/s41586-024-07534-6

Source: https://www.nature.com/articles/s41586-024-07534-6