Extended-range statistical ENSO prediction through operator-theoretic techniques for nonlinear dynamics
Extended-range statistical ENSO prediction through operator-theoretic techniques for nonlinear dynamics
复制标题
通过非线性动力学算子理论技术进行扩展范围统计 ENSO 预测
DOI：
10.1038/s41598-020-59128-7
复制
发表时间：
2020
影响因子：
4.6
通讯作者：
Giannakis, Dimitrios
中科院分区：
文献类型：
--
作者：
Wang, Xinyang;Slawinska, Joanna;Giannakis, Dimitrios
由于厄尔尼诺-南方涛动现象在气候动力学中的重要作用及其对全球社会经济的影响，预测厄尔尼诺-南方涛动一直是一个积极研究的课题。在过去几十年中，人们开发了许多预测厄尔尼诺/南方涛动的模型，其中，通过线性动力学近似厄尔尼诺/南方涛动演变的统计模型受到了极大的关注，因为它们简单，而且在短时间内可与第一原理模型相媲美。然而，由于高度非线性和混乱的动态（特别是在厄尔尼诺/南方涛动开始期间），这种模式在半年以上的长期预测方面的技能有限。为了解决这个问题，我们采用了一种基于模拟预测的非参数统计方法，称为核模拟预测（KAF），它通过使用非线性核方法进行机器学习和高维数据集的降维来避免对底层动态的假设。通过与动力系统的Koopman算子理论的严格联系，KAF产生了对未来ENSO状态的统计最佳预测作为有条件的期望，在预测初始化时给出了噪声和可能不完整的数据。在这里，使用工业时代的印度-太平洋海面温度（SST）作为训练数据，该方法被证明可以成功地预测1998-2017年验证期内的Niño 3.4指数，提前10个月，相当于增加3-8个月（取决于十年）超过基准线性逆模型（LIM），同时显着提高了ENSO的可预测性“春季障碍”。特别是，KAF早在2015年6月就成功地在初始时间预测了历史性的2015/16年厄尔尼诺现象，这与当前动力学模型的技能相当。综合气候模式（CCSM 4）的1300年控制集成的分析进一步表明，KAF提供的增强的可预测性保持在潜在的更长的时间，延长到24个月，而在基准LIM的18个月。厄尔尼诺/拉尼娜事件发生的概率预报也通过信息理论指标进行和评估，显示出LIM方法的技能改进，从而为各种情况下的环境风险评估开辟了一条途径。
Forecasting the El Niño-Southern Oscillation (ENSO) has been a subject of vigorous research due to the important role of the phenomenon in climate dynamics and its worldwide socioeconomic impacts. Over the past decades, numerous models for ENSO prediction have been developed, among which statistical models approximating ENSO evolution by linear dynamics have received significant attention owing to their simplicity and comparable forecast skill to first-principles models at short lead times. Yet, due to highly nonlinear and chaotic dynamics (particularly during ENSO initiation), such models have limited skill for longer-term forecasts beyond half a year. To resolve this limitation, here we employ a new nonparametric statistical approach based on analog forecasting, called kernel analog forecasting (KAF), which avoids assumptions on the underlying dynamics through the use of nonlinear kernel methods for machine learning and dimension reduction of high-dimensional datasets. Through a rigorous connection with Koopman operator theory for dynamical systems, KAF yields statistically optimal predictions of future ENSO states as conditional expectations, given noisy and potentially incomplete data at forecast initialization. Here, using industrial-era Indo-Pacific sea surface temperature (SST) as training data, the method is shown to successfully predict the Niño 3.4 index in a 1998–2017 verification period out to a 10-month lead, which corresponds to an increase of 3–8 months (depending on the decade) over a benchmark linear inverse model (LIM), while significantly improving upon the ENSO predictability “spring barrier”. In particular, KAF successfully predicts the historic 2015/16 El Niño at initialization times as early as June 2015, which is comparable to the skill of current dynamical models. An analysis of a 1300-yr control integration of a comprehensive climate model (CCSM4) further demonstrates that the enhanced predictability afforded by KAF holds over potentially much longer leads, extending to 24 months versus 18 months in the benchmark LIM. Probabilistic forecasts for the occurrence of El Niño/La Niña events are also performed and assessed via information-theoretic metrics, showing an improvement of skill over LIM approaches, thus opening an avenue for environmental risk assessment relevant in a variety of contexts.
通过非线性拉普拉斯谱分析比较综合气候模型中的低频和间歇性变化
DOI：
--
发表时间：
2012
期刊：
影响因子：
--
作者：
D. Giannakis;A. Majda
通讯作者：
A. Majda
南极绕极波及其季节性：固有旅行模式和厄尔尼诺-南方涛动遥相关
DOI：
--
发表时间：
2018
期刊：
International Journal of Climatology
影响因子：
--
作者：
Xinyang Wang;D. Giannakis;J. Slawinska
通讯作者：
J. Slawinska
复杂非线性系统随机模型的信息论、模型误差和预测技巧
DOI：
--
发表时间：
2012
期刊：
影响因子：
--
作者：
D. Giannakis;A. Majda;I. Horenko
通讯作者：
I. Horenko