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020 ▼a 9781392565964
035 ▼a (MiAaPQ)AAI22592092
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 551.5
1001 ▼a Wang, Xinyang.
24510 ▼a Dynamics-informed Machine Learning Approaches for El Nino Prediction and Its Teleconnection to Antarctica.
260 ▼a [S.l.]: ▼b New York University., ▼c 2019.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2019.
300 ▼a 144 p.
500 ▼a Source: Dissertations Abstracts International, Volume: 81-06, Section: B.
500 ▼a Includes supplementary digital materials.
500 ▼a Advisor: Giannakis, Dimitrios.
5021 ▼a Thesis (Ph.D.)--New York University, 2019.
506 ▼a This item must not be sold to any third party vendors.
506 ▼a This item must not be sold to any third party vendors.
506 ▼a This item must not be added to any third party search indexes.
506 ▼a This item must not be added to any third party search indexes.
520 ▼a Forecasting the El Nino-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 linear models have limited skill for longer-term forecasts beyond half a year. To resolve this limitation, we employ a recently introduced data analysis technique called nonlinear Laplacian spectral analysis (NLSA) and a new nonparametric prediction approach called kernel analog forecasting (KAF). These approaches use nonlinear kernel methods for machine learning and dimension reduction of high-dimensional datasets, and have rigorous connections with the spectral theory of dynamical systems. In particular, using the multiscale modes recovered by NLSA, KAF yields statistically optimal predictions of future ENSO states as conditional expectations without assumptions on the underlying dynamics. In Chapter 3, KAF is applied to ENSO prediction using modelled and observed sea surface temperature (SST) data in the Indo-Pacific region. In the observation case, KAF successfully predicts the Nino~3.4 index up to a 13-month lead, which corresponds to an increase of 6 months over a benchmark linear inverse model (LIM), while significantly improving upon the ENSO predictability ``spring barrier''. When applied to 1300-yr model data, the enhanced predictability afforded by KAF holds over potentially much longer leads, extending to 24 months versus 11 months in the benchmark LIM. Additionally, in Chapter 4, a strong connection between ENSO and Antarctic interannual variability has been found by the multiscale modes recovered from NLSA, providing a new approach to study the complex climate system around Antarctica which is still not well simulated by general circulation models. Motivated by this, we investigate regional forecasts of Antarctic sea ice via KAF in Chapter 5. Although Antarctic sea ice variability is much less predictable than ENSO, KAF shows improved performance over the benchmark persistence forecast.
590 ▼a School code: 0146.
650 4 ▼a Applied mathematics.
650 4 ▼a Atmospheric sciences.
690 ▼a 0364
690 ▼a 0725
71020 ▼a New York University. ▼b Mathematics.
7730 ▼t Dissertations Abstracts International ▼g 81-06B.
773 ▼t Dissertation Abstract International
790 ▼a 0146
791 ▼a Ph.D.
792 ▼a 2019
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15493212 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 202002 ▼f 2020
990 ▼a ***1008102
991 ▼a E-BOOK