New approach for optimal perturbation method in ensemble climate prediction with empirical singular vector SCIE SCOPUS

Cited 20 time in WEB OF SCIENCE Cited 22 time in Scopus
Title
New approach for optimal perturbation method in ensemble climate prediction with empirical singular vector
Author(s)
Kug, Jong-Seong; Ham, Yoo-Geun; Kimoto, Masahide; Jin, Fei-Fei; Kang, In-Sik
Publication Year
2010-08
Abstract
In this study, a new method is developed to generate optimal perturbations in ensemble climate prediction. In this method, the optimal perturbation in initial conditions is the 1st leading singular vector, calculated from an empirical linear operator based on a historical model integration. To verify this concept, this method is applied to a hybrid coupled model. It is demonstrated that the 1st leading singular vector from the empirical linear operator, to a large extent, represents the fast-growing mode in the nonlinear integration. Therefore, the forecast skill with the optimal perturbations is improved over most lead times and regions. In particular, the improvement of the forecast skill is significant where the signal-to-noise ratio is small, indicating that the optimal perturbation method is effective when the initial uncertainty is large. Therefore, the new optimal perturbation method has the potential to improve current seasonal prediction with state-of-the-art coupled GCMs.
ISSN
0930-7575
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/4061
DOI
10.1007/s00382-009-0664-y
Bibliographic Citation
CLIMATE DYNAMICS, v.35, no.2-3, pp.331 - 340, 2010
Publisher
SPRINGER
Subject
SURFACE TEMPERATURE ANOMALIES; NINO SOUTHERN OSCILLATION; OCEAN RECHARGE PARADIGM; COUPLED MODEL; OPTIMAL-GROWTH; BRED VECTORS; CONCEPTUAL-MODEL; ATMOSPHERE MODEL; ERROR GROWTH; ENSO
Keywords
Optimal perturbation method; Seasonal prediction; Ensemble prediction; Singular vector
Type
Article
Language
English
Document Type
Article
Publisher
SPRINGER
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