New approach for optimal perturbation method in ensemble climate prediction with empirical singular vector
SCIE
SCOPUS
Cited 21 time in
WEB OF SCIENCE
Cited 23 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
-
Alternative Author(s)
- 국종성
-
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
- Files in This Item:
-
There are no files associated with this item.
Items in ScienceWatch@KIOST are protected by copyright, with all rights reserved, unless otherwise indicated.