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Climate Prediction by a Hybrid Method with Emphasizing Future Precipitation Change of East Asia
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 Title & Authors
Climate Prediction by a Hybrid Method with Emphasizing Future Precipitation Change of East Asia
Lim, Yae-Ji; Jo, Seong-Il; Lee, Jae-Yong; Oh, Hee-Seok; Kang, Hyun-Suk;
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 Abstract
A canonical correlation analysis(CCA)-based method is proposed for prediction of future climate change which combines information from ensembles of atmosphere-ocean general circulation models(AOGCMs) and observed climate values. This paper focuses on predictions of future climate on a regional scale which are of potential economic values. The proposed method is obtained by coupling the classical CCA with empirical orthogonal functions(EOF) for dimension reduction. Furthermore, we generate a distribution of climate responses, so that extreme events as well as a general feature such as long tails and unimodality can be revealed through the distribution. Results from real data examples demonstrate the promising empirical properties of the proposed approaches.
 Keywords
Canonical correlation analysis;empirical orthogonal function;climate change;precipitation;prediction;
 Language
English
 Cited by
1.
Multimodel ensemble forecasting of rainfall over East Asia: regularized regression approach, International Journal of Climatology, 2014, 34, 14, 3720  crossref(new windwow)
 References
1.
Glahn, H. (1963). Canonical correlation and its relationship to discriminate analysis and multiple regression, Journal of the Atmospheric Sciences, 25, 23-31

2.
Greene, A. M., Goddard, L. and Lall, U. (2006). Probabilistic multimodel regional temperature change projections, Journal of Climate, 19, 4326-4343 crossref(new window)

3.
Landman, W. A. and Goddard, L. (2002). Statistical recalibration of GCM forecasts over southern Africa using model output statistics, Journal of Climate, 15, 2038-2055 crossref(new window)

4.
Parzen, E. (1962). On estimation of a probability density function and mode, The Annals of Mathematical Statistics, 33, 1065-1076 crossref(new window)

5.
Storch, H. V. and Zwiers, F. W. (1999). Statistical Analysis in Climate Research, Cambridge

6.
Wilks, D. S. (2006). Statistical Methods in the Atmospheric Sciences, Academic Press