Penerapan Analisis Korelasi Kanonik pada Kajian Enso dalam Identifikasi Hubungan Fitur Iklim

Miftahuddin Miftahuddin, Ria Andriani, Ichsan Setiawan, Adi Mulsandi

Abstract


There are several resulting arguments from the research done on climate variation in Indonesia stating that the observed affects are through various phenomena such as ENSO, monsoon, dipole mode event, and MJO. However, the magnitude of the effect varies for each region in Indonesia. This research aims to identify the relationship among the global climate features (GCFs) in the Nino3.4 (5°S–5°N, 120–170°W) with the local climate features (LCFs) in the Aceh regions which represented by: I(2–3°N, 95–98°E), II(3–4°N, 95–98°E), III(4–5°N, 95–98°E), and IV(5–6°N, 95–98°E) using canonical correlation analysis (CCA) in the ENSO phenomena. The analysis shows that global GCFs variations have strong correlation with LCFs variations with the correlation values, 0.893, 0.899, 0.900, and 0.901, respectively. The result show that when there is a global change in any feature of GCFs, the same change also appears in each feature of LCFs. The canonical loading shows that there are original variables which have strong correlation with the first canonical global variable (X1) with correlations 0.987, 0.969, 0.987, and 0.865,respectively, and the local wind (Y1) with correlations 0.974, 0.952, 0.979, and 0.845, respectively. All the other climate features have weak correlations with the first canonical variables. From the MANOVA, we can conclude that the climate features (wind, SST, SSTA, and SLP) affect climate changes in both study regions. Our results also reveal that LCFs are significantly affected in the Nino3.4 99.5% and in I, II, III, and IV for given correlations 99.8, 99.7, 99.6, and 99.5%, respectively.


Keywords


ƞ2 value, CCA, global and local climate features, loading canonic, MANOVA

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References


Anderson, T.W. 1958. An Introduction to Multivariate Statistical Analysis. New York: John Wiley & Sons.

Bannu. 2003. Analisis interaksi monsun, ENSO, dan dipole mode serta kaitannya dengan variabilitas curah hujan dan angin permukaan di benua maritim indonesia. Tesis, Institut Teknologi Bandung.

Bartlett, M.S. 1951. The goodness of Fit of A Single Hypothetical Discriminant Function in The Case of Several Groups. Ann. Eugen. Land. 16: 199–214.

Branston, A.G & Ropelewski, C.F. 1992. Prediction of ENSO episodes using canonical correlation analysis. J. Climate 5: 1316–1345.

Chen, C.C., McCarl, B.A & Adams R.M. 2000. Economic Implication of Potential ENSO Frequency and Strength Shifts. National Assessment of Climate Change, USA: Agriculture Focus Group.

Dillon, William R. & Matthew, G. 1984. Multivariate Analysis Methods and Applications. New York: John Wiley & Sons Inc.

Everitt & Brian. 2005. An R and S Plus Companion to Multivariate Analysis. London: Springer.

Everitt, Brian & Hothorn, Torsten. 2011. An Introduction to Applied Multivariate Analysis with R. Springer.

Gaspersz & Vincent. 1992. Teknik Analisis dalam Penelitian Percobaan. Bandung: Tarsito.

Hair, Joseph. F.J.R., Anderson., Rolh, E., Tatham., Ronald, L., Black & William, C. 1998, Multivariate Data Analysis 5th ed. New Jersey: Prentice Hall Inc.

Hardle, W.K & Simar, L. 2012. Applied Multivariate Statistical Analysis. Manchester: Springer Verlag.

Hardon, D.R., Szedmak, S. & Taylor, J.S. 2003. Canonical Correlation Analysis; An overview with application to learning methods. Technical Report. CSD-TR-03-02. Royal Holloway University of London.

Harijono & Sri Woro, B. 2008. Analisis dinamika atmosfer di bagian utara ekuator sumatera pada saat peristiwa el-

Nino dan dipole mode positif terjadi bersamaan. Jurnal Sains Dirgantara 5(2): 130–148.

Johnson, Richard, A & Wichern., Dean, W. 2007. Applied Multivariate Statistical Analysis 6th ed. New York: Prentice-Hall Inc.

Landman, W.A & Mason, S.J. 1999. Operational long-lead prediction of south african rainfall using canonical correlation analysis. Royal meteorological society. International Journal of Climatology 19(10): 1073– 1090.

Mardia, K.V., Kent, J.T & Bibby, J.M. 1979. Multivariate Analysis. California: Academic Press.

Raykov, T & Marcoulides, G.A. 2008. An Introduction to Applied Multivariate Analysis. New York, NY: Routledge. Read.

Rencher & Alvin, C. 2002. Methods of Multivariate Analysis. Second Edition. Wiley Interscience: Brigham Young University.

Stevans & James, P. 2009. Applied Multivariate Statistic for the Social Sciences. University of Cincinnati. Routledge: 5th ed.

Supranto. 2004. Analisis Multivariant: Arti dan Interpretasi. Jakarta: Rineka Cipta.

Timm & Neil, H. 2002. Applied Multivariate Analysis. New York, Inc: Springer Verlag.

Tjasyono, H.K & Bayong. 1999. Klimatologi Umum. Bandung: ITB.

Trenberth, K.E., Fasullo, J.T & Kiehl, J. 2009. Earth’s global energy budget. Bull. Am. Meteorol. Soc. 90(3): 311– 323.

Wang, C., et al. 2012. El Nino and Southern Oscillation (ENSO): A Review. A chapter for Springer book: Coral Reefs of the Eastern Pacific. USA.

Wiratmo & Joko. 1998. Sudah Benarkah Pemahaman Anda Tentang La Nina dan El Nino?. Bandung: ITB.

Yeh, S.W & Kirtman, B.P. 2007. ENSO amplitude changes due to climate change projections in different coupled models. J. Climate 20: 203–217.




DOI: http://dx.doi.org/10.31258/jnat.15.1.36-44

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