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Submitted
Nov 28, 2020
Accepted
Jan 30, 2021
Published
Jan 30, 2021
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Abstract

Climate change can create a considerable impact in Indonesia. Aceh province is a province located on the island of Sumatra and it are located around in the Indian Ocean. Aceh Province has a considerable impact of climate change caused by the Sea Surface Temperature Anomalies (SSTA). The SSTA in the Indian Ocean is a parameter that can affect climatic conditions in Indonesia. The SSTA changes can cause an extreme climate change on earth. There are several climate elements affected by SSTA including air temperature, rainfall, wind speed, solar radiation, and relative humidity. One of the methods used to look at SSTA's relationship with some climate elements is the Cross-Correlation method. The climate data used in this study was a daily time series data. The purpose of this study is to find out SSTA's relationship with some climate elements. The results showed that using the Pearson correlation, the highest relationship was SSTA and the air temperature was 0.45. Meanwhile, the lowest relationship was SSTA and the rainfall was -0.05. Similarly, the Cross-Correlation method where the highest relationship was SSTA and the air temperature was 0.469, and the lowest close relationship was SSTA and the rainfall was -0.075.

Keywords

SSTA cross-correlation climate elements

Article Details

How to Cite
Oktaviani, F., Miftahuddin, & Setiawan, I. (2021). Cross-correlation Analysis Between Sea Surface Temperature Anomalies and Several Climate Elements in The Indian Ocean. Parameter: Journal of Statistics, 1(1), 13-20. https://doi.org/10.22487/27765660.2021.v1.i1.15354

References

  1. Derrick, Timothy R., and Thomas, Joshua M. (2004). Time Series Analysis: The Cross-Correlation Function. Innovative Analyses of Human Movement. Vol 7: 189-205.
  2. Guilford, J.P. (1956). Fundamental Statistic in Psychology and Education. 3rd Ed. New York: McGraw-Hill Book Company, Inc.
  3. Hashmi,M.Z., Shamseldin, A.Y., and Melville, B.W. (2009). Statistical Downscaling of Precipitation: State of the Art and Application of Bayesian Multimodel Approach for Uncertainty Asessment. Hydrol Earth Sys Sci Discuss.Vol 6: 6536-6579.
  4. Hermawan, E., and Komalaningsih, K. (2008). Karakteristik Indian Ocean Dipole Mode di Samudera Hindia Hubungannya dengan Perilaku Curah Hujan di Kawasan Sumatera Barat Berbasis Analisis Mother. Jurnal Sains Dirgantara. 5(2): 109–129.
  5. Miftahuddin, Andriani,R., Setiawan I., dan Mulsandi, A., 2014, Penerapan Analisis Korelasi Kanonik pada Kajian ENSO dalam Identifikasi Hubungan Fitur Iklim, Jurnal Natur Indonesia, 15, 36-44, ISSN: 1410-9379.
  6. Nugroho, S., Akbar, S., and Vusvitasari, R. (2008). Kajian Hubungan Koefisien Korelasi Pearson (r), Spearman-rho(ρ), Kendall-tau (τ), Gamma (G), dan Somers(dyx). Journal Statistika, 41–54.
  7. Pramudia, A., Estiningtyas, W., Susanti, E., and Sucianti. (2015). Fenomena dan Perubahan Iklim Indonesia serta Pemanfaatan Informasi Iklim untuk Kalender Tanam. Badan Penelitian dan Pengembangan Pertanian, Departemen Pertanian
  8. Reid, C., Marshall, J. Logan, D., and Kleine, D. (2009). Terumbu Karang dan Perubahan Iklim. Terjemahan dari Coral Reefs and Climate Change, oleh Badan Penelitian dan Pengembangaan Kelautan dan Perikanan & Kementerian Kelautan dan Perikanan Indonesia. Panca Jaya, Jakarta.
  9. Reynolds, R.W., Rayner, N.A., Smith, T.M., Stokes, D.C., and Wang, W. (2001). An Improved in Situ and Satellite SST Analysis for Climate. Journal of Climate. 15(2): 1-3.
  10. Saji, N. H., Goswami, B.N., Vinayachandran, P.N., and Yamagata, T. (1999). Adipole Mode in the Tropical Indian Ocean. Journal Nature. 401:360-363.
  11. Shumway, Robert H., and Stoffer, David S. (2011). Time Series Analysis and Its Applications with R Examples Third Edition. Springer. USA.