OS Ocean Science OS Ocean Sci. 1812-0792 Copernicus Publications Göttingen, Germany 10.5194/os-6-933-2010 Spatio-Temporal Complexity analysis of the Sea Surface Temperature in the Philippines Botin Z. T. 1 David L. T. 2 del Rosario R. C. H. 1 4 Parrott L. 3 Institute of Mathematics, University of the Philippines Diliman, 1101 Quezon City, Philippines Marine Science Institute, University of the Philippines Diliman, 1101 Quezon City, Philippines Departement de Géographie, Université de Montréal, C.P. 6128 succursale centre-ville, Montréal, QC H3C 3JY, Canada now at: Genome Institute of Singapore, 60 Biopolis Street, #02-01 Genome, Singapore 09 11 2010 6 4 933 947 Copyright: © 2010 Z. T. Botin et al. 2010 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://os.copernicus.org/articles/6/933/2010/os-6-933-2010.html The full text article is available as a PDF file from https://os.copernicus.org/articles/6/933/2010/os-6-933-2010.pdf

A spatio-temporal complexity (STC) measure which has been previously used to analyze data from terrestrial ecosystems is employed to analyse 21 years of remotely sensed sea-surface temperature (SST) data from the Philippines. STC on the Philippine wide SST showed the monsoonal variability of the Philippine waters. STC is correlated with the SST mean (<i>R</i><sup>2</sup> &asymp; 0.7), and inversely correlated with the SST standard deviation (<i>R</i><sup>2</sup> &asymp; 0.9). Both STC and SST are highest during the middle of the year, which coincides with the Southwest Monsoon, but with the STC values being higher towards the end of the monsoon until the start of the inter-monsoon. In order to determine if STC has the potential to define limits of bio-regions, the spatial domain was subsequently divided into six thermal regions computed via clustering of temperature means. STC and EOF of the STC values were computed for each thermal region. Our STC analysis of the SST data, and comparisons with SST values suggest that the STC measure may be useful for characterising environmental heterogeneity over space and time for many long-term remotely sensed data.

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