Abstract
Sea surface temperature (SST) is one of the attributes of the world climate system and global warming. The relationship between SST and other climate parameters can be represented in a linearity approach. Through this approach, SST variability shows monthly and yearly effects. Information on these two time effects is important for knowing the period of peak effect as well as other statistical measures in the linear fitting model. The models used include transformation and without covariate transformation, interaction and without covariate interaction, and with centering and with the addition of time covariates in the model. The linear fitting model chosen as the basis for construction is a model with a combination effect of covariate interaction and transformation giving an increase in the magnitude of multiple R2 (56.62%) and adjusted R2 (56.13%) respectively 0.31% and 0.43%. This indicates that the time covariate has a very strong significant effect on the model compared to the continuous covariate. In general, the model has a statistical significance of p-value < 2.2e-16, as well as for the time covariate. However, because the model has an autocorrelation and a large AIC value, this effect is removed by means of an autoregressive moving average. The obtained linear fitting model for SST data is the model with AIC 403.2987.
Highlights
Sea surface temperature (SST) is one of the attributes of the world climate system and global warming
other climate parameters can be represented in a linearity approach
Information on these two time effects is important for knowing the period of peak effect
Summary
Salah satu parameter penting dalam sistem iklim dunia dan indikator dari pemanasan global adalah suhu permukaan laut atau lebih dikenal dengan sea surface temperature (SST). Laut merupakan bagian bumi yang termasuk dalam 70.9% dan sisanya adalah daratan. Dengan laut terluas adalah samudera Pasifik (64 million square miles) dan samudera Atlantik (41.1 msm) serta Hindia (28 msm) yang masing-masing kedua dan ketiga laut terluas. Data SST dapat menjadi indikator esensial untuk mengetahui variabilitas iklim bumi [2,3,4,5,9,11]. Indikator tersebut seperti adanya gejala El Nino dan La Nina di Samudera Pasifik yang berefek pada musim kemarau dan penghujan di wilayah sekitarnya termasuk Indonesia. Data SST tergolong memiliki struktur yang kompleks seperti data hilang (missing value) yang direpresentasikan dengan adanya gap, dimana panjang gap memiliki keragaman antar buoys
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