Modeling of sea surface temperature using linear models with autocorrelation Indian Ocean

Abstract

Abstract. Sea surface temperature (SST) is one of several indicators of climate system of the Earth. We used model SST to observe SST dataset from buoy arrays in the eastern equatorial Indian Ocean. Relationship between SST and other climate parameters can be represented in linearity approach. This approach shows that temporal variability of the SST as a dominant effect. Linear model fitting (LMF) has been examined with four treatments, with and without: covariate transformation, interaction, centering, and addition time covariate in the model. The LMF chosen as basic construction in the model with covariate interaction combination and transformation, which increases magnitude of multiple-R 2 (56.62%) and adjusted-R 2 (56.13%), i.e. 0.31% and 0.43% respectively. This shows that time covariates have a strong significance effect in the model, compared to continuous covariates. However, the model has autocorrelation, which has large Akaike Information Criterion (AIC) value then this deletion of effects can be done through the autoregressive moving average. Moreover we obtained that LMF which suitable to SST is model with AIC value 403.2987 by using three climate features include two time covariates. Furthermore, we observed that using GAM model fitting showed an increase in explained deviance to 65.90%, a significant decrease in AIC from 678.24 to 634.99 and significant increase in adjusted-R 2 from 51.20% to 64.40% by using sixteen climate features include two times covariates without interaction and transformation.

Modelling Indian Ocean Air Temperature Using Additive Model

“…Temperature is a state of hot air or cold air. The highest temperature on the Earth's surface based on latitude appears in tropical regions (near the equator) and temperatures are at its lowest in polar regions [7,8].…”

“…The increase in world population, the growth of urban and rural industries, the accumulated emissions of gases from transport and other technologies and electronic products, and human activities on natural resources, such as forest fires, land for houses, office buildings and toll roads, have caused the change in the quality of air temperature on the earth's surface. In addition to the above factors, changes in the earth's surface temperature are also considered to be caused by natural factors, such as rain, humidity, solar radiation, sea surface temperature and wind speed [7,8].…”

Modeling of Sea Surface Temperature Based on Partial Least Square - Structural Equation

“…Research on SST has been carried out using various methods such as detecting missing SST data in the Indian Ocean, model fitting of SST by using linear model with interaction , cross-correlation analysis of SST anomaly with several climate features in the Indian Ocean, specifically for forecasting using SARIMA ARCH/GARCH models , modeling of SST with generalized additive mixed models (GAMM) for risk detection (Humaira et al, 2019). Hereinafter, for detection of SSTA phenomena using vector autoregression (Miftahuddin et al, 2019), model fitting of SSTA with Generalized Additive Model (GAM), (Ananda and and model fitting of SST through linear model with autocorrelation (Miftahuddin, and Ilhamsyah, 2018 ). Furthermore, the statistical approach is focused on looking at weather and climate elements in a structure equation through the partial least squares (PLS) model.…”