Predicting column averaged dry-air mole fractions of carbon dioxide (XCO2) in Peninsular Malaysia by using GOSAT data

Abstract

Carbon dioxide (CO2) is the primary anthropogenic GHG and contribute up to 70% of the global warming. It has been associated with climate change which influences land and water resources, food and pasture availability, disappearance of plants and animal species and loss of habitat. The objective of this study was used multiple linear regression (MLR) method to analyze the relationship between the column averaged dry-air mole fractions of carbon dioxide (XCO2) and other atmospheric variables in Peninsular Malaysia based on Greenhouse Gases Observing Satellite (GOSAT) data for the period of 2009-2014. Then XCO2 was predicted using the obtained best-fitting MLR. The results indicated that prediction model with the measured data showed a high correlation coefficient (R2=0.9037), indicating the model's accuracy and efficiency. The GOSAT data are encouraging and capable to examine the increase of the atmosphere greenhouse gases over different regions in Peninsular Malaysia.