Predicting column averaged dry-air mole fractions of carbon dioxide (XCO 2 ) in Peninsular Malaysia by using GOSAT data

摘要: Carbon dioxide (CO 2 ) is the primary anthropogenic GHG and contribute up to 70% of global warming. It has been associated with climate change which influences land water resources, food pasture availability, disappearance plants animal species loss habitat. objective this study was used multiple linear regression (MLR) method analyze relationship between column averaged dry-air mole fractions carbon (XCO other atmospheric variables in Peninsular Malaysia based on Greenhouse Gases Observing Satellite (GOSAT) data for period 2009–2014. Then XCO predicted using obtained best-fitting MLR. The results indicated that prediction model measured showed a high correlation coefficient (R2=0.9037), indicating model's accuracy efficiency. GOSAT are encouraging capable examine increase atmosphere greenhouse gases over different regions Malaysia.