Analysis of Global Warming Using Machine Learning

Abstract

Climate change is a controversial topic of debate, especially in the US, where many do not believe in anthropogenic climate change. Because its consequences are predicted to be dire, such as a mass ocean extinction and frequent extreme weather events, it is important to learn what causes the warming in order to better combat it. In this study, the first challenge dwells on how to construct reliable statistical models based on massive climate data of 800,000 years and accurately capture the relationship between temperature and potential factors such as concentrations of carbon dioxide (CO 2), nitrous oxide (N 2 O), and methane (CH 4). We compared the performance several mainstream machine learning algorithms on our data, which includes linear regression, lasso, support vector regression and random forest, to build the state of the art model to verify the warming of the earth and identifying factors contributing the global warming. We found that random forest outper-forms other algorithms to create accurate climate models which use features including concentrations of different greenhouse gases to precisely forecast global atmosphere. The other challenges in identifying factor importance can be met by the feature of ensemble tree-based random forest algorithm. It was found that CO 2 is the largest contributor to temperature change, followed by CH 4 , then by N 2 O. They all had some sorts of impact, though, meaning their release into the atmosphere should all be controlled to help restrain temperature increase, and help prevent climate change's potential ramifications.