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 (CO2),
nitrous oxide (N2O), and methane (CH4). 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 outperforms 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 CO2 is the largest
contributor to temperature change, followed by CH4, then by N2O.
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.