Correlated Time Series Modeling of Carbon Emissions and Global Temperature Fluctuations

Global warming and climate change more generally, has become such a well documented phenomenon that it borders on social cliche. Yet, the prevalence of such discussion should not discount but rather underscore the gravity of the current climate crisis. Global temperatures do fluctuate naturally, yet recent trends observed over the past century are unprecedented and also likely the result of human interaction. This consistent increase in global temperature can be highly destructive to various components of the environment from Arctic Sea levels to atmospheric composition. Thus, the aim of this study is to model the global temperature trend in correlation with human interaction (most notably carbon emissions) in an attempt to provide insight as to how best mitigate our current predicament. Generalized linear regression and time series techniques were applied to a Mauna Loa data set (chronicling global carbon emissions) and a NASA data set (chronicling global temperature fluctuations). Seasonal trends were included where appropriate and the predictive models were used to forecast global temperatures, particularly if left independent of positive intervention.