Analysis of Greenhouse Gases
This work addresses climate modeling for researchers and policymakers, but it is incremental as it applies standard regression methods to existing data without novel methodological contributions.
The paper tackles predicting temperature anomalies using greenhouse gas data by applying Linear Regression, Quadratic Regression, and Gaussian Process Regression to historical monthly data, with results aligning closely with IPCC projections.
Climate change is a result of a complex system of interactions of greenhouse gases (GHG), the ocean, land, ice, and clouds. Large climate change models use several computers and solve several equations to predict the future climate. The equations may include simple polynomials to partial differential equations. Because of the uptake mechanism of the land and ocean, greenhouse gas emissions can take a while to affect the climate. The IPCC has published reports on how greenhouse gas emissions may affect the average temperature of the troposphere and the predictions show that by the end of the century, we can expect a temperature increase from 0.8 C to 5 C. In this article, I use Linear Regression (LM), Quadratic Regression and Gaussian Process Regression (GPR) on monthly GHG data going back several years and try to predict the temperature anomalies based on extrapolation. The results are quite similar to the IPCC reports.