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Analysis of Greenhouse Gases

2020-03-21 · Shalin Shah

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.

📄 PDF Abstract BibTeX arXiv:2003.11916

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GPRregression

Methods 이 논문이 사용한 방법론

Linear Regression Linear Regression is a method for modelling a relationship between a dependent variable and independent variables. These models can be fit with numerous approaches. The most…
Gaussian Process Gaussian Processes are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for…

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