Global temperature fuzzy model as a function of Carbon emissions a fuzzy ‘regression’ from historical data

There are several models that correlate global mean temperature with Carbon emissions using statistical analysis; in this study we approach the problem using fuzzy logic analysis and inference systems, which is a pioneer method in climate modelling. The process in which anthropogenic activity affects the atmospheric Carbon and therefore the global mean temperature, has been well studied but there are still a lot of unknown factors that play an important role in the process, e.g. punctual Carbon sequestration processes, economy-led emissions' fluctuations, etcetera. That way the process take no clear path and is when fuzzy logic is ideal to approach the system understanding. In this study a Fuzzy Inference System is developed, which model the problem using historical data from 1959 to present. Our model has good results quite comparable with statistical models and it can be used to project the future global mean temperature. The model was developed using SIMULINK extension from matlab.