Literature Review
\cite{Steinhaeuser_2011} This paper focuses on the " the extraction of ocean climate indices from observed climatological data. In this case, it is possible to quantify the relative performance of different methods. Specifically, we propose to extract indices with complex networks constructed from climate data, which have been shown to effectively capture the dynamical behavior of the global climate system, and compare their predictive power to candidate indices obtained using other popular clustering methods. Our results demonstrate that network-based clusters are statistically significantly better predictors of land climate than any other clustering method, which could lead to a deeper understanding of climate processes and complement physics-based climate models." We will in part build upon this paper to use GCM for validation
\cite{Chikamoto_2017} Spoke with Chikamoto on the memory of the oceans with a potential predictability of 3 years. Suggesting that mining ocean temperature data, in this case correlated with soil moisture is highly predictive. The problem with precipitation is that it is too noisy and some smoothing would be necessary
\cite{Steinbach_2003} This paper attempts to find new climate indexes through SVD, very intuitive approach anda clear background and understanding.