CCA is a commonly used tool in climate sciences to measure the linear relationship between two multidimensional variables. It also allows model building for forecasting. Being very similar to the Singular Value Decomposition (SVD) approach that Ingrid already supports, it made sense to develop a CCA for Ingrid, using its SVD. Here are presented three case studies illustrating how CCA analysis as well as model building and forecasting can be performed in Ingrid, prior to the set up of an Ingrid CCA function of its own.

A method has been developed to decompose an original yearly time series into three wannabe independant signals respectively representative of the global warming, the decadal variability and the inter-annual variability. The method does not guarantee the independance of the three decomposed signals, even though they covary very little. Here I will explore the nature of those covariances to better understand them.