Rapid Adaptive Climate Change Model: Application of a Probabilistic Centred Approach to the Minas Passage Bay of Fundy datasets

We are interested in modelling smaller climate datasets in order to generate more accurate forecasts. Our approach would be to develop a fractal-based system around the mathematical attractor for a particular dataset, building on the ideas developed by the authors of this paper, to “measure” the distance between the model and the subsequently observed dataset. The goal of the research would be to develop a model where climate data could be iterated comparatively between models and a meaningful comparison made from which forecasts can be drawn. This is expected to be particularly useful given the increasing rapidity of climate change. This paper presents the initial findings of this research focused around discoveries related to the number of Intrinsic Mode Functions (IMFs) required for the decomposition of the signal, obtained by the Empirical Mode Decomposition (EMD), independent from the number of observations.