A non-linear Granger causality framework to investigate climate–vegetation dynamics
Christina Papagiannopoulou1, Diego G. Miralles2,3, Niko E. C. Verhoest3, Wouter A. Dorigo4, and Willem Waegeman11Depart. of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Belgium 2Depart. of Earth Sciences, VU University Amsterdam, the Netherlands 3Laboratory of Hydrology and Water Management, Ghent University, Belgium 4Depart. of Geodesy and Geo-Information, Vienna University of Technology, Austria
Received: 11 Oct 2016 – Accepted for review: 09 Nov 2016 – Discussion started: 16 Nov 2016
Abstract. Satellite Earth observation has led to the creation of global climate data records of many important environmental and climatic variables. These take the form of multivariate time series with different spatial and temporal resolutions. Data of this kind provide new means to unravel the influence of climate on vegetation dynamics. However, as advocated in this article, existing statistical methods are often too simplistic to represent complex climate–vegetation relationships due to the assumption of linearity of these relationships. Therefore, as an extension of linear Granger causality analysis, we present a novel non-linear framework consisting of several components, such as data collection from various databases, time series decomposition techniques, feature construction methods and predictive modelling by means of random forests. Experimental results on global data sets indicate that with this framework it is possible to detect non-linear patterns that are much less visible with traditional Granger causality methods. In addition, we also discuss extensive experimental results that highlight the importance of considering the non-linear aspect of climate–vegetation dynamics.
Papagiannopoulou, C., Miralles, D. G., Verhoest, N. E. C., Dorigo, W. A., and Waegeman, W.: A non-linear Granger causality framework to investigate climate–vegetation dynamics, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-266, in review, 2016.