Articles | Volume 10, issue 5
Geosci. Model Dev., 10, 1945–1960, 2017
https://doi.org/10.5194/gmd-10-1945-2017
Geosci. Model Dev., 10, 1945–1960, 2017
https://doi.org/10.5194/gmd-10-1945-2017

Model evaluation paper 17 May 2017

Model evaluation paper | 17 May 2017

A non-linear Granger-causality framework to investigate climate–vegetation dynamics

Christina Papagiannopoulou et al.

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Cited articles

Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., et al.: The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present), J. Hydrometeorol., 4, 1147–1167, 2003.
Ancona, N., Marinazzo, D., and Stramaglia, S.: Radial basis function approach to nonlinear Granger causality of time series, Phys. Rev. E, 70, 056221, https://doi.org/10.1103/PhysRevE.70.056221, 2004.
Anderson, L. O., Malhi, Y., Aragão, L. E., Ladle, R., Arai, E., Barbier, N., and Phillips, O.: Remote sensing detection of droughts in Amazonian forest canopies, New Phytol., 187, 733–750, 2010.
Attanasio, A.: Testing for linear Granger causality from natural/anthropogenic forcings to global temperature anomalies, Theor. Appl. Climatol., 110, 281–289, 2012.
Attanasio, A., Pasini, A., and Triacca, U.: A contribution to attribution of recent global warming by out-of-sample Granger causality analysis, Atmos. Sci. Lett., 13, 67–72, 2012.
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Short summary
Global satellite observations provide a means to unravel the influence of climate on vegetation. Common statistical methods used to study the relationships between climate and vegetation are often too simplistic to capture the complexity of these relationships. Here, we present a novel causality framework that includes data fusion from various databases, time series decomposition, and machine learning techniques. Results highlight the highly non-linear nature of climate–vegetation interactions.