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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 10, issue 5
Geosci. Model Dev., 10, 1945–1960, 2017
https://doi.org/10.5194/gmd-10-1945-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 10, 1945–1960, 2017
https://doi.org/10.5194/gmd-10-1945-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

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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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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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.
Global satellite observations provide a means to unravel the influence of climate on vegetation....
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