Articles | Volume 10, issue 5
Geosci. Model Dev., 10, 1945–1960, 2017
Geosci. Model Dev., 10, 1945–1960, 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.

Related authors

Global hydro-climatic biomes identified via multitask learning
Christina Papagiannopoulou, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, and Willem Waegeman
Geosci. Model Dev., 11, 4139–4153,,, 2018
Short summary

Related subject area

Climate and Earth system modeling
Assessment of the Finite-VolumE Sea ice–Ocean Model (FESOM2.0) – Part 2: Partial bottom cells, embedded sea ice and vertical mixing library CVMix
Patrick Scholz, Dmitry Sidorenko, Sergey Danilov, Qiang Wang, Nikolay Koldunov, Dmitry Sein, and Thomas Jung
Geosci. Model Dev., 15, 335–363,,, 2022
Short summary
Evaluation and optimisation of the I/O scalability for the next generation of Earth system models: IFS CY43R3 and XIOS 2.0 integration as a case study
Xavier Yepes-Arbós, Gijs van den Oord, Mario C. Acosta, and Glenn D. Carver
Geosci. Model Dev., 15, 379–394,,, 2022
Short summary
Coupling the Community Land Model version 5.0 to the parallel data assimilation framework PDAF: description and applications
Lukas Strebel, Heye R. Bogena, Harry Vereecken, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 15, 395–411,,, 2022
Short summary
Convolutional conditional neural processes for local climate downscaling
Anna Vaughan, Will Tebbutt, J. Scott Hosking, and Richard E. Turner
Geosci. Model Dev., 15, 251–268,,, 2022
Short summary
Impact of increased resolution on long-standing biases in HighResMIP-PRIMAVERA climate models
Eduardo Moreno-Chamarro, Louis-Philippe Caron, Saskia Loosveldt Tomas, Javier Vegas-Regidor, Oliver Gutjahr, Marie-Pierre Moine, Dian Putrasahan, Christopher D. Roberts, Malcolm J. Roberts, Retish Senan, Laurent Terray, Etienne Tourigny, and Pier Luigi Vidale
Geosci. Model Dev., 15, 269–289,,, 2022
Short summary

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,, 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.
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.