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Causal inference promises new insight into biosphere–atmosphere interactions using time series only. To understand the behaviour of a specific method on such data, we used artificial and observation-based data. The observed structures are very interpretable and reveal certain ecosystem-specific behaviour, as only a few relevant links remain, in contrast to pure correlation techniques. Thus, causal inference allows to us gain well-constrained insights into processes and interactions.
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BG | Articles | Volume 17, issue 4
Biogeosciences, 17, 1033–1061, 2020
https://doi.org/10.5194/bg-17-1033-2020
Biogeosciences, 17, 1033–1061, 2020
https://doi.org/10.5194/bg-17-1033-2020

Research article 26 Feb 2020

Research article | 26 Feb 2020

Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach

Christopher Krich et al.

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Cited articles

Ammann, C., Spirig, C., Leifeld, J., and Neftel, A.: Assessment of the Nitrogen and Carbon Budget of Two Managed Temperate Grassland Fields, Agr. Ecosyst. Environ., 133, 150–162, https://doi.org/10.1016/j.agee.2009.05.006, 2009. a
Anthoni, P. M., Knohl, A., Rebmann, C., Freibauer, A., Mund, M., Ziegler, W., Kolle, O., and Schulze, E.-D.: Forest and Agricultural Land-Use-Dependent CO2 Exchange in Thuringia, Germany, Glob. Change Biol., 10, 2005–2019, https://doi.org/10.1111/j.1365-2486.2004.00863.x, 2004. a
Attanasio, A.: Testing for linear Granger causality from natural/anthropogenic forcings to global temperature anomalies, Theor. Appl. Climatol., 110, 281–289, https://doi.org/10.1007/s00704-012-0634-x, 2012. a
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, https://doi.org/10.1002/asl.365, 2012. a
Aubinet, M., Chermanne, B., Vandenhaute, M., Longdoz, B., Yernaux, M., and Laitat, E.: Long Term Carbon Dioxide Exchange above a Mixed Forest in the Belgian Ardennes, Agr. Forest Meteorol., 108, 293–315, https://doi.org/10.1016/S0168-1923(01)00244-1, 2001. a
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Short summary
Causal inference promises new insight into biosphere–atmosphere interactions using time series only. To understand the behaviour of a specific method on such data, we used artificial and observation-based data. The observed structures are very interpretable and reveal certain ecosystem-specific behaviour, as only a few relevant links remain, in contrast to pure correlation techniques. Thus, causal inference allows to us gain well-constrained insights into processes and interactions.
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