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Biogeosciences An interactive open-access journal of the European Geosciences Union
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BG | Articles | Volume 17, issue 4
Biogeosciences, 17, 1033–1061, 2020
https://doi.org/10.5194/bg-17-1033-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Biogeosciences, 17, 1033–1061, 2020
https://doi.org/10.5194/bg-17-1033-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

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.

Data sets

TIGRAMITE-Causal discovery for time series datasets J. Runge https://github.com/jakobrunge/tigramite/

Climatic Research Unit (CRU): Time-series (TS) datasets of variations in climate with variations in other phenomena v3 University of East Anglia Climatic Research Unit, P. D. Jones, and I. C. Harris http://catalogue.ceda.ac.uk/uuid/3f8944800cc48e1cbc29a5ee12d8542d

A Non-Stationary 1981–2012 AVHRR NDVI3g Time Series (https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1/) J. E. Pinzon and C. J. Tucker https://doi.org/10.3390/rs6086929

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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.
Causal inference promises new insight into biosphere–atmosphere interactions using time series...
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