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Biogeosciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/bg-2019-297
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-2019-297
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 13 Aug 2019

Submitted as: research article | 13 Aug 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Biogeosciences (BG).

Causal networks of biosphere–atmosphere interactions

Christopher Krich1,2, Jakob Runge3, Diego G. Miralles2, Mirco Migliavacca1, Oscar Perez-Priego1, Tarek El-Madany1, Arnaud Carrara4, and Miguel D. Mahecha1,5 Christopher Krich et al.
  • 1Max Planck Institute for Biogeochemistry, 07745 Jena, Germany
  • 2Laboratory of Hydrology and Water Management, Ghent University, Ghent 9000, Belgium
  • 3German Aerospace Center, Institute of Data Science, 07745, Jena, Germany
  • 4Fundación Centro de Estudios Ambientales del Mediterráneo (CEAM), 46980 Paterna, Spain
  • 5German Centre for Integrative Biodiversity Research (iDiv), Deutscher Platz 5e, 04103 Leipzig, Germany

Abstract. Local meteorological conditions and biospheric activity are tightly coupled. Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions. However, many empirical studies on the interaction between the biosphere and the atmosphere are based on correlative approaches that are not able to deduce causal paths, and only very few studies apply causal discovery methods. Here, we use a recently proposed causal graph discovery algorithm, which aims to reconstruct the causal dependency structure underlying a set of time series. We explore the potential of this method to infer temporal dependencies in biosphere-atmosphere interactions. Specifically we address the following questions: How do periodicity and heteroscedasticity influence causal detection rates, i.e. the detection of existing and non-existing links? How consistent are results for noise-contaminated data? Do results exhibit an increased information content that justifies the use of this causal-inference method? We explore the first question using artificial time series with well known dependencies that mimic real-world biosphere-atmosphere interactions. The two remaining questions are addressed jointly in two case studies utilizing observational data. Firstly, we analyse three replicated eddy covariance datasets from a Mediterranean ecosystem at half hourly time resolution allowing us to understand the impact of measurement uncertainties. Secondly, we analyse global NDVI time series (GIMMS 3g) along with gridded climate data to study large-scale climatic drivers of vegetation greenness. Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. The violation of the method's assumptions increases the likelihood to detect false links. Nevertheless, we consistently identify interaction patterns in observational data. Our findings suggest that estimating a directed biosphere-atmosphere network at the ecosystem level can offer novel possibilities to unravel complex multi-directional interactions. Other than classical correlative approaches, our findings are constrained to a few meaningful set of relations which can be powerful insights for the evaluation of terrestrial ecosystem models.

Christopher Krich et al.
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Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Christopher Krich et al.
Christopher Krich et al.
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Short summary
Causal inference promises new insight into biosphere-atmosphere interactions using time series only. To understand the behavior of a specific method on such data, we used artificial as well as observation based data. The observed structures are well interpretable and reveal certain ecosystem specific behavior as only few but relevant links remain which is in contrast to pure correlation techniques. Thus, causal inference allows to gain well constrained insights into processes and interactions.
Causal inference promises new insight into biosphere-atmosphere interactions using time series...
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