DOI: 10.5194/bg-17-1033-2020
论文题名: Estimating causal networks in biosphere-atmosphere interaction with the PCMCI approach
作者: Krich C. ; Runge J. ; Miralles D.G. ; Migliavacca M. ; Perez-Priego O. ; El-Madany T. ; Carrara A. ; Mahecha M.D.
刊名: Biogeosciences
ISSN: 17264170
出版年: 2020
卷: 17, 期: 4 语种: 英语
Scopus关键词: atmosphere-biosphere interaction
; biochemistry
; carbon cycle
; eddy covariance
; empirical analysis
; estimation method
; NDVI
; net ecosystem exchange
; periodicity
; terrestrial ecosystem
; time series analysis
英文摘要: The dynamics of biochemical processes in terrestrial ecosystems are tightly coupled to local meteorological conditions. Understanding these interactions is an essential prerequisite for predicting, e.g. the response of the terrestrial carbon cycle to climate change. However, many empirical studies in this field rely on correlative approaches and only very few studies apply causal discovery methods. Here we explore the potential for a recently proposed causal graph discovery algorithm to reconstruct the causal dependency structure underlying biosphere-atmosphere interactions. Using artificial time series with known dependencies that mimic real-world biosphere-atmosphere interactions we address the influence of non-stationarities, i.e. periodicity and heteroscedasticity, on the estimation of causal networks. We then investigate the interpretability of the method in two case studies. Firstly, we analyse three replicated eddy covariance datasets from a Mediterranean ecosystem. Secondly, we explore global Normalised Difference Vegetation Index time series (GIMMS 3g), along with gridded climate data to study large-scale climatic drivers of vegetation greenness. We compare the retrieved causal graphs to simple cross-correlation-based approaches to test whether causal graphs are considerably more informative. Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. For example, we find a complete decoupling of the net ecosystem exchange from meteorological variability during summer in the Mediterranean ecosystem. However, cautious interpretations are needed, as the violation of the method's assumptions due to non-stationarities increases the likelihood to detect false links. Overall, estimating directed biosphere-atmosphere networks helps unravel complex multidirectional process interactions. Other than classical correlative approaches, our findings are constrained to a few meaningful sets of relations, which can be powerful insights for the evaluation of terrestrial ecosystem models. © 2020. This work is distributed under the Creative Commons Attribution 4.0 License. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/159198
Appears in Collections: 气候变化与战略
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作者单位: Max Planck Institute for Biogeochemistry, Jena, 07745, Germany; Laboratory of Hydrology and Water Management, Ghent University, Ghent, 9000, Belgium; German Aerospace Center, Institute of Data Science, Jena, 07745, Germany; Fundación Centro de Estudios Ambientales Del Mediterráneo (CEAM), Paterna, 46980, Spain; German Centre for Integrative Biodiversity Research (IDiv), Deutscher Platz 5e, Leipzig, 04103, Germany
Recommended Citation:
Krich C.,Runge J.,Miralles D.G.,et al. Estimating causal networks in biosphere-atmosphere interaction with the PCMCI approach[J]. Biogeosciences,2020-01-01,17(4)