Abstract
Abstract. 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.
Highlights
The terrestrial biosphere responds to atmospheric drivers such as radiation intensity, temperature, vapour pressure deficit, and composition of trace gases
We explored two types of datasets that are highly relevant in biogeosciences: eddy covariance measurements of land–atmosphere fluxes and global satellite remote sensing of vegetation greenness
The causal graphs estimated from the eddy covariance data collected in a Mediterranean site confirm patterns we would expect in these ecosystems: during the dry season’s plants senescence, for instance, the ecosystem’s carbon cycle (NEE) decouples from meteorological variability
Summary
The terrestrial biosphere responds to atmospheric drivers such as radiation intensity, temperature, vapour pressure deficit, and composition of trace gases. The biosphere influences the atmosphere via partitioning incoming net radiation into sensible, latent, and ground heat fluxes, as well as via controlling the exchange of trace gases and volatile organic compounds. Monson and Baldocchi, 2014; McPherson, 2007, for overviews). There are still substantial unknowns regarding the exact causal dependencies among the different processes (Baldocchi et al, 2016; Miralles et al, 2018), which leads to large uncertainties when predicting, e.g. ecosystem responses to drought conditions (von Buttlar et al, 2018; Sippel et al, 2017).
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