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Understanding the dependencies of the terrestrial carbon and water cycle with meteorological conditions is a prerequisite to anticipate their behaviour under climate change conditions. However, terrestrial ecosystems and the atmosphere interact via a multitude of variables across temporal and spatial scales. Additionally these interactions might differ among vegetation types or climatic regions. Today, novel algorithms aim to disentangle the causal structure behind such interactions from empirical data. The estimated causal structures can be interpreted as networks, where nodes represent relevant meteorological variables or land-surface fluxes and the links represent the dependencies among them (possibly including time lags and link strength). Here we derived causal networks for different seasons at 119 eddy covariance flux tower observations in the FLUXNET network. We show that the networks of biosphere–atmosphere interactions are strongly shaped by meteorological conditions. For example, we find that temperate and high-latitude ecosystems during peak productivity exhibit biosphere–atmosphere interaction networks very similar to tropical forests. In times of anomalous conditions like droughts though, both ecosystems behave more like typical Mediterranean ecosystems during their dry season. Our results demonstrate that ecosystems from different climate zones or vegetation types have similar biosphere–atmosphere interactions if their meteorological conditions are similar. We anticipate our analysis to foster the use of network approaches, as they allow for a more comprehensive understanding of the state of ecosystem functioning. Long-term or even irreversible changes in network structure are rare and thus can be indicators of fundamental functional ecosystem shifts.

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.

Micrometeorological measurements (including eddy-covariance measurements of the surface fluxes of sensible and latent heat) were performed during the LITFASS-2003 experiment at 13 field sites over different types of land use (forest, lake, grassland, various agricultural crops) in a 20 × 20 km2 area around the Meteorological Observatory Lindenberg (MOL) of the German Meteorological Service (Deutscher Wetterdienst, DWD). Significant differences in the energy fluxes could be found between the major land surface types (forest, farmland, water), but also between the different agricultural crops (cereals, rape, maize). Flux ratios between the different surfaces changed during the course of the experiment as a result of increased water temperature of the lake, changing soil moisture, and of the vegetation development at the farmland sites. The measurements over grass performed at the boundary-layer field site Falkenberg of the MOL were shown to be quite representative for the farmland part of the area. Measurements from the 13 sites were composed into a time series of the area-averaged surface flux by taking into account the data quality of the single flux values from the different sites and the relative occurrence of each surface type in the area. Such composite fluxes could be determined for about 80% of the whole measurement time during the LITFASS-2003 experiment. Comparison of these aggregated surface fluxes with area-averaged fluxes from long-range scintillometer measurements and from airborne measurements showed good agreement.