Biosphere-atmosphere Interactions Research Articles

Development of a sampling protocol for collecting leaf surface material for multiphase chemistry studies.

Plant leaves and water drops residing on them interact with atmospheric oxidants, impacting the deposition and emission of trace gases and mediating leaf damage from air pollution. Characterizing the chemical composition and reactivity of the water-soluble material on leaf surfaces is thus essential for improving our understanding of atmosphere-biosphere interactions. However, the limited knowledge of sources and nature of these chemicals challenges sampling decisions. This work investigates how sampling variables and environmental factors impact the quantity and composition of water-soluble material sampled from wet leaves and proposes a flexible protocol for its collection. The ratio of solvent volume-to-leaf area, the solvent-to-leaf contact time, and environmental parameters - including the occurrence of rain, plant location and its metabolism - drive solute concentration in leaf soaks. Despite minor variations, UV-vis absorption spectra of leaf soaks are comparable to authentic raindrops collected from the same tree and share features with microbial dissolved organic matter - including overall low aromaticity, low chromophore content, and low average molecular weight. In addition to guiding the development of a sampling protocol, our data corroborate recent hypotheses on the amount, origin, nature, and reactivity of water-soluble organics on wet leaves, providing new directions of research into this highly interdisciplinary topic.

Forecasting Worldwide Temperature from Amazon Rainforest Deforestation Using a Long-Short Term Memory Model

Biosphere–atmosphere interactions are a critical component of the Earth’s climate system. Many of these interactions are currently contributing to temperature increases and accelerating global warming. One of the main factors responsible for this is land use and land cover changes; in particular, this work models the interaction between Amazon rainforest deforestation and global temperatures. A Long Short-Term Memory (LSTM) neural network is proposed to forecast temperature trends, including mean, average minimum, and average maximum temperatures, in 20 major cities worldwide. The Amazon rainforest, often referred to as the Earth’s “lungs”, plays a pivotal role in regulating global climate patterns. Over the past two decades, this region has experienced significant deforestation, largely due to human activities. We hypothesize that the extent of deforestation in the Amazon can serve as a valuable proxy for understanding and predicting temperature changes in distant urban centers. Using a dataset that tracks cumulative deforestation from 2001 to 2021 across 297 municipalities in the Amazon rainforest, a multivariate time series model was developed to forecast temperature trends worldwide up to 2030. The input data reveal a variety of behaviors, including complex deforestation patterns. Similarly, the forecasted temperature data showcases diverse trends. While some cities are expected to exhibit a steady temperature increase, others may experience gradual changes, while some cities may undergo drastic and rapid temperature shifts. Our findings contribute to a deeper understanding of the far-reaching impacts of deforestation on global climate patterns and underscore the importance of preserving vital ecosystems like the Amazon rainforest.

Prominent Impact of Diurnal Rainfall Variations on Evapotranspiration and Gross Primary Productivity in Forests over Low Latitudes

Rainfall amount and its variability on various time scales can significantly influence ecosystem evapotranspiration (ET) and gross primary productivity (GPP). However, little attention has been paid to how ET and GPP respond to diurnal rainfall variations. Here we investigate the effect of diurnal rainfall variations on ET and GPP, based on data at 14 forest sites between 35°S-35°N from FLUXNET2015. Diurnal rainfall variations are represented by four indices. The timing of precipitation within one day is represented by the fraction of daytime precipitation (Frc) and the rainfall peaking time hrpeak. The frequency of precipitation (Freq) is expressed in hours with rainfall out of 24 hours on the day. An Unranked Gini index (UGi) is newly introduced to represent the unevenness of diurnal rainfall distribution in both timing and frequency. Our results illustrate prominent influences of diurnal rainfall variations on ET and GPP at the studied forest sites in lower latitudes. Given an amount of daily total precipitation, more daytime rainfall either in intensity or frequency (larger Frc or Freq), especially occurring around noon (shorter period from hrpeak), could lead to less daily total ET and GPP. More unevenly distributed rainfall being more away from noon (larger UGi) could result in more daily total ET and GPP. These influences become stronger with more daily total rainfall except for Freq. The regression coefficients of ET and GPP to UGi could reach 0.6, which is twice of Frc and hrpeak. During a rainfall, solar radiation, air temperature and vapor pressure deficit would decrease, leading to decreases in ET and GPP. The concurrent diurnal variations in rainfall and other meteorological variables drive changes in ET and GPP. Considering diurnal variations of rainfall in observations and models would be helpful to improve our understanding of atmosphere-biosphere interactions in the future.

Intra- and interannual changes in isoprene emission from central Amazonia

Abstract. Isoprene emissions are a key component in biosphere–atmosphere interactions, and the most significant global source is the Amazon rainforest. However, intra- and interannual variations in biological and environmental factors that regulate isoprene emission from Amazonia are not well understood and, thereby, are poorly represented in models. Here, with datasets covering several years of measurements at the Amazon Tall Tower Observatory (ATTO) in central Amazonia, Brazil, we (1) quantified canopy profiles of isoprene mixing ratios across seasons of normal and anomalous years and related them to the main drivers of isoprene emission – solar radiation, temperature, and leaf phenology; (2) evaluated the effect of leaf age on the magnitude of the isoprene emission factor (Es) from different tree species and scaled up to canopy with intra- and interannual leaf age distribution derived by a phenocam; and (3) adapted the leaf age algorithm from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) with observed changes in Es across leaf ages. Our results showed that the variability in isoprene mixing ratios was higher between seasons (max during the dry-to-wet transition seasons) than between years, with values from the extreme 2015 El Niño year not significantly higher than in normal years. In addition, model runs considering in situ observations of canopy Es and the modification on the leaf age algorithm with leaf-level observations of Es presented considerable improvements in the simulated isoprene flux. This shows that MEGAN estimates of isoprene emission can be improved when biological processes are mechanistically incorporated into the model.

Isoprene and monoterpene simulations using the chemistry–climate model EMAC (v2.55) with interactive vegetation from LPJ-GUESS (v4.0)

Abstract. Earth system models (ESMs) integrate previously separate models of the ocean, atmosphere and vegetation into one comprehensive modelling system enabling the investigation of interactions between different components of the Earth system. Global isoprene and monoterpene emissions from terrestrial vegetation, which represent the most important source of volatile organic compounds (VOCs) in the Earth system, need to be included in global and regional chemical transport models given their major chemical impacts on the atmosphere. Due to the feedback of vegetation activity involving interactions with weather and climate, a coupled modelling system between vegetation and atmospheric chemistry is recommended to address the fate of biogenic volatile organic compounds (BVOCs). In this work, further development in linking LPJ-GUESS, a global dynamic vegetation model, to the atmospheric-chemistry-enabled atmosphere–ocean general circulation model EMAC is presented. New parameterisations are included to calculate the foliar density and leaf area density (LAD) distribution from LPJ-GUESS information. The new vegetation parameters are combined with existing LPJ-GUESS output (i.e. leaf area index and cover fractions) and used in empirically based BVOC modules in EMAC. Estimates of terrestrial BVOC emissions from EMAC's submodels ONEMIS and MEGAN are evaluated using (1) prescribed climatological vegetation boundary conditions at the land–atmosphere interface and (2) dynamic vegetation states calculated in LPJ-GUESS (replacing the “offline” vegetation inputs). LPJ-GUESS-driven global emission estimates for isoprene and monoterpenes from the submodel ONEMIS were 546 and 102 Tg yr−1, respectively. MEGAN determines 657 and 55 Tg of isoprene and monoterpene emissions annually. The new vegetation-sensitive BVOC fluxes in EMAC are in good agreement with emissions from the semi-process-based module in LPJ-GUESS. The new coupled system is used to evaluate the temperature and vegetation sensitivity of BVOC fluxes in doubling CO2 scenarios. This work provides evidence that the new coupled model yields suitable estimates for global BVOC emissions that are responsive to vegetation dynamics. It is concluded that the proposed model set-up is useful for studying land–biosphere–atmosphere interactions in the Earth system.

Editorial for the Special Issue “Understanding Biosphere–Atmosphere Interactions with Remote Sensing”

The terrestrial biosphere interacts with the free atmosphere through the exchange of momentum, energy and mass [...]

The Climate Science for Ecological Forecasting symposium

The Climate Science for Ecological Forecasting symposium

Strong isoprene emission response to temperature in tundra vegetation

Emissions of biogenic volatile organic compounds (BVOCs) are a crucial component of biosphere-atmosphere interactions. In northern latitudes, climate change is amplified by feedback processes in which BVOCs have a recognized, yet poorly quantified role, mainly due to a lack of measurements and concomitant modeling gaps. Hence, current Earth system models mostly rely on temperature responses measured on vegetation from lower latitudes, rendering their predictions highly uncertain. Here, we show how tundra isoprene emissions respond vigorously to temperature increases, compared to model results. Our unique dataset of direct eddy covariance ecosystem-level isoprene measurements in two contrasting ecosystems exhibited Q10 (the factor by which the emission rate increases with a 10 °C rise in temperature) temperature coefficients of up to 20.8, that is, 3.5 times the Q10 of 5.9 derived from the equivalent model calculations. Crude estimates using the observed temperature responses indicate that tundra vegetation could enhance their isoprene emissions by up to 41% (87%)-that is, 46% (55%) more than estimated by models-with a 2 °C (4 °C) warming. Our results demonstrate that tundra vegetation possesses the potential to substantially boost its isoprene emissions in response to future rising temperatures, at rates that exceed the current Earth system model predictions.

Greenland Ice Sheet Surfaces Colonized by Microbial Communities Emit Volatile Organic Compounds.

Volatile organic compounds (VOCs) are emitted by organisms for a range of physiological and ecological reasons. They play an important role in biosphere–atmosphere interactions and contribute to the formation of atmospheric secondary aerosols. The Greenland ice sheet is home to a variety of microbial communities, including highly abundant glacier ice algae, yet nothing is known about the VOCs emitted by glacial communities. For the first time, we present VOC emissions from supraglacial habitats colonized by active microbial communities on the southern Greenland ice sheet during July 2020. Emissions of C5–C30 compounds from bare ice, cryoconite holes, and red snow were collected using a push–pull chamber active sampling system. A total of 92 compounds were detected, yielding mean total VOC emission rates of 3.97 ± 0.70 μg m–2 h–1 from bare ice surfaces (n = 31), 1.63 ± 0.13 μg m–2 h–1 from cryoconite holes (n = 4), and 0.92 ± 0.08 μg m–2 h–1 from red snow (n = 2). No correlations were found between VOC emissions and ice surface algal counts, but a weak positive correlation (r = 0.43, p = 0.015, n = 31) between VOC emission rates from bare ice surfaces and incoming shortwave radiation was found. We propose that this may be due to the stress that high solar irradiance causes in bare ice microbial communities. Acetophenone, benzaldehyde, and phenylmaleic anhydride, all of which have reported antifungal activity, accounted for 51.1 ± 11.7% of emissions from bare ice surfaces, indicating a potential defense strategy against fungal infections. Greenland ice sheet microbial habitats are, hence, potential sources of VOCs that may play a role in supraglacial microbial interactions, as well as local atmospheric chemistry, and merit future research efforts.

Technical note: Incorporating expert domain knowledge into causal structure discovery workflows

Abstract. In this note, we argue that the outputs of causal discovery algorithms should not usually be considered end results but rather starting points and hypotheses for further study. The incentive to explore this topic came from a recent study by Krich et al. (2020), which gives a good introduction to estimating causal networks in biosphere–atmosphere interaction but confines the scope by investigating the outcome of a single algorithm. We aim to give a broader perspective to this study and to illustrate how not only different algorithms but also different initial states and prior information of possible causal model structures affect the outcome. We provide a proof-of-concept demonstration of how to incorporate expert domain knowledge with causal structure discovery and remark on how to detect and take into account over-fitting and concept drift.

Surface and atmospheric patterns for early and late rainy season onset years in South America

The biosphere–atmosphere interactions associated with the rainy season onset in South America (SA) are not well understood. This study aimed to analyze the atmospheric and surface patterns associated with early, neutral, and late rainy season onset in tropical regions of SA. The following years represented each rainy season onset: 1998, 2006, 2009 (early), 2001, 2004, 2005 (neutral), 2000, 2007, 2008 (late). The early (late) onset were negative (positive) rainy season onset date anomalies in comparison to the climatological mean (1998–2016) over central SA. Distinct atmospheric conditions were identified in the early and late rainy season onset. In the early onset, the northwesterly moisture flux and moisture advection were higher than average over central-east SA, where the precipitation increased. In the late onset, precipitation was enhanced in northwest SA and the configuration of multiple atmospheric blocking episodes contributed to delay the rainy season onset. Surface conditions also contributed to both the early/late rainy season onset. In the early onset, wetter and cooler pre-onset conditions over the central-east SA were verified. In the late onset, surface conditions were dry and warm before onset. Even though the atmospheric instability was promoted by the increase in sensible heating, dry atmospheric conditions were not favorable to deep convection, thus delaying the onset. These findings highlight how the onset variability promotes different atmospheric and surface patterns in SA. The results will contribute to the development of weather and climate models to better represent the rainy season onset focusing on biosphere–atmosphere processes improvements.

Variability, Trend, and Extremes of the South American Vegetation‐Climate System: Results From a Coupled Regional Model

AbstractSouth America, especially the Amazon region, is considered a hotspot of biosphere–atmosphere interactions and presents a unique challenge for regional climate modeling. Here, we evaluate the performance of a regional model in simulating the climate–vegetation system in South America and use the model to investigate the potential role of large‐scale warming in the recently observed trend of hydroclimate and vegetation. Compared with prescribing vegetation based on observational data, adding the predictive vegetation capacity to the regional climate model enabled the model to simulate the vegetation response to climate while sustaining the model performance in reproducing the mean, variability and extremes of the regional climate. The coupled vegetation–climate model captures the recent trends in hydroclimate and vegetation productivity and their spatial contrasts, including a trend toward warmer, drier, and less productive conditions in the Amazon and Nordeste regions and a trend toward cooler, wetter, and more productive condition in the La Plata region. Results from sensitivity experiment driven by detrended boundary forcing for the regional climate suggest that much of the trends in the Amazon and Nordeste regions can be attributed to the effects of large‐scale warming, but contribution from decadal variability also plays a role especially for the most recent decade. However, the trend in the La Plata region cannot be attenuated by the removal of the boundary forcing trend, indicating the role of large‐scale circulation pattern changes. The recent trends in vegetation productivity may be early manifestation of future changes in the Amazon and surrounding regions.

Isoprene in urban Atlantic forests: Variability, origin, and implications on the air quality of a subtropical megacity

Biosphere-atmosphere interactions play a key role in urban chemistry because of biogenic volatile organic compound (BVOC) emissions. Of the BVOC, isoprene is the most emitted compound; however, it also has anthropogenic origins in urban areas. In this study, we aimed to investigate the spatio-temporal variability and atmospheric impacts of biogenic and anthropogenic isoprene in the subtropical megacity of São Paulo (MASP), Brazil. Several measurement campaigns were conducted in three different urban Atlantic forests (Matão, PEFI, and RMG), and an urban background site (IAG); this equated to a total of 268 samples for the 2018–2019 period. For all sampling points, daytime average concentrations of isoprene were two to three times higher during the rainy season (IAG: 1.75 ± 0.93 ppb; Matão: 0.87 ± 0.35 ppb; PEFI: 0.50 ± 0.30 ppb; RMG: 0.37 ± 0.18 ppb), than those observed during the dry season (IAG: 0.46 ± 0.24 ppb; Matão: 0.31 ± 0.17 ppb; PEFI: 0.17 ± 0.11 ppb; RMG: 0.11 ± 0.07 ppb). Average isoprene concentrations were similar to those observed in other places worldwide, with the exception of the Amazon forest. Our results indicate differences in isoprene concentrations between sites, suggesting that environmental conditions such as the urban heat island and vegetation types, may play a role in spatial variability. Estimates of the isoprene fraction indicated that the biogenic fraction (85%) surpassed the anthropogenic fraction during the rainy season. By contrast, the anthropogenic fraction (52%) exceeded the biogenic fraction during dry periods. These fractions have an impact on potentially forming secondary pollutants gaseous (ozone formation potential: 7.19–33.32 μg m−3), and aerosols (secondary organic aerosols formation potential: 0.41–1.88 μg m−3). These results highlight the role of biogenic isoprene and its potential impact on urban air quality in subtropical megacities; this requires further investigation under future climate change scenarios.

Linkage between precipitation isotopes and biosphere-atmosphere interaction observed in northeast India

The intra-seasonal variation in precipitation isotopes shows a characteristic declining trend over northeast India. As of now, no mechanism offers a consistent explanation of this trend. We have performed the isotopic analysis of precipitation (rain) and estimated net ecosystem exchange and latent heat fluxes using an eddy-covariance system in northeast India. Additionally, we have used a diagnostic model to determine the recycled rainfall in this region. We find a strong link between the enhanced ecosystem productivity and isotopic enrichment in rainwater during the premonsoon season. Subsequently, on the advent of monsoon, the Bay of Bengal generated moisture enters this region and depletes the isotopic values. Additionally, the regional-scale convective activities produce periodic lows in the precipitation isotopes. Contrary to the general understanding, our study shows that the internal factors, such as the local land-atmosphere interactions, rather than the external influences, play a significant role in governing the precipitation isotopes in northeast India.

Evaluating atmospheric mercury (Hg) uptake by vegetation in a chemistry-transport model.

Mercury (Hg), a neurotoxic heavy metal, is transferred to marine and terrestrial ecosystems through atmospheric transport. Recent studies have highlighted the role of vegetation uptake as a sink for atmospheric elemental mercury (Hg0) and a source of Hg to soils. However, the global magnitude of the Hg0 vegetation uptake flux is highly uncertain, with estimates ranging 1000-4000 Mg per year. To constrain this sink, we compare simulations in the chemical transport model GEOS-Chem with a compiled database of litterfall, throughfall, and flux tower measurements from 93 forested sites. The prior version of GEOS-Chem predicts median Hg0 dry deposition velocities similar to litterfall measurements from Northern hemisphere temperate and boreal forests (∼0.03 cm s-1), yet it underestimates measurements from a flux tower study (0.04 cm s-1vs. 0.07 cm s-1) and Amazon litterfall (0.05 cm s-1vs. 0.17 cm s-1). After revising the Hg0 reactivity within the dry deposition parametrization to match flux tower and Amazon measurements, GEOS-Chem displays improved agreement with the seasonality of atmospheric Hg0 observations in the Northern midlatitudes. Additionally, the modelled bias in Hg0 concentrations in South America decreases from +0.21 ng m-3 to +0.05 ng m-3. We calculate a global flux of Hg0 dry deposition to land of 2276 Mg per year, approximately double previous model estimates. The Amazon rainforest contributes 29% of the total Hg0 land sink, yet continued deforestation and climate change threatens the rainforest's stability and thus its role as an important Hg sink. In an illustrative worst-case scenario where the Amazon is completely converted to savannah, GEOS-Chem predicts that an additional 283 Mg Hg per year would deposit to the ocean, where it can bioaccumulate in the marine food chain. Biosphere-atmosphere interactions thus play a crucial role in global Hg cycling and should be considered in assessments of future Hg pollution.

Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset

Diffuse solar radiation is an important, but understudied, component of the Earth’s surface radiation budget, with most global climate models not archiving this variable and a dearth of ground-based observations. Here, we describe the development of a global 40-year (1980–2019) monthly database of total shortwave radiation, including its diffuse and direct beam components, called BaRAD (Bias-adjusted RADiation dataset). The dataset is based on a random forest algorithm trained using Global Energy Balance Archive (GEBA) observations and applied to the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) dataset at the native MERRA-2 resolution (0.5° by 0.625°). The dataset preserves seasonal, latitudinal, and long-term trends in the MERRA-2 data, but with reduced biases than MERRA-2. The mean bias error is close to 0 (root mean square error = 10.1 W m−2) for diffuse radiation and −0.2 W m−2 (root mean square error = 19.2 W m−2) for the total incoming shortwave radiation at the surface. Studies on atmosphere-biosphere interactions, especially those on the diffuse radiation fertilization effect, can benefit from this dataset.

Bioaerosols in the Amazon rain forest: temporal variations and vertical profiles of Eukarya, Bacteria, and Archaea

Abstract. The Amazon rain forest plays a major role in global hydrological cycling, and biogenic aerosols are likely to influence the formation of clouds and precipitation. Information about the sources and altitude profiles of primary biological aerosol particles, however, is sparse. We used fluorescence in situ hybridization (FISH), a molecular biological staining technique largely unexplored in aerosol research, to investigate the sources and spatiotemporal distribution of Amazonian bioaerosols on the domain level. We found wet season bioaerosol number concentrations in the range of 1–5 × 105 m−3 accounting for > 70 % of the coarse mode aerosol. Eukaryotic and bacterial particles predominated, with fractions of ∼ 56 % and ∼ 26 % of the intact airborne cells. Archaea occurred at very low concentrations. Vertical profiles exhibit a steep decrease in bioaerosol numbers from the understory to 325 m height on the Amazon Tall Tower Observatory (ATTO), with a stronger decrease in Eukarya compared to Bacteria. Considering earlier investigations, our results can be regarded as representative for near-pristine Amazonian wet season conditions. The observed concentrations and profiles provide new insights into the sources and dispersion of different types of Amazonian bioaerosols as a solid basis for model studies on biosphere–atmosphere interactions such as bioprecipitation cycling.

A Regional Earth System Data Lab for Understanding Ecosystem Dynamics: An Example from Tropical South America

Tropical ecosystems experience particularly fast transformations largely as a consequence of land use and climate change. Consequences for ecosystem functioning and services are hard to predict and require analyzing multiple data sets simultaneously. Today, we are equipped with a wide range of spatio-temporal observation-based data streams that monitor the rapid transformations of tropical ecosystems in terms of state variables (e.g., biomass, leaf area, soil moisture) but also in terms of ecosystem processes (e.g., gross primary production, evapotranspiration, runoff). However, the underexplored joint potential of such data streams, combined with deficient access to data and processing, constrain our understanding of ecosystem functioning, despite the importance of tropical ecosystems in the regional-to-global carbon and water cycling. Our objectives are: 1. To facilitate access to regional “Analysis Ready Data Cubes” and enable efficient processing 2. To contribute to the understanding of ecosystem functioning and atmosphere-biosphere interactions. 3. To get a dynamic perspective of environmental conditions for biodiversity. To achieve our objectives, we developed a regional variant of an “Earth System Data Lab” (RegESDL) tailored to address the challenges of northern South America. The study region extensively covers natural ecosystems such as rainforest and savannas, and includes strong topographic gradients (0–6,500 masl). Currently, environmental threats such as deforestation and ecosystem degradation continue to increase. In this contribution, we show the value of the approach for characterizing ecosystem functioning through the efficient implementation of time series and dimensionality reduction analysis at pixel level. Specifically, we present an analysis of seasonality as it is manifested in multiple indicators of ecosystem primary production. We demonstrate that the RegESDL has the ability to underscore contrasting patterns of ecosystem seasonality and therefore has the potential to contribute to the characterization of ecosystem function. These results illustrate the potential of the RegESDL to explore complex land-surface processes and the need for further exploration. The paper concludes with some suggestions for developing future big-data infrastructures and its applications in the tropics.

An Improvement of SPME-Based Sampling Technique to Collect Volatile Organic Compounds from Quercus ilex at the Environmental Level.

Biogenic Volatile Organic Compounds (BVOCs) include many chemical compounds emitted by plants into the atmosphere. These compounds have a great effect on biosphere–atmosphere interactions and may affect the concentration of atmospheric pollutants, with further consequences on human health and forest ecosystems. Novel methods to measure and determine BVOCs in the atmosphere are of compelling importance considering the ongoing climate changes. In this study, we developed a fast and easy-to-handle analytical methodology to sample these compounds in field experiments using solid-phase microextraction (SPME) fibers at the atmospheric level. An improvement of BVOCs adsorption from SPME fibers was obtained by coupling the fibers with fans to create a dynamic sampling system. This innovative technique was tested sampling Q. ilex BVOCs in field conditions in comparison with the conventional static SPME sampling technique. The results showed a great potential of this dynamic sampling system to collect BVOCs at the atmosphere level, improving the efficiency and sensitivity of SPME fibers. Indeed, our novel device was able to reduce the sampling time, increase the amount of BVOCs collected through the fibers and add information regarding the emissions of these compounds at the environmental level.

Functional convergence of biosphere–atmosphere interactions in response to meteorological conditions

Abstract. 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.