218 research outputs found
Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach
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
Upscaled diurnal cycles of land–atmosphere fluxes: a new global half-hourly data product
Interactions between the biosphere and the atmosphere can be well
characterized by fluxes between the two. In particular, carbon and energy
fluxes play a major role in understanding biogeochemical processes on an
ecosystem level or global scale. However, the fluxes can only be measured at
individual sites, e.g., by eddy covariance towers, and an upscaling of these
local observations is required to analyze global patterns. Previous work
focused on upscaling monthly, 8-day, or daily average values, and global maps
for each flux have been provided accordingly. In this paper, we raise the
upscaling of carbon and energy fluxes between land and atmosphere to the next
level by increasing the temporal resolution to subdaily timescales. We
provide continuous half-hourly fluxes for the period from 2001 to 2014 at
0.5° spatial resolution, which allows for analyzing diurnal cycles
globally. The data set contains four fluxes: gross primary production (GPP),
net ecosystem exchange (NEE), latent heat (LE), and sensible heat (H). We
propose two prediction approaches for the diurnal cycles based on large-scale
regression models and compare them in extensive cross-validation experiments
using different sets of predictor variables. We analyze the results for a set
of FLUXNET tower sites showing the suitability of our approaches for this
upscaling task. Finally, we have selected one approach to calculate the
global half-hourly data products based on predictor variables from remote
sensing and meteorology at daily resolution as well as half-hourly potential
radiation. In addition, we provide a derived product that only contains
monthly average diurnal cycles, which is a lightweight version in terms of
data storage that still allows studying the important characteristics of
diurnal patterns globally. We recommend to primarily use these monthly
average diurnal cycles, because they are less affected by the impacts of
day-to-day variation, observation noise, and short-term fluctuations on
subdaily timescales compared to the full half-hourly flux products. The
global half-hourly data products are available at https://doi.org/10.17871/BACI.224.</p
SpectralIndices.jl: Streamlining spectral indices access and computation for Earth system research
Remote sensing is an essential technology in environmental science to study Earth surface processes. In optical remote sensing, spectral indices (SI) are widely used to quantify the properties of specific surface characteristics. SI mathematically combine reflectance values measured at different wavelengths. To gain an overview and access to such indices, comprehensive catalogs have been published and implemented in various programming languages. However, there is no Julia-based tool available for efficiently managing and using these indices. Here we introduce SpectralIndices.jl, a Julia package designed to retrieve and compute SI. Built on the Awesome Spectral Indices (ASI) catalog, our package enables rapid computation of SI using native functions. The multiple dispatch capability of Julia optimizes data handling across various storage types, ensuring quick load times. While primarily based on the ASI collection, SpectralIndices.jl also accommodates custom-made indices, offering users the flexibility to explore and compare alternative indices. The software is open source and available on github.com/awesome-spectral-indices/SpectralIndices.jl</code
Facilitating advanced Sentinel-2 analysis through a simplified computation of Nadir BRDF Adjusted Reflectance
The Sentinel-2 (S2) mission from the European Space Agency’s Copernicus program provides essential data for Earth surface analysis. Its Level-2A products deliver high-to-medium resolution (10–60 m) surface reflectance (SR) data through the MultiSpectral Instrument (MSI). To enhance the accuracy and comparability of SR data, adjustments simulating a nadir viewing perspective are essential. These corrections address the anisotropic nature of SR and the variability in sun and observation angles, ensuring consistent image comparisons over time and under different conditions. The c-factor method, a simple yet effective algorithm, adjusts observed S2 SR by using the MODIS BRDF model to achieve Nadir BRDF Adjusted Reflectance (NBAR). Despite the straightforward application of the c-factor to individual images, a cohesive Python framework for its application across multiple S2 images and Earth System Data Cubes (ESDCs) from cloud-stored data has been lacking. Here we introduce sen2nbar, a Python package crafted to convert S2 SR data to NBAR, supporting both individual images and ESDCs derived from cloud-stored data. This package simplifies the conversion of S2 SR data to NBAR via a single function, organized into modules for efficient process management. By facilitating NBAR conversion for both SAFE files and ESDCs from SpatioTemporal Asset Catalogs (STAC), sen2nbar is developed as a flexible tool that can handle diverse data format requirements. We anticipate that sen2nbar will considerably contribute to the standardization and harmonization of S2 data, offering a robust solution for a diverse range of users across various applications. sen2nbar is an open-source tool available at https://github.com/ESDS-Leipzig/sen2nbar
Extreme events in gross primary production: a characterization across continents
Climate extremes can affect the functioning of terrestrial ecosystems, for
instance via a reduction of the photosynthetic capacity or alterations of
respiratory processes. Yet the dominant regional and seasonal effects of
hydrometeorological extremes are still not well documented and in the focus
of this paper. Specifically, we quantify and characterize the role of large
spatiotemporal extreme events in gross primary production (GPP) as triggers
of continental anomalies. We also investigate seasonal dynamics of extreme
impacts on continental GPP anomalies. We find that the 50 largest positive
extremes (i.e., statistically unusual increases in carbon uptake rates) and
negative extremes (i.e., statistically unusual decreases in carbon uptake
rates) on each continent can explain most of the continental variation in
GPP, which is in line with previous results obtained at the global scale. We
show that negative extremes are larger than positive ones and demonstrate
that this asymmetry is particularly strong in South America and Europe. Our
analysis indicates that the overall impacts and the spatial extents of GPP
extremes are power-law distributed with exponents that vary little across
continents. Moreover, we show that on all continents and for all data sets
the spatial extents play a more important role for the overall impact of GPP
extremes compared to the durations or maximal GPP. An analysis of possible
causes across continents indicates that most negative extremes in GPP can be
attributed clearly to water scarcity, whereas extreme temperatures play a
secondary role. However, for Europe, South America and Oceania we also
identify fire as an important driver. Our findings are consistent with remote
sensing products. An independent validation against a literature survey on
specific extreme events supports our results to a large extent
Characterizing ecosystem-atmosphere interactions from short to interannual time scales
International audienceCharacterizing ecosystem-atmosphere interactions in terms of carbon and water exchange on different time scales is considered a major challenge in terrestrial biogeochemical cycle research. The respective time series currently comprise an observation period of up to one decade. In this study, we explored whether the observation period is already sufficient to detect cross-relationships between the variables beyond the annual cycle, as they are expected from comparable studies in climatology. We investigated the potential of Singular System Analysis (SSA) to extract arbitrary kinds of oscillatory patterns. The method is completely data adaptive and performs an effective signal to noise separation. We found that most observations (Net Ecosystem Exchange, NEE, Gross Primary Productivity, GPP, Ecosystem Respiration, Reco, Vapor Pressure Deficit, VPD, Latent Heat, LE, Sensible Heat, H, Wind Speed, u, and Precipitation, P) were influenced significantly by low-frequency components (interannual variability). Furthermore, we extracted a set of nontrivial relationships and found clear seasonal hysteresis effects except for the interrelation of NEE with Global Radiation (Rg). SSA provides a new tool for the investigation of these phenomena explicitly on different time scales. Furthermore, we showed that SSA has great potential for eddy covariance data processing, since it can be applied as a novel gap filling approach relying on the temporal correlation structure of the time series structure only
Characterizing Ecosystem-Atmosphere Interactions from Short to Interannual Time Scales
Characterizing ecosystem-atmosphere interactions in terms of carbon and water exchange on different time scales is considered a major challenge in terrestrial biogeochemical cycle research. The respective time series are now partly comprising an observation 5 period of one decade. In this study, we explored whether the observation period is already sufficient to detect cross relationships of the variables beyond the annual cycle as they are expected from comparable studies in climatology. We explored the potential of Singular System Analysis (SSA) to extract arbitrary kinds of oscillatory patterns. The method is completely data adaptive and performs an 10 effective signal to noise separation.
We found that most observations (NEE, GP P , Reco, V P D, LE, H, u, P ) were influenced significantly by low frequency components (interannual variability). Furthermore we extracted a set of nonlinear relationships and found clear annual hysteresis effects except for the NEE-Rg relationship which turned out to be the sole linear relationship 15 in the observation space. SSA provides a new tool to investigate these phenomena explicitly on different time scales. Furthermore, we showed that SSA has great potential for eddy covariance data processing since it can be applied as novel gap fillingapproach relying on the temporal time series structure only.JRC.H.2-Climate chang
Detecting impacts of extreme events with ecological in situ monitoring networks
Extreme hydrometeorological conditions typically impact ecophysiological
processes on land. Satellite-based observations of the terrestrial biosphere
provide an important reference for detecting and describing the
spatiotemporal development of such events. However, in-depth investigations
of ecological processes during extreme events require additional in situ
observations. The question is whether the density of existing ecological
in situ networks is sufficient for analysing the impact of extreme events,
and what are expected event detection rates of ecological in situ networks of
a given size. To assess these issues, we build a baseline of extreme
reductions in the fraction of absorbed photosynthetically active
radiation (FAPAR), identified by a
new event detection method tailored to identify extremes of regional
relevance. We then investigate the event detection success rates of
hypothetical networks of varying sizes. Our results show that large extremes
can be reliably detected with relatively small networks, but also reveal a
linear decay of detection probabilities towards smaller extreme events in
log–log space. For instance, networks with  ≈  100 randomly placed
sites in Europe yield a  ≥  90 % chance of detecting the eight
largest (typically very large) extreme events; but only a  ≥  50 %
chance of capturing the 39 largest events. These findings are consistent with
probability-theoretic considerations, but the slopes of the decay rates
deviate due to temporal autocorrelation and the exact implementation of the
extreme event detection algorithm. Using the examples of AmeriFlux and NEON,
we then investigate to what degree ecological in situ networks can capture
extreme events of a given size. Consistent with our theoretical
considerations, we find that today's systematically designed networks (i.e.
NEON) reliably detect the largest extremes, but that the extreme event
detection rates are not higher than would be achieved by randomly designed
networks. Spatio-temporal expansions of ecological in situ monitoring
networks should carefully consider the size distribution characteristics of
extreme events if the aim is also to monitor the impacts of such events in
the terrestrial biosphere.</p
Contrasting biosphere responses to hydrometeorological extremes: revisiting the 2010 western Russian heatwave
Combined droughts and heatwaves are among those compound extreme
events that induce severe impacts on the terrestrial biosphere and human
health. A record breaking hot and dry compound event hit western Russia in
summer 2010 (Russian heatwave, RHW). Events of this kind are relevant from a
hydrometeorological perspective, but are also interesting from a biospheric
point of view because of their impacts on ecosystems, e.g., reductions in the
terrestrial carbon storage. Integrating both perspectives might facilitate
our knowledge about the RHW. We revisit the RHW from both a biospheric and a
hydrometeorological perspective. We apply a recently developed multivariate
anomaly detection approach to a set of hydrometeorological variables, and
then to multiple biospheric variables relevant to describe the RHW. One main
finding is that the extreme event identified in the hydrometeorological
variables leads to multidirectional responses in biospheric variables, e.g.,
positive and negative anomalies in gross primary production (GPP). In
particular, the region of reduced summer ecosystem production does not match
the area identified as extreme in the hydrometeorological variables. The
reason is that forest-dominated ecosystems in the higher latitudes respond
with unusually high productivity to the RHW. Furthermore, the RHW was
preceded by an anomalously warm spring, which leads annually integrated to a
partial compensation of 54 % (36 % in the preceding spring, 18 %
in summer) of the reduced GPP in southern agriculturally dominated
ecosystems. Our results show that an ecosystem-specific and multivariate
perspective on extreme events can reveal multiple facets of extreme events by
simultaneously integrating several data streams irrespective of impact
direction and the variables' domain. Our study exemplifies the need for
robust multivariate analytic approaches to detect extreme events in both
hydrometeorological conditions and associated biosphere responses to fully
characterize the effects of extremes, including possible compensatory effects
in space and time.</p
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