16 research outputs found

    State-of-the-art capabilities in LPJ-GUESS

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    LPJ-GUESS is an advanced DGVM including detailed forest demography and management, croplands, wetlands, specialised arctic processes, emissions of nonCO2 GHGs and a highly flexible land-use change scheme which tracks transitions between different land-uses. It is the vegetation component of the EC-Earth CMIP6 ESM, the RCA-GUESS regional ESM, and also has a European mode operating at tree species level

    Earth system data cubes unravel global multivariate dynamics

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    Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model-data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model-data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model-data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved

    European mushroom assemblages are darker in cold climates

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    Abstract: Thermal melanism theory states that dark-colored ectotherm organisms are at an advantage at low temperature due to increased warming. This theory is generally supported for ectotherm animals, however, the function of colors in the fungal kingdom is largely unknown. Here, we test whether the color lightness of mushroom assemblages is related to climate using a dataset of 3.2 million observations of 3,054 species across Europe. Consistent with the thermal melanism theory, mushroom assemblages are significantly darker in areas with cold climates. We further show differences in color phenotype between fungal lifestyles and a lifestyle differentiated response to seasonality. These results indicate a more complex ecological role of mushroom colors and suggest functions beyond thermal adaption. Because fungi play a crucial role in terrestrial carbon and nutrient cycles, understanding the links between the thermal environment, functional coloration and species’ geographical distributions will be critical in predicting ecosystem responses to global warming

    A dynamic model for strategies and dynamics of plant water-potential regulation under drought conditions

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    Vegetation responds to drought through a complex interplay of plant hydraulic mechanisms, posing challenges for model development and parameterization. We present a mathematical model that describes the dynamics of leaf water-potential over time while considering different strategies by which plant species regulate their water-potentials. The model has two parameters: the parameter λ describing the adjustment of the leaf water potential to changes in soil water potential, and the parameter Δψww describing the typical ‘well-watered’ leaf water potentials at non-stressed (near-zero) levels of soil water potential. Our model was tested and calibrated on 110 time-series datasets containing the leaf- and soil water potentials of 66 species under drought and non-drought conditions. Our model successfully reproduces the measured leaf water potentials over time based on three different regulation strategies under drought. We found that three parameter sets derived from the measurement data reproduced the dynamics of 53% of an drought dataset, and 52% of a control dataset [root mean square error (RMSE) < 0.5 MPa)]. We conclude that, instead of quantifying water-potential-regulation of different plant species by complex modeling approaches, a small set of parameters may be sufficient to describe the water potential regulation behavior for large-scale modeling. Thus, our approach paves the way for a parsimonious representation of the full spectrum of plant hydraulic responses to drought in dynamic vegetation models

    Impacts of land use change and atmospheric CO2_2 on gross primary productivity (GPP), evaporation, and climate in Southern Amazon

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    International audienceRecent publications indicate that the Amazon may be acting more as a carbon source than a sink in some regions. Moreover, the Amazon is a source of moisture for other regions in the continent, and deforestation over the years may be reducing this function. In this work, we analyze the impacts of elevated CO2 (eCO2) and land use change (LUC) on gross primary productivity (GPP) and evaporation in the southern Amazon (7°S 14°S, 66°W 51°W), which suffered strong anthropogenic influence in the period of 1981‒2010. We ran four dynamic global vegetation models (DGVMs), isolating historical CO2, constant CO2, LUC, and potential natural vegetation scenarios with three climate variable data sets: precipitation, temperature, and shortwave radiation. We compared the outputs to five “observational” data sets obtained through eddy covariance, remote sensing, meteorological measurements, and machine learning. The results indicate that eCO2 may have offset deforestation, with GPP increasing by ∼13.5% and 9.3% (dry and rainy seasons, respectively). After isolating the LUC effect, a reduction in evaporation of ∼4% and ∼1.2% (dry and rainy seasons, respectively) was observed. The analysis of forcings in subregions under strong anthropogenic impact revealed a reduction in precipitation of ∼15 and 30 mm, and a temperature rise of 1°C and 0.6°C (dry and rainy seasons, respectively). Differences in the implementation of plant physiology and leaf area index in the DGVMs introduced some uncertainties in the interpretation of the results. Nevertheless, we consider that it was an important exercise given the relevance
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