{"type": "FeatureCollection", "features": [{"id": "21.11116/0000-0005-8A29-2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:29:25Z", "type": "Journal Article", "created": "2019-04-09", "title": "Comparison With Global Soil Radiocarbon Observations Indicates Needed Carbon Cycle Improvements in the E3SM Land Model", "description": "Abstract

We evaluated global soil organic carbon (SOC) stocks and turnover time predictions from a global land model (ELMv1\uffe2\uff80\uff90ECA) integrated in an Earth System Model (E3SM) by comparing them with observed soil bulk and \uffce\uff9414C values around the world. We analyzed observed and simulated SOC stocks and \uffce\uff9414C values using machine learning methods at the Earth System Model grid cell scale (~200\uffc2\uffa0km). In grid cells with sufficient observations, the model provided reasonable estimates of soil carbon stocks across soil depth and \uffce\uff9414C values near the surface but underestimated \uffce\uff9414C at depth. Among many explanatory variables, soil albedo index, soil order, plant function type, air temperature, and SOC content were major factors affecting predicted SOC \uffce\uff9414C values. The influences of soil albedo index, soil order, and air temperature were primarily important in the shallow subsurface (\uffe2\uff89\uffa430\uffc2\uffa0cm). We also performed sensitivity studies using different vertical root distributions and decomposition turnover times and compared to observed SOC stock and \uffce\uff9414C profiles. The analyses support the role of vegetation in affecting soil carbon turnover, particularly in deep soil, possibly through supplying fresh carbon and degrading physical\uffe2\uff80\uff90chemical protection of SOC via root activities. Allowing for grid cell\uffe2\uff80\uff90specific rooting and decomposition rates substantially reduced discrepancies between observed and predicted \uffce\uff9414C values and SOC content. Our results highlight the need for more explicit representation of roots, microbes, and soil physical protection in land models.Droughts affect ecosystems at multiple time scales, but their sub\uffe2\uff80\uff90seasonal legacy effects on vegetation activity remain unclear. Combining the satellite\uffe2\uff80\uff90based enhanced vegetation index MODIS EVI with a novel location\uffe2\uff80\uff90specific definition of the growing season, we quantify drought impacts on sub\uffe2\uff80\uff90seasonal vegetation activity and the subsequent recovery in the Northern Hemisphere. Drought legacy effects are quantified as changes in post\uffe2\uff80\uff90drought greenness and sensitivity to climate. We find that greenness losses under severe drought are partially compensated by a \uffe2\uff88\uffbc+5% greening within 2\uffe2\uff80\uff936 growing\uffe2\uff80\uff90season months following the droughts, both in woody and herbaceous vegetation but at different timings. In addition, post\uffe2\uff80\uff90drought sensitivity of herbaceous vegetation to hydrological conditions increases noticeably at high latitudes compared with the local normal conditions, regardless of the choice of drought time scales. In general, the legacy effects on sensitivity are larger in herbaceous vegetation than in woody vegetation.We evaluated global soil organic carbon (SOC) stocks and turnover time predictions from a global land model (ELMv1\uffe2\uff80\uff90ECA) integrated in an Earth System Model (E3SM) by comparing them with observed soil bulk and \uffce\uff9414C values around the world. We analyzed observed and simulated SOC stocks and \uffce\uff9414C values using machine learning methods at the Earth System Model grid cell scale (~200\uffc2\uffa0km). In grid cells with sufficient observations, the model provided reasonable estimates of soil carbon stocks across soil depth and \uffce\uff9414C values near the surface but underestimated \uffce\uff9414C at depth. Among many explanatory variables, soil albedo index, soil order, plant function type, air temperature, and SOC content were major factors affecting predicted SOC \uffce\uff9414C values. The influences of soil albedo index, soil order, and air temperature were primarily important in the shallow subsurface (\uffe2\uff89\uffa430\uffc2\uffa0cm). We also performed sensitivity studies using different vertical root distributions and decomposition turnover times and compared to observed SOC stock and \uffce\uff9414C profiles. The analyses support the role of vegetation in affecting soil carbon turnover, particularly in deep soil, possibly through supplying fresh carbon and degrading physical\uffe2\uff80\uff90chemical protection of SOC via root activities. Allowing for grid cell\uffe2\uff80\uff90specific rooting and decomposition rates substantially reduced discrepancies between observed and predicted \uffce\uff9414C values and SOC content. Our results highlight the need for more explicit representation of roots, microbes, and soil physical protection in land models.

Abstract. Citizen science, scientific work and data collection conducted by or with non-experts, is rapidly growing. Although the potential of citizen science activities to generate enormous amounts of data otherwise not feasible is widely recognized, the obtained data are often treated with caution and scepticism. Their quality and reliability is not fully trusted since they are obtained by non-experts using low-cost instruments or scientifically non-verified methods. In this study, we evaluate the performance of Parrot's Flower Power soil moisture sensor used within the European citizen science project the GROW Observatory (GROW; https://growobservatory.org, last access: 30\u00a0March\u00a02020). The aim of GROW is to enable scientists to validate satellite-based soil moisture products at an unprecedented high spatial resolution through crowdsourced data. To this end, it has mobilized thousands of citizens across Europe in science and climate actions, including hundreds who have been empowered to monitor soil moisture and other environmental variables within 24 high-density clusters around Europe covering different climate and soil conditions. Clearly, to serve as reference dataset, the quality of ground observations is crucial, especially if obtained from low-cost sensors. To investigate the accuracy of such measurements, the Flower Power sensors were evaluated in the lab and field. For the field trials, they were installed alongside professional soil moisture probes in the Hydrological Open Air Laboratory (HOAL) in Petzenkirchen, Austria. We assessed the skill of the low-cost sensors against the professional probes using various methods. Apart from common statistical metrics like correlation, bias, and root-mean-square difference, we investigated and compared the temporal stability, soil moisture memory, and the flagging statistics based on the International Soil Moisture Network (ISMN) quality indicators. We found a low intersensor variation in the lab and a high temporal agreement with the professional sensors in the field. The results of soil moisture memory and the ISMN quality flags analysis are in a comparable range for the low-cost and professional probes; only the temporal stability analysis shows a contrasting outcome. We demonstrate that low-cost sensors can be used to generate a dataset valuable for environmental monitoring and satellite validation and thus provide the basis for citizen-based soil moisture science.

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