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Ensemble Machine Learning Approach Improves Predicted Spatial Variation of Surface Soil Organic Carbon Stocks in Data-Limited Northern Circumpolar Region
<span title="2020-10-28">2020</span>
<i title="Frontiers Media SA">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zsdtezgyevhsxncisld4z5oxfe" style="color: black;">Frontiers in Big Data</a>
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predict the spatial variation of surface (0-30 cm) soil organic carbon (SOC) stocks at 250-m spatial resolution across the northern circumpolar permafrost region. ...
spatial variation of surface SOC stocks. ...
ACKNOWLEDGMENTS This research was performed for the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Science Focus Area (RUBISCO SFA), which is sponsored by the ...
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fdata.2020.528441">doi:10.3389/fdata.2020.528441</a>
<a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33693410">pmid:33693410</a>
<a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7931963/">pmcid:PMC7931963</a>
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Apparent ecosystem carbon turnover time: uncertainties and robust features
<span title="2020-10-16">2020</span>
<i title="Copernicus GmbH">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6jl75d2hqzctnd54k5gccy4xny" style="color: black;">Earth System Science Data</a>
</i>
uncertainties in high spatial resolution (0.083∘) using multiple, state-of-the-art, observation-based datasets of soil organic carbon stock (Csoil), vegetation biomass (Cveg) and gross primary productivity ...
Using this new ensemble of data, we estimated the global median τ to be 43-7+7 yr (median-difference to percentile 25+difference to percentile 75) when the full soil is considered, in contrast to limiting ...
We would like to acknowledge the support of Tomislav Hengl for providing soil carbon data and discussions regarding the analysis. ...
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5194/essd-12-2517-2020">doi:10.5194/essd-12-2517-2020</a>
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Data-mining analysis of the global distribution of soil carbon in observational databases and Earth system models
<span title="2017-03-28">2017</span>
<i title="Copernicus GmbH">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rqk7miszmbcxtcrxk7hjwolghq" style="color: black;">Geoscientific Model Development</a>
</i>
We used a data-mining (machine-learning) (boosted regression trees – BRT) scheme to identify the factors affecting the SOC stock. ...
Earth system models (ESMs) are used to understand the current climate and to project future climate conditions, but the soil organic carbon (SOC) stock simulated by ESMs and those of observational databases ...
This database is a spatial database of SOC stock of the northern circumpolar permafrost region. ...
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5194/gmd-10-1321-2017">doi:10.5194/gmd-10-1321-2017</a>
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<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171202102410/https://www.geosci-model-dev.net/10/1321/2017/gmd-10-1321-2017.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
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Global soil-climate-biome diagram: linking surface soil properties to climate and biota
<span title="2019-01-29">2019</span>
<i title="Copernicus GmbH">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lsqxuqfufrh7bbwdcrceloas2y" style="color: black;">Biogeosciences Discussions</a>
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STN] density; and soil C<span class="thinspace"></span>:<span class="thinspace"></span>N mass ratios) in the surface (0&ndash;30<span class="thinspace"></span>cm) soil layer based on machine learning ...
Surface soil physical and chemical properties also showed remarkable variations across biomes. ...
Spatial variability of soil properties within each biome was estimated as standard deviations. ...
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5194/bg-2018-449">doi:10.5194/bg-2018-449</a>
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Global soil–climate–biome diagram: linking surface soil properties to climate and biota
<span title="2019-07-25">2019</span>
<i title="Copernicus GmbH">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sqg6wlenofcqpjpw2z6rnhi7xq" style="color: black;">Biogeosciences</a>
</i>
STN, density; and soil <span class="inline-formula">C:N</span> mass ratios) in the surface soil layer (0–30&thinsp;cm), based on machine learning algorithms, and demonstrated the quantitative linkages ...
Surface soil physical and chemical properties also showed remarkable variation across biomes. ...
We thank Huifeng Hu, Zhaodi Guo, Chao Yue, Jinyu Sun, Bing Xu and Shengli Tao for their assistance in data collection and preparation of the manuscript. ...
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5194/bg-16-2857-2019">doi:10.5194/bg-16-2857-2019</a>
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SoilGrids250m: Global gridded soil information based on machine learning
<span title="2017-02-16">2017</span>
<i title="Public Library of Science (PLoS)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a>
</i>
SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard ...
images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods-random forest and gradient boosting and/or multinomial logistic regression-as implemented ...
and Forest Ecosystem Carbon Database (FECD), ISRIC-WISE [88] , The Northern Circumpolar Soil Carbon Database [26] , eSOTER profiles [89] , SPADE [90] , ...
<span class="external-identifiers">
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0169748">doi:10.1371/journal.pone.0169748</a>
<a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28207752">pmid:28207752</a>
<a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5313206/">pmcid:PMC5313206</a>
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A review of the global soil property maps for Earth system models
<span title="2019-07-05">2019</span>
<i title="Copernicus GmbH">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3xdpbdhbonaina3o4qcu67xjkm" style="color: black;">SOIL</a>
</i>
Aggregation and upscaling of soil data are needed for model use, but can be avoided by using a subgrid method in ESMs at the expense of increases in model complexity. ...
</strong> Soil is an important regulator of Earth system processes, but remains one of the least well-described data layers in Earth system models (ESMs). ...
We thank two anonymous referees and the editors for their comments which helped us improve the paper much. Review statement. This paper was edited by Axel Don and reviewed by two anonymous referees. ...
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5194/soil-5-137-2019">doi:10.5194/soil-5-137-2019</a>
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Validation of terrestrial biogeochemistry in CMIP6 Earth system models: a review
<span title="2021-09-27">2021</span>
<i title="Copernicus GmbH">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rqk7miszmbcxtcrxk7hjwolghq" style="color: black;">Geoscientific Model Development</a>
</i>
The vital role of terrestrial biogeochemical cycles in influencing global climate change is explored by modelling groups internationally through land surface models (LSMs) coupled to atmospheric and oceanic ...
Despite these common experimental protocols, a variety of terrestrial biogeochemical cycle validation approaches were adopted by CMIP6 participants, leading to ambiguous model performance assessment and ...
This research was supported by the National Science and Engineering Research Council of Canada (NSERC) Discovery Grant Program. ...
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5194/gmd-14-5863-2021">doi:10.5194/gmd-14-5863-2021</a>
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Biophysical controls of increased tundra productivity in the western Canadian Arctic
<span title="">2021</span>
<i title="Elsevier BV">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lm57ugzxwjehna44tee4sjxr4u" style="color: black;">Remote Sensing of Environment</a>
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Our work highlights the potential of the joint analysis of annual remotely sensed vegetation indices and broad-scale biophysical data to understand spatial variation in tundra vegetation change. ...
These findings suggest that the response of tundra vegetation to warming is mediated by regional-and landscape-scale variation in microclimate, topography and soil moisture, and physiological differences ...
Lorraine Allison Scholarship: AC), the Northern Scientific Training Program and the Polar Continental Shelf Program. ...
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Evaluation of terrestrial pan-Arctic carbon cycling using a data-assimilation system
<span title="2018-05-22">2018</span>
<i title="Copernicus GmbH">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/brycqcrcsrg2pnqubtuu5vefay" style="color: black;">Earth System Dynamics Discussions</a>
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The Arctic accounts for approximately 50<span class="thinspace"></span>% of the global soil organic C stock, emphasizing the important role of Arctic regions in the global C cycle. ...
Our approach integrates a range of data (soil organic C, leaf area index, biomass, and climate) to determine the most likely state of the high latitude C cycle at a 1&deg;<span class="thinspace"></ ...
These range from machine-learning based upscaling of FLUXNET data, remotelysensed biomass products or the creation of a harmonized soil databases. ...
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LPJmL4 – a dynamic global vegetation model with managed land: Part II – Model evaluation
<span title="2017-07-27">2017</span>
<i title="Copernicus GmbH">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2jbssxnaxjasfbnf4v6yvqz3ve" style="color: black;">Geoscientific Model Development Discussions</a>
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We apply a range of metrics to evaluate the quality of the model to simulate stocks and flows of carbon and water in natural and managed ecosystems at different temporal and spatial scales. ...
We show that an advanced phenology scheme improves the simulation of seasonal fluctuations in the atmospheric CO<sub>2</sub> concentration while the permafrost scheme improves estimates of carbon stocks ...
a machine learning approach. ...
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5194/gmd-2017-146">doi:10.5194/gmd-2017-146</a>
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The Arctic Carbon Cycle and Its Response to Changing Climate
<span title="2021-02-02">2021</span>
<i title="Springer Science and Business Media LLC">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ika6w3q2jrb5fkppp42ypz5f4i" style="color: black;">Current Climate Change Reports</a>
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In addition, we re-evaluate some of the observational evidence for changing Arctic carbon budgets. Recent Findings Observations suggest a more active CO2 cycle in high northern latitude ecosystems. ...
Purpose of Review The Arctic has experienced the most rapid change in climate of anywhere on Earth, and these changes are certain to drive changes in the carbon budget of the Arctic as vegetation changes ...
Acknowledgments The authors would like to acknowledge Marielle Saunois and all of those who submitted their inverse modeling results to the Global Carbon Project for use of their results in this paper. ...
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LPJmL4 – a dynamic global vegetation model with managed land – Part 2: Model evaluation
<span title="2018-04-12">2018</span>
<i title="Copernicus GmbH">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rqk7miszmbcxtcrxk7hjwolghq" style="color: black;">Geoscientific Model Development</a>
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We apply a range of metrics to evaluate the quality of the model to simulate stocks and flows of carbon and water in natural and managed ecosystems at different temporal and spatial scales. ...
We show that an advanced phenology scheme improves the simulation of seasonal fluctuations in the atmospheric CO<sub>2</sub> concentration, while the permafrost scheme improves estimates of carbon stocks ...
This study was supported by the German Federal Ministry of Education and Research (BMBF) project "PalMod 2.3 Methankreislauf, Teilprojekt 2 Modellierung der Methanemissionen von Feucht-und Permafrostgebieten ...
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Carbon response to changing winter conditions in northern regions: Current understanding and emerging research needs
<span title="2019-03-18">2019</span>
<i title="Canadian Science Publishing">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hwkqqj6yofaeffvb4s3cva7qia" style="color: black;">Environmental Reviews</a>
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incubation experiments aimed at isolating influential factors in controlled environments, and time series of climate and carbon data that evaluate long-term natural variation and trends. ...
of how the snow-covered period influences carbon cycling, thereby improving our ability to predict future responses to climate change. ...
The authors thank Mahsa Haei who helped with earlier versions of this manuscript, and Rebecca Sanders-DeMott and Andrew Reinmann for providing helpful comments on a previous draft. ...
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Evaluation of terrestrial pan-Arctic carbon cycling using a data-assimilation system
<span title="2019-04-24">2019</span>
<i title="Copernicus GmbH">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xmod3vi2ybbb5b64zveedgalwu" style="color: black;">Earth System Dynamics</a>
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The Arctic, accounting for approximately 50&thinsp;% of the global soil organic C stocks, has an important role in the global C cycle. ...
Mapping of vegetation C stocks and change over time and soil C ages linked to soil C stocks is required for better analytical constraint. ...
S1 in the Supplement) corresponds to the extent of the Northern Circumpolar Soil Carbon Database version 2 (NCSCDv2) dataset (Hugelius et al., 2013a, b) , bounded by 42-80 • N and 180 • W-180 • E and ...
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5194/esd-10-233-2019">doi:10.5194/esd-10-233-2019</a>
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<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200212152655/https://www.earth-syst-dynam.net/10/233/2019/esd-10-233-2019.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
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