0000000000745812

AUTHOR

V. Haverd

showing 2 related works from this author

Understanding the uncertainty in global forest carbon turnover

2020

Abstract. The length of time that carbon remains in forest biomass is one of the largest uncertainties in the global carbon cycle, with both recent historical baselines and future responses to environmental change poorly constrained by available observations. In the absence of large-scale observations, models used for global assessments tend to fall back on simplified assumptions of the turnover rates of biomass and soil carbon pools. In this study, the biomass carbon turnover times calculated by an ensemble of contemporary terrestrial biosphere models (TBMs) are analysed to assess their current capability to accurately estimate biomass carbon turnover times in forests and how these times a…

0106 biological sciences010504 meteorology & atmospheric sciencesEnvironmental changelcsh:Life01 natural sciencesCarbon cyclelcsh:QH540-549.5ddc:550Baseline (configuration management)Ecology Evolution Behavior and Systematics0105 earth and related environmental sciencesEarth-Surface Processes[SDU.OCEAN]Sciences of the Universe [physics]/Ocean AtmosphereBiomass (ecology)lcsh:QE1-996.5BiosphereSoil carbon15. Life on landPlant functional typelcsh:GeologyEarth scienceslcsh:QH501-531[SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology13. Climate actionTurnoverlcsh:EcologyPhysical geography010606 plant biology & botanyBiogeosciences
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Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach

2020

FLUXNET comprises globally distributed eddy-covariance-based estimates of carbon fluxes between the biosphere and the atmosphere. Since eddy covariance flux towers have a relatively small footprint and are distributed unevenly across the world, upscaling the observations is necessary to obtain global-scale estimates of biosphere–atmosphere exchange. Based on cross-consistency checks with atmospheric inversions, sun-induced fluorescence (SIF) and dynamic global vegetation models (DGVMs), here we provide a systematic assessment of the latest upscaling efforts for gross primary production (GPP) and net ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine learning methods…

Meteorologie en Luchtkwaliteit010504 meteorology & atmospheric sciencesMeteorology and Air Qualitylcsh:LifeEddy covarianceFlux010501 environmental sciencesAtmospheric sciences01 natural sciencesCarbon cycle03 medical and health sciencesFluxNetLaboratory of Geo-information Science and Remote Sensinglcsh:QH540-549.5ddc:550Life ScienceLaboratorium voor Geo-informatiekunde en Remote SensingBiogeosciences[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environmentScalingEcology Evolution Behavior and Systematics030304 developmental biology0105 earth and related environmental sciencesCarbon fluxEarth-Surface ProcessesSDG 15 - Life on Land[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere0303 health sciencesWIMEKlcsh:QE1-996.5Carbon sinkBiospherePrimary production15. Life on landlcsh:GeologyEarth scienceslcsh:QH501-53113. Climate actionGreenhouse gasEnvironmental sciencelcsh:Ecology
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