Articles | Volume 10, issue 3
https://doi.org/10.5194/essd-10-1327-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-10-1327-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Upscaled diurnal cycles of land–atmosphere fluxes: a new global half-hourly data product
Max Planck Institute for Biogeochemistry, Jena, Germany
Martin Jung
Max Planck Institute for Biogeochemistry, Jena, Germany
Fabian Gans
Max Planck Institute for Biogeochemistry, Jena, Germany
Miguel D. Mahecha
Max Planck Institute for Biogeochemistry, Jena, Germany
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
Michael Stifel Center Jena (MSCJ) for Data-Driven & Simulation Science, Jena, Germany
Markus Reichstein
Max Planck Institute for Biogeochemistry, Jena, Germany
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
Michael Stifel Center Jena (MSCJ) for Data-Driven & Simulation Science, Jena, Germany
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- Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests S. Besnard et al. 10.1371/journal.pone.0211510
- Effects of Land Cover Change on Vegetation Carbon Source/Sink in Arid Terrestrial Ecosystems of Northwest China, 2001–2018 H. Tu et al. 10.3390/rs15092471
- The effects of spatiotemporal patterns of atmospheric CO2 concentration on terrestrial gross primary productivity estimation Z. Sun et al. 10.1007/s10584-020-02903-2
- Spatiotemporal lagging of predictors improves machine learning estimates of atmosphere–forest CO2 exchange M. Kämäräinen et al. 10.5194/bg-20-897-2023
- Crowd‐sourced plant occurrence data provide a reliable description of macroecological gradients M. Mahecha et al. 10.1111/ecog.05492
- A framework for constructing machine learning models with feature set optimisation for evapotranspiration partitioning A. Stapleton et al. 10.1016/j.acags.2022.100105
- Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations O. Peltola et al. 10.5194/essd-11-1263-2019
- Global variability in belowground autotrophic respiration in terrestrial ecosystems X. Tang et al. 10.5194/essd-11-1839-2019
- Exploring evapotranspiration changes in a typical endorheic basin through the integrated observatory network Z. Xu et al. 10.1016/j.agrformet.2020.108010
- Net ecosystem exchange (NEE) estimates 2006–2019 over Europe from a pre-operational ensemble-inversion system S. Munassar et al. 10.5194/acp-22-7875-2022
- Resolve the Clear‐Sky Continuous Diurnal Cycle of High‐Resolution ECOSTRESS Evapotranspiration and Land Surface Temperature J. Wen et al. 10.1029/2022WR032227
- Generating high-resolution total canopy SIF emission from TROPOMI data: Algorithm and application Z. Zhang et al. 10.1016/j.rse.2023.113699
- Estimating the Net Ecosystem Exchange at Global FLUXNET Sites Using a Random Forest Model N. Huang et al. 10.1109/JSTARS.2021.3114190
- A decreasing carbon allocation to belowground autotrophic respiration in global forest ecosystems X. Tang et al. 10.1016/j.scitotenv.2021.149273
- A Hybrid Model Coupling Physical Constraints and Machine Learning to Estimate Daily Evapotranspiration in the Heihe River Basin X. Li et al. 10.3390/rs16122143
- What is global photosynthesis? History, uncertainties and opportunities Y. Ryu et al. 10.1016/j.rse.2019.01.016
- Gap‐filling approaches for eddy covariance methane fluxes: A comparison of three machine learning algorithms and a traditional method with principal component analysis Y. Kim et al. 10.1111/gcb.14845
- Statistical Modeling to Predict Climate Change Effects on Watershed Scale Evapotranspiration R. Khanal et al. 10.3390/atmos12121565
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Latest update: 04 Oct 2024
Short summary
We provide continuous half-hourly carbon and energy fluxes for 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). In addition, we provide a derived product that only contains monthly average diurnal cycles but which also enables us to study the important characteristics of subdaily patterns at a global scale.
We provide continuous half-hourly carbon and energy fluxes for 2001 to 2014 at 0.5° spatial...
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