Articles | Volume 18, issue 7
https://doi.org/10.5194/bg-18-2405-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-18-2405-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Apr 2021
Research article |
| 16 Apr 2021
| 16 Apr 2021
Plant phenology evaluation of CRESCENDO land surface models – Part 1: Start and end of the growing season
Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, CSP, Bologna, Italy
Deborah Hemming
Met Office Hadley Centre, Exeter, UK
Stefano Materia
Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, CSP, Bologna, Italy
Christine Delire
Centre National de Recherches Météorologiques, UMR3589, Université de Toulouse/Météo-France/CNRS, Toulouse, France
Yuanchao Fan
NORCE Norwegian Research Centre AS, Bjerknes Centre for Climate Research, Bergen, Norway
Center for the Environment, Harvard University, Cambridge, USA
Emilie Joetzjer
Centre National de Recherches Météorologiques, UMR3589, Université de Toulouse/Météo-France/CNRS, Toulouse, France
Hanna Lee
NORCE Norwegian Research Centre AS, Bjerknes Centre for Climate Research, Bergen, Norway
Julia E. M. S. Nabel
Max Planck Institute for Meteorology, Hamburg, Germany
Taejin Park
NASA Ames Research Centre, Moffett Field, CA, USA
Bay Area Environmental Research Institute, Moffett Field, CA, USA
Philippe Peylin
Laboratoire des Sciences du Climat et l'Environnement, Gif-sur-Yvette, France
David Wårlind
Department of Physical Geography and Ecosystem Science, Faculty of Science, Lund University, Lund, Sweden
Andy Wiltshire
Met Office Hadley Centre, Exeter, UK
Global Systems Institute, University of Exeter, Exeter, UK
Sönke Zaehle
Max Planck Institute for Biogeochemistry, Jena, Germany
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- DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology G. Liu et al. 10.5194/gmd-17-6683-2024
- Evaluation and modification of ELM seasonal deciduous phenology against observations in a southern boreal peatland forest L. Meng et al. 10.1016/j.agrformet.2021.108556
- Arctic warming-induced cold damage to East Asian terrestrial ecosystems J. Kim et al. 10.1038/s43247-022-00343-7
- Regional estimates of gross primary production applying the Process-Based Model 3D-CMCC-FEM vs. Remote-Sensing multiple datasets D. Dalmonech et al. 10.1080/22797254.2023.2301657
- CMIP6 Simulations With the CMCC Earth System Model (CMCC‐ESM2) T. Lovato et al. 10.1029/2021MS002814
- Drivers of intermodel uncertainty in land carbon sink projections R. Padrón et al. 10.5194/bg-19-5435-2022
- Regulation of biophysical drivers on carbon and water fluxes over a warm-temperate plantation in northern China P. Yu et al. 10.1016/j.scitotenv.2023.167408
- Calibration and validation of phenological models for Biome-BGCMuSo in the grasslands of Tibetan Plateau using remote sensing data L. Zheng et al. 10.1016/j.agrformet.2022.109001
- Diverging Northern Hemisphere Trends in Meteorological Versus Ecological Indicators of Spring Onset in CMIP6 X. Li et al. 10.1029/2023GL102833
- Exploring the environmental drivers of vegetation seasonality changes in the northern extratropical latitudes: a quantitative analysis * X. Kong et al. 10.1088/1748-9326/acf728
- Sensitivity of Spring Phenology Simulations to the Selection of Model Structure and Driving Meteorological Data R. Dávid et al. 10.3390/atmos12080963
- A Comparison of Land Surface Phenology in the Northern Hemisphere Derived from Satellite Remote Sensing and the Community Land Model X. Li et al. 10.1175/JHM-D-21-0169.1
- Climate change is enforcing physiological changes in Arctic Ecosystems N. Madani et al. 10.1088/1748-9326/acde92
- A high spatial resolution land surface phenology dataset for AmeriFlux and NEON sites M. Moon et al. 10.1038/s41597-022-01570-5
- A comparative study of 17 phenological models to predict the start of the growing season Y. Mo et al. 10.3389/ffgc.2022.1032066
- Impacts of shifting phenology on boundary layer dynamics in North America in the CESM X. Li et al. 10.1016/j.agrformet.2022.109286
- Towards a standardized, ground-based network of hyperspectral measurements: Combining time series from autonomous field spectrometers with Sentinel-2 P. Naethe et al. 10.1016/j.rse.2024.114013
- Gross primary productivity and the predictability of CO2: more uncertainty in what we predict than how well we predict it I. Dunkl et al. 10.5194/bg-20-3523-2023
Latest update: 16 Nov 2024
Short summary
Global climate models are the scientist’s tools used for studying past, present, and future climate conditions. This work examines the ability of a group of our tools in reproducing and capturing the right timing and length of the season when plants show their green leaves. This season, indeed, is fundamental for CO2 exchanges between land, atmosphere, and climate. This work shows that discrepancies compared to observations remain, demanding further polishing of these tools.
Global climate models are the scientist’s tools used for studying past, present, and future...
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