Plant phenology evaluation of CRESCENDO land surface models using satellite-derived Leaf Area Index – Part 2: Seasonal trough, peak, and amplitude
Abstract. Leaf area index is an important metric for characterising the structure of vegetation canopies and scaling up leaf and plant processes to assess their influence on regional and global climate. Earth observation estimates of leaf area index have increased in recent decades, providing a valuable resource for monitoring vegetation changes and evaluating their representation in land surface and earth system models. The study presented here uses satellite leaf area index products to quantify regional to global variations in the seasonal timing and value of the leaf area index trough, peak, and amplitude, and evaluate how well these variations are simulated by seven land surface models, which are the land components of state-of-the-art earth system models. Results show that the models simulate widespread delays, of up to 3 months, in the timing of leaf area index troughs and peaks compared to satellite products. These delays are most prominent across the Northern Hemisphere and support the findings of previous studies that have shown similar delays in the timing of spring leaf out simulated by some of these land surface models. The modelled seasonal amplitude differs by less than 1 m2 m−2 compared to the satellite-derived amplitude across more than half of the vegetated land area. This study highlights the relevance of vegetation phenology as an indicator of climate, hydrology, soil, and plant interactions, and the need for further improvements in the modelling of phenology in land surface models in order to capture the correct seasonal cycles, and potentially also the long-term trends, of carbon, water and energy within global earth system models.
Authors
- Sönke Zaehle (ORCID: https://orcid.org/0000-0001-5602-7956)
- Stefano Materia (ORCID: https://orcid.org/0000-0001-5635-2847)
- Yuanchao Fan (ORCID: https://orcid.org/0000-0002-3462-1820)
- Hanna Lee (ORCID: https://orcid.org/0000-0002-2003-4377)
- Julia E. M. S. Nabel (ORCID: https://orcid.org/0000-0002-8122-5206)
- Daniele Peano (ORCID: https://orcid.org/0000-0002-6975-4447)
- Christine Delire (ORCID: https://orcid.org/0000-0002-6114-3211)
- Deborah Hemming (ORCID: https://orcid.org/0000-0001-5245-0546)
- David Wårlind (ORCID: https://orcid.org/0000-0002-6257-0338)
- Taejin Park (ORCID: https://orcid.org/0000-0003-0698-6942)
- Andy Wiltshire
Institutions
- Ames Research Center (US)
- Met Office (GB)
- Centre National de la Recherche Scientifique (FR)
- Université Fédérale de Toulouse Midi-Pyrénées (FR)
- Lund University (SE)
- Norwegian University of Science and Technology (NO)
- University of Exeter (GB)
- Barcelona Supercomputing Center (ES)
- Bjerknes Centre for Climate Research (NO)
- Centre National de Recherches Météorologiques (FR)
- NORCE Research AS (NO)
- Bay Area Environmental Research Institute (US)
- CMCC Foundation - Euro-Mediterranean Center on Climate Change (IT)
- Météo-France (FR)
- Tsinghua–Berkeley Shenzhen Institute (CN)
- Max Planck Institute for Biogeochemistry (DE)
- Max Planck Institute for Meteorology (DE)
- Universitat Politècnica de Catalunya (ES)
Publication Details
- Journal
- Biogeosciences
- Published
- 2025-11-24
- DOI
- https://doi.org/10.5194/bg-22-7117-2025
- Citations
- 1
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 1.72