Preprints
https://doi.org/10.5194/egusphere-2025-5731
https://doi.org/10.5194/egusphere-2025-5731
12 Jan 2026
 | 12 Jan 2026
Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

A general physiologically driven representation of leaf turnover in grasslands in the QUINCY land surface model (revision: 974a6b7f)

Josua Seitz, Midori Yajima, Yu Zhu, Lumnesh Swaroop Kumar Joseph, Jinyan Yang, Fabrice Lacroix, Yunpeng Luo, Andreas Schaumberger, Michael Bahn, Sönke Zaehle, and Silvia Caldararu

Abstract. Terrestrial vegetation plays an important role in shaping the Earth’s climate due to its control on the global carbon cycle. Understanding and predicting vegetation phenology and biomass turnover into soil organic matter is therefore of great importance for our understanding and quantification of carbon exchange with the atmosphere, which varies seasonally. In the past, models of tree phenology have been developed extensively, but equivalent models for herbaceous ecosystems, which cover a significant area of the terrestrial land surface and provide many ecosystem services to humans, have been much more poorly developed for both the start and the end of season. These limitations may be due to spatially and temporally sparse observational data in grasslands, but more importantly, their distribution across a large range of climatic and environmental conditions, as well as a lack of understanding of underlying processes. It follows that a refined autumn phenology model for grasslands is a necessary component of land surface models (LSMs). Here we present a novel approach to grassland autumn phenology by introducing a general, dynamic leaf turnover model controlled by environmental conditions into the QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) LSM and show that decoupling leaf senescence from growing season triggers improves site-level carbon dynamics in herbaceous systems globally. We tested the model at 59 sites with differing climates and show that our model was able to reduce errors in gross primary productivity (GPP) predictions as well as in the timing of the onset of leaf senescence, especially in seasonally dry and very cold sites. Our model is able to reduce the root mean square error (RMSE) of daily GPP at a seasonally dry site from 1.25 to 0.76 g C m−2 d−1. At a seasonally cold and light-limited site, RMSE decreased from 0.6 to 0.46 g C m−2 d−1 and at a temperate, oceanic site, from 1.56 to 1.20 g C m−2 d−1. Our study provides a way forward towards general, non PFT or site-specific autumn phenology modules in LSMs, as well as improving predictions of carbon fluxes in grassland ecosystems globally.

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Josua Seitz, Midori Yajima, Yu Zhu, Lumnesh Swaroop Kumar Joseph, Jinyan Yang, Fabrice Lacroix, Yunpeng Luo, Andreas Schaumberger, Michael Bahn, Sönke Zaehle, and Silvia Caldararu

Status: open (until 09 Mar 2026)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Josua Seitz, Midori Yajima, Yu Zhu, Lumnesh Swaroop Kumar Joseph, Jinyan Yang, Fabrice Lacroix, Yunpeng Luo, Andreas Schaumberger, Michael Bahn, Sönke Zaehle, and Silvia Caldararu

Data sets

PLUMBER2: forcing and evaluation datasets for a model intercomparison project for land surface models. Anna Ukkola https://researchdata.edu.au/plumber2-forcing-evaluation-surface-models/1656048

Majadas de Tietar: Ecosystem level and understorey carbon, water, and energy fluxes in a Mediterranean tree-grass ecosystem Arnaund Carrara et al. https://doi.org/10.5281/zenodo.1314194

PhenoCam An ecosystem phenology camera network Andrew Richardson https://phenocam.nau.edu/webcam/

Model code and software

QUINCY land surface model Tea Thum https://www.bgc-jena.mpg.de/en/bsi/projects/quincy/software

Josua Seitz, Midori Yajima, Yu Zhu, Lumnesh Swaroop Kumar Joseph, Jinyan Yang, Fabrice Lacroix, Yunpeng Luo, Andreas Schaumberger, Michael Bahn, Sönke Zaehle, and Silvia Caldararu
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Latest update: 12 Jan 2026
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Short summary
This study presents a new global leaf turnover model for grasslands in the QUINCY land surface model. Land surface models often struggle to simulate grassland carbon dynamics and phenology accurately. By allowing environmental conditions to directly control leaf senescence we improve its timing as well as the accuracy of whole-season carbon dynamics across a wide range of climates and grassland ecosystems.
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