EGU25-16191, updated on 15 Mar 2025
https://doi.org/10.5194/egusphere-egu25-16191
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Northern Phenology Under Climate Warming: Evaluating TRENDY Models Against Remote Sensing Data with the Plant Phenology Index
Hanna Marsh1, Hongxiao Jin1, Zheng Duan1, and Wenxin Zhang1,2
Hanna Marsh et al.
  • 1Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden (hanna.marsh@nateko.lu.se)
  • 2School of Geographical and Earth Sciences, University of Glasgow, Glasgow, UK

Phenology, encompassing the timing of the start, end, and duration of the growing season, is influenced by climate warming in northern regions. Altered phenological patterns carry significant implications for the global carbon cycle by disrupting the seasonal balance between gross primary productivity (GPP) and ecosystem respiration and complicating vegetation reproductive cycles. However, many current Earth system models, including those used in the “Trends and drivers of the regional scale terrestrial sources and sinks of carbon dioxide” (TRENDY) project, may inadequately capture recent phenological trends in northern ecosystems (Sitch et al., 2024). In this study, we aim to present a comprehensive analysis of phenology patterns across northern latitudes (>45°N) over the past two decades, using outputs from twelve state-of-the-art vegetation models included in the TRENDY project. These outputs, along with the TRENDY model ensemble average, are intercompared with a remote sensing-based phenology dataset derived using the Plant Phenology Index (PPI) and MODIS data. Compared with the in-situ measurements, the PPI has demonstrated improved accuracy in capturing northern phenology, particularly for boreal evergreen forests, by reducing the confounding effects of snowmelt and soil background signals (Jin et al., 2017). Furthermore, the PPI has proven effective in estimating large-scale GPP across diverse northern ecosystems, providing a robust benchmark for evaluating the performance of vegetation models (Marsh et al., 2024). We further examine the primary climatic drivers of phenological shifts (air temperature, precipitation and radiation) and assess the extent to which TRENDY models capture these drivers and the associated phenological responses to climate warming. Our findings highlight the current gap between model projections and observed phenology, offering insights into the limitations of current carbon cycle models in representing northern ecosystem dynamics. Our study contributes to advancing our understanding of the roles of northern ecosystems in the global carbon cycle.

References

Jin, H., Jönsson, A. M., Bolmgren, K., Langvall, O., & Eklundh, L. (2017). Disentangling remotely-sensed plant phenology and snow seasonality at northern Europe using MODIS and the plant phenology index. Remote Sensing of Environment198, 203-212.

Marsh, H., Jin, H., Duan, Z., Holst, J., Eklundh, L., & Zhang, W. (2025). Plant Phenology Index leveraging over conventional vegetation indices to establish a new remote sensing benchmark of GPP for northern ecosystems. International Journal of Applied Earth Observation and Geoinformation136, 104289.

Sitch, S., O’sullivan, M., Robertson, E., Friedlingstein, P., Albergel, C., Anthoni, P., ... & Zaehle, S. (2024). Trends and drivers of terrestrial sources and sinks of carbon dioxide: An overview of the TRENDY project. Global Biogeochemical Cycles38(7), e2024GB008102.

How to cite: Marsh, H., Jin, H., Duan, Z., and Zhang, W.: Northern Phenology Under Climate Warming: Evaluating TRENDY Models Against Remote Sensing Data with the Plant Phenology Index, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16191, https://doi.org/10.5194/egusphere-egu25-16191, 2025.