Improved permafrost soil representation in a dynamic global vegetation model enhances simulations of carbon dynamics and vegetation structure in boreal forests of Northeast China
- 1Northeast Forestry University (Harbin, China)
- 2Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung (Bremerhaven, Germany)
Abstract
Boreal forests play an important role in the global terrestrial carbon cycle, yet the effects of climate-driven permafrost degradation on ecosystem carbon dynamics remain poorly represented in current models. Earth system models often simplify subsurface thermal and hydrological processes, limiting their ability to capture ecosystem responses in permafrost-affected regions. Here, we improve the soil module of the LPJ-GUESS model by implementing a deep soil profile (3 m) with refined vertical discretization (30 layers) and explicit ice-impedance effects. The improved LPJ-GUESS configuration is then applied to simulate carbon and hydrothermal dynamics in the climate-sensitive boreal forests of Northeast China. Model evaluation indicates that the improved configuration generally enhances the simulation of soil hydrothermal dynamics relative to the default LPJ-GUESS configuration. The correlation coefficient for deep soil temperature (1.0 m) increases from 0.818 to 0.894, while the systematic overestimation of soil water content is substantially reduced, with the bias at 1.0 m reduced from 0.321 to −0.006. These improvements are associated with better agreement between simulated and observed vegetation composition. The simulated dominance of needleleaf forests decreases from near-complete dominance to 24.1 %, resulting in vegetation patterns that are more consistent with regional observations. Consequently, biases in carbon estimates are reduced, with simulated net primary productivity decreasing from 583.5 to 560.1 g C m ⁻² yr ⁻¹ and aboveground biomass from 112.9 to 99.0 t ha ⁻¹ , resulting in better agreement with estimates from satellite-based observations. Model sensitivity analysis indicates that vegetation composition is highly sensitive to changes in soil hydrothermal conditions compared with the control simulation. Pronounced shifts in vegetation composition occur when soil temperature increases exceed 0.6 °C and soil water deficits exceed 0.6 m ³ m ⁻³ compared to the default model configuration. Overall, this study highlights the importance of improving soil hydrothermal representation for simulating vegetation dynamics and carbon cycling in permafrost-affected boreal ecosystems.
Versions
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BibTeX
@article{yang2026improved,
title = {{Improved permafrost soil representation in a dynamic global vegetation model enhances simulations of carbon dynamics and vegetation structure in boreal forests of Northeast China}},
author = {Hong Yang and Simone Maria Stuenzi and Yanqiu Xing and Fulai Feng and Moritz Langer},
journal = {Biogeosciences},
year = {2026},
month = nov,
doi = {10.5194/bg-23-7743-2026},
url = {https://bg.copernicus.org/articles/23/7743/2026/},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Improved permafrost soil representation in a dynamic global vegetation model enhances simulations of carbon dynamics and vegetation structure in boreal forests of Northeast China
AU - Hong Yang
AU - Simone Maria Stuenzi
AU - Yanqiu Xing
AU - Fulai Feng
AU - Moritz Langer
PY - 2026
DA - 2026/11/05
JO - Biogeosciences
DO - 10.5194/bg-23-7743-2026
UR - https://bg.copernicus.org/articles/23/7743/2026/
AB - Boreal forests play an important role in the global terrestrial carbon cycle, yet the effects of climate-driven permafrost degradation on ecosystem carbon dynamics remain poorly represented in current models. Earth system models often simplify subsurface thermal and hydrological processes, limiting their ability to capture ecosystem responses in permafrost-affected regions. Here, we improve the soil module of the LPJ-GUESS model by implementing a deep soil profile (3 m) with refined vertical discretization (30 layers) and explicit ice-impedance effects. The improved LPJ-GUESS configuration is then applied to simulate carbon and hydrothermal dynamics in the climate-sensitive boreal forests of Northeast China. Model evaluation indicates that the improved configuration generally enhances the simulation of soil hydrothermal dynamics relative to the default LPJ-GUESS configuration. The correlation coefficient for deep soil temperature (1.0 m) increases from 0.818 to 0.894, while the systematic overestimation of soil water content is substantially reduced, with the bias at 1.0 m reduced from 0.321 to −0.006. These improvements are associated with better agreement between simulated and observed vegetation composition. The simulated dominance of needleleaf forests decreases from near-complete dominance to 24.1 %, resulting in vegetation patterns that are more consistent with regional observations. Consequently, biases in carbon estimates are reduced, with simulated net primary productivity decreasing from 583.5 to 560.1 g C m ⁻² yr ⁻¹ and aboveground biomass from 112.9 to 99.0 t ha ⁻¹ , resulting in better agreement with estimates from satellite-based observations. Model sensitivity analysis indicates that vegetation composition is highly sensitive to changes in soil hydrothermal conditions compared with the control simulation. Pronounced shifts in vegetation composition occur when soil temperature increases exceed 0.6 °C and soil water deficits exceed 0.6 m ³ m ⁻³ compared to the default model configuration. Overall, this study highlights the importance of improving soil hydrothermal representation for simulating vegetation dynamics and carbon cycling in permafrost-affected boreal ecosystems.
SN - 1726-4189
ER -