## 2 Materials and Methods
### The LPJ-GUESS Model  
The Lund-Potsdam-Jena general ecosystem simulator (LPJ-GUESS model) is developed based on the dynamic global vegetation model (LPJ-DGVM model), which combines the generalized physiological and biological processes in the LPJ-DGVM with representations of the tree population, demography, and canopy structures [29]. The LPJ-GUESS model combines terrestrial vegetation dynamics and land-atmosphere-carbon-water exchanges in a modular framework [30]. The model can simulate vegetation dynamics and carbon and water fluxes at species, communities, ecosystems, and even global scales. This model can predict vegetation changes under future climate scenarios [24], and it has been successfully used for modeling vegetation structure and biomass around the world [31; 32]. The model simulates soil freeze-thaw processes and is suitable for the studies of processes at northern high latitudes. Therefore, we used it to study the future vegetation change in the Arctic ecosystem.  
The LPJ-GUESS 4.1 version considers the soil freeze-thaw process and therefore performs well over the high latitudes in the north [31]. The data required to drive the model include monthly meteorological data (monthly temperature, monthly precipitation, and monthly solar radiation), annual atmospheric CO\({}_{2}\) concentration, and soil texture. The soil texture data applied in the LPJ-GUESS model were the soil dataset from the Food and Agriculture Organization of the United Nations (FAO) [33]. The soil type code is extracted from the file that comes with the model based on the geographic coordinates of the simulated points. In this model, vegetation is defined as a competing set of plant functional types (PFTs), grouped by similar attributes such as phenology, growth, life history, and bioclimatic constraints. The vegetation functional types used in this study consisted of three tree PFTs, a boreal needle-leaved evergreen tolerant tree (BNE), a boreal needle-leaved evergreen intolerant tree (BINE), and a broad-leaved evergreen intolerant tree (IBS), and a generic herbaceous PFT, grass (C3G) (Table 3). For the vegetation growth and carbon exchange simulation [34], the main output variables used in this study included the leaf area index (LAI), carbon biomass (C biomass), net primary production of ecosystems (NPP), net ecosystem exchange (NEE), and gross primary production of ecosystems (GPP).  
In this study, climate data from 1989 to 2014 were used as input data for spin-up to repeatedly simulate the 200-year spin cycle and establish the equilibrium state of vegetation (Table 4). The input data of the model are SSP scenario data, such as temperature, precipitation, solar radiation, and carbon dioxide concentration, with a spatial resolution of 250 km. The vegetation conditions of the Arctic sites, such as the ATQ site and PRR site under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios in the future are simulated correspondingly.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Parameter Value** \\ \hline vegmode & cohort \\ nyear\_spinup & 200 \\ npatch & 25 \\ patcharea & 1000 \\ \hline \end{tabular}
\end{table}
Table 4: Parameter values in LPJ-GUESS Model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**PFT** & **Distribution** & **Growth Form** & **Leaf Phenology** & **Shade-Tolerance** & \begin{tabular}{c} **Photosynthetic** \\ **Pathway** \\ \end{tabular} \\ \hline BNE & \begin{tabular}{c} Boreal \\ BINE \\ BES \\ \end{tabular} & \begin{tabular}{c} Needle-leaved tree \\ BES \\ \end{tabular} & \begin{tabular}{c} Evergreen \\ Broad-leaved tree \\ \end{tabular} & \begin{tabular}{c} Tolerant \\ Evergreen \\ \end{tabular} & \
\begin{tabular}{c} C3 \\ Intolerant \\ \end{tabular} &
\begin{tabular}{c} C3 \\ C3 \\ C3G \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 3: Plant functional types used in this study.