## 3 Results
### Responses of the Annual Carbon Fluxes to Environmental Changes  
To further investigate the different responses of carbon fluxes to environmental changes, we used the path analysis method on the data after removing the correlation between the \begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multicolumn{1}{c}{**Trend**} & \multicolumn{1}{c}{**Site**} & \multicolumn{1}{c}{\(\mathbf{TA_{SON}}^{\leftrightarrow}\)} & \multicolumn{1}{c}{\(\mathbf{TA_{SON}}^{\leftrightarrow}\)} & \multicolumn{1}{c}{\(\mathbf{TA_{SON}}^{\leftrightarrow}\)} & \multicolumn{1}{c}{\(\mathbf{P_{SON}}^{\leftrightarrow}\)} & \multicolumn{1}{c}{\(\mathbf{P_{SON}}^{\leftrightarrow}\)} & \multicolumn{1}{c}{\(\mathbf{SW_{SON}}^{\leftrightarrow}\)} \\  & & \(\mathbf{P_{SON}}\) & \(\mathbf{SW_{SON}}\) & \(\mathbf{VPD_{SON}}\) & \(\mathbf{SW_{SON}}\) & \(\mathbf{VPD_{SON}}\) & \(\mathbf{VPD_{SON}}\) & \(\mathbf{VPD_{SON}}\) \\ \hline \multirow{6}{*}{Consistent and significant trends} & FL-Hyy & 0.31 & 0.02 & 0.10 & \(-\)0.71 ** & \(-\)0.39 & 0.79 ** \\  & DE-Tha & \(-\)0.47 * & 0.41 * & 0.53 ** & \(-\)0.81 ** & \(-\)0.79 ** & 0.86 ** \\  & US-GLE & \(-\)0.55 * & 0.44 & 0.65 ** & \(-\)0.72 ** & \(-\)0.71 ** & 0.84 ** \\  & CZ-BKI & \(-\)0.17 & 0.35 & 0.47 & \(-\)0.80 * & \(-\)0.74 * & 0.82 ** \\  & US-NRI & \(-\)0.24 & 0.39 & 0.89 ** & \(-\)0.85 ** & \(-\)0.55 * & 0.70 * \\  & CA-TP3 & \(-\)0.45 & 0.56 * & 0.80 ** & \(-\)0.28 & \(-\)0.37 & 0.73 ** \\ \hline \multirow{6}{*}{Consistent but not significant trends} & DE-Obe & \(-\)0.47 & 0.30 & 0.38 & \(-\)0.66 * & \(-\)0.64 * & 0.81 ** \\  & FI-Let & 0.10 & 0.13 & 0.14 & \(-\)0.66 * & \(-\)0.77 ** & 0.67 * \\  & IT-Ren & \(-\)0.08 & 0.02 & 0.39 & \(-\)0.67 ** & \(-\)0.57 ** & 0.72 ** \\  & US-Ho2 & \(-\)0.26 & \(-\)0.06 & 0.63 ** & \(-\)0.11 & \(-\)0.43 & 0.44 \\  & FI-Sod & \(-\)0.53 * & \(-\)0.15 & 0.24 & \(-\)0.74 ** & \(-\)0.10 & 0.49 \\  & CA-Man & 0.07 & \(-\)0.30 & 0.08 & \(-\)0.74 ** & \(-\)0.54 * & 0.77 ** \\  & RU-Fyo & 0.11 & \(-\)0.21 & 0.11 & \(-\)0.68 ** & \(-\)0.62 ** & 0.88 ** \\ \hline \multicolumn{1}{c}{Decoupled} & CA-Obs & \(-\)0.31 & 0.04 & \(-\)0.19 & \(-\)0.84 ** & \(-\)0.41 & 0.70 * \\ \hline \hline \end{tabular}  
* Trend at 1% significance level or 99% confidence level.
* Trend at 5% significance level or 95% confidence level.  
\end{table} 
Table 6: Correlations between autumn environmental factors. Figure 2: Environmental conditions affecting the consistency and decoupling of GPP and NEP. The vertical axis represents the 14 selected forest sites, where CA-Obs is the decoupled site. The horizontal axis represents the relationship between environmental factors at different time scales. The values in each box are the correlation coefficients between the two environmental factors. Red denotes a positive connection and blue denotes a negative correlation; a stronger association is shown by a deeper colored bar. independent variables by ridge regression to analyze the response patterns of annual GPP and NEP in sites with both consistent and decoupled trends at annual (Figure 3) and seasonal scales (Figure 4). At all 14 forest sites, the direct effects of environmental factors on NEP were relatively weak, with the main mechanism being an indirect impact on NEP through the direct regulation of GPP and RE. In all sites, we found a significant promotion of NEP by GPP and a significant inhibition of NEP by RE, while RE at the decoupled forest site had a greater inhibitory effect on NEP than in sites with consistent trends, and the inhibitory effect of RE was stronger than that of GPP (Figure 3). At forest sites with a consistent trend, TA\({}_{\text{year}}\) was significantly positively correlated with GPP and RE, and it played a crucial role in the regulation of GPP and RE (PC\({}_{\text{GPP}}\) = 0.81, PC\({}_{\text{RE}}\) = 0.65) (Figure 3a), suggesting a dominant role for temperature in carbon flux regulation. And interannual trends in TA\({}_{\text{year}}\) with GPP and RE were consistent at consistent forest sites (Table A1).