## 3 Results and Discussion
### Effects on Land Variables
Although, in south China, phosphorus may be the main limiter for carbon allocation ([PERSON] et al., 2015), the model did not take this into account, which may have led to an unreasonable root profile, and an inaccurate ET estimation. In R4, the water stress was heavier when considering the dynamic rooting scheme, with less soil water absorbed for ET, so the ET simulation for this region by the NEW run was further underestimated. In R2 and R3, it was interesting that the R values were relatively lower ([PERSON] et al., 2009). We found that both regions were characterized by a relatively sparse network of towers; however, the MTE algorithm strongly relied on observations, so this may be one reason why the MTE data did not match well with the mechanism-based model simulations. On the other hand, the plant types in the tropical regions were abundant, and the PFTs data used in this study may also deviate from the observations. Figure 2: Annual soil moisture (SM, m\({}^{3}\) m\({}^{-3}\)) over 1982–2005 for (**a**) the original CESM1.2.0 (CTL) run; (**b**) the CESM1.2.0_DR coupled with the dynamic rooting scheme (NEW) run; (**c**) the difference between NEW and CTL. (Grid shaded regions are significant at the 95% confidence level according to Student’s \(t\)-test). Figure 4: Annual evapotranspiration (ET, mm year\({}^{-1}\)) over 1982–2005 for **(a)** the CTL run; **(b)** the NEW run; **(c)** Model Tree Ensembles (MTE); **(d)** the difference between NEW and CTL. (Grid shaded regions are significant at the 95% confidence level according to Student’s \(t\)-test, and the red frames outline typical regions with significant differences, denoted as R1, R2, R3, R4, and R5.). Figure 3: Annual sensible heat (SH, W m\({}^{-2}\)) over 1982–2005 for **(a)** the CTL run; **(b)** the NEW run; **(c)** the difference between NEW and CTL. (Grid shaded regions are significant at the 95% confidence level according to Student’s \(t\)-test.). Figure 5 shows the spatial patterns of mean GPP. Both the CTL and NEW simulations of GPP (denoted as CTL_GPP and NEW_GPP) and the benchmarking products (MTE_GPP) showed broadly similar patterns, with apparent latitude-related characteristics because ET decreased with increasing latitude. The spatial correlation coefficients between the simulated GPP and the MTE_GPP were 0.86 for CTL and 0.89 for NEW (both significant at the 99% confidence level, \(p<\) 0.01). However, the CTL run did underestimate GPP in tropical regions and overestimate it in mid- and high-latitude regions (Figure 5a, Table 2). When the dynamic rooting scheme was incorporated, the NEW run substantially eliminated underestimation in tropical regions, especially for South America, central Africa, and Southeast Asia (Figure 5b, Table 2), which are denoted as R2, R3, and R5 respectively. In addition, the overestimation of high latitudes was overcome to some extent in the areas denoted as R1 in North America and R2 in Northern Europe and Russia (Figure 5d). With further analyses, it was found that the dynamic rooting scheme can reduce soil water stress (i.e., \(\beta_{t}\); see Section 2.1.2) in tropical areas (not shown), thus alleviating water deficiency. This development would promote photosynthesis, ultimately increasing GPP in these regions. On the other hand, increased water stress at high latitudes caused GPP to decrease. Globally, the total GPP for the CTL run was 114 Pg C year\({}^{-1}\), and for the MTE product, it was 133 Pg C year\({}^{-1}\). For the NEW run, the annual GPP increased to 118 Pg C year\({}^{-1}\), showing that the dynamic rooting scheme improved underestimation in most tropical regions, bringing it closer to MTE.