# A Simulation Study of Global Evapotranspiration Components Using the Community Land Model
## 3 Results Analysis
### Vegetation Transpiration  
Figure 6 shows the seasonal spatial pattern of the simulation results of CLM4.5 and the reanalysis data of global Et. As observed from Figure 6a-d, similar to Ec, Et also has obvious seasonal variations. Et is concentrated in southern Africa and southeastern Latin America in January, and in the Northern Hemisphere, Et increases gradually in April in Europe, Southeast Asia and southern North America and reaches its maximum in July with Et mainly concentrated in the Northern Hemisphere. Et concentration then gradually moves toward the Southern Hemisphere in October. As observed from Figure 6e-h, the model effectively simulates the seasonal variation of Et and the distribution of high and low value centers. Table 2 shows the RMSEs, means and spatial correlation coefficients of the simulation results of CLM4.5 and reanalysis data of the global Et. Table 2 demonstrates that spatial correlation coefficients of the simulated results of CLM4.5 and the reanalysis data are approximately 0.89, indicating that the simulated spatial distribution in CLM4.5 is close to the reanalysis data. Additionally, it can be observed from Table 2 that the simulation results of CLM4.5 on Et are relatively larger than that of the reanalysis data. Figure 7 shows the differences among the reanalysis data, CLM4.5 and CLM4.0. As observed from Figure 7a-d, the simulated Et in CLM4.5 is relatively large in most areas compared with the reanalysis, and the bias between the models is especially concentrated near the equator and South Asia. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**RMSE (Units: W/m\({}^{2}\))**} & \multicolumn{2}{c}{**Mean (Units: W/m\({}^{2}\))**} & \multicolumn{2}{c}{**SCC**} \\ \cline{2-7}  & **CLM4** & **CLM4.5** & **CLM4** & **CLM4.5** & **NOAA** & **CLM4** & **CLM4.5** \\ \hline January & 8.7 & 7.3 & 8.2 & 7.5 & 6.2 & 0.88 & 0.89 \\ April & 9.6 & 7.5 & 9.7 & 9.4 & 7.5 & 0.84 & 0.88 \\ July & 12.8 & 11.0 & 17.7 & 18.1 & 17.3 & 0.84 & 0.89 \\ October & 10.5 & 8.0 & 9.3 & 8.9 & 7.1 & 0.84 & 0.89 \\ Annual mean & 8.3 & 6.0 & 11.0 & 10.7 & 9.4 & 0.86 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RMSEs, means and spatial correlation coefficients (SCC) for globally Et in January, April, July and October and annual mean. Figure 5: Difference in global Ec (units: W/m\({}^{2}\)) between the simulation results of CLM4.5 and reanalysis (NOAA) in January (**a**), April (**b**), July (**c**) and October (**d**). Figure 6: Spatial distribution of global Et (units: W/m\({}^{2}\)): (**a–d**) NOAA reanalysis data and (**e–h**) simulated by CLM4.5. Due to the work of [PERSON] et al. [16], [PERSON] et al. [17] and [PERSON] et al. [18], the simulation results of CLM4.5 and CLM4.0 on vegetation transpiration were significantly different (see Figure 7i-l). Comparing Figure 7e-h with i-l, CLM4.5 has clearly reduced the simulation bias in CLM4.0, and the spatial correlation coefficients in January, April, July and October are \(-0.54\), \(-0.67\), \(-0.53\) and \(-0.71\), respectively. As seen from Table 2, the RMSEs of CLM4.5 are clearly smaller than those of CLM4.0. Although the simulation result of Et from CLM4.5 is larger than that of CLM4.0 in the middle and high latitudes in July, which produces a large simulation bias for the mean, it is encouraging that the simulated means in CLM4.5 in January, April and October are closer to the reanalysis data. The reason is that [PERSON] et al. [16] had improved the leaf parameterization process, which changed the stomatal conductance, and resulted in the limitation of photosynthesis, especially in the equatorial region, and this improvement significantly reduced the simulated mean of Et in CLM4.0. [PERSON] et al. [18] found that dry soil biases result from the CLM4.0's formulation of soil hydraulic permeability when soil ice is present and excessively dry soils in permafrost regions.