# A Simulation Study of Global Evapotranspiration Components Using the Community Land Model
## 3 Results Analysis
### Latent Heat Flux  
Figure 10 shows the seasonal spatial pattern of the simulation results of CLM4.5 and the reanalysis data of global latent heat flux. From Figure 10, it can be seen that CLM4.5 can effectively simulate the seasonal variation and the center position of the high and low values of the latent heat flux. Table 5 shows the RMSEs, means and spatial correlation coefficients of the simulated results of CLM4.5, and reanalysis data of the global latent heat flux. From Table 5, we can see that the spatial correlation coefficients of the simulated results of CLM4.5 and the reanalysis data in January, April, July and October and the annual average are 0.97, 0.92, 0.88, 0.96 and 0.91, respectively. This indicates that CLM4.5 has relatively strong simulation ability for global latent heat flux spatial distribution in January and October. In addition, the simulated means of latent heat flux in CLM4.5 are relatively larger than those of the reanalysis data. Figure 11 shows the differences among the reanalysis data, CLM4.5 and CLM4.0. As seen from Figure 11a-d, the negative bias is primarily concentrated in the middle and high latitudes in the Northern Hemisphere, while the Southern Hemisphere is dominated by positive bias. The spatial correlation coefficients between the bias distribution of global latent heat flux and Et calculated by the difference between the simulation results of CLM4.5 and the  
Figure 9: Spatial distribution of global Es (units: W/m\({}^{2}\)): (**a–d**) difference between CLM4.5 and CFSR, (**e–h**) difference between CLM4.0 and CFSR, and (**i–l**) difference between CLM4.5 and CLM4.0. reanalysis data (see Figures 11a-d and 7a-d) in January, April, July and October are 0.38, 0.36, 0.18 and 0.41, respectively, but the spatial correlation coefficients between the bias distribution of global latent heat flux and Es (see Figures 11a-d and 9a-d) for the same months are 0.69, 0.66, 0.53 and 0.54, respectively. These data indicate that the bias distribution of latent heat flux is primarily determined by the bias distribution of Es, in which reducing the simulation bias of Es can be helpful in improving the simulation of latent heat flux in CLM4.5. In order to further validate the point established above and to discuss the regional differences, we calculated the temporal correlation coefficient of the bias between latent heat flux and its components in some problematic regions (Table 6). The findings revealed that the results in southeast China, Siberia, southeast U.S. and Europe are consistent with the above research. However, the bias of latent heat flux is significant related to the bias of Et over Amazon. It is conjectured that the bias of latent heat flux is primarily determined by Es or Et in regional scale depending on the PFTs of the land surface. Although the bias distribution in the equator region, southwestern North America, and Europe in Figure 11e-h is opposite of that in Figure 11i-l, it can be seen from Table 5, that unlike the relatively significant improvement in Et and Es, the spatial correlation coefficients of CLM4.5 have hardly changed relative to those of CLM4.0. This shows that the ability of CLM4.5 to simulate the spatial distribution of global latent heat flux shows little improvement compared with CLM4.0. However, the RMSEs of CLM4.5 are relatively smaller than those of CLM4.0, and in addition to October, the simulation results of CLM4.5 on the means of latent heat flux have been improved relative to CLM4.0. The bias fields of latent heat flux of CLM4.5 and CLM4.0 are the comprehensive reflection of the models' bias fields of Ec, Et and Es. Although the improvement of CLM4.5's ability to simulate latent heat flux compared with CLM4.0 is roughly consistent with the conclusion of [PERSON] et al. (2016), there are some contrasting results in some regions (eastern United States, Alaska, China, etc.).