# A Simulation Study of Global Evapotranspiration Components Using the Community Land Model
## 3 Results Analysis  
### Canopy Interception Evaporation  
In Figure 3, the seasonality spatial pattern of global Ec is given from the simulation results of CLM4.5 and the reanalysis data. From the reanalysis data shown in Figure 3a-d, global Ec presents zonal distribution characteristics and seasonal variation. Ec is mainly present in the Southern Hemisphere in January; in July, it is mainly present in the Northern Hemisphere; and in April and October, it decreases moving from the equator to the poles. Figure 4 shows the seasonal distribution of global precipitation derived from Qian atmospheric forcing data, and it can be seen from Figures 2 and 4 that the seasonal variation of the spatial pattern of Ec is determined by the seasonal variation of the spatial pattern of precipitation and the sum of LAI and SAI. In terms of climate type, the large values of Ec are mainly concentrated in the tropical rainforest and tropical monsoon climate zones. When we calculate the annual average of Ec between 15\({}^{\circ}\) S and 15\({}^{\circ}\) N from the reanalysis data, we find that Ec in this area account for 46.1% of the global total. It can be seen from Figure 1 that the tropical broad-leaved forest is the dominant vegetation type in that area, and the sum of LAI and SAI is relatively large (see Figure 2), which results in the stronger interception capacity of canopies in these areas. In addition, these areas have abundant water vapors (see Figure 4). Therefore, the combined effects of sufficient precipitation and the strong interception ability make Ec in these areas larger than that of other regions. However, in some desert areas (e.g., northern Africa, the Middle East Peninsula, Central Eurasia, and South-Western Australia), polar regions (e.g., Greenland and northeast Canada), glaciers and permafrost regions (e.g., the Rocky Mountains, the Andes and the Tibetan Plateau), Ec is relatively small. This is because the vegetation types in these areas are mostly non-vegetation, shrubs and cold grasslands. Vegetation coverage is low, and the surface roughness is relatively small, which is not conducive to the flux of water vapor. The surface albedo is also large, so the temperature of sinking air flow increases and compensates for energy loss at the ground [(33)]. As a result, in these areas, there is less precipitation and a weaker canopy interception capacity, so Ec is relatively lower than the other regions. As observed from Figure 3e-h, the simulation results of seasonal variation and distribution characteristics of Ec in CLM4.5 are consistent with the reanalysis data. The annual average of Ec between 15\({}^{\circ}\) S and 15\({}^{\circ}\) N is also calculated and is found to account for 58.5% of the total global Ec, which is larger than the amount found in the reanalysis data (46.1%). Figure 2: Spatial distribution of the sum of leaf area index and stem area index in January (**a**), April (**b**), July (**c**) and October (**d**). We use CLM4.5 as the representative model because CLM4.5 and CLM4.0 have the same canopy interception parameterized scheme and, therefore, the simulated results in both models are almost identical. Table 1 shows the root mean square errors (RMSEs), means and spatial correlation coefficients of the simulated results of CLM4.5 and the reanalysis data of the global Ec. From Table 1, we can see that the spatial correlation coefficients of simulated results and the reanalysis data in January, April, July and October and the annual average are 0.91, 0.91, 0.85, 0.89 and 0.90, respectively, indicating that the model has a relatively strong ability to simulate the spatial distribution of Ec in January and April. However, compared with the reanalysis data, the simulated mean of Ec is relatively small, especially in July, and the RMSE is large, which indicates that there may be a systematic weakness in the simulation of Ec in CLM4.5.