## 3 Results
### Spatial Patterns  
The zonal anomalies of FPAR from 2003 to 2005 are displayed in Figure 7 and the correlation coefficients between CLM4 and RS FPAR are also calculated. CLM4 FPAR has a very similar pattern to that of remote sensing observations. Statistics show that they are highly correlated (0.879 for GIMMS3g, 0.997 for MODIS and MERIS, and 0.990 for SeaWiFS), and these correlations are all significant (\(p<5\%\)). They all have peaks around the equator and 25 N, and troughs around 30 S and 15 N. The difference between FPAR at the equator and the 25 N in CLM4 simulation is around 0.071, which is much smaller than that in GIMMS3g (0.203) and MODIS (0.162), and close to that in  
Figure 6: Global distribution of linear regression slopes in (**a**) GIMMS FPAR3g and (**b**) CLM4 FPAR from 1982 to 2009. Grids with slopes exceeding the 90% confidence level are marked with black dots.  
SeaWiFS (0.113) and MERIS (0.079). Figure 8(g) shows the month of maximum FPAR simulated by the CLM4. Compared with remote sensing observations (Figure 8(a-d)), the CLM4 does well in capturing the main characteristics of the FPAR peaking in June-July-August (JJA) in the northern hemisphere and in December-January-February-March (DJFM) in the southern hemisphere. However, the peak months in the CLM4 are generally one or two months earlier. The CLM4 simulations also have much less spatial heterogeneity in the month of maximum FPAR. Take north hemisphere for instance, satellite observations show FPAR peaks in June for Southern north America and west Europe, in July and August for northern north America and most Eurasia. The CLM4, however, estimates FPAR in most north hemisphere peaks in June with small high latitude area peaks in July.  
In the Amazon, CLM4 FPAR and satellite-based FPAR have an interesting discrepancy. Satellite-observed FPAR for evergreen broadleaf forest shows weak seasonality (Figure 4(c)), but the spatial distribution of month with the maximum FPAR value has an unexpected pattern. Divided by the equator, FPAR in the northern part of the Amazon peaks around November, December, and January, while in the southern part it peaks around June, July, August, and September (Figure 9(a,b,d)). The probability density function of month with maximum FPAR value (Figure 9(f,g)) verifies that such differences exist among satellite-based observations. However, CLM4 FPAR has an opposite temporal-spatial distribution as we can see from Figure 8(g). The probability density function of month with maximum FPAR value (Figure 9(f,g)) also verifies such differences between the CLM4 FPAR and satellite-based FPAR.  
Figure 7: Comparison of zonal mean FPAR between remote sensing observations and CLM4 FPAR averaged from 2003 to 2005. Correlation coefficients (_i.e._, correlation and _p_-value) are calculated between CLM4 zonal mean FPAR and each satellite FPAR dataset.  
Figure 8: Comparison of month of maximum FPAR between RS observations and CLM4 FPAR averaged from 2003 to 2005, (**a**) GIMMS3g, (**b**) MODIS, (**c**) MERIS, (**d**) SeaWiFS, and (**e**) CLM4 simulation.  
Figure 9: Month of maximum FPAR in the Amazon from (**a**) GIMMS3g, (**b**) MODIS, (**c**) MERIS, (**d**) SeaWiFS, and (**e**) CLM4 simulation. Probability density function of maximum FPAR months in the northern (**f**) and southern (**g**) parts of the Amazon, divided by the equator.