## 4 Discussion  
The first important point to address is the validity of the satellite data. Satellite data does not represent true observations per se, but rather a model in itself. However, it is the closest product to observations, and available globally. It has been widely validated, but nevertheless there are some important issues that need to be considered. The satellite LAI product may have some problems detecting LAI in wintertime, since there is little sunlight in high latitudes. Sun angles are low and the satellite signals are heavily corrupted. Additionally snow cover affects reflection in winter and early spring. Hence, in the processing of any satellite data, there is a sun-angle cut-off. In these regions in the winter period  
Figure 9: Growing season length trends over the period 1986–2005 for 8 LSMs and the satellite observations. Brown indicates an increase in the length of the growing season and green a decrease (days/year). In the white boxes, the values of the spatial correlation between each model and the satellite observations are given. there is little or no data. This partially explains the difference with modeled dormancy dates. However, this does not matter since the soil during this time is frozen and the plants are not photosynthesizing, hence there are no changes in LAI. The methods used to detect the growing season will also ignore this period, since we are only interested in LAI when it starts to change, during the spring. In the region occupied by boreal forests, the same applies. The majority of the Boreal forests are photosynthetically inactive since they are covered in snow. They do however have green needles. These will begin to appear in late winter and early spring as radiation increases. The sun angles in some regions are above the processing cut-off limits and the satellite sensor will begin to register NDVI values. However the ground is still frozen and therefore there is no photosynthetic activity even if the air temperatures begin to rise above freezing during some hours of the day [36]. Over the boreal forest region (55 \(\lx@math@degree\)-65 \(\lx@math@degree\) N), all models exhibit an overestimation in LAI of 2-3 units compared to the satellite but also when compared with literature estimates [39]. We know that the satellite has an error precision of 20% at a pixel scale [40,41], field measurements have reported values around 2.7 \(\pm\) 1.18 for the evergreen boreal forest [41] and 2.3 \(\pm\)0.6 for the deciduous forest [42]. The annual average over this region is around 1-2, which is inline with the satellite observations plus error. The average model values in this region are around 4, more similar to the expected maximum [39], than the expected mean (2.6-2.7). It seems that modeled LAI is higher all year round. These values are similar to the temperate forests, which suggests that having only one PFT for broadleaf forest might not be sufficient as is the case for TRIFFID, SDGVM and VEGAS. Moreover, models that include a wider range of PFTs, such as ORCHIDEE and LPJ-GUESS, are more similar to the satellite observations. Another possible explanation lies in the fact that models based on observed vegetation perform better than dynamic models. The lack of important ecosystem processes such as gap dynamics and fire, could be leading to the simulation of a mature forest state, which ultimately increases the PFT LAI. There is great discrepancy in the calculation of the GSL with values that differ for more than a month, due to differences in the phenology module of each model. CLM4 CN is one of the models that best predicts the GSL, since its LAI is derived from simulated leaf carbon and balanced with nitrogen [29]. More interestingly, models that use a thermal gradient to determine LAI (e.g., LPJ and LPJ-GUESS) [6,30] more accurately simulate the GSL than models with a more complex phenology, such as models where LAI is calculated from the leaf biomass (e.g., OCN [31], ORCHIDEE [32] and VEGAS [35]) or those that use a hydrological budget (e.g., SDGVM [32]).