## 3 Results  
### Mean LAI  
All of the models overestimate mean LAI, LAI trend and interannual variability (IAV) over the high-latitude Northern Hemisphere compared to the satellite observations (Figure 2). In general, models with the highest average LAI also have strong positive trends. This occurs regardless of whether the models use imposed or dynamic vegetation, or the number of PFTs implemented. Interestingly, models with a trend and average LAI closest to the satellite records, such as ORCHIDEE, OCN and TRIFFID have very different values of IAV, ranging from values similar to the satellite data up to 4 times higher. On the contrary, the most dissimilar models to the observations, such as LPJ and CLM4 CN, have larger IAV.  
Looking at the spatial distribution of LAI, most of the models simulate the observed spatial distribution pattern (Figure 3). Peaks in LAI are evident over the boreal forest (55 \(\circ\)-65 N) and the North American temperate forest (30 \(\circ\)-55 N). The lowest values are found over the cold Gobi plateau and the Siberian Tundra. As noted above, there is a general overestimation of mean LAI in the models, relative to observations. LAI values range from 0 to 2.5 in the satellite data, while for the models they are as high as 5. Models and observations agree on values over the deserts and low-LAI regions but the differences  
Figure 2: Linear trend against average LAI for each model and satellite observations, with IAV represented as colors. The data represents the whole high-latitude Northern Hemisphere (30 \(\circ\)-90 ) for the time period 1986–2005.  
are higher (3-4) over the boreal region. As shown by spatial correlations, differences between satellite data and models are higher in VEGAS and TRIFFID, and smaller in LPJ and LPJ-GUESS (Figure 3). It is noteworthy that much of the discrepancies occur over evergreen vegetation, suggesting that the lack of regenerative vegetative states, fire and gap dynamics over this region lead to an overestimation of the number of fully grown trees on models, which ultimately means a much higher LAI than observed. However, satellite signal saturation--this is the inability of the satellite to distinguish between areas with high LAI- could be leading to an underestimation of LAI in dense forested areas such as the boreal forest, which might also account for the lower LAI over this area.  
The seasonal amplitude patterns show large disagreements between the models and the satellite data (Figure 4). Most models overestimate the mean amplitude (RSME = 1.02-2.21), which is particularly evident over Europe and Eastern North America. The exception here is SDGVM, which displays little seasonality and performs better than the rest of the models in reproducing the satellite-derived observations. The RSME show that models using dynamic vegetation are less similar to observations than those using imposed vegetation. Regardless, most models correctly simulate the spatial variability of the seasonal amplitude; this is true for CLM, GUESS, OCN and VEGAS to some extent. TRIFFID shows almost no seasonality over this area, which is mainly driven by the omnipresence of the evergreen PFT over the Northern Hemisphere (not shown) (Figure 4).  
Figure 3: Spatially distributed annual mean LAI for 8 LSMs (1–8) and satellite observations over the Northern Hemisphere (30 \(\lx@math@degree\)90 \(\aleph\)N), for the period 1986–2005. Spatial correlations between each model and observations are given in the white boxes.