## 4 Discussion
The overestimation of LAI is found consistently over the boreal forest (55\({}^{\circ}\)N) when compared to the satellite observations, with better agreement over areas with scarce vegetation. When comparing models with the same vegetation model (TRIFFID _vs._ HADGEM2-CC, ORCHIDEE _vs._ IPSL-CM5A-MR and CLM _vs._ NorESM1 or CESM1-BGC) there are little differences in the distribution  
Figure 9: Mean growing season dormancy (day) as simulated by 11 Earth System Models (ESMs) and satellite observations over the Northern Hemisphere (30\({}^{\circ}\)–90\({}^{\circ}\)N) computed using the Gross Primary Production (GPP). For each model we masked out all the grid points where the Leaf Area Index (LAI) seasonal amplitude is less than 0.5 (see Figure 3). In the box the value of root mean square error (in days) and spatial correlation, as computed from mean annual data, are presented for each model against the observations. of LAI, suggesting that climatic variations in the coupled models are less important in controlling the distribution of LAI than having the correct vegetation distribution and parameterizations. The onset patterns are similar among all coupled and uncoupled models, with the latest onset occurring over the boreal region. The similarities are even stronger over the dormancy where all models display a general overestimation over the boreal region, possibly explained by the late leaf shed in all models [94]. These results suggest that both coupled and uncoupled models predict a later dormancy (day) and a longer growing season length in comparison to satellite observation (Table 3). It seems that leaves in the models remain for longer than they should. However the late dormancy is not in line with the vegetation photosynthetic activity: in fact, when the same methodology to calculate the end of the photosynthetic active period was applied to the gross primary productivity (GPP), we found that the dormancy began at \(277\pm 7\) days in the uncoupled models and \(287\pm 13\) days in case of CMIP5 models, which is remarkably earlier than previously predicted by LAI, and much closer to the observed value of 295 days. It is evident that all models are keeping inactive leaves for longer than they should, which does not have any impact on the carbon cycle but could potentially modify surface radiation budget and turbulent fluxes, affecting therefore the PBL dynamics, which in turn could lead to potential bias in lower atmospheric dynamics simulated by ESMs. In addition to those ESMs having an interactive tropospheric chemistry component, the presence of inactive leaves could modify the deposition fluxes that strongly depend on the area of the canopy [11]. Conversely, the longer offset simulated by offline models does not affect simulation results since the climate is provided as input data and the feedbacks between the land surface and the atmosphere are not taken into account. Looking at the LAI trends, all the coupled models show a clear greening over the whole Northern America and Eurasia, consistent with satellite data, while not all the offline models show the same pattern over the high latitudes of the Northern Hemisphere. Considering all the ESMs, the greening of the high latitudes is likely driven by positive temperature trend (Figure 1) but in some of the offline models we observe a browning over the same region, suggesting that offline modelled LAI is also sensitive to moisture changes, as most of the browning occurs over areas where precipitation shows a decrease (not shown). The previous similarities between coupled and uncoupled models, similar geographical distribution of LAI with higher values than the satellite data, and an extended growing season mostly driven by a later dormancy all suggests that the correct initialization, distribution, and parameterization of vegetation in the models is the most important feature in the correct representation of LAI. Nevertheless climatic variables, temperature in particular, have proven to be the main drivers of changes over time [95].