## 4 Discussion  
Results show that all coupled models correctly reproduce the spatial pattern of LAI (Figure 2), although an overall overestimation is found (Figure 1). GFDL-ESM2G clearly shows a strong overestimation over the Northern Hemisphere. Such overestimation in boreal forest is related to the substitution of tundra with coniferous forests; this result is supported by the low seasonal amplitude found over the whole Northern region of Eurasia. Table 2 reports the comparison of simulated LAI and the leaf phenology, averaged over the whole domain of interest, against satellite observations. Looking at Table 2, it is clear that all the models overestimate not only the average LAI (except CanESM2), but also the mean dormancy and length of the growing season (except CanESM2 and MIROC-ESM), while the onset shows much agreement between model means and observations (Table 2). Satellite LAI average for the Northern Hemisphere is 0.83 while LAI from the models varies between 0.8 to 2.7. Growing season onset was earlier in 5 out of 11 models, while dormancy came between 9 to 75 days later in the models. However, when the dormancy period is calculated based on the Gross Primary Production (GPP) the modelled values become much closer to the satellite values, with an average offset occurring at \(287\pm 13\) days, very similar to the 295 days from the satellite data. In addition, the RMSE shows a noticeable decrease when compared to results of Figure 5. Besides, looking at the spatial pattern, when the offset is computed using the GPP instead of the LAI, all the models show a geographical distribution very similar to the observations (Figure 9). This is confirmed by an increase in the spatial correlations found in 7 out of 11 models compared to results of Figure 5. These results suggest that the leaves in the models remain for longer than they should (discussed later). \begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **LAI** & **Onset** & **Dormancy** & **Length** \\ \hline BCC-CSM1 & 1.54 & 102 & 304 (274) & 203 (146) \\ BNU-ESM & 1.75 & 109 & 323 (280) & 214 (148) \\ CanESM2 & 0.80 & 140 & 318 (295) & 178 (149) \\ CESM1-BGC & 1.20 & 109 & 343 (305) & 233 (190) \\ GFDL-ESM2G & 2.70 & 131 & 330 (304) & 199 (152) \\ HadGEM2-CC & 1.17 & 115 & 325 (279) & 210 (132) \\ INMCM4 & 1.00 & 106 & 329 (289) & 223 (164) \\ IPSL-CM5A-MR & 1.68 & 121 & 370 (276) & 249 (131) \\ MIROC-ESM & 1.66 & 124 & 307 (276) & 183 (130) \\ MPI-ESM-MR & 1.35 & 121 & 347 (274) & 227 (147) \\ NorESM1-ME & 1.30 & 109 & 342 (303) & 219 (186) \\
**LAI3g** & **0.83** & **111** & **295** & **184** \\ \hline \end{tabular}
\end{table} 
Table 2: Average Leaf Area Index (LAI), onset, dormancy and growing season length average for the Northern Hemisphere for each model and satellite observations. The values for dormancy and length based on Gross Primary Production (GPP) are presented in brackets. Although the wet bias found in most of the analysed CMIP5 models could explain the positive bias in LAI, this overestimation of the mean LAI is consistent with results from the uncoupled models (Table 3), suggesting that it is unlikely that differences in climate in the coupled models are solely responsible for this positive bias. This general overestimation could also be explained by a combination of underestimation of observed LAI, likely due to a saturation of satellite instrumentation, particularly on areas with dense vegetation, and by missing parameterizations of disturbances in the models (e.g., pollution, insect attack, nutrient limitation, grazing, fire dynamics), which leads to a larger amount of carbon stored in the biomass, which, in turn, leads to a larger LAI. The combination of these two effects explains why we found a relevant overestimation of simulated LAI in both coupled and uncoupled models. The geographical pattern of average LAI is also similar between coupled and uncoupled models [73].