## 3 Results
### Growing Season  
Figure 4 displays the spatial distribution of the mean onset dates of green-up as calculated from the CMIP5 ESMs and satellite observation for the period 1986-2005. As expected the satellite data show that the mean green-up date is progressively delayed with increasing latitude and increasing continentally [93]. The latest dates of green-up occur in northern Siberia, northern Canada, and over the Tibetan Plateau, owing to low temperatures. The growing season onset derived from CMIP5 ESMs shows that quite a few models do correctly reproduce the observed spatial pattern, as confirmed by the relatively high spatial correlation (\(>\)0.5). The exceptions are BCC-CSM1 who has some patchy areas of agreement with satellite data and HadGEM2-CC who has an earlier onset in the whole Arctic area than in the temperate regions. In the latter case, the wrong pattern leads also to a negative correlation (\(-\)0.13). The models that correctly reproduce the green-up spatial pattern show an overestimation of the onset day, namely these models generally predict later onset values, particularly over the boreal forests  
Figure 4: Mean growing season onset (day) as simulated by 11 Earth System Models (ESMs) and satellite observations over the Northern Hemisphere (30\({}^{\circ}\)–90\({}^{\circ}\)N). For each model we masked out all the grid points where the 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 Siberia and Northern America. This leads to the large RMSE values found for the onset in most of the models, ranging from 24 to 38 days. Considering GFDL-ESM2G, this model shows a larger onset date in the few \"non-masked\" grid points (Figure 4), while a large area of Eurasia and Northern America shows a seasonal amplitude less than 0.5 (Figure 3). This suggests a problem in the initialization of the vegetation during the spin up phase: in fact the GFDL land model only allows coniferous trees to grow in cold climates, _i.e._, deciduous trees and grass do not grow in these cold regions. As a result, coniferous trees are established in areas where there should be tundra or cold deciduous trees, and therefore the seasonal amplitude is lower than expected. Satellite data shows that the dates of vegetation dormancy (Figure 5) occur in reverse order of the green-up onset, namely the green-up wave progresses northwards and dormancy wave progresses southwards [93]. Considering all the 11 ESMs only BNU-ESM, INMCM4, GFDL-ESM2G, and CanESM2 have a dormancy distribution similar to the observed pattern, the spatial correlation being larger than 0.4. The remaining models have some patchy area of agreement, mainly over Europe and the temperate region of North America, while we found relevant problems over the whole Arctic area in HadGEM2-CC, IPSL-CM5A-MR and MPI-ESM-MR, with an offset occurring after DOY 365. Figure 5: 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). For each model we masked out all the grid points where the 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. In these regions, where the dominant vegetation is composed by boreal deciduous forest, modelled dormancy can happen after the end of the year (DOY higher than 365). However, the snow corrupts the satellite signal in those months and this partially explains why the errors are large in the dormancy date. This is particularly evident in IPSL-CM5A-MR that shows a dormancy date around February-March of the new year over the whole Arctic area, while satellite data shows that the offset occurs 5-6 months before. This explains the large RMSE (82 days) found in this model.