## 3 Results
### Mean Leaf Area Index (LAI)
The interannual variability of the reference data is about 85 mm/yr and only INMCM4, IPSL-CM5A-MR, GFDL-ESM2G and MIROC-ESM well reproduce this value, while CanESM2 (\(\sim\)80 mm/yr), NorESM1-ME (\(\sim\)80 mm/yr) and BCC-CSM1 (75 mm/yr) have a slightly lower IAV and the remaining models show a larger IAV. It is noteworthy that MPI-ESM-MR has a IAV two times larger than the reference data. Looking at the temperature, all the models are clustered around the reference data and only HadGEM2-CC (cold bias) and MIROC-ESM (warm bias) show a bias greater than 1.5 \({}^{\circ}\)C. In addition, all the models predict a warming in the Northern Hemisphere during the last 20 years; the weaker trends have been found in HadGEM2-CC and INMCM4 being about 4 times smaller than the one reported by CRU. The observed temperature interannual variability is about 0.8 \({}^{\circ}\)C and only INMCM4 and MIROC-ESM have a similar IAV; all the other models show a larger IAV than CRU with NorESM1-ME having an IAV of about 1 \({}^{\circ}\)C.  
Although models in general show good skills in reproducing the observed climate, we would highlight that this agreement in the mean values over a large region could arise from a compensation between  
Figure 1: _Cont._  
overestimation in some points of the domain and underestimation in other points [85]. This suggests that to perform an exhaustive model validation we should look at the spatial patterns (e.g., maps). For this reason in Figure 2 we show the spatial distribution of the mean annual LAI in the Northern Hemisphere as calculated from the CMIP5 ESMs and observed by satellite over the period 1986-2005. Results are projected over a stereographic projection from the North Pole, with the latitude ranging from 30\({}^{\circ}\)N to 90\({}^{\circ}\)N.  
The observed spatial pattern of LAI is characterized by a wide maximum over Northern America and by a negative gradient extending from central Europe to Northern-Eastern Asia, with a broad minimum in the Tibetan plateau due to the sparse vegetation. Although there is an overall overestimation by most of the CMIP5 models, 10 out of 11 models correctly reproduce this pattern: in particular CESM1-BGC, IPSL-CM5A-MR, and NorESM1-ME show a very good agreement with observations in terms of locations of the maximum and minimum values, as well as fairly simulating the gradient over the Eurasian region. This is confirmed by the relatively high value (\(>\)0.6) of the spatial correlation computed between the models and the reference data. Conversely, GFDL-ESM2G is not able to reproduce this spatial pattern, and LAI values above 5 are simulated over the whole North America and Asia; for this reason this model exhibits the lowest spatial correl  
Figure 2: Spatial distribution of mean annual leaf area index (LAI) as simulated by 11 Earth System Models (ESMs) and observed by satellite over the period 1986–2005 in the Northern Hemisphere (30\({}^{\circ}\)–90\({}^{\circ}\)N). The value in the box represents the spatial correlation between modelled and satellite values. noted that, since ESMs generate their own climate, there is no reason to expect models and observations to agree on the phasing of interannual variations [85]; for this reason we did not account the RMSE for the mean annual LAI. The seasonal amplitude patterns show large disagreement between the models and the satellite data (Figure 3). Some models (e.g., BNU-ESM and MIROC-ESM) clearly overestimate the mean amplitude, which is particularly evident over the whole North America and Eurasia. Other models (e.g., CESM1-BGC, HadGEM2-CC, MPI-ESM-MR, and NorESM1-ME) show a smaller seasonality than satellite data, while INMCM4, CanESM2 and BCC-CSM1 perform better than the rest of the models in reproducing the satellite-derived observations.