## 3 Results  
### Analysis Impact on Assimilated Variables  
Being the model equivalents of the assimilated observations, LAI and soil moisture from the second layer of soil are expected to be the two variables most affected by the assimilation. Figure 2 presents a 10-day time series of LAI averaged over the whole domain for the 2010-2016 time period. From Figure 2, one can see that the open-loop simulation tends to overestimate the observed LAI in winter periods and that the senescence phase of vegetation is too late over the autumn when compared with the observations. In that respect, the assimilation is efficiently correcting the model; however, analysed LAI does not reach LAI maximal values of the observations. Figure 3 shows maps of LAI for the model (Figure 3a), the observations (Figure 3b), and the analysis (Figure 3c) averaged over 2010-2016. It is clearly visible that the model overestimates LAI in the eastern part of the domain. Also, some geographical patterns visible in the observations (e.g., the Mississippi area in Figure 3b) are not represented in the model (Figure 3a). After assimilation, the analysis presents reduced LAI values in the eastern part of the domain and the above-mentioned geographical patterns are visible too (Figure 3c). This shows the ability of the assimilation to integrate geographical information into the model. Figure 3 also presents seasonal scores between the model and the observations and between the analysis and the observations for RMSD and R over the 2010-2016 time period. The analysis leads to a better fit between the model forecasts and the subsequent assimilated observations for both metrics. On average for the whole period, RMSD values drop from 1.10 m\({}^{2}\) m\({}^{-2}\) (model vs. observations) to 0.65 m\({}^{2}\) m\({}^{-2}\) (analysis vs. observations), while R values increase from 0.69 (model vs. observations) to 0.88 (analysis vs. observations). Figure 4 presents the same information content for soil moisture. As ESA CCI SSM was rescaled in order to match the first two moments of the modelled SSM cumulative distribution function, the impact is marginal and differences are hardly visible from Figure 4a-c. From Figure 4d,e, however, one can appreciate the added value of the analysis: RMSD values drop from 0.046 m\({}^{3}\) m\({}^{-3}\) (model vs. observations) to 0.044 m\({}^{3}\) m\({}^{-3}\) (analysis vs. observations), while R values increase from 0.85 (model vs. observations) to 0.87 (analysis vs. observations). It is worth mentioning that the good level of scores (prior as well as after assimilation) are linked to the rescaling of the ESA CCI SSM data to the model climatology. Finally, Figure 5 shows maps of analysis increments for 4 (out of 8) control variables averaged over the whole 2010-2016 time period (LAI, second, fourth, and sixth layers of soil from left to right, respectively). It can be noticed that the magnitude of increments is decreasing with depth. It can also be noticed that over almost the whole domain, the analysis tends to add water in the soil near the surface (positive increments), while it dries layers where the roots are mainly located (from layer 4 to 6, negative increments).  
Figure 3: Top row; leaf area index from (**a**) the model, (**b**) the observations, and (**c**) the analysis averaged over the 2010–2016 time period. Bottom row: seasonal (**d**) root mean square difference (RMSD) and (**e**) correlation values between leaf area index (LAI) from the model (in blue), the analysis (in red) and GEOV1 LAI estimates from the Copernicus Global Land Service project from 2010 to 2016.  
Figure 2: Leaf area index time series from the model (blue line), the observations (green dots and dashed line), and the analysis (red line) averaged over the whole domain from 2010 to 2017.