## 3 Results
### Evaluation Using Independent Datasets  
#### 3.2.1 Evapotranspiration and GPP  
Table 2 presents the statistical scores from the evaluation of both the open-loop and the analysis with respect to evapotranspiration and GPP averaged over 2010-2016, as well as the mean of the evaluation data set over the considered area. On average, R increases from 0.80 to 0.81 when comparing evapotranspiration from the model and from the analysis, respectively, to the independent estimates. Average RMSD decreases from 0.89 kg m\({}^{-2}\) d\({}^{-1}\) to 0.85 kg m\({}^{-2}\) d\({}^{-1}\). When compared with GPP estimates, averaged correlations rise from 0.74 to 0.78 and RMSD drops from 2.20 g(C) m\({}^{-2}\) d\({}^{-1}\) to 1.91 g(C) m\({}^{-2}\) d\({}^{-1}\) when considering the model or the analysis, respectively. Figure 6 presents spatial maps of N\({}_{\text{RMSD}}\) (Figure 6a,c) and NIC\({}_{\text{R}}\) (Figure 6b,d) resulting from the comparison with evapotranspiration (Figure 6, top row) and GPP (Figure 6, bottom row) of their modelled and analysed equivalent. For N\({}_{\text{RMSD}}\) (Figure 6a,c), blue colours represent an improvement from the analysis regarding RMSDs (i.e., the latter better represents either evapotranspiration or GPP than the model), while for NIC\({}_{\text{R}}\) (Figure 6b,d), red colours depict an improvement from the analysis. Figure 6 shows that both evapotranspiration and GPP are improved almost everywhere in terms of correlation and RMSD, and that the impact of the assimilation is stronger for GPP than for evapotranspiration. At the seasonal scale (not shown), the assimilation leads to a positive impact all year long in the representation of GPP in terms of both RMSD and R values. Impact from the assimilation on evapotranspiration is smaller (as seen in Table 2), while RMSD values are slightly improved all year long, R values are slightly improved from April to October and slightly degraded from November to March. Figure 4: (**a–e**) same as Figure 3 for soil moisture. Figure 5: Analysis increments averaged over the 2010–2016 time period for (**a**) LAI in m\({}^{2}\) m\({}^{-2}\), (**b**) second, (**c**) fourth, and (**d**) sixth layer of soil moisture in m\({}^{3}\) m\({}^{-3}\). Geographical patterns, as seen on the middle of Figure 6a,c (transition from relative wet to dry areas) areas of the Midwest, are also visible in the soil moisture increments maps of Figure 5b,c, where stronger increments occur. It is difficult to state whether or not those strong corrections reflect atmospheric forcing errors, for example, precipitation, in this area as to date, very few studies have evaluated ERA5 precipitation errors. Ref. [52] have evaluated a large amount of precipitation products, including ERA5, using the high-resolution (4 km) stage-IV gauge-radar precipitation data set as a reference over CONUS using several metrics for 2008-2017. Figure 1d of [52], illustrates the Kling-Gupta efficiency (KGE) scores between ERA5 precipitation and stage-IV reference. If lower values seem to be observed in the above-mentioned transition area, it is not clear enough to incriminate precipitation errors. However, this geographical pattern corresponds to a specific soil moisture regime, the Ustic regime, where moisture is present in the soil, but limited, at times in which conditions are suitable for plant growth (as visible on the following USDA map: [[https://www.nrcs.usda.gov/Internet/FSE_MEDIA/nrcs142p2_050436.jpg](https://www.nrcs.usda.gov/Internet/FSE_MEDIA/nrcs142p2_050436.jpg)]([https://www.nrcs.usda.gov/Internet/FSE_MEDIA/nrcs142p2_050436.jpg](https://www.nrcs.usda.gov/Internet/FSE_MEDIA/nrcs142p2_050436.jpg)), last access October 2018). This area has specific soil properties including clay, with high swelling potential [53]. Such soil properties are likely to be misrepresented in the model, possibly leading to stronger increments in the analysis.