## 3 Results
### LDAS-Monde Impact
#### 3.2.2 Assimilation Impact on LAI and Soil Moisture  
In order to obtain the land surface analysis, satellite derived observations are combined with the model simulations through the above-mentioned data assimilation technique. In doing that, the resulting analysis is expected to be closer to the assimilated observations (LAI and SSM) than the open-loop (i.e., model with no assimilation). The largest impact is observed for LAI. Figure 7 shows maps of the monthly average values of precipitation from ERA5 as well as LAI from the LDAS-ERA5 openloop, the observations, LDAS-ERA5 analysis, and the difference between the analysis and the openloop. Observations indicate a sharp jump of LAI in April, very likely in response to the increase of soil moisture availability due to the start of the rainy season. However, an increase in LAI is also observed earlier in the SG region (southern part of the domain) from February to May; i.e., before the first rains. Interestingly, this peculiar behaviour would be consistent with findings from in situ studies from [76; 77; 78], which point to some tree species that put on leaves before the first rains of the season. This functioning does not appear to be linked to soil moisture [79]. It could involve rises of the air temperature or humidity, though the precise mechanisms at play are still unknown. In any case, this process is not represented in the ISBA LSM, leading to a temporal shift of two to three months in the leaf onset and an underestimation of the observed LAI. Figure 7 also shows little impact of the assimilation on the estimated LAI where observations are below 0.4 m\({}^{2}\cdot\)m\({}^{-2}\). This is due to a limitation of the ISBA LSM. As mentioned in Section 2.1.1, a minimum LAI threshold is prescribed to 0.3 m\({}^{2}\cdot\)m\({}^{-2}\) for every vegetation type (exceptiferous forests) in ISBA. To satisfy that threshold, we force the SEKF to reset every LAI estimate below 0.3 m\({}^{2}\cdot\)m\({}^{-2}\) to that value, thus limiting the impact of assimilation. The analysis is efficient at compensating for these two model caveats, as shown in Figures 7 and 8. This is also clear in Figure 9, which shows the LDAS-ERA5 configuration LAI monthly mean time series averaged values over the whole domain for January 2001 to June 2018. Observations indicate interannual fluctuations in the yearly maximum of LAI, with, for instance, higher values in 2003, 2010, and 2012 and lower values in 2002, 2011, and 2017 (consistent with [80; 81]). Beyond differences in the structure of their annual cycles, the model and the analysis both capture part of this interannual variability. Figure 8 presents similar maps for the following months, from July to December. While the model underestimates LAI in spring, it significantly overestimates the LAI in November, after the demise of the rainy season. The improved annual cycle of LAI provided by the analysis is associated with a higher correlation and a positive impact on the SDD, Bias, and RMSD scores over the study region in all months (as seen on Figure 10). It is also visible that the analyses add skill to both configurations, LDAS-ERA5 and LDAS-ERAI, which indicates the healthy behavior from the land data assimilation system. Considering now the three climatic regions of BF, it appears that the correlation between the analyzed and observed LAI is lower during the rainy season in the SG region while it is higher in the SS and SH regions (Figure 11b for the LDAS-ERA5 configuration). This reduction of R values in the analysis during the rainy season in the SG region could be related to a decrease in the number of the observed LAI linked to cloud cover (see Table 4). Overall, the assimilation corrects the model seasonal responses by increasing the LAI during the rainy season and decreasing the LAI after the rainy season, especially in the Sudanian region (SS and SG regions).