# Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation
## 3 Results and Discussions
### Anticipating the Impact of Heat Waves on Vegetation  
Two other experiments are presented in order to (i) study the possibility of forecasting the impact of extreme events on vegetation few days in advance and (ii) highlighting the fact that a forecast initialized by an analyzed state can have more skills than an open loop. For the whole 2018 and for each daily analysis from LDAS-HRES, 2 forecast experiments (2-day and 8-day forecast, see Table 1) were conducted. The atmospheric forcing forecast is coming from HRES, as described in the materials and methods sections. For the sake of clarity, only forecasts with lead time of 2 and 8 days are presented (LDAS_fc_d2 and LDAS_fc_d8, respectively). Figure 9a illustrates LAI time-series from the open-loop, the analysis (ran for 2018, only) as well as the 2 forecast experiments (LDAS_fc_d2 and LDAS_fc_d8) for 2018 averaged over a domain defined as: longitudes from 4\({}^{\circ}\)W to 15\({}^{\circ}\)E and latitudes from 48\({}^{\circ}\)N to 55\({}^{\circ}\)N. According to Figure 2, this domain was more severely affected by the heatwave, and is represented by Figure 9c. Firstly, the large error between all the experiments and the observations for the start of the growing season is noticeable (Figure 9a). Secondly, from March to June LDAS_HRES analysis as well as LDAS_fc_d2 and LDAS_fc_d8 are only slightly correcting for this issue. This is a known issue as already mentioned by [(18)], the CO\({}_{2}\)-responsive version of ISBA is such that during the growing phase, enhanced photosynthesis corresponds to a CO\({}_{2}\) uptake, which results in vegetation  
Figure 8: Monthly (**a**) RMSD and (**b**) correlation values between leaf area index (LAI) from the model forced by either HRES_010 or ERA5_010 (ERA5 forcing down-scaled to HRES spatial resolution) and GEOV2 LAI estimates from the Copernicus Global Land Service project for the year 2017.  
growth from a prescribed LAI minimum threshold (1 m\({}^{2}\)m\({}^{-2}\) for coniferous forest or 0.3 m\({}^{2}\)m\({}^{-2}\) for other vegetation types). These thresholds are likely too low and are currently being revisited using the CGLS LAI long term dataset. This is expected to lead to better representation of LAI during the vegetation growing phase [78]. However, during the senescence phase (see zoom on Figure 9b), the analysis is quite efficient in reducing the differences with the observed LAI and it is interesting to notice that so are the 2-d and 8-d forecasts of LAI initialized by the analysis. This suggests that the impact of assimilating satellite observations in LDAS-Monde has the capacity to mitigate model deficiencies, leading to better estimates of the system states and that this impact can last in time. From the two first panels of Figure 9, one may see that LDAS_fc_d2 and LDAS_fc_d8 are closer to the observations than the open-loop. Figure 9c represents RMSD values between the open-loop (ran for 2018, only) and the LAI GEOV2 observations and Figure 9d the RMSD differences between the open-loop (analysis) and the LAI GEOV2. Negative (blue shaded areas) values indicate areas where the analysis has smaller (i.e., better) RMSD values than the open-loop. Figure 9d is dominated by negative values with blue shaded areas representing 82% of the domain. It shows the added value of the analysis over the open-loop. Finally, Figure 9e presents RMSD differences between the open-loop (LDAS_fc_d8) and the LAI GEOV2 observations and it is very interesting to notice than an 8-day forecast initialized by an analysis presents better skills in capturing LAI than an open-loop for most of the domain with blue shaded areas (negative values) still representing ~49% of the studied domain. Figure 9: (**a**) LAI time series from the model (LDAS-HRES Open-loop in blue), the analysis (LDAS-HRES Analysis in red), the 2-d and 8-d forecasts from the analysis (LDAS_Fc_d2 in green, LDAS_Fc_d8 in cyan respectively) as well as the observations from the Copernicus Global Land Service (LAI GEOV2, red stars) for 2018.