# Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation
## 3 Results and Discussions
### Monitoring the Heatwave Impact on Vegetation Lising LDAS-Monde  
LDAS-Monde, being an offline reanalysis of the land surface variables, is forced by atmospheric datasets: ERA5 and HRES in this study. Using both datasets to force LDAS-Monde produces a long reanalysis of the LSVs (from the use of ERA5) with real-time and even forecast capacity (from the use of HRES). As ERA5 is available over a large temporal extent (from 2000 at the time of study) it can be useful to study recent climate changes. Anomaly time-series of air temperature and precipitation from ERA5 are presented in Figure 3. While it is not our intention to repeat the study from [49] on predicting the summer 2018 heatwave, Figure 3 highlights that the April to August period in 2018 exhibits rather large positive anomaly values of air temperature (Figure 3a) with July 2018 being the highest value observed between January 2001 and October 2018. For precipitation, all months from May to October 2018 present large negative anomalies with July 2018 being the third lowest within the considered period. One may also note the coherence between air temperature and precipitation from ERA5 and the satellite derived observation presented above for this 2018 heatwave event, particularly for LAI. As seen from Figures 1a and 3, large positive anomalies of air temperature (Figure 3a) are associated with large negative anomalies of precipitation (Figure 3b) as well as large negative anomalies of LAI (Figure 1a). In the beginning of 2007 temperature and precipitation show positive anomalies which reflect on LAI presenting large positive anomalies. While in the beginning of 2013, both air temperature and LAI show negative anomalies. When LDAS-Monde is driven by ERA5 and integrates LAI and SSM through data assimilation, those anomalies should be reflected on analyzed land surface conditions and their impact propagated to other land surface variables through biophysical processes and feedback in the model. Figure 4a illustrates observed CGLS GFOV2 Leaf Area Index (LAI), over 2008-2018 as well as LDAS-Monde LAI time-series forced by either ERA5 (LDAS-ERA5) over January 2008-October 2018 or HRES (LDAS-HRES) over April 2016-December 2018. Figure 4b shows the same as Figure 4a for the common April 2014 to October 2018 period. From Figure 4 one may notice the good agreement between the analyzed LAI and the observed annual cycle. While neither the open-loop nor the analysis capture  
Figure 3: Monthly Anomaly time-series (scaled by the standard deviation, expressed in units of standard deviation) of air temperature (**a**) and precipitations (**b**) from ERA5 atmospheric reanalysis dataset over January 2001–October 2018. Months of July are highlighted in red, straight dashed lines represent values for July 2018.  
the maximum LAI peak well (as already observed by [18]), the analysis efficiently corrects for the open-loop delay during the senescence phase. Considering the period where both ERA5 and HRES are available to force LDAS-Monde (April 2016 to October 2018), one may notice the relative good agreement between LDAS-ERA5 and LDAS-HRES, both in the open-loops and analyses, as well as the senescence phase being very well represented by LDAS-HRES analysis (which failed to capture the LAI peak intensity though). Upper panel of Figure 5 illustrates seasonal RMSD (Figure 5a) and correlation (Figure 5b) values between LAI from the model forced by either ERA5 (LDAS-ERA5 Open-loop) or HRES (LDAS-HRES Open-loop), the analysis forced by either ERA5 (LDAS-ERA5 Analysis) or HRES (LDAS-HRES Open-loop) and GEOV2 LAI estimates from CGLS from April 2016 to October 2018. Figure 5 lower panel shows the same between modeled/analyzed soil moisture from the second layer of soil (1-4 cm) and ASCAT surface soil moisture estimates from CGLS (and converted into the model space, in m\({}^{3}\)m\({}^{-3}\), as detailed in Section 2.1).