# Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation
## 4 Conclusions and Perspectives  
This study has investigated the capability of LDAS-Monde offline land data assimilation system to represent the impact of the summer 2018 heatwave on vegetation. Satellite derived leaf area index and surface soil moisture were assimilated in LDAS-Monde forced by either ERA5 reanalyses (0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) spatial resolution) or IFS HRES operational product (0.10\({}^{\circ}\)\(\times\) 0.10\({}^{\circ}\) spatial resolution) from ECMWF. Both analysis experiments were able to represent the impact of the heatwave on vegetation well. While there is a surface physiography and modeling advantage to the HRES configuration, there is added value in down-scaling ERA5 to HRES spatial resolution, too. It would allow consistent, long term and high-resolution reanalysis of the LSVs. The possibility of forecasting LSVs was tested and it was shown that a forecast of LAI from analyzed initial conditions has more skill than an open-loop (with a persistence of at least 8 days). Combining ERA5 atmospheric re-analysis, HRES analysis and its forecast within LDAS-Monde is highly relevant to foster research for land applications at various timescales from daily to annual. The use of HRES data to force LDAS-Monde is very promising and it can be complemented by ECMWF 51-member ensemble forecasts (\(\sim\)18 km spatial resolution). Use of the ECMWF ensemble in LDAS-Monde could help to capture uncertainties in the representation of LSVs. It would open the possibility to anticipate the impact of heatwaves at monthly temporal scales using a probabilistic method. Moreover, since one member of the ensemble is similar to HRES at a coarser spatial resolution, and the ensemble is available up to 15-days lead time (twice a day and up to 45 days twice a week) it can be used to test longer range forecast of LSVs than when using HRES.  
One of the limitations of the use of the discussed land data assimilation system at a high spatial resolution, for example using grid cells of 1 km or 300 m, is that analyzed atmospheric forcings are not available at these scales. While downscaling atmospheric forcing like the IFS HRES (e.g., from 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) to 0.01\({}^{\circ}\)\(\times\) 0.01\({}^{\circ}\) spatial resolution) is likely to add uncertainties, their impact on the representation of the LSVs can be reduced through the dynamic integration of satellite-derived LAI  
Figure 10: Monthly (**a**) RMSD and (**b**) R values between LAI from the model (LDAS-HRES open-loop in blue), analysis (LDAS-HRES Analysis in red), the 2-d and 8-d forecast experiments initialized by the analysis and the Copernicus Global Land Service LAI GEOV2 over 2018.  
observations at fine scale like the 300 m spatial resolution product from Copernicus Global Land Service. For the meteorological forcing, the use of AROME (Application de la Recherche a l'Operationnel a Meso-Echelle), the operational numerical prediction model from Meteo-France, atmospheric variables to drive the LDAS could also be investigated as its spatial resolution is already of 1.3 km \(\times\) 1.3 km over France. The process of comparing Land Surface Models and observations, e.g. through data assimilation, can highlight model deficiencies. It is likely that the model would benefit from new LAI minimal values parameterization that are currently being revisited at Meteo-France using the long-term CGLS data-set including more than 19-yr of LAI data.  
Conceptualization, C.A.; Investigation, C.A.; Methodology, C.A., E.D. and G.B.; Writing--original draft, C.A.; Writing--review and editing, C.A., E.D., B.B., Y.Z., S.M., G.B., P.d.R., J.M.-S. and J.-C.C.  
This research received no external funding.  
Results were generated using the Copernicus Climate Change Service Information, 2017. The Authors would like to thanks the Copernicus Global Land Service for providing the satellite derived Leaf Area Index and Surface Soil Moisture.  
The authors declare no conflict of interest.