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Open AccessFeature PaperArticle

Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation

CNRM—Université de Toulouse, Météo-France, CNRS, 31057 Toulouse, France
Instituto Dom Luiz, IDL, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon, Portugal
European Centre for Medium-range Weather Forecasts (ECMWF), Reading RG2 9AX, UK
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(5), 520;
Received: 9 January 2019 / Revised: 14 February 2019 / Accepted: 27 February 2019 / Published: 4 March 2019
This study aims to assess the potential of the LDAS-Monde platform, a land data assimilation system developed by Météo-France, to monitor the impact on vegetation state of the 2018 summer heatwave over Western Europe. The LDAS-Monde is driven by ECMWF’s (i) ERA5 reanalysis, and (ii) the Integrated Forecasting System High Resolution operational analysis (IFS-HRES), used in conjunction with the assimilation of Copernicus Global Land Service (CGLS) satellite-derived products, namely the Surface Soil Moisture (SSM) and the Leaf Area Index (LAI). The study of long time series of satellite derived CGLS LAI (2000–2018) and SSM (2008–2018) highlights marked negative anomalies for July 2018 affecting large areas of northwestern Europe and reflects the impact of the heatwave. Such large anomalies spreading over a large part of the domain of interest have never been observed in the LAI product over this 19-year period. LDAS-Monde land surface reanalyses were produced at spatial resolutions of 0.25° × 0.25° (January 2008 to October 2018) and 0.10° × 0.10° (April 2016 to December 2018). Both configurations of LDAS-Monde forced by either ERA5 or HRES capture well the vegetation state in general and for this specific event, with HRES configuration exhibiting better monitoring skills than ERA5 configuration. The consistency of ERA5- and IFS HRES-driven simulations over the common period (April 2016 to October 2018) allowed to disentangle and appreciate the origin of improvements observed between the ERA5 and HRES. Another experiment, down-scaling ERA5 to HRES spatial resolutions, was performed. Results suggest that land surface spatial resolution is key (e.g., associated to a better representation of the land cover, topography) and using HRES forcing still enhances the skill. While there are advantages in using HRES, there is added value in down-scaling ERA5, which can provide consistent, long term, high resolution land reanalysis. If the improvement from LDAS-Monde analysis on control variables (soil moisture from layers 2 to 8 of the model representing the first meter of soil and LAI) from the assimilation of SSM and LAI was expected, other model variables benefit from the assimilation through biophysical processes and feedback in the model. Finally, we also found added value of initializing 8-day land surface HRES driven forecasts from LDAS-Monde analysis when compared with model-only initial conditions. View Full-Text
Keywords: land surface modeling; data assimilation; leaf area index; surface soil moisture; summer 2018 heatwave land surface modeling; data assimilation; leaf area index; surface soil moisture; summer 2018 heatwave
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MDPI and ACS Style

Albergel, C.; Dutra, E.; Bonan, B.; Zheng, Y.; Munier, S.; Balsamo, G.; de Rosnay, P.; Muñoz-Sabater, J.; Calvet, J.-C. Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation. Remote Sens. 2019, 11, 520.

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