The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released the first 7-year segment of its latest atmospheric reanalysis: ERA-5 over the period 2010–2016. ERA-5 has important changes relative to the former ERA-Interim atmospheric reanalysis including higher spatial and temporal resolutions as well as a more recent model and data assimilation system. ERA-5 is foreseen to replace ERA-Interim reanalysis and one of the main goals of this study is to assess whether ERA-5 can enhance the simulation performances with respect to ERA-Interim when it is used to force a land surface model (LSM). To that end, both ERA-5 and ERA-Interim are used to force the ISBA (Interactions between Soil, Biosphere, and Atmosphere) LSM fully coupled with the Total Runoff Integrating Pathways (TRIP) scheme adapted for the CNRM (Centre National de Recherches Météorologiques) continental hydrological system within the SURFEX (SURFace Externalisée) modelling platform of Météo-France. Simulations cover the 2010–2016 period at half a degree spatial resolution. The ERA-5 impact on ISBA LSM relative to ERA-Interim is evaluated using remote sensing and in situ observations covering a substantial part of the land surface storage and fluxes over the continental US domain. The remote sensing observations include (i) satellite-driven model estimates of land evapotranspiration, (ii) upscaled ground-based observations of gross primary production, (iii) satellite-derived estimates of surface soil moisture and (iv) satellite-derived estimates of leaf area index (LAI). The in situ observations cover (i) soil moisture, (ii) turbulent heat fluxes, (iii) river discharges and (iv) snow depth. ERA-5 leads to a consistent improvement over ERA-Interim as verified by the use of these eight independent observations of different land status and of the model simulations forced by ERA-5 when compared with ERA-Interim. This is particularly evident for the land surface variables linked to the terrestrial hydrological cycle, while variables linked to vegetation are less impacted. Results also indicate that while precipitation provides, to a large extent, improvements in surface fields (e.g. large improvement in the representation of river discharge and snow depth), the other atmospheric variables play an important role, contributing to the overall improvements. These results highlight the importance of enhanced meteorological forcing quality provided by the new ERA-5 reanalysis, which will pave the way for a new generation of land-surface developments and applications.

In this study, a global land data assimilation system (LDAS-Monde) is applied over Europe and the Mediterranean basin to increase monitoring accuracy for land surface variables. LDAS-Monde is able to ingest information from satellite-derived surface soil moisture (SSM) and leaf area index (LAI) observations to constrain the interactions between soil–biosphere–atmosphere (ISBA, Interactions between Soil, Biosphere and Atmosphere) land surface model (LSM) coupled with the CNRM (Centre National de Recherches Météorologiques) version of the Total Runoff Integrating Pathways (ISBA-CTRIP) continental hydrological system. It makes use of the CO₂-responsive version of ISBA which models leaf-scale physiological processes and plant growth. Transfer of water and heat in the soil rely on a multilayer diffusion scheme. SSM and LAI observations are assimilated using a simplified extended Kalman filter (SEKF), which uses finite differences from perturbed simulations to generate flow dependence between the observations and the model control variables. The latter include LAI and seven layers of soil (from 1 to 100 cm depth). A sensitivity test of the Jacobians over 2000–2012 exhibits effects related to both depth and season. It also suggests that observations of both LAI and SSM have an impact on the different control variables. From the assimilation of SSM, the LDAS is more effective in modifying soil moisture (SM) from the top layers of soil, as model sensitivity to SSM decreases with depth and has almost no impact from 60 cm downwards. From the assimilation of LAI, a strong impact on LAI itself is found. The LAI assimilation impact is more pronounced in SM layers that contain the highest fraction of roots (from 10 to 60 cm). The assimilation is more efficient in summer and autumn than in winter and spring. Results shows that the LDAS works well constraining the model to the observations and that stronger corrections are applied to LAI than to SM. A comprehensive evaluation of the assimilation impact is conducted using (i) agricultural statistics over France, (ii) river discharge observations, (iii) satellite-derived estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project and (iv) spatially gridded observation-based estimates of upscaled gross primary production and evapotranspiration from the FLUXNET network. Comparisons with those four datasets highlight neutral to highly positive improvement.

Interannual global mean sea level (GMSL) variations and El Nino-Southern Oscillation (ENSO) are highly correlated, with positive/negative GMSL anomalies during El Nino/La Nina events. In a previous study, we showed that interannual GMSL and total land water storage variations are inversely correlated, with lower-than-average total water storage on land and higher-than-average GMSL during El Nino. This result is in agreement with the observed rainfall deficit/excess over land/oceans during El Nino (and vice versa during La Nina). It suggests that the positive GMSL anomaly observed during El Nino is likely due to an ocean mass rather than thermal expansion increase. Here, we analyze the respective contribution of the Atlantic, Indian, and Pacific oceans to the interannual (ENSO-related) GMSL anomalies observed during the altimetry era (i.e., since 1993) with an emphasis on the 1997/1998 El Nino event. For each oceanic region, we compute the steric contribution, and remove it from the altimetry-based mean sea level to estimate the ocean mass component. We find that mass changes of the tropical Pacific Ocean, mainly in the region within 0–25°N, are mostly responsible for the observed 1997/1998 ENSO-related GMSL anomaly. The ocean mass excess of this region almost perfectly compensates the total land water deficit during the 1997/1998 El Nino. An estimate of the ocean-atmosphere water balance of this region shows that the time derivative of the ocean mass component is well correlated with net P-E (precipitation minus evaporation) over most of the study period, except during the 1997/1998 ENSO event, where there is a temporary ocean mass increase, not compensated by the net P-E. We thus propose that the 1997/1998 ocean mass increase of this north tropical Pacific area be linked to an imbalance between the inflow/outflow entering/leaving the north tropical Pacific. A preliminary qualitative analysis indicates that a significant reduction of the Makassar Strait transport, (about 80% of the total Indonesian throughflow), as previously reported in the literature during the strong 1997/1998 El Nino event, could explain the north tropical Pacific Ocean mass excess reported in this study, hence the observed positive GMSL anomaly.

The future Surface Water and Ocean Topography (SWOT) satellite mission will provide two‐dimensional maps of water elevation for rivers with width greater than 100 m globally. We describe a modeling framework and an automatic control algorithm that prescribe optimal releases from the Selingue dam in the Upper Niger River Basin, with the objective of understanding how SWOT data might be used to the benefit of operational water management. The modeling framework was used in a twin experiment to simulate the “true” system state and an ensemble of corrupted model states. Virtual SWOT observations of reservoir and river levels were assimilated into the model with a repeat cycle of 21 days. The updated state was used to initialize a Model Predictive Control (MPC) algorithm that computed the optimal reservoir release that meets a minimum flow requirement 300 km downstream of the dam. The data assimilation results indicate that the model updates had a positive effect on estimates of both water level and discharge. The “persistence,” which describes the duration of the assimilation effect, was clearly improved (greater than 21 days) by integrating a smoother into the assimilation procedure. We compared performances of the MPC with SWOT data assimilation to an open‐loop MPC simulation. Results show that the data assimilation resulted in substantial improvements in the performances of the Selingue dam management with a greater ability to meet environmental requirements (the number of days the target is missed falls to zero) and a minimum volume of water released from the dam.

In this study, we applied the integration methodology developed in the companion paper by Aires (2014) by using real satellite observations over the Mississippi Basin. The methodology provides basin‐scale estimates of the four water budget components (precipitation P, evapotranspiration E, water storage change ΔS, and runoff R) in a two‐step process: the Simple Weighting (SW) integration and a Postprocessing Filtering (PF) that imposes the water budget closure. A comparison with in situ observations of P and E demonstrated that PF improved the estimation of both components. A Closure Correction Model (CCM) has been derived from the integrated product (SW+PF) that allows to correct each observation data set independently, unlike the SW+PF method which requires simultaneous estimates of the four components. The CCM allows to standardize the various data sets for each component and highly decrease the budget residual (P − E − ΔS − R). As a direct application, the CCM was combined with the water budget equation to reconstruct missing values in any component. Results of a Monte Carlo experiment with synthetic gaps demonstrated the good performances of the method, except for the runoff data that has a variability of the same order of magnitude as the budget residual. Similarly, we proposed a reconstruction of ΔS between 1990 and 2002 where no Gravity Recovery and Climate Experiment data are available. Unlike most of the studies dealing with the water budget closure at the basin scale, only satellite observations and in situ runoff measurements are used. Consequently, the integrated data sets are model independent and can be used for model calibration or validation.

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.