###### Abstract
In the past recent years, several land data assimilation system (LDAS) have emerged at different spatial scales: \"site level\", like the data assimilation system for LSMs using CLM4.5 (Community Land Model 4.5, [5]); regional, like the coupled land vegetation LDAS (CLVLDAS, [6; 7]) and the famine early warning systems network (FEWSNET) LDAS (FLDAS, [8]); continental, like the North American LDAS (NLDAS, [9; 10]) and the National Climate Assessment LDAS (NCA-LDAS [11]); as well as at global scale, like the global land data assimilation (GLDAS, [12]) and, more recently, LDAS-Monde [13]. LDAS-Monde has been developed to constrain the CO\({}_{2}\)-responsive version of the ISBA (interactions between soil, biosphere, and atmosphere) LSM [14; 15; 16; 17] using satellite derived observations within the open-source SURFEX modelling platform (SURFace Externalise, [18]) of Meteo-France. LDAS-Monde has been implemented in a monitoring chain of terrestrial water and carbon fluxes. Unlike most of the above mentioned LDAS, LDAS-Monde is able to jointly and sequentially assimilate vegetation products such as leaf area index (LAI) together with surface soil moisture (SSM) observations [13; 19; 20; 21]. Ref. [13] tested LDAS-Monde over Europe and the Mediterranean basin for the 2000-2012 period. A long term, global scale, multi-sensor satellite-derived surface soil moisture dataset (ESA CCI SSM, [22; 23; 24; 25]) along with satellite derived LAI (GEOV1, [[http://land.copernicus.eu/global/](http://land.copernicus.eu/global/)]([http://land.copernicus.eu/global/](http://land.copernicus.eu/global/)) last access, June 2018), were jointly assimilated. LDAS-Monde was forced by WFDEI (WATCH-forcing-data-ERA-interim) observations based atmospheric forcing dataset [26; 27] at half degree spatial resolution. Analysis impact was successfully carried out 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 observations-based estimates of up-scaled gross primary production and evapotranspiration from the FLUXNET network [13]. In this study, LDAS-Monde is applied and tested in a data-rich area: the CONtiguous US (CONUS, defined here as longitudes from 130.0\({}^{\circ}\)W to 60.0\({}^{\circ}\)W, latitudes from 20.0\({}^{\circ}\)N to 55.0\({}^{\circ}\)N, as shown in Figure 1). LDAS-Monde is forced by the latest ERA-5 atmospheric reanalysis from the European Center for Medium Range Weather Forecast (ECMWF) from 2010 to 2016 leading to a seven-year, quarter degree spatial resolution offline reanalysis of the land surface variables (LSVs). Ref. [28] assessed ERA-5 ability to force the ISBA LSM by comparison to satellite-derived products and in situ observations covering a substantial part of the land surface storage and fluxes. They found that using ERA-5 in place of its predecessor, ERA-Interim, led to significant improvements in the representation of the LSVs linked to the terrestrial water cycle (surface soil moisture, river discharges, snow depth, and turbulent atmospheric fluxes), but did not improve the LSVs linked to the vegetation cycle (evapotranspiration, carbon uptake, and LAI). In that respect, the assimilation of LAI through ERA-5 driven reanalysis from LDAS-Monde is expected to bring clear improvements [13]. In this study, the impact of LDAS-Monde analysis with respect to an open-loop (i.e., model run without assimilation) is assessed using satellite-driven model estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project and upscaled ground-based observations of gross primary productivity from the FLUXCOM project, together with river discharges from the United States Geophysical Survey (USGS) and the Global Runoff Data Centre (GRDC). Over CONUS, in situ measurements of soil moisture from the USCRN network (US Climate Reference Network) are also used in the evaluation.