## Data and Methods
### LDAS-Monde System Components
#### 2.1.2 ESA CCI Surface Soil Moisture and CGLS Leaf Area Index  
In this study the European Space Agency and Climate Change Initiative (ESA CCI) SSM-combined version of the product (v4.1) is assimilated into LDAS-Monde ([[http://www.esa-soilmoisture-cci.org](http://www.esa-soilmoisture-cci.org)]([http://www.esa-soilmoisture-cci.org](http://www.esa-soilmoisture-cci.org)), last access June 2018). The CCI merges SSM observations from seven different microwave radiometers (SMMR, SSM/I, TMI, ASMR-E, WindSat, AMSR2, SMOS) and four different scatterometers (ERS-1 and 2 AMI, and MetOp-A and B ASCAT) into a single combined data set covering the time period from November 1978 to December 2016. Data are expressed in volumetric (m\({}^{3}\) m\({}^{-3}\)) units and quality flags are provided (i.e., snow coverage or temperature below 0\({}^{\circ}\) and dense vegetation). For a more comprehensive overview of the product, see [24; 25]. Topographic relief is known to negatively affect satellite remote sensing retrievals of SSM [37], hence the time series for pixels whose average altitude exceeds 1500 m above sea level were not accounted for. Data on pixels with urban land cover fractions larger than 15% were discarded too, to limit the effects of artificial surfaces. These thresholds were set according to [13; 20; 38], who processed satellite-based SSM retrievals for data assimilation experiments with the ISBA LSM. Data are available almost every day with a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\). To assimilate SSM data, it is important to rescale the observations such that they are consistent with the model climatology [39; 40]. Hence, similarly to previous studies, the ESA CCI SSM product has been transformed into the model-equivalent SSM to address possible misspecification of physiographic parameters, such as the wilting point and the field capacity. The linear rescaling approach described in [41] (using the first two moments of the cumulative distribution function, CDF) was used. It consists of a linear rescaling enabling a correction of the differences in the mean and variance of the distribution. It has been applied at a seasonal scale (i.e., for each specific month) following [13].  
The GEOV1 LAI is also assimilated. It is produced by the European Copernicus Global Land Service project ([[http://land.copernicus.eu/global/](http://land.copernicus.eu/global/)]([http://land.copernicus.eu/global/](http://land.copernicus.eu/global/)), last access June 2018). Ref. [42] proposed an evaluation of this product in the context of Numerical Weather Prediction (NWP). LAI observations are retrieved from the SPOT-VGT (from 1999 to 2014) and then from PROBA-V (from 2014 to present) satellite data according to the methodology proposed by [43]. The 1 km spatial resolution observations are interpolated by an arithmetic average to the 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) model grid points, if at least 50% of the observation grid points are observed (i.e., half the maximum amount). LAI observations have a temporal frequency of 10 days at best (e.g., in presence of clouds, no observations are available). Both assimilated datasets are illustrated by Figure 1, averaged over 2010-2016.