## 3 Model Development
### Land Biosphere Representation
#### 3.1.1 Land Use/Land Cover and Leaf Area Index  
Current Land Use/Land Cover (LU/LC) are from the dated GLCCv1.2 data [34] which is based on observations from the Advanced Very High Resolution Radiometer (AVHRR) covering 1992-1993. An upgrade to more accurate and up-to-date vegetation LU/LC maps is introduced with a new automated framework that enables an easy use of the latest maps. These new maps are based on the ESA-CCI LU/LC brokered through the Copernicus Climate Change Services (C3S), which provides consistent maps at 300 m spatial resolution on an annual basis from 1992 to present (currently 2019). The maps have a total of 22 land cover classes based on the land cover classification system (LCCS) developed by the United Nations Food and Agriculture Organization (UN FAO). To be used in the model, the 22 LCCS classes are converted to the Biosphere-Atmosphere Transfer Scheme (BATS) classes through a new cross-walking table which allows removing \"residual\" (non pure) vegetation types such as the interrupted forest (which covers about 25% of the land points in the current ECMWF maps). It is important to note that having actual/pure plant types allows a better characterization of the model parameters, especially for parameters based on observed quantities. The vegetation types in ECLand are categorised as low for crops, grass and herbaceous types and high for forest and trees types and their corresponding climatological cover is extracted form satellite based observations such as the the GLCC or ESA-CCI LU/LC products (please see the Supplementary Materials for the detailed equations and types classification). The difference in high and low vegetation covers between ESA-CCI and GLCCv1.2 based maps (Figure 2) shows an increase of low vegetation cover at the expense of the high vegetation cover in forest areas and a decrease of low vegetation cover favouringmore bareground, especially over the Siberian and Australian deserts. The vegetation types show a split of the mixed and interrupted forest types between pure forest and low vegetation types. These differences suggest a substantial impact on the energy fluxes which will be explored in the evaluation section. Additional developments are focused on improving the model phenology and its seasonality, which is represented in the model by the Leaf Area Index (LAI). To be used in the model with the tiles concept, the LAI needs to be split into low and high vegetation components. An update of this LAI disaggregation operator is developed. The current operator tends to overestimate the LAI during the transition periods (spring and autumn), while the new operator is more conservative of the observed LAI. The currently used operator relies on a LAI lookup table that represents the yearly maximum LAI per vegetation type and applies a seasonal cycle based on a MODIS LAI climatology [10]. The new operator extracts a seasonally varying LAI lookup table per vegetation type. In combination with the vegetation types map it defines the ratio of low and high LAI components which is then applied to disaggregate the satellite based climatological LAI. The revised operator allows thus to derive low and high LAI climatology components which when combined  
Figure 2: Vegetation cover difference: ESA-CCI – GLCCv1.2 based maps: (**a**) for the high vegetation types (forest and trees); (**b**) for the low vegetation types (crops, grass and herbaceous types). tend to conserve the observed total LAI climatology, in particular for the transition seasons. It also prepares for an operational assimilation of the LAI to allow better monitoring of the surface conditions inter-annual variability and representation of extremes as found in [PERSON] et al. (2018). Following the changes in the high and low vegetation coverages, Figure 3 shows that for July the new LAI components tend to increase for high vegetation by up to 80%, while for low vegetation it mostly decreases by up to 60% in low vegetation dominated areas.