## 4 Evaluation of Model Developments
### Evaluation of Updated Land Cover and Vegetation
#### 4.3.2 Evaluation of Dynamic Vegetation Seasonal Vegetation Cover Evaluation  
The vegetation cover seasonality is also evaluated with global offline and forecast coupled simulations. Figure 4b shows low vegetation cover difference between January and July when using a clumping based seasonality. This is in contrast with the current static vegetation cover field, which has the same vegetation cover all year round. Figure 4a shows the difference of the seasonal bareground cover for July as compared with the current static map. This variability in cover impacts the global circulation as it is directly linked with the surface roughness. Moreover it affects surface albedo especially in winter and spring during the snow season for example, through the shading of snow under high vegetation with a strong impact on energy fluxes. This configuration is also assessed in [PERSON] et al. [88] and showed an overall bias reduction of the maximum LSA-SAF LST with some scattered deterioration, especially in south America which was attributed to a misrepresentation of land use related parameters. In the forecast experiments, this change is tested on top of the previous ESA-CCI + LAI (CLAI) configuration. The impact on the near surface parameters in this case also showed mixed patterns which tend to amplify both positive and negative signals found when using the CLAI configuration only. Figure 12 illustrates the 2 m temperature normalised RMSE difference between the coupled forecast simulations based on the seasonal vegetation cover (FSCV) and the simulation based on the current operational static cover map (CTR). The operational analysis for the 4 seasons is used as a proxy for observations. The mixed signals in the verification emphasize that vegetation changes are not independent of other model settings which often require further optimisation of vegetation related parameters such us roughness length, vegetation minimum stomatal resistance and the surface skin conductivity. They all modulate the exchanges between the surface and the atmosphere and point to the need to further adjust this development as operationally viable for future cycles. Figure 12: 2 m temperature normalised RMSE difference between forecasts using the seasonal vegetation cover (FSCV) and the control simulations with static vegetation cover (CTR) for (**a**) DJF, (**b**) MAM, (**c**) JJA and (**d**) SON. Prognostic Vegetation Phenology Evaluation  
The prognostic phenology evaluation is performed with offline in-situ and offline global simulations driven by ERA-Interim forcing. A relaxation coefficient \(r_{B}\) (Equation (4)) of 0.05 day\({}^{-1}\) was used in this case. The results show that this simple growth model is able to detect growth anomalies with regards to a multi-year climatology. Figure 13 illustrates the above ground anomaly index (\(AI_{AGB}\)) for November 2010 with regards to the 1999-2013 climatology defined as  
\[AI_{AGB}=\frac{AGB_{Nor2010}-AGB_{CLIM_{1999-2013}}}{AGB_{CLIM_{1999-2013}}}. \tag{6}\]  
November 2010 is chosen because it is characterized by a drought in the Horn of Africa and a wet event corresponding to drought recovery in central and eastern Australia. Given the tight link assumed in this parametrization between the above ground biomass and LAI, this map correlates well with both Copernicus global land (CGLS) observation-based and simulated LAI anomaly maps of the same period (not shown). In the Horn of Africa, the above-ground biomass can be less than 20% of the 15 year mean (\(AI_{AGB}<-80\%\)), while for the Australia wet case, the anomaly is positive and in some areas it exceeds the 15 year mean by 60%. The in-situ evaluation is performed with the Flux tower network (FLUXNET) data ([PERSON] et al. [79]) and the WOrId FOod Studies (WOFOST) products which are currently developed by the Crop Growth Monitoring System (CGMS) within the Monitoring Agricultural Resources unit (MARS) of the European Joint Research Centre (JRC).