## 4 Evaluation of Model Developments
### Evaluation of Updated Snow
#### 4.4.3 Global Coupled Evaluation  
Evaluation of the multi-layer snow scheme in coupled forecasts has been performed over multiple winter periods looking at the impact on weather parameters and surface fluxes using a wide range of diagnostics ([PERSON] et al., 2018; [PERSON] et al., 2018). Using a thinner snow layer with a low thermal inertia enables better representation of strong cooling events occurring in snow-covered conditions, because the snow surface responds on quicker time-scales to changes in the atmospheric forcing. This leads to an increase of the amplitude of the diurnal cycle of 2 m temperature over snow-covered surfaces, as well as to an increase of its variability.  
Figure 18 shows the absolute mean bias and root-mean-square error differences over a winter period (DJF) between coupled forecasts at a lead time of 2 days using the multi-layer snow scheme and the current single-layer snow scheme. Two metre temperature observations from the synoptic station network, used routinely in the ECMWF assimilation system for monitoring and diagnostics, are used as a benchmark.  
Figure 16: Time series of snow water equivalent (SWE), snow Depth, and snow Density for Swamp Angel (swa) from observations (Obs, black dots) and the offline experiments using the single-layer (SL, red lines), multilayer (ML5, blue lines), and multilayer without the snow depth-dependent snow layer thickness described in Section 3.2 (ML-noOro, green lines). The latter configuration corresponds to a simulation with the same vertical discretization of the snowpack as used by [PERSON] et al. (2018).  
Results indicate a reduction of the mean error in particular in the high-latitude regions, which can be associated to an improved prediction of very cold temperatures over snow-covered regions. The RMSE of T2 m is reduced in the snow multi-layer forecasts over most of Eurasia regions, however it increases over certain regions, for instance in Alaska and West Scandinavia. This can be partly associated to errors in other variables and processes, like cloud cover and/or cloud microphysics, for which the new scheme is more exposed more \"responsive\", as described in [41]. However, the increase of RMSE over complex terrain can be also related to an increase of the preexisting biases in the control forecasts, which can be partly due to the relatively low horizontal resolution used in these experiments (25 km). The increase in variability can be advantageous in a probabilistic framework, through the increase in forecast spread [92]. The overall results of the updated snow scheme indicate its readiness for implementation in the next operational cycle.  
Figure 17: (**Left**) RMSE difference of snow depth between global offline simulation using the multi-layer (ML5) and single-layer (SL) snow schemes for December to May period from 2014 to 2018. Blue colours indicate reduced RMSE in ML5. (**Right**) boxplot of snow depth statistics (bias and RMSE) using stations considered in left panel.  
Figure 18: Temperature verification at screen level (T2m). (**a**) Absolute mean error difference (amel) at a lead time of 2 days between coupled forecasts with the multi-layer (snowML) and single-layer (snowSL) snow schemes; (**b**) Same as (**a**), but showing root-mean-square-error difference (rmsef).