## 3 Earth Surface Modelling Advances and Links with EO Datasets
### Ocean Cryosphere: Snow and Ice
Commonly used sea-ice models, like the LIM2 sea ice model currently operational at ECMWF, feature a simple visco-plastic rheology and a single sea-ice thickness category. This can be improved upon e.g., by introducing multiple sea-ice categories that allow treatment of sub-grid-scale variability in the ice thickness, but large-scale observations of sea-ice thickness are urgently needed to validate these new model developments. At present, there are large discrepancies in the representation of thickness of thin sea ice between observational products derived from L-band and ocean analyses with a prognostic ice model, assimilating sea-ice concentration but not sea-ice thickness [292]. This is not entirely due to limitations in the sea-ice model or data assimilation methods, but systematic biases in the observational data set also play a role. Further improvements in the retrieval methods are clearly needed [293], which is also important for the development of the ocean-ice model. Figure 24: Daily-mean fields for 16 April 2017 showing **(a)** brightness temperature in L-band from SMOS (K); **(b)** sea-ice thickness from the University of Hamburg SMOS sea-ice thickness product (m); and **(c)** observed sea-ice concentration from the EUMETSAT Ocean and Sea–Ice Satellite Application Facility (OSI-SAF). Brightness temperatures over land are masked out. The greyed-out area around the North Pole does not have observations due to the characteristics of orbit and instrumentation of the SMOS satellite. Figure 23: Evolution of sea ice concentration and the preceding 24-h mean 10-m wind field for forecasts initialised on 18 February 2018 and valid on **(a)** 19 February; **(b)** 21 February and **(c)** 24 February; **(d)** shows the Synthetic Aperture Radar (SAR) image from the Sentinel-1B satellite for 24 February around Cape Morris Jesup. (Satellite image: European Space Agency via Danish Meteorological Institute). but the current sea-ice thickness data sets from L-band radiance have already proven highly valuable for exposing weaknesses in currently used sea-ice models. Hence, sea-ice thickness observations from L-band are essential to further improve prognostic sea-ice models to the benefit of advancing characterization and prediction of polar regions. Another sea ice model development has been the parametrization of melt ponds, a feature essential for correct representation of the surface albedo of the ice and therefore capturing the correct evolution of the sea ice melt during the summer. In the past melt pond fractions have been estimated from field campaigns (e.g., [294]), but coverage of the whole Arctic basin are now possible, such as the data set produced by [295], which enables assessment of model parametrization schemes (e.g., [296])  
For processes and parameters that are particularly important for Earth system model development, such as near surface temperature over polar oceans, there is a reliance on reanalysis data which can only be assessed in terms of performance with relatively limited in situ observational data within the ice [297] or using land based in situ data [298]. Sea ice and snow surface temperature data sets derived from different satellite products are starting to be used in data assimilation and are allowing the ongoing development of the sea ice model themselves [299]. Another example from the coupled system development is that data from CALIPSO has helped to identify model weakness in representing marine stratus cloud. It also helped to identify areas of model physics that would benefit from further development [300].