## 3 Earth Surface Modelling Advances and Links with EO Datasets
### Ocean Cryosphere: Snow and Ice  
The presence of sea ice on the ocean drastically alters air-sea fluxes of heat and momentum. Hence, sea ice is an important component of Earth system models at high latitudes. As operational  
Figure 22: AMSR2 Observations of SST on 10 July 2014 after the passage of Tropical Cyclone Neoguri (**upper**), ECMWF uncoupled day-5 forecast (**middle**), ECMWF coupled ocean forecast (**lower**) valid at observation time. centres incorporate the ocean cryosphere within seamless prediction systems (i.e., in forecast systems spanning days to decades), there is an increasing demand for high-latitude coverage of observations to compare models for assessment of forecast performance and support ongoing development of the model, its coupling and data assimilation systems. As the polar regions have relatively sparse in situ observations, model development relies heavily on the earth observation data. It is important to note that this also requires the EO products to have a good estimate of the uncertainty as there are limited independent data sets to validate against. Recent advances in EO products are allowing model developments to be compared across the entire Arctic domain rather than just using limited in situ data at a single point. For the Arctic cryosphere, this is also important as the region is changing so rapidly. Field campaigns that have provided large datasets for model development such as SHEBA [279] may well be less representative of the ice conditions in recent years as much of the multi-year ice has been lost. There are several products that are available operationally to describe the sea ice field. They are derived from brightness temperatures from various instruments and scatterometer data. These describe: the cover in terms of concentration, type, and edge; physical properties such as emissivity, skin temperature and thickness; and the dynamic properties in the form of motion (drift). Of these EO measurements, the most established for use in NWP is sea ice concentration. It is one of the few fields routinely assimilated by operational centres (e.g., [280; 281; 282]) and also the main type of observation that the models are verified against (e.g., [283; 284; 285; 286; 287]). An example case study is shown in Figure 23 of an unusual event occurring to the north of Greenland that was captured in the model and also observed. Here, the performance of the coupled ECMWF HRES forecast is compared to the sea ice concentration as shown in the SAR image. In recent years, it has become increasingly clear that thickness, i.e., the vertical distance from the air-ice interface at the top to the ice-water interface at the bottom, is essential for sea-ice modelling and prediction [288; 289; 290]. This is because thin ice allows much stronger conductive heat fluxes than thick ice, and it evolves much more quickly than thick ice because it is more susceptible to dispersion or compression by winds, and it can melt or grow more rapidly. One way to observe the thickness of sea ice from space is to measure the surface emissivity in the L-band, which is sensitive to sea-ice thickness if the surface is dry, the ice temperature is well below freezing, and the ice is thinner than about 1 m [291]. This allows detection of areas which are covered by thin ice even though the sea-ice area fraction might be close to 100%. Typically, this occurs close to the ice edge, where new ice forms during seasonal ice advance. However, thin ice is also present in polynyas and fracture zones within the ice pack. These are frequent in winter where the old ice breaks up under the force of winds, and new ice forms rapidly on the exposed water surface (see Figure 24). This thin ice is difficult to model because it requires a good representation of the complex mechanical properties of sea ice combined with a good representation of its sub-grid-scale thickness distribution.