## 3 Earth Surface Modelling Advances and Links with EO Datasets
### Land-Atmosphere Coupling  
A promising method of model evaluation is to study the dynamics of the system rather than its states. The response of the land surface to atmospheric drivers repeats after each rainfall event. The repeating wetting-drying cycle can be studied using different observable properties to identify the time response of the surface to the driver which is dependent on evapotranspiration. Refs. [251; 252] developed a systematic way of analysing the time-response of evaporation after a rainfall event by analysing the land surface temperature increase. The theory is that a dry soil with low evapotranspiration will warm up more quickly than a wet surface with high-evaporation. Since the dynamical time-response of the system is an inherent property of the land surface, rather than a result of the driving data, the method can be applied to coupled model simulations. The method was applied to evaluate a model in coupled mode by [253]. Two model configurations were tested and Figure 21 shows how the data favours one of the model configurations, at least for the spring months. In a similar vein, but at a much slower time-scale, EO observations of Total Water Storage (TWS) have been used [254] at the seasonal time-scale to evaluate evapotranspiration in a suite of land-surface models. Key properties of the models can be obtained. Similarly, a dynamic soil moisture global dataset based on EO data [255] has been obtained by characterizing the dry-down trends. Since EO soil moisture only captures near surface water, it can only be used for two of the three evaporation components (soil surface and interception) and not the larger transpiration. Ref. [256] quantified the power spectra of rainfall, EO soil moisture and modelled soil moisture allowing for inferring the low-pass filter effect of soil hydrology. Neither of these analyses have yet been used to evaluate land-surface model systematically, but the principle shows great promise. These new methods can further be backed up with in situ observations such as drying rates from Flux Tower or soil moisture sensors. Producing accurate forecasts of near-surface parameters becomes increasingly important as near-surface temperature, humidity and wind are used in a widening range of applications and are themselves highly relevant during extreme events (e.g., wind-storms, droughs and heat-waves). The representation of land-atmosphere interactions depend on a large number of processes. The exchange of energy or moisture between land surface and the atmosphere plays an important role for near-surface temperatures and humidity [70] while the roughness of the surface exerts a major control on near-surface winds. However, the parameterisation of these interactions is hindered by difficulties in estimating the land-atmosphere coupling strength from theory or observations. Thus, the parameterisation relies on a number of parameters, set to poorly constrained values, that depend at most on the vegetation type or sub-grid information (tile fractions). The skin layer conductivity, the roughness length for heat or momentum, the minimum canopy resistance, the dependence of the canopy resistance on the water vapour pressure deficit and the root distribution over the soil layers are just a few examples. In the past years, efforts have been made to (i) evaluate the sensitivity of near-surface variables to the values chosen for such parameters, and (ii) find ways in which to better constrain them [106]. The first obvious candidate was the roughness length for momentum, which has a strong influence on the representation of 10-m wind speeds. For many years, ECMWF forecasts overestimated the near-surface (10 m) wind speed over land. The mean forecast errors, with respect to routine SYNOP observations, range roughly between 0.5 and 1 m/s for various regions/times of the day. A stratification of the 10-m wind speed forecast errors by vegetation type showed that these  
Figure 21: Example of usage of land surface temperature during dry episodes after a precipitation event.