A new parametrization is presented for the representation in large‐scale models of turbulent form drag due to subgrid orography with horizontal scales smaller than around 5000 m. The scheme is based on earlier work in which surface stress and its vertical distribution is formulated in terms of a slope parameter for sinusoidal hills. The new elements of the current parametrization are: (i) the spectrum of the orography is represented by a piecewise empirical power law, (ii) the turbulent form drag scheme integrates over the spectral orography to represent all the relevant scales, and (iii) the wind forcing level of the drag scheme depends on the horizontal scale of the orography, which implies that the smaller scales respond to lighter winds than do the larger scales.The scheme is controlled by a single parameter, namely the spectral density of the orography at scales of a few kilometres, as can be accurately obtained from a global 1 km data set. The empirical orographic power‐law spectrum is derived by analysing fine resolution data as available for the USA. In order to be applicable in a large‐scale model, simplifications are applied. Single‐column simulations are performed to assess the consequences of these simplifications. Comparisons of single‐column simulations with output from fine‐scale simulations for sinusoidal hills show that the approximations are within the uncertainty of the fine‐scale models. Furthermore, it is felt that the scheme is sufficiently accurate given the uncertainty in the characterization of the orography with current global datasets. © Royal Meteorological Society, 2004. Contributions by A. R. Brown and N. Wood are Crown copyright.

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

The land-surface developments of the European Centre for Medium-range Weather Forecasts (ECMWF) are based on the Carbon-Hydrology Tiled Scheme for Surface Exchanges over Land (CHTESSEL) and form an integral part of the Integrated Forecasting System (IFS), supporting a wide range of global weather, climate and environmental applications. In order to structure, coordinate and focus future developments and benefit from international collaboration in new areas, a flexible system named ECLand, which would facilitate modular extensions to support numerical weather prediction (NWP) and society-relevant operational services, for example, Copernicus, is presented. This paper introduces recent examples of novel ECLand developments on (i) vegetation; (ii) snow; (iii) soil; (iv) open water/lake; (v) river/inundation; and (vi) urban areas. The developments are evaluated separately with long-range, atmosphere-forced surface offline simulations and coupled land-atmosphere-ocean experiments. This illustrates the benchmark criteria for assessing both process fidelity with regards to land surface fluxes and reservoirs of the water-energy-carbon exchange on the one hand, and on the other hand the requirements of ECMWF’s NWP, climate and atmospheric composition monitoring services using an Earth system assimilation and prediction framework.

A generalized coupling is proposed between atmospheric models and surface schemes (land and ocean). A set of input and output variables is defined for this purpose in such a way that it can be used by many current and future models, including mosaic or tile schemes. The basic concept is to pass atmospheric variables from the lowest model level and their relation to corresponding fluxes to the surface scheme. The surface scheme returns the fluxes. In this framework, there is no need for the atmospheric model to have detailed information about the surface. Only the result of the surface computations is needed; namely, the fluxes, which are applied as a boundary condition. The equations for fully implicit coupling are derived, and the relevance for numerical stability is demonstrated. It is also shown that fully implicit coupling in a tile scheme leads to more robust results than partially implicit coupling.

Coupling the atmosphere with the underlying surface presents numerical stability challenges in cost-effective model integrations used for operational weather prediction or climate simulations. These are due to the choice of large integration time steps compared to the physical timescale of the problem, aiming at reducing computational burden, and to an explicit flux coupling formulation, often preferred for its simplicity and modularity. Atmospheric models therefore use the surface-layer temperatures (representative of the uppermost soil, snow, ice, water, etc.) at the previous integration time step in all surface–atmosphere heat-flux calculations and prescribe fluxes to be used in the surface model integrations. Although both models may use implicit formulations for the time steps, the explicit flux coupling can still lead to instabilities.In this study, idealized simulations with a fully coupled implicit system are performed to derive an empirical relation between surface heat flux and surface temperature at the new time level. Such a relation mimics the fully implicit formulation by allowing one to estimate the surface temperature at the new time level without solving the surface heat diffusion problem. It is based on similarity reasoning and applies to any medium with constant heat diffusion and heat capacity parameters. The advantage is that modularity of the code is maintained and that the heat flux can be computed in the atmospheric model in such a way that instabilities in the snow or ice code are avoided. Applicability to snow–ice–soil models with variable density is discussed, and the loss of accuracy turns out to be small. A formal stability analysis confirms that the parametrized implicit-flux coupling is unconditionally stable.

The numerical aspects of physical parametrization are discussed mainly in the context of the ECMWF Integrated Forecasting System. Two time integration techniques are discussed. With parallel splitting the tendencies of all the parametrized processes are computed independently of each other. With sequential splitting, tendencies of the explicit processes are computed first and are used as input to the subsequent implicit fast process. It is argued that sequential splitting is better than parallel splitting for problems with multiple time scales, because a balance between processes is obtained during the time integration. It is shown that sequential splitting applied to boundary layer diffusion in the ECMWF model leads to much smaller time truncation errors than does parallel splitting. The so called Semi-Lagrangian Averaging of Physical Parametrizations (SLAVEPP), as implemented in the ECMWF model, is explained. The scheme reduces time truncation errors compared to standard first order methods, although a few implementation questions remain. In the scheme fast and slow processes are handled differently and it remains a research topic to find the optimal way of handling convection and clouds. Process specific numerical issues are discussed in the context of the ECMWF parametrization package. Examples are the non-linear stability problems in the vertical diffusion scheme, the stability related mass flux limit in the convection scheme and the fast processes in the cloud microphysics. Vertical resolution in the land surface scheme is inspired by the requirement to represent diurnal to annual time scales. Finally, a coupling strategy between atmospheric models and land surface schemes is discussed. It allows for fully implicit coupling also for tiled land surface schemes.