Other model parameterizations in global models

parameterizations over the next 5 to 10 years will result, with concomitant improvements in global weather and climate predictions.

10.2.2 Other model parameterizations in global models

With the availability of more computer power, the resolution of global models is increasing to the point where convection is partially resolved (as discussed above), orography is better represented, and the land characterization can have more details. Also, data assimilation is expected to make better use of satellite observations that are affected by clouds, precipitation and land surface. The observations will not only help to define the state variables of the atmosphere, but will also inspire and inform parameterization development. All these aspects will raise new parameterization issues and will require extensive research. In principle, some of these issues are already addressed now in the context of high-resolution regional models (see Section 10.2.3). However, the requirements of global models put a much stronger constraint on sub-grid schemes as they have to work adequately in different climate regimes.

Clouds

At a recent European Center for Medium range Weather Forecasting (ECMWF) workshop on cloud parameterization (ECMWF, 2013), it was concluded that the following areas require more attention:

(i) microphysics, (ii) the representation of sub-grid variability, and (iii) the use of observations. Bulk microphysics schemes will be necessary at all resolutions, but it is by no means clear what

complexity is optimal at what resolution. The representation of microphysics needs to be, as much as possible, resolution independent or resolution aware. Current complexity in global models appears appropriate with prognostic variables for cloud water, cloud ice, rain and snow, but in future additional variables for number concentrations may be beneficial. The interaction of aerosols with microphysics still requires more research before conclusions for parameterization can be drawn.

Regarding the representation of sub-grid variability of clouds, various approaches are in use e.g.

through a PDF scheme with prognostic variables for higher order moments (Golaz et al. 2002) or simply cloud cover (Ahlgrimm and Forbes, 2014). At this stage it is not clear what the optimal approach is and at what resolution an all or nothing scheme will suffice. It should be noted that cloud/radiation interaction is highly non-linear and therefore a representation of cloud

heterogeneity is likely to remain an important aspect at all resolutions. The ECMWF workshop concluded that a hybrid formulation based on the use of a cloud cover variable and assumptions about in-cloud PDF’s may be a suitable research direction for many years. The role of LES models is believed to be important as these models can provide sources and sinks for the prognostic equations and information about the PDF’s in various cloud regimes. Another major issue is the role and behaviour of mixed phase clouds, because the way these clouds are maintained is still very uncertain (Shupe et al. 2008). Observations and verification will play a key role in all aspects of the cloud parameterization research. At this stage only a small fraction of the available passive microwave and active radar/lidar observations has been explored (Illingworth et al. 2015), so it is expected that further exploitation will lead to many improvements in cloud and precipitation formulations of global models.

Radiation

Radiation is probably the least controversial of all model processes and is well supported by line-by-line computations. However, it still poses major parameterization issues. Current radiation schemes can compute clear sky radiation to a high level of accuracy, but such computations tend to be expensive and the available codes make compromises between accuracy and efficiency, e.g.

by limiting spectral, spatial and temporal resolution. This will remain an active area of research also in view of changing computer architectures (highly parallel and/or supported by dedicated

accelerator processors). Cloud optical properties and the representation of cloud heterogeneity will remain an important topic (Hogan and Illingworth, 2000). Also, the representation of 3D effects will become increasingly relevant at high resolution (Hogan and Kew, 2006), (Wissmeier et al. 2013).

The two way interaction between cloud dynamics and radiation, e.g. through cloud top cooling, will become more important, and might require high frequency coupling between the radiation and cloud schemes.

The planetary boundary layer

Parameterizations of the boundary layer and shallow convection are still needed even when deep convection is reasonably resolved at e.g. 500 m resolution. The boundary layer parameterization problem will only be alleviated when another order of magnitude resolution increase can be

achieved, e.g. 100 m resolution. In all cases an appropriate 3D sub-grid model will be required, but the expectation is that the sensitivity to the representation of sub-grid turbulence becomes less dominant in LES where the sub-grid model is in the isotropic turbulence cascade regime (at least for convective boundary layers). Simulations at LES resolution of 100 m can already be performed over considerable domains and will help to address the parameterization issues at resolutions between 20 km and 1 km resolution, which will be the resolution of many global models over the coming 10 years. Current boundary layer schemes tend to use a combination of diffusion for dry turbulent transport and a mass flux approach for cloudy boundary layers. Some of them are supported by a turbulent kinetic energy equation. Interestingly, many traditional issues with boundary layer, and boundary layer cloud, representations in large scale models are still

unresolved: notable examples are the lack of wind turning (Brown et al. 2006), the lack of response of surface wind speed to stability (Chelton et al. 2001), the excessive diffusion in stable situations and the associated underestimation of nocturnal jets (Holtslag et al. 2013), the uncertainty in cloud forcing in climate models (Bony and Dufresne, 2005), the difficulties with the transition from

cumulus to stratocumulus, and the lack of understanding of the physics of mixed phase clouds (Shupe et al. 2008). Another issue is the representation of the surface boundary condition for momentum over heterogeneous terrain.

The need for excessive diffusion in the stable boundary layer, often formulated by so-called long-tail stability functions, is probably one of the more pressing and poorly understood issues as it hinders the introduction of Turbulent Kinetic Energy (TKE) formulations. The strong diffusion affects surface drag, the thermal coupling with the underlying surface (particularly at high latitudes) and the amplitude of the diurnal cycle for temperature. It is very well possible that the underlying reason for the excessive diffusion in the stable boundary layer is the lack of shallow vertical shears in large scale models which might be due to unresolved mesoscale variability, e.g. variability related to inertial and gravity waves over land and topography. Many of the issues listed above can be studied by making use of LES simulations over a large domain. It is important of course to verify whether the fine scale simulations and the derived parameterizations show the correct dependence on forcing parameters as observed. Examples of such critical dependencies are the relation of boundary layer cloud cover with inversion strength (Klein and Hartmann, 1993) and the

dependence of the amplitude of the diurnal cycle on wind speed and radiation (Betts, 2006).

Land momentum issues and sub-grid orography

It is well known that numerical weather prediction models respond strongly to the formulation of various surface drag formulations (Sandu et al. 2013). Drag at the surface is exerted by the

resolved orography, sub-grid orography schemes and the boundary layer scheme, and the relative magnitudes of these contributions depend on model resolution. None of these contributions can be evaluated from observations on a routine basis, and experimental campaigns over heterogeneous terrain are extremely rare and often limited (e.g. Bougeault et al. 1997). The basic concepts to represent the different types of drag are well established: i) empirical tables to link surface roughness to vegetation type; ii) effective roughness (Grant and Mason, 2006) or turbulent orographic form drag (Beljaars et al. 2004); iii) flow blocking by sub-grid orography (Lott and Miller, 1997); and iv) gravity wave generation by sub-grid orography (Miller et al. 1989). However, these schemes are often developed with idealized topography in mind, they are difficult to connect to real terrain characteristics and they have many empirical constants. Because direct verification is difficult or impossible, the drag representation is uncertain, and considerable optimization/tuning is necessary on the basis of forecast experiments. In a recent WMO/WGNE initiative, different operational Numerical Weather Prediction (NWP) models were compared and it was concluded that

surface drag is rather different in the models. The differences are particularly large for the

individual components and for the diurnal cycle, illustrating that the modulation of drag by stability is completely different in different models. The main difficulty in models is, on the one hand, to characterize the land surface in terms of heterogeneity (from vegetation cover to complex

orography) and on the other hand, to have formulations that use the land information and convert it into reasonable drag values dependent on wind speed and stability. Most of the theory and

schemes have also been developed for uniform flow and stability whereas in reality wind and stability vary with height. To progress in this area of research, more comprehensive use should be made of simulations over real terrain. Routinely run limited area models could be used to try and diagnose resolved drag for topography and provide “ground truth” over a limited area for the global parameterized models. However this in itself needs careful study as these models are not using

‘real’ orographies and will have biases in boundary layer characteristics. Similar simulations could be performed at much higher resolutions by LES models with the purpose of developing

parameterizations of drag for heterogeneously vegetated terrain (or effective roughness including stability dependence). This has to be accompanied by activities to map and characterize

heterogeneous terrain e.g. from space (lidar) observations.

Land surface schemes

The main role of a land surface scheme in atmospheric models is to provide a surface boundary condition for heat and moisture fluxes (see Balsamo et al. (2014) for a review). The more

comprehensive versions of these models also handle carbon, aerosols and other tracers. State-of-the-art land surface schemes in large-scale models are necessarily highly empirical as it is

currently impossible to describe the relevant processes in all their complexity and detail. However, these complex processes, occurring over a wide range of spatial and temporal scales, govern the energy and water cycles at global scale, and the large-scale budgets are obviously a priority in global models. The water budget is a clear example, as precipitation falls on the ground in a non-uniform way, some of it is intercepted by the vegetation and evaporates again, and another part falls through and can either run off on the surface or infiltrate into the soil. Soil texture, being highly heterogeneous, affects vertical transport and horizontal water transport to rivers. Evaporation is another important component of the water budget. It is linked to the available energy, but is also highly regulated by vegetation through root distribution and plant physiological processes. The consequence of all this complexity is that it is difficult to build a land surface scheme from the smallest scale of individual leaves and plants and the smallest elements of soil heterogeneity and to integrate such a description to effective scales of the order of 10 km. A related difficulty is that it is impossible to characterize the surface vegetation and soil with sufficient accuracy over the whole globe since accurate datasets to support such characterization do not presently exist. This is the reason that a bulk parameterization of land surface processes is necessary and inevitably there are strong elements of inverse modelling (i.e. parameters have to be optimized on the basis of the results). Having observational information strongly related to the processes that are modelled is important; otherwise the introduction of compensating errors is very likely. Major challenges for the coming years are: i) to achieve consistency between model components e.g. between carbon uptake by vegetation and transpiration (Boussetta et al. 2013a); ii) to represent all the time scales, e.g. from the fast evaporation from plant-intercepted water to the diurnal and seasonal time scales (Gentine et al. 2011); iii) to avoid compensating errors e.g. between bare soil evaporation and transpiration (Lawrence et al. 2007); iv) to integrate as much as possible all available observations e.g. skin temperature and vegetation observations (Trigo and Viterbo, 2003; Bousetta et al.

2013b); and v) to develop a comprehensive benchmarking system e.g. covering the full water budget from precipitation and evaporation to runoff (Hirschi et al. 2006; Blyth et al. 2011).

Integrating diverse observational sources will require a more holistic approach to parameterization testing, and should be combined with innovative applications of data assimilation techniques extending parameter space. Improved understanding of surface process mechanisms will rely on the combination of optimal estimation techniques and modelling, to identify optimal parameters and also to better identify certain limits within the existing schemes.