Advances in simulating atmospheric variability with the ECMWF model are presented that stem from revisions of the convection and diffusion parametrizations. The revisions concern in particular the introduction of a variable convective adjustment time-scale, a convective entrainment rate proportional to the environmental relative humidity, as well as free tropospheric diffusion coefficients for heat and momentum based on Monin-Obukhov functional dependencies.The forecasting system is evaluated against analyses and observations using high-resolution medium-range deterministic and ensemble forecasts, monthly and seasonal integrations, and decadal integrations with coupled atmosphere-ocean models. The results show a significantly higher and more realistic level of model activity in terms of the amplitude of tropical and extratropical mesoscale, synoptic and planetary perturbations. Importantly, with the higher variability and reduced bias not only the probabilistic scores are improved, but also the midlatitude deterministic scores in the short and medium ranges. Furthermore, for the first time the model is able to represent a realistic spectrum of convectively coupled equatorial Kelvin and Rossby waves, and maintains a realistic amplitude of the Madden-Julian oscillation (MJO) during monthly forecasts. However, the propagation speed of the MJO is slower than observed. The higher tropical tropospheric wave activity also results in better stratospheric temperatures and winds through the deposition of momentum.The partitioning between convective and resolved precipitation is unaffected by the model changes with roughly 62% of the total global precipitation being of the convective type. Finally, the changes in convection and diffusion parametrizations resulted in a larger spread of the ensemble forecasts, which allowed the amplitude of the initial perturbations in the ensemble prediction system to decrease by 30%.

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Abstract. Polar stratospheric clouds (PSCs) at 22-26 km were observed over the Norwegian mountains by airborne lidar on January 15, 1995. Simulations using a mesoscale model reveal that they were caused by mountain-induced gravity waves. The clouds had a highly detailed filamentary structure with bands as thin as 100 m in the vertical, and moved insignificantly over 4 hours, suggesting them to be quasi-stationary. The aircraft flight path was parallel or close to parallel with the wind at cloud level. Such a quasi-Lagrangian observation, together with the presence of distinct aerosol layers, •11ows •n air parcel trajectory through the cloud to be constructed and enables the lidar images to be simulated using a microphysical box model and light scattering calculations. The results yield detailed information about particle evolution in PSCs and suggest that water ice nucleated directly from liquid HNO3/H2SO4/H20 droplets as much as 4 K below the ice frost point. The observation of solid nitric acid hydrate particles downwind of the mountains shows that such mesoscale events can generate solid PSC particles that can persist on the synoptic scale. We also draw attention to the possible role of mesoscale PSCs in chlorine activation and subsequent ozone destruction.

Understanding model error in state-of-the-art numerical weather prediction models and representing its impact on flow-dependent predictability remains a complex and mostly unsolved problem. Here, a spectral stochastic kinetic energy backscatter scheme is used to simulate upscale-propagating errors caused by unresolved subgrid-scale processes. For this purpose, stochastic streamfunction perturbations are generated by autoregressive processes in spectral space and injected into regions where numerical integration schemes and parameterizations in the model lead to excessive systematic kinetic energy loss. It is demonstrated how output from coarse-grained high-resolution models can be used to inform the parameters of such a scheme. The performance of the spectral backscatter scheme is evaluated in the ensemble prediction system of the European Centre for Medium-Range Weather Forecasts. Its implementation in conjunction with reduced initial perturbations results in a better spread–error relationship, more realistic kinetic-energy spectra, a better representation of forecast-error growth, improved flow-dependent predictability, improved rainfall forecasts, and better probabilistic skill. The improvement is most pronounced in the tropics and for large-anomaly events. It is found that whereas a simplified scheme assuming a constant dissipation rate already has some positive impact, the best results are obtained for flow-dependent formulations of the unresolved processes.

Members in ensemble forecasts differ due to the representations of initial uncertainties and model uncertainties. The inclusion of stochastic schemes to represent model uncertainties has improved the probabilistic skill of the ECMWF ensemble by increasing reliability and reducing the error of the ensemble mean. Recent progress, challenges and future directions regarding stochastic representations of model uncertainties at ECMWF are described in this article. The coming years are likely to see a further increase in the use of ensemble methods in forecasts and assimilation. This will put increasing demands on the methods used to perturb the forecast model. An area that is receiving greater attention than 5–10 years ago is the physical consistency of the perturbations. Other areas where future efforts will be directed are the expansion of uncertainty representations to the dynamical core and other components of the Earth system, as well as the overall computational efficiency of representing model uncertainty.