[1] In the 1990s, scientists at European Centre for Medium-Range Weather Forecasts (ECMWF) suggested that artificially enhancing turbulent diffusion in stable conditions improves the representation of two important aspects of weather forecasts, i.e., near-surface temperatures and synoptic cyclones. Since then, this practice has often been used for tuning the large-scale performance of operational numerical weather prediction (NWP) models, although it is widely recognized to be detrimental for an accurate representation of stable boundary layers. Here we investigate why, 20 years on, such a compromise is still needed in the ECMWF model. We find that reduced turbulent diffusion in stable conditions improves the representation of winds in stable boundary layers, but it deteriorates the large-scale flow and the near-surface temperatures. This suggests that enhanced diffusion is still needed to compensate for errors caused by other poorly represented processes. Among these, we identify the orographic drag, which influences the large-scale flow in a similar way to the turbulence closure for stable conditions, and the strength of the land-atmosphere coupling, which partially controls the near-surface temperatures. We also take a closer look at the relationship between the turbulence closure in stable conditions and the large-scale flow, which was not investigated in detail with a global NWP model. We demonstrate that the turbulent diffusion in stable conditions affects the large-scale flow by modulating not only the strength of synoptic cyclones and anticyclones, but also the amplitude of the planetary-scale standing waves.Citation: Sandu, I., A. Beljaars, P. Bechtold, T. Mauritsen, and G. Balsamo, Why is it so difficult to represent stably stratified conditions in numerical weather prediction (NWP) models?, J. Adv. Model. Earth Syst., 5, 117-133,
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.
Large-eddy simulation (LES) is used to explore the role of various processes in regulating the stratocumulus to cumulus transition (SCT). Simulations are based on a composite case derived from a Lagrangian analysis of 2 yr of data from the northeastern Pacific. The simulations reproduce well the observed transition from a compact stratocumulus layer to more broken fields of cumulus, simply as a response to increasing sea surface temperatures (SSTs) along the transition. In so doing they support earlier theoretical work that argued that the SCT was a response of boundary layer circulations to increased forcing by surface latent heat fluxes. Although the basic features of the SCT imposed by the increase in SST are robust, a variety of other factors affect the detailed character of the SCT. For example, enhanced precipitation or increased downwelling longwave radiative fluxes can accelerate the reduction in cloud cover that accompanies the SCT, while a gradual decrease in the large-scale divergence can make changes in cloud cover that accompany the SCT relatively more modest. The simulations also demonstrate that the pace of the SCT is mainly set by the strength of the temperature inversion capping the initial stratocumulus-topped boundary layer.
[1] CGILS-the CFMIP-GASS Intercomparison of Large Eddy Models (LESs) and single column models (SCMs)-investigates the mechanisms of cloud feedback in SCMs and LESs under idealized climate change perturbation. This paper describes the CGILS results from 15 SCMs and 8 LES models. Three cloud regimes over the subtropical oceans are studied: shallow cumulus, cumulus under stratocumulus, and wellmixed coastal stratus/stratocumulus. In the stratocumulus and coastal stratus regimes, SCMs without activated shallow convection generally simulated negative cloud feedbacks, while models with active shallow convection generally simulated positive cloud feedbacks. In the shallow cumulus alone regime, this relationship is less clear, likely due to the changes in cloud depth, lateral mixing, and precipitation or a combination of them. The majority of LES models simulated negative cloud feedback in the wellmixed coastal stratus/stratocumulus regime, and positive feedback in the shallow cumulus and stratocumulus regime. A general framework is provided to interpret SCM results: in a warmer climate, the moistening rate of the cloudy layer associated with the surface-based turbulence parameterization is enhanced; together with weaker VOL. 5, 826-842, doi:10.1002/2013MS000246, 2013 large-scale subsidence, it causes negative cloud feedback. In contrast, in the warmer climate, the drying rate associated with the shallow convection scheme is enhanced. This causes positive cloud feedback. These mechanisms are summarized as the ''NESTS'' negative cloud feedback and the ''SCOPE'' positive cloud feedback (Negative feedback from Surface Turbulence under weaker Subsidence-Shallow Convection PositivE feedback) with the net cloud feedback depending on how the two opposing effects counteract each other. The LES results are consistent with these interpretations.Citation: Zhang, M., et al. (2013), CGILS: Results from the first phase of an international project to understand the physical mechanisms of low cloud feedbacks in single column models, J. Adv. Model. Earth Syst., 5, 826-842,
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