The parameterization of the stably stratified atmospheric boundary layer is a difficult issue, having a significant impact on medium-range weather forecasts and climate integrations. To pursue this further, a moderately stratified Arctic case is simulated by nineteen single-column turbulence schemes. Statistics from a large-eddy simulation intercomparison made for the same case by eleven different models are used as a guiding reference. The single-column parameterizations include research and operational schemes from major forecast and climate research centres. Results from first-order schemes, a large number of turbulence kinetic energy closures, and other models were used. There is a large spread in the results; in general, the operational schemes mix over a deeper layer than the research schemes, and the turbulence kinetic energy and other higher-order closures give results closer to the statistics obtained from the large-eddy simulations. The sensitivities of the schemes to the parameters of their turbulence closures are partially explored.

This study examines the spatial variability of the nocturnal wind field using eight networks of surface observations ranging in horizontal width from 500 m to 65 km. The wind field is partitioned into small-scale variability (submeso motions) and the spatially-averaged wind vector. The vector-averaged wind is analogous to the wind resolved by a numerical model, posed here in terms of the wind that is vector averaged over an observational network. The small-scale variability represents the unresolved subgrid (sub-network) variation estimated in terms of the spatial variation of the wind vector within the observational domain. The bulk formula for the spatially-averaged heat flux is modified to account for the subgrid variation of the wind field. Investigation of the spatial variability of the wind field is also motivated by the need to estimate the representativeness of observations of the wind vector at an individual measurement site with respect to the wind field over the surrounding landscape. The small-scale variability of the observed wind field is contrasted between the networks as a function of the spatially-averaged wind vector, stratification, size of the network, and the topography. A strong dependence on topography emerges in spite of different instrumentation, deployment strategy, and processing for each network. Even weak topography can be important. A better design for future observational networks is briefly discussed.

In this study, turbulent heat flux data from two sites within the Baltic Sea are compared with estimates from two models. The main focus is on the latent heat flux. The measuring sites are located on small islands close to the islands of Bornholm and Gotland. Both sites have a wide wind direction sector with undisturbed over-water fetch. Mean parameters and direct fluxes were measured on masts during May to December 1998. The two models used in this study are the regional-scale atmospheric model HIRLAM and the ocean model PROBE-Baltic. It is shown that both models overestimate the sensible and latent heat fluxes. The overestimation can, to a large extent, be explained by errors in the air-water temperature and humidity differences. From comparing observed and modelled data, the estimated 8-month mean errors in temperature and humidity are up to 1 degreesC and 1 g kg(-1),respectively. The mean errors in the sensible and latent heat fluxes for the same period are approximately 15 and 30 W m(-2), respectively. Bulk transfer coefficients used for calculating heat and humidity fluxes at the surface were shown to agree rather well with the measurements, at least for the unstable data. For stable stratification, the scatter in data is generally large, and it appears that the bulk formulation chosen overestimates turbulent heat fluxes.

A new spectral closure model of stably stratified turbulence is used to develop a K - epsilon model suitable for applications to the atmospheric boundary layer. This K - epsilon model utilizes vertical viscosity and diffusivity obtained from the spectral theory. In the epsilon equation, the Coriolis parameter-dependent formulation of the coefficient C-1 suggested by Detering and Etling is generalized to include the dependence on the Brunt-Vaisala frequency, N. The new K - epsilon model is tested in simulations of the ABL over sea ice and compared with observations from BASE as simulated in large-eddy simulations by Kosovic and Curry, and observations from SHEBA.

A primary climate change signal in the central Arctic is the melting of sea ice. This is dependent on the interplay between the atmosphere and the sea ice, which is critically dependent on the exchange of momentum, heat and moisture at the surface. In assessing the realism of climate change scenarios it is vital to know the quality by which these exchanges are modelled in climate simulations. Six state-of-the-art regional-climate models are run for one year in the western Arctic, on a common domain that encompasses the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment ice-drift track. Surface variables, surface fluxes and the vertical structure of the lower troposphere are evaluated using data from the SHEBA experiment. All the models are driven by the same lateral boundary conditions, sea-ice fraction and sea and sea-ice surface temperatures. Surface pressure, near-surface air temperature, specific humidity and wind speed agree well with observations, with a falling degree of accuracy in that order. Wind speeds have systematic biases in some models, by as much as a few metres per second. The surface radiation fluxes are also surprisingly accurate, given the complexity of the problem. The turbulent momentum flux is acceptable, on average, in most models, but the turbulent heat fluxes are, however, mostly unreliable. Their correlation with observed fluxes is, in principle, insignificant, and they accumulate over a year to values an order of magnitude larger than observed. Typical instantaneous errors are easily of the same order of magnitude as the observed net atmospheric heat flux. In the light of the sensitivity of the atmosphere-ice interaction to errors in these fluxes, the ice-melt in climate change scenarios must be viewed with considerable caution.

A combination of methods originating from non-stationary time-series analysis is applied to two datasets of near-surface turbulence in order to gain insights on the non-stationary enhancement mechanism of intermittent turbulence in the stable atmospheric boundary layer (SBL). We identify regimes of SBL turbulence for which the range of time scales of turbulence and submeso motions, and hence their scale separation (or lack of separation), differs. Ubiquitous flow structures, or events, are extracted from the turbulence data in each flow regime. We relate flow regimes characterized by very stable stratification, but differing in the dynamical interactions and in the transport properties of different scales of motion, to a signature of flow structures thought to be submeso motions.