No Arabic abstract
The daily cycle of heating and cooling of the near-surface ocean may be quite different in a shallow lagoon with a few meters deep seafloor that can be heated directly by the sun. If important, the solar radiation will affect the local benthic communities. To study the physical processes associated with the daily cycle of south-Pacific lagoon Bora Bora, a vertical string of five high-resolution temperature sensors was moored at a 2-m deep site for 3 weeks. Besides the standard ocean warming (approximately during daytime) and cooling (approximately nighttime), the sensors show relatively highest temperature near the lagoon-floor during the warming phase and a weakly stable stratification towards the end of the cooling phase. During the warming phase, highly variable stratification is observed extending into the water column under calm weather and turbid waters, otherwise not. Under trade wind and clear waters, the lowest sensor(s) show(s) consistently higher temperature variability than sensors higher-up with spectral slopes indicative of shear- and/or convective turbulence. During the cooling phase, the lower sensor shows consistently very low variance (non-turbulent), while other sensors show a spectral slope around the buoyancy frequency evidencing weakly stratified waters supporting internal waves. These observations contrast with open-ocean near-surface observations of stable stratification during the warming phase and of turbulent free convection during the cooling phase. Thus, lagoons seem to more resemble the atmosphere than the ocean in daytime thermodynamics and possibly act as a natural solar pond with bottom conductive heating (when salinity compensates for unstable temperature variations).
Knowledge about the characteristics of the atmospheric boundary layer are vital for the redistribution of air and suspended contents that are particularly driven by turbulent motions. Despite many modelling studies, detailed observations are still demanded of the development of turbulent exchange under stable and unstable conditions. In this paper we present an attempt to observationally detail atmospheric internal waves, under stable conditions, and associated turbulent overturning, under quasi-stable and unstable conditions. Therefore, we mounted 198 high-resolution temperature T-sensors on a cable. The instrumented cable was attached along the 213 m tall mast of Cabauw, the Netherlands, during late-summer 2017. The mast has standard and special meteorological equipment at extendable booms every 20 m in height. A sonic turbulence anemometer is at 60 m above ground. The extra, originally underwater-, T-sensor cable was suspended down from the 206-m level, temporarily for about 3 months. While in water the sensors have a response time of tw=0.4 s and drift of 0.001 degC per month, in air the response time ta=3 s is relatively slow and the apparent drift of about 0.1 degC per month relatively large. Least performance is during daytime. These T-sensor characteristics hamper quantitative atmospheric turbulence research, as it results in a relatively narrow inertial subrange of only one order of magnitude. Nevertheless, height-time images from two contrasting days show common nocturnal marginally stable density stratification supporting internal waves up to the buoyancy period of about 300 s, shear and convective deformation of the stratification over the entire 197 m range of observations.
Ocean swell plays an important role in the transport of energy across the ocean, yet its evolution is still not well understood. In the late 1960s, the nonlinear Schr{o}dinger (NLS) equation was derived as a model for the propagation of ocean swell over large distances. More recently, a number of dissipative generalizations of the NLS equation based on a simple dissipation assumption have been proposed. These models have been shown to accurately model wave evolution in the laboratory setting, but their validity in modeling ocean swell has not previously been examined. We study the efficacy of the NLS equation and four of its generalizations in modeling the evolution of swell in the ocean. The dissipative generalizations perform significantly better than conservative models and are overall reasonable models for swell amplitudes, indicating dissipation is an important physical effect in ocean swell evolution. The nonlinear models did not out-perform their linearizations, indicating linear models may be sufficient in modeling ocean swell evolution.
Stochastic parametrisations are used in weather and climate models to improve the representation of unpredictable unresolved processes. When compared to a deterministic model, a stochastic model represents `model uncertainty, i.e., sources of error in the forecast due to the limitations of the forecast model. We present a technique for systematically deriving new stochastic parametrisations or for constraining existing stochastic approaches. A high-resolution model simulation is coarse-grained to the desired forecast model resolution. This provides the initial conditions and forcing data needed to drive a Single Column Model (SCM). By comparing the SCM parametrised tendencies with the evolution of the high resolution model, we can estimate the error in the SCM tendencies that a stochastic parametrisation seeks to represent. We use this approach to assess the physical basis of the widely used Stochastically Perturbed Parametrisation Tendencies (SPPT) scheme. We find justification for the multiplicative nature of SPPT, and for the use of spatio-temporally correlated stochastic perturbations. We find evidence that the stochastic perturbation should be positively skewed, indicating that occasional large-magnitude positive perturbations are physically realistic. However other key assumptions of SPPT are less well justified, including coherency of the stochastic perturbations with height, coherency of the perturbations for different physical parametrisation schemes, and coherency for different prognostic variables. Relaxing these SPPT assumptions allows for an error model that explains a larger fractional variance than traditional SPPT. In particular, we suggest that independently perturbing the tendencies associated with different parametrisation schemes is justifiable, and would improve the realism of the SPPT approach.
We construct a network from climate records of atmospheric temperature at surface level, at different geographical sites in the globe, using reanalysis data from years 1948-2010. We find that the network correlates with the North Atlantic Oscillation (NAO), both locally in the north Atlantic, and through coupling to the southern Pacific Ocean. The existence of tele-connection links between those areas and their stability over time allows us to suggest a possible physical explanation for this phenomenon.
Sea surface height anomalies observed by satellites in 1993--2012 are combined with simulation and observations by surface drifters and Argo floats to study water flow pattern in the Near Strait (NS) connected the Pacific Ocean with the Bering Sea. Daily Lagrangian latitudinal maps, computed with the AVISO surface velocity field, and calculation of the transport across the strait show that the flow through the NS is highly variable and controlled by mesoscale and submesoscale eddies in the area. On the seasonal scale, the flux through the western part of the NR is negatively correlated with the flux through its eastern part ($r=-0.93$). On the interannual time scale, a significant positive correlation ($r=0.72$) is diagnosed between the NS transport and the wind stress in winter. Increased southward component of the wind stress decreases the northward water transport through the strait. Positive wind stress curl over the strait area in winter--spring generates the cyclonic circulation and thereby enhances the southward flow in the western part ($r=-0.68$) and northward flow in the eastern part ($r=0.61$) of the NR. In fall, the water transport in different parts of the NS is determined by the strength of the anticyclonic mesoscale eddy located in the Alaskan Stream area.