Modelling the biogeochemical cycles of elements limiting primary production in the English channel .1. Role of thermohaline stratification

This paper presents the general framework of an ecological model of the English Channel. The model is a result of combining a physical sub-model with a biological one. in the physical submodel, the Channel is divided into 71 boxes and water fluxes between them are calculated automatically. A 2-layer, vertical thermohaline model was then linked with the horizontal circulation scheme. This physical sub-model exhibits thermal stratification in the western Channel during spring and summer and haline stratification in the Bay of Seine due to high flow rates from the river. The biological sub-model takes 2 elements, nitrogen and silicon, into account and divides phytoplankton into diatoms and dinoflagellates. Results from this ecological model emphasize the influence of stratification on chlorophyll a concentrations as well as on primary production. Stratified waters appear to be much less productive than well-mixed ones. Nevertheless, when simulated production values are compared with literature data, calculated production is shown to be underestimated. This could be attributed to a lack of refinement of the 2-layer box-model or processes omitted from the biological model, such as production by nanoplankton.

Optimizing PAT data transmission: assessing the accuracy of temperature summary data to estimate environmental conditions

Pop-up archival tags (PAT) provide summary and high-resolution time series data at predefined temporal intervals. The limited battery capabilities of PATs often restrict the transmission success and thus temporal coverage of both data products. While summary data are usually less affected by this problem, as a result of its lower size, it might be less informative. We here investigate the accuracy and feasibility of using temperature at depth summary data provided by PATs to describe encountered oceanographic conditions. Interpolated temperature at depth summary data was found to provide accurate estimates of three major thermal water column structure indicators: thermocline depth, stratification and ocean heat content. Such indicators are useful for the interpretation of the tagged animal's horizontal and vertical behaviour. The accuracy of these indicators was found to be particularly sensitive to the number of data points available in the first 100 m, which in turn depends on the vertical behaviour of the tagged animal. Based on our results, we recommend the use of temperature at depth summary data as opposed to temperature time series data for PAT studies; doing so during the tag programming will help to maximize the amount of transmitted time series data for other key data types such as light levels and depth.

Extrapolating Satellite Winds to Turbine Operating Heights

Ocean wind retrievals from satellite sensors are typically performed for the standard level of 10 m. This restricts their full exploitation for wind energy planning, which requires wind information at much higher levels where wind turbines operate. A new method is presented for the vertical extrapolation of satellite-based wind maps. Winds near the sea surface are obtained from satellite data and used together with an adaptation of the Monin–Obukhov similarity theory to estimate the wind speed at higher levels. The thermal stratification of the atmosphere is taken into account through a long-term stability correction that is based on numerical weather prediction (NWP) model outputs. The effect of the long-term stability correction on the wind profile is significant. The method is applied to Envisat Advanced Synthetic Aperture Radar scenes acquired over the south Baltic Sea. This leads to maps of the long-term stability correction and wind speed at a height of 100 m with a spatial resolution of 0.02°. Calculations of the corresponding wind power density and Weibull parameters are shown. Comparisons with mast observations reveal that NWP model outputs can correct successfully for long-term stability effects and also, to some extent, for the limited number of satellite samples. The satellite-based and NWP-simulated wind profiles are almost equally accurate with respect to those from the mast. However, the satellite-based maps have a higher spatial resolution, which is particularly important in nearshore areas where most offshore wind farms are built.