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River biofilms that grow on wet benthic surface are mainly composed of bacteria, algae, cyanobacteria and protozoa embedded in a polysaccharide matrix. The effects of increased river water temperature on biofilm formation were investigated. A laboratory experiment was designed employing two temperatures (11.1-13.2°C, night-day; 14.7-16.0°C, night-day) and two nutrient levels (0.054 mg P l-1, 0.75 mg N l-1; 0.54 mg P l-1, 7.5 mg N l-1). Biofilm formation at the higher temperature was faster, while the biomass of the mature biofilm was mainly determined by nutrient availability. The specific response of the three microbial groups that colonized the substrata (algae, bacteria and ciliates) was modulated by interactions between them. The greater bacterial growth rate and earlier bacterial colonization at the higher temperature and higher nutrient status was not translated into the accrual of higher bacterial biomass. This may result from ciliates grazing on the bacteria, as shown by an earlier increase in peritrichia at higher temperatures, and especially at high nutrient conditions. Temperature and ciliate grazing might determine the growth of a distinctive bacterial community under warming conditions. Warmer conditions also produced a thicker biofilm, while functional responses were much less evident (increases in the heterotrophic utilization of polysaccharides and peptides, but no increase in primary production and respiration). Increasing the temperature of river water might lead to faster biofilm recolonization after disturbances, with a distinct biofilm community structure that might affect the trophic web. Warming effects would be expected to be more relevant under eutrophic conditions. [ABSTRACT FROM AUTHOR]

The European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission aims at obtaining global maps of soil moisture and sea surface salinity from space for large-scale and climatic studies. It uses an L-band (1400-1427 MHz) Microwave Interferometric Radiometer by Aperture Synthesis to measure brightness temperature of the earth's surface at horizontal and vertical polarizations (Th and Tv). These two parameters will be used together to retrieve the geophysical parameters. The retrieval of salinity is a complex process that requires the knowledge of other environmental information and an accurate processing of the radiometer measurements. Here, we present recent results obtained from several studies and field experiments that were part of the SMOS mission, and highlight the issues still to be solved. [ABSTRACT FROM AUTHOR]

Abstract: Argo is an international project that is deploying an array of temperature and salinity profiling floats over the global ocean. Here we use the error formulation derived from Optimal Statistical Interpolation to estimate statistical errors associated with the recovery of the temperature field in the North-East Atlantic ocean. Results indicate that with the present distribution of floats (119 in the considered domain), scales of wavelength larger than 500 km can be recovered with a relative uncertainty (rms error relative to the standard deviation of the field) of about 7% at 50 m, 8% at 200 m and 10% at 1000 m. This corresponds to mean absolute errors of 0.111 °C at 50 m, 0.104 °C at 200 m and 0.073 °C at 1000 m. The splitting of total errors into instrumental and sampling contributions reveals that, in the present scenario, errors are more due to the small number of floats than to instrumental errors, especially at upper levels. For scales larger than 500 km this will hold true until 200–250 floats are deployed (less than 200 for deep levels). In such a simulated scenario, the number of observations and the technology become approximately equally limiting factors for the accuracy of the temperature field mapping, with total relative errors of less than 2% at upper levels and about 3% at 1000 m. [Copyright &y& Elsevier]

Abstract: Linear and non-linear empirical models for salinity (S) are estimated from the Argo temperature (T) and salinity (delayed) data. This study focuses on the reconstruction of salinity in the upper 1200m of the eastern North Atlantic Ocean, a region characterized by the presence of many different water masses. While previous studies have found it necessary to split this region by boxes to fit different polynomial models in each box, a unique model valid for the entire region is fitted here. Argo profiles are randomly distributed on two sets: one for fitting the models and one for testing them. Non-linear regressions are built using neural networks with a single hidden layer and the fitting data set is further divided into two subsets: one for adjusting the coefficients (training data) and one for early stopping of the fitting (validation data). Our results indicate that linear regressions perform better than the climatologic T–S relationship, but that non-linear regressions perform better than the linear ones. Non-linear training using a three-data subsets strategy successfully prevents overfitting even when networks with 90 neurons in the hidden layer are being trained. While the presence of local minima may complicate the generalization of non-linear models to new data, network committees (created by training the same network from different random initial weights) are shown to better reproduce the test data. Several predictors are tested, and the results show that geographical, or surface, information does provide significant information. These results highlight the potential applications of future satellite missions measuring sea-surface salinity to reconstruct, when combined with temperature profiles, vertical salinity profiles. [Copyright &y& Elsevier]