Georeferenced standardized apparent wave power along the coastline of Cabrera National Park for analysis of essential habitats of littoral species

Wind- induced exposure is one of the major forces shaping the geomorphology and biota in coastal areas. The effect of wave exposure on littoral biota is well known in marine environments (Ekebon et al., 2003; Burrows et al., 2008). In the Cabrera Archipelago National Park wave exposure has demostrated to have an effect on the spatial distribution of different stages of E.marginatus (Alvarez et al., 2010). Standarized average wave exposures during 2008 along the Cabrera Archipelago National park coast line were calculated to be applied in studies of littoral species distribution within the archipelago. Average wave exposure (or apparent wave power) was calculated for points located 50 m equidistant on the coastline following the EXA methodology (EXposure estimates for fragmented Archipelagos) (Ekebon et al., 2003). The average wave exposures were standardized from 1 to 100 (minimum and maximum in the area), showing coastal areas with different levels of mea wave exposure during the year. Input wind data (direction and intensity) from 2008 was registered at the Cabrera mooring located north of Cabrera Archipelago. Data were provided by IMEDEA (CSIC-UIB, TMMOS http://www.imedea.uib-csic.es/tmoos/boyas/). This cartography has been developed under the framework of the project EPIMHAR, funded by the National Park's Network (Spanish Ministry of Environment, Maritime and Rural Affairs, reference: 012/2007 ). Part of this work has been developed under the research programs funded by "Fons de Garantia Agrària i Pesquera de les Illes Balears (FOGAIBA)".

Case studies of the effects of a total solar eclipse on weather with varying degrees of cloud cover using a local numerical weather prediction model

The importance of renewable energies for the European electricity market is growing rapidly. This presents transmission grids and the power market in general with new challenges which stem from the higher spatiotemporal variability of power generation. This uncertainty is due to the fact that renewable power production results from weather phenomena, thus making it difficult to plan and control. We present a sensitivity study of a total solar eclipse in central Europe in March. The weather in Germany and Europe was modeled using the German Weather Service's local area models COSMO-DE and COSMO-EU, respectively (http://www.cosmo-model.org/). The simulations were performed with and without considering a solar eclipse for the following 3 situations: 1. An idealized, clear-sky situation for the entire model area (Europe, COSMO-EU) 2. A real weather situation with mostly cloudy skies (Germany, COSMO-DE) 3. A real weather situation with mostly clear skies (Germany, COSMO-DE) The data should help to evaluate the effects of a total solar eclipse on the weather in the planetary boundary layer. The results show that a total solar eclipse has significant effects particularly on the main variables for renewable energy production, such as solar irradiation and temperature near the ground.

Probabilistic models for the prediction of a ship performance in dynamic ice

We introduce two probabilistic, data-driven models that predict a ship's speed and the situations where a ship is probable to get stuck in ice based on the joint effect of ice features such as the thickness and concentration of level ice, ice ridges, rafted ice, moreover ice compression is considered. To develop the models to datasets were utilized. First, the data from the Automatic Identification System about the performance of a selected ship was used. Second, a numerical ice model HELMI, developed in the Finnish Meteorological Institute, provided information about the ice field. The relations between the ice conditions and ship movements were established using Bayesian learning algorithms. The case study presented in this paper considers a single and unassisted trip of an ice-strengthened bulk carrier between two Finnish ports in the presence of challenging ice conditions, which varied in time and space. The obtained results show good prediction power of the models. This means, on average 80% for predicting the ship's speed within specified bins, and above 90% for predicting cases where a ship may get stuck in ice. We expect this new approach to facilitate the safe and effective route selection problem for ice-covered waters where the ship performance is reflected in the objective function.

Ensemble prediction distribution maps of macroalgae for current conditions and four climate change scenarios and high resultion bathymetry for Potter Cove, WAP, Antarctica

Species distribution models (SDM) predict species occurrence based on statistical relationships with environmental conditions. The R-package biomod2 which includes 10 different SDM techniques and 10 different evaluation methods was used in this study. Macroalgae are the main biomass producers in Potter Cove, King George Island (Isla 25 de Mayo), Antarctica, and they are sensitive to climate change factors such as suspended particulate matter (SPM). Macroalgae presence and absence data were used to test SDMs suitability and, simultaneously, to assess the environmental response of macroalgae as well as to model four scenarios of distribution shifts by varying SPM conditions due to climate change. According to the averaged evaluation scores of Relative Operating Characteristics (ROC) and True scale statistics (TSS) by models, those methods based on a multitude of decision trees such as Random Forest and Classification Tree Analysis, reached the highest predictive power followed by generalized boosted models (GBM) and maximum-entropy approaches (Maxent). The final ensemble model used 135 of 200 calculated models (TSS > 0.7) and identified hard substrate and SPM as the most influencing parameters followed by distance to glacier, total organic carbon (TOC), bathymetry and slope. The climate change scenarios show an invasive reaction of the macroalgae in case of less SPM and a retreat of the macroalgae in case of higher assumed SPM values.