Influence of the orographic roughness of glacier valleys across the Transantarctic Mountains in an atmospheric regional model

Glacier valleys across the Transantarctic Mountains are not properly taken into account in climate models, because of their coarse resolution. Nonetheless, glacier valleys control katabatic winds in this region, and the latter are thought to affect the climate of the Ross Sea sector, frsater formation to snow mass balance. The purpose of this paper is to investigate the role of the production of turbulent kinetic energy by the subgrid-scale orography in the Transantarctic Mountains using a 20-km atmospheric regional model. A classical orographic roughness length parametrization is modified to produce either smooth or rough valleys. A one-year simulation shows that katabatic winds in the Transantarctic Mountains are strongly improved using smooth valleys rather than rough valleys. Pressure and temperature fields are affected by the representation of the orographic roughness, specifically in the Transantarctic Mountains and over the Ross Ice Shelf. A smooth representation of escarpment regions shows better agreement with automatic weather station observations than a rough representation. This work stresses the need to improve the representation of subgrid-scale orography to simulate realistic katabatic flows. This paper also provides a way of improving surface winds in an atmospheric model without increasing its resolution.     

UV transit observations of EUV-heated expanded thermospheres of Earth-like exoplanets around M-stars: testing atmosphere evolution scenarios

The detection and investigation of EUV heated, extended and non-hydrostatic upper atmospheres around terrestrial exoplanets would provide important insights into the interaction of the host stars plasma environment as well as the evolution of Earth-type planets their atmospheres and possible magnetic environments. We discuss different scenarios where one can expect that Earth-like planets should experience non-hydrostatic upper atmosphere conditions so that dynamically outward flowing neutral atoms can interact with the stellar plasma flow so that huge hydrogen coronae and energetic neutral atoms (ENA) can be produced via charge exchange. By observing the size of the extended upper atmospheres and related ENA-clouds and by determining the velocities of the surrounding hydrogen atoms, conclusions can be drawn in respect to the origin of these features. Due to the large number of M-type stars in our neighbourhood and their long periods of strong and moderate stellar activity in comparison to G-stars, we expect that M-type stars represent the most promising candidates for the detection of hydrogen ENA-clouds and the subsequent study of the interaction between the host star and the planets?? upper atmosphere. We show that the low mass of M-type stars also makes them preferable targets to observe extended hydrogen clouds around terrestrial exoplanets with a mass as low as one Earth mass. Transit follow-up observations in the UV-range of terrestrial exoplanets around M-type stars with space observatories such as the World Space Observatory-UV (WSO-UV) would provide a unique opportunity to shed more light on the early evolution of Earth-like planets, including those of our own Solar System.     

Long‐term nitrogen and phosphorus fertilization effects on N2 fixation rates and nifH gene community patterns in mangrove sediments

The bioavailability of nutrients is important in controlling ecological processes and nitrogen cycling in oligotrophic mangrove forests, yet the variation of diazotrophic community structure and activity with nutrient availability in sediments remains largely unexplored. To investigate for the first time how nutrients in sediments affect spatial and temporal patterns of diazotrophic community structure and activity, the sedimentary environment of Twin Cays, Belize, was examined with respect to the effects of long‐term fertilization [treatments: control (Ctrl), nitrogen (N), and phosphorus (P)] on N₂ fixation rates and nifH gene community structure. We found that N₂ fixation rates were significantly higher at the P‐treatment, intermediate at the Ctrl‐treatment and lower in the N‐treatment (P: 4.2 ± 0.5, Crtl: 0.8 ± 0.1, N: 0.4 ± 0.1 nmol·N·g^?1·h^?1; P < 0.001) with spatial (Ctrl‐ and P‐treatments) and temporal (only P‐treatment) variability positively correlated with live root abundance (r² = 0.473, P < 0.001) and concentration (r² = 0.458, P < 0.0001). The community structure of diazotrophs showed larger spatial and temporal variability in the fertilized treatments than in the Ctrl‐treatment, with the relative abundance of OTUs (nifH operational taxonomic units) at the fertilized treatments inversely related to live root abundance. Overall, long‐term fertilization (with either N or P) affects not only nutrient levels in mangrove sediments directly, but also spatial and temporal patterns of both community structure and activity and likely plant‐microbe interactions as well. Our findings suggest that the maintenance of natural nutrient conditions in mangrove sediments is important to ensure the stability of microbial functional groups like diazotrophs.     

High-performance three-horizon composition algorithm for large-scale terrains

This work presents a high-performance algorithm to compute the horizon in very large high-resolution DEMs. We used Stewart's algorithm as the core of our implementation and considered that the horizon has three components: the ground, near, and far horizons. To eliminate the edge-effect, we introduced a multi-resolution halo method. Moreover, we used a new data partition approach, to substantially increase the parallelism in the algorithm. In addition, several optimizations have been applied to considerably reduce the number of arithmetical operations in the core of the algorithm. The experimental results have demonstrated that by applying the above-described contributions, the proposed algorithm is more than twice faster than Stewart's algorithm while maintaining the same accuracy.     

Detecting abrupt climate changes on different time scales

Two concepts are introduced for detecting abrupt climate changes. In the first case, the sampling frequency of climate data is high as compared to the frequency of climate events examined. The method is based on a separation of trend and noise in the data and is applicable to any dataset that satisfies some mild smoothness and statistical dependence conditions for the trend and the noise, respectively. We say that an abrupt change occurs when the first derivative of the trend function has a discontinuity and the task is to identify such points. The technique is applied to Northern Hemisphere temperature data from 1850 to 2009, Northern Hemisphere temperature data from proxy data, a.d. 200?C1995 and Holocene ??¹⁸O values going back to 11,700 years BP. Several abrupt changes are detected that are, among other things, beneficial for determining the Medieval Warm Period, Little Ice Age and Holocene Climate Optimum. In the second case, the sampling frequency is low relative to the frequency of climate events studied. A typical example includes Dansgaard?COeschger events. The methodology used here is based on a refinement of autoregressive conditional heteroscedastic models. The key element of this approach is the volatility that characterises the time-varying variance, and abrupt changes are defined by high volatilities. The technique applied to ??¹⁸O values going back to 122,950 years BP is suitable for identifying DO events. These two approaches for the two cases are closely related despite the fact that at first glance, they seem quite different.     

Comparison of models calculating the sky view factor used for urban climate investigations

The sky view factor (SVF) describes the surface geometry and is a commonly used and important measure in urban climate investigations whose aim is the exploration of effects of a complex urban surface on climatological processes in built-up areas. A selection of methods and models for calculating the SVF was compared. For this purpose, fish eye images were taken at several locations in the city of Szeged, southern Hungary. The fish eye images equidistantly follow linear transects to cover a range of SVF values and to analyze the reaction of the methods to a continuously changing environment. The fish eye pictures were evaluated by three methods: the method according to Steyn (Atmos-Ocean 18(3):245?C258, 1980) implemented in a GIS-Script, the ??Edit free sky view factor?? tool of the RayMan model and BMSkyView. The SVF values at the coordinates of the fish eye pictures were calculated with three numerical models (SkyHelios, ArcView SVF extension, and SOLWEIG) with a 3D building data base as input. After comparing the results of the first run, a deviation occurs. The deviation disappears after implementing an option to include a weighting factor in some of the models.     

Ecological status of seagrass ecosystems: An uncertainty analysis of the meadow classification based on the Posidonia oceanica multivariate index (POMI)

Quantifying the uncertainty associated with monitoring protocols is essential to prevent the misclassification of ecological status and to improve sampling design. We assessed the Posidonia oceanica multivariate index (POMI) bio-monitoring program for its robustness in classifying the ecological status of coastal waters within the Water Framework Directive. We used a 7-year data set covering 30 sites along 500 km of the Catalonian coastline to examine which version of POMI (14 or 9 metrics) maximises precision in classifying the ecological status of meadows. Five factors (zones within a site, sites within a water body, depth, years and surveyors) that potentially generate classification uncertainty were examined in detail. Of these, depth was a major source of uncertainty, while all the remaining spatial and temporal factors displayed low variability. POMI 9 matched POMI 14 in all factors, and could effectively replace it in future monitoring programs.     

Compositional Bayesian indicator estimation

Indicator kriging is widely used for mapping spatial binary variables and for estimating the global and local spatial distributions of variables in geosciences. For continuous random variables, indicator kriging gives an estimate of the cumulative distribution function, for a given threshold, which is then the estimate of a probability. Like any other kriging procedure, indicator kriging provides an estimation variance that, although not often used in applications, should be taken into account as it assesses the uncertainty of the estimate. An alternative approach to indicator estimation is proposed in this paper. In this alternative approach the complete probability density function of the indicator estimate is evaluated. The procedure is described in a Bayesian framework, using a multivariate Gaussian likelihood and an a priori distribution which are both combined according to Bayes theorem in order to obtain a posterior distribution for the indicator estimate. From this posterior distribution, point estimates, interval estimates and uncertainty measures can be obtained. Among the point estimates, the median of the posterior distribution is the maximum entropy estimate because there is a fifty-fifty chance of the unknown value of the estimate being larger or smaller than the median; that is, there is maximum uncertainty in the choice between two alternatives. Thus in some sense, the latter is an indicator estimator, alternative to the kriging estimator, that includes its own uncertainty. On the other hand, the mode of the posterior distribution estimator, assuming a uniform prior, is coincidental with the simple kriging estimator. Additionally, because the indicator estimate can be considered as a two-part composition which domain of definition is the simplex, the method is extended to compositional Bayesian indicator estimation. Bayesian indicator estimation and compositional Bayesian indicator estimation are illustrated with an environmental case study in which the probability of the content of a geochemical element in soil being over a particular threshold is of interest. The computer codes and its user guides are public domain and freely available.     

Adapting a texture synthesis algorithm for conditional multiple point geostatistical simulation

Computer vision provides several tools for analyzing and simulating textures. The principles of these techniques are similar to those in multiple-point geostatistics, namely, the reproduction of patterns and consistency in the results from a perceptual point of view, thus, ensuring the reproduction of long range connectivity. The only difference between these techniques and geostatistical simulation accounting for multiple-point statistics is that conditioning is not an issue in computer vision. We present a solution to the problem of conditioning simulated fields while simultaneously honoring multiple-point (pattern) statistics. The proposal is based on a texture synthesis algorithm where a fixed search (causal) pattern is used. Conditioning is achieved by adding a non-causal search neighborhood that modifies the conditional distribution from which the simulated category is drawn, depending on the conditioning information. Results show an excellent reproduction of the features from the training image, while respecting the conditioning information. Some issues related to the data structure and to the computer efficiency are discussed.