Arctic sea ice and the Madden–Julian Oscillation (MJO)

Arctic sea ice responds to atmospheric forcing in primarily a top-down manner, whereby near-surface air circulation and temperature govern motion, formation, melting, and accretion. As a result, concentrations of sea ice vary with phases of many of the major modes of atmospheric variability, including the North Atlantic Oscillation, the Arctic Oscillation, and the El Niño-Southern Oscillation. However, until this present study, variability of sea ice by phase of the leading mode of atmospheric intraseasonal variability, the Madden–Julian Oscillation (MJO), which has been found to modify Arctic circulation and temperature, remained largely unstudied. Anomalies in daily change in sea ice concentration were isolated for all phases of the real-time multivariate MJO index during both summer (May–July) and winter (November–January) months. The three principal findings of the current study were as follows. (1) The MJO projects onto the Arctic atmosphere, as evidenced by statistically significant wavy patterns and consistent anomaly sign changes in composites of surface and mid-tropospheric atmospheric fields. (2) The MJO modulates Arctic sea ice in both summer and winter seasons, with the region of greatest variability shifting with the migration of the ice margin poleward (equatorward) during the summer (winter) period. Active regions of coherent ice concentration variability were identified in the Atlantic sector on days when the MJO was in phases 4 and 7 and the Pacific sector on days when the MJO was in phases 2 and 6, all supported by corresponding anomalies in surface wind and temperature. During July, similar variability in sea ice concentration was found in the North Atlantic sector during MJO phases 2 and 6 and Siberian sector during MJO phases 1 and 5, also supported by corresponding anomalies in surface wind. (3) The MJO modulates Arctic sea ice regionally, often resulting in dipole-shaped patterns of variability between anomaly centers. These results provide an important first look at intraseasonal variability of sea ice in the Arctic.     

The Kozeny‐Carman Equation with a Percolation Threshold

A procedure has been developed for calculating permeability (k) from the Kozeny‐Carman equation, a procedure that links ideas from percolation theory with the ideas of Koltermann and Gorelick (1995) and Esselburn et al. (2011) . The approach focuses on the proportion of coarser pores that are occupied by finer sediments relative to a percolation threshold proportion (ω_c). If the proportion occupied is below ω_c, then the unoccupied coarser pores percolate. Otherwise they do not percolate. Following the ideas of Koltermann and Gorelick (1995) , the effective grain‐size term in the Kozeny‐Carman equation is calculated using the geometric mean if the unoccupied coarse pores percolate, and using the harmonic mean if otherwise. Following ideas of Esselburn et al. (2011) , this approach is implemented by evaluating the potential for grains in each size category to occupy pores among sediment of each larger‐size category. Application of these ideas to physical sediment models for sands and gravels, which have known k, indicates that a threshold does indeed exist. Results also suggest that the Kozeny‐Carman equation is robust and gives representative values for k, even though ω_c is not precisely known.     

Probabilistic GIS-based method for delineation of urban flooding risk hotspots

Identifying urban flooding risk hotspots is one of the first steps in an integrated methodology for urban flood risk assessment and mitigation. This work employs three GIS-based frameworks for identifying urban flooding risk hotspots for residential buildings and urban corridors. This is done by overlaying a map of potentially flood-prone areas [estimated through the topographic wetness index (TWI)], a map of residential areas and urban corridors [extracted from a city-wide assessment of urban morphology types (UMT)], and a geo-spatial census dataset. A maximum likelihood method (MLE) is employed for estimating the threshold used for identifying the flood-prone areas (the TWI threshold) based on the inundation profiles calculated for various return periods within a given spatial window. Furthermore, Bayesian parameter estimation is employed in order to estimate the TWI threshold based on inundation profiles calculated for more than one spatial window. For different statistics of the TWI threshold (e.g. MLE estimate, 16th percentile, 50th percentile), the map of the potentially flood-prone areas is overlaid with the map of urban morphology units, identified as residential and urban corridors, in order to delineate the urban hotspots for both UMT. Moreover, information related to population density is integrated by overlaying geo-spatial census datasets in order to estimate the number of people affected by flooding. Differences in exposure characteristics have been assessed for a range of different residential types. As a demonstration, urban flooding risk hotspots are delineated for different percentiles of the TWI value for the city of Addis Ababa, Ethiopia.     

DXL: a sounding rocket mission for the study of solar wind charge exchange and local hot bubble X-ray emission

The Diffuse X-rays from the Local galaxy (DXL) mission is an approved sounding rocket project with a first launch scheduled around December 2012. Its goal is to identify and separate the X-ray emission generated by solar wind charge exchange from that of the local hot bubble to improve our understanding of both. With 1,000 cm² proportional counters and grasp of about 10 cm² sr both in the 1/4 and 3/4 keV bands, DXL will achieve in a 5-min flight what cannot be achieved by current and future X-ray satellites.     

Image analysis algorithms for estimating porous media multiphase flow variables from computed microtomography data: a validation study

Image analysis of three-dimensional microtomographic image data has become an integral component of pore scale investigations of multiphase flow through porous media. This study focuses on the validation of image analysis algorithms for identifying phases and estimating porosity, saturation, solid surface area, and interfacial area between fluid phases from gray-scale X-ray microtomographic image data. The data used in this study consisted of (1) a two-phase high precision bead pack from which porosity and solid surface area estimates were obtained and (2) three-phase cylindrical capillary tubes of three different radii, each containing an air–water interface, from which interfacial area was estimated. The image analysis algorithm employed here combines an anisotropic diffusion filter to remove noise from the original gray-scale image data, a k-means cluster analysis to obtain segmented data, and the construction of isosurfaces to estimate solid surface area and interfacial area. Our method was compared with laboratory measurements, as well as estimates obtained from a number of other image analysis algorithms presented in the literature. Porosity estimates for the two-phase bead pack were within 1.5% error of laboratory measurements and agreed well with estimates obtained using an indicator kriging segmentation algorithm. Additionally, our method estimated the solid surface area of the high precision beads within 10% of the laboratory measurements, whereas solid surface area estimates obtained from voxel counting and two-point correlation functions overestimated the surface area by 20–40%. Interfacial area estimates for the air–water menisci contained within the capillary tubes were obtained using our image analysis algorithm, and using other image analysis algorithms, including voxel counting, two-point correlation functions, and the porous media marching cubes. Our image analysis algorithm, and other algorithms based on marching cubes, resulted in errors ranging from 1% to 20% of the analytical interfacial area estimates, whereas voxel counting and two-point correlation functions overestimated the analytical interfacial area by 20–40%. In addition, the sensitivity of the image analysis algorithms on the resolution of the microtomographic image data was investigated, and the results indicated that there was little or no improvement in the comparison with laboratory estimates for the resolutions and conditions tested.     

An occurrence of cadmiferous phosphorite soil concretions in Jamaica

Bauxite exploration drilling revealed the presence of phosphate minerals through the chemical and XRD analysis of recovered drill hole samples at Spitzbergen, in Manchester Parish, Jamaica. A subsequent pit led to the discovery of phosphorite concretions composed of hard competent masses of finely crystalline fluorapatite, with some minor crandallite. The phosphorite contains anomalously high levels of Zn (>5000 mg kg⁻¹), Cd (>1.1%), Ag (>20 mg kg⁻¹), Be (>80 mg kg⁻¹) and, to a lesser extent, U. Textural and geochemical evidence indicates that the phosphorite concretions were most likely formed by the replacement of limestone by secondary deposition proximal to fossil guano deposits, postulated to be Late Miocene or Pliocene sea-bird colonies. Mechanical dispersion of the phosphorite concretions through karst weathering processes has led to their wider spatial distribution than the original guano deposits. Subsequent weathering of the concretions and the admixture of their decomposition products into the bauxitic and Terra Rossa soils is postulated to be the cause of the widespread anomalous levels of Zn, Cd and Be in these soils in central Jamaica, and the elevated levels of P in the bauxite.     

Heavy Metal Concentrations in European Mosses: 2000/2001 Survey

The heavy metals in mosses survey was originally established in 1980 as a joint Danish–Swedish initiative under the leadership of Åke Rühling, Sweden and has, since then, been repeated at five-yearly intervals with an increasing number of countries and individuals participating. Twenty-eight European countries, almost 7000 sites and about 100 individuals have been involved in the most recent survey in 2000/2001. The survey provides data on concentrations of 10 heavy metals (arsenic, cadmium, chromium, copper, iron, lead, mercury, nickel, vanadium, zinc) in naturally growing mosses throughout Europe. The technique of moss analysis provides a surrogate measure of the spatial patterns of heavy metal deposition from the atmosphere to terrestrial systems, and is easier and cheaper than conventional precipitation analysis. The aims of the survey are to determine patterns of variation in the heavy metal concentration of mosses across Europe, identify the main polluted areas, produce regional maps and further develop the understanding of long-range transboundary pollution.As in previous surveys, there was an east/west decrease in heavy metal concentrations in mosses, related in particular to industrial emissions. Former industrial sites and historic mines accounted for the location of some high concentrations in areas without contemporary industries. Long-range transboundary transport appears to account for elevated concentrations of heavy metals in areas without emission sources, such as lead in southern Scandinavia (presumably from emission sources elsewhere in Europe).