Causes and effects of spatial and temporal variations of cold period in Chinese oases between 1960 and 2014

Based on daily average temperatures and observation data from 74 meteorological stations in Chinese oases, we calculate five-day (pentad) average temperature ≤0°C for the start and end pentad as well as pentads of cold period using linear regression analysis, nonparametric Mann-Kendall tests, the Morlet wavelet power spectrum, and correlation analysis. We also analyze spatial and temporal variations and their effects on the start and end pentad as well as pentads of cold period in Chinese oases. Results show that over the last 55 years, the start pentad of cold period has been postponed while the end pentad has been advanced. Overall, the pentads have gradually shortened over time at trend rates that are 0.3 p/10a,–0.27 p/10a, and–0.58 p/10a, respectively. Spatial differences are significant, especially for the Qaidam Basin oasis where the start pentad is the earliest, the end pentad is the latest, and the trend of change is most obvious. Mutation points for the start and end pentad as well as pentads of cold period were observed in 1990, 1998, and 1994, respectively. Of these, the start pentad and pentads of cold period show a periodic cycle, related to atmospheric circulation and El Nino events, while the end pentad exhibits a periodic cycle, related to solar activity. The Tibetan Plateau index (TPI), the Asian polar vortex area index (APVAI), and carbon dioxide emissions (CDE) are the main factors affecting cold period in the study area, whereas the South Asian summer monsoon (SASM) index exerts the greatest effect on the Qaidam Basin oasis. The start and end pentad as well as pentads of cold period increase in concert with latitude, longitude, and altitude; in response to these changes, the start pentad is advanced, the end pentad is postponed, and pentads of cold period are gradually extended. Results show that change in latitude is most significant. Overall, the start and end pentad as well as pentads of cold period show clear responses to regional warming, but there are different effects on each.     

Stable isotope study of base metal deposits from the Eastern High Atlas,Morocco

Tertiary epigenetic lead ± zinc and copper mineralizations occur in the Mesozoic carbonate cover and stibnite, barite and copper mineralization in the Precambrian to Palaezoic basement in the Eastern High Atlas, Morocco. The carbon isotope data from host carbonates range from +3 to -1‰ PDB. The data are typical for marine carbon. There is no difference between surrounding dolomites and younger vein dolomites. The oxygen isotopic composition of the samples is enriched in ¹⁶O; the range is from -3 to -11‰ PDB with respect to that of carbonates in equilibrium with marine water. It is postulated that the isotopic composition was changed during dolomitization being caused by transition of connate formation waters diluted to some degree by meteoritic water. The bacteriogenically reduced sulphur from syngenetic sulphides and Mesozoic marine sulphate mobilized during Tertiary orogenesis are thought to be the source of H₂S and SO₄. Cambro-Ordovicien sulphate is also supposed to be the source of sulphur in the Jebel Zelmou barite deposit in the basement. Isotopic composition of lead from galenas in the Tertiary deposits from the Mesozoic cover are strikingly homogenous. Model ages vary between 230–180 Ma. It is suggested that important mobilization processes are responsible for Tertiary metallogeny in Morocco.

     

Eagle Nebula Pillars: From Models to Observations

Over the past few years, our group has been developing hydrodynamic models to simulate formation of the Eagle Nebula pillars. The true test of any model is, of course, how well it can reproduce the observations. Here, we discuss how we go about testing our models against observations. We describe the process by which we “observe” the model data to create synthetic maps. We show an example of this technique using one of our model runs and compare the resultant synthetic map to the real one.     

A Multiple Flare Scenario where the Classic Long-Duration Flare Was Not the Source of a CME

A series of flares (GOES class M, M and C) and a CME were observed in close succession on 20 January 2004 in NOAA 10540. Radio observations, which took the form of types II, III and N bursts, were associated with these events. We use the combined observations from TRACE, EIT, Hα images from Kwasan, MDI magnetograms and GOES to understand the complex development of this event. Contrary to a standard interpretation, we conclude that the first two impulsive flares are part of the CME launch process while the following long-duration event flare represents simply the recovery phase. Observations show that the flare ribbons not only separate but also shift along the magnetic inversion line so that magnetic reconnection progresses stepwise to neighboring flux tubes. We conclude that “tether cutting” reconnection in the sheared arcade progressively transforms it to a twisted flux tube, which becomes unstable, leading to a CME. We interpret the third flare, a long-duration event, as a combination of the classical two-ribbon flare with the relaxation process following forced reconnection between the expanding CME structure and neighboring magnetic fields. Electronic Supplementary Material  The online version of this article () contains supplementary material, which is available to authorized users.     

Detection and extraction of signals from the epoch of reionization using higher-order one-point statistics

Detecting redshifted 21-cm emission from neutral hydrogen in the early Universe promises to give direct constraints on the epoch of reionization (EoR). It will, though, be very challenging to extract the cosmological signal (CS) from foregrounds and noise which are orders of magnitude larger. Fortunately, the signal has some characteristics which differentiate it from the foregrounds and noise, and we suggest that using the correct statistics may tease out signatures of reionization. We generate mock data cubes simulating the output of the Low Frequency Array (LOFAR) EoR experiment. These cubes combine realistic models for Galactic and extragalactic foregrounds and the noise with three different simulations of the CS. We fit out the foregrounds, which are smooth in the frequency direction, to produce residual images in each frequency band. We denoise these images and study the skewness of the one-point distribution in the images as a function of frequency. We find that, under sufficiently optimistic assumptions, we can recover the main features of the redshift evolution of the skewness in the 21-cm signal. We argue that some of these features – such as a dip at the onset of reionization, followed by a rise towards its later stages – may be generic, and give us a promising route to a statistical detection of reionization.     

A back-propagation neural-network-based displacement back analysis for the identification of the geomechanical parameters of the Yonglang landslide in China

Xigeda formation is a type of hundred-meter-thick lacustrine sediments of being prone to triggering landslides along the trunk channel and tributaries of the upper Yangtze River in China. The Yonglang landslide located near Yonglang Town of Dechang County in Sichuan Province of China, which was a typical Xigeda formation landslide, was stabilized by anti-slide piles. Loading tests on a loading-test pile were conducted to measure the displacements and moments. The uncertainty of the tested geomechanical parameters of the Yonglang landslide over certain ranges would be problematic during the evaluation of the landslide. Thus, uniform design was introduced in the experimental design, and by which, numerical analyses of the loading-test pile were performed using Fast Lagrangian Analysis of Continua (FLAC^3D) to acquire a database of the geomechanical parameters of the Yonglang landslide and the corresponding displacements of the loading-test pile. A three-layer back-propagation neural network was established and trained with the database, and then tested and verified for its accuracy and reliability in numerical simulations. Displacement back analysis was conducted by substituting the displacements of the loading-test pile to the well-trained three-layer back-propagation neural network so as to identify the geomechanical parameters of the Yonglang landslide. The neural-network-based displacement back analysis method with the proposed methodology is verified to be accurate and reliable for the identification of the uncertain geomechanical parameters of landslides.