136 research outputs found

    Interplay of atomic displacements in the quantum magnet (CuCl)LaNb2O7

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    We report on the crystal structure of the quantum magnet (CuCl)LaNb2O7 that was controversially described with respect to its structural organization and magnetic behavior. Using high-resolution synchrotron powder x-ray diffraction, electron diffraction, transmission electron microscopy, and band structure calculations, we solve the room-temperature structure of this compound [alpha-(CuCl)LaNb2O7] and find two high-temperature polymorphs. The gamma-(CuCl)LaNb2O7 phase, stable above 640K, is tetragonal with a(sub) = 3.889 A, c(sub) = 11.738 A, and the space group P4/mmm. In the gamma-(CuCl)LaNb2O7 structure, the Cu and Cl atoms are randomly displaced from the special positions along the {100} directions. The beta-phase [a(sub) x 2a(sub) x c(sub), space group Pbmm] and the alpha-phase [2a(sub) x 2a(sub) x c(sub), space group Pbam] are stable between 640 K and 500 K and below 500 K, respectively. The structural changes at 500 K and 640 K are identified as order-disorder phase transitions. The displacement of the Cl atoms is frozen upon the gamma --> beta transformation, while a cooperative tilting of the NbO6 octahedra in the alpha-phase further eliminates the disorder of the Cu atoms. The low-temperature alpha-(CuCl)LaNb2O7 structure thus combines the two types of the atomic displacements that interfere due to the bonding between the Cu atoms and the apical oxygens of the NbO6 octahedra. The precise structural information resolves the controversy between the previous computation-based models and provides the long-sought input for understanding the magnetic properties of (CuCl)LaNb2O7.Comment: 12 pages, 10 figures, 5 tables; crystallographic information (cif files) include

    A more accurate scheme for calculating Earth's skin temperature

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    The theoretical framework of the vertical discretization of a ground column for calculating Earth’s skin temperature is presented. The suggested discretization is derived from the evenly heat-content discretization with the optimal effective thickness for layer-temperature simulation. For the same level number, the suggested discretization is more accurate in skin temperature as well as surface ground heat flux simulations than those used in some state-of-the-art models. A proposed scheme (“op(3,2,0)”) can reduce the normalized root–mean–square error (or RMSE/STD ratio) of the calculated surface ground heat flux of a cropland site significantly to 2% (or 0.9 W m−2), from 11% (or 5 W m−2) by a 5-layer scheme used in ECMWF, from 19% (or 8 W m−2) by a 5-layer scheme used in ECHAM, and from 74% (or 32 W m−2) by a single-layer scheme used in the UCLA GCM. Better accuracy can be achieved by including more layers to the vertical discretization. Similar improvements are expected for other locations with different land types since the numerical error is inherited into the models for all the land types. The proposed scheme can be easily implemented into state-of-the-art climate models for the temperature simulation of snow, ice and soil

    Lingulodinium machaerophorum expansion over the last centuries in the Caspian Sea reflects global warming

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    This article is made available through the Brunel Open Access Publishing Fund. Copyright @ Author(s) 2012. This work is distributed under the Creative Commons Attribution 3.0 License.We analysed dinoflagellate cyst assemblages in four short sediment cores, two of them dated by radionuclides, taken in the south basin of the Caspian Sea. The interpretation of the four sequences is supported by a collection of 27 lagoonal or marine surface sediment samples. A sharp increase in the biomass of the dinocyst occurs after 1967, especially owing to Lingulodinium machaerophorum. Considering nine other cores covering parts or the whole of Holocene, this species started to develop in the Caspian Sea only during the last three millennia. By analysing instrumental data and collating existing reconstructions of sea level changes over the last few millennia, we show that the main forcing of the increase of L. machaerophorum percentages and of the recent dinocyst abundance is global climate change, especially sea surface temperature increase. Sea level fluctuations likely have a minor impact. We argue that the Caspian Sea has entered the Anthropocene

    Impacts of variations in Caspian Sea surface area on catchment-scale and large-scale climate

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    The Caspian Sea (CS) is the largest inland lake in the world. Large variations in sea level and surface area occurred in the past and are projected for the future. The potential impacts on regional and large-scale hydroclimate are not well understood. Here, we examine the impact of CS area on climate within its catchment and across the northern hemisphere, for the first time with a fully coupled climate model. The Community Earth System Model (CESM1.2.2) is used to simulate the climate of four scenarios: (a) larger than present CS area, (b) current area, (c) smaller than present area, and (d) no-CS scenario. The results reveal large changes in the regional atmospheric water budget. Evaporation (e) over the sea increases with increasing area, while precipitation (P) increases over the south-west CS with increasing area. P-E over the CS catchment decreases as CS surface area increases, indicating a dominant negative lake-evaporation feedback. A larger CS reduces summer surface air temperatures and increases winter temperatures. The impacts extend eastwards, where summer precipitation is enhanced over central Asia and the north-western Pacific experiences warming with reduced winter sea ice. Our results also indicate weakening of the 500-hPa troughs over the northern Pacific with larger CS area. We find a thermal response triggers a southward shift of the upper troposphere jet stream during summer. Our findings establish that changing CS area results in climate impacts of such scope that CS area variations should be incorporated into climate model simulations, including palaeo and future scenarios. © 2021. The Authors

    Forecasting global ENSO-related climate anomalies

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    Long-range global climate forecasts have been made by use of a model for predicting a tropical Pacific sea surface temperature (SST) in tandem with an atmospheric general circulation model. The SST is predicted first at long lead times into the future. These ocean forecasts are then used to force the atmospheric model and so produce climate forecasts at lead times of the SST forecasts. Prediction of the wintertime 500 mb height, surface air temperature and precipitation for seven large climatic events of the 1970 to 1990s by this two-tiered technique agree well in general with observations over many regions of the globe. The levels of agreement are high enough in some regions to have practical utility. -Author

    The unusual electronic structure of the "pseudo-ladder" compound CaCu2O3

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    Experimental and theoretical studies of the unoccupied electronic structure of CaCu2O3 single crystals have been performed using polarization-dependent x-ray absorption spectroscopy and band structure calculations. The measured hole distribution shows an unusual large number of holes in orbitals parallel to the interlayer direction which is in agreement with the theoretical analysis. CaCu2O3 deviates significantly from the standard pd-sigma cuprate picture. The corresponding strong interlayer exchange is responsible for the missing spin gap generic for other two-leg ladder cuprates.Comment: 4 pages, 3 figures include

    Climatology and variability in the ECHO coupled GCM

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    ECHO is a new global coupled ocean-atmosphere general circulation model (GCM), consisting of the Hamburg version of the European Centre atmospheric GCM (ECHAM) and the Hamburg Primitive Equation ocean GCM (HOPE). We performed a 20-year integration with ECHO. Climate drift is significant, but typical annual mean errors in sea surface temperature (SST) do not exceed 2° in the open oceans. Near the boundaries, however, SST errors are considerably larger. The coupled model simulates an irregular ENSO cycle in the tropical Pacific, with spatial patterns similar to those observed. The variability, however, is somewhat weaker relative to observations. ECHO also simulates significant interannual variability in mid-latitudes. Consistent with observations, variability over the North Pacific can be partly attributed to remote forcing from the tropics. In contrast, the interannual variability over the North Atlantic appears to be generated locally

    Forecasting global ENSO-related climate anomalies

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    Long-range global climate forecasts have been made by use of a model for predicting a tropical Pacific sea surface temperature (SST) in tandem with an atmospheric general circulation model. The SST is predicted first at long lead times into the future. These ocean forecasts are then used to force the atmospheric model and so produce climate forecasts at lead times of the SST forecasts. Prediction of the wintertime 500mb height, surface air temperature and precipitation for seven large climatic events of the 1970 1990s by this two-tiered technique agree well in general with observations over many regions of the globe. The levels of agreement are high enough in some regions to have practical utility

    Detecting controlling nodes of boolean regulatory networks

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    Boolean models of regulatory networks are assumed to be tolerant to perturbations. That qualitatively implies that each function can only depend on a few nodes. Biologically motivated constraints further show that functions found in Boolean regulatory networks belong to certain classes of functions, for example, the unate functions. It turns out that these classes have specific properties in the Fourier domain. That motivates us to study the problem of detecting controlling nodes in classes of Boolean networks using spectral techniques. We consider networks with unbalanced functions and functions of an average sensitivity less than 23k, where k is the number of controlling variables for a function. Further, we consider the class of 1-low networks which include unate networks, linear threshold networks, and networks with nested canalyzing functions. We show that the application of spectral learning algorithms leads to both better time and sample complexity for the detection of controlling nodes compared with algorithms based on exhaustive search. For a particular algorithm, we state analytical upper bounds on the number of samples needed to find the controlling nodes of the Boolean functions. Further, improved algorithms for detecting controlling nodes in large-scale unate networks are given and numerically studied
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