The impact of el Nino‐Southern oscillation on the temperature field over Canada: Research note

Abstract

The impact of the two phases of El Niño‐Southern Oscillation (ENSO), namely El Niño and La Niña, on the surface and lower tropospheric temperature fields over Canada is documented. Gridded surface temperature data for 91 years (1900–1990) and 500–1000 hPa thickness data for 49 years (1946–1994) have been analyzed statistically in the context of El Niño, La Niña and normal years.

Using a composite analysis, the present study conclusively demonstrates that significant positive surface temperature anomalies spread eastward from the west coast of Canada to the Labrador coast from the late fall to early spring (November through May) following the onset of El Niño episodes. The accompanying temperatures in the lower troposphere show a transition from the Pacific/North American (PNA) pattern to the Tropical/Northern Hemisphere (TNH) pattern over the North American sector during the same period. Conversely, significant negative surface temperature anomalies spread southeastward from the Yukon and extend into the upper Great Lakes region by the winter season following the onset of La Niña episodes. Furthermore, the lower tropospheric temperatures show a negatively‐phased PNA‐like pattern in early winter which weakens considerably by May of the following year. Thus, while western Canadian surface temperatures are influenced during both phases of ENSO, eastern Canadian surface temperature effects are found during the El Niño phase only. The impact of ENSO on the Canadian surface temperatures is the strongest during the winter season and nearly disappears by spring (April and May). The largest positive (negative) anomalies are found to be centred over two separate regions, one over the Yukon and the other just west of Hudson Bay in the El Niño (La Niña) years. Over western Canada, mean wintertime temperature distribution of the El Niño (La Niña) years is found to be shifted towards warmer (colder) values relative to the distribution of the normal years.

This study suggests the possibility of developing a long‐range forecasting technique for Canada using ENSO related indices.     

Empirical modeling of plate load test moduli of soil via gene expression programming

New empirical models were developed to predict the soil deformation moduli using gene expression programming (GEP). The principal soil deformation parameters formulated were secant (E_s) and reloading (E_r) moduli. The proposed models relate E_s and E_r obtained from plate load-settlement curves to the basic soil physical properties. The best GEP models were selected after developing and controlling several models with different combinations of the influencing parameters. The experimental database used for developing the models was established upon a series of plate load tests conducted on different soil types at depths of 1–24 m. To verify the applicability of the derived models, they were employed to estimate the soil moduli of a part of test results that were not included in the analysis. The external validation of the models was further verified using several statistical criteria recommended by researchers. A sensitivity analysis was carried out to determine the contributions of the parameters affecting E_s and E_r. The proposed models give precise estimates of the soil deformation moduli. The E_s prediction model provides considerably better results in comparison with the model developed for E_r. The simplified formulation for E_s significantly outperforms the empirical equations found in the literature. The derived models can reliably be employed for pre-design purposes.     

Non-stationary extreme value analysis in a changing climate

This paper introduces a framework for estimating stationary and non-stationary return levels, return periods, and risks of climatic extremes using Bayesian inference. This framework is implemented in the Non-stationary Extreme Value Analysis (NEVA) software package, explicitly designed to facilitate analysis of extremes in the geosciences. In a Bayesian approach, NEVA estimates the extreme value parameters with a Differential Evolution Markov Chain (DE-MC) approach for global optimization over the parameter space. NEVA includes posterior probability intervals (uncertainty bounds) of estimated return levels through Bayesian inference, with its inherent advantages in uncertainty quantification. The software presents the results of non-stationary extreme value analysis using various exceedance probability methods. We evaluate both stationary and non-stationary components of the package for a case study consisting of annual temperature maxima for a gridded global temperature dataset. The results show that NEVA can reliably describe extremes and their return levels.     

Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features

One important tool for water resources management in arid and semi-arid areas is groundwater potential mapping. In this study, four data-mining models including K-nearest neighbor (KNN), linear discriminant analysis (LDA), multivariate adaptive regression splines (MARS), and quadric discriminant analysis (QDA) were used for groundwater potential mapping to get better and more accurate groundwater potential maps (GPMs). For this purpose, 14 groundwater influence factors were considered, such as altitude, slope angle, slope aspect, plan curvature, profile curvature, slope length, topographic wetness index (TWI), stream power index, distance from rivers, river density, distance from faults, fault density, land use, and lithology. From 842 springs in the study area, in the Khalkhal region of Iran, 70 % (589 springs) were considered for training and 30 % (253 springs) were used as a validation dataset. Then, KNN, LDA, MARS, and QDA models were applied in the R statistical software and the results were mapped as GPMs. Finally, the receiver operating characteristics (ROC) curve was implemented to evaluate the performance of the models. According to the results, the area under the curve of ROCs were calculated as 81.4, 80.5, 79.6, and 79.2 % for MARS, QDA, KNN, and LDA, respectively. So, it can be concluded that the performances of KNN and LDA were acceptable and the performances of MARS and QDA were excellent. Also, the results depicted high contribution of altitude, TWI, slope angle, and fault density, while plan curvature and land use were seen to be the least important factors.     

Geochemistry and zircon U-Pb geochronology of Aligoodarz granitoid complex, Sanandaj-Sirjan Zone, Iran

The Aligoodarz granitoid complex (AGC) is located in the Sanandaj-Sirjan Zone (SSZ), western Iran and consists of quartz-diorites, granodiorites and subordinate granites. Whole rock major and trace element data mostly define linear trends on Harker diagrams suggesting a cogenetic origin of the different rock types. (⁸⁷Sr/⁸⁶Sr)_i and εNd_t ratios are in the ranges 0.7074-0.7110 and −3.56 to −5.50, respectively. The trace elements and Sr-Nd isotopic composition suggest that the granitoids from the AGC are similar to crustal derived I-type granitoids of continental arcs. The whole rock suite was produced by assimilation and fractional crystallization starting from a melt with intermediate composition likely possessing a mantle component. In situ zircon U-Pb data on the granites with LA-ICP-MS yield a crystallization age of ∼165 Ma. Inherited grains spanning in age from ∼180 Ma up to 2027 Ma were also found and confirm that assimilation of country rock has occurred.Chemical and chronological data on the AGC were compared with those available for other granitoid complexes of the central SSZ (e.g., Dehno, Boroujerd and Alvand). The comparison reveals that in spite of the different origins that have been proposed, all these granitoid complexes are likely genetically related. They share many chemical features and are derived from crustal melts with minor differences. Alvand granites have the most peculiar compositions most likely related to the presence of abundant pelitic component. All these intrusions are coeval and reveal the presence of an extensive magmatic activity in the central sector of the SSZ during middle Jurassic.     

Time-frequency decomposition of seismic signals via quantum swarm evolutionary matching pursuit

Matching pursuit belongs to the category of spectral decomposition approaches that use a pre-defined discrete wavelet dictionary in order to decompose a signal adaptively. Although disengaged from windowing issues, matching point demands high computational costs as extraction of all local structure of signal requires a large size dictionary. Thus in order to find the best match wavelet, it is required to search the whole space. To reduce the computational cost of greedy matching pursuit, two artificial intelligence methods, (1) quantum inspired evolutionary algorithm and (2) particle swarm optimization, are introduced for two successive steps: (a) initial estimation and (b) optimization of wavelet parameters. We call this algorithm quantum swarm evolutionary matching pursuit. Quantum swarm evolutionary matching pursuit starts with a small colony of population at which each individual, is potentially a transformed form of a time-frequency atom. To attain maximum pursuit of the potential candidate wavelets with the residual, the colony members are adjusted in an evolutionary way. In addition, the quantum computing concepts such as quantum bit, quantum gate, and superposition of states are introduced into the method. The algorithm parameters such as social and cognitive learning factors, population size and global migration period are optimized using seismic signals. In applying matching pursuit to geophysical data, typically complex trace attributes are used for initial estimation of wavelet parameters, however, in this study it was shown that using complex trace attributes are sensitive to noisy data and would have lower rate of convergence. The algorithm performance over noisy signals, using non-orthogonal dictionaries are investigated and compared with other methods such as orthogonal matching pursuit. The results illustrate that quantum swarm evolutionary matching pursuit has the least sensitivity to noise and higher rate of convergence. Finally, the algorithm is applied to both modelled seismograms and real data for detection of low frequency anomalies to validate the findings.     

Assessment of geomorphological bank evolution of the alluvial threshold rivers based on entropy concept parameters

The complex stream bank profiles in alluvial channels and rivers that are formed after reaching equilibrium has been a popular topic of research for many geomorphologists and river engineers. The entropy theory has recently been successfully applied to this problem. However, the existing methods restrict the further application of the entropy parameter to determine the cross-section slope of the river banks. To solve this limitation, we introduce a novel approach in the extraction of the equation based on the calculation of the entropy parameter (λ) and the transverse slope of the bank profile at threshold channel conditions. The effects of different hydraulic and geometric parameters are evaluated on a variation of the entropy parameter. Sensitivity analysis on the parameters affecting the entropy parameter shows that the most effective parameter on the λ-slope multiplier is the maximum slope of the bank profile and the dimensionless lateral distance of the river banks.     

Limit states and failure mechanisms of viscous dampers and the implications for large earthquakes

Fluid viscous dampers are used to control story drifts and member forces in structures during earthquake events. These elements provide satisfactory performance at the design‐level or maximum considered earthquake. However, buildings using fluid viscous dampers have not been subjected to very large earthquakes with intensities greater than the design and maximum considered events. Furthermore, an extensive database of viscous damper performance during large seismic events does not exist. To address these issues, a comprehensive analytical and experimental investigation was conducted to determine the performance of damped structures subjected to large earthquakes. A critical component of this research was the development and verification of a detailed viscous damper mathematical model that incorporates limit states. The development of this model and the laboratory and simulation results conclude good correlation with the new model and the damper limit states and provide superior results compared with the typical damper model when considering near collapse evaluation of structures. Copyright © 2010 John Wiley & Sons, Ltd.     

The chronostratigraphy of a quaternary sequence at the distal part of the nile littoral cell,Haifa Bay,Israel

The current detailed chronostratigraphic framework of the last 1 Ma of an eastern Mediterranean sequence (Haifa Bay, Israel) aims to examine the relative roles of sea‐level changes, climate and tectonics. Seven continuous marine cores, up to ～120 m long, were recovered from shallow water depths. The cores were dated by optically stimulated luminescence, ¹⁴C, magnetostratigraphy, ²³⁰Th/²³⁴U, ²⁶Al/¹⁰Be, occurrence of index fossils and correlated to the global sea‐level curve and Marine Isotope Stages (MIS). The sedimentary sequence accumulated during the last ca. 1.0 Ma consists of 21 transgression–regression units with hiatuses between them. Five marine/terrestrial cycles, which occur in the lower part of the sequence, are attributed to the Jaramillo subchron and the Brunhes–Matuyama boundary, and correspond to MIS 29–21. The top ～50 m includes three sedimentary cycles deposited in the last ca. 400 ka. The regressive phases during this interval correspond to Glacial MIS 8, 6 and 2, while the transgressions correspond to Interglacial MIS 11, 7, 5 and 1. Thus, for the first time, this study documents the longest Quaternary succession dated so far in a key area of the Levant, sensitive to global history of sea‐level changes and glacial/interglacial fluctuations. Copyright © 2012 John Wiley & Sons, Ltd.