An example of fingerprint detection of greenhouse climate change

As an example of the technique of fingerprint detection of greenhouse climate change, a multivariate signal or fingerprint of the enhanced greenhouse effect is defined using the zonal mean atmospheric temperature change as a function of height and latitude between equilibrium climate model simulations with control and doubled CO₂ concentrations. This signal is compared with observed atmospheric temperature variations over the period 1963 to 1988 from radiosonde-based global analyses. There is a significant increase of this greenhouse signal in the observational data over this period.These results must be treated with caution. Upper air data are available for a short period only, possibly too short to be able to resolve any real greenhouse climate change. The greenhouse fingerprint used in this study may not be unique to the enhanced greenhouse effect and may be due to other forcing mechanisms. However, it is shown that the patterns of atmospheric temperature change associated with uniform global increases of sea surface temperature, with El NinoSouthern Oscillation events and with decreases of stratospheric ozone concentrations individually are different from the greenhouse fingerprint used here.     

华北北部中生代岩墙群

岩墙群是深源岩浆浅侵位的产物，它可提供有关壳-幔演化的重要信息。通过华北北部大同早中生代碳酸岩-煌斑岩岩墙群、赤峰早中生代闪长玢岩岩墙群、林西中生代辉绿岩岩墙群、京北晚中生代双峰式岩墙群的对比研究，揭示了华北中生代存在的两次底侵作用及其相关的伸展作用。华北早、晚中生代岩墙群与晋北地区元古代岩墙群的初步对比，从一个侧面反映了华北壳幔的演变。     

The Sabzevar blueschists of the North-Central Iranian micro-continent as remnants of the Neotethys-related oceanic crust subduction

The Sabzevar ophiolites mark the Neotethys suture in east-north-central Iran. The Sabzevar metamorphic rocks, as part of the Cretaceous Sabzevar ophiolitic complex, consist of blueschist, amphibolite and greenschist. The Sabzevar blueschists contain sodic amphibole, epidote, phengite, calcite ± omphacite ± quartz. The epidote amphibolite is composed of sodic-calcic amphibole, epidote, albite, phengite, quartz ± omphacite, ilmenite and titanite. The greenschist contains chlorite, plagioclase and pyrite, as main minerals. Thermobarometry of a blueschist yields a pressure of 13–15.5 kbar at temperatures of 420–500 °C. Peak metamorphic temperature/depth ratios were low (~12 °C/km), consistent with metamorphism in a subduction zone. The presence of epidote in the blueschist shows that the rocks were metamorphosed entirely within the epidote stability field. Amphibole schist samples experienced pressures of 5–7 kbar and temperatures between 450 and 550 °C. The presence of chlorite, actinolite, biotite and titanite indicate greenschist facies metamorphism. Chlorite, albite and biotite replacing garnet or glaucophane suggests temperatures of >300 °C for greenschist facies. The formation of high-pressure metamorphic rocks is related to north-east-dipping subduction of the Neotethys oceanic crust and subsequent closure during lower Eocene between the Central Iranian Micro-continent and Eurasia (North Iran).     

Soil temperature modeling at different depths using neuro-fuzzy,neural network,and genetic programming techniques

The applicability of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) techniques in estimating soil temperatures (ST) at different depths is investigated in this study. Weather data from two stations, Mersin and Adana, Turkey, were used as inputs to the applied models in order to model monthly STs. The first part of the study focused on comparison of ANN, ANFIS, and GP models in modeling ST of two stations at the depths of 10, 50, and 100 cm. GP was found to perform better than the ANN and ANFIS-SC in estimating monthly ST. The effect of periodicity (month of the year) on models’ accuracy was also investigated. Including periodicity component in models’ inputs considerably increased their accuracies. The root mean square error (RMSE) of ANN models was respectively decreased by 34 and 27 % for the depths of 10 and 100 cm adding the periodicity input. In the second part of the study, the accuracies of the ANN, ANFIS, and GP models were compared in estimating ST of Mersin Station using the climatic data of Adana Station. The ANN models generally performed better than the ANFIS-SC and GP in modeling ST of Mersin Station without local climatic inputs.     

Spatial characterisation of the Benguela ecosystem for ecosystem-based management

The three countries of the Benguela Current Large Marine Ecosystem (BCLME), namely Angola, Namibia and South Africa, have committed to implementing ecosystem-based management (EBM) including an ecosystem approach to fisheries (EAF) in the region, to put in practice the principles of sustainable development in ocean-related matters. There is also recognition of the need for marine spatial planning (MSP) as a process for informing EBM with regard to the allocation and siting of ocean uses so that ecosystem health is ensured and trade-offs between ecosystem services are appropriately dealt with. Marine spatial planning is both an integrated and an area-based process, and this paper produces a spatial characterisation of the BCLME for achieving a common basis for MSP in the region, focusing on the oceanography, biology and fisheries. Recognising spatial variation in physical driving forces, primary and secondary production, trophic structures and species richness, four different subsystems are characterised: (1) north of the Angola–Benguela Front, (2) from the Angola–Benguela Front to Lüderitz, (3) from Lüderitz to Cape Agulhas, and (4) from Cape Agulhas to Port Alfred on the south-east coast of South Africa. Research and monitoring requirements of relevance for MSP and EBM in the region are identified, focusing on understanding variability and change, including with regard to the boundary areas identified for the system. To this end, 14 cross-shelf monitoring transects are proposed (including seven that are already being monitored) to estimate fluxes of biota, energy and materials within and between the subsystems. The usefulness of models for understanding ecosystem variability and changes is recognised and the need for fine-scale resolution of both sampling and modelling for adequate MSP as input to EBM for the often-conflicting interests of conserving biodiversity, and managing fisheries, recreation, offshore oil and gas exploration and exploitation, offshore mining and shipping routes, is emphasised.     

Monotone multiscale finite volume method

The MultiScale Finite Volume (MSFV) method is known to produce non-monotone solutions. The causes of the non-monotone solutions are identified and connected to the local flux across the boundaries of primal coarse cells induced by the basis functions. We propose a monotone MSFV (m-MSFV) method based on a local stencil-fix that guarantees monotonicity of the coarse-scale operator, and thus, the resulting approximate fine-scale solution. Detection of non-physical transmissibility coefficients that lead to non-monotone solutions is achieved using local information only and is performed algebraically. For these ‘critical’ primal coarse-grid interfaces, a monotone local flux approximation, specifically, a Two-Point Flux Approximation (TPFA), is employed. Alternatively, a local linear boundary condition can be used for the dual basis functions to reduce the degree of non-monotonicity. The local nature of the two strategies allows for ensuring monotonicity in local sub-regions, where the non-physical transmissibility occurs. For practical applications, an adaptive approach based on normalized positive off-diagonal coarse-scale transmissibility coefficients is developed. Based on the histogram of these normalized coefficients, one can remove the large peaks by applying the proposed modifications only for a small fraction of the primal coarse grids. Though the m-MSFV approach can guarantee monotonicity of the solutions to any desired level, numerical results illustrate that employing the m-MSFV modifications only for a small fraction of the domain can significantly reduce the non-monotonicity of the conservative MSFV solutions.     

Prediction of porosity and water saturation using pre-stack seismic attributes: a comparison of Bayesian inversion and computational intelligence methods

Rock physical parameters such as porosity and water saturation play an important role in the mechanical behavior of hydrocarbon reservoir rocks. A valid and reliable prediction of these parameters from seismic data is essential for reservoir characterization, management, and also geomechanical modeling. In this paper, the application of conventional methods such as Bayesian inversion and computational intelligence methods, namely support vector regression (SVR) optimized by particle swarm optimization (PSO) and adaptive network-based fuzzy inference system-subtractive clustering method (ANFIS-SCM), is demonstrated to predict porosity and water saturation. The prediction abilities offered by Bayesian inversion, SVR-PSO, and ANFIS-SCM were presented using a synthetic dataset and field data available from a gas carbonate reservoir in Iran. In these models, seismic pre-stack data and attributes were utilized as the input parameters, while the porosity and water saturation were the output parameters. Various statistical performance indexes were utilized to compare the performance of those estimation models. The results achieved indicate that the ANFIS-SCM model has strong potential for indirect estimation of porosity and water saturation with high degree of accuracy and robustness from seismic data and attributes in both synthetic and real cases of this study.     

Expert views on biodiversity conservation in an era of climate change

Adapting conservation policy to the impacts of climate change has emerged as a central and unresolved challenge. In this paper, we report on the results of 21 in-depth interviews with biodiversity and climate change adaptation experts on their views of the implications of climate change for conservation policy. We find a diversity of views across a set of topics that included: changing conservation objectives, conservation triage and its criteria, increased management interventions in protected areas, the role of uncertainty in decision-making, and evolving standards of conservation success. Notably, our findings reveal active consideration among experts with some more controversial elements of policy adaptation (including the role of disturbance in facilitating species transitions, and changing standards of conservation success), despite a comparative silence on these topics in the published literature. Implications of these findings are discussed with respect to: (a) identifying future research and integration needs and (b) providing insight into the process of policy adaptation in the context of biodiversity conservation.