混积型硅质碎屑—碳酸盐沉积体系中最大海泛事件的识别——全球…

沉积层序中的最大管事件是地层对比的重要资料，因为它实质上代表等时的地层。这种事件是根据被动大陆边缘背景下滨岸到大陆斜坡环境硅质碎屑沉积中的特殊物理面和／或微化石组合（相对化石丰度峰值）的显著变化加以识别的。     

Consistent fractionation of 13C in nature and in the laboratory: growth-rate effects in some haptophyte algae

The carbon isotopic fractionation accompanying formation of biomass by alkenone-producing algae in natural marine environments varies systematically with the concentration of dissolved phosphate. Specifically, if the fractionation is expressed by epsilon p approximately delta e - delta p, where delta e and delta p are the delta 13C values for dissolved CO2 and for algal biomass (determined by isotopic analysis of C37 alkadienones), respectively, and if Ce is the concentration of dissolved CO2, micromole kg-1, then b = 38 + 160*[PO4], where [PO4] is the concentration of dissolved phosphate, microM, and b = (25 - epsilon p)Ce. The correlation found between b and [PO4] is due to effects linking nutrient levels to growth rates and cellular carbon budgets for alkenone-containing algae, most likely by trace-metal limitations on algal growth. The relationship reported here is characteristic of 39 samples (r2 = 0.95) from the Santa Monica Basin (six different times during the annual cycle), the equatorial Pacific (boreal spring and fall cruises as well as during an iron-enrichment experiment), and the Peru upwelling zone. Points representative of samples from the Sargasso Sea ([PO4] < or = 0.1 microM) fall above the b = f[PO4] line. Analysis of correlations expected between mu (growth rate), epsilon p, and Ce shows that, for our entire data set, most variations in epsilon p result from variations in mu rather than Ce. Accordingly, before concentrations of dissolved CO2 can be estimated from isotopic fractionations, some means of accounting for variations in growth rate must be found, perhaps by drawing on relationships between [PO4] and Cd/Ca ratios in shells of planktonic foraminifera.     

On North Pacific circulation and associated marine debris concentration

Marine debris in the oceanic realm is an ecological concern, and many forms of marine debris negatively affect marine life. Previous observations and modeling results suggest that marine debris occurs in greater concentrations within specific regions in the North Pacific Ocean, such as the Subtropical Convergence Zone and eastern and western "Garbage Patches". Here we review the major circulation patterns and oceanographic convergence zones in the North Pacific, and discuss logical mechanisms for regional marine debris concentration, transport, and retention. We also present examples of meso- and large-scale spatial variability in the North Pacific, and discuss their relationship to marine debris concentration. These include mesoscale features such as eddy fields in the Subtropical Frontal Zone and the Kuroshio Extension Recirculation Gyre, and interannual to decadal climate events such as El Ni?o and the Pacific Decadal Oscillation/North Pacific Gyre Oscillation.     

Predicting conductance due to upconing using neural networks

Artificial neural networks (ANNs) were developed to accurately predict highly time-variable specific conductance values in an unconfined coastal aquifer. Conductance values in the fresh water lens aquifer change in response to vertical displacements of the brackish zone and fresh water-salt water interface, which are caused by variable pumping and climate conditions. Unlike physical-based models, which require hydrologic parameter inputs, such as horizontal and vertical hydraulic conductivities, porosity, and fluid densities, ANNs can "learn" system behavior from easily measurable variables. In this study, the ANN input predictor variables were initial conductance, total precipitation, mean daily temperature, and total pumping extraction. The ANNs were used to predict salinity (specific conductance) at a single monitoring well located near a high-capacity municipal-supply well over time periods ranging from 30 d to several years. Model accuracy was compared against both measured/interpolated values and predictions were made with linear regression, and in general, excellent prediction accuracy was achieved. For example, although the average percent change of conductance over 90-d periods was 39%, the absolute mean prediction error achieved with the ANN was only 1.1%. The ANNs were also used to conduct a sensitivity analysis that quantified the importance of each of the four predictor variables on final conductance values, providing valuable insights into the dynamics of the system. The results demonstrate that the ANN technology can serve as a powerful and accurate prediction and management tool, minimizing degradation of ground water quality to the extent possible by identifying appropriate pumping policies under variable and/or changing climate conditions.     

A neural network model for predicting aquifer water level elevations

Artificial neural networks (ANNs) were developed for accurately predicting potentiometric surface elevations (monitoring well water level elevations) in a semiconfined glacial sand and gravel aquifer under variable state, pumping extraction, and climate conditions. ANNs "learn" the system behavior of interest by processing representative data patterns through a mathematical structure analogous to the human brain. In this study, the ANNs used the initial water level measurements, production well extractions, and climate conditions to predict the final water level elevations 30 d into the future at two monitoring wells. A sensitivity analysis was conducted with the ANNs that quantified the importance of the various input predictor variables on final water level elevations. Unlike traditional physical-based models, ANNs do not require explicit characterization of the physical system and related physical data. Accordingly, ANN predictions were made on the basis of more easily quantifiable, measured variables, rather than physical model input parameters and conditions. This study demonstrates that ANNs can provide both excellent prediction capability and valuable sensitivity analyses, which can result in more appropriate ground water management strategies.     

青藏高原地表对大气红外冷却廓线的影响

青藏高原地表对大气红外冷却廓线的影响ＥｒｉｃＡ．Ｓｍｉｔｈ；ＬｅｔＳｈｉ（佛罗里达州立大学气象系和巨型计算研究所）１引言青藏高原对大气环流的动力和热力影响是众所周知的。很多学者对青藏高原作为一个巨大的、抬高的热源对大气的影响做过深入的研究。这些研究重...