Thallium: a sensitive indicator of rock/seawater interaction and of sulfur saturation of silicate melts

Radiochemical neutron activation analysis for Tl in a number of young pillow basalts, hydrothermally altered basalts and associated hydrothermal and hydrogenous Mn crusts from the Mid-Atlantic Ridge and adjacent regions of the North Atlantic indicate that Tl is a sensitive indicator of both S saturation of silicate melts and of rock/seawater interactions. Rb-Cs-K-Tl trend lines for fresh MAR basalts (whose melts remained saturated with S during silicate fractionation) are distinctly different to Hawaiian basalts whose melts lost S during or prior to eruption, but were saturated with S at an earlier stage. Varying degrees of Tl enrichment are found in the hydrogenous Mn nodules (91,000 ppb), hydrothermal Mn crusts (2300–32,000 ppb), palagonitized glass (300–2700 ppb), hydrothermally altered basalts (1140–4560 ppb), and even slightly altered pillow interiors (11–45 ppb) relative to the fresh glasses (6–12 ppb). This enrichment has taken place due to incorporation of Tl into secondary silicate phases along with the alkalis and also due to co-precipitation of Tl with ferromanganese oxides and hydroxides. Thallium enrichment in the hydrothermal products is interpreted as being due to cooling and oxidation of hydrothermal fluids as these approached the sea floor. Haloes of Tl-enriched country rock may occur around sulfide deposits in which seawater has acted as the ore fluid.     

Spatial Variability of Turbulent Fluxes in the Roughness Sublayer of an Even-Aged Pine Forest

The spatial variability of turbulent flow statistics in the roughness sublayer (RSL) of a uniform even-aged 14 m (= h) tall loblolly pine forest was investigated experimentally. Using seven existing walkup towers at this stand, high frequency velocity, temperature, water vapour and carbon dioxide concentrations were measured at 15.5 m above the ground surface from October 6 to 10 in 1997. These seven towers were separated by at least 100m from each other. The objective of this study was to examine whether single tower turbulence statistics measurements represent the flow properties of RSL turbulence above a uniform even-aged managed loblolly pine forest as a best-case scenario for natural forested ecosystems. From the intensive space-time series measurements, it was demonstrated that standard deviations of longitudinal and vertical velocities (_u, _w) and temperature (_T) are more planar homogeneous than their vertical flux of momentum (u_* ²) and sensible heat (H) counterparts. Also, the measured H is more horizontally homogeneous when compared to fluxes of other scalar entities such as CO₂ and water vapour. While the spatial variability in fluxes was significant (>15 %), this unique data set confirmed that single tower measurements represent the canonical structure of single-point RSL turbulence statistics, especially flux-variance relationships. Implications to extending the moving-equilibrium hypothesis for RSL flows are discussed. The spatial variability in all RSL flow variables was not constant in time and varied strongly with spatially averaged friction velocity u_*, especially when u_* was small. It is shown that flow properties derived from two-point temporal statistics such as correlation functions are more sensitive to local variability in leaf area density when compared to single point flow statistics. Specifically, that the local relationship between the reciprocal of the vertical velocity integral time scale (I_w) and the arrival frequency of organized structures (/h) predicted from a mixing-layer theory exhibited dependence on the local leaf area index. The broader implications of these findings to the measurement and modelling of RSL flows are also discussed.     

A Sensitivity Analysis of Timing and Costs of Greenhouse Gas Emission Reductions

This paper analyses the optimal timing and macro-economic costs of carbon emission reductions that mitigate the global average atmospheric temperature increase. We use a macro-economic model in which there are two competing energy sources, fossil-fuelled and non-fossil-fuelled. Technological change is represented endogenously through learning curves, and niche markets exist implying positive demand for the relatively expensive non-fossil-fuelled energy source. Under these conditions, with a temperature increase constraint of 2 ° C, early abatement is found to be optimal, and, comparedto the results of many existing top-down models, the costs of this strategy prove to be low. We perform an extensive sensitivity analysis of our results regarding the uncertainties that dominate various economic and technological modeling parameters. Uncertainties in the learning rate and the elasticity of substitution between the two different energy sources most significantly affect the robustness of our findings.     

Derivation of streamflow statistics based on a filtered point process

This work presents the derivation of general streamflow cumulants from daily rainfall time series. The general streamflow cumulants can be used to compute basic streamflow statistics such as mean, variance, coefficient of skewness, and correlation coefficient. Streamflow is considered as a filtered point process where the input is a daily rainfall time series assumed to be a marked point process. The marks of the process are the daily rainfall amounts which are assumed independent and identically distributed. The number of rainfall occurrences is a counting process represented by either the binomial, the Poisson, or the negative binomial probability distribution depending on its ratio of mean to variance. The first three cumulants and the covariance function of J-day averaged streamflows are deduced based on the characteristic function of a filtered point process. These cumulants are functions of the stochastic properties of the daily rainfall process and the basin-response function representing the causal relationship between rainfall and runoff.     

Retrospective multivariate Bayesian change-point analysis: A simultaneous single change in the mean of several hydrological sequences

Although hydrological time series for different sites in a given region are usually correlated and that climate changes should have a regional impact on water resources, very little has appeared in the literature about multivariate change-point analysis. This paper generalizes the univariate Bayesian approach for the detection of a single shift in the mean level to study a change in the mean-vector of a sequence of multivariate normal vectors. Two different problems are considered: the first one is the estimation of the unknown regional change-point under the hypothesis that a shift occurred, while the second one is the overall assessment of change versus no change. This method is illustrated by an application to streamflow data series for six rivers situated in the Northern Québec Labrador region.     

Method of moments of the Halphen distribution parameters

Halphen laws have been proposed as a complete system of distributions with sufficient statistics that lead to estimation with minimum variance. The Halphen system provides a flexibility to fit a large variety of data sets from natural events. In this paper we present the method of moments (MM) to estimate the Halphen type B and IB distribution parameters. Their computation is very fast when compared to those given by the maximum likelihood method (ML). Furthermore, this estimation method is very easy to implement since the formulae are explicit. Some simulations show the equivalence of both methods when estimating the quantiles for finite sample size.     

Designing policy for deployment of CCS in industry

Attaining deep greenhouse gas (GHG) emission reductions in industry in order to support a stringent climate change control target will be difficult without recourse to CO₂ capture and storage (CCS). Using the insights from a long-term bottom-up energy systems model, and undertaking a sectoral assessment, we investigated the importance of CCS in the industrial sector. Under climate policy aimed at limiting atmospheric concentration of GHGs to 650 ppm CO₂e, costs could increase fivefold when CCS is excluded from the portfolio of mitigation option measures in the industry sector as compared to when CCS is excluded in the power sector. This effect is driven largely by the lack of alternatives for deep emission reductions in industry. Our main policy conclusion is that a broader recognition of CCS in industrial applications in both current policy discussions and research, development, and demonstration funding programmes is justified. In recognition of the heterogeneity of the many types of industrial production processes, the size and location of industrial CO₂ sources, the specific need for CCS-retrofitting, and the exposure of most industrial sectors to international trade, policies aimed at supporting CCS must distinguish between the different challenges faced by the power and industrial sectors.