How robust are observed and simulated precipitation responses to tropical ocean warming?

Robust responses and links between the tropical energy and water cycles are investigated using multiple datasets and climate models over the period 1979-2006. Atmospheric moisture and net radiative cooling provide powerful constraints upon future changes in precipitation. While moisture amount is robustly linked with surface temperature, the response of atmospheric net radiative cooling, derived from satellite data, is less coherent. Precipitation trends and relationships with surface temperature are highly sensitive to the data product and the time-period considered. Data from the Special Sensor Microwave Imager (SSM/I) produces the strongest trends in precipitation and response to warming of all the datasets considered. The tendency for moist regions to become wetter while dry regions become drier in response to warming is captured by both observations and models. Citation: John, V. O., R. P. Allan, and B. J. Soden (2009), How robust are observed and simulated precipitation responses to tropical ocean warming?

The release of silicon from amorphous silica and rice straw in Sri Lankan soils

Silicon release from rice straw and amorphous silica when shaken in solution with five Sri Lankan soils was studied indirectly using sorption isotherms and changes in concentration and directly using straw in dialysis bags examined using electron microscopy. The aim was to further our understanding of the processes and factors affecting the release of straw-Si in soils and its availability to rice. The soils (alfisols and ultisols) shaken with 0.1 M NaCl (5 g per 125 mL for 250 days) produced concentrations of 1 - 4 mg L-1 of monosilicic acid-Si. Amorphous silica added to these suspensions (36.5 mg, containing 17 mg Si) raised the concentrations to 20 - 40 mg L-1, and added rice straw (0.5 g, containing 17 mg Si) gave 10 - 25 mg L-1. Sorption isotherms (7 days equilibrations) were used to calculate from the concentrations the amounts of Si released ( 24 - 38% and 8 - 21%, respectively). Both materials gave about 40 mg L-1 of monosilicic acid-Si plus 30 mg L-1 of disilicic acid-Si when shaken in solution alone (5 g per 125 mL). Straw in dialysis bags ( 0.5 g per 25 mL in 0.1 M NaCl) was shaken in soil suspension ( 5 g per 100 mL) for 60 days. Similar concentrations and releases were measured to those obtained above. About one fifth of the mass of straw was lost by decomposition in the first 15 days. A chloroform treatment prevented decomposition, but Si release was unaffected. Disintegration continued throughout the experiments, with phytoliths being exposed and dissolved. Compared to the rate of release from straw into solution without soil, the release of Si into soil suspensions was increased during the first 20 days by adsorption on the soil, but was then reduced probably through the effect of Fe and Al on the phytolith surfaces. The extent of this blocking effect varied between soils and was not simply related to soil pH.

Determining the effect of asymmetric data on the variogram. II. Outliers

Asymmetry in a distribution can arise from a long tail of values in the underlying process or from outliers that belong to another population that contaminate the primary process. The first paper of this series examined the effects of the former on the variogram and this paper examines the effects of asymmetry arising from outliers. Simulated annealing was used to create normally distributed random fields of different size that are realizations of known processes described by variograms with different nugget:sill ratios. These primary data sets were then contaminated with randomly located and spatially aggregated outliers from a secondary process to produce different degrees of asymmetry. Experimental variograms were computed from these data by Matheron's estimator and by three robust estimators. The effects of standard data transformations on the coefficient of skewness and on the variogram were also investigated. Cross-validation was used to assess the performance of models fitted to experimental variograms computed from a range of data contaminated by outliers for kriging. The results showed that where skewness was caused by outliers the variograms retained their general shape, but showed an increase in the nugget and sill variances and nugget:sill ratios. This effect was only slightly more for the smallest data set than for the two larger data sets and there was little difference between the results for the latter. Overall, the effect of size of data set was small for all analyses. The nugget:sill ratio showed a consistent decrease after transformation to both square roots and logarithms; the decrease was generally larger for the latter, however. Aggregated outliers had different effects on the variogram shape from those that were randomly located, and this also depended on whether they were aggregated near to the edge or the centre of the field. The results of cross-validation showed that the robust estimators and the removal of outliers were the most effective ways of dealing with outliers for variogram estimation and kriging. (C) 2007 Elsevier Ltd. All rights reserved.

Characterization and identification of mixed-metal phosphates in soils: the application, of Raman spectroscopy

The development of protocols for the identification of metal phosphates in phosphate-treated, metal-contaminated soils is a necessary yet problematical step in the validation of remediation schemes involving immobilization of metals as phosphate phases. The potential for Raman spectroscopy to be applied to the identification of these phosphates in soils has yet to be fully explored. With this in mind, a range of synthetic mixed-metal hydroxylapatites has been characterized and added to soils at known concentrations for analysis using both bulk X-ray powder diffraction (XRD) and Raman spectroscopy. Mixed-metal hydroxylapatites in the binary series Ca-Cd, Ca-Pb, Ca-Sr and Cd-Pb synthesized in the presence of acetate and carbonate ions, were characterized using a range of analytical techniques including XRD, analytical scanning electron microscopy (SEM), infrared spectroscopy (IR), inductively coupled plasma-atomic emission spectrometry (ICP-AES) and Raman spectroscopy. Only the Ca-Cd series displays complete solid solution, although under the synthesis conditions of this study the Cd-5(PO4)(3)OH end member could not be synthesized as a pure phase. Within the Ca-Cd series the cell parameters, IR active modes and Raman active bands vary linearly as a function of Cd content. X-ray diffraction and extended X-ray absorption fine structure spectroscopy (EXAFS) suggest that the Cd is distributed across both the Ca(1) and Ca(2) sites, even at low Cd concentrations. In order to explore the likely detection limits for mixed-metal phosphates in soils for XRD and Raman spectroscopy, soils doped with mixed-metal hydroxylapatites at concentrations of 5, 1 and 0.5 wt.% were then studied. X-ray diffraction could not confirm unambiguously the presence or identity of mixed-metal phosphates in soils at concentrations below 5 wt.%. Raman spectroscopy proved a far more sensitive method for the identification of mixed-metal hydroxylapatites in soils, which could positively identify the presence of such phases in soils at all the dopant concentrations used in this study. Moreover, Raman spectroscopy could also provide an accurate assessment of the degree of chemical substitution in the hydroxylapatites even when present in soils at concentrations as low as 0.1%.

The release of silicon from amorphous silica and rice straw in Sri Lankan soils

Silicon release from rice straw and amorphous silica when shaken in solution with five Sri Lankan soils was studied indirectly using sorption isotherms and changes in concentration and directly using straw in dialysis bags examined using electron microscopy. The aim was to further our understanding of the processes and factors affecting the release of straw-Si in soils and its availability to rice. The soils (alfisols and ultisols) shaken with 0.1 M NaCl (5 g per 125 mL for 250 days) produced concentrations of 1 - 4 mg L-1 of monosilicic acid-Si. Amorphous silica added to these suspensions (36.5 mg, containing 17 mg Si) raised the concentrations to 20 - 40 mg L-1, and added rice straw (0.5 g, containing 17 mg Si) gave 10 - 25 mg L-1. Sorption isotherms (7 days equilibrations) were used to calculate from the concentrations the amounts of Si released ( 24 - 38% and 8 - 21%, respectively). Both materials gave about 40 mg L-1 of monosilicic acid-Si plus 30 mg L-1 of disilicic acid-Si when shaken in solution alone (5 g per 125 mL). Straw in dialysis bags ( 0.5 g per 25 mL in 0.1 M NaCl) was shaken in soil suspension ( 5 g per 100 mL) for 60 days. Similar concentrations and releases were measured to those obtained above. About one fifth of the mass of straw was lost by decomposition in the first 15 days. A chloroform treatment prevented decomposition, but Si release was unaffected. Disintegration continued throughout the experiments, with phytoliths being exposed and dissolved. Compared to the rate of release from straw into solution without soil, the release of Si into soil suspensions was increased during the first 20 days by adsorption on the soil, but was then reduced probably through the effect of Fe and Al on the phytolith surfaces. The extent of this blocking effect varied between soils and was not simply related to soil pH.

Robust dynamics of Amazon dieback to climate change with perturbed ecosystem model parameters

Climate change science is increasingly concerned with methods for managing and integrating sources of uncertainty from emission storylines, climate model projections, and ecosystem model parameterizations. In tropical ecosystems, regional climate projections and modeled ecosystem responses vary greatly, leading to a significant source of uncertainty in global biogeochemical accounting and possible future climate feedbacks. Here, we combine an ensemble of IPCC-AR4 climate change projections for the Amazon Basin (eight general circulation models) with alternative ecosystem parameter sets for the dynamic global vegetation model, LPJmL. We evaluate LPJmL simulations of carbon stocks and fluxes against flux tower and aboveground biomass datasets for individual sites and the entire basin. Variability in LPJmL model sensitivity to future climate change is primarily related to light and water limitations through biochemical and water-balance-related parameters. Temperature-dependent parameters related to plant respiration and photosynthesis appear to be less important than vegetation dynamics (and their parameters) for determining the magnitude of ecosystem response to climate change. Variance partitioning approaches reveal that relationships between uncertainty from ecosystem dynamics and climate projections are dependent on geographic location and the targeted ecosystem process. Parameter uncertainty from the LPJmL model does not affect the trajectory of ecosystem response for a given climate change scenario and the primary source of uncertainty for Amazon 'dieback' results from the uncertainty among climate projections. Our approach for describing uncertainty is applicable for informing and prioritizing policy options related to mitigation and adaptation where long-term investments are required.

Characterisation of amorphous and nanocrystalline molecular materials by total scattering

The use of high-energy X-ray total scattering coupled with pair distribution function analysis produces unique structural fingerprints from amorphous and nanostructured phases of the pharmaceuticals carbamazepine and indomethacin. The advantages of such facility-based experiments over laboratory-based ones are discussed and the technique is illustrated with the characterisation of a melt-quenched sample of carbamazepine as a nanocrystalline (4.5 nm domain diameter) version of form III.

Dynamic load balancing for the distributed mining of molecular structures

In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable for large-scale, multi-domain, heterogeneous environments, such as computational grids.

A customizable multi-agent system for distributed data mining

We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances.