The isotopic effects of electron transfer: An explanation for Fe isotope fractionation in nature

Isotope fractionation of electroplated Fe was measured as a function of applied electrochemical potential. As plating voltage was varied from −0.9 V to 2.0 V, the isotopic signature of the electroplated iron became depleted in heavy Fe, with δ⁵⁶Fe values (relative to IRMM-14) ranging from −0.18(±0.02) to −2.290(±0.006) ‰, and corresponding δ⁵⁷Fe values of −0.247(±0.014) and −3.354(±0.019) ‰. This study demonstrates that there is a voltage-dependent isotope fractionation associated with the reduction of iron. We show that Marcus’s theory for the kinetics of electron transfer can be extended to include the isotope effects of electron transfer, and that the extended theory accounts for the voltage dependence of Fe isotope fractionation. The magnitude of the electrochemically-induced fractionation is similar to that of Fe reduction by certain bacteria, suggesting that similar electrochemical processes may be responsible for biogeochemical Fe isotope effects. Charge transfer is a fundamental physicochemical process involving Fe as well as other transition metals with multiple isotopes. Partitioning of isotopes among elements with varying redox states holds promise as a tool in a wide range of the Earth and environmental sciences, biology, and industry.     

Using raw regional climate model outputs for quantifying climate change impacts on hydrology

General circulation model outputs are rarely used directly for quantifying climate change impacts on hydrology, due to their coarse resolution and inherent bias. Bias correction methods are usually applied to correct the statistical deviations of climate model outputs from the observed data. However, the use of bias correction methods for impact studies is often disputable, due to the lack of physical basis and the bias nonstationarity of climate model outputs. With the improvement in model resolution and reliability, it is now possible to investigate the direct use of regional climate model (RCM) outputs for impact studies. This study proposes an approach to use RCM simulations directly for quantifying the hydrological impacts of climate change over North America. With this method, a hydrological model (HSAMI) is specifically calibrated using the RCM simulations at the recent past period. The change in hydrological regimes for a future period (2041–2065) over the reference (1971–1995), simulated using bias‐corrected and nonbias‐corrected simulations, is compared using mean flow, spring high flow, and summer–autumn low flow as indicators. Three RCMs driven by three different general circulation models are used to investigate the uncertainty of hydrological simulations associated with the choice of a bias‐corrected or nonbias‐corrected RCM simulation. The results indicate that the uncertainty envelope is generally watershed and indicator dependent. It is difficult to draw a firm conclusion about whether one method is better than the other. In other words, the bias correction method could bring further uncertainty to future hydrological simulations, in addition to uncertainty related to the choice of a bias correction method. This implies that the nonbias‐corrected results should be provided to end users along with the bias‐corrected ones, along with a detailed explanation of the bias correction procedure. This information would be especially helpful to assist end users in making the most informed decisions.     

The supergene origin of ruthenian hexaferrum in Ni‐laterites

For two decades, the nature of Fe‐rich, oxygen‐bearing, Ru–Os compounds found in the supergene environment has been debated. Ru–Os–Fe‐oxides and nano‐intergrowths of ruthenium with magnetite have been proposed. We applied FE‐SEM, EMPA, μ‐Raman spectroscopy and synchrotron tts‐μXRD to Ru–Os–Fe compounds recovered from Ni‐laterites from the Dominican Republic. The results demonstrate that a significant portion of Fe exists in a common structure with the Ru–Os alloy, that is, ruthenian hexaferrum. This mineral occurs both as nanoparticles and as micrometric patches within a matrix of Fe‐oxide(s). Our data suggest that supergene ruthenian hexaferrum with a (Ru_0.4(Os,Ir)_0.1Fe_0.5)_?1.0 stoichiometry represents the most advanced weathering product of primary laurite within Ni‐laterites from the Dominican Republic.     

Assessing regression‐based statistical approaches for downscaling precipitation over North America

This paper assesses linear regression‐based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45‐ and 15‐km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger‐scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher‐resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation‐generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression‐based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd.     

A gridded sea surface salinity data set for the tropical Pacific with sample applications (1950–2008)

We present a gridded data set of Sea Surface Salinity (SSS) for the tropical Pacific (120°E–70°W; 30°N–30°S), with a grid resolution of 1° longitude, 1° latitude and 1 month, from 1950 to 2008. The product, together with its associated error field, is derived from an objective analysis of about 10 million validated SSS records, with most of the data originating from Voluntary Observing Ships, TAO/TRITON moorings and Argo profilers (during the most recent period). We expect this product to benefit studies in oceanography, meteorology and paleoceanography. As examples of applications, we analyse: (a) the seasonal and ENSO (El Niño Southern Oscillation) modes of observed SSS variability, (b) the ability of 23 coupled models used in the Intergovernmental Panel for Climate Change 4th Assessment Report (IPCC AR4) to simulate the mean SSS and these two time varying modes, and (c) the usefulness of the SSS product and of its associated error field in calibrating and validating the paleo-salinity time series. We anticipate improvements and regular updates to our product, as more SSS data become available from in situ networks and from the ongoing and near-future satellite-derived observations by SMOS (Soil Moisture and Ocean Salinity) and Aquarius.     

Calibration of paired watersheds: Utility of moving sums in presence of externalities

Historically, paired watershed studies have been used to quantify the hydrological effects of land use and management practices by concurrently monitoring 2 similar watersheds during calibration (pretreatment) and post‐treatment periods. This study characterizes seasonal water table and flow response to rainfall during the calibration period and tests a change detection technique of moving sums of recursive residuals (MOSUM) to select calibration periods for each control–treatment watershed pair when the regression coefficients for daily water table elevation were most stable to minimize regression model uncertainty. The control and treatment watersheds were 1 watershed of 3–4‐year‐old intensely managed loblolly pine (Pinus taeda L.) with natural understory, 1 watershed of 3–4‐year‐old loblolly pine intercropped with switchgrass (Panicum virgatum), 1 watershed of 14–15‐year‐old thinned loblolly pine with natural understory (control), and 1 watershed of switchgrass only. The study period spanned from 2009 to 2012. Silvicultural operational practices during this period acted as external factors, potentially shifting hydrologic calibration relationships between control and treatment watersheds. MOSUM results indicated significant changes in regression parameters due to silvicultural operations and were used to identify stable relationships for water table elevation. None of the calibration relationships developed using this method were significantly different from the classical calibration relationship based on published historical data. We attribute that to the similarity of historical and 2010–2012 leaf area index on control and treatment watersheds as moderated by the emergent vegetation. Although the MOSUM approach does not eliminate the need for true calibration data or replace the classic paired watershed approach, our results show that it may be an effective alternative approach when true data are unavailable, as it minimizes the impacts of external disturbances other than the treatment of interest.     

Intra-interstadial environmental changes in Last Glacial loess revealed by molluscan assemblages from the Upper Palaeolithic site of Amiens-Renancourt 1 (Somme,France)

The Amiens-Renancourt 1 site recently yielded one of the most important Upper Palaeolithic human occupations of northern France by the number of flint artefacts and especially by the presence of Venus figurines. All the material comes from a single archaeological layer located in a tundra gley bracketed by loess units. A multi-proxy study combining a detailed stratigraphy, luminescence and radiocarbon datings and high-resolution (5 cm per sample) grain size and molluscan analyses was therefore carried out to reconstruct and date the associated environmental changes and to determine the exact context of the human occupation. The chronological frame thus established supports the correlations of the archaeology-bearing tundra gley and of an underlying arctic brown soil with Greenland interstadials GI-4 and GI-3. Composition changes in the molluscan population enabled the identification of transitional and optimum phases and sub-phases within these two pedogenetic horizons. A conceptual correlation model linking molluscan phases with millennial-scale variations of Greenland ice-core and Sieben Hengste speleothem climate records is proposed. The Human occupation appears contemporaneous to the end of the stadial–interstadial transition of GI-3. Synchronous in Amiens-Renancourt 1 and Nussloch, subsequent micro-gleys may also result from a regional/global forcing. Such a level of detail is unprecedented in a loess sequence.