Electronic Structure of Calcium Hexaboride within the Weighted Density Approximation

We report calculations of the electronic structure of CaB$_6$ using the weighted density approximation (WDA) to density functional theory. We find a semiconducting band structure with a sizable gap, in contrast to local density approximation (LDA) results, but in accord with recent experimental data. In particular, we find an $X$-point band gap of 0.8 eV. The WDA correction of the LDA error in describing the electronic structure of CaB$_6$ is discussed in terms of the orbital character of the bands and the better cancelation of self-interactions within the WDA.Comment: 1 figur

Carbon stable isotopes as a palaeoclimate proxy in vascular plant dominated peatlands

Carbon stable isotope (δ¹³C) records from vascular plant dominated peatlands have been used as a palaeoclimate proxy, but a better empirical understanding of fractionation processes in these ecosystems is required. Here, we test the potential of δ¹³C analysis of ombrotrophic restiad peatlands in New Zealand, dominated by the wire rush (Empodisma spp.), to provide a methodology for developing palaeoclimatic records. We took surface plant samples alongside measurements of water table depth and (micro)climate over spatial (six sites spanning > 10 latitude) and temporal (monthly measurements over 1 year) gradients and analysed the relationships between cellulose δ¹³C values and environmental parameters. We found strong, significant negative correlations between δ¹³C and temperature, photosynthetically active radiation and growing degree days above 0 C. No significant relationships were observed between δ¹³C and precipitation, relative humidity, soil moisture or water table depth, suggesting no growing season water limitation and a decoupling of the expected link between δ¹³C in vascular plants and hydrological variables. δ¹³C of Empodisma spp. roots may therefore provide a valuable temperature proxy in a climatically sensitive region, but further physiological and sub-fossil calibration studies are required to fully understand the observed signal

Preface paper to the Semi-Arid Land-Surface-Atmosphere (SALSA) Program special issue

The Semi-Arid Land-Surface-Atmosphere Program (SALSA) is a multi-agency, multi-national research effort that seeks to evaluate the consequences of natural and human-induced environmental change in semi-arid regions. The ultimate goal of SALSA is to advance scientific understanding of the semi-arid portion of the hydrosphere–biosphere interface in order to provide reliable information for environmental decision making. SALSA approaches this goal through a program of long-term, integrated observations, process research, modeling, assessment, and information management that is sustained by cooperation among scientists and information users. In this preface to the SALSA special issue, general program background information and the critical nature of semi-arid regions is presented. A brief description of the Upper San Pedro River Basin, the initial location for focused SALSA research follows. Several overarching research objectives under which much of the interdisciplinary research contained in the special issue was undertaken are discussed. Principal methods, primary research sites and data collection used by numerous investigators during 1997–1999 are then presented. Scientists from about 20 US, five European (four French and one Dutch), and three Mexican agencies and institutions have collaborated closely to make the research leading to this special issue a reality. The SALSA Program has served as a model of interagency cooperation by breaking new ground in the approach to large scale interdisciplinary science with relatively limited resources

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

What is the Oxygen Isotope Composition of Venus? The Scientific Case for Sample Return from Earth’s “Sister” Planet

Venus is Earth’s closest planetary neighbour and both bodies are of similar size and mass. As a consequence, Venus is often described as Earth’s sister planet. But the two worlds have followed very different evolutionary paths, with Earth having benign surface conditions, whereas Venus has a surface temperature of 464 °C and a surface pressure of 92 bar. These inhospitable surface conditions may partially explain why there has been such a dearth of space missions to Venus in recent years.The oxygen isotope composition of Venus is currently unknown. However, this single measurement (Δ17O) would have first order implications for our understanding of how large terrestrial planets are built. Recent isotopic studies indicate that the Solar System is bimodal in composition, divided into a carbonaceous chondrite (CC) group and a non-carbonaceous (NC) group. The CC group probably originated in the outer Solar System and the NC group in the inner Solar System. Venus comprises 41% by mass of the inner Solar System compared to 50% for Earth and only 5% for Mars. Models for building large terrestrial planets, such as Earth and Venus, would be significantly improved by a determination of the Δ17O composition of a returned sample from Venus. This measurement would help constrain the extent of early inner Solar System isotopic homogenisation and help to identify whether the feeding zones of the terrestrial planets were narrow or wide.Determining the Δ17O composition of Venus would also have significant implications for our understanding of how the Moon formed. Recent lunar formation models invoke a high energy impact between the proto-Earth and an inner Solar System-derived impactor body, Theia. The close isotopic similarity between the Earth and Moon is explained by these models as being a consequence of high-temperature, post-impact mixing. However, if Earth and Venus proved to be isotopic clones with respect to Δ17O, this would favour the classic, lower energy, giant impact scenario.We review the surface geology of Venus with the aim of identifying potential terrains that could be targeted by a robotic sample return mission. While the potentially ancient tessera terrains would be of great scientific interest, the need to minimise the influence of venusian weathering favours the sampling of young basaltic plains. In terms of a nominal sample mass, 10 g would be sufficient to undertake a full range of geochemical, isotopic and dating studies. However, it is important that additional material is collected as a legacy sample. As a consequence, a returned sample mass of at least 100 g should be recovered.Two scenarios for robotic sample return missions from Venus are presented, based on previous mission proposals. The most cost effective approach involves a “Grab and Go” strategy, either using a lander and separate orbiter, or possibly just a stand-alone lander. Sample return could also be achieved as part of a more ambitious, extended mission to study the venusian atmosphere. In both scenarios it is critical to obtain a surface atmospheric sample to define the extent of atmosphere-lithosphere oxygen isotopic disequilibrium. Surface sampling would be carried out by multiple techniques (drill, scoop, “vacuum-cleaner” device) to ensure success. Surface operations would take no longer than one hour.Analysis of returned samples would provide a firm basis for assessing similarities and differences between the evolution of Venus, Earth, Mars and smaller bodies such as Vesta. The Solar System provides an important case study in how two almost identical bodies, Earth and Venus, could have had such a divergent evolution. Finally, Venus, with its runaway greenhouse atmosphere, may provide data relevant to the understanding of similar less extreme processes on Earth. Venus is Earth’s planetary twin and deserves to be better studied and understood. In a wider context, analysis of returned samples from Venus would provide data relevant to the study of exoplanetary systems

Earlier snowmelt may lead to late season declines in plant productivity and carbon sequestration in Arctic tundra ecosystems

Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO2 later in the season

Impact of different eddy covariance sensors, site set-up, and maintenance on the annual balance of CO2 and CH4 in the harsh Arctic environment

Improving year-round data coverage for CO2 and CH4 fluxes in the Arctic is critical for refining the global C budget but continuous measurements are very sparse due to the remote location limiting instrument maintenance, to low power availability, and to extreme weather conditions. The need for tailoring instrumentation, site set up, and maintenance at different sites can add uncertainty to estimates of annual C budgets from different ecosystems. In this study, we investigated the influence of different sensor combinations on fluxes of sensible heat, CO2, latent heat (LE), and CH4, and assessed the differences in annual CO2 and CH4 fluxes estimated with different instrumentation at the same sites. Using data from four sites across the North Slope of Alaska, we found that annual CO2 fluxes estimated with heated (7.5 ± 1.4 gC m−2 yr−1) and non-heated (7.9 ± 1.3 gC m−2 yr−1) anemometers were within uncertainty bounds. Similarly, despite elevated noise in 30-min flux data, we found that summer CO2 fluxes from open (−17.0 ± 1.1 gC m−2 yr−1) and close-path (−14.2 ± 1.7 gC m−2 yr−1) gas analyzers were not significantly different. Annual CH4 fluxes were also within uncertainty bounds when comparing both open (4.5 ± 0.31 gC m−2 yr−1) and closed-path (4.9 ± 0.27 gC m−2 yr−1) gas analyzers as well as heated (3.7 ± 0.26 gC m−2 yr−1) and non-heated (3.7 ± 0.28 gC m−2 yr−1) anemometers. A continuously heated anemometer increased data coverage (64%) relative to non-heated anemometers (47–52%). However, sensible heat fluxes were over-estimated by 12%, on average, with the heated anemometer, contributing to the overestimation of CO2, CH4, and LE fluxes (mean biases of −0.03 μmol m−2 s−1, −0.05 mgC m−2 h−1, and −3.77 W m−2, respectively). To circumvent this potential bias and reduce power consumption, we implemented an intermittent heating strategy whereby activation only occurred when ice or snow blockage of the transducers was detected. This resulted in comparable coverage (50%) during winter to the continuously heated anemometer (46%), while avoiding flux over-estimation. Closed and open-path analyzers showed good agreement, but data coverage was generally greater when using closed-path, especially during winter. Winter data coverage of 26–32% was obtained with closed-path devices, vs 10–14% for the open-path devices with unheated anemometers or up to 46% and 35% using closed and open-path analyzers, respectively with heated anemometers. Accurate estimation of LE remains difficult in the Arctic due to strong attenuation in closed-path systems, even when intake tubes are heated, and due to poor data coverage from open-path sensors in such a harsh environment