Superconductivity in a Ferromagnetic Layered Compound

We examine superconductivity in layered systems with large Fermi-surface splitting due to coexisting ferromagnetic layers. In particular, the hybrid ruthenate-cuprate compound RuSr_2GdCu_2O_8 is examined on the coexistence of the superconductivity and the ferromagnetism, which has been observed recently. We calculate critical fields of the superconductivity taking into account the Fulde-Ferrell-Larkin-Ovchinnikov state in a model with Fermi-surfaces which shapes are similar to those obtained by a band calculation. It is shown that the critical field is enhanced remarkably due to a Fermi-surface effect, and can be high enough to make the coexistence possible in a microscopic scale. We also clarify the direction of the spatial oscillation of the order parameter, which may be observed by scanning tunneling microscope experiments.Comment: 4 pages, 4 figures, (Latex, revtex.sty, epsf.sty

Spin Glass Behavior in RuSr2Gd1.5Ce0.5Cu2O10

The dynamics of the magnetic properties of polycrystalline RuSr2Gd1.5Ce0.5Cu2O10 (Ru-1222) have been studied by ac susceptibility and dc magnetization measurements, including relaxation and ageing studies. Ru-1222 is a reported magneto-superconductor with Ru spins magnetic ordering at temperatures near 100 K and superconductivity in Cu-O2 planes below Tc ~ 40 K. The exact nature of Ru spins magnetic ordering is still debated and no conclusion has been reached yet. In this work, a frequency-dependent cusp was observed in ac susceptibility vs. T measurements, which is interpreted as a spin glass transition. The change in the cusp position with frequency follows the Vogel-Fulcher law, which is commonly accepted to describe a spin glass with magnetically interacting clusters. Such interpretation is supported by themoremanaent magnetization (TRM) measurements at T = 60 K. TRM relaxations are well described by a stretched exponential relation, and present significant ageing effects.Comment: 4 pages, 6 figures, submitted to Phys. Rev.

The importance of comprehensive parameter sampling and multiple observations for robust constraint of aerosol radiative forcing

© 2018 Author(s). Observational constraint of simulated aerosol and cloud properties is an essential part of building trustworthy climate models for calculating aerosol radiative forcing. Models are usually tuned to achieve good agreement with observations, but tuning produces just one of many potential variants of a model, so the model uncertainty cannot be determined. Here we estimate the uncertainty in aerosol effective radiative forcing (ERF) in a tuned climate model by constraining 4 million variants of the HadGEM3-UKCA aerosol-climate model to match nine common observations (top-of-atmosphere shortwave flux, aerosol optical depth, PM2.5, cloud condensation nuclei at 0.2% supersaturation (CCN0.2), and concentrations of sulfate, black carbon and organic carbon, as well as decadal trends in aerosol optical depth and surface shortwave radiation.) The model uncertainty is calculated by using a perturbed parameter ensemble that samples 27 uncertainties in both the aerosol model and the physical climate model, and we use synthetic observations generated from the model itself to determine the potential of each observational type to constrain this uncertainty. Focusing over Europe in July, we show that the aerosol ERF uncertainty can be reduced by about 30% by constraining it to the nine observations, demonstrating that producing climate models with an observationally plausible base state can contribute to narrowing the uncertainty in aerosol ERF. However, the uncertainty in the aerosol ERF after observational constraint is large compared to the typical spread of a multi-model ensemble. Our results therefore raise questions about whether the underlying multi-model uncertainty would be larger if similar approaches as adopted here were applied more widely. The approach presented in this study could be used to identify the most effective observations for model constraint. It is hoped that aerosol ERF uncertainty can be further reduced by introducing process-related constraints; however, any such results will be robust only if the enormous number of potential model variants is explored

Stability of circular orbits of spinning particles in Schwarzschild-like space-times

Circular orbits of spinning test particles and their stability in Schwarzschild-like backgrounds are investigated. For these space-times the equations of motion admit solutions representing circular orbits with particles spins being constant and normal to the plane of orbits. For the de Sitter background the orbits are always stable with particle velocity and momentum being co-linear along them. The world-line deviation equations for particles of the same spin-to-mass ratios are solved and the resulting deviation vectors are used to study the stability of orbits. It is shown that the orbits are stable against radial perturbations. The general criterion for stability against normal perturbations is obtained. Explicit calculations are performed in the case of the Schwarzschild space-time leading to the conclusion that the orbits are stable.Comment: eps figures, submitted to General Relativity and Gravitatio

National Beef Quality Audit-2016: Transportation, mobility, and harvest-floor assessments of targeted characteristics that affect quality and value of cattle, carcasses, and by-products

The National Beef Quality Audit-2016 (NBQA-2016) was conducted to assess current transportation, mobility, and quality characteristics of U.S. fed steers and heifers. Data were collected at 17 beef processing facilities between March and November 2016. About 8,000 live cattle were evaluated for transportation and mobility, and about 25,000 carcasses were evaluated on the slaughter floor. Cattle were in transit to the slaughter facility for a mean duration of 2.7 h from a mean distance of 218.5 km using trailers with dimensions ranging from 17.84 m2 to 59.09 m2. Area allotted per animal averaged 1.13 m2 and ranged from 0.85 m2 to 2.28 m2. A total of 96.8% of cattle received a mobility score of 1 (walks easily, no apparent lameness). Identification types (35.1% had multiple) were lot visual tags (61.5%), individual tags (55.0%), electronic tags (16.9%), metal-clip tags (9.2%), bar-coded tags (0.05%), wattles (0.01%), and other (2.6%). Cattle were black-hided (57.8%), Holstein (20.4%), red-hided (10.5%), yellow-hided (4.8%), gray-hided (2.9%), brown-hided (1.3%), and white-hided (1.1%). Unbranded hides were observed on 74.3% of cattle; 18.6% had brands located on the butt, 6.3% on the side, and 1.3% on the shoulder (values exceed 100% due to multiple brands). For hide-on carcasses, 37.7% displayed no mud or manure; specific locations for mud or manure were legs (40.8%), belly (33.0%), tail region (15.5%), side (6.8%), and top-line (3.9%). Cattle without horns represented 83.3% of the sample, and cattle that did have horns measured: \u3c 2.54 cm (5.5%), 2.54 to 12.7 cm (8.3%), and \u3e 12.7 cm (2.9%). Carcasses without bruises represented 61.1% of those sampled, whereas 28.2% had 1, 8.2% had 2, 2.1% had 3, and 0.3% had 4 bruises. Of those carcasses with a bruise, the bruise was located on the loin (29.7%), round (27.8%), chuck (16.4%), rib (14.4%), and brisket/plate/flank (11.6%). Frequencies of offal condemnations were livers (30.8%), lungs (18.2%), viscera (16.3%), hearts (11.1%), heads (2.7%), and tongues (2.0%). Compared to NBQA-2011, fewer cattle were identified for traceability, fewer were black-hided, a greater number were Holstein cattle, more with no brand and no horns, fewer without bruises, more liver, lung, and viscera condemnations, and fewer heads and tongues were condemned. The NBQA remains an influential survey for the U.S. beef industry to provide benchmarks and strategic plans for continued improvement of beef quality and consistency

Identifying climate model structural inconsistencies allows for tight constraint of aerosol radiative forcing

Aerosol radiative forcing uncertainty affects estimates of climate sensitivity and limits model skill in terms of making climate projections. Efforts to improve the representations of physical processes in climate models, including extensive comparisons with observations, have not significantly constrained the range of possible aerosol forcing values. A far stronger constraint, in particular for the lower (most-negative) bound, can be achieved using global mean energy balance arguments based on observed changes in historical temperature. Here, we show that structural deficiencies in a climate model, revealed as inconsistencies among observationally constrained cloud properties in the model, limit the effectiveness of observational constraint of the uncertain physical processes. We sample the uncertainty in 37 model parameters related to aerosols, clouds, and radiation in a perturbed parameter ensemble of the UK Earth System Model and evaluate 1 million model variants (different parameter settings from Gaussian process emulators) against satellite-derived observations over several cloudy regions. Our analysis of a very large set of model variants exposes model internal inconsistencies that would not be apparent in a small set of model simulations, of an order that may be evaluated during model-tuning efforts. Incorporating observations associated with these inconsistencies weakens any forcing constraint because they require a wider range of parameter values to accommodate conflicting information. We show that, by neglecting variables associated with these inconsistencies, it is possible to reduce the parametric uncertainty in global mean aerosol forcing by more than 50 %, constraining it to a range (around −1.3 to −0.1 W m−2) in close agreement with energy balance constraints. Our estimated aerosol forcing range is the maximum feasible constraint using our structurally imperfect model and the chosen observations. Structural model developments targeted at the identified inconsistencies would enable a larger set of observations to be used for constraint, which would then very likely narrow the uncertainty further and possibly alter the central estimate. Such an approach provides a rigorous pathway to improved model realism and reduced uncertainty that has so far not been achieved through the normal model development approach

Identifying climate model structural inconsistencies allows for tight constraint of aerosol radiative forcing

Aerosol radiative forcing uncertainty affects estimates of climate sensitivity and limits model skill in terms of making climate projections. Efforts to improve the representations of physical processes in climate models, including extensive comparisons with observations, have not significantly constrained the range of possible aerosol forcing values. A far stronger constraint, in particular for the lower (most-negative) bound, can be achieved using global mean energy balance arguments based on observed changes in historical temperature. Here, we show that structural deficiencies in a climate model, revealed as inconsistencies among observationally constrained cloud properties in the model, limit the effectiveness of observational constraint of the uncertain physical processes. We sample the uncertainty in 37 model parameters related to aerosols, clouds, and radiation in a perturbed parameter ensemble of the UK Earth System Model and evaluate 1 million model variants (different parameter settings from Gaussian process emulators) against satellite-derived observations over several cloudy regions. Our analysis of a very large set of model variants exposes model internal inconsistencies that would not be apparent in a small set of model simulations, of an order that may be evaluated during model-tuning efforts. Incorporating observations associated with these inconsistencies weakens any forcing constraint because they require a wider range of parameter values to accommodate conflicting information. We show that, by neglecting variables associated with these inconsistencies, it is possible to reduce the parametric uncertainty in global mean aerosol forcing by more than 50 %, constraining it to a range (around −1.3 to −0.1 W m−2) in close agreement with energy balance constraints. Our estimated aerosol forcing range is the maximum feasible constraint using our structurally imperfect model and the chosen observations. Structural model developments targeted at the identified inconsistencies would enable a larger set of observations to be used for constraint, which would then very likely narrow the uncertainty further and possibly alter the central estimate. Such an approach provides a rigorous pathway to improved model realism and reduced uncertainty that has so far not been achieved through the normal model development approach

Landscape-scale species monitoring of agri-environment schemes (LandSpAES project). Final project report, 2022

In this project, we applied a novel, pseudo-experimental design in order to collect a baseline survey dataset of the responses of mobile taxa to local and landscape AES gradients over four years, from 54 survey squares across six regions (NCAs) in England. This is the first project to monitor the responses of multiple mobile taxa to generalised AES gradients across large spatial extents, which were applied to arable, grassland and upland agricultural systems, in order specifically to address impacts beyond AES option or agreement boundaries. This baseline dataset supported a spatial assessment of relationships between the AES gradients and taxon abundance (or activity), species richness and diversity. Strong evidence for relationships with local and / or landscape AES gradients were found for one or more response variable for butterflies, moths and bats. Little or no evidence of AES gradient relationships were found for either bees or hoverflies and weak evidence for associations with bird metrics. A future resurvey would allow analyses of the longer-term changes in target taxa in response to AES management, against this baseline. The identification of various spatial relationships is encouraging in terms of the likely power to detect AES effects on biodiversity change in the future

The AeroCom evaluation and intercomparison of organic aerosol in global models

This paper evaluates the current status of global modeling of the organic aerosol (OA) in the troposphere and analyzes the differences between models as well as between models and observations. Thirty-one global chemistry transport models (CTMs) and general circulation models (GCMs) have participated in this intercomparison, in the framework of AeroCom phase II. The simulation of OA varies greatly between models in terms of the magnitude of primary emissions, secondary OA (SOA) formation, the number of OA species used (2 to 62), the complexity of OA parameterizations (gas-particle partitioning, chemical aging, multiphase chemistry, aerosol microphysics), and the OA physical, chemical and optical properties. The diversity of the global OA simulation results has increased since earlier AeroCom experiments, mainly due to the increasing complexity of the SOA parameterization in models, and the implementation of new, highly uncertain, OA sources. Diversity of over one order of magnitude exists in the modeled vertical distribution of OA concentrations that deserves a dedicated future study. Furthermore, although the OA / OC ratio depends on OA sources and atmospheric processing, and is important for model evaluation against OA and OC observations, it is resolved only by a few global models. The median global primary OA (POA) source strength is 56 Tg a−1 (range 34–144 Tg a−1) and the median SOA source strength (natural and anthropogenic) is 19 Tg a−1 (range 13–121 Tg a−1). Among the models that take into account the semi-volatile SOA nature, the median source is calculated to be 51 Tg a−1 (range 16–121 Tg a−1), much larger than the median value of the models that calculate SOA in a more simplistic way (19 Tg a−1; range 13–20 Tg a−1, with one model at 37 Tg a−1). The median atmospheric burden of OA is 1.4 Tg (24 models in the range of 0.6–2.0 Tg and 4 between 2.0 and 3.8 Tg), with a median OA lifetime of 5.4 days (range 3.8–9.6 days). In models that reported both OA and sulfate burdens, the median value of the OA/sulfate burden ratio is calculated to be 0.77; 13 models calculate a ratio lower than 1, and 9 models higher than 1. For 26 models that reported OA deposition fluxes, the median wet removal is 70 Tg a−1 (range 28–209 Tg a−1), which is on average 85% of the total OA deposition. Fine aerosol organic carbon (OC) and OA observations from continuous monitoring networks and individual field campaigns have been used for model evaluation. At urban locations, the model–observation comparison indicates missing knowledge on anthropogenic OA sources, both strength and seasonality. The combined model–measurements analysis suggests the existence of increased OA levels during summer due to biogenic SOA formation over large areas of the USA that can be of the same order of magnitude as the POA, even at urban locations, and contribute to the measured urban seasonal pattern. Global models are able to simulate the high secondary character of OA observed in the atmosphere as a result of SOA formation and POA aging, although the amount of OA present in the atmosphere remains largely underestimated, with a mean normalized bias (MNB) equal to −0.62 (−0.51) based on the comparison against OC (OA) urban data of all models at the surface, −0.15 (+0.51) when compared with remote measurements, and −0.30 for marine locations with OC data. The mean temporal correlations across all stations are low when compared with OC (OA) measurements: 0.47 (0.52) for urban stations, 0.39 (0.37) for remote stations, and 0.25 for marine stations with OC data. The combination of high (negative) MNB and higher correlation at urban stations when compared with the low MNB and lower correlation at remote sites suggests that knowledge about the processes that govern aerosol processing, transport and removal, on top of their sources, is important at the remote stations. There is no clear change in model skill with increasing model complexity with regard to OC or OA mass concentration. However, the complexity is needed in models in order to distinguish between anthropogenic and natural OA as needed for climate mitigation, and to calculate the impact of OA on climate accurately