Seismic Constraints on Helium Abundances from the TESS Southern CVZ

Poster for Cool Stars 21 Stellar helium abundances strongly determine their structure and evolution. However, since helium cannot be detected directly in the photospheres of cool stars, helium abundances are one of the most poorly-constrained inputs to stellar models. It is therefore typical to assume a relationship with the initial abundances of other heavy elements, typically of linear form described by a gradient ΔY/ΔZ. Attempts to determine from globular-cluster stellar populations and Galactic H-II regions have so far not yielded any consensus about empirically reasonable values of ΔY/ΔZ, or, for that matter, even whether such a linear relation is observationally justifiable. Separately, asteroseismology permits the inference of stellar helium abundances, either directly through acoustic-glitch measurements, or indirectly through the forward modelling of stellar oscillation mode frequencies. Using constraints on the initial helium abundance derived from ensemble asteroseismology and stellar forward modelling against individual mode frequencies of a collection of field stars in the TESS, Kepler, and K2 fields, we characterise the helium-metallicity relation of the brightest cool stars in the solar neighbourhood. We find a large spread of seismic initial helium abundances for any given metallicity, rather than a single well-defined linear enrichment law

Discovery of binarity, spectroscopic frequency analysis, and mode identification of the delta Sct star 4CVn

More than 40 years of ground-based photometric observations of the delta Sct star 4CVn revealed 18 independent oscillation frequencies, including radial as well as non-radial p-modes of low spherical degree l<=2. From 2008 to 2011, more than 2000 spectra were obtained at the 2.1-m Otto-Struve telescope at the McDonald Observatory. We present the analysis of the line-profile variations, based on the Fourier-parameter fit method, detected in the absorption lines of 4CVn, which carry clear signatures of the pulsations. From a non-sinusoidal, periodic variation of the radial velocities, we discovered that 4CVn is an eccentric binary system, with an orbital period Porb = 124.44 +/- 0.03 d and an eccentricity e = 0.311 +/- 0.003. We firmly detect 20 oscillation frequencies, 9 of which are previously unseen in photometric data, and attempt mode identification for the two dominant modes, f1 = 7.3764 c/d and f2 = 5.8496 c/d, and determine the prograde or retrograde nature of 7 of the modes. The projected rotational velocity of the star, vsini ~ 106.7 km/s, translates to a rotation rate of veq/vcrit >= 33%. This relatively high rotation rate hampers unique mode identification, since higher-order effects of rotation are not included in the current methodology. We conclude that, in order to achieve unambiguous mode identification for 4CVn, a complete description of rotation and the use of blended lines have to be included in mode-identification techniques.Comment: 18 pages, 18 figures (including Appendices), accepted for publication in A&

Арап элифбесинде нешир этильген къырымтатар грамматикаларнынъ тенъештирме талили

Статья посвящена сопоставительному анализу имени существительного и глагола в арабографических грамматиках крымскотатарского языка.Стаття присвячена порівняльному аналізу іменника і дієслова в арабографічних граматиках кримськотатарської мови.The article annotation is devoted to the comparative analysis of the noun and the verb in arabographis grammars of the Crimean Tatar language

Stochastic Model Output Statistics for Bias Correcting and Downscaling Precipitation Including Extremes

Precipitation is highly variable in space and time; hence, rain gauge time series generally exhibit additional random small-scale variability compared to area averages. Therefore, differences between daily precipitation statistics simulated by climate models and gauge observations are generally not only caused by model biases, but also by the corresponding scale gap. Classical bias correction methods, in general, cannot bridge this gap; they do not account for small-scale random variability and may produce artifacts. Here, stochastic model output statistics is proposed as a bias correction framework to explicitly account for random small-scale variability. Daily precipitation simulated by a regional climate model (RCM) is employed to predict the probability distribution of local precipitation. The pairwise correspondence between predictor and predictand required for calibration is ensured by driving the RCM with perfect boundary conditions. Wet day probabilities are described by a logistic regression, and precipitation intensities are described by a mixture model consisting of a gamma distribution for moderate precipitation and a generalized Pareto distribution for extremes. The dependence of the model parameters on simulated precipitation is modeled by a vector generalized linear model. The proposed model effectively corrects systematic biases and correctly represents local-scale random variability for most gauges. Additionally, a simplified model is considered that disregards the separate tail model. This computationally efficient model proves to be a feasible alternative for precipitation up to moderately extreme intensities. The approach sets a new framework for bias correction that combines the advantages of weather generators and RCMs

Process-conditioned bias correction for seasonal forecasting: a case-study with ENSO in Peru

This work assesses the suitability of a first simple attempt for process-conditioned bias correction in the context of seasonal forecasting. To do this, we focus on the northwestern part of Peru and bias correct 1- and 4-month lead seasonal predictions of boreal winter (DJF) precipitation from the ECMWF System4 forecasting system for the period 1981–2010. In order to include information about the underlying large-scale circulation which may help to discriminate between precipitation affected by different processes, we introduce here an empirical quantile–quantile mapping method which runs conditioned on the state of the Southern Oscillation Index (SOI), which is accurately predicted by System4 and is known to affect the local climate. Beyond the reduction of model biases, our results show that the SOI-conditioned method yields better ROC skill scores and reliability than the raw model output over the entire region of study, whereas the standard unconditioned implementation provides no added value for any of these metrics. This suggests that conditioning the bias correction on simple but well-simulated large-scale processes relevant to the local climate may be a suitable approach for seasonal forecasting. Yet, further research on the suitability of the application of similar approaches to the one considered here for other regions, seasons and/or variables is needed.This work has received funding from the MULTI-SDM project (MINECO/FEDER, CGL2015-66583-R). The authors are grateful to SENAMHI for the observational data, which are publicly available from http://www.senamhi.gob.pe/?p=data-historica, and to the European Center for Medium-Range Weather Forecast (ECMWF), for the access to the System4 seasonal forecasting hindcast

Young [alpha/Fe]-enhanced stars discovered by CoRoT and APOGEE: What is their origin?

We report the discovery of a group of apparently young CoRoT red-giant stars exhibiting enhanced [_/Fe] abundance ratios (as determined from APOGEE spectra) with respect to solar values. Their existence is not explained by standard chemical evolution models of the Milky Way, and shows that the chemical-enrichment history of the Galactic disc is more complex. We find similar stars in previously published samples for which isochrone-ages could be reliably obtained, although in smaller relative numbers. This might explain why these stars have not previously received attention. The young [_/Fe]-rich stars are much more numerous in the CoRoT APOGEE (CoRoGEE) inner-field sample than in any other high-resolution sample available at present because only CoRoGEE can explore the inner-disc regions and provide ages for its field stars. The kinematic properties of the young [_/Fe]-rich stars are not clearly thick-disc like, despite their rather large distances from the Galactic mid-plane. Our tentative interpretation of these and previous intriguing observations in the Milky Way is that these stars were formed close to the end of the Galactic bar, near corotation – a region where gas can be kept inert for longer times than in other regions that are more frequently shocked by the passage of spiral arms. Moreover, this is where the mass return from older inner-disc stellar generations is expected to be highest (according to an inside-out disc-formation scenario), which additionally dilutes the in-situ gas. Other possibilities to explain these observations (e.g., a recent gas-accretion event) are also discussed

Daily precipitation statistics in a EURO-CORDEX RCM ensemble: added value of raw and bias-corrected high-resolution simulations

Daily precipitation statistics as simulated by the ERA-Interim-driven EURO-CORDEX regional climate model (RCM) ensemble are evaluated over two distinct regions of the European continent, namely the European Alps and Spain. The potential added value of the high-resolution 12 km experiments with respect to their 50 km resolution counterparts is investigated. The statistics considered consist of wet-day intensity and precipitation frequency as a measure of mean precipitation, and three precipitation-derived indicators (90th percentile on wet days?90pWET, contribution of the very wet days to total precipitation?R95pTOT and number of consecutive dry days?CDD). As reference for model evaluation high resolution gridded observational data over continental Spain (Spain011/044) and the Alpine region (EURO4M-APGD) are used. The assessment and comparison of the two resolutions is accomplished not only on their original horizontal grids (approximately 12 and 50 km), but the high-resolution RCMs are additionally regridded onto the coarse 50 km grid by grid cell aggregation for the direct comparison with the low resolution simulations. The direct application of RCMs e.g. in many impact modelling studies is hampered by model biases. Therefore bias correction (BC) techniques are needed at both resolutions to ensure a better agreement between models and observations. In this work, the added value of the high resolution (before and after the bias correction) is assessed and the suitability of these BC methods is also discussed. Three basic BC methods are applied to isolate the effect of biases in mean precipitation, wet-day intensity and wet-day frequency on the derived indicators. Daily precipitation percentiles are strongly affected by biases in the wet-day intensity, whereas the dry spells are better represented when the simulated precipitation frequency is adjusted to the observed one. This confirms that there is no single optimal way to correct for RCM biases, since correcting some distributional features typically leads to an improvement of some aspects but to a deterioration of others. Regarding mean seasonal biases before the BC, we find only limited evidence for an added value of the higher resolution in the precipitation intensity and frequency or in the derived indicators. Thereby, evaluation results considerably depend on the RCM, season and indicator considered. High resolution simulations better reproduce the indicators? spatial patterns, especially in terms of spatial correlation. However, this improvement is not statistically significant after applying specific BC methods.The authors are grateful to Prof. C. Schär for his helpful comments and E. van Meijgaard for making available the RACMO model data. We acknowledge the observational data providers. Calculations for WRF311F were made using the TGCC super computers under the GENCI time allocation GEN6877. The WRF331A from CRP-GL (now LIST) was funded by the Luxembourg National Research Fund (FNR) through grant FNR C09/SR/16 (CLIMPACT). The KNMI-RACMO2 simulations were supported by the Dutch Ministry of Infrastructure and the Environment. The CCLM and REMO simulations were supported by the Federal Ministry of Education and Research (BMBF) and performed under the Konsortial share at the German Climate Computing Centre (DKRZ). The CCLM simulations were furthermore supported by the Swiss National Supercomputing Centre (CSCS) under project ID s78. Part of the SMHI contribution was carried out in the Swedish Mistra-SWECIA programme founded by Mistra (the Foundation for Strategic Environmental Research). This work is supported by CORWES (CGL2010-22158-C02) and EXTREMBLES (CGL2010-21869) projects funded by the Spanish R&D programme and the European COST ACTION VALUE (ES1102). A. C. thanks the Spanish Ministry of Economy and Competitiveness for the funding provided within the FPI programme (BES-2011-047612 and EEBB-I-13-06354). We also thank two anonymous referees for their useful comments that helped to improve the original manuscript

Age dating of an early Milky Way merger via asteroseismology of the naked-eye star νν Indi

Over the course of its history, the Milky Way has ingested multiple smaller satellite galaxies. While these accreted stellar populations can be forensically identified as kinematically distinct structures within the Galaxy, it is difficult in general to precisely date the age at which any one merger occurred. Recent results have revealed a population of stars that were accreted via the collision of a dwarf galaxy, called \textit{Gaia}-Enceladus, leading to a substantial pollution of the chemical and dynamical properties of the Milky Way. Here, we identify the very bright, naked-eye star $\nu$\,Indi as a probe of the age of the early in situ population of the Galaxy. We combine asteroseismic, spectroscopic, astrometric, and kinematic observations to show that this metal-poor, alpha-element-rich star was an indigenous member of the halo, and we measure its age to be $11.0 \pm 0.7$ (stat) $\pm 0.8$ (sys)$\,\rm Gyr$. The star bears hallmarks consistent with it having been kinematically heated by the \textit{Gaia}-Enceladus collision. Its age implies that the earliest the merger could have begun was 11.6 and 13.2 Gyr ago at 68 and 95% confidence, respectively. Input from computations based on hierarchical cosmological models tightens (i.e. reduces) slightly the above limits

Can bias correction and statistical downscaling methods improve the skill of seasonal precipitation forecasts?

Statistical downscaling methods are popular post-processing tools which are widely used in many sectors to adapt the coarse-resolution biased outputs from global climate simulations to the regional-to-local scale typically required by users. They range from simple and pragmatic Bias Correction (BC) methods, which directly adjust the model outputs of interest (e.g. precipitation) according to the available local observations, to more complex Perfect Prognosis (PP) ones, which indirectly derive local predictions (e.g. precipitation) from appropriate upper-air large-scale model variables (predictors). Statistical downscaling methods have been extensively used and critically assessed in climate change applications; however, their advantages and limitations in seasonal forecasting are not well understood yet. In particular, a key problem in this context is whether they serve to improve the forecast quality/skill of raw model outputs beyond the adjustment of their systematic biases. In this paper we analyze this issue by applying two state-of-the-art BC and two PP methods to downscale precipitation from a multimodel seasonal hindcast in a challenging tropical region, the Philippines. To properly assess the potential added value beyond the reduction of model biases, we consider two validation scores which are not sensitive to changes in the mean (correlation and reliability categories). Our results show that, whereas BC methods maintain or worsen the skill of the raw model forecasts, PP methods can yield significant skill improvement (worsening) in cases for which the large-scale predictor variables considered are better (worse) predicted by the model than precipitation. For instance, PP methods are found to increase (decrease) model reliability in nearly 40% of the stations considered in boreal summer (autumn). Therefore, the choice of a convenient downscaling approach (either BC or PP) depends on the region and the season.This study was partially supported by the SPECS and EUPORIAS projects, funded by the European Commission through the Seventh Framework Programme for Research under grant agreements 308378 and 308291, respectively. JMG acknowledges partial support from the project MULTI-SDM (CGL2015-66583-R, MINECO/FEDER)