Topological superconductivity mediated by magnons of helical magnetic states

We recently showed that spin fluctuations of noncoplanar magnetic states can induce topological superconductivity in an adjacent normal metal [K. M{\ae}land et al., Phys. Rev. Lett. 130, 156002 (2023)]. The noncolinear nature of the spins was found to be essential for this result, while the necessity of noncoplanar spins was unclear. In this paper we show that magnons in coplanar, noncolinear magnetic states can mediate topological superconductivity in a normal metal. Two models of the Dzyaloshinskii-Moriya interaction are studied to illustrate the need for a sufficiently complicated Hamiltonian describing the magnetic insulator. The Hamiltonian, in particular the specific form of the Dzyaloshinskii-Moriya interaction, affects the magnons and by extension the effective electron-electron interaction in the normal metal. We solve a linearized gap equation in the case of weak-coupling superconductivity. The result is a time-reversal-symmetric topological superconductor, as confirmed by calculating the topological invariant. In analogy with magnon-mediated superconductivity from antiferromagnets, Umklapp scattering enhances the critical temperature of superconductivity for certain Fermi momenta.Comment: 12 pages, 6 figure

Tracking of lung function from 10 to 35 years after being born extremely preterm or with extremely low birth weight

Background Lifelong pulmonary consequences of being born extremely preterm or with extremely low birth weight remain unknown. We aimed to describe lung function trajectories from 10 to 35 years of age for individuals born extremely preterm, and address potential cohort effects over a period that encompassed major changes in perinatal care. Methods We performed repeated spirometry in three population-based cohorts born at gestational age ≤28 weeks or with birth weight ≤1000 g during 1982–85, 1991–92 and 1999–2000, referred to as extremely preterm-born, and in term-born controls matched for age and gender. Examinations were performed at 10, 18, 25 and 35 years. Longitudinal data were analysed using mixed models regression, with the extremely preterm-born stratified by bronchopulmonary dysplasia (BPD). Results We recruited 148/174 (85%) eligible extremely preterm-born and 138 term-born. Compared with term-born, the extremely preterm-born had lower z-scores for forced expiratory volume in 1 s (FEV1) at most assessments, the main exceptions were in the groups without BPD in the two youngest cohorts. FEV1 trajectories were largely parallel for the extremely preterm- and term-born, also during the period 25–35 years that includes the onset of the age-related decline in lung function. Extremely preterm-born had lower peak lung function than term-born, but z-FEV1 values improved for each consecutive decade of birth (p=0.009). More extremely preterm—than term-born fulfilled the spirometry criteria for chronic obstructive pulmonary disease, 44/148 (30%) vs 7/138 (5%), p<0.001. Conclusions Lung function after extremely preterm birth tracked in parallel, but significantly below the trajectories of term-born from 10 to 35 years, including the incipient age-related decline from 25 to 35 years. The deficits versus term-born decreased with each decade of birth from 1980 to 2000.publishedVersio

Resource-oriented therapeutic models in psychiatry:conceptual review

International audienc

A statistical downscaling framework for environmental mapping

In recent years, knowledge extraction from data has become increasingly popular, with many numerical forecasting models, mainly falling into two major categories—chemical transport models (CTMs) and conventional statistical methods. However, due to data and model variability, data-driven knowledge extraction from high-dimensional, multifaceted data in such applications require generalisations of global to regional or local conditions. Typically, generalisation is achieved via mapping global conditions to local ecosystems and human habitats which amounts to tracking and monitoring environmental dynamics in various geographical areas and their regional and global implications on human livelihood. Statistical downscaling techniques have been widely used to extract high-resolution information from regional-scale variables produced by CTMs in climate model. Conventional applications of these methods are predominantly dimensional reduction in nature, designed to reduce spatial dimension of gridded model outputs without loss of essential spatial information. Their downside is twofold—complete dependence on unlabelled design matrix and reliance on underlying distributional assumptions. We propose a novel statistical downscaling framework for dealing with data and model variability. Its power derives from training and testing multiple models on multiple samples, narrowing down global environmental phenomena to regional discordance through dimensional reduction and visualisation. Hourly ground-level ozone observations were obtained from various environmental stations maintained by the US Environmental Protection Agency, covering the summer period (June–August 2005). Regional patterns of ozone are related to local observations via repeated runs and performance assessment of multiple versions of empirical orthogonal functions or principal components and principal fitted components via an algorithm with fully adaptable parameters. We demonstrate how the algorithm can be extended to weather-dependent and other applications with inherent data randomness and model variability via its built-in interdisciplinary computational power that connects data sources with end-users

The VALUE perfect predictor experiment: evaluation of temporal variability

Temporal variability is an important feature of climate, comprising systematic vari-ations such as the annual cycle, as well as residual temporal variations such asshort-term variations, spells and variability from interannual to long-term trends.The EU-COST Action VALUE developed a comprehensive framework to evaluatedownscaling methods. Here we present the evaluation of the perfect predictorexperiment for temporal variability. Overall, the behaviour of the differentapproaches turned out to be as expected from their structure and implementation.The chosen regional climate model adds value to reanalysis data for most consid-ered aspects, for all seasons and for both temperature and precipitation. Bias cor-rection methods do not directly modify temporal variability apart from the annualcycle. However, wet day corrections substantially improve transition probabilitiesand spell length distributions, whereas interannual variability is in some cases dete-riorated by quantile mapping. The performance of perfect prognosis (PP) statisticaldownscaling methods varies strongly from aspect to aspect and method to method,and depends strongly on the predictor choice. Unconditional weather generatorstend to perform well for the aspects they have been calibrated for, but underrepre-sent long spells and interannual variability. Long-term temperature trends of thedriving model are essentially unchanged by bias correction methods. If precipita-tion trends are not well simulated by the driving model, bias correction furtherdeteriorates these trends. The performance of PP methods to simulate trendsdepends strongly on the chosen predictors.VALUE has been funded as EU COST Action ES1102

Towards a fair comparison of statistical and dynamical downscaling in the framework of the EURO-CORDEX initiative

Both statistical and dynamical downscaling methods are well established techniques to bridge the gap between the coarse information produced by global circulation models and the regional-to-local scales required by the climate change Impacts, Adaptation, and Vulnerability (IAV) communities. A number of studies have analyzed the relative merits of each technique by inter-comparing their performance in reproducing the observed climate, as given by a number of climatic indices (e.g. mean values, percentiles, spells). However, in this paper we stress that fair comparisons should be based on indices that are not affected by the calibration towards the observed climate used for some of the methods. We focus on precipitation (over continental Spain) and consider the output of eight Regional Climate Models (RCMs) from the EURO-CORDEX initiative at 0.44? resolution and five Statistical Downscaling Methods (SDMs) ?analog resampling, weather typing and generalized linear models? trained using the Spain044 observational gridded dataset on exactly the same RCM grid. The performance of these models is inter-compared in terms of several standard indices ?mean precipitation, 90th percentile on wet days, maximum precipitation amount and maximum number of consecutive dry days? taking into account the parameters involved in the SDM training phase. It is shown, that not only the directly affected indices should be carefully analyzed, but also those indirectly influenced (e.g. percentile-based indices for precipitation) which are more difficult to identify. We also analyze how simple transformations (e.g. linear scaling) could be applied to the outputs of the uncalibrated methods in order to put SDMs and RCMs on equal footing, and thus perform a fairer comparison.We acknowledge the World Climate Research Programme’s Working Group on Regional Climate, and theWorking Group on CoupledModelling, former coordinating body of CORDEX and responsible panel for CMIP5. We also thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. We also acknowledge the Earth System Grid Federation infrastructure and AEMET and University of Cantabria for the Spain02 dataset (available at http: //www.meteo.unican.es/en/datasets/spain02). All the statistical downscaling experiments have been computed using theMeteoLab software (http://www.meteo.unican.es/software/meteolab), which is an open-source Matlab toolbox for statistical downscaling. This work has been partially supported by CORWES (CGL2010-22158-C02) and EXTREMBLES (CGL2010-21869) projects funded by the Spanish R&D programme. AC thanks the Spanish Ministry of Economy and Competitiveness for the funding provided within the FPI programme (BES-2011-047612 and EEBB-I-13-06354), JMG acknowledges the support from the SPECS project (FP7-ENV-2012-308378) and JF is grateful to the EUPORIAS project (FP7-ENV-2012-308291). We also thank three anonymous referees for their useful comments that helped to improve the original manuscript

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

Testing MOS precipitation downscaling for ENSEMBLES regional climate models over Spain

Model Output Statistics (MOS) has been recently proposed as an alternative to the standard perfect prognosis statistical downscaling approach for Regional Climate Model (RCM) outputs. In this case, the model output for the variable of interest (e.g. precipitation) is directly downscaled using observations. In this paper we test the performance of a MOS implementation of the popular analog methodology (referred to as MOS analog) applied to downscale daily precipitation outputs over Spain. To this aim, we consider the state‐of‐the‐art ERA40‐driven RCMs provided by the EU‐funded ENSEMBLES project and the Spain02 gridded observations data set, using the common period 1961–2000. The MOS analog method improves the representation of the mean regimes, the annual cycle, the frequency and the extremes of precipitation for all RCMs, regardless of the region and the model reliability (including relatively low‐performing models), while preserving the daily accuracy. The good performance of the method in this complex climatic region suggests its potential transferability to other regions. Furthermore, in order to test the robustness of the method in changing climate conditions, a cross‐validation in driest or wettest years was performed. The method improves the RCM results in both cases, especially in the former

Electron-magnon coupling and magnon-induced superconductivity in hybrid structures of metals and magnets with non-collinear ground states

Elektron-magnon interaksjoner i et dobbeltlag av monoatomisk metall og en ferromagnetisk isolator med ikke-kolineær spiralfase er studert på et triangulært gitter, motivert av nylig interesse i liknende systemer med enklere kolineære magnetiske lag. Med dette formål presenteres et rammeverk for å beregne kobling mellom elektroner og ikke-kolineære spin, som er en modifisering av eksisterende teori for kolineære spinstrukturer basert på en kanonisk transformasjon til en effektiv Hamilton-operator. Dette er gjort ved å ta i bruk Fishman og Haraldsen sin generelle rotasjonsmodell for ikke-kolineære spinstrukturer. Systemets egenskaper som en ukonvensjonell magnon-formidlet superleder er undersøkt, med en analyse av hvordan "squeezing" av magnonene fra den valgte spinstrukturen påvirker styrken av BCS par-dannelse og hva slags type symmetrier man kan forvente å finne for den superledende tilstanden. Dobbeltlaget som har en en ferromagnetisk isolator med ikke-kolineær spiralfase finner vi at kan ha p-bølge superledning i teorien, men forventer at styrken på BCS interaksjonen blir mindre enn for tilfellet med en kolineær ferromagnetisk isolator. Den lineæriserte gaplikningen ble løst nær Fermiflaten for et approksimert potensial, og en analyse ble forsøkt for en fullstendig beregning over hele første Brillouinsone, men oppløsningen i k-rom ble for lav. Derimot var det interessant å observere at det dukker opp interaksjons-termer i Hamilton-operatoren for spredning av elektronpar uten spinflip som et resultat av den ikke-kolineære strukturen, hvilket kan gi opphav til Sz=±1 polariserte Cooper par. Slike polariserte Cooper par har interessante bruksområder i spintronikk, og selv om de upolariserte parene dominerer for eksempelet studert i denne masteroppgaven kunne man tenke seg at det finnes andre strukturer der de polariserte parene spiller en viktigere rolle. Ideene fra rammeverket presentert i denne oppgaven kan med litt arbeid generaliseres for å studere mer kompliserte 3D spinstrukturer