Fulde-Ferrell-Larkin-Ovchinnikov pairing as leading instability on the square lattice

We study attractively interacting spin-1/2 fermions on the square lattice subject to a spin population imbalance. Using unbiased diagrammatic Monte Carlo simulations we find an extended region in the parameter space where the Fermi liquid is unstable towards formation of Cooper pairs with non-zero center-of-mass momentum, known as the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state. In contrast to earlier mean-field and quasi-classical studies we provide quantitative and well-controlled predictions on the existence and location of the relevant Fermi-liquid instabilities. The highest temperature where the FFLO instability can be observed is about half of the superfluid transition temperature in the unpolarized system.Comment: 7 pages, 4 figures; v2: improved references and discussion, added calculations with larger cutoff order that corroborate our earlier result

Measured and modelled source water δ18O based on tree-ring cellulose of larch and pine trees from the permafrost zone

To identify source water for trees growing on permafrost in Siberia, we applied mechanistic models that quantify physical and biochemical fractionation processes, leading to oxygen isotope variation (δ18O) in plant organic matter. These models allowed us to investigate the influence of a variety of climatic factors on tree-ring cellulose from two dominant species: Larix cajanderi Mayr. from northeastern Yakutia (69° 22′ N, 148° 25′ E, ~ 250 m a.s.l.) and Pinus sylvestris L. from Central Yakutia (62°14′ N, 129°37′ E, ~ 220 m a.s.l.). The climate of the region is highly continental with short growing seasons, low amount of precipitation and these forest ecosystems are growing on permafrost, which in turn impact the water cycle and climate variation in the δ18O of source water. We compared outputs of the Land surface Processes and eXchanges (LPX-Bern v. 1.3), and Roden-Lin-Ehleringer (RLE) models for the common period from 1945 to 2004. Based on our findings, trees from northeastern and central Yakutia may have access to additional thawed permafrost water during dry summer periods. Owing to differences in the soil structure, active thaw soil depth and root systems of trees at two Siberian sites, Larix cajanderi Mayr. trees can access water not more than from 50 cm depth, in contrast to Pinus sylvestris L. in Central Yakutia which can acquire water from up to 80 cm soil depth. The results enhance our understanding of the growth and survival of the trees in this extreme environment

Tolerance of intravenous methylprednisolone for relapse treatment in demyelinating CNS disease

In Switzerland, the first course of intravenous steroids for treatment of episodes of demyelinating CNS disease is usually administered in an inpatient setting. We prospectively evaluated short term tolerance of treatment with special emphasis on sleep quality

Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling

Evapotranspiration (ET) is critical in linking global water, carbon and energy cycles. However, direct measurement of global terrestrial ET is not feasible. Here, we first reviewed the basic theory and state-of-the-art approaches for estimating global terrestrial ET, including remote-sensing-based physical models, machine-learning algorithms and land surface models (LSMs). We then utilized 4 remote-sensing-based physical models, 2 machine-learning algorithms and 14 LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the ensemble means of annual global terrestrial ET estimated by these three categories of approaches agreed well, with values ranging from 589.6 mm yr−1 (6.56×104 km3 yr−1) to 617.1 mm yr−1 (6.87×104 km3 yr−1). For the period from 1982 to 2011, both the ensembles of remote-sensing-based physical models and machine-learning algorithms suggested increasing trends in global terrestrial ET (0.62 mm yr−2 with a significance level of p0.05), although many of the individual LSMs reproduced an increasing trend. Nevertheless, all 20 models used in this study showed that anthropogenic Earth greening had a positive role in increasing terrestrial ET. The concurrent small interannual variability, i.e., relative stability, found in all estimates of global terrestrial ET, suggests that a potential planetary boundary exists in regulating global terrestrial ET, with the value of this boundary being around 600 mm yr−1. Uncertainties among approaches were identified in specific regions, particularly in the Amazon Basin and arid/semiarid regions. Improvements in parameterizing water stress and canopy dynamics, the utilization of new available satellite retrievals and deep-learning methods, and model–data fusion will advance our predictive understanding of global terrestrial ET

Are Terrestrial Biosphere Models Fit for Simulating the Global Land Carbon Sink?

The Global Carbon Project estimates that the terrestrial biosphere has absorbed about one-third of anthropogenic CO$_2$ emissions during the 1959–2019 period. This sink-estimate is produced by an ensemble of terrestrial biosphere models and is consistent with the land uptake inferred from the residual of emissions and ocean uptake. The purpose of our study is to understand how well terrestrial biosphere models reproduce the processes that drive the terrestrial carbon sink. One challenge is to decide what level of agreement between model output and observation-based reference data is adequate considering that reference data are prone to uncertainties. To define such a level of agreement, we compute benchmark scores that quantify the similarity between independently derived reference data sets using multiple statistical metrics. Models are considered to perform well if their model scores reach benchmark scores. Our results show that reference data can differ considerably, causing benchmark scores to be low. Model scores are often of similar magnitude as benchmark scores, implying that model performance is reasonable given how different reference data are. While model performance is encouraging, ample potential for improvements remains, including a reduction in a positive leaf area index bias, improved representations of processes that govern soil organic carbon in high latitudes, and an assessment of causes that drive the inter-model spread of gross primary productivity in boreal regions and humid tropics. The success of future model development will increasingly depend on our capacity to reduce and account for observational uncertainties