Mechanisms involved in the amplification of the 11-year solar cycle signal in the tropical Pacific Ocean

It is debated whether the response of the tropical Pacific Ocean to the 11-yr solar cycle forcing resembles a La Niña– or El Niño–like signal. To address this issue, ensemble simulations employing an atmospheric general circulation model with and without ocean coupling are conducted. The coupled simulations show no evidence for a La Niña–like cooling in solar maxima. Instead, the tropical sea surface temperature rises almost in phase with the 11-yr solar cycle. A basinwide warming of about 0.1 K is simulated in the tropical Pacific, whereas the warming in the tropical Indian and Atlantic Oceans is weaker. In the western Pacific, the region of deep convection shifts to the east, thus reducing the surface easterlies. This shift is independent of the ocean coupling because deep convection moves to the east in the uncoupled simulations too. The reduced surface easterlies cool the subsurface but warm the surface due to the reduction of heat transport divergence. The latter mechanism operates together with water vapor feedback, resulting in a stronger tropical Pacific warming relative to the warming over the tropical Indian and Atlantic Oceans. These results suggest that the atmospheric response to the 11-yr solar cycle drives the tropical Pacific response, which is amplified by atmosphere–ocean feedbacks operating on decadal time scales. Based on the coupled simulations, it is concluded that the tropical Pacific Ocean should warm when the sun is more active

Single-cell transcriptomics characterizes cell types in the subventricular zone and uncovers molecular defects impairing adult neurogenesis

Neural stem cells (NSCs) contribute to plasticity and repair of the adult brain. Niches harboring NSCs regulate stem cell self-renewal and differentiation. We used comprehensive and untargeted single-cell RNA profiling to generate a molecular cell atlas of the largest germinal region of the adult mouse brain, the subventricular zone (SVZ). We characterized >20 neural and non-neural cell types and gained insights into the dynamics of neurogenesis by predicting future cell states based on computational analysis of RNA kinetics. Furthermore, we applied our single-cell approach to document decreased numbers of NSCs, reduced proliferation activity of progenitors, and perturbations in Wnt and BMP signaling pathways in mice lacking LRP2, an endocytic receptor required for SVZ maintenance. Our data provide a valuable resource to study adult neurogenesis and a proof of principle for the power of single-cell RNA sequencing to elucidate neural cell-type-specific alterations in loss-of-function models

The role of the oceans in shaping the tropospheric response to the 11 year solar cycle

Observational data indicate a weakening and poleward shift of the subtropical tropospheric jets in the maximum phase of the 11 year solar cycle, commonly explained in terms of a direct "top-down" propagation of solar signals from the stratosphere to the troposphere. We here demonstrate possible linkages to oceanic variability, instead. The observed response of the jets is qualitatively and quantitatively reproduced in an ensemble of simulations with a global model forced only at the lower boundary by the observed sea surface temperatures and sea ice concentrations, while keeping solar cycle forcing constant. The twentieth century reanalysis, in which only surface observations are assimilated, is characterized by a similar shift of the jets. These findings suggest that changes at the ocean surface could contribute considerably to the poleward shift of the subtropical tropospheric jets, although a top-down influence on the oceans and hence indirectly on the jets cannot be excluded. © 2013

Decline in Etesian winds after large volcanic eruptions in the last millennium

The northerly Etesian winds are a stable summertime circulation system in the eastern Mediterranean, emerging from a steep pressure gradient between the central Europe and Balkans high-pressure and the Anatolian low-pressure systems. Etesian winds are influenced by the variability in the Indian summer monsoon (ISM), but their sensitivity to external forcing on interannual and longer timescales is not well understood. Here, for the first time, we investigate the sensitivity of Etesian winds to large volcanic eruptions in a set of model simulations over the last millennium and reanalysis of the 20th century. We provide model evidence for significant volcanic signatures, manifested as a robust reduction in the wind speed and the total number of days with Etesian winds in July and August. These are robust responses to all strong eruptions in the last millennium, and in the extreme case of Samalas, the ensemble-mean response suggests a post-eruption summer without Etesians. The significant decline in the number of days with Etesian winds is attributed to the weakening of the ISM in the post-eruption summers, which is associated with a reduced large-scale subsidence and weakened surface pressure gradients in the eastern Mediterranean. Our analysis identifies a stronger sensitivity of Etesian winds to the Northern Hemisphere volcanic forcing, particularly for volcanoes before the 20th century, while for the latest large eruption of Pinatubo modelled and observed responses are insignificant. These findings could improve seasonal predictions of the wind circulation in the eastern Mediterranean in the summers after large volcanic eruptions.</p

Self-supervised Metric Learning

H Μάθηση Μετρικής είναι ένα σημαντικό παράδειγμα για μία πληθώρα προβλημάτων της Μηχανικής Μάθησης και της Όρασης Υπολογιστών. Έχει επιτυχημένα εφαρμοστεί σε ε- φαρμογές όπως η λεπτομερής ταξινόμηση, ανάκτηση πληροφορίας, αναγνώριση προσώ- που κ.α. Αφορά την εκμάθηση μιας μετρικής απόστασης που βασίζεται στον προσδιορι- σμό ομοιοτήτων ή ανομοιοτήτων μεταξύ των δειγμάτων. Στόχος της είναι να μειωθεί η απόσταση μεταξύ παρόμοιων δειγμάτων και ταυτόχρονα να αυξηθεί η απόσταση μεταξύ ανόμοιων. Ως εκ τούτου, είναι σημαντικό η μάθηση μετρικής να είναι εκπαιδευόμενη ώστε να προσαρμόζεται σε δεδομένα από διαφορετικούς τομείς. Η εκπαίδευση ενός Συνελικτικού Νευρωνικού Δικτύου ώστε να διακρίνει παρόμοιες από ανόμοιες εικόνες απαιτεί κάποιου είδους επίβλεψη. Στην εποχή του μεγάλου όγκου δεδο- μένων, λόγω του περιορισμένου αριθμού των ανθρωπίνως επισημειωμένων δεδομένων, οι μέθοδοι βαθιάς μάθησης προσαρμόστηκαν να λειτουργούν χωρίς επίβλεψη. Οι αυτοεπιβλεπόμενες μέθοδοι μπορούν να θεωρηθούν ως μια ειδική μορφή μεθόδων μάθησης χωρίς επίβλεψη με εποπτευόμενη μορφή, όπου η εποπτεία πηγάζει από αυτοε- ποπτευόμενες εργασίες και όχι από προκαθορισμένη προηγούμενη γνώση. Σε αντίθεση με μια εντελώς μη επιβλεπόμενη διεργασία, η αυτοεπιβλεπόμενη μάθηση χρησιμοποιεί πληροφορίες από το ίδιο το σύνολο δεδομένων για να δημιουργήσει ψευδο-ετικέτες. Στην παρούσα εργασία εξετάζουμε ορισμένες αυτοεπιβλεπόμενες μεθόδους μετρικής εκ- μάθησης που χρησιμοποιούν διαφορετικές τεχνικές εξόρυξης δειγμάτων καθώς και συ- ναρτήσεις κόστους με σκοπό τη διερεύνηση της αποτελεσματικότητάς τους τόσο στη χρή- ση προεκπαιδευμένου δικτύου στο ImageNet όσο και στην χρήση τυχαία αρχικοποιημέ- νου δικτύου. Η αξιολόγηση των μεθόδων πραγματοποιείται στα πιο διαδεδομένα σύνολα δεδομένων ανάκτησης πληροφορίας και μάθησης μετρικής. Παρατηρείται πως οι ήπιες συναρτήσεις κόστους εκμεταλλεύονται τις ομοιότητες μεταξύ των δειγμάτων λαμβάνοντας υπόψιν τους γείτονές τους, έχουν καλύτερα αποτελέσματα σε σχέση με τις απόλυτες συ- ναρτήσεις κόστους που χρησιμοποιούν τις ομοιότητες κατά ζεύγη. Επιπλέον, φαίνεται πως η τεχνητή επάυξηση των αρχικών εικόνων του συνόλου δεδομένων για την δημιουρ- γία θετικών ζευγών μπορεί να βοηθήσει την αυτοεπιβλεπόμενη μάθηση και ιδιαίτερα στο ξεκίνημά της.Metric learning is an important paradigm for a variety of problems in machine learning and computer vision. It has been successfully employed for fine-grained classification, retrieval, face recognition, person re-identification and few-shot learning, among other tasks. Metric learning is an approach based on a distance metric that aims to determine similarities or dissimilarities between samples. The goal is to reduce the distance between similar samples and at the same time to increase the distance of dissimilar ones. Therefore, it is crucial that the distance measure is learnable to adapt to data from different domains. Training a Convolutional Neural Network to distinguish similar from dissimilar images requires some kind of supervision. In the era of big data, due to limited human-powered annotated data, deep learning methods are recently adapted to work without supervision. Self-supervised methods can be considered as a special form of unsupervised learning methods with a supervised form, where supervision is induced by self-supervised tasks rather than predetermined prior knowledge. Unlike a completely unsupervised setting, self-supervised learning uses information from the dataset itself to generate pseudolabels. In this work we consider some self-supervised metric learning methods which use different sample mining techniques as well as loss functions to investigate its effectiveness in both using pre-trained network on ImageNet and initialized from scratch. The evaluation is performed on four benchmark metric learning and retrieval datasets. It appears that soft loss functions that exploit contextual similarities between samples outperform hard ones that use pairwise similarities. Furthermore, it seems that augmented versions of the original images can be used as positive pairs to initiate the self-supervised training process

Solar Signals in CMIP-5 Simulations: The Ozone Response

A multiple linear regression statistical method is applied to model data taken from the Coupled Model Intercomparison Project, phase 5 (CMIP-5) to estimate the 11-yr solar cycle responses of stratospheric ozone, temperature, and zonal wind during the 1979-2005 period. The analysis is limited to the six CMIP-5 models that resolve the stratosphere (high-top models) and that include interactive ozone chemistry. All simulations assumed a conservative 11-yr solar spectral irradiance (SSI) variation based on the NRL model. These model responses are then compared to corresponding observational estimates derived from two independent satellite ozone profile data sets and from ERA Interim Reanalysis meteorological data. The models exhibit a range of 11-yr responses with three models (CESM1-WACCM, MIROC-ESM-CHEM, and MRI-ESM1) yielding substantial solar-induced ozone changes in the upper stratosphere that compare favorably with available observations. The remaining three models do not, apparently because of differences in the details of their radiation and photolysis rate codes. During winter in both hemispheres, the three models with stronger upper stratospheric ozone responses produce relatively strong latitudinal gradients of ozone and temperature in the upper stratosphere that are associated with accelerations of the polar night jet under solar maximum conditions. This behavior is similar to that found in the satellite ozone and ERA Interim data except that the latitudinal gradients tend to occur at somewhat higher latitudes in the models. The sharp ozone gradients are dynamical in origin and assist in radiatively enhancing the temperature gradients, leading to a stronger zonal wind response. These results suggest that simulation of a realistic solar-induced variation of upper stratospheric ozone, temperature and zonal wind in winter is possible for at least some coupled climate models even if a conservative SSI variation is adopted

Similar patterns of tropical precipitation and circulation changes under solar and greenhouse gas forcing

Abstract: Theory and model evidence indicate a higher global hydrological sensitivity for the same amount of surface warming to solar as to greenhouse gas (GHG) forcing, but regional patterns are highly uncertain due to their dependence on circulation and dynamics. We analyse a multi-model ensemble of idealized experiments and a set of simulations of the last millennium and we demonstrate similar global signatures and patterns of forced response in the tropical Pacific, of higher sensitivity for the solar forcing. In the idealized simulations, both solar and GHG forcing warm the equatorial Pacific, enhance precipitation in the central Pacific, and weaken and shift the Walker circulation eastward. Centennial variations in the solar forcing over the last millennium cause similar patterns of enhanced equatorial precipitation and slowdown of the Walker circulation in response to periods with stronger solar forcing. Similar forced patterns albeit of considerably weaker magnitude are identified for variations in GHG concentrations over the 20th century, with the lower sensitivity explained by fast atmospheric adjustments. These findings differ from previous studies that have typically suggested divergent responses in tropical precipitation and circulation between the solar and GHG forcings. We conclude that tropical Walker circulation and precipitation might be more susceptible to solar variability rather than GHG variations during the last-millennium, assuming comparable global mean surface temperature changes

Cell type atlas and lineage tree reconstruction of whole adult animals by single-cell transcriptomics

Flatworms of the species Schmidtea mediterranea are immortal –adult animals contain a large pool of pluripotent stem cells that continuously differentiate to all adult cell types. Therefore, single-cell transcriptome profiling of adult animals should reveal mature and progenitor cells. Here, by combining perturbation experiments, gene expression analysis, a computational method that predicts future cell states from the transcriptional changes, and a novel lineage reconstruction method, we placed all major cell types onto a single lineage tree that connects all cells to a single stem cell compartment. We characterize gene expression changes during differentiation and discover cell types important for regeneration. Our results demonstrate the importance of single-cell transcriptome analysis for mapping and reconstructing fundamental processes of developmental and regenerative biology at unprecedented resolution

Historical simulations with HadGEM3-GC3.1 for CMIP6

We describe and evaluate historical simulations which use the third Hadley Centre Global Environment Model in the Global Coupled configuration 3.1 (HadGEM3-GC3.1) model and which form part of the UK's contribution to the sixth Coupled Model Intercomparison Project, CMIP6. These simulations, run at two resolutions, respond to historically evolving forcings such as greenhouse gases, aerosols, solar irradiance, volcanic aerosols, land use, and ozone concentrations. We assess the response of the simulations to these historical forcings and compare against the observational record. This includes the evolution of global mean surface temperature, ocean heat content, sea ice extent, ice sheet mass balance, permafrost extent, snow cover, North Atlantic sea surface temperature and circulation, and decadal precipitation. We find that the simulated time evolution of global mean surface temperature broadly follows the observed record but with important quantitative differences which we find are most likely attributable to strong effective radiative forcing from anthropogenic aerosols and a weak pattern of sea surface temperature response in the low to middle latitudes to volcanic eruptions. We also find evidence that anthropogenic aerosol forcings play a role in driving the Atlantic Multidecadal Variability and the Atlantic Meridional Overturning Circulation, which are key features of the North Atlantic ocean. Overall, the model historical simulations show many features in common with the observed record over the period 1850–2014 and so provide a basis for future in-depth study of recent climate change