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Estimating climate response to observed and projected increases in atmospheric greenhouse gases usually requires averaging among multiple independent simulations of computationally expensive global climate models to filter out the spontaneous internal climate variability. Studies have shown that advanced pattern recognition methods allow one to obtain accurate estimates of the forced climate signal from just a handful of such climate realizations. The accuracy of these methods for a fixed ensemble size, however, decreases with an increasing magnitude of the low-frequency, decadal and longer internal climate variability. Here we generalize a previously developed Bayesian methodology of Linear Dynamical Mode (LDM) decomposition for spatially extended time series to enable joint identification and analysis of forced signal and internal variability in ensembles of climate simulations, a methodology dubbed here an ensemble LDM, or ELDM. The new ELDM method is shown to outperform its pattern-recognition competitors by more accurately isolating the forced signal in small ensembles of both toy- and state-of-the-art climate-model simulations. It is able to do so by explicitly recognizing a non-random structure of the internal variability, identified by the ELDM algorithm alongside the optimal forced-signal estimate, which allows one to study possible dynamical connections between the two types of variability. The optimal ELDM filtering provides a unique opportunity for objective intercomparison of decadal and longer climate variability across different global climate models --- a task that proved difficult due to uncertainties associated with the noisy character and limited length of historical climate simulations combined with parameter uncertainties of alternative signal-detection methods.

Two-dimensional flow around a semi-circular profile at the zero angle of attack and at Re = 50000 on the self-oscillatory period is extensively studied by the URANS method involving the standard semi-empirical SST turbulence models, the SST turbulence model with the correction for streamline curvature modified within the Rodi-Leschziner-Isaev and Smirnov-Menter approaches, as well as involving Hanjalic's four-parameter eddy viscosity elliptic relaxation model and its analog - eddy viscosity elliptic blending model proposed in the present work. This has been done with the use of different-structure grids (multiblock with structured overlapping and unstructured composite). Different numerical approximation methods realized in six codes (VP2/3, SigmaFlow, Fluent, CFX, OpenFOAM, and StarCCM+) are used. An underestimation (up to 30%) of time-averaged integral aerodynamic loads is revealed by means of the standard near-wall SST model. This is explained by the high vortex viscosity production in the profile wake. Wind tunnel tests show that the location of cutoff washers on the semi-circular profile provides a quasi-two-dimensional flow around it and allows applying measurement data to verify two-dimensional turbulent flow. The best agreement of experimental results and numerical predictions when comparing the Strouhal number and time-averaged surface pressure coefficient distributions is achieved using both the modified SST model with the correction for streamline curvature and the modified eddy viscosity elliptic blending model. When the SST model with the correction for streamline curvature, modified within the Rodi-Leschziner-Isaev, Smirnov-Menter and Durbin approaches, is used, all the above codes yield close predictions of a vertical aerodynamic load on the oscillation period.

Currently, causes of the middle Pleistocene transition (MPT) – the onset of large-amplitude glacial variability with 100 kyr time scale instead of regular 41 kyr cycles before – are a challenging puzzle in Paleoclimatology. Here we show how a Bayesian data analysis based on machine learning approaches can help to reveal the main mechanisms underlying the Pleistocene variability, which most likely explain proxy records and can be used for testing existing theories. We construct a Bayesian data-driven model from benthic δ 18O records (LR04 stack) accounting for the main factors which may potentially impact climate of the Pleistocene: internal climate dynamics, gradual trends, variations of insolation, and millennial variability. In contrast to some theories, we uncover that under long-term trends in climate, the strong glacial cycles have appeared due to internal nonlinear oscillations induced by millennial noise. We find that while the orbital Milankovitch forcing does not matter for the MPT onset, the obliquity oscillation phase-locks the climate cycles through the meridional gradient of insolation.

The loss of autocorrelations of tropical sea surface temperatures (SST) during late spring, also called the spring predictability barrier (SPB), is a factor that strongly limits the predictability ...

Annual cycle events may be interlinked, influence following annual cycle stages, and may alter performance of individuals. Such links, called carry-over effects, can explain individual variation in timing or reproductive success in migratory species. Identifying the key links affecting fitness may reveal the mechanisms of species population dynamics but the current evidence for the strongest carry-over effects is equivocal. Here, we aim to assess the carry-over effects in great reed warblers Acrocephalus arundinaceus, a long-distance migratory songbird, using 103 full-annual tracks from three European and two Asian breeding populations. Our results showed strong positive relationships within autumn and spring migration periods and buffering capacity of the non-breeding period preventing events to carry over between these periods. Moreover, we found no profound relation between the non-breeding habitat quality or seasonality (quantified using stable isotopes and remote sensing data) and the timing of spring migration. The strongest carry-over effects occurred in individuals from the southern European breeding population compared to the northern and the central European populations. A moderate relationship between the habitat seasonality during moult and the spring migration timing indicates the importance of the complete moult. The overall weak carry-over effects of non-breeding habitat conditions found in this study contrast with previous results and imply between-species differences in these crucial relationships. Moreover, the population-specific carry-over effects highlight the importance of multi-population approach and advise caution in interpretation of results from single-population studies. Finally, the carry-over effect from the moulting period indicates the significance of a so-far neglected link in the species., + Publisher's Statement: This is a post-peer-review, pre-copyedit version of an article published in Behavioral Ecology and Sociobiology. The final authenticated version is available online at: https://doi.org/10.1007/s00265-020-02929-7.

The article considers the influence of the relief, river, and urban development on the formation of vortex structures in the atmosphere and the spread of pollutants in the city of Krasnoyarsk in winter. The weak influence of urban development on the appearance of large vortex structures over the river is shown. However, in the ground layer, it significantly changes the flow pattern and determines the character of the distribution of pollutants.

The paper presents the numerical simulation of the distribution of pollutants in winter in the atmosphere of Krasnoyarsk city. The source of the pollution is the suburban areas of the city of Krasnoyarsk, in which coal stove heating prevails. A micro-scale mathematical model based on the solution of the system of non-stationary Reynolds equations is used for modeling. The influence of various factors, such as elements of terrain and urban development, on the distribution of pollutants is studied in the paper.

The paper presents the results of Newtonian-fluid spiral flow simulation in an annular channel. The simulation parameters were dimeters ratio of 0.5; Re = 100 ÷ 10,000 and dimensionless inner cylinder rotation rate χ = 0.2 ÷ 5. The turbulent flow regimes were simulated using URANS, a non-stationary statistical turbulence model. While simulation, the following flow regimes were detected: homogeneous flow without pronounced structures (Re = 100, χ⩽3 and Re=300, χ⩽0.5); flow with Görtler-type continuous spiral vortices near inner cylinder (Re = 300, χ=1 and Re = 1000 ÷ 10000, χ = 0.5); flow with Taylor-type vortices (Re = 100, χ⩾3 and Re = 300, χ⩾3); flow with small-scale Görtler-type vortices near both channel walls (Re ⩾ 1000 and χ⩾1). The spiral spin of Görtler-type vortices near the outer cylinder was co-directed with the rotation of the inner cylinder, while the vortices near the inner cylinder spin against the rotation. Formation of large Taylor-type vortex structures led to a decrease in the channel skin friction coefficient faRe. Görtler-type vortices interaction near the channel walls triggered the formation of small-scale structures increasing in the channel resistance as the Reynolds number and rotation speed grew.

The paper reveals the capabilities of SigmaFlow CFD code to predict wind conditions in terms of pedestrian comfort as illustrated by a model problem. The proposed numerical model was verified by comparing with experimental data. A group of buildings consisting of low-rise buildings and a high-rise building was considered. A comparative analysis of five computational variants with different grid saturation was performed. The results of mathematical modeling allow observing the vortex flow structure that is generated when streamlining buildings.

The article analyzes the environmental situation in the city of Krasnoyarsk based on data of the Ministry of Natural Resources and the Ministry of Ecology and Environmental Management of the Krasnoyarsk Territory. The influence of the non-freezing Yenisei River on the movement of air masses over the city is considered based on numerical simulation. The obtained results demonstrate the ability to quickly simulate the wind pattern of the city, taking into account the heterogeneous nature of the terrain, heat transfer, wind load, and the influence of the river.

House sparrows (Passer domesticus) are a hugely successful anthrodependent species; occurring on nearly every continent. Yet, despite their ubiquity and familiarity to humans, surprisingly little is known about their origins. We sought to investigate the evolutionary history of the house sparrow and identify the processes involved in its transition to a human-commensal niche. We used a whole genome resequencing dataset of 120 individuals from three Eurasian species, including three populations of Bactrianus sparrows, a non-commensal, divergent house sparrow lineage occurring in the Near East. Coalescent modelling supports a split between house and Bactrianus sparrow 11 Kya and an expansion in the house sparrow at 6 Kya, consistent with the spread of agriculture following the Neolithic revolution. Commensal house sparrows therefore likely moved into Europe with the spread of agriculture following this period. Using the Bactrianus sparrow as a proxy for a pre-commensal, ancestral house population, we performed a comparative genome scan to identify genes potentially involved with adaptation to an anthropogenic niche. We identified potential signatures of recent, positive selection in the genome of the commensal house sparrow that are absent in Bactrianus populations. The strongest selected region encompasses two major candidate genes;COL11A—which regulates craniofacial and skull development andAMY2A, part of the amylase gene family which has previously been linked to adaptation to high-starch diets in humans and dogs. Our work examines human-commensalism in an evolutionary framework, identifies genomic regions likely involved in rapid adaptation to this new niche and ties the evolution of this species to the development of modern human civilization.

The present paper is the second part of a two-part study on empirical modeling and prediction of climate variability. This paper deals with spatially distributed data, as opposed to the univariate data of Part I. The choice of a basis for effective data compression becomes of the essence. In many applications, it is the set of spatial empirical orthogonal functions that provides the uncorrelated time series of principal components (PCs) used in the learning set. In this paper, the basis of the learning set is obtained instead by applying multichannel singular-spectrum analysis to climatic time series and using the leading spatiotemporal PCs to construct a reduced stochastic model. The effectiveness of this approach is illustrated by predicting the behavior of the Jin–Neelin–Ghil (JNG) hybrid seasonally forced coupled ocean–atmosphere model of El Niño–Southern Oscillation. The JNG model produces spatially distributed and weakly nonstationary time series to which the model reduction and prediction methodology is applied. Critical transitions in the hybrid periodically forced coupled model are successfully predicted on time scales that are substantially longer than the duration of the learning sample.

The paper presents the results of the simulation of cuttings transport (solid spherical particles) by water through a long annular axisymmetric inclined channel. At the inlet uniform distribution of particles and fluid velocity was set. Inclination angle varied from 0 to 90 degrees. Modes of stationary and moving dunes and their transition into wave-shaped and stationary sediment were observed. Moving dunes could oscillate laterally and did not form a crest that occupied the entire width of the channel. In this case, dense sediment formed an S-shaped path, moving along the stream. Despite the general downstream movement of the S-shaped track (dune), a downward flow might occur in the low area with high concentration. Five basic flow regimes were identified: constant sediment, moving wave-like sediment, separated stationary dunes, S-shaped sediment and homogeneous flow. The basic flow pattern could be complicated by lateral motion and backward flow in a region of high concentration. A variant of the flow pattern map is suggested.

This paper presents the implementation of the immersed boundary method (IBM) for grids consisting of arbitrary cells. This approach allows to combine the IBM with the body-fitted method (BFM). The implementation is based on the combination of the “ghost-cell” method and penalization method. The paper presents the IBM to determine the basic design parameters and the results of the verification of the method for laminar and turbulent flow regimes for both Newtonian and viscoplastic fluids. In the last test, the method was used to simulate a flow in an annular channel with the orbital motion of the inner cylinder.

The paper presents the results of the implementation of computational algorithms of hydrodynamics for using graphics processor units. The implementation was carried out on the basis of the in-house CFD code SigmaFlow. Numerical simulations were based on the solution of the Navier-Stokes equations using SIMPLE-like procedure. The discretization of the differential equations was based on the control volume method on unstructured mesh. In the case of multiple CPU/GPU, parallel calculations were performed by means of domain decomposition. In the GPU-version of the code, basic computational functions were implemented as CUDA kernels to perform on GPUs. The code has been verified using several test cases. The computational efficiencies of several GPUs were compared with each other and that of modern CPUs. A modern GPU can increase the calculation performance of CFD problems by more than two times compared to a modern six-core CPU.

The current study is devoted to simulating cuttings transport by drilling fluid through a horizontal section of borehole with an annular cross section. Drill pipe rotates in fixed eccentric position. Steady-state flow is considered. Cuttings are rigid spheres with equal diameters. The carrying fluid is drilling mud with Herschel-Bulkley rheology. Suspension rheology depends on local shear rate and particles concentration. Continuous mixture model with algebraic equation for particles slipping velocity is used. Two hydrodynamic regimes are considered: axial flow without drill pipe rotation and with drill pipe rotation. In the case of axial flow was shown that increasing of power index n and consistency factor k increases pressure gradient and decreases cuttings concentration. Increasing of yield stress leads to increasing of pressure gradient and cuttings concentration. Cuttings concentration achieves constant value for high yield stress and not depends on it. Rotation of the drill pipe significantly changes the flow structure: pressure loss occurs and particles concentration decreases in the cross section. Two basic regimes of rotational flow are observed: domination of primary vortex around drill pipe and domination secondary vorticity structures. Transition between regimes leads to significant changes of flow integral parameters.

The article describes the concept of the methodology of intellectual labor automation, a formal description of the conceptual model (CM) at the object level, and also a description of the analytical processing of CM. The conceptual model is the basis for the design of the system. The conceptual model of an applied task reflects the semantics (the system of knowledge) of the subject field in the form of static and dynamic structures and their linkage. The elements of the static structure are subject categories defined by classes: cycle, process, task, component, object, feature, value. The dynamic structure reflects the system of subject dependences which describes the process of performing a subject task. The main stage of conceptual model analytical processing is the restructuring of the dynamic component. The method and technique of descriptions restructuring of subject dependences system at the level of features are developed. The method allows to provide reduction of time and spatial expenses on performance of process of the decision of the applied task in the computing environment at early stages of information-active systems design

We suggest a new nonlinear expansion of space-distributed observational time series. The expansion allows constructing principal nonlinear manifolds holding essential part of observed variability. It yields low-dimensional hidden time series interpreted as internal modes driving observed multivariate dynamics as well as their mapping to a geographic grid. Bayesian optimality is used for selecting relevant structure of nonlinear transformation, including both the number of principal modes and degree of nonlinearity. Furthermore, the optimal characteristic time scale of the reconstructed modes is also found. The technique is applied to monthly sea surface temperature (SST) time series having a duration of 33 years and covering the globe. Three dominant nonlinear modes were extracted from the time series: the first efficiently separates the annual cycle, the second is responsible for ENSO variability and combinations of the second and the third modes explain substantial parts of Pacific and Atlantic dynamics. A relation of the obtained modes to decadal natural climate variability including current hiatus in global warming is exhibited and discussed.

This paper overviews and analyzes progressive trends in modern data center, and existing solutions to build the distributed cloud data center. Authors present the hierarchical distributed agent-based control plane architecture to build a web-scale control layer based on software-defined domains. The goal of this approach is to design the simple extensible agent that could be used for any management purposes, just by adding some specific code. Using of this approach makes easier the scalability and increases the efficiency of the management of a multi-site environment. There are five main use-cases of using this approach: distributed cloud, hybrid cloud, hyperscale data center, IoT, Continuous Integration.

The paper presents numerical simulation of the flow in hydraulic turbine based on the experimental data of the II Francis-99 workshop. The calculation domain includes the wicket gate, runner and draft tube with rotating reference frame for the runner zone. Different turbulence models such as k-ω SST, ζ-f and RSM were considered. The calculations were performed by means of in-house CFD code SigmaFlow. The numerical simulation for part load, high load and best efficiency operation points were performed.

We performed numerical simulation of flow in a laboratory model of a Francis hydroturbine at three regimes, using two eddy-viscosity- (EVM) and a Reynolds stress (RSM) RANS models (realizable k-, k-ω SST, LRR) and detached-eddy-simulations (DES), as well as large-eddy simulations (LES). Comparison of calculation results with the experimental data was carried out. Unlike the linear EVMs, the RSM, DES, and LES reproduced well the mean velocity components, and pressure pulsations in the diffusor draft tube. Despite relatively coarse meshes and insufficient resolution of the near-wall region, LES, DES also reproduced well the intrinsic flow unsteadiness and the dominant flow structures and the associated pressure pulsations in the draft tube.

Results of unsteady two-dimensional numerical simulations for the turbulent flow over the semi-cylindrical profile at Reynolds number 45000 are presented. Three different turbulence RANS eddy viscosity models are used for the simulation. The calculations show the formation of alternating large-scale vortices shed in the wake of the body. The calculated averaged pressure distribution on the contour demonstrate a good agreement with the experimental data. A series of calculations have been performed for the estimation of influence of mesh resolution in the wake of streamlined body on the unsteady solution accuracy. It was found that a mesh size ~0.04 of chord length is sufficient to resolve the vortex street.