73 research outputs found

    Improved linear response for stochastically driven systems

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    The recently developed short-time linear response algorithm, which predicts the average response of a nonlinear chaotic system with forcing and dissipation to small external perturbation, generally yields high precision of the response prediction, although suffers from numerical instability for long response times due to positive Lyapunov exponents. However, in the case of stochastically driven dynamics, one typically resorts to the classical fluctuation-dissipation formula, which has the drawback of explicitly requiring the probability density of the statistical state together with its derivative for computation, which might not be available with sufficient precision in the case of complex dynamics (usually a Gaussian approximation is used). Here we adapt the short-time linear response formula for stochastically driven dynamics, and observe that, for short and moderate response times before numerical instability develops, it is generally superior to the classical formula with Gaussian approximation for both the additive and multiplicative stochastic forcing. Additionally, a suitable blending with classical formula for longer response times eliminates numerical instability and provides an improved response prediction even for long response times

    THE ROLE OF THE STRUCTURAL PROTEINS IN THE NON-VIRAEMIC TRANSMISSION OF TICK-BORNE ENCEPHALITIS VIRUS

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    There are 3 subtypes of tick-borne encephalitis virus (TBEV) distinguished, at present time: European (EU), Siberian (SIB) and Far Eastern (FE). The former one is associated with Ixodes ricinus tick, whereas the latter two - with I. persulcatus. The circulation of TBEV in nature is mediated by the non-viraemic transmission, between infected and. uninfected ticks co-feeding on the same hosts and. it was shown that transmission rate of «Hypr» strain (EU subtype) is much higher than rate of «Vasilchenko» strain (SIB subtype) - 60 and 5 % respectively. To reveal the viral determinants of transmission efficacy, we constructed the series of recombinant viruses with gradually exchanged genes coding structural (str) and. non-structural (ns) viral proteins. The recombinant virus Hypr[str]Vs[ns] achieved the rate of non-viraemic transmission of 70 %. The introduction of separate structural genes of Hypr into Vs infectious clone has enhanced the transmission efficacy as well, though not to such extent as entire structural region but up to 33 % only. Thus, it was shown that efficacy of non-viraemic transmission of TBEV depends from properties of viral structural proteins mainly

    Beyond forcing scenarios: predicting climate change through response operators in a coupled general circulation model

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    Global Climate Models are key tools for predicting the future response of the climate system to a variety of natural and anthropogenic forcings. Here we show how to use statistical mechanics to construct operators able to flexibly predict climate change for a variety of climatic variables of interest. We perform our study on a fully coupled model - MPI-ESM v.1.2 - and for the first time we prove the effectiveness of response theory in predicting future climate response to CO2 increase on a vast range of temporal scales, from inter-annual to centennial, and for very diverse climatic quantities. We investigate within a unified perspective the transient climate response and the equilibrium climate sensitivity and assess the role of fast and slow processes. The prediction of the ocean heat uptake highlights the very slow relaxation to a newly established steady state. The change in the Atlantic Meridional Overturning Circulation (AMOC) and of the Antarctic Circumpolar Current (ACC) is accurately predicted. The AMOC strength is initially reduced and then undergoes a slow and only partial recovery. The ACC strength initially increases as a result of changes in the wind stress, then undergoes a slowdown, followed by a recovery leading to a overshoot with respect to the initial value. Finally, we are able to predict accurately the temperature change in the Northern Atlantic

    Predicting climate change using response theory: global averages and spatial patterns

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    The provision of accurate methods for predicting the climate response to anthropogenic and natural forcings is a key contemporary scientific challenge. Using a simplified and efficient open-source general circulation model of the atmosphere featuring O(105105) degrees of freedom, we show how it is possible to approach such a problem using nonequilibrium statistical mechanics. Response theory allows one to practically compute the time-dependent measure supported on the pullback attractor of the climate system, whose dynamics is non-autonomous as a result of time-dependent forcings. We propose a simple yet efficient method for predicting—at any lead time and in an ensemble sense—the change in climate properties resulting from increase in the concentration of CO22 using test perturbation model runs. We assess strengths and limitations of the response theory in predicting the changes in the globally averaged values of surface temperature and of the yearly total precipitation, as well as in their spatial patterns. The quality of the predictions obtained for the surface temperature fields is rather good, while in the case of precipitation a good skill is observed only for the global average. We also show how it is possible to define accurately concepts like the inertia of the climate system or to predict when climate change is detectable given a scenario of forcing. Our analysis can be extended for dealing with more complex portfolios of forcings and can be adapted to treat, in principle, any climate observable. Our conclusion is that climate change is indeed a problem that can be effectively seen through a statistical mechanical lens, and that there is great potential for optimizing the current coordinated modelling exercises run for the preparation of the subsequent reports of the Intergovernmental Panel for Climate Change

    Tick-borne encephalitis virus in dogs - is this an issue?

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    The last review on Tick-borne encephalitis (TBE) in dogs was published almost ten years ago. Since then, this zoonotic tick-borne arbovirus has been geographically spreading and emerging in many regions in Eurasia and continues to do so. Dogs become readily infected with TBE virus but they are accidental hosts not capable to further spread the virus. They seroconvert upon infection but they seem to be much more resistant to the clinical disease than humans. Apart from their use as sentinels in endemic areas, however, an increasing number of case reports appeared during the last decade thus mirroring the rising public health concerns. Owing to the increased mobility of people travelling to endemic areas with their companion dogs, this consequently leads to problems in recognizing and diagnosing this severe infection in a yet non-endemic area, simply because the veterinarians are not considering TBE. This situation warrants an update on the epidemiology, clinical presentation and possible preventions of TBE in the dog

    Resonances in a chaotic attractor crisis of the Lorenz Flow

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    Local bifurcations of stationary points and limit cycles have successfully been characterized in terms of the critical exponents of these solutions. Lyapunov exponents and their associated covariant Lyapunov vectors have been proposed as tools for supporting the understanding of critical transitions in chaotic dynamical systems. However, it is in general not clear how the statistical properties of dynamical systems change across a boundary crisis during which a chaotic attractor collides with a saddle. This behavior is investigated here for a boundary crisis in the Lorenz flow, for which neither the Lyapunov exponents nor the covariant Lyapunov vectors provide a criterion for the crisis. Instead, the convergence of the time evolution of probability densities to the invariant measure, governed by the semigroup of transfer operators, is expected to slow down at the approach of the crisis. Such convergence is described by the eigenvalues of the generator of this semigroup, which can be divided into two families, referred to as the stable and unstable Ruelle--Pollicott resonances, respectively. The former describes the convergence of densities to the attractor (or escape from a repeller) and is estimated from many short time series sampling the state space. The latter is responsible for the decay of correlations, or mixing, and can be estimated from a long times series, invoking ergodicity. It is found numerically for the Lorenz flow that the stable resonances do approach the imaginary axis during the crisis, as is indicative of the loss of global stability of the attractor. On the other hand, the unstable resonances, and a fortiori the decay of correlations, do not flag the proximity of the crisis, thus questioning the usual design of early warning indicators of boundary crises of chaotic attractors and the applicability of response theory close to such crises
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