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

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?

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

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

Introduction to the special issue on the statistical mechanics of climate

We introduce the special issue on the Statistical Mechanics of Climate by presenting an informal discussion of some theoretical aspects of climate dynamics that make it a topic of great interest for mathematicians and theoretical physicists. In particular, we briefly discuss its nonequilibrium and multiscale properties, the relationship between natural climate variability and climate change, the different regimes of climate response to perturbations, and critical transitions

Micro-connectomics: probing the organization of neuronal networks at the cellular scale.

Defining the organizational principles of neuronal networks at the cellular scale, or micro-connectomics, is a key challenge of modern neuroscience. In this Review, we focus on graph theoretical parameters of micro-connectome topology, often informed by economical principles that conceptually originated with Ramón y Cajal's conservation laws. First, we summarize results from studies in intact small organisms and in samples from larger nervous systems. We then evaluate the evidence for an economical trade-off between biological cost and functional value in the organization of neuronal networks. Various results suggest that many aspects of neuronal network organization are indeed the outcome of competition between these two fundamental selection pressures.This work was supported by the National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre.This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by the Nature Publishing Group

Biological and Phylogenetic Characteristics of Yellow Fever Virus Lineages from West Africa

The yellow fever virus (YFV), the first proven human-pathogenic virus, although isolated in 1927, is still a major public health problem, especially in West Africa where it causes outbreaks every year. Nevertheless, little is known about its genetic diversity and evolutionary dynamics, mainly due to a limited number of genomic sequences from wild virus isolates. In this study, we analyzed the phylogenetic relationships of 24 full-length genomes from YFV strains isolated between 1973 and 2005 in a sylvatic context of West Africa, including 14 isolates that had previously not been sequenced. By this, we confirmed genetic variability within one genotype by the identification of various YF lineages circulating in West Africa. Further analyses of the biological properties of these lineages revealed differential growth behavior in human liver and insect cells, correlating with the source of isolation and suggesting host adaptation. For one lineage, repeatedly isolated in a context of vertical transmission, specific characteristics in the growth behavior and unique mutations of the viral genome were observed and deserve further investigation to gain insight into mechanisms involved in YFV emergence and maintenance in nature