Impact of Local Winter Cooling on the Melt of Pine Island Glacier, Antarctica

The rapid thinning of the ice shelves in the Amundsen Sea is generally attributed to basal melt driven by warm water originating from the continental slope. We examine the hypothesis that processes taking place on the continental shelf contribute significantly to the interannual variability of the ocean heat content and ice shelf melt rates. A numerical model is used to simulate the circulation of ocean heat and the melt of the ice shelves over the period 2006–2013. The fine model grid (grid spacing 1.5 km) explicitly resolves the coastal polynyas and mesoscale processes. The ocean heat content of the eastern continental shelf exhibits recurrent decreases around September with a magnitude that varies from year to year. The heat loss is primarily caused by surface heat fluxes along the eastern shore in areas of low ice concentration (polynyas). The cold winter water intrudes underneath the ice shelves and reduces the basal melt rates. Ocean temperatures upstream (i.e., at the shelf break) are largely constant over the year and cannot account for the cold events. The cooling is particularly marked in 2012 and its effect on the ocean heat content remains visible over the following years. The study suggests that ocean-atmosphere interactions in coastal polynyas contribute to the interannual variability of the melt of Pine Island Glacier

Localization, epidemic transitions, and unpredictability of multistrain epidemics with an underlying genotype network

Mathematical disease modelling has long operated under the assumption that any one infectious disease is caused by one transmissible pathogen spreading among a population. This paradigm has been useful in simplifying the biological reality of epidemics and has allowed the modelling community to focus on the complexity of other factors such as population structure and interventions. However, there is an increasing amount of evidence that the strain diversity of pathogens, and their interplay with the host immune system, can play a large role in shaping the dynamics of epidemics. Here, we introduce a disease model with an underlying genotype network to account for two important mechanisms. One, the disease can mutate along network pathways as it spreads in a host population. Two, the genotype network allows us to define a genetic distance across strains and therefore to model the transcendence of immunity often observed in real world pathogens. We study the emergence of epidemics in this model, through its epidemic phase transitions, and highlight the role of the genotype network in driving cyclicity of diseases, large scale fluctuations, sequential epidemic transitions, as well as localization around specific strains of the associated pathogen. More generally, our model illustrates the richness of behaviours that are possible even in well-mixed host populations once we consider strain diversity and go beyond the "one disease equals one pathogen" paradigm

Observations of fresh, anticyclonic eddies in the Hudson Strait outflow

Author Posting. © The Author(s), 2010. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Journal of Marine Systems 88 (2011): 375-384, doi:10.1016/j.jmarsys.2010.12.004.The waters that flow out through Hudson Strait, a coastal system that connects Hudson Bay with the Labrador Sea, constitute the third largest freshwater contribution to the northern North Atlantic. Recent studies have documented the mean structure and transport of the outflow, as well as highlighting significant variability on synoptic scales (days–week). This study examines the outflow’s variability on these synoptic scales through the use of observations collected by a mooring array from 2005-2006. We focus on the mechanisms that cause the freshwater export to be concentrated in a series of discrete pulses during the fall/winter season. We find that the pulses occur once every 4.4 days on average and are associated with anticyclonic, surface-trapped eddies propagated through the strait by the mean outflow. Their occurrence is related to the passage of storms across Hudson Bay, although local instability processes also play a role in their formation. The eddies are responsible for approximately 40% of the mean volume transport and 50% of the mean freshwater transport out of the strait. We discuss the implications of this freshwater release mechanism on the delivery of nutrient-rich and highly stratified waters to the Labrador shelf, a productive region south of Hudson Strait.This work was funded by National Science Foundation grant OCE-0751554, with additional funding from the Office of Naval Research grant N00014-08-10490

Efficient sampling of spreading processes on complex networks using a composition and rejection algorithm

Efficient stochastic simulation algorithms are of paramount importance to the study of spreading phenomena on complex networks. Using insights and analytical results from network science, we discuss how the structure of contacts affects the efficiency of current algorithms. We show that algorithms believed to require $\mathcal{O}(\log N)$ or even $\mathcal{O}(1)$ operations per update---where $N$ is the number of nodes---display instead a polynomial scaling for networks that are either dense or sparse and heterogeneous. This significantly affects the required computation time for simulations on large networks. To circumvent the issue, we propose a node-based method combined with a composition and rejection algorithm, a sampling scheme that has an average-case complexity of $\mathcal{O} [\log(\log N)]$ per update for general networks. This systematic approach is first set-up for Markovian dynamics, but can also be adapted to a number of non-Markovian processes and can enhance considerably the study of a wide range of dynamics on networks.Comment: 12 pages, 7 figure

Social confinement and mesoscopic localization of epidemics on networks

Recommendations around epidemics tend to focus on individual behaviors, with much less efforts attempting to guide event cancellations and other collective behaviors since most models lack the higher-order structure necessary to describe large gatherings. Through a higher-order description of contagions on networks, we model the impact of a blanket cancellation of events larger than a critical size and find that epidemics can suddenly collapse when interventions operate over groups of individuals rather than at the level of individuals. We relate this phenomenon to the onset of mesoscopic localization, where contagions concentrate around dominant groups

Master equation analysis of mesoscopic localization in contagion dynamics on higher-order networks

Simple models of infectious diseases tend to assume random mixing of individuals, but real interactions are not random pairwise encounters: they occur within various types of gatherings such as workplaces, households, schools, and concerts, best described by a higher-order network structure. We model contagions on higher-order networks using group-based approximate master equations, in which we track all states and interactions within a group of nodes and assume a mean-field coupling between them. Using the Susceptible-Infected-Susceptible dynamics, our approach reveals the existence of a mesoscopic localization regime, where a disease can concentrate and self-sustain only around large groups in the network overall organization. In this regime, the phase transition is smeared, characterized by an inhomogeneous activation of the groups. At the mesoscopic level, we observe that the distribution of infected nodes within groups of a same size can be very dispersed, even bimodal. When considering heterogeneous networks, both at the level of nodes and groups, we characterize analytically the region associated with mesoscopic localization in the structural parameter space. We put in perspective this phenomenon with eigenvector localization and discuss how a focus on higher-order structures is needed to discern the more subtle localization at the mesoscopic level. Finally, we discuss how mesoscopic localization affects the response to structural interventions and how this framework could provide important insights for a broad range of dynamics.Comment: 15 pages, 10 figure

Social confinement and mesoscopic localization of epidemics on networks

Recommendations around epidemics tend to focus on individual behaviors, with much less efforts attempting to guide event cancellations and other collective behaviors since most models lack the higher-order structure necessary to describe large gatherings. Through a higher-order description of contagions on networks, we model the impact of a blanket cancellation of events larger than a critical size and find that epidemics can suddenly collapse when interventions operate over groups of individuals rather than at the level of individuals. We relate this phenomenon to the onset of mesoscopic localization, where contagions concentrate around dominant groups.Comment: 5 pages, 4 figure