Changes in column inventories of carbon and oxygen in the Atlantic Ocean

Increasing concentrations of dissolved inorganic carbon (DIC) in the interior ocean are expected as a direct consequence of increasing concentrations of CO<sub>2</sub> in the atmosphere. This extra DIC is often referred to as anthropogenic carbon (C<sub>ant</sub>), and its inventory, or increase rate, in the interior ocean has previously been estimated by a multitude of observational approaches. Each of these methods is associated with hard to test assumptions since C<sub>ant</sub> cannot be directly observed. Results from a simpler concept with fewer assumptions applied to the Atlantic Ocean are reported on here using two large data collections of carbon relevant bottle data. The change in column inventory on decadal time scales, i.e. the storage rate, of DIC, respiration compensated DIC and oxygen is calculated for the Atlantic Ocean. We report storage rates and the confidence intervals of the mean trend at the 95% level (CI), reflecting the mean trend but not considering potential biasing effects of the spatial and temporal sampling. For the whole Atlantic Ocean the mean trends for DIC and oxygen are non-zero at the 95% confidence level: DIC: 0.86 (CI: 0.72–1.00) and oxygen: −0.24 (CI: −0.41–(−0.07)) mol m<sup>−2</sup> yr<sup>−1</sup>. For oxygen, the whole Atlantic trend is dominated by the subpolar North Atlantic, whereas for other regions the O<sub>2</sub> trends are not significant. The storage rates are similar to changes found by other studies, although with large uncertainty. For the subpolar North Atlantic the storage rates show significant temporal and regional variation of all variables. This seems to be due to variations in the prevalence of subsurface water masses with different DIC and oxygen concentrations leading to sometimes different signs of storage rates for DIC compared to published C<sub>ant</sub> estimates. This study suggest that accurate assessment of the uptake of CO<sub>2</sub> by the oceans will require accounting not only for processes that influence C<sub>ant</sub> but also additional processes that modify CO<sub>2</sub> storage

Thermodynamics and Excitations of Condensed Polaritons in Disordered Microcavities

We study the thermodynamic condensation of microcavity polaritons using a realistic model of disorder in semiconductor quantum wells. This approach correctly describes the polariton inhomogeneous broadening in the low density limit, and treats scattering by disorder to all orders in the condensed regime. While the weak disorder changes the thermodynamic properties of the transition little, the effects of disorder in the condensed state are prominent in the excitations and can be seen in resonant Rayleigh scattering.Comment: 5 pages, 3 eps figures (published version

Polariton condensation with localised excitons and propagating photons

We estimate the condensation temperature for microcavity polaritons, allowing for their internal structure. We consider polaritons formed from localised excitons in a planar microcavity, using a generalised Dicke model. At low densities, we find a condensation temperature T_c \propto \rho, as expected for a gas of structureless polaritons. However, as T_c becomes of the order of the Rabi splitting, the structure of the polaritons becomes relevant, and the condensation temperature is that of a B.C.S.-like mean field theory. We also calculate the excitation spectrum, which is related to observable quantities such as the luminescence and absorption spectra.Comment: 5 pages, 4 figures, Corrected typos, replaced figure

Partitioning of the global fossil CO2 sink using a 19-year trend in atmospheric O2

O2/N2 is measured in the Cape Grim Air Archive (CGAA), a suite of tanks filled with background air at Cape Grim, Tasmania (40.7°S, 144.8°E) between April 1978 and January 1997. Derived trends are compared with published O2/N2 records and assessed against limits on interannual variability of net terrestrial exchanges imposed by trends of δ13C in CO2. Two old samples from 1978 and 1987 and eight from 1996/97 survive critical selection criteria and give a mean 19-year trend in δ(O2/N2) of -16.7 ± 0.5 per meg yr-1, implying net storage of +2.3 ± 0.7 GtC (1015 g carbon) yr-1 of fossil fuel CO2 in the oceans and +0.2 ± 0.9 GtC yr-1 in the terrestrial biosphere. The uptake terms are consistent for both O2/N2 and δ13C tracers if the mean 13C isotopic disequilibrium flux, combining terrestrial and oceanic contributions, is 93 ± 15 GtC ‰ yr-1. Copyright 1999 by the American Geophysical Union

The impact of contact tracing in clustered populations

The tracing of potentially infectious contacts has become an important part of the control strategy for many infectious diseases, from early cases of novel infections to endemic sexually transmitted infections. Here, we make use of mathematical models to consider the case of partner notification for sexually transmitted infection, however these models are sufficiently simple to allow more general conclusions to be drawn. We show that, when contact network structure is considered in addition to contact tracing, standard “mass action” models are generally inadequate. To consider the impact of mutual contacts (specifically clustering) we develop an improvement to existing pairwise network models, which we use to demonstrate that ceteris paribus, clustering improves the efficacy of contact tracing for a large region of parameter space. This result is sometimes reversed, however, for the case of highly effective contact tracing. We also develop stochastic simulations for comparison, using simple re-wiring methods that allow the generation of appropriate comparator networks. In this way we contribute to the general theory of network-based interventions against infectious disease

Extracting the time-dependent transmission rate from infection data via solution of an inverse ODE problem

The transmission rate of many acute infectious diseases varies significantly in time, but the underlying mechanisms are usually uncertain. They may include seasonal changes in the environment, contact rate, immune system response, etc. The transmission rate has been thought difficult to measure directly. We present a new algorithm to compute the time-dependent transmission rate directly from prevalence data, which makes no assumptions about the number of susceptible or vital rates. The algorithm follows our complete and explicit solution of a mathematical inverse problem for SIR-type transmission models. We prove that almost any infection profile can be perfectly fitted by an SIR model with variable transmission rate. This clearly shows a serious danger of overfitting such transmission models. We illustrate the algorithm with historic UK measles data and our observations support the common belief that measles transmission was predominantly driven by school contacts

Beyond clustering: mean-field dynamics on networks with arbitrary subgraph composition

Clustering is the propensity of nodes that share a common neighbour to be connected. It is ubiquitous in many networks but poses many modelling challenges. Clustering typically manifests itself by a higher than expected frequency of triangles, and this has led to the principle of constructing networks from such building blocks. This approach has been generalised to networks being constructed from a set of more exotic subgraphs. As long as these are fully connected, it is then possible to derive mean-field models that approximate epidemic dynamics well. However, there are virtually no results for non-fully connected subgraphs. In this paper, we provide a general and automated approach to deriving a set of ordinary differential equations, or mean-field model, that describes, to a high degree of accuracy, the expected values of system-level quantities, such as the prevalence of infection. Our approach offers a previously unattainable degree of control over the arrangement of subgraphs and network characteristics such as classical node degree, variance and clustering. The combination of these features makes it possible to generate families of networks with different subgraph compositions while keeping classical network metrics constant. Using our approach, we show that higher-order structure realised either through the introduction of loops of different sizes or by generating networks based on different subgraphs but with identical degree distribution and clustering, leads to non-negligible differences in epidemic dynamics

Targeting vaccination against novel infections: risk, age and spatial structure for pandemic influenza in Great Britain

The emergence of a novel strain of H1N1 influenza virus in Mexico in 2009, and its subsequent worldwide spread, has focused attention to the question of optimal deployment of mass vaccination campaigns. Here, we use three relatively simple models to address three issues of primary concern in the targeting of any vaccine. The advantages of such simple models are that the underlying assumptions and effects of individual parameters are relatively clear, and the impact of uncertainty in the parametrization can be readily assessed in the early stages of an outbreak. In particular, we examine whether targeting risk-groups, age-groups or spatial regions could be optimal in terms of reducing the predicted number of cases or severe effects; and how these targeted strategies vary as the epidemic progresses. We examine the conditions under which it is optimal to initially target vaccination towards those individuals within the population who are most at risk of severe effects of infection. Using age-structured mixing matrices, we show that targeting vaccination towards the more epidemiologically important age groups (5–14 year olds and then 15–24 year olds) leads to the greatest reduction in the epidemic growth and hence reduces the total number of cases. Finally, we consider how spatially targeting the vaccine towards regions of country worst affected could provide an advantage. We discuss how all three of these priorities change as both the speed at which vaccination can be deployed and the start of the vaccination programme is varied

Epidemics in Networks of Spatially Correlated Three-dimensional Root Branching Structures

Using digitized images of the three-dimensional, branching structures for root systems of bean seedlings, together with analytical and numerical methods that map a common 'SIR' epidemiological model onto the bond percolation problem, we show how the spatially-correlated branching structures of plant roots affect transmission efficiencies, and hence the invasion criterion, for a soil-borne pathogen as it spreads through ensembles of morphologically complex hosts. We conclude that the inherent heterogeneities in transmissibilities arising from correlations in the degrees of overlap between neighbouring plants, render a population of root systems less susceptible to epidemic invasion than a corresponding homogeneous system. Several components of morphological complexity are analysed that contribute to disorder and heterogeneities in transmissibility of infection. Anisotropy in root shape is shown to increase resilience to epidemic invasion, while increasing the degree of branching enhances the spread of epidemics in the population of roots. Some extension of the methods for other epidemiological systems are discussed.Comment: 21 pages, 8 figure

ENSO and the Carbon Cycle

This is the author accepted manuscript. The final version is available from the American Geophysical Union via the DOI in this recordObservational studies of atmospheric CO2, land ecosystems, and ocean processes show that variability in the carbon cycle is closely related with ENSO. Years with a warm anomaly in the tropical Pacific show a faster CO2 rise due to weaker land carbon sinks, particularly in the tropics, with a partial offset by stronger net uptake by oceans. The opposite happens in years with cool Pacific SST anomalies. This relationship holds for small ENSO SST anomalies as well as large ones and is robust enough for the annual CO2 growth rate anomaly to be highly predictable on the basis of SST observations and forecasts. Generally, variability in the land‐atmosphere carbon flux is mainly driven by physiological processes (photosynthesis and/or respiration), with a smaller contribution from fire. Fire was important in the 1997–1998 El Niño, making a major contribution to the CO2 rise, which can be viewed as anthropogenic in nature since the ignition was caused by humans. However, in the 2015–2016 El Niño event, the change in land carbon flux was mainly due to physiological processes, particularly reduced productivity. In the oceans, El Niño conditions involve decreased upwelling of carbon in the equatorial Pacific due to a weakening of the trade winds, causing this region to become a weaker sink of CO2, or near neutral if the El Niño event is strong. The year‐to‐year variations in the rate of CO2 rise can be successfully reconstructed and predicted on the basis of sea surface temperatures in the Pacific. ENSO‐CO2 relationships may also provide an emergent constraint on the strength of climate‐carbon cycle feedbacks on future anthropogenic climate change