A parametric physical model for the intracluster medium and its use in joint SZ/X-ray analyses of galaxy clusters

We present a parameterized model of the intra-cluster medium that is suitable for jointly analysing pointed observations of the Sunyaev-Zel'dovich (SZ) effect and X-ray emission in galaxy clusters. The model is based on assumptions of hydrostatic equilibrium, the Navarro, Frenk and White (NFW) model for the dark matter, and a softened power law profile for the gas entropy. We test this entropy-based model against high and low signal-to-noise mock observations of a relaxed and recently-merged cluster from N-body/hydrodynamic simulations, using Bayesian hyper-parameters to optimise the relative statistical weighting of the mock SZ and X-ray data. We find that it accurately reproduces both the global values of the cluster temperature, total mass and gas mass fraction (fgas), as well as the radial dependencies of these quantities outside of the core (r > kpc). For reference we also provide a comparison with results from the single isothermal beta model. We confirm previous results that the single isothermal beta model can result in significant biases in derived cluster properties.Comment: Published in MNRAS. 20 pages. 9 figure

Patient and public involvement in a study of multimedia clinical trial information for children, young people and families

© 2020 Sheridan, Preston, Stones, Ainsworth, Taylor, Challinor, Ainsworth, Martin-Kerry, Brady and Knapp. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY) 4.0. https://creativecommons.org/licenses/by/4.0/.There is increasing recognition of the need to involve the public in health research, but accounts of how best to achieve this are scarce. This article describes public involvement in the TRials Engagement in Children and Adolescents (TRECA) study, which is developing and evaluating multimedia information resources to inform children, young people and their familes about clinical trials. A dedicated group of young people with long-term health conditions and their parents met regularly throughout the study; further involvement was sought when specific input was required. Review of formal impact records and informal discussions highlighted how public involvement can positively influence research practice and the people involved. By detailing the methods of involvement used, this work also provides guidance for successfully implementing public involvement in research, and highlights challenges that should be considered in future research projects.Peer reviewe

The influence of magnetic fields on the Sunyaev Zel'dovich effect in clusters of galaxies

We study the influence of intracluster large scale magnetic fields on the thermal Sunyaev-Zel'dovich (SZ) effect. In a macroscopic approach we complete the hydrostatic equilibrium equation with the magnetic field pressure component. Comparing the resulting mass distribution with a standard one, we derive a new electron density profile. For a spherically symmetric cluster model, this new profile can be written as the product of a standard ($\beta$-) profile and a radius dependent function, close to unity, which takes into account the magnetic field strength. For non-cooling flow clusters we find that the observed magnetic field values can reduce the SZ signal by $\sim 10$ with respect to the value estimated from X-ray observations and the $\beta$-model. If a cluster harbours a cooling flow, magnetic fields tend to weaken the cooling flow influence on the SZ-effect.Comment: Accepted for publication in New Astronom

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects

Use of agro-climate ensembles for quantifying uncertainty and informing adaptation

Significant progress has been made in the use of ensemble agricultural and climate modelling, and observed data, to project future productivity and to develop adaptation options. An increasing number of agricultural models are designed specifically for use with climate ensembles, and improved methods to quantify uncertainty in both climate and agriculture have been developed. Whilst crop-climate relationships are still the most common agricultural study of this sort, on-farm management, hydrology, pests, diseases and livestock are now also examined. This paper introduces all of these areas of progress, with more detail being found in the subsequent papers in the special issue. Remaining scientific challenges are discussed, and a distinction is developed between projection- and utility-based approaches to agro-climate ensemble modelling. Recommendations are made regarding the manner in which uncertainty is analysed and reported, and the way in which models and data are used to make inferences regarding the future. A key underlying principle is the use of models as tools from which information is extracted, rather than as competing attempts to represent reality

Systemic risk and food security. Emerging trends and future avenues for research

The unanticipated international food price spike of 2008 has raised concerns about global food security. Might food systems lastingly fail to supply, trade, and distribute food? Might widespread unsustainable agricultural practices irreversibly alter ecosystems? Or might large scale food shortages trigger political unrest? To answer these questions, we reflect upon the concept of systemic risk and conduct a review of the literature on systemic risks and food security. First, we present the concept of systemic risk and current trends in systemic risk research. We then analyze contributions on systemic risk and food security. We first show that the literature has so far focused on a) agricultural production and correlated yield-losses, and on ways of pooling risk at regional or global-level, and b) the role of international trade in increasing or decreasing systemic risk. We then identify avenues for further research, highlighting the impact of intensive farming on ecosystems. Finally, we discuss the concept of systemic risk: we show that scholars need to be careful when assuming that there exists just one global food system; we show that systemic risk can be understood in various ways, beyond the domino effect paradigm. © 202