Halving warming with stratospheric aerosol geoengineering moderates policy-relevant climate hazards

Stratospheric aerosol geoengineering is a proposal to artificially thicken the layer of reflective aerosols in the stratosphere and it is hoped that this may offer a means of reducing average climate changes. However, previous work has shown that it could not perfectly offset the effects of climate change and there is a concern that it may worsen climate impacts in some regions. One approach to evaluating this concern is to test whether the absolute magnitude of climate change at each location is significantly increased (exacerbated) or decreased (moderated) relative to the period just preceding deployment. In prior work it was found that halving warming with an idealized solar constant reduction would substantially reduce climate change overall, exacerbating change in a small fraction of places. Here, we test if this result holds for a more realistic representation of stratospheric aerosol geoengineering using the data from the geoengineering large ensemble (GLENS). Using a linearized scaling of GLENS we find that halving warming with stratospheric aerosols moderates important climate hazards in almost all regions. Only 1.3% of land area sees exacerbation of change in water availability, and regions that are exacerbated see wetting not drying contradicting the common assumption that solar geoengineering leads to drying in general. These results suggest that halving warming with stratospheric aerosol geoengineering could potentially reduce key climate hazards substantially while avoiding some problems associated with fully offsetting warming

Elicitation of US and Chinese expert judgments show consistent views on solar geoengineering

Expert judgments on solar geoengineering (SG) inform policy decisions and influence public opinions. We performed face-to-face interviews using formal expert elicitation methods with 13 US and 13 Chinese climate experts randomly selected from IPCC authors or supplemented by snowball sampling. We compare their judgments on climate change, SG research, governance, and deployment. In contrast to existing literature that often stress factors that might differentiate China from western democracies on SG, we found few significant differences between quantitative judgments of US and Chinese experts. US and Chinese experts differed on topics, such as desired climate scenario and the preferred venue for international regulation of SG, providing some insight into divergent judgments that might shape future negotiations about SG policy. We also gathered closed-form survey results from 19 experts with >10 publications on SG. Both expert groups supported greatly increased research, recommending SG research funding of ~5% on average (10th–90th percentile range was 1–10%) of climate science budgets compared to actual budgets of <0.3% in 2018. Climate experts chose far less SG deployment in future climate policies than did SG experts

What is macroecology?

The symposium 'What is Macroecology?' was held in London on 20 June 2012. The event was the inaugural meeting of the Macroecology Special Interest Group of the British Ecological Society and was attended by nearly 100 scientists from 11 countries. The meeting reviewed the recent development of the macroecological agenda. The key themes that emerged were a shift towards more explicit modelling of ecological processes, a growing synthesis across systems and scales, and new opportunities to apply macroecological concepts in other research fields

Purposeful selection of variables in logistic regression

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De Broglie Wavelength of a Nonlocal Four-Photon

Superposition is one of the most distinct features of quantum theory and has been demonstrated in numerous realizations of Young's classical double-slit interference experiment and its analogues. However, quantum entanglement - a significant coherent superposition in multiparticle systems - yields phenomena that are much richer and more interesting than anything that can be seen in a one-particle system. Among them, one important type of multi-particle experiments uses path-entangled number-states, which exhibit pure higher-order interference and allow novel applications in metrology and imaging such as quantum interferometry and spectroscopy with phase sensitivity at the Heisenberg limit or quantum lithography beyond the classical diffraction limit. Up to now, in optical implementations of such schemes lower-order interference effects would always decrease the overall performance at higher particle numbers. They have thus been limited to two photons. We overcome this limitation and demonstrate a linear-optics-based four-photon interferometer. Observation of a four-particle mode-entangled state is confirmed by interference fringes with a periodicity of one quarter of the single-photon wavelength. This scheme can readily be extended to arbitrary photon numbers and thus represents an important step towards realizable applications with entanglement-enhanced performance.Comment: 19 pages, 4 figures, submitted on November 18, 200

Simultaneous non-negative matrix factorization for multiple large scale gene expression datasets in toxicology

Non-negative matrix factorization is a useful tool for reducing the dimension of large datasets. This work considers simultaneous non-negative matrix factorization of multiple sources of data. In particular, we perform the first study that involves more than two datasets. We discuss the algorithmic issues required to convert the approach into a practical computational tool and apply the technique to new gene expression data quantifying the molecular changes in four tissue types due to different dosages of an experimental panPPAR agonist in mouse. This study is of interest in toxicology because, whilst PPARs form potential therapeutic targets for diabetes, it is known that they can induce serious side-effects. Our results show that the practical simultaneous non-negative matrix factorization developed here can add value to the data analysis. In particular, we find that factorizing the data as a single object allows us to distinguish between the four tissue types, but does not correctly reproduce the known dosage level groups. Applying our new approach, which treats the four tissue types as providing distinct, but related, datasets, we find that the dosage level groups are respected. The new algorithm then provides separate gene list orderings that can be studied for each tissue type, and compared with the ordering arising from the single factorization. We find that many of our conclusions can be corroborated with known biological behaviour, and others offer new insights into the toxicological effects. Overall, the algorithm shows promise for early detection of toxicity in the drug discovery process

Prediction of landing gear loads using machine learning techniques

This article investigates the feasibility of using machine learning algorithms to predict the loads experienced by a landing gear during landing. For this purpose, the results on drop test data and flight test data will be examined. This article will focus on the use of Gaussian process regression for the prediction of loads on the components of a landing gear. For the learning task, comprehensive measurement data from drop tests are available. These include measurements of strains at key locations, such as on the side-stay and torque link, as well as acceleration measurements of the drop carriage and the gear itself, measurements of shock absorber travel, tyre closure, shock absorber pressure and wheel speed. Ground-to-tyre loads are also available through measurements made with a drop test ground reaction platform. The aim is to train the Gaussian process to predict load at a particular location from other available measurements, such as accelerations, or measurements of the shock absorber. If models can be successfully trained, then future load patterns may be predicted using only these measurements. The ultimate aim is to produce an accurate model that can predict the load at a number of locations across the landing gear using measurements that are readily available or may be measured more easily than directly measuring strain on the gear itself (for example, these may be measurements already available on the aircraft, or from a small number of sensors attached to the gear). The drop test data models provide a positive feasibility test which is the basis for moving on to the critical task of prediction on flight test data. For this, a wide range of available flight test measurements are considered for potential model inputs (excluding strain measurements themselves), before attempting to refine the model or use a smaller number of measurements for the prediction

A risk-based framework for assessing the effectiveness of stratospheric aerosol geoengineering

Open Access journalCopyright: © 2014 Ferraro et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Geoengineering by stratospheric aerosol injection has been proposed as a policy response to warming from human emissions of greenhouse gases, but it may produce unequal regional impacts. We present a simple, intuitive risk-based framework for classifying these impacts according to whether geoengineering increases or decreases the risk of substantial climate change, with further classification by the level of existing risk from climate change from increasing carbon dioxide concentrations. This framework is applied to two climate model simulations of geoengineering counterbalancing the surface warming produced by a quadrupling of carbon dioxide concentrations, with one using a layer of sulphate aerosol in the lower stratosphere, and the other a reduction in total solar irradiance. The solar dimming model simulation shows less regional inequality of impacts compared with the aerosol geoengineering simulation. In the solar dimming simulation, 10% of the Earth's surface area, containing 10% of its population and 11% of its gross domestic product, experiences greater risk of substantial precipitation changes under geoengineering than under enhanced carbon dioxide concentrations. In the aerosol geoengineering simulation the increased risk of substantial precipitation change is experienced by 42% of Earth's surface area, containing 36% of its population and 60% of its gross domestic product.Natural Environment Research Council (NERC

Effect on life expectancy of temporal sequence in a multimorbidity cluster of psychosis, diabetes, and congestive heart failure among 1·7 million individuals in Wales with 20-year follow-up: a retrospective cohort study using linked data

BACKGROUND: To inform targeted public health strategies, it is crucial to understand how coexisting diseases develop over time and their associated impacts on patient outcomes and health-care resources. This study aimed to examine how psychosis, diabetes, and congestive heart failure, in a cluster of physical-mental health multimorbidity, develop and coexist over time, and to assess the associated effects of different temporal sequences of these diseases on life expectancy in Wales. METHODS: In this retrospective cohort study, we used population-scale, individual-level, anonymised, linked, demographic, administrative, and electronic health record data from the Wales Multimorbidity e-Cohort. We included data on all individuals aged 25 years and older who were living in Wales on Jan 1, 2000 (the start of follow-up), with follow-up continuing until Dec 31, 2019, first break in Welsh residency, or death. Multistate models were applied to these data to model trajectories of disease in multimorbidity and their associated effect on all-cause mortality, accounting for competing risks. Life expectancy was calculated as the restricted mean survival time (bound by the maximum follow-up of 20 years) for each of the transitions from the health states to death. Cox regression models were used to estimate baseline hazards for transitions between health states, adjusted for sex, age, and area-level deprivation (Welsh Index of Multiple Deprivation [WIMD] quintile). FINDINGS: Our analyses included data for 1 675 585 individuals (811 393 [48·4%] men and 864 192 [51·6%] women) with a median age of 51·0 years (IQR 37·0-65·0) at cohort entry. The order of disease acquisition in cases of multimorbidity had an important and complex association with patient life expectancy. Individuals who developed diabetes, psychosis, and congestive heart failure, in that order (DPC), had reduced life expectancy compared with people who developed the same three conditions in a different order: for a 50-year-old man in the third quintile of the WIMD (on which we based our main analyses to allow comparability), DPC was associated with a loss in life expectancy of 13·23 years (SD 0·80) compared with the general otherwise healthy or otherwise diseased population. Congestive heart failure as a single condition was associated with mean a loss in life expectancy of 12·38 years (0·00), and with a loss of 12·95 years (0·06) when preceded by psychosis and 13·45 years (0·13) when followed by psychosis. Findings were robust in people of older ages, more deprived populations, and women, except that the trajectory of psychosis, congestive heart failure, and diabetes was associated with higher mortality in women than men. Within 5 years of an initial diagnosis of diabetes, the risk of developing psychosis or congestive heart failure, or both, was increased. INTERPRETATION: The order in which individuals develop psychosis, diabetes, and congestive heart failure as combinations of conditions can substantially affect life expectancy. Multistate models offer a flexible framework to assess temporal sequences of diseases and allow identification of periods of increased risk of developing subsequent conditions and death. FUNDING: Health Data Research UK