COP21 climate negotiators' responses to climate model forecasts

Policymakers involved in climate change negotiations are key users of climate science. It is therefore vital to understand how to communicate scientific information most effectively to this group. We tested how a unique sample of policymakers and negotiators at the Paris COP21 conference update their beliefs on year 2100 global mean temperature increases in response to a statistical summary of climate models' forecasts. We randomized the way information was provided across participants using three different formats similar to those used in Intergovernmental Panel on Climate Change reports. In spite of having received all available relevant scientific information, policymakers adopted such information very conservatively, assigning it less weight than their own prior beliefs. However, providing individual model estimates in addition to the statistical range was more effective in mitigating such inertia. The experiment was repeated with a population of European MBA students who, despite starting from similar priors, reported conditional probabilities closer to the provided models' forecasts than policymakers. There was also no effect of presentation format in the MBA sample. These results highlight the importance of testing visualization tools directly on the population of interest

A person-time analysis of hospital activity among cancer survivors in England

BACKGROUND: There are around 2 million cancer survivors in the UK. This study describes the inpatient and day case hospital activity among the population of cancer survivors in England. This is one measure of the burden of cancer on the individual and the health service. METHODS: The national cancer registry data set for England (1990-2006) is linked to the NHS Hospital Episode Statistics (HES) database. Cohorts of survivors were defined as those people recorded in the cancer registry data with a diagnosis of breast, colorectal, lung or prostate cancer before 2007. The person-time of prevalence in 2006 for each cohort of survivors was calculated according to the cancer type, sex, age and time since diagnosis. The corresponding HES episodes of care in 2006 were used to calculate the person-time of admitted hospital care for each cohort of survivors. The average proportion of time spent in hospital by survivors in each cohort was calculated as the summed person-time of hospital activity divided by the summed person-time of prevalence. The analysis was conducted separately for cancer-related episodes and non-cancer-related episodes. RESULTS: Lung cancer survivors had the highest intensity of cancer-related hospital activity. For all cancers, cancer-related hospital activity was highest in the first year following diagnosis. Breast and prostate cancer survivors had peaks of cancer-related hospital activity in the relatively young and relatively old age groups. The proportion of time spent in hospital for non-cancer-related care was much lower than that for cancer-related care and increased gradually with age but was generally constant regardless of time since diagnosis. CONCLUSION: The person-time approach used in this study is more revealing than a simple enumeration of cancer survivors and hospital admissions. Hospital activity among cancer survivors is highest soon after diagnosis. The effect of age on the amount of hospital activity is different for each type of cancer

Exploring the science–policy interface on climate change: The role of the IPCC in informing local decision-making in the UK

Building on the Intergovernmental Panel on Climate Change’s (IPCC) review of how to make its Assessment Reports (ARs) more accessible in the future, the research reported here assesses the extent to which the ARs are a useful tool through which scientific advice informs local decision-making on climate change in the United Kingdom. Results from interviews with local policy representatives and three workshops with UK academics, practitioners and local decision makers are presented. Drawing on these data, we outline three key recommendations made by participants on how the IPCC ARs can be better utilized as a form of scientific advice to inform local decision-making on climate change. First, to provide more succinct summaries of the reports paying close attention to the language, content, clarity, context and length of these summaries; second, to better target and frame the reports from a local perspective to maximize engagement with local stakeholders; and third, to work with local decision makers to better understand how scientific advice on climate change is being incorporated in local decision-making. By adopting these, the IPCC would facilitate local decision-making on climate change and provide a systematic review of how its reports are being used locally. We discuss implications of these recommendations and their relevance to the wider debate within and outside the IPCC as to the most effective way the IPCC can more effectively tailor its products to user needs without endangering the robustness of its scientific findings. This article is published as part of a collection on scientific advice to government

Cognitive and psychological science insights to improve climate change data visualization

Visualization of climate data plays an integral role in the communication of climate change findings to both expert and non-expert audiences. The cognitive and psychological sciences can provide valuable insights into how to improve visualization of climate data based on knowledge of how the human brain processes visual and linguistic information. We review four key research areas to demonstrate their potential to make data more accessible to diverse audiences: directing visual attention, visual complexity, making inferences from visuals, and the mapping between visuals and language. We present evidence-informed guidelines to help climate scientists increase the accessibility of graphics to non-experts, and illustrate how the guidelines can work in practice in the context of Intergovernmental Panel on Climate Change graphics

Forecasting the duration of volcanic eruptions: an empirical probabilistic model

The ability to forecast future volcanic eruption durations would greatly benefit emergency response planning prior to and during a volcanic crises. This paper introduces a probabilistic model to forecast the duration of future and on-going eruptions. The model fits theoretical distributions to observed duration data and relies on past eruptions being a good indicator of future activity. A dataset of historical Mt. Etna flank eruptions is presented and used to demonstrate the model. The data has been compiled through critical examination of existing literature along with careful consideration of uncertainties on reported eruption start and end dates between the years 1300 AD and 2010 and data following 1600 is considered to be reliable and free of reporting biases. The distribution of eruption durations between the years 1600 and 1670 is found to be statistically different from that following 1670 and represents the culminating phase of a century-scale cycle. The forecasting model is run on two datasets ofMt. Etna flank eruption durations; 1600-2010 and 1670-2010. Each dataset is modelled using a log-logistic distribution with parameter values found by maximum likelihood estimation. Survivor function statistics are applied to the model distributions to forecast (a) the probability of an eruption exceeding a given duration, (b) the probability of an eruption that has already lasted a particular number of days exceeding a given total duration and (c) the duration with a given probability of being exceeded. Results show that excluding the 1600-1670 data has little effect of the forecasting model result, especially where short durations are involved. By assigning the terms ‘likely’ and ‘unlikely’ to probabilities of 66 % and 33 %, respectively the forecasting model is used on the 1600-2010 dataset to indicate that a future flank eruption on Mt. Etna would be likely to exceed 20 days (± 7 days) but unlikely to exceed 68 days (± 29 days). This model can easily be adapted for use on other highly active, well-documented volcanoes or for different duration data such as the duration of explosive episodes or the duration of repose periods between eruptions

Signaling probabilities in ambiguity: who reacts to vague news?

Ambiguity affects decisions of people who exhibit a distaste of and require a premium for dealing with it. Do ambiguity-neutral subjects completely disregard ambiguity and respond to any vague news? We couple decision-making in ambiguity with a preliminary information processing stage, where news is used to test prior beliefs and, possibly but not necessarily, update them. All decision-makers, including ambiguity-neutral, recognize and account for ambiguity at this stage; higher confidence makes ambiguity-neutral subjects less susceptible to vague news. In a two-color Ellsberg experiment with imprecise signals about the unknown probability of success they are less likely to respond to signals; the difference between them and non-neutral to ambiguity subjects vanishes for high precision signals. Less than 60% subjects choose the ambiguous urn, even for high communicated probabilities of success, suggesting many participants, especially ambiguity-neutral, discard vague news at the information processing stage. JEL: C90, D01, D81, as well as seminar participants at ETH-Zürich, University of Essex, University of Glasgow and University of Hamburg, and participants of iCare conference at HSE in Perm and JE on Ambiguity and Strategic Interactions at the University of Grenoble for helpful comments, suggestions and encouragement. All remaining errors are ours