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Major explosions and paroxysms, respectively, have been the most powerful explosive phenomena at Stromboli in recent centuries. These two categories of explosions, although not sharply separable in terms of eruptive mechanisms and hazards, can produce ballistic projectiles affecting trails and observation sites in the summit area (both major explosions and paroxysms) as well as lower elevation areas of the volcano, down to the coast (paroxysms only). Time series analysis of reconstructed activity since the end of the XIXth Century highlights that such unordinary explosions are strongly non-homogeneous in time and often show notable temporal clustering. We perform a critical review of the volcanic catalogs produced by the Italian volcanological observatories in the last ~40 years. In this review, we evaluate the effect of uncertainties on the characterization of such major explosions, in contrast to intense ‘ordinary’ Strombolian explosions that do not eject large ballistic projectiles outside the Craters Terrace and the upper portion of Sciara del Fuoco. Where sufficient information is available for major explosions, we devise an analytical summary and explore comparative mapping of field data related to the dispersal areas of ballistic projectiles, taking into account relevant uncertainties. Using Monte Carlo simulations, we propose preliminary probabilistic hazard maps for areas potentially exposed to future events of this kind, varying the radius and angle-size of the circular sectors affected. We also evaluate lateral hazard modulation in terms of the density variability of ballistic projectiles per square meter of ground, based on literature review and spatial statistics of newly collected UAV data from the ballistic deposits of the 3rd July 2019 paroxysm on the slopes above Ginostra village. These new hazard maps, once combined with vulnerability and exposure data, allow preliminary quantitative estimates of individual risk exposure levels for guides, volcanologists, and tourists spending time in areas exposed to these unordinary events. Through a retrospective counterfactual analysis of the July 2019 eruption, we demonstrate how, in a future Strombolian paroxysm at another time of day, these risk rates might result in major casualty numbers.

Following a month of unrest in June-July 2019 and three months of effusive activity from late December 2020 to March 2021, La Soufrière volcano, St. Vincent, transitioned to a two-week period of explosions on 2021/04/09. During initial unrest, there was one working seismograph station on the volcano, providing the only information for tracking fluctuations in seismicity. Subsequently, full capability for locating volcanic earthquakes was achieved on 2021/01/27. Because events prior to this did not have reliable locations, unconventional data tracking approaches were adopted for assessing unrest evolution. Holistic inferences, suggested by combining these novel analyses with decades of knowledge of Lesser Antilles volcanoes, provided punctual evidential support for assessing the possibility that unrest could culminate in explosive activity. However, this case history is not offered as a paradigm for minimalist seismic monitoring at active volcanoes, notwithstanding information gains were possible. This said, evaluations of seismic moment release, using event duration magnitudes as proxies for moment magnitude, allowed the magma volume intruded or activated in the volcano to be estimated: 68 ± 14 × 10 6 m 3 DRE magma; value remarkably similar to a geological estimate of total erupted volume (71 ± 14 × 10 6 m 3 DRE), even down to matching uncertainties. Supplementary material at https://doi.org/10.6084/m9.figshare.c.6492941

Improved high-resolution paleo records of atmospheric carbon dioxide (CO2) concentrations and reconstructions of Earth’s surface temperature are available. We analyze one authoritative Pleistocene data set to explore how the climate sensitivity parameter S varies under different system states, using linear regression of mean annual surface temperature changes against CO2 forcing changes. On the whole data set, S = 2.04K/Wm^(-2) and CO2 forcing explains 64% of the variance in temperature. Partitioning the data by path (glaciation / de-glaciation); during de-glaciation episodes, S = 2.34K/Wm^(-2), explaining 75% of the temperature variance; during glaciations, S = 1.59K/Wm^(-2) explaining 48% of the temperature variance. Further partitioning into epochs before and after the Marine Isotope Stage 11 424 kaBP (thousand years before present); for de-glaciation paths after 424 kaBP, S = 2.63 K/Wm^(-2) explaining 83% of the variance. Partitioning into levels of CO2 concentration has much less explanatory power; 20% for concentrations above 210 ppmv and 2% for concentrations below 210 ppmv. These partitions conflate glaciation and de-glaciation episodes and pre/post 424 kaBP epochs. Possible process-related explanations for the path-related differences are conjectured. The common assumption that changes in temperature are proportional to changes in CO2 forcing constrains the regression intercept to pass through the origin and inflates the regression error to the extent that the regression model becomes a worse predictor of temperature changes than simply predicting the population mean for all values of CO2 forcing. The consequences of this assumption are substantive in the context of estimating future temperature trajectories and should be carefully weighed.

Significance Future sea level rise (SLR) poses serious threats to the viability of coastal communities, but continues to be challenging to project using deterministic modeling approaches. Nonetheless, adaptation strategies urgently require quantification of future SLR uncertainties, particularly upper-end estimates. Structured expert judgement (SEJ) has proved a valuable approach for similar problems. Our findings, using SEJ, produce probability distributions with long upper tails that are influenced by interdependencies between processes and ice sheets. We find that a global total SLR exceeding 2 m by 2100 lies within the 90% uncertainty bounds for a high emission scenario. This is more than twice the upper value put forward by the Intergovernmental Panel on Climate Change in the Fifth Assessment Report., Despite considerable advances in process understanding, numerical modeling, and the observational record of ice sheet contributions to global mean sea-level rise (SLR) since the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change, severe limitations remain in the predictive capability of ice sheet models. As a consequence, the potential contributions of ice sheets remain the largest source of uncertainty in projecting future SLR. Here, we report the findings of a structured expert judgement study, using unique techniques for modeling correlations between inter- and intra-ice sheet processes and their tail dependences. We find that since the AR5, expert uncertainty has grown, in particular because of uncertain ice dynamic effects. For a +2 °C temperature scenario consistent with the Paris Agreement, we obtain a median estimate of a 26 cm SLR contribution by 2100, with a 95th percentile value of 81 cm. For a +5 °C temperature scenario more consistent with unchecked emissions growth, the corresponding values are 51 and 178 cm, respectively. Inclusion of thermal expansion and glacier contributions results in a global total SLR estimate that exceeds 2 m at the 95th percentile. Our findings support the use of scenarios of 21st century global total SLR exceeding 2 m for planning purposes. Beyond 2100, uncertainty and projected SLR increase rapidly. The 95th percentile ice sheet contribution by 2200, for the +5 °C scenario, is 7.5 m as a result of instabilities coming into play in both West and East Antarctica. Introducing process correlations and tail dependences increases estimates by roughly 15%.

1.AbstractThe SARS-CoV-2 epidemic continues to have major impacts on children’s education, with schools required to implement infection control measures that have led to long periods of absence and classroom closures. We have developed an agent-based epidemiological model of SARS-CoV-2 transmission that allows us to quantify projected infection patterns within primary school classrooms, and related uncertainties; the basis of our approach is a contact model constructed using random networks, informed by structured expert judgement. The effectiveness of mitigation strategies are considered in terms of effectiveness at supressing infection outbreaks and limiting pupil absence. Covid-19 infections in schools in the UK in Autumn 2020 are re-examined and the model used for forecasting infection levels in autumn 2021, as the more infectious Delta-variant was emerging and school transmission thought likely to play a major role in an incipient new wave of the epidemic. Our results were in good agreement with available data. These findings indicate that testing-based surveillance of infections in the classroom population with isolation of positive cases is a more effective mitigation measure than bubble quarantine, both for reducing transmission in primary schools and for avoiding pupil absence, even accounting for insensitivity of self-administered tests. Bubble quarantine entails large numbers of pupils being absent from school, with only modest impact on classroom infection levels. However, maintaining reduced contact rates within the classroom can have a major beneficial impact for managing Covid-19 in school settings.

Co-auteur étranger; International audience; Future occurrence of explosive eruptive activity at Cotopaxi and Guagua Pichincha volcanoes, Ecuador, is assessed probabilistically, utilizing expert elicitation. Eight eruption types were considered for each volcano. Type event probabilities were evaluated for the next eruption at each volcano and for at least one of each type within the next 100 years. For each type, we elicited relevant eruption source parameters (duration, average plume height, and total tephra mass). We investigated the robustness of these elicited evaluations by deriving probability uncertainties using three expert scoring methods. For Cotopaxi, we considered both rhyolitic and andesitic magmas. Elicitation findings indicate that the most probable next eruption type is an andesitic hydrovolcanic/ash-emission (~ 26–44% median probability), which has also the highest median probability of recurring over the next 100 years. However, for the next eruption at Cotopaxi, the average joint probabilities for sub-Plinian or Plinian type eruption is of order 30–40%—a significant chance of a violent explosive event. It is inferred that any Cotopaxi rhyolitic eruption could involve a longer duration and greater erupted mass than an andesitic event, likely producing a prolonged emergency. For Guagua Pichincha, future eruption types are expected to be andesitic/dacitic, and a vulcanian event is judged most probable for the next eruption (median probability ~40–55%); this type is expected to be most frequent over the next 100 years, too. However, there is a substantial probability (possibly >40% in average) that the next eruption could be sub-Plinian or Plinian, with all that implies for hazard levels.

Risk assessment in communities or regions typically relies on the determination of hazard scenarios and an evaluation of their impact on local systems and structures. One of the challenges of risk assessment for infrastructure operators is how to identify the most critical scenarios that are likely to represent unacceptable risks to such assets in a given time frame. This study develops a novel approach for prioritizing hazards for the risk assessment of infrastructure. Central to the proposed methodology is an expert elicitation technique termed paired comparison which is based on a formal mathematical technique for quantifying the range and variance in the judgements of a group of stakeholders. The methodology is applied here to identify and rank natural and operational hazard scenarios that could cause serious disruption or have disastrous effects to the infrastructure in the transnational Øresund region over a period of five years. The application highlighted substantial divergences of views among the stakeholders on identifying a single ‘most critical’ natural or operational hazard scenario. Despite these differences, it was possible to flag up certain cases as critical among the natural hazard scenarios, and others among the operational hazards.

Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c. 5280319., The support of the Royal Society education policy unit and the RAMP initiative for COVID-19 are acknowledged. Funding to L.D. and E.B.-P. from MRC grants MR/V038613/1 and MC-PC-19067 is acknowledged., Personal contacts drive COVID-19 infections. After being closed (23 March 2020) UK primary schools partially re-opened on 1 June 2020 with social distancing and new risk mitigation strategies. We conducted a structured expert elicitation of teachers to quantify primary school contact patterns and how contact rates changed upon re-opening with risk mitigation measures in place. These rates, with uncertainties, were determined using a performance-based algorithm. We report mean number of contacts per day for four cohorts within schools, with associated 90% confidence ranges. Prior to lockdown, younger children (Reception and Year 1) made 15 contacts per day [range 8.35] within school, older children (Year 6) 18 contacts [range 5.55], teaching staff 25 contacts [range 4.55] and non-classroom staff 11 contacts [range 2.27]. After re-opening, the mean number of contacts was reduced by 53% for young children, 62% for older children, 60% for classroom staff and 64% for other staff. Contacts between teaching and non-teaching staff reduced by 80%. The distributions of contacts per person are asymmetric with heavy tail reflecting a few individuals with high contact numbers. Questions on risk mitigation and supplementary structured interviews elucidated how new measures reduced daily contacts in-school and contribute to infection risk reduction., Royal Society education policy unit, RAMP initiative for COVID-19, UK Research & Innovation (UKRI) Medical Research Council UK (MRC) MR/V038613/1 MC-PC-19067

There was no direct funding for this study but the support of the Royal Society education policy unit and the Royal Society RAMP initiative for COVID-19 are acknowledged. The `COVID-19 Mapping and Mitigation in Schools' (CoMMinS) project was supported by the Medical Research Council (grant no. MR/V0285545/1) and hosted within the UK MRC Integrative Epidemiology Unit at the University of Bristol (grant no. MC_UU_00011/5)., Drawing on risk methods from volcano crises, we developed a rapid COVID-19 infection model for the partial return of pupils to primary schools in England in June and July 2020, and a full return in September 2020. The model handles uncertainties in key parameters, using a stochastic re-sampling technique, allowing us to evaluate infection levels as a function of COVID-19 prevalence and projected pupil and staff headcounts. Assuming average national adult prevalence, for the first scenario (as at 1 June 2020) we found that between 178 and 924 [90% CI] schools would have at least one infected individual, out of 16 769 primary schools in total. For the second return (July), our estimate ranged between 336 (2%) and 1873 (11%) infected schools. For a full return in September 2020, our projected range was 661 (4%) to 3310 (20%) infected schools, assuming the same prevalence as for 5 June. If national prevalence fell to one-quarter of that, the projected September range would decrease to between 381 (2%) and 900 (5%) schools but would increase to between 2131 (13%) and 9743 (58%) schools if prevalence increased to 4× June level. When regional variations in prevalence and school size distribution were included in the model, a slight decrease in the projected number of infected schools was indicated, but uncertainty on estimates increased markedly. The latter model variant indicated that 82% of infected schools would be in areas where prevalence exceeded the national average and the probability of multiple infected persons in a school would be higher in such areas. Post hoc, our model projections for 1 September 2020 were seen to have been realistic and reasonable (in terms of related uncertainties) when data on schools' infections were released by official agencies following the start of the 2020/2021 academic year., Royal Society education policy unit, Royal Society RAMP initiative for COVID-19, UK Research & Innovation (UKRI) Medical Research Council UK (MRC) European Commission MR/V0285545/1, UK MRC Integrative Epidemiology Unit at the University of Bristol MC_UU_00011/5

In this chapter, I trace my introduction to the Classical Model and to the thoughts and philosophy of Roger Cooke, and then go on to recount some experiences of acting as a facilitator in many real world and sometimes crucial expert elicitations. The essence of my own history is that it took me a very long time to start to understand, and appreciate, the elegance of Roger’s Classical Model (Cooke 1991), its mathematical probity and how it is best deployed in application. I am sure I still haven’t fully mastered the probability calculus entirely, and don’t doubt others might quarrel with my preferred way of conducting elicitations that rely on the Classical Model, using a plenary workshop approach. This said, I can’t find, devise or even imagine, a better alternative to the Classical Model. And, on top of this extraordinary intellectual achievement, Roger Cooke has been a beneficent collaborator nonpareil, always willing to help me, and anyone else, avoid self-inflicted elicitation and probabilistic infelicities.

SummaryWe have constructed a COVID-19 infection hazard model for the return of pupils to the 16,769 state Primary Schools in England that takes into account uncertainties in model input parameters. The basic probabilistic model estimates likely number of primary schools with one or more infected persons under three different return-to-school circumstances. Inputs to the infection hazard model are: the inventory of children, teachers and support staff; the prevalence of COVID-19 in the general community including its spatial variation, and the ratio of adult susceptibility to that of children. Three scenarios of inventory are: the counts on 1stJune when schools re-opened to Nursery, Reception, Year 1 and Year 6 children, when approximately one-third of eligible children attended; a scenario assuming a full return of eligible children in those cohorts; and a return of all primary age children, scheduled for September. With a national average prevalence, we find that for the first scenario between 178 and 924 schools out of 16,769 in total (i.e. about 1% and 5.5% respectively) may have infected individuals present, expressed as a 90% credible interval. For the second scenario, the range is between 336 (2%) and 1873 (11%) schools with one (or more) infected persons, while for the third scenario the range is 661 (4%) to 3310 (20%) schools, assuming that the prevalence is the same as it was on 5thJune. The range decreases to between 381 (2%) and 900 (5%) schools with an infected person if prevalence is one-quarter that of 5thJune, and increases to between 2131 (13%) and 9743 (58%) schools for the situation where prevalence increases to 4 times the 5thJune level. Net prevalence of COVID-19 in schools is reduced relative to the general community because of the lower susceptibility of primary age children to infection. When regional variations in prevalence and school size distribution are taken into account there is a slight decrease in number of infected schools, but the uncertainty on these projected numbers increases markedly. The probability of having an infected school in a community is proportional to the local prevalence and school size. Analysis of a scenario equivalent to a full return to school with an average national prevalence of 1 in 1700 and spatial prevalence variations, estimated from data for late June, indicates 82% of infected schools would be located in areas where prevalence exceeds the national average. The probability of having multiple infected persons in a school increases markedly in high prevalence areas. Assuming national prevalence characteristic of early June, individual, operational and societal risk will increase if schools reopen fully in September due to both increases in numbers of children and the increased challenges of sustaining mitigation measures. Comparison between incidents in primary schools with positive tests in June and July and our estimates of number of infected schools indicates at least an order of magnitude difference. The much lower number of incidents reflects several factors, including effective reduction in transmission resulting from risk mitigation measure instigated by schools.

Stromboli volcano (Italy), always active with low energy explosive activity, is a very attractive place for visitors, scientists, and inhabitants of the island. Nevertheless, occasional more intense eruptions can present a serious danger. This study focuses on the modeling and estimation of their inter-event time and temporal rate. With this aim we constructed a new historical catalog of major explosions and paroxysms through a detailed review of scientific literature of the last ca. 140 years. The catalog includes the calendar date and phenomena descriptions for 180 explosive events, of which 36 were paroxysms. We evaluated the impact of the main sources of uncertainty affecting the historical catalog. In particular, we categorized as uncertain 45 major explosions that reportedly occurred before 1985 and tested the effect of excluding these events from our analysis. Moreover, after analyzing the entire record in the period [1879, 2020], we separately considered, as sequences, events in [1879, 1960] and in [1985, 2020] because of possible under recording issues in the period [1960, 1985]. Our new models quantify the temporal rate of major explosions and paroxysms as a function of time passed since the last event occurred. Recurrence hazard levels are found to be significantly elevated in the weeks and months following a major explosion or paroxysm, and then gradually decrease over longer periods. Computed hazard functions are also used to illustrate a methodology for estimating order-of-magnitude individual risk of fatality under certain basis conditions. This study represents a first quantitatively formal advance in determining long-term hazard levels at Stromboli.

Presentation (plus narrated version) at EGU General Assembly 2020: Sharing Geoscience Online, 4-8 May 2020, and comments available online at https://doi.org/10.5194/egusphere-egu2020-5747

Improved high-resolution paleo records of atmospheric carbon dioxide (CO2) concentrations and reconstructions of Earth’s surface temperature are available. We analyse one authoritative Pleistocene dataset to explore how the climate sensitivity parameter S varies under different system states, using linear regression of mean annual surface temperature changes against CO2 forcing changes. Data are partitioned by path (deglaciation or glaciation). On the whole data set, S = 2.04 K/Wm−2 and CO2 forcing explains 64 % of the variance in temperature. During deglaciation periods, S = 2.34 K/Wm−2, explaining 75 % of the temperature variance; during glaciations, S = 1.59 K/Wm−2 and explains 48 % of the temperature variance. Possible process-related explanations are conjectured.

The study focuses on the estimation and modeling of the temporal rates of major explosions and paroxysms at Stromboli volcano (also named small-scale and large-scale paroxysms respectively). The an...

Volcano-tectonic (VT) earthquake swarms are usual precursors to volcanic eruptions and their dynamic phenomenologies are an invaluable eruption forecasting tool. A power-law relationship between cumulative seismic moment release and intrusion volume has been proposed for both well injection sites and VT swarms. Here we compile data from 17 geodetically-studied VT swarms and use a Bayesian methodology to assess the relationship between cumulative moment release and subsurface volume change. We find that the empirical relationship derived from injection sites systematically underpredicts volume changes observed during VT swarms near volcanoes. A new relationship derived specifically from VT swarms provides a better fit, but large uncertainties mean that estimates of intruded volume range by three orders of magnitude (95% confidence) for a given cumulative seismic moment release. We further subdivide the dataset, and although the sample size is too small to meet conventional statistic hypothesis significance tests, qualitative inspection suggests that the seismic moment release is proportionally larger for unrest swarms which culminate in eruption. We conclude that the currently available dataset is too sparse and the uncertainties too large for use for reliably forecasting eruption magnitude; however, as the dataset of cases grows, further analysis along these same lines may provide improved diagnostic insights into eruption-related processes that may be driving seismic moment release in VT swarms.

In this study, we combine reconstructions of volcanological data sets and inputs from a structured expert judgment to produce a first long-term probability map for vent opening location for the next Plinian or sub-Plinian eruption of Somma-Vesuvio. In the past, the volcano has exhibited significant spatial variability in vent location; this can exert a significant control on where hazards materialize (particularly of pyroclastic density currents). The new vent opening probability mapping has been performed through (i) development of spatial probability density maps with Gaussian kernel functions for different data sets and (ii) weighted linear combination of these spatial density maps. The epistemic uncertainties affecting these data sets were quantified explicitly with expert judgments and implemented following a doubly stochastic approach. Various elicitation pooling metrics and subgroupings of experts and target questions were tested to evaluate the robustness of outcomes. Our findings indicate that (a) Somma-Vesuvio vent opening probabilities are distributed inside the whole caldera, with a peak corresponding to the area of the present crater, but with more than 50% probability that the next vent could open elsewhere within the caldera; (b) there is a mean probability of about 30% that the next vent will open west of the present edifice; (c) there is a mean probability of about 9.5% that the next medium-large eruption will enlarge the present Somma-Vesuvio caldera, and (d) there is a nonnegligible probability (mean value of 6–10%) that the next Plinian or sub-Plinian eruption will have its initial vent opening outside the present Somma-Vesuvio caldera.

This study presents new and revised data sets about the spatial distribution of past volcanic vents, eruptive fissures, and regional/local structures of the Somma-Vesuvio volcanic system (Italy). The innovative features of the study are the identification and quantification of important sources of uncertainty affecting interpretations of the data sets. In this regard, the spatial uncertainty of each feature is modeled by an uncertainty area, i.e., a geometric element typically represented by a polygon drawn around points or lines. The new data sets have been assembled as an updatable geodatabase that integrates and complements existing databases for Somma-Vesuvio. The data are organized into 4 data sets and stored as 11 feature classes (points and lines for feature locations and polygons for the associated uncertainty areas), totaling more than 1700 elements. More specifically, volcanic vent and eruptive fissure elements are subdivided into feature classes according to their associated eruptive styles: (i) Plinian and sub-Plinian eruptions (i.e., large- or medium-scale explosive activity); (ii) violent Strombolian and continuous ash emission eruptions (i.e., small-scale explosive activity); and (iii) effusive eruptions (including eruptions from both parasitic vents and eruptive fissures). Regional and local structures (i.e., deep faults) are represented as linear feature classes. To support interpretation of the eruption data, additional data sets are provided for Somma-Vesuvio geological units and caldera morphological features. In the companion paper, the data presented here, and the associated uncertainties, are used to develop a first vent opening probability map for the Somma-Vesuvio caldera, with specific attention focused on large or medium explosive events.

The detection of prion protein in widely used urine-derived fertility drugs has raised the possibility that prions from urine donors with (asymptomatic) prion disease could be present in these drugs. A high level of uncertainty exists regarding this issue. An international expert panel provided judgments related to prion disease transmission through fertility drug use in a structured expert elicitation. The elicitation gauged expert judgements about the uncertainty surrounding potential prion disease risks associated with urine-derived fertility drugs and emphasised the scientific ambiguity surrounding disease transmission risk factors associated with urine-derived fertility drugs. Group aggregated responses indicate that the theoretical risk of prion disease transmission with urine-derived fertility drugs was judged to be very low. The experts judged recombinant fertility drugs produced with bovine serum to possess 10-fold lower risk compared to urine-derived fertility drugs. Fertility drugs made without fetal bovine serum were judged to present a risk approximately 1,200 times lower compared to urine-derived counterparts. This elicitation indicates recombinant fertility drugs carry relatively less risk than urine-derived fertility drugs. However, the associated uncertainties are significant and pro-active surveillance of possible new routes of transmission of human prion disease warrants consideration of new scientific data as it becomes available.

IntroductionFood insecurity is associated with increased risk for several health conditions and with poor chronic disease management. Key determinants for household food insecurity are income and food costs. Whereas short-term household incomes are likely to remain static, increased food prices would be a significant driver of food insecurity.ObjectivesTo investigate food price drivers for household food security and its health consequences in the UK under scenarios of Deal and No-deal for Britain’s exit from the European Union. To estimate the 5% and 95% quantiles of the projected price distributions.DesignStructured expert judgement elicitation, a well-established method for quantifying uncertainty, using experts. In July 2018, each expert estimated the median, 5% and 95% quantiles of changes in price for 10 food categories under Brexit Deal and No-deal to June 2020 assuming Brexit had taken place on 29 March 2019. These were aggregated based on the accuracy and informativeness of the experts on calibration questions.ParticipantsTen specialists with expertise in food procurement, retail, agriculture, economics, statistics and household food security.ResultsWhen combined in proportions used to calculate Consumer Price Index food basket costs, median food price change for Brexit with a Deal is expected to be +6.1% (90% credible interval −3% to +17%) and with No-deal +22.5% (90% credible interval +1% to +52%).ConclusionsThe number of households experiencing food insecurity and its severity is likely to increase because of expected sizeable increases in median food prices after Brexit. Higher increases are more likely than lower rises and towards the upper limits, these would entail severe impacts. Research showing a low food budget leads to increasingly poor diet suggests that demand for health services in both the short and longer terms is likely to increase due to the effects of food insecurity on the incidence and management of diet-sensitive conditions.

We clarify key aspects of the evaluation, by de Vries and van de Wal (2015), of our expert elicitation paper on the contributions of ice sheet melting to sea level rise due to future global temperature rise scenarios (Bamber and Aspinall 2013), and extend the conversation with further analysis of their proposed approach for combining expert uncertainty judgments. We thank de Vries and van de Wal (2015: [VW15]) for their detailed consideration of Bamber and Aspinall (2013: [BA13]), and welcome this opportunity to clarify the work presented in BA13 and extend the analysis of VW15. The problem of finding a science-based quantification of uncertainty for poorly constrained physical models with large societal impacts deserves high priority in the climate community. This entails crossing discipline boundaries and will take that community outside its usual scientific comfort zone. We therefore salute the authors of VW13 for venturing into this alien terrain and welcome the opportunity to address some of the issues they raise. The present commentary discusses certain important and unique attributes of BA13’s expert weighting scheme that are misinterpreted in VW15, then addresses the Bconsensus distribution^ of VW15, their Blevel of consensus^, and the issue of lognormal fitting elicited data.

Uncertainties in natural hazard risk assessment are generally dominated by the sources arising from lack of knowledge or understanding of the processes involved. There is a lack of knowledge about frequencies, process representations, parameters, present and future boundary conditions, consequences and impacts, and the meaning of observations in evaluating simulation models. These are the epistemic uncertainties that can be difficult to constrain, especially in terms of event or scenario probabilities, even as elicited probabilities rationalized on the basis of expert judgements. This paper reviews the issues raised by trying to quantify the effects of epistemic uncertainties. Such scientific uncertainties might have significant influence on decisions that are made for risk management, so it is important to communicate the meaning of an uncertainty estimate and to provide an audit trail of the assumptions on which it is based. Some suggestions for good practice in doing so are made.

When a volcano emerges from dormancy into a phase of unrest, the civil protection authorities charged with managing societal risks have the unenviable responsibility of making difficult decisions balancing numerous competing societal, political and economic considerations. A volcano that is threatening to erupt requires sound risk assessments incorporating trusted hazard assessments that are timely, relevant and comprehensible. Foreseeable challenges arise when the inevitable uncertainties of hazard assessment and communication meet societal and political demands for certitude. In some regions that host volcanic hazards, it would be both realistic and prudent to adopt three working assumptions. The complex legal and administrative infrastructures of risk governance will be largely untested and possibly inadequate. Many volcano observatory scientists, and probably even more risk managers and at-risk individuals/communities, will have inadequate recent experience of the challenges of hazard communication during a period of unrest. And lastly, the scientists may also have inadequate practical experience of the needs and management capacities of the risk-mitigation decision makers with whom they must communicate. “Practice doesn’t make perfect. Practice reduces the imperfection.” (Beta 2011). If this statement is correct, volcanic unrest simulation exercises (VUSE) have a vital role to play within the complex processes of volcanic risk governance. Consistent with the broad approach of the Sendai Framework for Risk Reduction 2015–30, this chapter argues that practical knowledge of VUSE can and should be analysed and recorded so that key lessons can be shared for the widest possible benefit. This chapter investigates five recent simulation exercises and presents six complementary checklists based upon data, insights and practice pointers derived from those exercises. The use of checklists, supported by guidance notes, is commended as a pragmatic way to create, test and develop acceptable standards of governance practice. It is argued here that well planned and executed simulation exercises are capable of informing and motivating a wide range of risk governance stakeholders. They can identify process and individual shortcomings that can be mitigated. Simulation exercises can and should play a vital role in reducing volcanic risks.

Injection depth matters for induced earthquakes Wastewater injection has induced earthquakes in Oklahoma, but the relative importance of operational and geologic parameters in triggering such earthquakes is unclear. Hincks et al. developed an advanced Bayesian network to determine the interplay between these parameters in Oklahoma. The injection depth above the crystalline basement was the most important parameter when considering the potential for release of seismic energy. This modeling strategy may provide a way to improve forecasts of the impact of proposed regulatory changes on induced seismicity. Science , this issue p. 1251

Campi Flegrei is an active volcanic area situated in the Campanian Plain (Italy) and dominated by a resurgent caldera. The great majority of past eruptions have been explosive, variable in magnitude, intensity, and in their vent locations. In this hazard assessment study we present a probabilistic analysis using a variety of volcanological data sets to map the background spatial probability of vent opening conditional on the occurrence of an event in the foreseeable future. The analysis focuses on the reconstruction of the location of past eruptive vents in the last 15 ka, including the distribution of faults and surface fractures as being representative of areas of crustal weakness. One of our key objectives was to incorporate some of the main sources of epistemic uncertainty about the volcanic system through a structured expert elicitation, thereby quantifying uncertainties for certain important model parameters and allowing outcomes from different expert weighting models to be evaluated. Results indicate that past vent locations are the most informative factors governing the probabilities of vent opening, followed by the locations of faults and then fractures. Our vent opening probability maps highlight the presence of a sizeable region in the central eastern part of the caldera where the likelihood of new vent opening per kilometer squared is about 6 times higher than the baseline value for the whole caldera. While these probability values have substantial uncertainties associated with them, our findings provide a rational basis for hazard mapping of the next eruption at Campi Flegrei caldera.

Campi Flegrei (CF) is an example of an active caldera containing densely populated settlements at very high risk of pyroclastic density currents (PDCs). We present here an innovative method for assessing background spatial PDC hazard in a caldera setting with probabilistic invasion maps conditional on the occurrence of an explosive event. The method encompasses the probabilistic assessment of potential vent opening positions, derived in the companion paper, combined with inferences about the spatial density distribution of PDC invasion areas from a simplified flow model, informed by reconstruction of deposits from eruptions in the last 15 ka. The flow model describes the PDC kinematics and accounts for main effects of topography on flow propagation. Structured expert elicitation is used to incorporate certain sources of epistemic uncertainty, and a Monte Carlo approach is adopted to produce a set of probabilistic hazard maps for the whole CF area. Our findings show that, in case of eruption, almost the entire caldera is exposed to invasion with a mean probability of at least 5%, with peaks greater than 50% in some central areas. Some areas outside the caldera are also exposed to this danger, with mean probabilities of invasion of the order of 5–10%. Our analysis suggests that these probability estimates have location-specific uncertainties which can be substantial. The results prove to be robust with respect to alternative elicitation models and allow the influence on hazard mapping of different sources of uncertainty, and of theoretical and numerical assumptions, to be quantified.

The Classical Model (CM) is a performance-based approach for mathematically aggregating judgements from multiple experts, when reasoning about target questions under uncertainty. Individual expert performance is assessed against a set of seed questions, items from their field, for which the analyst knows or will know the true values, but the experts do not; the experts are, however, expected to provide accurate and informative distributional judgements that capture these values reliably. Performance is measured according to metrics for each expert’s statistical accuracy and informativeness, and the two metrics are convolved to determine a weight for each expert, with which to modulate their contribution when pooling them together for a final combined assessment of the desired target values. This chapter provides mathematical and practical details of the CM, including describing the method for measuring expert performance and discussing approaches for devising good seed questions.

Siting and designing technological facilities that need to be located in regions susceptible to major tectonic events require evaluation of the full range of knowledge and appraisal of plausible alternative models and interpretations, all within a probabilistic framework. This challenge has been clearly demonstrated by the extreme effects of the March 2011 Tohoku earthquake and is nowhere more problematic than in siting facilities with hazard potentials that last for thousands of years, such as geological repositories for radioactive waste. The use of formalised expert elicitation to help derive credible impact scenarios and their likelihoods of occurrence is being trialled for the first time in the Japanese geological disposal programme. This paper looks at the methodology for eliciting expert judgement under uncertainty, and explores the broader possibilities of this approach for tectonic hazard forecasting.

Following a cessation in eruptive activity it is important to understand how a volcano will behave in the future and when it may next erupt. Such an assessment can be based on the volcano's long-term pattern of behaviour and insights into its current state via monitoring observations. We present a Bayesian network that integrates these two strands of evidence to forecast future eruptive scenarios using expert elicitation. The Bayesian approach provides a framework to quantify the magmatic causes in terms of volcanic effects (i.e., eruption and unrest). In October 2013, an expert elicitation was performed to populate a Bayesian network designed to help forecast future eruptive (in-)activity at Soufrière Hills Volcano. The Bayesian network was devised to assess the state of the shallow magmatic system, as a means to forecast the future eruptive activity in the context of the long-term behaviour at similar dome-building volcanoes. The findings highlight coherence amongst experts when interpreting the current behaviour of the volcano, but reveal considerable ambiguity when relating this to longer patterns of volcanism at dome-building volcanoes, as a class. By asking questions in terms of magmatic causes, the Bayesian approach highlights the importance of using short-term unrest indicators from monitoring data as evidence in long-term forecasts at volcanoes. Furthermore, it highlights potential biases in the judgements of volcanologists and identifies sources of uncertainty in terms of magmatic causes rather than scenario-based outcomes.

Two project case histories for geological disposal of nuclear waste are discussed in this and a companion contribution ( Chapter 21 ) with emphasis on the application of formalized treatments of scientific uncertainties in siting considerations. In this chapter, a decision support approach is described, governed by a formalized basis for eliciting and aggregating expert judgments in a rational and auditable way when reasoning under scientific uncertainties. The Classical Model for structured expert judgment elicitation is the theme common to both case histories, serving as a means for determining inputs to a logic tree assessment of the potential evolution of multiple tectonic hazards over extended future periods in the present case history, and providing a way for characterizing potential impacts of climate change on repository performance in the second case history. This chapter first notes the emerging role of structured expert judgment in radioactive waste management and geological disposal facility siting decisions, and describes the properties and attributes of the elicitation method adopted for both case histories. The first, discussed here, is a contribution to a major geological disposal facility siting program in Japan, where expert judgments were elicited in a pioneering approach for parameterizing a logic tree assessment of site-specific impacts due to hazards arising from different long-term tectonic evolution scenarios. Some generic insights on expert elicitation are summarized in the context of facility siting considerations, and suggestions made for further applications, research and methodology developments.

This chapter presents the main methods and findings of a pilot study to demonstrate potential applications for Bayes Networks (BNs) in the management of uncertainty in the long-term safety case for a radioactive waste geological repository. The study, carried out with experts from the Radioactive Waste Management Directorate of NDA, demonstrates a method for determining uncertainty distributions for a probabilistic analysis of potential effects on geological barriers due to climate-driven changes in related processes and factors. Two complementary, decision support approaches are applied: (1) the Classical Model for expert judgment elicitation (introduced in Chapter 20 ) provides a structured basis for eliciting and aggregating expert judgments in a formal and auditable way and (2) a graphical Bayes Network is employed to assess the evidential worth of uncertain data, including expert judgments. The BN was constructed by performing a systematic review of the key features, events, and processes relating specifically to the single issue of the extent to which climate change could affect biosphere radiological risks due to a release from a repository; in this exploratory exercise, the approach was applied to four different climate change scenarios over the next 1 million years. Although the model developed for this pilot study was necessarily highly simplified and considers a limited number of alternative climate scenarios, it demonstrates a methodology for identifying states and scenarios not currently captured by the reference case model in the post-closure performance assessment that could lead to significant increases in risk and/or uncertainty. An important outcome is the ability to evaluate relative changes in key risk factors, compared to a defined base climate scenario with a known level of risk.

This study uses a formalized pooling of expert views on uncertainties in the future contributions of melting ice sheets to sea-level rise, using a structured elicitation approach. The median estimate obtained is substantially larger than that found by previous studies. Expert opinion is shown to be both very uncertain and undecided on the key issue of whether recent ice-sheet behaviour is a long-term trend or due to natural variability.

A key question for volcanic hazard assessment is the extent to which information can be exchanged between vol- canoes. This question is particularly pertinent to hazard forecasting for dome-building volcanoes, where effusive activity may persist for years to decades, and may be punctuated by periods of repose, and sudden explosive ac- tivity. Here we review historical eruptive activity of fifteen lava dome-building volcanoes over the past two cen- turies, with the goal of creating a hierarchy of exchangeable (i.e., similar) behaviours. Eruptive behaviour is classified using empirical observations that include patterns of SO2 flux, eruption style, and magma composition. We identify two eruptive regimes: (i) an episodic regime where eruptions are much shorter than intervening pe- riods of repose, and degassing is temporally correlated with lava effusion; and (ii) a persistent regime where erup- tions are comparable in length to periods of repose and gas emissions do not correlate with eruption rates. A corollary to these two eruptive regimes is that there are also two different types of repose: (i) inter-eruptive re- pose separates episodic eruptions, and is characterised by negligible gas emissions and (ii) intra-eruptive repose is observed in persistently active volcanoes, and is characterised by continuous gas emissions. We suggest that these different patterns of can be used to infer vertical connectivity within mush-dominated magmatic systems. We also note that our recognition of two different types of repose raises questions about traditional definitions of historical volcanism as a point process. This is important, because the ontology of eruptive activity (that is, the definition of volcanic activity in time) influences both analysis of volcanic data and, by extension, interpretations of magmatic processes. Our analysis suggests that one identifying exchangeable traits or behaviours provides a starting point for developing robust ontologies of volcanic activity. Moreover, by linking eruptive regimes to con- ceptual models of magmatic processes, we illustrate a path towards developing a conceptual framework not only for comparing data between different volcanoes but also for improving forecasts of eruptive activity.

A high degree of uncertainty exists for chronic wasting disease (CWD) transmission factors in farmed and wild cervids. Evaluating the factors is important as it helps to inform future risk management strategies. Expert opinion is often used to assist decision making in a number of health, science, and technology domains where data may be sparse or missing. Using the "Classical Model" of elicitation, a group of experts was asked to estimate the most likely values for several risk factors affecting CWD transmission. The formalized expert elicitation helped structure the issues and hence provide a rational basis for estimating some transmission risk factors for which evidence is lacking. Considered judgments regarding environmental transmission, latency of CWD transmission, management, and species barrier were provided by the experts. Uncertainties for many items were determined to be large, highlighting areas requiring more research. The elicited values may be used as surrogate values until research evidence becomes available.

Based on rapid analyses done in near real-time and in support of scientific advice, I present two cases of prima facie evidence for an earth tide triggering eect on earthquake activity during two volcanic eruption episodes: the first, in relation to large magnitude earthquakes, occurred during August2014 in the 2014 2015 Bar﷿arbunga-Holuhruan, Iceland, eruption; and the second concerns the timing of VT strings at the Soufriere Hills Volcano, Montserrat. In both cases, statistical testing of the hypothesis that the timings of the seismic events of interest and vertical earth tide phase were not correlated produced probabilities at or below a 5% significance level, suggesting rejection of the hypothesis can be argued. This note outlines the method used for computing earth tide amplitudes at the two volcanoes and the basis and results of the Schuster test for relative phase timings; some comments on the volcanological contexts are added. These findings suggest that further, more formal analysis may be warranted.

Carbon capture and geological storage (CCS) is identified within the portfolio of mitigation options for climate change. Each value chain activity of large scale integrated projects (capture, trans...

There is a high level of uncertainty surrounding the potential for iatrogenic prion transmission through transplantation, medical instrument reuse, blood transfusion, and blood product use due to a lack of evidence-based research on this important risk issue. A group of specialists was enlisted to evaluate some of the knowledge gaps in this area using the "Classical Model," a structured elicitation procedure for weighting and pooling expert judgment. The elicitation exercise was undertaken in March 2009 with 11 transmissible spongiform encephalopathy (TSE) experts who were first calibrated using a series of seed questions for which the answers are known; they were then asked to answer a number of target questions that are important for risk assessment purposes, but for which there remains high uncertainty at this time. The target questions focused on variant Creutzfeldt-Jakob disease (vCJD) prevalence, incubation times for vCJD, genetic susceptibility to prion disease, blood infectivity, prion reduction of blood and blood products, surgical instrument risks, and interspecies transmission of TSEs. The experts were also asked to perform pairwise risk rankings for 12 different potential routes of infection. Dura mater transplantation was seen as having the highest risk, while dental tissue grafts were viewed as presenting the lowest risk of iatrogenic transmission. The structured elicitation procedure provides a rational, auditable, and repeatable basis for obtaining useful information on prion disease risk issues, for which data are sparse.