A methodology for the risk assessment of climate variability and change under uncertainty

Existing methods for the assessment of the potential impacts of climate change in productive activities and sectors are usually limited to point estimates that do not consider the inherent variability and uncertainty of climatic and socioeconomic variables. This is a major drawback given that only a limited and potentially misleading estimation of risk can be expected when ignoring such determinant factors. In this paper, a new methodology is introduced that is capable of integrating the agent's beliefs and expert judgment into the assessment of the potential impacts of climate change in a quantitative manner by means of an objective procedure. The goal is to produce tailor-made information to assist decision-making under uncertainty in a way that is consistent with the current state of knowledge and the available subjective "expert" information. Time-charts of the evolution of different risk measures, that can be relevant for assisting decision-making and planning, can be constructed using this new methodology. This methodology is illustrated with a case study of coffee production in Mexico. Time-dependent probabilistic scenarios for coffee production and income, conditional on the agent's beliefs and expert judgment, are developed for the average producer under uncertain future conditions. It is shown that variability in production and income, generated by introducing climate variability and uncertainty are important factors affecting decision-making and the assessment of economic viability that are frequently ignored. The concept of Value at Risk, commonly applied in financial risk management, is introduced as a means for estimating the maximum expected loss for a previously chosen confidence level. Results are tailor-made for agents that have incomplete information and different beliefs. In this case study, the costs of climate change for coffee production in Veracruz are estimated to have a present value representing from 3 to 14 times the current annual value of coffee production in the state. © 2011 The Author(s)

Impact of impaired fractional flow reserve after coronary interventions on outcomes: a systematic review and meta-analysis

BACKGROUND: FFR is routinely used to guide percutaneous coronary interventions (PCI). Visual assessment of the angiographic result after PCI has limited efficacy. Even when the angiographic result seems satisfactory FFR after a PCI might be useful for identifying patients with a suboptimal interventional result and higher risk for poor clinical outcome who might benefit from additional procedures. The aim of this meta-analysis was to investigate available data of studies that examined clinical outcomes of patients with impaired vs. satisfactory fractional flow reserve (FFR) after percutaneous coronary interventions (PCI). METHODS: This meta-analysis was carried out according to the Cochrane Handbook for Systematic Reviews. The Mantel-Haenszel method using the fixed-effect meta-analysis model was used for combining the results. Studies were identified by searching the literature through mid-January, 2016, using the following search terms: fractional flow reserve, coronary circulation, after, percutaneous coronary intervention, balloon angioplasty, stent implantation, and stenting. Primary endpoint was the rate of major adverse cardiac events (MACE). Secondary endpoints included rates of death, myocardial infarction (MI), repeated revascularisation. RESULTS: Eight relevant studies were found including a total of 1337 patients. Of those, 492 (36.8 %) had an impaired FFR after PCI, and 853 (63.2 %) had a satisfactory FFR after PCI. Odds ratios indicated that a low FFR following PCI was associated with an impaired outcome: major adverse cardiac events (MACE, OR: 4.95, 95 % confidence interval [CI]: 3.39–7.22, p <0.001); death (OR: 3.23, 95 % CI: 1.19–8.76, p = 0.022); myocardial infarction (OR: 13.83, 95 % CI: 4.75–40.24, p <0.0001) and repeated revascularisation (OR: 4.42, 95 % CI: 2.73–7.15, p <0.0001). CONCLUSIONS: Compared to a satisfactory FFR, a persistently low FFR following PCI is associated with a worse clinical outcome. Prospective studies are needed to identify underlying causes, determine an optimal threshold for post-PCI FFR, and clarify whether simple additional procedures can influence the post-PCI FFR and clinical outcome. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12872-016-0355-7) contains supplementary material, which is available to authorized users

Paths to Innovation in Supply Chains: The Landscape of Future Research

This chapter presents a Strategic Research and Innovation Agenda for supply chain and it is the result of an intensive work jointly performed involving a wide network of stakeholders from discrete manufacturing, process industry and logistics sector to put forward a vision to strengthen European Supply Chains for the next decade. The work is based on matching visions from literature and from experts with several iterations between desk research and workshops, focus groups and interviews. The result is a detailed analysis of the supply chain strategies identified as most relevant for the next years and definition of the related research and innovation topics as future developments and steps for the full implementation of the strategies, thus proposing innovative and cutting-edge actions to be implemented based on technological development and organisational change

Robustness and uncertainties in global multivariate wind-wave climate projections

Understanding climate-driven impacts on the multivariate global wind-wave climate is paramount to effective offshore/coastal climate adaptation planning. However, the use of single-method ensembles and variations arising from different methodologies has resulted in unquantified uncertainty amongst existing global wave climate projections. Here, assessing the first coherent, community-driven, multi-method ensemble of global wave climate projections, we demonstrate widespread ocean regions with robust changes in annual mean significant wave height and mean wave period of 5–15% and shifts in mean wave direction of 5–15°, under a high-emission scenario. Approximately 50% of the world’s coastline is at risk from wave climate change, with ~40% revealing robust changes in at least two variables. Furthermore, we find that uncertainty in current projections is dominated by climate model-driven uncertainty, and that single-method modelling studies are unable to capture up to ~50% of the total associated uncertainty

A Bayesian hierarchical approach for spatial analysis of climate model bias in multi-model ensembles

Coupled atmosphere–ocean general circulation models are key tools to investigate climate dynamics and the climatic response to external forcings, to predict climate evolution and to generate future climate projections. Current general circulation models are, however, undisputedly affected by substantial systematic errors in their outputs compared to observations. The assessment of these so-called biases, both individually and collectively, is crucial for the models’ evaluation prior to their predictive use. We present a Bayesian hierarchical model for a unified assessment of spatially referenced climate model biases in a multi-model framework. A key feature of our approach is that the model quantifies an overall common bias that is obtained by synthesizing bias across the different climate models in the ensemble, further determining the contribution of each model to the overall bias. Moreover, we determine model-specific individual bias components by characterizing them as non-stationary spatial fields. The approach is illustrated based on the case of near-surface air temperature bias in the tropical Atlantic and bordering regions from a multi-model ensemble of historical simulations from the fifth phase of the Coupled Model Intercomparison Project. The results demonstrate the improved quantification of the bias and interpretative advantages allowed by the posterior distributions derived from the proposed Bayesian hierarchical framework, whose generality favors its broader application within climate model assessment

Structural decomposition of decadal climate prediction errors: A Bayesian approach

Decadal climate predictions use initialized coupled model simulations that are typically affected by a drift toward a biased climatology determined by systematic model errors. Model drifts thus reflect a fundamental source of uncertainty in decadal climate predictions. However, their analysis has so far relied on ad-hoc assessments of empirical and subjective character. Here, we define the climate model drift as a dynamical process rather than a descriptive diagnostic. A unified statistical Bayesian framework is proposed where a state-space model is used to decompose systematic decadal climate prediction errors into an initial drift, seasonally varying climatological biases and additional effects of co-varying climate processes. An application to tropical and south Atlantic sea-surface temperatures illustrates how the method allows to evaluate and elucidate dynamic interdependencies between drift, biases, hindcast residuals and background climate. Our approach thus offers a methodology for objective, quantitative and explanatory error estimation in climate predictions

Insights into elevation-dependent warming in the Tibetan Plateau-Himalayas from CMIP5 model simulations

We use the output of twenty-seven Global Climate Models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5) to investigate the temperature changes and their dependence on the elevation in the Tibetan Plateau, Himalaya and Karakoram mountains and in the surrounding areas in historical model simulations and in future projections. The aim of this study is to explore if and to what extent the CMIP5 models show elevation-dependent warming (EDW) in this part of the globe and to investigate what are the driving factors at play and their relative importance. Our results indicate that the models show enhanced rates of warming at higher elevations in the Tibetan Plateau-Himalayan region in the twentieth century, and this phenomenon is projected to strengthen by the end of the twenty-first century under a high-emission scenario. We find a nonlinear relationship between the warming rates and the elevation, for both the minimum and the maximum temperature: regions with temperatures below the freezing level of water show more warming than the regions with temperatures above, likely suggesting a key role of mechanisms involving water phase changes, the presence/absence of snow and the snow-albedo feedback. We consider the main variables simulated by the CMIP5 models whose change may be related to temperature changes at higher elevations. We find that changes in surface albedo, atmospheric humidity and downward longwave radiation are relevant factors for EDW in the Tibetan Plateau-Himalayas, with surface albedo being the leading driver