Sensitivity of simulated regional Arctic climate to the choice of coupled model domain

The climate over the Arctic has undergone changes in recent decades. In order to evaluate the coupled response of the Arctic system to external and internal forcing, our study focuses on the estimation of regional climate variability and its dependence on large-scale atmospheric and regional ocean circulations. A global ocean–sea ice model with regionally high horizontal resolution is coupled to an atmospheric regional model and global terrestrial hydrology model. This way of coupling divides the global ocean model setup into two different domains: one coupled, where the ocean and the atmosphere are interacting, and one uncoupled, where the ocean model is driven by prescribed atmospheric forcing and runs in a so-called stand-alone mode. Therefore, selecting a specific area for the regional atmosphere implies that the ocean–atmosphere system can develop ‘freely’ in that area, whereas for the rest of the global ocean, the circulation is driven by prescribed atmospheric forcing without any feedbacks. Five different coupled setups are chosen for ensemble simulations. The choice of the coupled domains was done to estimate the influences of the Subtropical Atlantic, Eurasian and North Pacific regions on northern North Atlantic and Arctic climate. Our simulations show that the regional coupled ocean–atmosphere model is sensitive to the choice of the modelled area. The different model configurations reproduce differently both the mean climate and its variability. Only two out of five model setups were able to reproduce the Arctic climate as observed under recent climate conditions (ERA-40 Reanalysis). Evidence is found that the main source of uncertainty for Arctic climate variability and its predictability is the North Pacific. The prescription of North Pacific conditions in the regional model leads to significant correlation with observations, even if the whole North Atlantic is within the coupled model domain. However, the inclusion of the North Pacific area into the coupled system drastically changes the Arctic climate variability to a point where the Arctic Oscillation becomes an ‘internal mode’ of variability and correlations of year-to-year variability with observational data vanish. In line with previous studies, our simulations provide evidence that Arctic sea ice export is mainly due to ‘internal variability’ within the Arctic region. We conclude that the choice of model domains should be based on physical knowledge of the atmospheric and oceanic processes and not on ‘geographic’ reasons. This is particularly the case for areas like the Arctic, which has very complex feedbacks between components of the regional climate system.

Normality under uncertainty

Consider the demand for a good whose consumption be chosen prior to the resolution of uncertainty regarding income. How do changes in the distribution of income affect the demand for this good? In this paper we show that normality, is sufficient to guarantee that consumption increases of the Radon-Nikodym derivative of the new distribution with respect to the old is non-decreasing in the whole domain. However, if only first order stochastic dominance is assumed more structure must be imposed on preferences to guanantee the validity of the result. Finally a converse of the first result also obtains. If the change in measure is characterized by non-decreasing Radon-Nicodyn derivative, consumption of such a good will always increase if and only if the good is normal.

Uncertainty aversion and the optmal choice of portfolio

In this paper we apply the theory of declsion making with expected utility and non-additive priors to the choice of optimal portfolio. This theory describes the behavior of a rational agent who i5 averse to pure 'uncertainty' (as well as, possibly, to 'risk'). We study the agent's optimal allocation of wealth between a safe and an uncertain asset. We show that there is a range of prices at which the agent neither buys not sells short the uncertain asset. In contrast the standard theory of expected utility predicts that there is exactly one such price. We also provide a definition of an increase in uncertainty aversion and show that it causes the range of prices to increase.

Portfolio choice with a correlated background risk : theory and evidence

We extend the static portfolio choice problem with a small background risk to the case of small partially correlated background risks. We show that respecting the theories under which risk substitution appears, except for the independence of background risk, it is perfectly rational for the individual to increase his optimal exposure to portfolio risk when risks are partially negatively correlated. Then, we test empirically the hypothesis of risk substitutability using INSEE data on French households. We find that households respond by increasing their stockholdings in response to the increase in future earnings uncertainty. This conclusion is in contradiction with results obtained in other countries. So, in light of these results, our model provides an explanation to account for the lack of empirical consensus on cross-country tests of risk substitution theory that encompasses and criticises all of them.

Ucides cordatus cordatus (Linnaeus, 1763) (Crustacea, Decapoda): complete larval development under laboratory conditions and its systematic position

Six zoeal stages and the megalopa of the comestible crab Ucides cordatus cordatus (Linnaeus, 1763) are described and illustrated. The larvae were reared in the laboratory at temperature 25 ± 1ºC and water salinity of 24 ; duration of stages and survival rates were measured. Comparisons with the descriptions of the zoeal morphologic characters of Ocypodidae and Gecarcinidae permited to include definitively U. c. cordatus in the family Ocypodidae, subfamily Ocypodinae.

A comparison of deterministic, reliability-based and risk-based structural optimization under uncertainty

In this paper, the effects of uncertainty and expected costs of failure on optimum structural design are investigated, by comparing three distinct formulations of structural optimization problems. Deterministic Design Optimization (DDO) allows one the find the shape or configuration of a structure that is optimum in terms of mechanics, but the formulation grossly neglects parameter uncertainty and its effects on structural safety. Reliability-based Design Optimization (RBDO) has emerged as an alternative to properly model the safety-under-uncertainty part of the problem. With RBDO, one can ensure that a minimum (and measurable) level of safety is achieved by the optimum structure. However, results are dependent on the failure probabilities used as constraints in the analysis. Risk optimization (RO) increases the scope of the problem by addressing the compromising goals of economy and safety. This is accomplished by quantifying the monetary consequences of failure, as well as the costs associated with construction, operation and maintenance. RO yields the optimum topology and the optimum point of balance between economy and safety. Results are compared for some example problems. The broader RO solution is found first, and optimum results are used as constraints in DDO and RBDO. Results show that even when optimum safety coefficients are used as constraints in DDO, the formulation leads to configurations which respect these design constraints, reduce manufacturing costs but increase total expected costs (including expected costs of failure). When (optimum) system failure probability is used as a constraint in RBDO, this solution also reduces manufacturing costs but by increasing total expected costs. This happens when the costs associated with different failure modes are distinct. Hence, a general equivalence between the formulations cannot be established. Optimum structural design considering expected costs of failure cannot be controlled solely by safety factors nor by failure probability constraints, but will depend on actual structural configuration. (c) 2011 Elsevier Ltd. All rights reserved.

Cost Sharing under Uncertainty: An Algorithmic Approach to Cooperative Interval-Valued Games

Recently, Branzei, Dimitrov, and Tijs (2003) introduced cooperative interval-valued games. Among other insights, the notion of an interval core has been coined and proposed as a solution concept for interval-valued games. In this paper we will present a general mathematical programming algorithm which can be applied to find an element in the interval core. As an example, we discuss lot sizing with uncertain demand to provide an application for interval-valued games and to demonstrate how interval core elements can be computed. Also, we reveal that pitfalls exist if interval core elements are computed in a straightforward manner by considering the interval borders separately.

Networks of Information Exchange Under Uncertainty: The Case of Unconventional Gas Development in the United Kingdom

Modern policy-making is increasingly influenced by different types of uncertainty. Political actors are supposed to behave differently under the context of uncertainty then in “usual” decision-making processes. Actors exchange information in order to convince other actors and decision-makers, to coordinate their lobbying activities and form coalitions, and to get information and learn on the substantive issue. The literature suggests that preference similarity, social trust, perceived power and functional interdependence are particularly important drivers of information exchange. We assume that social trust as well as being connected to scientific actors is more important under uncertainty than in a setting with less uncertainty. To investigate information exchange under uncertainty analyze the case of unconventional shale gas development in the UK from 2008 till 2014. Our study will rely on statistical analyses of survey data on a diverse set of actors dealing with shale gas development and regulation in the UK.

Policy Instrument Selection under Uncertainty: The Case of Micropollution Regulation

The paper addresses the question of which factors drive the formation of policy preferences when there are remaining uncertainties about the causes and effects of the problem at stake. To answer this question we examine policy preferences reducing aquatic micropollutants, a specific case of water protection policy and different actor groups (e.g. state, science, target groups). Here, we contrast two types of policy preferences: a) preventive or source-directed policies, which mitigate pollution in order to avoid contact with water; and b) reactive or end-of-pipe policies, which filter water already contaminated by pollutants. In a second step, we analyze the drivers for actors’ policy preferences by focusing on three sets of explanations, i.e. participation, affectedness and international collaborations. The analysis of our survey data, qualitative interviews and regression analysis of the Swiss political elite show that participation in the policy-making process leads to knowledge exchange and reduces uncertainties about the policy problem, which promotes preferences for preventive policies. Likewise, actors who are affected by the consequences of micropollutants, such as consumer or environmental associations, opt for anticipatory policies. Interestingly, we find that uncertainties about the effectiveness of preventive policies can promote preferences for end-of-pipe policies. While preventive measures often rely on (uncertain) behavioral changes of target groups, reactive policies are more reliable when it comes to fulfilling defined policy goals. Finally, we find that in a transboundary water management context, actors with international collaborations prefer policies that produce immediate and reliable outcomes.

Collaboration patterns, external shocks and uncertainty: Swiss nuclear energy politics before and after Fukushima

Energy shocks like the Fukushima accident can have important political consequences. This article examines their impact on collaboration patterns between collective actors in policy processes. It argues that external shocks create both behavioral uncertainty, meaning that actors do not know about other actors' preferences, and policy uncertainty on the choice and consequences of policy instruments. The context of uncertainty interacts with classical drivers of actor collaboration in policy processes. The analysis is based on a dataset comprising interview and survey data on political actors in two subsequent policy processes in Switzerland and Exponential Random Graph Models for network data. Results first show that under uncertainty, collaboration of actors in policy processes is less based on similar preferences than in stable contexts, but trust and knowledge of other actors are more important. Second, under uncertainty, scientific actors are not preferred collaboration partners.

Multi-agent Architecture for Heterogeneous Reasoning under Uncertainty Combining MSBN and Ontologies in Distributed Network Diagnosis

This article proposes a MAS architecture for network diagnosis under uncertainty. Network diagnosis is divided into two inference processes: hypothesis generation and hypothesis confirmation. The first process is distributed among several agents based on a MSBN, while the second one is carried out by agents using semantic reasoning. A diagnosis ontology has been defined in order to combine both inference processes. To drive the deliberation process, dynamic data about the influence of observations are taken during diagnosis process. In order to achieve quick and reliable diagnoses, this influence is used to choose the best action to perform. This approach has been evaluated in a P2P video streaming scenario. Computational and time improvements are highlight as conclusions.

Supporting decision making under uncertainty: Development of a participatory integrated model for water management in the middle Guadiana river basin.

Following the Integrated Water Resources Management approach, the European Water Framework Directive demands Member States to develop water management plans at the catchment level. Those plans have to integrate the different interests and must be developed with stakeholder participation. To face these requirements, managers need tools to assess the impacts of possible management alternatives on natural and socio-economic systems. These tools should ideally be able to address the complexity and uncertainties of the water system, while serving as a platform for stakeholder participation. The objective of our research was to develop a participatory integrated assessment model, based on the combination of a crop model, an economic model and a participatory Bayesian network, with an application in the middle Guadiana sub-basin, in Spain. The methodology is intended to capture the complexity of water management problems, incorporating the relevant sectors, as well as the relevant scales involved in water management decision making. The integrated model has allowed us testing different management, market and climate change scenarios and assessing the impacts of such scenarios on the natural system (crops), on the socio-economic system (farms) and on the environment (water resources). Finally, this integrated assessment modelling process has allowed stakeholder participation, complying with the main requirements of current European water laws.

Participatory modelling to support decision making in water management under uncertainty: Two comparative case studies in the Guadiana river basin, Spain.

A participatory modelling process has been conducted in two areas of the Guadiana river (the upper and the middle sub-basins), in Spain, with the aim of providing support for decision making in the water management field. The area has a semi-arid climate where irrigated agriculture plays a key role in the economic development of the region and accounts for around 90% of water use. Following the guidelines of the European Water Framework Directive, we promote stakeholder involvement in water management with the aim to achieve an improved understanding of the water system and to encourage the exchange of knowledge and views between stakeholders in order to help building a shared vision of the system. At the same time, the resulting models, which integrate the different sectors and views, provide some insight of the impacts that different management options and possible future scenarios could have. The methodology is based on a Bayesian network combined with an economic model and, in the middle Guadiana sub-basin, with a crop model. The resulting integrated modelling framework is used to simulate possible water policy, market and climate scenarios to find out the impacts of those scenarios on farm income and on the environment. At the end of the modelling process, an evaluation questionnaire was filled by participants in both sub-basins. Results show that this type of processes are found very helpful by stakeholders to improve the system understanding, to understand each others views and to reduce conflict when it exists. In addition, they found the model an extremely useful tool to support management. The graphical interface, the quantitative output and the explicit representation of uncertainty helped stakeholders to better understand the implications of the scenario tested. Finally, the combination of different types of models was also found very useful, as it allowed exploring in detail specific aspects of the water management problems.

Optimization methods for the operating room management under uncertainty: stochastic programming vs. decomposition approach

The operating theatres are the engine of the hospitals; proper management of the operating rooms and its staff represents a great challenge for managers and its results impact directly in the budget of the hospital. This work presents a MILP model for the efficient schedule of multiple surgeries in Operating Rooms (ORs) during a working day. This model considers multiple surgeons and ORs and different types of surgeries. Stochastic strategies are also implemented for taking into account the uncertain in surgery durations (pre-incision, incision, post-incision times). In addition, a heuristic-based methods and a MILP decomposition approach is proposed for solving large-scale ORs scheduling problems in computational efficient way. All these computer-aided strategies has been implemented in AIMMS, as an advanced modeling and optimization software, developing a user friendly solution tool for the operating room management under uncertainty.