Ornamental Mediterranean plants in the UK: root adaptations to hypoxia and anoxia

Mediterranean species are popular landscape plants in the UK and well suited to the predicted climate change scenarios of hotter, drier summers. What is less clear is how these species will respond to the more unpredictable rainfall patterns also anticipated, where soil water-logging may become more prevalent, especially in urban environments where soil sealing can restrict drainage. Pot experiments on flooding of four Mediterranean species (Cistus × hybridus, Lavandula angustifolia ‘Munstead’, Salvia officinalis and Stachys byzantina) showed that the effects of waterlogging were only severe when the temperature was high and flooding prolonged. All plants survived the flooding in winter, but during the summer a 17-day flood resulted in the death of 30-40% of the Salvia officinalis and Cistus × hybridus. To examine the response of roots to oxygen deprivation over a range of conditions from total absence of oxygen (anoxia), low oxygen (hypoxia) and full aeration, rooted cuttings of Salvia officinalis were grown in a hydroponic-based system and mixtures of oxygen and nitrogen gases bubbled through the media. Anoxia was found to reduce root development dramatically. When the plants were subjected to a period of hypoxia they responded by increasing the production of lateral roots close to the surface thus enabling them to acclimate to subsequent anoxia. This greatly increased their chances of survival.

Changing climate and the Irish landscape

The impacts of current and future changes in climate have been investigated for Irish vegetation. Warming has been observed over the last two decades, with impacts that are also strongly influenced by natural oscillations of the surrounding ocean, seen as fluctuations in the North Atlantic Oscillation and the Atlantic Multidecadal Oscillation. Satellite observations show that vegetation greenness increases in warmer years, a feature mirrored by increases in net ecosystem production observed for a grassland and a plantation forest. An ensemble of general circulation model simulations of future climates indicate temperature rises over the twenty-first century ranging from 1°C to 7°C, depending on future scenarios of greenhouse gas emissions. Net primary production is simulated to increase under all scenarios, due to the positive impacts of rising temperature, a modest rise of precipitation and rising carbon dioxide concentrations. In an optimistic scenario of reducing future emissions, CO2 concentration is simulated to flatten from about 2070, although temperatures continue to increase. Under this scenario Ireland could become a source of carbon, whereas under all other emission scenarios Ireland is a sink for carbon that may increase by up to three-fold over the twenty-first century. A likely and unavoidable impact of changing climate is the arrival of alien plant species, which may disrupt ecosystems and exert negative impacts on native biodiversity. Alien species arrive continually, with about 250 dated arrivals in the twentieth century. A simulation model indicates that this rate of alien arrival may increase by anything between two and ten times, dependent on the future climatic scenario, by 2050. Which alien species may become severely disruptive is, however, not known.

From ensemble forecasts to predictive distribution functions

The translation of an ensemble of model runs into a probability distribution is a common task in model-based prediction. Common methods for such ensemble interpretations proceed as if verification and ensemble were draws from the same underlying distribution, an assumption not viable for most, if any, real world ensembles. An alternative is to consider an ensemble as merely a source of information rather than the possible scenarios of reality. This approach, which looks for maps between ensembles and probabilistic distributions, is investigated and extended. Common methods are revisited, and an improvement to standard kernel dressing, called ‘affine kernel dressing’ (AKD), is introduced. AKD assumes an affine mapping between ensemble and verification, typically not acting on individual ensemble members but on the entire ensemble as a whole, the parameters of this mapping are determined in parallel with the other dressing parameters, including a weight assigned to the unconditioned (climatological) distribution. These amendments to standard kernel dressing, albeit simple, can improve performance significantly and are shown to be appropriate for both overdispersive and underdispersive ensembles, unlike standard kernel dressing which exacerbates over dispersion. Studies are presented using operational numerical weather predictions for two locations and data from the Lorenz63 system, demonstrating both effectiveness given operational constraints and statistical significance given a large sample.

Control without deception

Lying to participants offers an experimenter the enticing prospect of making “others' behaviour” a controlled variable, but is eschewed by experimental economists because it may pollute the pool of subjects. This paper proposes and implements a new experimental design, the Conditional Information Lottery, which offers all the benefits of deception without actually deceiving anyone. The design should be suitable for most economics experiments, and works by a modification of an already standard device, the Random Lottery incentive system. The deceptive scenarios of designs which use deceit are replaced with fictitious scenarios, each of which, from a subject's viewpoint, has a chance of being true. The design is implemented in a sequential play public good experiment prompted by Weimann's (1994) result, from a deceptive design, that subjects are more sensitive to freeriding than cooperation on the part of others. The experiment provides similar results to Weimann's, in that subjects are at least as cooperative when uninformed about others' behaviour as they are if reacting to high contributions. No deception is used and the data cohere well both internally and with other public goods experiments. In addition, simultaneous play is found to be more efficient than sequential play, and subjects contribute less at the end of a sequence than at the start. The results suggest pronounced elements of overconfidence, egoism and (biased) reciprocity in behaviour, which may explain decay in contributions in repeated play designs. The experiment shows there is a workable alternative to deception.

Future changes in atmospheric rivers and their implications for winter flooding in Britain

Within the warm conveyor belt of extra-tropical cyclones, atmospheric rivers (ARs) are the key synoptic features which deliver the majority of poleward water vapour transport, and are associated with episodes of heavy and prolonged rainfall. ARs are responsible for many of the largest winter floods in the mid-latitudes resulting in major socioeconomic losses; for example, the loss from United Kingdom (UK) flooding in summer/winter 2012 is estimated to be about $1.6 billion in damages. Given the well-established link between ARs and peak river flows for the present day, assessing how ARs could respond under future climate projections is of importance in gauging future impacts from flooding. We show that North Atlantic ARs are projected to become stronger and more numerous in the future scenarios of multiple simulations from five state-of-the-art global climate models (GCMs) in the fifth Climate Model Intercomparison Project (CMIP5). The increased water vapour transport in projected ARs implies a greater risk of higher rainfall totals and therefore larger winter floods in Britain, with increased AR frequency leading to more flood episodes. In the high emissions scenario (RCP8.5) for 2074–2099 there is an approximate doubling of AR frequency in the five GCMs. Our results suggest that the projected change in ARs is predominantly a thermodynamic response to warming resulting from anthropogenic radiative forcing.

Statistical-dynamical downscaling for wind energy potentials: evaluation and applications to decadal hindcasts and climate change projections

A statistical–dynamical downscaling (SDD) approach for the regionalization of wind energy output (Eout) over Europe with special focus on Germany is proposed. SDD uses an extended circulation weather type (CWT) analysis on global daily mean sea level pressure fields with the central point being located over Germany. Seventy-seven weather classes based on the associated CWT and the intensity of the geostrophic flow are identified. Representatives of these classes are dynamically downscaled with the regional climate model COSMO-CLM. By using weather class frequencies of different data sets, the simulated representatives are recombined to probability density functions (PDFs) of near-surface wind speed and finally to Eout of a sample wind turbine for present and future climate. This is performed for reanalysis, decadal hindcasts and long-term future projections. For evaluation purposes, results of SDD are compared to wind observations and to simulated Eout of purely dynamical downscaling (DD) methods. For the present climate, SDD is able to simulate realistic PDFs of 10-m wind speed for most stations in Germany. The resulting spatial Eout patterns are similar to DD-simulated Eout. In terms of decadal hindcasts, results of SDD are similar to DD-simulated Eout over Germany, Poland, Czech Republic, and Benelux, for which high correlations between annual Eout time series of SDD and DD are detected for selected hindcasts. Lower correlation is found for other European countries. It is demonstrated that SDD can be used to downscale the full ensemble of the Earth System Model of the Max Planck Institute (MPI-ESM) decadal prediction system. Long-term climate change projections in Special Report on Emission Scenarios of ECHAM5/MPI-OM as obtained by SDD agree well to the results of other studies using DD methods, with increasing Eout over northern Europe and a negative trend over southern Europe. Despite some biases, it is concluded that SDD is an adequate tool to assess regional wind energy changes in large model ensembles.

Can the Iberian floristic diversity withstand near-future climate change?

We assess how effectively the current network of protected areas (PAs) across the Iberian Peninsula will conserve plant diversity under near-future (2020) climate change. We computed 3267 MAXENT environmental niche models (ENMs) at 1-km spatial resolution for known Iberian plant species under two climate scenarios (1950-2000 baseline & 2020). To predict near-future species distributions across the network of Iberian and Balearics PAs, we combined projections of species’ ENMs with simulations of propagule dispersal by using six scenarios of annual dispersal rates (no dispersal, 0.1 km, 0.5 km, 1 km, 2 km and unlimited). Mined PA grid cell values for each species were then analyzed. We forecast 3% overall floristic diversity richness loss by 2020. The habitat of regionally extant species will contract on average by 13.14%. Niche movement exceeds 1 km per annum for 30% of extant species. While the southerly range margin of northern plant species retracts northward at 8.9 km per decade, overall niche movement is more easterly and westerly than northerly. There is little expansion of the northern range margin of southern plant species even under unlimited dispersal. Regardless of propagule dispersal rate, altitudinal niche movement of +25 m per decade is strongest for northern species. Pyrenees flora is most vulnerable to near-future climate change with many northern plant species responding by shifting their range westerly and easterly rather than northerly. Northern humid habitats will be particularly vulnerable to near-future climate change. Andalusian National Parks will become important southern biodiversity refuges. With limited human intervention (particularly in the Pyrenees), we conclude that floristic diversity in Iberian PAs should withstand near-future climate change.

Focus on environmental risks and migration: causes and consequences

Environmental change poses risks to societies, including disrupting social and economic systems such as migration. At the same time, migration is an effective adaptation to environmental and other risks. We review novel science on interactions between migration, environmental risks and climate change. We highlight emergent findings, including how dominant flows of rural to urban migration mean that populations are exposed to new risks within destination areas and the requirement for urban sustainability. We highlight the issue of lack of mobility as a major issue limiting the effectiveness of migration as an adaptation strategy and leading to potentially trapped populations. The paper presents scenarios of future migration that show both displacement and trapped populations over the incoming decades. Papers in the special issue bring new insights from demography, human geography, political science and environmental science to this emerging field.

PETS 2015: datasets and challenge

This paper presents the two datasets (ARENA and P5) and the challenge that form a part of the PETS 2015 workshop. The datasets consist of scenarios recorded by us- ing multiple visual and thermal sensors. The scenarios in ARENA dataset involve different staged activities around a parked vehicle in a parking lot in UK and those in P5 dataset involve different staged activities around the perimeter of a nuclear power plant in Sweden. The scenarios of each dataset are grouped into ‘Normal’, ‘Warning’ and ‘Alarm’ categories. The Challenge specifically includes tasks that account for different steps in a video understanding system: Low-Level Video Analysis (object detection and tracking), Mid-Level Video Analysis (‘atomic’ event detection) and High-Level Video Analysis (‘complex’ event detection). The evaluation methodology used for the Challenge includes well-established measures.

Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5

We present the global general circulation model IPSL-CM5 developed to study the long-term response of the climate system to natural and anthropogenic forcings as part of the 5th Phase of the Coupled Model Intercomparison Project (CMIP5). This model includes an interactive carbon cycle, a representation of tropospheric and stratospheric chemistry, and a comprehensive representation of aerosols. As it represents the principal dynamical, physical, and bio-geochemical processes relevant to the climate system, it may be referred to as an Earth System Model. However, the IPSL-CM5 model may be used in a multitude of configurations associated with different boundary conditions and with a range of complexities in terms of processes and interactions. This paper presents an overview of the different model components and explains how they were coupled and used to simulate historical climate changes over the past 150 years and different scenarios of future climate change. A single version of the IPSL-CM5 model (IPSL-CM5A-LR) was used to provide climate projections associated with different socio-economic scenarios, including the different Representative Concentration Pathways considered by CMIP5 and several scenarios from the Special Report on Emission Scenarios considered by CMIP3. Results suggest that the magnitude of global warming projections primarily depends on the socio-economic scenario considered, that there is potential for an aggressive mitigation policy to limit global warming to about two degrees, and that the behavior of some components of the climate system such as the Arctic sea ice and the Atlantic Meridional Overturning Circulation may change drastically by the end of the twenty-first century in the case of a no climate policy scenario. Although the magnitude of regional temperature and precipitation changes depends fairly linearly on the magnitude of the projected global warming (and thus on the scenario considered), the geographical pattern of these changes is strikingly similar for the different scenarios. The representation of atmospheric physical processes in the model is shown to strongly influence the simulated climate variability and both the magnitude and pattern of the projected climate changes.

The old metal-poor open cluster ESO 92-SC05: accreted from a dwarf galaxy ?

The study of old open clusters outside the solar circle can bring constraints on formation scenarios of the outer disc. In particular, accretion of dwarf galaxies has been proposed as a likely mechanism in the area. We use BVI photometry for determining fundamental parameters of the faint open cluster ESO 92-SC05. Colour-magnitude diagrams are compared with Padova isochrones, in order to derive age, reddening and distance. We derive a reddening E(B - V) = 0.17, and an old age of similar to 6.0 Gyr. It is one of the rare open clusters known to be older than 5 Gyr. A metallicity of Z similar to 0.004 or [M/H] similar to -0.7 is found. The rather low metallicity suggests that this cluster might be the result of an accretion episode of a dwarf galaxy.

Bayesian network classifiers: Beyond classification accuracy

This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing the tradeoff between the precise probability estimates produced by time consuming unrestricted Bayesian networks and the computational efficiency of Naive Bayes (NB) classifiers. The proposed approach is based on the fundamental principles of the Heuristic Search Bayesian network learning. The Markov Blanket concept, as well as a proposed ""approximate Markov Blanket"" are used to reduce the number of nodes that form the Bayesian network to be induced from data. Consequently, the usually high computational cost of the heuristic search learning algorithms can be lessened, while Bayesian network structures better than NB can be achieved. The resulting algorithms, called DMBC (Dynamic Markov Blanket Classifier) and A-DMBC (Approximate DMBC), are empirically assessed in twelve domains that illustrate scenarios of particular interest. The obtained results are compared with NB and Tree Augmented Network (TAN) classifiers, and confinn that both proposed algorithms can provide good classification accuracies and better probability estimates than NB and TAN, while being more computationally efficient than the widely used K2 Algorithm.

Avaliação dos impactos de uma proposta de escalonamento de horários em um campus universitário

O trabalho apresenta uma avaliação dos prováveis impactos do escalonamento dos horários das aulas noturnas em uma Universidade. Neste trabalho foi investigada, a preferência dos usuários quanto às possíveis alterações nos horários de entrada de aulas, e os impactos do escalonamento na rede viária no entorno do campus. O trabalho envolveu a construção de diversos cenários de alterações nos horários de início das aulas. Os cenários foram construídos visando atender as preferências da população universitária e reduzir a interferência do pico de chegada de alunos no sistema viário. A avaliação dos impactos destes cenários no tráfego foi realizada através de simulação, com a utilização do modelo TSIS/NETSIM. As análises foram baseadas em dois indicadores de desempenho: a velocidade média e o tempo de atraso médio na rede.

Fuel ethanol price drivers in the Atlantic basin : Brazil & the U.S. traders perspectives

Como a oportunidade de exportação de etanol combustível é muito recente, o agronegócio carece de literatura para formação de gestores e estrategistas. Este trabalho tem como objetivo descrever o mecanismo de formação de preços de etanol tanto no Brasil como nos Estados Unidos da America. Decupei as variáveis que impactam na formação de preços, custo de produção de etanol de cana-de-açúcar, custo da logística e desidratação no Caribe, custo do etanol de milho e fiz algumas análises de cenários de formação de preços das principais variáveis e consegui organizar este conhecimento em um gráfico que contém no eixo superior preço do etanol hidratado na usina no Brasil e no eixo inferior o preço do bushel de milho em Chicago cujas interceções nas retas da taxa do dólar e do preço do gás natural, fornecem no eixo Y o preço do produto no porto de Nova York, ficando uma visualização simples das possibilidades da janela de exportação. A metodologia permite assumir diferentes cenários de oferta, demanda e preços e estabelecer diferentes estratégias de comercialização.

Avaliação de projetos de investimento em cogeração de energia utilizando bagaço de cana-de-açúcar em biorrefinarias a partir do uso da teoria das opções reais

O cenário de continuo aumento do consumo de derivados do petróleo aliado a conscientização de que é necessário existir um equilíbrio com relação a exploração de recursos naturais e preservação do meio ambiente, vem impulsionando a busca por fontes alternativas de energia. Esse crescente interesse vem se aplicando a geração de energia a partir de biomassa da cana de açúcar, que vem se tornando cada vez mais comuns no Brasil, porém ainda existe um imenso potencial a ser explorado. Dentro deste contexto, se torna relevante a tomada de decisão de investimentos em projetos de cogeração e este trabalho busca incrementar a analise e tomada de decisão com a utilização da Teoria das Opções Reais, uma ferramenta de agregação de valor às incertezas, cabendo perfeitamente ao modelo energético brasileiro, onde grandes volatilidades do preço de energia são observadas ao longo dos anos. O objetivo do trabalho é determinar o melhor momento para uma biorrefinaria investir em unidades de cogeração. A estrutura do trabalho foi dividida em três cenários de porte de biorrefinarias, as de 2 milhões de capacidade de moagem de cana-de-açúcar por ano, as de 4 milhões e as de 6 milhões, visando assim ter uma representação amostral das biorrefinarias do país. Além disso, analisaram-se três cenários de volatilidade atrelados ao preço futuro de energia, dado que a principal variável de viabilização deste tipo de projeto é o preço de energia. As volatilidades foram calculadas de acordo com histórico do ambiente regulado, o dobro do ambiente regulado e projeção de PLD, representando, respectivamente, níveis baixos, médios e altos, de volatilidade do preço de energia. Após isso, foram elaboradas as nove árvores de decisão, que demonstram para os gestores de investimento que em um cenário de baixa volatilidade cria-se valor estar posicionado e ter a opção real de investir ou adiar investimento para qualquer porte de usina. No cenário de média volatilidade de preço, aconselha-se ao gestor estar posicionado em usinas de médio a grande porte para viabilização do investimento. Por fim, quando o cenário de preços é de grande volatilidade, tem-se um maior risco e existe a maior probabilidade de viabilização do investimento em usinas de grande porte.