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The impending threat of global climate change and its regional manifestations is among the most important and urgent problems facing humanity. Society needs accurate and reliable estimates of changes in the probability of regional weather variations to develop science-based adaptation and mitigation strategies. Recent advances in weather prediction and in our understanding and ability to model the climate system suggest that it is both necessary and possible to revolutionize climate prediction to meet these societal needs. However, the scientific workforce and the computational capability required to bring about such a revolution is not available in any single nation. Motivated by the success of internationally funded infrastructure in other areas of science, this paper argues that, because of the complexity of the climate system, and because the regional manifestations of climate change are mainly through changes in the statistics of regional weather variations, the scientific and computational requirements to predict its behavior reliably are so enormous that the nations of the world should create a small number of multinational high-performance computing facilities dedicated to the grand challenges of developing the capabilities to predict climate variability and change on both global and regional scales over the coming decades. Such facilities will play a key role in the development of next-generation climate models, build global capacity in climate research, nurture a highly trained workforce, and engage the global user community, policy-makers, and stakeholders. We recommend the creation of a small number of multinational facilities with computer capability at each facility of about 20 peta-flops in the near term, about 200 petaflops within five years, and 1 exaflop by the end of the next decade. Each facility should have sufficient scientific workforce to develop and maintain the software and data analysis infrastructure. Such facilities will enable questions of what resolution, both horizontal and vertical, in atmospheric and ocean models, is necessary for more confident predictions at the regional and local level. Current limitations in computing power have placed severe limitations on such an investigation, which is now badly needed. These facilities will also provide the world's scientists with the computational laboratories for fundamental research on weather--climate interactions using 1-km resolution models and on atmospheric, terrestrial, cryospheric, and oceanic processes at even finer scales. Each facility should have enabling infrastructure including hardware, software, and data analysis support, and scientific capacity to interact with the national centers and other visitors. This will accelerate our understanding of how the climate system works and how to model it. It will ultimately enable the climate community to provide society with climate predictions, which are based on our best knowledge of science and the most advanced technology. [ABSTRACT FROM AUTHOR]

Weather and climate predictions are uncertain, because both forecast initial conditions and the computational representation of the known equations of motion are uncertain. Ensemble prediction systems provide the means to estimate the flow-dependent growth of uncertainty during a forecast. Sources of uncertainty must therefore be represented in such systems. In this paper, methods used to represent model uncertainty are discussed. It is argued that multimodel and related ensembles are vastly superior to corresponding single-model ensembles, but do not provide a comprehensive representation of model uncertainty. A relatively new paradigm is discussed, whereby unresolved processes are represented by computationally efficient stochastic-dynamic schemes. [ABSTRACT FROM AUTHOR]

The control of epidemic malaria is a priority for the international health community and specific targets for the early detection and effective control of epidemics have been agreed. Interannual climate variability is an important determinant of epidemics in parts of Africa where climate drives both mosquito vector dynamics and parasite development rates. Hence, skilful seasonal climate forecasts may provide early warning of changes of risk in epidemic-prone regions. Here we discuss the development of a system to forecast probabilities of anomalously high and low malaria incidence with dynamically based, seasonal-timescale, multi-model ensemble predictions of climate, using leading global coupled ocean–atmosphere climate models developed in Europe. This forecast system is successfully applied to the prediction of malaria risk in Botswana, where links between malaria and climate variability are well established, adding up to four months lead time over malaria warnings issued with observed precipitation and having a comparably high level of probabilistic prediction skill. In years in which the forecast probability distribution is different from that of climatology, malaria decision-makers can use this information for improved resource allocation. [ABSTRACT FROM AUTHOR]

Contact-free handling, trapping and precise transfer of small polarizable neutral objects in solution based on dielectrophoresis is demonstrated. Laser-direct-write-created concentric aluminum ring electrode microcages are generated coaxially on top of a glass capillary that enables the direct, fast and precise transfer of specimens to and from the cage’s trapping region. In this way, aggregates of pre-selected and desired size can be formed as demonstrated for pairs, triples and also larger clusters. Using a combination of two traps, which can freely be positioned with respect to each other, pairs of objects can be separated, keeping one of the constituents trapped. [ABSTRACT FROM AUTHOR]

A multi-model ensemble-based system for seasonal-to-interannual prediction has been developed in a joint European project known as DEMETER (Development of a European Multimodel Ensemble Prediction System for Seasonal to Interannual Prediction). The DEMETER system comprises seven global atmosphere–ocean coupled models, each running from an ensemble of initial conditions. Comprehensive hindcast evaluation demonstrates the enhanced reliability and skill of the multimodel ensemble over a more conventional single-model ensemble approach. In addition, innovative examples of the application of seasonal ensemble forecasts in malaria and crop yield prediction are discussed. The strategy followed in DEMETER deals with important problems such as communication across disciplines, downscaling of climate simulations, and use of probabilistic forecast information in the applications sector, illustrating the economic value of seasonal-to-interannual prediction for society as a whole. [ABSTRACT FROM AUTHOR]