Visualising Key Parameters of Climate Change
Falcão, António1; Lecomte, Pascal2; Petit, David3

Data visualisation is the key to both the understanding of complex science issues and to their communication. Convincing non-expert communities (the public, government, and industry) is increasingly the role of scientists, as research moves into an era where its outputs are of major relevance to our economies, in some cases determining future global policy on the environment, energy, the use of satellite resources, and many others. The present context is rather fragmented, with a concerted approach to data visualisation slow to emerge. Although there is considerable knowledge and skill related to scientific data visualisation, this tends to be concentrated in single research establishments, and even worse, small teams or individuals in these establishments. Results are often presented effectively, with a sound mastery of the visualisation technology. However, the context lacks coordination, and lacks standard methods of representing 3D datasets for communications purposes. The result is that entities external to this confined scientific community get a disjointed, sometimes contradictory message. In the context, visualisation of climate change key indicators present a particular challenge, given the multi-dimensional and time-varying feature of the data, as well as the large amount of different parameters and overwhelming size of the datasets. These data are usually geo-referenced, which calls for visualisation tools that can also handle this particular characteristic, while being able to provide an interactive and responsive manipulation of large datasets. The requirements for such a visualisation framework must encompass an integrated environment for the collaborative exploration and analysis of the data, and provide mechanisms to easily communicate the results to a wide variety of target audiences. Any new architecture must allow information to be presented in the same way across a domain / community, and must support effective and recognised ways of presenting this information to non-expert, decision making bodies within, or external to, the community. This is particularly important when the domain in question has a major influence on the way society behaves: energy, remote sensing, climate, meteorology, are only a few of a much longer list. It is therefore the case that we must try to satisfy at least two aims simultaneously: visualising the datasets of our own domains / communities in a standardised and interchangeable, and explaining the significance of these datasets to the people in society with a decisional role. It is also critical that the visualisation systems of the future make it much easier for different kinds of data from different sources to be combined more easily in order to support research and decision-making. Fulfilling these objectives will serve the needs of the community and society as a whole