In recent decades, science has experienced a revolutionary shift. The development and extensive application of computer modelling and simulation has transformed the knowledge-making practices of scientific fields as diverse as astro-physics, genetics, robotics and demography. This epistemic transformation has brought with it a simultaneous heightening of political relevance and a renewal of international policy agendas, raising crucial questions about the nature and application of simulation knowledges throughout public policy. Through a diverse range of case studies, spanning over a century of theoretical and practical developments in the atmospheric and environmental sciences, this book argues that computer modelling and simulation have substantially changed scientific and cultural practices and shaped the emergence of novel 'cultures of prediction'. Making an innovative, interdisciplinary contribution to understanding the impact of computer modelling on research practice, institutional configurations and broader cultures, this volume will be essential reading for anyone interested in the past, present and future of climate change and the environmental sciences.

In recent decades, science has experienced a revolutionary shift. The development and extensive application of computer modelling and simulation has transformed the knowledge-making practices of scientific fields as diverse as astro-physics, genetics, robotics and demography. This epistemic transformation has brought with it a simultaneous heightening of political relevance and a renewal of international policy agendas, raising crucial questions about the nature and application of simulation knowledges throughout public policy. Through a diverse range of case studies, spanning over a century of theoretical and practical developments in the atmospheric and environmental sciences, this book argues that computer modelling and simulation have substantially changed scientific and cultural practices and shaped the emergence of novel 'cultures of prediction'. Making an innovative, interdisciplinary contribution to understanding the impact of computer modelling on research practice, institutional configurations and broader cultures, this volume will be essential reading for anyone interested in the past, present and future of climate change and the environmental sciences.

The debate on how mankind should respond to climate change is diverse, as the appropriate strategy depends on global as well as local circumstances. As scientists are denied the possibility of conducting experiments with the real climate, only climate models can give insights into man-induced climate change, by experimenting with digital climates under varying conditions and by extrapolating past and future states into the future. But the 'nature' of models is a purely representational one. A model is good if it is believed to represent the relevant processes of a natural system well. However, a model and its results, in particular in the case of climate models which interconnect countless hypotheses, is only to some extent testable, although an advanced infrastructure of evaluation strategies has been developed involving strategies of model intercomparison, ensemble prognoses, uncertainty metrics on the system and component levels. The complexity of climate models goes hand in hand with uncertainties, but uncertainty is in conflict with socio-political expectations. However, certain predictions belong to the realm of desires and ideals rather than to applied science. Today's attempt to define and classify uncertainty in terms of likelihood and confidence reflect this awareness of uncertainty as an integral part of human knowledge, in particular on knowledge about possible future developments. The contributions in this book give a first hand insight into scientific strategies in dealing with uncertainty by using simulation models and into social, political and economical requirements in future projections on climate change. Do these strategies and requirements meet each other or fail? The debate on how mankind should respond to climate change is diverse, as the appropriate strategy depends on global as well as local circumstances. As scientists are denied the possibility of conducting experiments with the real climate, only climate models can give insights into man-induced climate change, by experimenting with digital climates under varying conditions and by extrapolating past and future states into the future. But the 'nature' of models is a purely representational one. A model is good if it is believed to represent the relevant processes of a natural system well. However, a model and its results, in particular in the case of climate models which interconnect countless hypotheses, is only to some extent testable, although an advanced infrastructure of evaluation strategies has been developed involving strategies of model intercomparison, ensemble prognoses, uncertainty metrics on the system and component levels. The complexity of climate models goes hand in hand with uncertainties, but uncertainty is in conflict with socio-political expectations. However, certain predictions belong to the realm of desires and ideals rather than to applied science. Today's attempt to define and classify uncertainty in terms of likelihood and confidence reflect this awareness of uncertainty as an integral part of human knowledge, in particular on knowledge about possible future developments. The contributions in this book give a first hand insight into scientific strategies in dealing with uncertainty by using simulation models and into social, political and economical requirements in future projections on climate change. Do these strategies and requirements meet each other or fail? Gabriele Gramelsberger is Principal Investigator of the Collaborative Research Project is Principal Investigator of the Collaborative Research Project

The debate on how mankind should respond to climate change is diverse, as the appropriate strategy depends on global as well as local circumstances.

As scientists are denied the possibility of conducting experiments with the real climate, only climate models can give insights into man-induced climate change, by experimenting with digital climates under varying conditions and by extrapolating past and future states into the future.

But the nature of models is a purely representational one. A model is good if it is believed to represent the relevant processes of a natural system well. However, a model and its results, in particular in the case of climate models which interconnect countless hypotheses, is only to some extent testable, although an advanced infrastructure of evaluation strategies has been developed involving strategies of model intercomparison, ensemble prognoses, uncertainty metrics on the system and component levels. The complexity of climate models goes hand in hand with uncertainties, but uncertainty is in conflict with socio-political expectations. However, certain predictions belong to the realm of desires and ideals rather than to applied science. Today s attempt to define and classify uncertainty in terms of likelihood and confidence reflect this awareness of uncertainty as an integral part of human knowledge, in particular on knowledge about possible future developments. The contributions in this book give a first hand insight into scientific strategies in dealing with uncertainty by using simulation models and into social, political and economical requirements in future projections on climate change. Do these strategies and requirements meet each other or fail?

The debate on how mankind should respond to climate change is diverse, as the appropriate strategy depends on global as well as local circumstances. As scientists are denied the possibility of conducting experiments with the real climate, only climate models can give insights into man-induced climate change, by experimenting with digital climates under varying conditions and by extrapolating past and future states into the future. But the 'nature' of models is a purely representational one. A model is good if it is believed to represent the relevant processes of a natural system well. However, a model and its results, in particular in the case of climate models which interconnect countless hypotheses, is only to some extent testable, although an advanced infrastructure of evaluation strategies has been developed involving strategies of model intercomparison, ensemble prognoses, uncertainty metrics on the system and component levels. The complexity of climate models goes hand in hand with uncertainties, but uncertainty is in conflict with socio-political expectations. However, certain predictions belong to the realm of desires and ideals rather than to applied science. Today's attempt to define and classify uncertainty in terms of likelihood and confidence reflect this awareness of uncertainty as an integral part of human knowledge, in particular on knowledge about possible future developments. The contributions in this book give a first hand insight into scientific strategies in dealing with uncertainty by using simulation models and into social, political and economical requirements in future projections on climate change. Do these strategies and requirements meet each other or fail?

Gabriele Gramelsberger is Principal Investigator of the Collaborative Research Project is Principal Investigator of the Collaborative Research Project

Computer-based technologies for the production and analysis of data have been an integral part of biological research since the 1990s at the latest. This not only applies to genomics and its offshoots but also to less conspicuous subsections such as ecology. But little consideration has been given to how this new technology has changed research practically. How and when do data become questionable? To what extent does necessary infrastructure influence the research process? What status is given to software and algorithms in the production and analysis of data? These questions are discussed by the biologists Philipp Fischer and Hans Hofmann, the philosopher Gabriele Gramelsberger, the historian of science and biology Hans-Joerg Rheinberger, the science theorist Christoph Hoffmann, and the artist Hannes Rickli. The conditions of experimentation in the digital sphere are examined in four chapters--"Data," "Software," "Infrastructure," and "in silico"--in which the different perspectives of the discussion partners complement one another. Rather than confirming any particular point of view, Natures of Data deepens understanding of the contemporary basis of biological research.

Digitalization has transformed the discourse of architecture: that discourse is now defined by a wealth of new terms and concepts that previously either had no meaning, or had different meanings, in the context of architectural theory and design. Its concepts and strategies are increasingly shaped by influences emerging at the intersection with scientific and cultural notions from modern information technology. The new series Context Architecture seeks to take a critical selection of concepts that play a vital role in the current discourse and put them up for discussion.

In the context of discussions of the medial, the notion of simulation plays a central role in architecture as illusion and imitation. In dialogue with information technology and computer science, however, that notion has now taken on a new quality in architectural discourse. Today when we speak of simulation we primarily think of "computer simulation," the technical ability to simulate processes.

Whereas simulation used to refer to a mode of presentation, it now connects architecture with the sciences and represents a strategic and methodological instrument, a tool of discovery. With the scientific principle of simulation the focus shifts to the idea of "modeling a dynamic system" (Norbert Wiener), not just presenting finished products but going in search of solutions and developing systems!