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How scientists used transformative new technologies to understand the complexities of weather and the atmosphere, told through the intertwined careers of three key figures. “The goal of meteorology is to portray everything atmospheric, everywhere, always,” declared John Bellamy and Harry Wexler in 1960, soon after the successful launch of TIROS 1, the first weather satellite. Throughout the twentieth century, meteorological researchers have had global ambitions, incorporating technological advances into their scientific study as they worked to link theory with practice. Wireless telegraphy, radio, aviation, nuclear tracers, rockets, digital computers, and Earth-orbiting satellites opened up entirely new research horizons for meteorologists. In this book, James Fleming charts the emergence of the interdisciplinary field of atmospheric science through the lives and careers of three key figures: Vilhelm Bjerknes (1862–1951), Carl-Gustaf Rossby (1898–1957), and Harry Wexler (1911–1962). In the early twentieth century, Bjerknes worked to put meteorology on solid observational and theoretical foundations. His younger colleague, the innovative and influential Rossby, built the first graduate program in meteorology (at MIT), trained aviation cadets during World War II, and was a pioneer in numerical weather prediction and atmospheric chemistry. Wexler, one of Rossby's best students, became head of research at the U.S. Weather Bureau, where he developed new technologies from radar and rockets to computers and satellites, conducted research on the Antarctic ice sheet, and established carbon dioxide measurements at the Mauna Loa Observatory in Hawaii. He was also the first meteorologist to fly into a hurricane—an experience he chose never to repeat. Fleming maps both the ambitions of an evolving field and the constraints that checked them—war, bureaucracy, economic downturns, and, most important, the ultimate realization (prompted by the formulation of chaos theory in the 1960s by Edward Lorenz) that perfectly accurate measurements and forecasts would never be possible.
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