000			03999nam a22004215i 4500
001			978-3-642-17700-2
003			DE-He213
005			20140220083751.0
007			cr nn 008mamaa
008			110329s2011 gw | s |||| 0|eng d
020			_a9783642177002 _9978-3-642-17700-2
024	7		_a10.1007/978-3-642-17700-2 _2doi
100	1		_aGramelsberger, Gabriele. _eeditor.
245	1	0	_aClimate Change and Policy _h[electronic resource] : _bThe Calculability of Climate Change and the Challenge of Uncertainty / _cedited by Gabriele Gramelsberger, Johann Feichter.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2011.
300			_aVIII, 241p. 19 illus., 18 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_a1 Introduction to the Volume -- 2 Modelling the Climate System – An Overview -- 3 Climate Simulation, Uncertainty, and Policy Advice - The Case of the IPCC.- 4 Dealing with Uncertainty – From climate research to integrated assessment of policy options -- 5 Uncertainty in Climate Policy – Impacts on Market Mechanisms -- 6 Insuring climate change – Managing political and economic uncertainties in flood management -- 7 Climate Science, Weather and Climate Engineering, and Scientific Objectivity -- 8 Utilizing participatory scenario-based approaches to design proactive responses to climate change in the face of uncertainties -- 9 Image Politics – Picturing uncertainty. The role of images in climatology and climate policy -- 10 Glossary and Abbreviations -- 11 Authors -- 12 Index.
520			_aThe 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?
650		0	_aGeography.
650		0	_aMeteorology.
650		0	_aClimatic changes.
650		0	_aPolitical science.
650	1	4	_aEarth Sciences.
650	2	4	_aMeteorology/Climatology.
650	2	4	_aPolitical Science.
650	2	4	_aClimate Change.
700	1		_aFeichter, Johann. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642176999
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-17700-2
912			_aZDB-2-EES
999			_c107288 _d107288