Analysis of global water vapour trends based on satellite data - Mieruch, Sebastian
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Water vapour is the most important greenhouse gas on Earth. Without atmospheric water vapour, the Earth's surface temperature would be about 20 K lower than today. Life, as we know it, would not be possible. In the context of global climate change, especially the temperature increase due to anthropogenic emissions of CO2 (carbon dioxide) and CH4 (methane), water vapour is of utmost importance because of the strong temperature water vapour feedback. The present book concerns mainly on three parts. First, water vapour trends based on monthly mean satellite data from 1996 to 2007 are estimated on…mehr

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Produktbeschreibung
Water vapour is the most important greenhouse gas on Earth. Without atmospheric water vapour, the Earth's surface temperature would be about 20 K lower than today. Life, as we know it, would not be possible. In the context of global climate change, especially the temperature increase due to anthropogenic emissions of CO2 (carbon dioxide) and CH4 (methane), water vapour is of utmost importance because of the strong temperature water vapour feedback. The present book concerns mainly on three parts. First, water vapour trends based on monthly mean satellite data from 1996 to 2007 are estimated on a 0.5x0.5 degree global grid. Locally significant trends up to +-5 % are found. The second part investigates the comparison of the derived water vapour trends from satellite observations with globally distributed measurements from groundstations using a Bayesian model selection. Finally, part three presents a study of the interaction of water vapour and temperature by the application of a Markov chain analysis, which elucidates the potential of the development of a new climate classification scheme.
Autorenporträt
received his Ph.D. degree in physics in 2009from the University of Bremen, Germany at the Institute ofEnvironmental Physics (IUP). His research interests concern thedetection of climate change signals from environmental timeseries, statistical climatology, climate variability as well asclimate extremes and prediction.