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Multi-platform remote sensing of isotopologues for.. (MUSICA)
Multi-platform remote sensing of isotopologues for investigating the cycle of atmospheric water
(MUSICA)
Date du début: 1 févr. 2011,
Date de fin: 31 juil. 2016
PROJET
TERMINÉ
MUSICA aims to understand the atmospheric water cycle and its interplay with climate change applying unique long-term high quality and global remote sensing observations of tropospheric stable water vapour isotopologues. It is well established that water in its various forms plays a dominant role in nearly all aspects of the Earth s climate system. Understanding the full cycle of evaporation, cloud formation, and precipitation is of highest scientific priority for predicting climate change.The ratio of the isotopologues (e.g. HD16O/H216O) is affected by evaporation, condensation, and cloud processes, and offers a unique opportunity for investigating how water moves through the troposphere. Incorporating isotopologues in atmospheric general circulation models (AGCM) and comparing the isotopologue simulations to observations has the potential to test the models ability of reproducing the global atmospheric water cycle and its interplay with climate change. So far this research field has not been explored due to the lack of consistent, long-term, high-quality, and area-wide observational data. MUSICA will for the first time combine long-term ground- and space-based remote sensing measurements in a consistent manner, and will generate novel tropospheric HD16O/H216O data, taking benefit from both the high and well documented quality of the ground-based observations and the wide geographical coverage of the space-based observations. This unique observational data set will allow a new dimension of water cycle research.MUSICA will collaborate with the Stable Water Isotope Intercomparison Group (SWING) in order to improve current state-of-the-art water isotope AGCMs. MUSICA will investigate and improve the understanding of tropospheric water vapour sources and transport pathways, and empirically assess how well climate feedbacks are captured by current climate models and thereby it will constrain a major uncertainty of climate projections.
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