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Haddeland, I.; Clark, D. B.; Franssen, W.; Ludwig, F.; Voß, F.; Arnell, Nigel W.; Bertrand, N.; Best, M.; Folwell, S.; Kabat, P; Koirala, S.; Oki, T.; Polcher, J.; Stacke, T.; Viterbo, P.; Weedon, G. P.; Yehm, P.; Gerten, D.; Gomes, S.; Gosling, S. N.; Hagemann, S.; Hanasaki, N.; Harding, R.; Heinke, J. (2011)
Publisher: American Meteorological Society
Languages: English
Types: Article
Subjects: [SDE.MCG] Environmental Sciences/Global Changes
International audience; Six land surface models and five global hydrological models participate in a model intercomparison project [WaterModel Intercomparison Project (WaterMIP)], which for the first time compares simulation results of these different classes of models in a consistent way. In this paper, the simulation setup is described and aspects of the multimodel global terrestrial water balance are presented. All models were run at 0.58 spatial resolution for the global land areas for a 15-yr period (1985-99) using a newly developed global meteorological dataset. Simulated global terrestrial evapotranspiration, excluding Greenland and Antarctica, ranges from 415 to 586 mm yr -1 (from 60 000 to 85 000 km 3 yr -1), and simulated runoff ranges from 290 to 457 mm yr -1 (from 42 000 to 66 000 km 3 yr -1). Both the mean and median runoff fractions for the land surface models are lower than those of the global hydrological models, although the range is wider. Significant simulation differences between land surface and global hydrological models are found to be caused by the snow scheme employed. The physically based energy balance approach used by land surface models generally results in lower snow water equivalent values than the conceptual degreeday approach used by global hydrological models. Some differences in simulated runoff and evapotranspiration are explained by model parameterizations, although the processes included and parameterizations used are not distinct to either land surface models or global hydrological models. The results show that differences between models are a major source of uncertainty. Climate change impact studies thus need to use not only multiple climate models but also some other measure of uncertainty (e.g., multiple impact models). © 2011 American Meteorological Society.
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