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Downscaling extreme month-long anomalies in southern South America

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/persons/resource/persons37139

Elizalde,  A.
The Atmosphere in the Earth System, MPI for Meteorology, Max Planck Society;

/persons/resource/persons37190

Jacob,  D.
The Atmosphere in the Earth System, MPI for Meteorology, Max Planck Society;
B 2 - Land Use and Land Cover Change, Research Area B: Climate Manifestations and Impacts, The CliSAP Cluster of Excellence, External Organizations;

/persons/resource/persons37293

Pfeiffer,  S.
The Atmosphere in the Earth System, MPI for Meteorology, Max Planck Society;

/persons/resource/persons37353

Teichmann,  C.
The Atmosphere in the Earth System, MPI for Meteorology, Max Planck Society;
IMPRS on Earth System Modelling, MPI for Meteorology, Max Planck Society;

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Fulltext (public)

ClimaticChange_98-379.pdf
(Publisher version), 8MB

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Citation

Menéndez, C. G., de Castro, M., Boulanger, J.-P., D'Onofrio, A., Sanchez, E., Sörensson, A. A., et al. (2010). Downscaling extreme month-long anomalies in southern South America. Climatic Change, 98, 379-403. doi:10.1007/s10584-009-9739-3.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0011-F699-F
Abstract
We investigate the performance of one stretched-grid atmospheric global model, five different regional climate models and a statistical downscaling technique in simulating 3 months (January 1971, November 1986, July 1996) characterized by anomalous climate conditions in the southern La Plata Basin. Models were driven by reanalysis (ERA-40). The analysis has emphasized on the simulation of the precipitation over land and has provided a quantification of the biases of and scatter between the different regional simulations. Most but not all dynamical models underpredict precipitation amounts in south eastern South America during the three periods. Results suggest that models have regime dependence, performing better for some conditions than others. The models' ensemble and the statistical technique succeed in reproducing the overall observed frequency of daily precipitation for all periods. But most models tend to underestimate the frequency of dry days and overestimate the amount of light rainfall days. The number of events with strong or heavy precipitation tends to be under simulated by the models.