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Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user

Maraun, D; Wetterhall, F; Ireson, AM; Chandler, RE; Kendon, EJ; Widmann, M; ... Thiele-Eich, I; + view all (2010) Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user. Reviews of Geophysics , 48 , Article RG3003. 10.1029/2009RG000314. Green open access

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Abstract

Precipitation downscaling improves the coarse resolution and poor representation of precipitation in global climate models and helps end users to assess the likely hydrological impacts of climate change. This paper integrates perspectives from meteorologists, climatologists, statisticians, and hydrologists to identify generic end user (in particular, impact modeler) needs and to discuss downscaling capabilities and gaps. End users need a reliable representation of precipitation intensities and temporal and spatial variability, as well as physical consistency, independent of region and season. In addition to presenting dynamical downscaling, we review perfect prognosis statistical downscaling, model output statistics, and weather generators, focusing on recent developments to improve the representation of space-time variability. Furthermore, evaluation techniques to assess downscaling skill are presented. Downscaling adds considerable value to projections from global climate models. Remaining gaps are uncertainties arising from sparse data; representation of extreme summer precipitation, subdaily precipitation, and full precipitation fields on fine scales; capturing changes in small-scale processes and their feedback on large scales; and errors inherited from the driving global climate model.

Type:Article
Title:Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user
Open access status:An open access version is available from UCL Discovery
DOI:10.1029/2009RG000314
Publisher version:http://dx.doi.org/10.1029/2009RG000314
Language:English
Additional information:Copyright 2010 by the American Geophysical Union
Keywords:Stochastic weather generators, Hidden Markov model, Generalized additive-models, Northwestern United-States, Finding coupled patterns, Daily rainfall data, Regional-climate, Extreme precipitation, Atmospheric circulation, North-Atlantic
UCL classification:UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science

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