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Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user
Author(s) -
Maraun D.,
Wetterhall F.,
Ireson A. M.,
Chandler R. E.,
Kendon E. J.,
Widmann M.,
Brienen S.,
Rust H. W.,
Sauter T.,
Themeßl M.,
Venema V. K. C.,
Chun K. P.,
Goodess C. M.,
Jones R. G.,
Onof C.,
Vrac M.,
ThieleEich I.
Publication year - 2010
Publication title -
reviews of geophysics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.087
H-Index - 156
eISSN - 1944-9208
pISSN - 8755-1209
DOI - 10.1029/2009rg000314
Subject(s) - downscaling , precipitation , climatology , environmental science , climate change , climate model , representation (politics) , computer science , scale (ratio) , consistency (knowledge bases) , meteorology , geography , geology , artificial intelligence , oceanography , cartography , politics , political science , law
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.