Climate model bias correction and the role of timescales
By Jan O. Haerter, Stefan Hagemann, C. Moseley, C. Piani. 2010
311 citations recorded by the source corpus.
Abstract. It is well known that output from climate models cannot be used to force hydrological simulations without some form of preprocessing to remove the existing biases. In principle, statistical bias correction methodologies act on model output so the statistical properties of the corrected data match those of the observations. However, the improvements to the statistical properties of the data are limited to the specific timescale of the fluctuations that are considered. For example, a statistical bias correction methodology for mean daily temperature values might be detrimental to monthly statistics. Also, in applying bias corrections derived from present day to scenario simulations, an assumption is made on the stationarity of the bias over the largest timescales. First, we point out several conditions that have to be fulfilled by model data to make the application of a statistical bias correction meaningful. We then examine the effects of mixing fluctuations on different timescales and suggest an alternative statistical methodology, referred to here as a cascade bias correction method, that eliminates, or greatly reduces, the negative effects.
Subfields: Atmospheric Science, Global and Planetary Change, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/52bea935e76a02b9dd677604809bfd5942d63e96
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In Atmospheric Science
In Global and Planetary Change
Citations in the corpus
- On the bias correction of general circulation model output for Indian summer monsoon (2013, methodology): “For example, same statistical bias correction for daily values may not work on a monthly scale (Haerter et al., 2011).”