Does Applying Subsampling in Quantile Mapping Affect the Climate Change Signal?

Bias in regional climate model (RCM) data makes bias correction (BC) a necessary pre-processing step in climate change impact studies. Among a variety of different BC methods, quantile mapping (QM) is a popular and powerful BC method. Studies have shown that QM may be vulnerable to reductions in cal...

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Bibliographic Details
Published inHydrology Vol. 11; no. 9; p. 143
Main Authors Reiter, Philipp, Casper, Markus C.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2024
Subjects
Analysis
Bias
bias adjustment
bias correction
Climate change
Climate models
Climatic changes
Datasets
Environmental impact
Forecasts and trends
Mapping
Methods
Precipitation
quantile mapping
quantile matching
Quantile regression
Quantiles
Selection bias
subsampling
Time
timescale
Trends
Germany
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Summary:Bias in regional climate model (RCM) data makes bias correction (BC) a necessary pre-processing step in climate change impact studies. Among a variety of different BC methods, quantile mapping (QM) is a popular and powerful BC method. Studies have shown that QM may be vulnerable to reductions in calibration sample size. The question is whether this also affects the climate change signal (CCS) of the RCM data. We applied four different QM methods without subsampling and with three different subsampling timescales to an ensemble of seven climate projections. BC generally improved the RCM data relative to observations. However, the CCS was significantly modified by the BC for certain combinations of QM method and subsampling timescale. In conclusion, QM improves the RCM data that are fundamental for climate change impact studies, but the optimal subsampling timescale strongly depends on the chosen QM method.
ISSN:2306-5338
2306-5338
DOI:10.3390/hydrology11090143