Scaled distribution mapping: A bias correction method that preserves raw climate model projected changes

B. Matthew Switanek, Peter A Troch, Christopher Castro, Armin Leuprecht, Hsin I. Chang, Rajarshi Mukherjee, M. C.Eleonora Demaria

Research output: Contribution to journalArticle

26 Scopus citations

Abstract

Commonly used bias correction methods such as quantile mapping (QM) assume the function of error correction values between modeled and observed distributions are stationary or time invariant. This article finds that this function of the error correction values cannot be assumed to be stationary. As a result, QM lacks justification to inflate/deflate various moments of the climate change signal. Previous adaptations of QM, most notably quantile delta mapping (QDM), have been developed that do not rely on this assumption of stationarity. Here, we outline a methodology called scaled distribution mapping (SDM), which is conceptually similar to QDM, but more explicitly accounts for the frequency of rain days and the likelihood of individual events. The SDM method is found to outperform QM, QDM, and detrended QM in its ability to better preserve raw climate model projected changes to meteorological variables such as temperature and precipitation.

Original languageEnglish (US)
Pages (from-to)2649-2666
Number of pages18
JournalHydrology and Earth System Sciences
Volume21
Issue number6
DOIs
StatePublished - Jun 6 2017

ASJC Scopus subject areas

  • Water Science and Technology
  • Earth and Planetary Sciences (miscellaneous)

Fingerprint Dive into the research topics of 'Scaled distribution mapping: A bias correction method that preserves raw climate model projected changes'. Together they form a unique fingerprint.

  • Cite this