Abstract
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.
Highlights
Bias correction of climate model projections is often performed in order to properly assess the impacts of climate change on human and environmental resources (Berg et al, 2003; Ines and Hansen, 2006; Muerth et al, 2013; Teng et al, 2015)
We focus our study on univariate bias correction methods
Our proposed bias correction methodology, scaled distribution mapping (SDM), share some similarities with quantile delta mapping (QDM); there are three important distinctions: (1) SDM uses a parametric model instead of a nonparametric one, (2) SDM and QDM handle days with zero rainfall very differently, and (3) SDM more accurately accounts for the differences in the modeled variances, for temperature, between the period of interest and the calibration period
Summary
Bias correction of climate model projections is often performed in order to properly assess the impacts of climate change on human and environmental resources (Berg et al, 2003; Ines and Hansen, 2006; Muerth et al, 2013; Teng et al, 2015). Standard QM assumes that the function of error correction values found in a calibration period can be applied to any time period of interest This is referred to as the stationarity assumption or the time-invariant assumption (Christensen et al, 2008; Maraun, 2012; Themeßl et al, 2012; Brekke et al, 2013; Chen et al, 2013). The assumption of stationarity, in QM, is responsible for inflating (or altering) the raw model projections of climate change (Maurer and Pierce, 2014). The method can alter the raw model projected changes (Themeßl et al, 2012; Maurer and Pierce, 2014) This inflation or deflation of the raw simulated climate change signal exists as an artifact of the stationarity assumption. Our proposed bias correction methodology, SDM, share some similarities with QDM; there are three important distinctions: (1) SDM uses a parametric model instead of a nonparametric one, (2) SDM and QDM handle days with zero rainfall very differently, and (3) SDM more accurately accounts for the differences in the modeled variances, for temperature, between the period of interest and the calibration period
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