Editing IPCC:AR6/WGI/Chapter-10 (section)

===== 10.3.1.3.2 Bias adjustment =====

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Bias adjustment is a statistical post-processing technique used to pragmatically reduce the mismatch between the statistics of climate model output and observations. The approach estimates the bias or relative error between a chosen simulated statistical property (such as the long-term mean or specific quantiles of the climatological distribution) and that observed over a calibration period; the simulated statistic is then adjusted taking into account the simulated deviation. Bias adjustment methods are regularly applied on a spatial scale similar to that of the simulation being adjusted, but they are often used as a simple statistical downscaling method by calibrating them between coarse resolution (e.g., global) model output and finer observations ( [[#Maraun--2018b|Maraun and Widmann, 2018b]] ).

Typical implementations of bias adjustment are (i) additive adjustments, where the model data is adjusted by adding a constant, (ii) rescaling, where the model data is adjusted by a factor, and (iii) more flexible quantile mapping approaches that adjust different ranges of a distribution individually. Hempel et al. (2013), [[#Pierce--2015|Pierce et al. (2015)]] , [[#Switanek--2017|Switanek et al. (2017)]] , and [[#Lange--2019|Lange (2019)]] developed variants of quantile mapping that preserve trends in the mean or even further distributional statistics. Multivariate bias adjustment extends univariate methods, which adjust statistics of individual variables separately, to joint adjustment of multiple variables simultaneously. Implementations remove biases in (i) specific measures of multivariate dependence, like correlation structure, via linear transformations ( [[#Bárdossy--2012|Bárdossy and Pegram, 2012]] ; [[#Cannon--2016|Cannon, 2016]] ), or, more flexibly, (ii) the full multivariate distribution via non-linear transformations ( [[#Vrac--2015|Vrac and Friederichs, 2015]] ; [[#Dekens--2017|Dekens et al., 2017]] ; [[#Cannon--2018|Cannon, 2018]] ; [[#Vrac--2018|Vrac, 2018]] ; [[#Robin--2019|Robin et al., 2019]] ). Other research strands focus on the explicit separation of bias adjustment and downscaling ( [[#10.3.1.3.5|Section 10.3.1.3.5]] ), or the integration of process understanding ( [[#Maraun--2017|Maraun et al., 2017]] ), such as by conditioning the adjustment on the occurrence of relevant phenomena ( [[#Addor--2016|Addor et al., 2016]] ; [[#Verfaillie--2017|Verfaillie et al., 2017]] ; [[#Manzanas--2019|Manzanas and Gutiérrez, 2019]] ). Some authors suggest to mitigate the influence of large-scale temperature or circulation biases by performing a bias adjustment of the driving fields prior to dynamical downscaling ( [[#Colette--2012|Colette et al., 2012]] ; [[#Hernández-Díaz--2013|Hernández-Díaz et al., 2013]] , 2019). Issues that may arise when using bias adjustment are discussed in Cross-Chapter Box 10.2.

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