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

===== 10.3.3.7.2 Performance of bias adjustment methods =====

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This subsection assesses the performance of bias adjustment in a perfect predictor context. In practice, climate model imperfections may cause substantial additional issues in the application of bias adjustment. These are assessed separately in Cross-Chapter Box 10.2.

Bias adjustment methods, if driven by reanalysis predictors, in principle adjust well all the aspects that they intend to address ( [[#Maraun--2018b|Maraun and Widmann, 2018b]] ). For temperature, all univariate methods are good for adjusting means, variance, and high quantiles ( [[#Gutiérrez--2019|Gutiérrez et al., 2019]] ; [[#Hertig--2019|Hertig et al., 2019]] ). For precipitation, means, intensities, wet-day frequencies, and wet–dry and dry–wet transitions are well-adjusted ( [[#Gutiérrez--2019|Gutiérrez et al., 2019]] ; [[#Maraun--2019b|Maraun et al., 2019b]] ). The representation of high quantiles depends on the chosen method, although flexible quantile mapping performs best ( [[#Hertig--2019|Hertig et al., 2019]] ). Empirical (non-parametric) methods perform better than parametric methods over the observed range, but it is unclear how this translates into extrapolation to unobserved values (IPCC, 2015; [[#Hertig--2019|Hertig et al., 2019]] ). Many quantile mapping methods overestimate interannual variability ( [[#Maraun--2019b|Maraun et al., 2019b]] ). Temporal and spatial dependence are usually not adjusted and thus inherited from the driving model ( [[#Maraun--2019b|Maraun et al., 2019b]] ; [[#Widmann--2019|Widmann et al., 2019]] ). Spatial fields are thus typically too smooth in space, even after bias adjustment ( [[#Widmann--2019|Widmann et al., 2019]] ).

Several studies show improved simulations of present-day impacts, when the impact model is fed with bias-adjusted climate model output, including the assessment of river discharge ( [[#Rojas--2011|Rojas et al., 2011]] ; [[#Muerth--2013|Muerth et al., 2013]] ; [[#Montroull--2018|Montroull et al., 2018]] ), forest fires ( [[#Migliavacca--2013|Migliavacca et al., 2013]] ), crop production ( [[#Ruiz-Ramos--2016|Ruiz-Ramos et al., 2016]] ), and regional ocean modelling ( [[#Macias--2018|Macias et al., 2018]] ).

There is ''high confidence'' that bias adjustment can improve the marginal distribution of simulated climate variables, if applied to a climate model that adequately represents the processes relevant for a given application (Cross-Chapter Box 10.2).

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