HESS Opinions "Should we apply bias correction to global and regional climate model data?"

By Uwe Ehret, E. Zehe, Volker Wulfmeyer, K. Warrach‐Sagi, J. Liebert. 2012

739 citations recorded by the source corpus.

Despite considerable progress in recent years, output of both global and regional circulation models is still afflicted with biases to a degree that precludes its direct use, especially in climate change impact studies. This is well known, and to overcome this problem, bias correction (BC; i.e. the correction of model output towards observations in a post-processing step) has now become a standard procedure in climate change impact studies. In this paper we argue that BC is currently often used in an invalid way: it is added to the GCM/RCM model chain without sufficient proof that the consistency of the latter (i.e. the agreement between model dynamics/model output and our judgement) as well as the generality of its applicability increases. BC methods often impair the advantages of circulation models by altering spatiotemporal field consistency, relations among variables and by violating conservation principles. Currently used BC methods largely neglect feedback mechanisms, and it is unclear whether they are time-invariant under climate change conditions. Applying BC increases agreement of climate model output with observations in hindcasts and hence narrows the uncertainty range of simulations and predictions without, however, providing a satisfactory physical justification. This is in most cases not transparent to the end user. We argue that this hides rather than reduces uncertainty, which may lead to avoidable forejudging of end users and decision makers. We present here a brief overview of state-of-the-art bias correction methods, discuss the related assumptions and implications, draw conclusions on the validity of bias correction and propose ways to cope with biased output of circulation models in the short term and how to reduce the bias in the long term. The most promising strategy for improved future global and regional circulation model simulations is the increase in model resolution to the convection-permitting scale in combination with ensemble predictions based on sophisticated approaches for ensemble perturbation. With this article, we advocate communicating the entire uncertainty range associated with climate change predictions openly and hope to stimulate a lively discussion on bias correction among the atmospheric and hydrological community and end users of climate change impact studies.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/4c08ab52988f9a107a2dd7308b988679cf90b901

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Citations in the corpus

Does applying quantile mapping to subsamples improve the bias correction of daily precipitation? (2018, background): “Regional climate model (RCM) output is known to be afflicted with systematic biases compared to observations, which is why bias correction (BC) is ‘a standard procedure in climate change impact studies’ (Ehret et al., 2012).”
Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes (2016, methodology): “The application of these methods, however, is not without controversy (Ehret et al., 2012).”
Climate Model Dependence and the Ensemble Dependence Transformation of CMIP Projections (2015, background): “While there are compelling reasons not to do this (Ehret et al. 2012), it remains standard practice in the climate community (e.g., Meehl et al. 2007b) and greatly simplifies the solutions to the problems posed below.”
Joint bias correction of temperature and precipitation in climate model simulations (2014, background): “Although global climate modeling has evolved rapidly in recent years, output from climate models is still prone to biases [Ehret et al., 2012; Mehran et al., 2014; Mueller and Seneviratne, 2014].”
DEALINGWITHUNCERTAINTY INAGENT-BASED SIMULATION:FARM-LEVELMODELING OFADAPTATION TOCLIMATECHANGE INSOUTHWESTGERMANY (2015, neutral): “…models can be tuned to fit almost any observation, restricting simulations to the use of one best-fitting parameter combination hides the uncertainty in model Electronic copy available at: https://ssrn.com/abstract=3574231 inputs and results, rather than truly reducing it (Ehret et al. 2012).”
The effect of univariate bias adjustment on multivariate hazard estimates (2019, background): “…through the modeling chain (Wilby and Dessai, 2010; Frieler et al., 2017), uncertainties associated with the choice of the applied bias-adjustment method are rarely reported (Ehret et al., 2012), though there are exceptions (Chen et al., 2011; Bosshard et al., 2013; Addor and Fischer, 2015).”
Direct and component-wise bias correction of multi-variate climate indices: the percentile adjustment function diagnostic tool (2018, methodology): “Due to the purely statistical nature of bias adjustment, the potential modifica- tion of the inter-variable dependencies caused by uni-variate techniques is an inherent caveat of the methodology (Piani et al. 2010b; Ehret et al. 2012; Muerth et al. 2013).”
A method to preserve trends in quantile mapping bias correction of climate modeled temperature (2017, background): “…the physical consistency of climate variables, the mass/energy balance and the omission of correction feedback mechanisms to other climate variables (Ehret et al., 2012) – were not examined in this work, despite the existence of methods that preserve consistency between specific variables (Sippel…”
A nonstationary bias‐correction technique to remove bias in GCM simulations (2016, background): “This is because the priority of statistical bias correction is to increase the agreement between GCM outputs and observations, which usually neglects the underlying physical mechanisms [Ehret et al., 2012].”
Global projections of river flood risk in a warmer world (2017, background): “…of the observational dataset on the output product; (3) bias-corrected datasets are often associated with a decrease in model resolution; (4) questionable benefits of the bias correction for extreme events [Ehret et al., 2012; Themeßl et al., 2012; Muerth et al., 2013; Huang et al., 2014].”
Multimodel assessment of water scarcity under climate change (2013, methodology): “All required climate variables have been bias-corrected (55) toward an observation-based dataset (56) using a newly developed method (21) that builds on earlier approaches (57) but was specifically designed to preserve the long-term trends in temperature and precipitation projections to facilitate c”
Multivariate stochastic bias corrections with optimal transport (2018, methodology): “The main goal of bias correction (BC) is to match the statistical features of climate model outputs with observations (see, e.g., Ehret et al., 2012; Gudmundsson et al., 2012).”