Statistical bias correction for daily precipitation in regional climate models over Europe

By C. Piani, Jan O. Haerter, E. Coppola. 2010

1,162 citations recorded by the source corpus.

We design, apply, and validate a methodology for correcting climate model output to produce internally consistent fields that have the same statistical intensity distribution as the observations. We refer to this as a statistical bias correction. Validation of the methodology is carried out using daily precipitation fields, defined over Europe, from the ENSEMBLES climate model dataset. The bias correction is calculated using data from 1961 to 1970, without distinguishing between seasons, and applied to seasonal data from 1991 to 2000. This choice of time periods is made to maximize the lag between calibration and validation within the ERA40 reanalysis period. Results show that the method performs unexpectedly well. Not only are the mean and other moments of the intensity distribution improved, as expected, but so are a drought and a heavy precipitation index, which depend on the autocorrelation spectra. Given that the corrections were derived without seasonal distinction and are based solely on intensity distributions, a statistical quantity oblivious of temporal correlations, it is encouraging to find that the improvements are present even when seasons and temporal statistics are considered. This encourages the application of this method to multi-decadal climate projections.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/72fde922aa97a5746b14722f8ccb8abfcac5aa32

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Citations in the corpus

Climate model bias correction and the role of timescales (2010, methodology): “In summary, we have shown that the statistical bias correction as used in Piani et al. (2010b) does equalize the statistics of the daily observations.”
Changes in Spatiotemporal Precipitation Patterns in Changing Climate Conditions (2016, methodology): “…are bias-correcting model output based on observations (of means or marginal distributions) (e.g., Ines and Hansen 2006; Christensen et al. 2008; Piani et al. 2010a,b; Teutschbein and Seibert 2012) and ‘‘delta’’ methods that adjust observations by modelprojected changes (in means or marginal…”
The impact of climate change on the levelised cost of wind energy (2017, background): “Climate models exhibit systematic errors in their absolute outputs, such as temperature or precipitation estimates [75,76].”
The effect of univariate bias adjustment on multivariate hazard estimates (2019, background): “However, biases continue to persist in global (Flato et al., 2013; Wang et al., 2014) and regional climate models (Christensen et al., 2008; Kotlarski et al., 2014), rendering bias adjustment an undesired but often unavoidable data processing step for climate impact modeling (Piani et al., 2010).”
Stochastic Model Output Statistics for Bias Correcting and Downscaling Precipitation Including Extremes (2014, methodology): “…we bias correct and downscale daily precipitation simulated by the Consortium for Small-Scale Modeling in Climate Mode (COSMO-CLIM) version 4.8 RCM (Rockel et al. 2008) to a set of rain gauges across the United Kingdom separately for winter [December–February (DJF)] and summer [June–August (JJA)].”
Multi-variable error correction of regional climate models (2013, methodology): “One might circumvent these issues by fitting theoretical distributions or some functions to the data (e.g. Piani et al. 2009; Rojas et al. 2011), but this would lead to less flexibility and the need for a specific implementation for each variable and probably also for each climate regime.”
Equiratio cumulative distribution function matching as an improvement to the equidistant approach in bias correction of precipitation (2014, methodology): “Quantile mapping is a very efficient bias correction technique with many applications (e.g. Sharma et al., 2007; Piani et al., 2010).”
Multisegment statistical bias correction of daily GCM precipitation output (2013, background): “…bias correction methods based on transfer functions establish statistical relationships between cumulative density functions (CDFs) of a common period between observed and simulated precipitation and apply it to the projected precipitation [Deque, 2007; Block et al., 2009; Piani et al., 2010].”
Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes (2016, methodology): “Over the years, numerous bias correction methods have been developed using univariate and multivariate approaches (Gellens and Roulin, 1998; Wood et al., 2004; Schmidli et al., 2006; Boé et al., 2007; Leander and Buishand, 2007; Lenderink et al., 2007; Li et al., 2010; Maraun et al., 2010; Piani et ”
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).”
Projecting future crop productivity for global economic modeling (2014, methodology): “The bias correction method builds on Piani et al. (2010) but preserves 77 absolute temperature and relative precipitation changes.”
An improved statistical analogue downscaling procedure for seasonal precipitation forecast (2013, methodology): “Q transformation has been used widely as a post bias correction for the GCM output to translate long-range climate predictions to the realm of hydrology in hydrological modeling (Wood et al. 2002, 2005; Luo and Wood 2008; Ines and Hansen 2006; Sharma et al. 2007; Piani et al. 2010).”
A Localized Particle Filter for High-Dimensional Nonlinear Systems (2016, methodology): “…06:57 AM UTC b. Probability mapping step After updating the particles within the localization region using (13), higher-order corrections are then made using probability mapping methods used frequently for debiasing model output (e.g., Ines and Hansen 2006; Piani et al. 2010;Haerter et al. 2011).”
A nonstationary bias‐correction technique to remove bias in GCM simulations (2016, methodology): “…developed by Panofsky and Brier [1968] and has been successfully implemented in hydrologic applications [Boé et al., 2007; Maurer and Hidalgo, 2008; Wood et al., 2004] and, more recently, in bias correction of climate models [Dobler and Ahrens, 2008; Grillakis et al., 2013; Piani et al., 2010].”
Robust bias-correction of precipitation extremes using a novel hybrid empirical quantile-mapping method (2021, methodology): “For both bias-correction methods, twelve TFs were constructed, one for each month of the year (Jakob Themeßl et al. 2011; Piani et al. 2010) using model data topographically downscaled to GHCND stations and GHCND station data.”
Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables (2017, background): “To address the first problem, speed of convergence, Pitié et al. (2005, 2007) suggest replacing the sequence of random orthogonal rotation matrices with a deterministic selection that instead maximizes the distance between rotation axis sets. As rotation axes are likely to be less correlated in high”