Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?

Alex J. Cannon; Stephen R. Sobie; Trevor Q. Murdock

doi:10.1175/jcli-d-14-00754.1

journal article Sep 01, 2015

Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?

Alex J. Cannon

Stephen R. Sobie Trevor Q. Murdock

Journal of Climate Vol. 28 No. 17 pp. 6938-6959 · American Meteorological Society

View at Publisher Save 10.1175/jcli-d-14-00754.1

Abstract

Abstract
Quantile mapping bias correction algorithms are commonly used to correct systematic distributional biases in precipitation outputs from climate models. Although they are effective at removing historical biases relative to observations, it has been found that quantile mapping can artificially corrupt future model-projected trends. Previous studies on the modification of precipitation trends by quantile mapping have focused on mean quantities, with less attention paid to extremes. This article investigates the extent to which quantile mapping algorithms modify global climate model (GCM) trends in mean precipitation and precipitation extremes indices. First, a bias correction algorithm, quantile delta mapping (QDM), that explicitly preserves relative changes in precipitation quantiles is presented. QDM is compared on synthetic data with detrended quantile mapping (DQM), which is designed to preserve trends in the mean, and with standard quantile mapping (QM). Next, methods are applied to phase 5 of the Coupled Model Intercomparison Project (CMIP5) daily precipitation projections over Canada. Performance is assessed based on precipitation extremes indices and results from a generalized extreme value analysis applied to annual precipitation maxima. QM can inflate the magnitude of relative trends in precipitation extremes with respect to the raw GCM, often substantially, as compared to DQM and especially QDM. The degree of corruption in the GCM trends by QM is particularly large for changes in long period return values. By the 2080s, relative changes in excess of +500% with respect to historical conditions are noted at some locations for 20-yr return values, with maximum changes by DQM and QDM nearing +240% and +140%, respectively, whereas raw GCM changes are never projected to exceed +120%.

Topics

No keywords indexed for this article. Browse by subject →

References

71

[1]

Adamowski "Regional rainfall distribution for Canada" Atmos. Res. (1996) 10.1016/0169-8095(95)00054-2

[2]

Alila "A hierarchical approach for the regionalization of precipitation annual maxima in Canada" J. Geophys. Res. (1999) 10.1029/1999jd900764

[3]

Arora "Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases" Geophys. Res. Lett. (2011) 10.1029/2010gl046270

[4]

Berg "Strong increase in convective precipitation in response to higher temperatures" Nat. Geosci. (2013) 10.1038/ngeo1731

[5]

Boé "Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies" Int. J. Climatol. (2007) 10.1002/joc.1602

[6]

Bronaugh, D. , 2014: climdex.pcic: PCIC implementation of Climdex routines. Pacific Climate Impacts Consortium, R package version 1.1-1 [Available online at http://CRAN.R-project.org/package=climdex.pcic.]

[7]

Bürger "Estimates of future flow, including extremes, of the Columbia River headwaters" Water Resour. Res. (2011) 10.1029/2010wr009716

[8]

Bürger "Downscaling extremes—An intercomparison of multiple statistical methods for present climate" J. Climate (2012) 10.1175/jcli-d-11-00408.1

[9]

Bürger "Downscaling extremes: An intercomparison of multiple methods for future climate" J. Climate (2013) 10.1175/jcli-d-12-00249.1

[10]

Cannon "Probabilistic multisite precipitation downscaling by an expanded Bernoulli-gamma density network" J. Hydrometeor. (2008) 10.1175/2008jhm960.1

[11]

Cannon "A flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology" Hydrol. Processes (2010) 10.1002/hyp.7506

[12]

Cannon "Quantile regression neural networks: Implementation in R and application to precipitation downscaling" Comput. Geosci. (2011) 10.1016/j.cageo.2010.07.005

[13]

Cannon "Neural networks for probabilistic environmental prediction: Conditional Density Estimation Network Creation and Evaluation (CaDENCE) in R" Comput. Geosci. (2012) 10.1016/j.cageo.2011.08.023

[14]

Cannon "An intercomparison of regional and at-site rainfall extreme value analyses in southern British Columbia, Canada" Can. J. Civ. Eng. (2015) 10.1139/cjce-2014-0361

[15]

Cannon "Revisiting the nonlinear relationship between ENSO and winter extreme station precipitation in North America" Int. J. Climatol. (2015) 10.1002/joc.4263

[16]

Chen "Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America" Water Resour. Res. (2013) 10.1002/wrcr.20331

[17]

Coles (2001) 10.1007/978-1-4471-3675-0

[18]

Deser "Uncertainty in climate change projections: The role of internal variability" Climate Dyn. (2012) 10.1007/s00382-010-0977-x

[19]

Eden "Skill, correction, and downscaling of GCM-simulated precipitation" J. Climate (2012) 10.1175/jcli-d-11-00254.1

[20]

Ehret "HESS opinions: “Should we apply bias correction to global and regional climate model data?”" Hydrol. Earth Syst. Sci. (2012) 10.5194/hess-16-3391-2012

[21]

Finkelstein "A review of the fundamental concepts of measurement" Measurement (1984) 10.1016/0263-2241(84)90020-4

[22]

Flato "Evaluation of climate models" (2013)

[23]

Gent "The Community Climate System Model version 4" J. Climate (2011) 10.1175/2011jcli4083.1

[24]

Gudmundsson, L. , 2014: qmap: Statistical transformations for post-processing climate model output. R package version 1.0-3 [Available online at http://cran.r-project.org/web/packages/qmap/.]

[25]

Gudmundsson "Technical note: Downscaling RCM precipitation to the station scale using statistical transformations—A comparison of methods" Hydrol. Earth Syst. Sci. (2012) 10.5194/hess-16-3383-2012

[26]

Gutmann "An intercomparison of statistical downscaling methods used for water resource assessments in the United States" Water Resour. Res. (2014) 10.1002/2014wr015559

[27]

Hagemann "Impact of a statistical bias correction on the projected hydrological changes obtained from three GCMs and two hydrology models" J. Hydrometeor. (2011) 10.1175/2011jhm1336.1

[28]

A trend-preserving bias correction – the ISI-MIP approach

S. Hempel, K. Frieler, L. Warszawski et al.

Earth System Dynamics 2013 10.5194/esd-4-219-2013

[29]

Hopkinson "Impact of aligning climatological day on gridding daily maximum-minimum temperature and precipitation over Canada" J. Appl. Meteor. Climatol. (2011) 10.1175/2011jamc2684.1

[30]

IPCC (2013)

[31]

Kendon "Heavier summer downpours with climate change revealed by weather forecast resolution model" Nat. Climate Change (2014) 10.1038/nclimate2258

[32]

Kharin "Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations" J. Climate (2007) 10.1175/jcli4066.1

[33]

Kharin "Changes in temperature and precipitation extremes in the CMIP5 ensemble" Climatic Change (2013) 10.1007/s10584-013-0705-8

[34]

Koenker "Quantile spline models for global temperature change" Climatic Change (1994) 10.1007/bf01104081

[35]

Statistics of extremes and estimation of extreme rainfall: I. Theoretical investigation / Statistiques de valeurs extrêmes et estimation de précipitations extrêmes: I. Recherche théorique

Demetris Koutsoyiannis

Hydrological Sciences Journal 2004 10.1623/hysj.49.4.575.54430

[36]

Li "Joint bias correction of temperature and precipitation in climate model simulations" J. Geophys. Res. Atmos. (2014) 10.1002/2014jd022514

[37]

Li "Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching" J. Geophys. Res. (2010) 10.1029/2009jd012882

[38]

Maraun "Nonstationarities of regional climate model biases in European seasonal mean temperature and precipitation sums" Geophys. Res. Lett. (2012) 10.1029/2012gl051210

[39]

Maraun "Bias correction, quantile mapping, and downscaling: Revisiting the inflation issue" J. Climate (2013) 10.1175/jcli-d-12-00821.1

[40]

Maraun "The representation of location by regional climate models in complex terrain" Hydrol. Earth Syst. Sci. Discuss. (2015) 10.5194/hessd-12-3011-2015

[41]

Maraun "Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user" Rev. Geophys. (2010) 10.1029/2009rg000314

[42]

Martins "Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data" Water Resour. Res. (2000) 10.1029/1999wr900330

[43]

Maurer "Utility of daily vs. monthly large-scale climate data: An intercomparison of two statistical downscaling methods" Hydrol. Earth Syst. Sci. (2008) 10.5194/hess-12-551-2008

[44]

Maurer "Bias correction can modify climate model simulated precipitation changes without adverse effect on the ensemble mean" Hydrol. Earth Syst. Sci. (2014) 10.5194/hess-18-915-2014

[45]

Maurer "The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California" Hydrol. Earth Syst. Sci. (2010) 10.5194/hess-14-1125-2010

[46]

McKenney "Customized spatial climate models for North America" Bull. Amer. Meteor. Soc. (2011) 10.1175/2011bams3132.1

[47]

Mearns "The North American Regional Climate Change Assessment Program: Overview of phase I results" Bull. Amer. Meteor. Soc. (2012) 10.1175/bams-d-11-00223.1

[48]

Muerth "On the need for bias correction in regional climate scenarios to assess climate change impacts on river runoff" Hydrol. Earth Syst. Sci. (2013) 10.5194/hess-17-1189-2013

[49]

O’Gorman "Sensitivity of tropical precipitation extremes to climate change" Nat. Geosci. (2012) 10.1038/ngeo1568

[50]

O’Gorman "How closely do changes in surface and column water vapor follow Clausius–Clapeyron scaling in climate change simulations?" Environ. Res. Lett. (2010) 10.1088/1748-9326/5/2/025207

Showing 50 of 71 references

Cited By

1,227

Validation of SMAP Surface Soil Moisture Using In Situ Measurements in Diverse Agroecosystems Across Texas, US

Sanjita Gurau, Gebrekidan W. Tefera · 2026

Remote Sensing

XGBoost-based bias correction of CMIP6 precipitation in the Wabi Shebelle Basin, Ethiopia: a comparative study with empirical quantile mapping

Amanuel Tsegaye Tadase, Andinet Kebede Tekile · 2026

Journal of Water and Climate Change

Upper-tail correction of multivariate synthetic environmental series using annual maxima

Víctor Collado, Fernando J. Méndez · 2026

Stochastic Environmental Research a...

Widespread increase in frequency and duration of European wind droughts based on CMIP6 projections

Idunn Aamnes Mostue, Guillermo Valenzuela-Venegas · 2026

iScience

Probabilistic assessment of multi-mode failure of levees under climate change

Yanhao Zheng, Jinhui Li · 2026

Reliability Engineering & Syste...

Dynamic effect of climate change on flood damage cost in the Andean region of Colombia using an ARDL-ECM model and climate change projections

Camilo A. Rodriguez-Espinosa, Carlos F. Valencia · 2025

Sustainable Cities and Society

The first emergence of unprecedented global water scarcity in the Anthropocene

Vecchia P. Ravinandrasana, Christian L. E. Franzke · 2025

Nature Communications

Nonlinear association between wet-bulb globe temperature and maternal hypertensive disorders burden: a global analysis from 1990 to 2021

Boya Zhao, Yonghui Zhao · 2025

Frontiers in Public Health

Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River

Ningpeng Dong, Haoran Hao · 2025

Hydrology and Earth System Sciences

Spatial and temporal variability of future extreme precipitation in Bangladesh using CMIP6 models

Rifat Ara Mishu, Javed Mallick · 2025

Journal of Water and Climate Change

Substantial increases in compound climate extremes and associated socio-economic exposure across China under future climate change

Pei Fang, Taihua Wang · 2025

npj Climate and Atmospheric Science

Permafrost Degradation and Concomitant Hydrological Changes Dominated by Anthropogenic Greenhouse Gas Emissions in the Northeastern Tibetan Plateau

Pei Fang, Taihua Wang · 2025

Geophysical Research Letters

Global increases of salt intrusion in estuaries under future environmental conditions

Jiyong Lee, Bouke Biemond · 2025

Nature Communications

Current progress in subseasonal-to-decadal prediction based on machine learning

Zixiong Shen, Qiming Sun · 2024

Applied Computing and Geosciences

Response of runoff and its components to climate change and its future trend in the source region of the Yangtze River

Tailai Gao, Jiadi Li · 2024

Hydrological Processes

Climate change impacts on global photovoltaic variability

Xie Chen, Hongzhi Mao · 2024

Applied Energy

Research on the optimal allocation of agricultural water and soil resources in the Heihe River Basin based on SWAT and intelligent optimization

Zepeng Zhang, Qingzheng Wang · 2023

Agricultural Water Management

CLIMBra - Climate Change Dataset for Brazil

André S. Ballarin, Jullian Souza Sone · 2023

Scientific Data

A comprehensive assessment of RCP4.5 projections and bias-correction techniques in a complex coastal karstic aquifer in the Mediterranean

Maria Rosaria Alfio, Vassilios Pisinaras · 2023

Frontiers in Earth Science

Deep Learning for Bias‐Correcting CMIP6‐Class Earth System Models

Philipp Hess, Stefan Lange · 2023

Earth's Future

Metrics

1,227

Citations

71

References

Details

Published: Sep 01, 2015
Vol/Issue: 28(17)
Pages: 6938-6959

Authors

A

Alex J. Cannon

Pacific Climate Impacts Consortium, University of Victoria, Victoria, British Columbia, Canada

S

Stephen R. Sobie

Pacific Climate Impacts Consortium, University of Victoria, Victoria, British Columbia, Canada

T

Trevor Q. Murdock

Pacific Climate Impacts Consortium, University of Victoria, Victoria, British Columbia, Canada

Cite This Article

Alex J. Cannon, Stephen R. Sobie, Trevor Q. Murdock (2015). Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?. Journal of Climate, 28(17), 6938-6959. https://doi.org/10.1175/jcli-d-14-00754.1

Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?

You May Also Like