Abstract
The use and development of bias correction (BC) methods has grown fast in recent years, due to the increased demand of unbiased projections by many sectoral climate change impact applications. Case studies are frequently based on multi-variate climate indices (CIs) combining two or more essential climate variables that are frequently individually corrected prior to CI calculation. This poses the question of whether the BC method modifies the inter-variable dependencies and eventually the climate change signal. The direct bias correction of the multi-variate CI stands as a usual alternative, since it preserves the physical and temporal coherence among the primary variables as represented in the dynamical model output, at the expense of incorporating the individual biases on the CI with an effect difficult to foresee, particularly in the case of complex CIs bearing in their formulation non-linear relationships between components. Such is the case of the Fire Weather Index (FWI), a meteorological fire danger indicator frequently used in forest fire prevention and research. In the present work, we test the suitability of the direct BC approach on FWI as a representative multi-variate CI, assessing its performance in present climate conditions and its effect on the climate change signal when applied to future projections. Moreover, the results are compared with the common approach of correcting the input variables separately. To this aim, we apply the widely used empirical quantile mapping method (QM), adjusting the 99 empirical percentiles. The analysis of the percentile adjustment function (PAF) provides insight into the effect of the QM on the climate change signal. Although both approaches present similar results in the present climate, the direct correction introduces a greater modification of the original change signal. These results warn against the blind use of QM, even in the case of essential climate variables or uni-variate CIs.
Similar content being viewed by others
References
Addor N, Rohrer M, Furrer R, Seibert J (2016) Propagation of biases in climate models from the synoptic to the regional scale: implications for bias adjustment. J Geophys Res.-Atmos 121(5):2075–2089. https://doi.org/10.1002/2015JD024040
Andrews D (1974) A robust method for multiple linear-regression. Technometrics 16(4):523–531. https://doi.org/10.2307/1267603
Bedia J, Herrera S, San-Martín D, Koutsias N, Gutiérrez JM (2013) Robust projections of fire weather index in the mediterranean using statistical downscaling. Clim Chang 120(1-2):229–247. https://doi.org/10.1007/s10584-013-0787-3
Bedia J, Herrera S, Camia A, Moreno JM, Gutiérrez JM (2014) Forest fire danger projections in the Mediterranean using ENSEMBLES regional climate change scenarios. Clim Chang 122(1-2):185–199. https://doi.org/10.1007/s10584-013-1005-z
Bedia J, Herrera S, Gutiérrez JM, Benali A, Brands S, Mota B, Moreno JM (2015) Global patterns in the sensitivity of burned area to fire-weather: implications for climate change. Agric For Meteorol 214215:369–379. https://doi.org/10.1016/j.agrformet.2015.09.002
Bedia J, Golding N, Casanueva A, Iturbide M, Buontempo C, Gutierrez JM (2017) Seasonal predictions of Fire Weather Index: paving the way for their operational applicability in Mediterranean Europe. Climate Services. https://doi.org/10.1016/j.cliser.2017.04.001
Brands S, Herrera S, Gutiérrez J (2014) Is Eurasian snow cover in October a reliable statistical predictor for the wintertime climate on the Iberian Peninsula?. Int J Climatol 34(5):1615–1627. https://doi.org/10.1002/joc.3788
Cannon AJ (2016) Multivariate bias correction of climate model output: matching marginal distributions and intervariable dependence structure. J Clim 29(19):7045–7064. https://doi.org/10.1175/JCLI-D-15-0679.1 https://doi.org/10.1175/JCLI-D-15-0679.1
Casanueva A, Frías MD, Herrera S, San-Martín D, Zaninovic K, Gutiérrez JM (2014) Statistical downscaling of climate impact indices: testing the direct approach. Clim Chang 127(3-4):547–560. https://doi.org/10.1007/s10584-014-1270-5
Casanueva A, Kotlarski S, Herrera S, Fernández J, Gutiérrez J, Boberg B, Colette A, Christensen OB, Goergen K, Jacob D, Keuler K, Nikulin G, Teichmann C, Vautard R (2016) Daily precipitation statistics in a EURO-CORDEX RCM ensemble: added value of raw and bias-corrected high-resolution simulations. Clim Dyn 47:719–737. https://doi.org/10.1007/s00382-015-2865-x
Christensen JH, Carter TR, Rummukainen M, Amanatidis G (2007) Evaluating the performance and utility of regional climate models: the PRUDENCE project. Clim Chang 81(1):1–6. https://doi.org/10.1007/s10584-006-9211-6 https://doi.org/10.1007/s10584-006-9211-6
Christensen JH, Boberg F, Christensen OB, Lucas-Picher P (2008) On the need for bias correction of regional climate change projections of temperature and precipitation. Geophys Res Lett 35(20):L20,709. https://doi.org/10.1029/2008GL035694
Cofiño A, Bedia J, Iturbide M, Vega M, Herrera S, Fernández J, Frías M, Manzanas R, Gutiérrez J (2017) The ecoms user data gateway: towards seasonal forecast data provision and research reproducibility in the era of climate services. Climate Services. https://doi.org/10.1016/j.cliser.2017.07.001. http://www.sciencedirect.com/science/article/pii/S2405880717300079
Déqué M (2007) Frequency of precipitation and temperature extremes over France in an anthropogenic scenario: model results and statistical correction according to observed values. Glob Planet Chang 57(1-2):16–26. https://doi.org/10.1016/j.gloplacha.2006.11.030
Dimitrakopoulos A, Bemmerzouk A, Mitsopoulos I (2011) Evaluation of the Canadian fire weather index system in an eastern Mediterranean environment. Meteorol Appl 18:83–93
Ehret U, Zehe E, Wulfmeyer V, Warrach-Sagi K, Liebert J (2012) HESS opinions “should we apply bias correction to global and regional climate model data?”. Hydrol Earth Syst Sci 16(9):3391–3404. https://doi.org/10.5194/hess-16-3391-2012
Gobiet A, Suklitsch M, Heinrich G (2015) The effect of empirical-statistical correction of intensity-dependent model errors on the temperature climate change signal. Hydrol Earth Syst Sci 19(10):4055–4066. https://doi.org/10.5194/hess-19-4055-2015
Hagemann S, Chen C, Haerter JO, Heinke J, Gerten D, Piani C (2011) Impact of a statistical bias correction on the projected hydrological changes obtained from three GCMs and two hydrology models. J Hydrometeor 12(4):556–578. https://doi.org/10.1175/2011JHM1336.1
Herrera S, Fita L, Fernández J, Gutiérrez JM (2010) Evaluation of the mean and extreme precipitation regimes from the ENSEMBLES regional climate multimodel simulations over Spain. J Geophys Res 115(D21):D21,117. https://doi.org/10.1029/2010JD013936
Herrera S, Bedia J, Gutiérrez J, Fernández J, Moreno J (2013) On the projection of future fire danger conditions with various instantaneous/mean-daily data sources 118:827–840
IPCC (2014). In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds) Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, p 1132
Kotlarski S, Keuler K, Christensen OB, Colette A, Déqué M, Gobiet A, Goergen K, Jacob D, Lüthi D, van Meijgaard E, Nikulin G, Schär C, Teichmann C, Vautard R, Warrach-Sagi K, Wulfmeyer V (2014) Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble. Geosci Model Dev 7(4):1297–1333. https://doi.org/10.5194/gmd-7-1297-2014
Li C, Sinha E, Horton DE, Diffenbaugh NS, Michalak AM (2014) Joint bias correction of temperature and precipitation in climate model simulations. J Geophys Res.-Atmos 119(23):13,153–13,162. https://doi.org/10.1002/2014JD022514
Maraun D (2013) Bias correction, quantile mapping, and downscaling: revisiting the inflation issue. J Climate 26(6):2137–2143. https://doi.org/10.1175/JCLI-D-12-00821.1
Maraun D (2016) Bias correcting climate change simulations—a critical review. Current Climate Change Reports 2(4):211–220. https://doi.org/10.1007/s40641-016-0050-x
Maraun D, Shepherd TG, Widmann M, Zappa G, Walton D, Gutiérrez J, Hagemann S, Richter I, Soares PMM, Hall A, Mearns LO (2017) Towards process-informed bias correction of climate change simulations. Nature Climate Change 7:764–773. https://doi.org/10.1038/nclimate3418
Moriondo M, Good P, Durao R, Bindi M, Giannakopoulos C, Corte-Real J (2006) Potential impact of climate change on fire risk in the Mediterranean area 31:85–95
Muerth MJ, Gauvin St-Denis B, Ricard S, Velázquez JA, Schmid J, Minville M, Caya D, Chaumont D, Ludwig R, Turcotte R (2013) On the need for bias correction in regional climate scenarios to assess climate change impacts on river runoff. Hydrol Earth Syst Sci 17(3):1189–1204. https://doi.org/10.5194/hess-17-1189-2013. https://www.hydrol-earth-syst-sci.net/17/1189/2013/
Pal JS, Giorgi F, Bi X, Elguindi N, Solmon F, Rauscher SA, Gao X, Francisco R, Zakey A, Winter J, Ashfaq M, Syed FS, Sloan LC, Bell JL, Diffenbaugh NS, Karmacharya J, Konar A, Martinez D, da Rocha RP, Steiner AL (2007) Regional climate modeling for the developing world: the ICTP RegCM3 and RegCNET. Bull Amer Meteor Soc 88(9):1395–1409. https://doi.org/10.1175/BAMS-88-9-1395
Panofsky HA, Brier GW (1968) Some applications of statistics to meteorology University Park : Penn. State University, College of Earth and Mineral Sciences
Pechony O, Shindell DT (2010) Driving forces of global wildfires over the past millennium and the forthcoming century. P Natl Acad Sci USA 107(45):19,167–19,170. https://doi.org/10.1073/pnas.1003669107
Piani C, Haerter JO, Coppola E (2010a) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 99 (1-2):187–192. https://doi.org/10.1007/s00704-009-0134-9
Piani C, Weedon G, Best M, Gomes S, Viterbo P, Hagemann S, Haerter J (2010b) Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. J Hydrol 395(3):199–215. https://doi.org/10.1016/j.jhydrol.2010.10.024. http://www.sciencedirect.com/science/article/pii/S0022169410006475
Quintana Seguí P, Ribes A, Martin E, Habets F, Boé J (2010) Comparison of three downscaling methods in simulating the impact of climate change on the hydrology of Mediterranean basins. J Hydrol 383 (1-2):111–124. https://doi.org/10.1016/j.jhydrol.2009.09.050
Rocheta E, Evans JP, Sharma A (2014) Assessing atmospheric bias correction for dynamical consistency using potential vorticity. Environ Res Lett 9(12):124,010. http://stacks.iop.org/1748-9326/9/i=12/a=124010
San-Miguel-Ayanz J, Schulte E, Schmuck G, Camia A (2013) The European forest fire information system in the context of environmental policies of the European Union. Forest Policy Econ 29(SI):19–25. https://doi.org/10.1016/j.forpol.2011.08.012
Stocks B, Fosberg M, Lynham T, Mearns L, Wotton B, Yang Q, Jin J, Lawrence K, Hartley G, Mason J, McKenney D (1998) Climate change and forest fire potential in russian and canadian boreal forests. Clim Chang 38:1–13
Teng J, Potter NJ, Chiew FHS, Zhang L, Wang B, Vaze J, Evans JP (2015) How does bias correction of regional climate model precipitation affect modelled runoff?. Hydrol Earth Syst Sci 19(2):711–728. https://doi.org/10.5194/hess-19-711-2015
Teutschbein C, Seibert J (2012) Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. J Hydrol 456-457:12–29
Themeßl MJ, Gobiet A, Heinrich G (2012) Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal. Clim Chang 112(2):449–468. https://doi.org/10.1007/s10584-011-0224-4
Turco M, Sanna A, Herrera S, Llasat MC, Gutiérrez JM (2013) Large biases and inconsistent climate change signals in ensembles regional projections. Clim Chang 120(4):859–869. https://doi.org/10.1007/s10584-013-0844-y https://doi.org/10.1007/s10584-013-0844-y
van der Linden P, Mitchell J (eds.) (2009) ENSEMBLES: climate change and its impacts: summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK
van Meijgaard E, van Ulft L, van de Berg W, Bosveld B, van der Hurk B, Gand Siebesma LA (2008) The KNMI regional atmospheric climate model RACMO version 2.1. Tech Rep 302
van Wagner C, Pickett T (1985) Equations and FORTRAN program for the Canadian forest fire weather index system. Forestry Tech. Rep. 33 Canadian Forestry Service, Ottawa, Canada
van Wagner CE (1987) Development and structure of the Canadian Forest Fire Weather Index. Forestry Tech. Rep. 35 Canadian Forestry Service, Ottawa, Canada
Viegas D, Bovio G, Ferreira A, Nosenzo A, Sol B (1999) Comparative study of various methods of fire danger evaluation in Southern Europe. Int J Wildland Fire 9:235–246
Vrac M, Friederichs P (2015) Multivariate–intervariable, spatial, and temporal–bias correction. J Climate 28(1):218–237. https://doi.org/10.1175/JCLI-D-14-00059.1
Wilby RL, Hay LE, Gutowski WJ, Arritt RW, Takle ES, Pan Z, Leavesley GH, Clark MP (2000) Hydrological responses to dynamically and statistically downscaled climate model output. Geophys Res Lett 27(8):1199–1202. https://doi.org/10.1029/1999GL006078
Wilcke RAI, Mendlik T, Gobiet A (2013) Multi-variable error correction of regional climate models. Clim Chang 120(4):871–887. https://doi.org/10.1007/s10584-013-0845-x
Williams A, Karoly D, Tapper N (2001) The sensitivity of australian fire danger to climate change. Clim Chang 49(1-2):171–191
Wotton BM (2009) Interpreting and using outputs from the Canadian forest fire danger rating system in research applications. Environ Ecol Stat 16:107–131. https://doi.org/10.1007/s10651-007-0084-2
Yang W, Gardelin M, Olsson J, Bosshard T (2015) Multi-variable bias correction: application of forest fire risk in present and future climate in Sweden. Nat Hazards Earth Syst Sci 15(9):2037–2057. https://doi.org/10.5194/nhess-15-2037-2015
Acknowledgements
All the statistical downscaling experiments have been computed using the MeteoLab software (http://www.meteo.unican.es/software/meteolab), an open-source Matlab toolbox for statistical downscaling. The authors are grateful to the Spanish Meteorological Agency (AEMET) for providing the observational data and Erika Coppola from the International Center of Theoretical Physics (ICTP) and Erik van Meijgaard from the Royal Netherlands Meteorological Institute (KNMI) for making available the ENSEMBLES RegCM3 and RACMO2 regional climate models, respectively. We also thank two anonymous referees for their useful comments that helped to improve the original manuscript.
Funding
A.C. thanks the Spanish Ministry of Economy and Competitiveness for the funding provided within the FPI program (BES-2011-047612). J.F. acknowledges support from the INSIGNIA project, co-funded by the Spanish R&D program (CGL2016-79210-R) and the European Regional Development Fund. This work was partially supported by the project MULTI-SDM (CGL2015-66583-R, MINECO/FEDER).
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Casanueva, A., Bedia, J., Herrera, S. et al. Direct and component-wise bias correction of multi-variate climate indices: the percentile adjustment function diagnostic tool. Climatic Change 147, 411–425 (2018). https://doi.org/10.1007/s10584-018-2167-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10584-018-2167-5