Skip to main content

Advertisement

Log in

Comparing correction methods of RCM outputs for improving crop impact projections in the Iberian Peninsula for 21st century

  • Published:
Climatic Change Aims and scope Submit manuscript

Abstract

Assessment of climate change impacts on crops in regions of complex orography such as the Iberian Peninsula (IP) requires climate model output which is able to describe accurately the observed climate. The high resolution of output provided by Regional Climate Models (RCMs) is expected to be a suitable tool to describe regional and local climatic features, although their simulation results may still present biases. For these reasons, we compared several post-processing methods to correct or reduce the biases of RCM simulations from the ENSEMBLES project for the IP. The bias-corrected datasets were also evaluated in terms of their applicability and consequences in improving the results of a crop model to simulate maize growth and development at two IP locations, using this crop as a reference for summer cropping systems in the region. The use of bias-corrected climate runs improved crop phenology and yield simulation overall and reduced the inter-model variability and thus the uncertainty. The number of observational stations underlying each reference observational dataset used to correct the bias affected the correction performance. Although no single technique showed to be the best one, some methods proved to be more adequate for small initial biases, while others were useful when initial biases were so large as to prevent data application for impact studies. An initial evaluation of the climate data, the bias correction/reduction method and the consequences for impact assessment would be needed to design the most robust, reduced uncertainty ensemble for a specific combination of location, crop, and crop management.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Asseng S, Ewert F, Rosenzweig C et al (2013) Uncertainty in simulating wheat yields under climate change. Nat Clim Chang 3:827–832. doi:10.1038/nclimate1916

    Article  Google Scholar 

  • Baigorria GA, Jones JW, Shin DW, Mishra A, O’Brien JJ (2007) Assessing uncertainties in crop model simulations using daily bias-corrected regional circulation model outputs. Clim Res 34(3):211–222. doi:10.3354/cr00703

    Article  Google Scholar 

  • Bassu S, Brisson N, Durand JL et al (2014) How do various maize crop models vary in their responses to climate change factors? Glob Chang Biol 20(7):2301–2320. doi:10.1111/gcb.12520

    Article  Google Scholar 

  • Boberg F, Christensen JH (2012) Overestimation of Mediterranean summer temperature projections due to model deficiencies. Nat Clim Chang 2:433–436. doi:10.1038/NCLIMATE1454

    Article  Google Scholar 

  • Bosshard T, Carambia M, Görgen K, Kotlarski S, Krahe P, Zappa M, Schär C (2013) Quantifying uncertainty sources in an ensemble of hydrological climate-impact projections. Water Resour Res 49. doi:10.1029/2011WR011533

  • 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, L20709. doi:10.1029/2008GL035694

    Article  Google Scholar 

  • Ministerio de Agricultura, Alimentación y Medio Ambiente (MAGRAMA) (2014) Avance Anuario de Estadística, NIPO: 280-15-063-O, pp. 887

  • Domínguez M, Romera R, Sánchez E, Fita L et al (2013) Present-climate precipitation and temperature extremes over Spain from a set of high resolution RCMs. Clim Res 58:149–164

    Article  Google Scholar 

  • Dosio A, Paruolo P (2011) Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models: evaluation on the present climate. J Geophys Res 116, D16106. doi:10.1029/2011JD015934

    Article  Google Scholar 

  • Dosio A, Paruolo P, Rojas R (2012) Bias correction of the ENSEMBLES high resolution climate change projections for use by impact models: analysis of the climate change signal. J Geophys Res 117:D17. doi:10.1029/2012JD017968

    Google Scholar 

  • Giorgi F (1990) Simulation of regional climate using a limited area model nested in general circulation model. J Clim 3:941–963. doi:10.1175/1520-0442(1990)003<0941:SORCUA>2.0.CO;2

    Article  Google Scholar 

  • Glotter M, Elliott J, McInerney D, Best N, Foster I, Moyer EJ (2014) Evaluating the utility of dynamical downscaling in agricultural impacts projections. Proc Natl Acad Sci U S A 111(24):8776–8781

    Article  Google Scholar 

  • Haylock MR, Hofstra N, Klein Tank AMG, Klok EJ, Jones PD, New M (2008) A European daily high-resolution gridded dataset of surface temperature and precipitation. J Geophys Res Atmos 113, D20119. doi:10.1029/2008JD10201

    Article  Google Scholar 

  • Herrera S et al (2012) Development and analysis of a 50 year high-resolution daily gridded precipitation dataset over Spain (Spain02). Int J Climatol 32:74–85. doi:10.1002/joc.2256

    Article  Google Scholar 

  • Hoffmann H, Rath T (2012) Meteorologically consistent bias correction of climate time series for agricultural models. Theor Appl Climatol 110:129–141. doi:10.1007/s00704-012-0618-x

    Article  Google Scholar 

  • Ines AVM, Hansen JW (2006) Bias correction of daily GCM rainfall for crop simulation studies. Agric For Meteorol 138:44–53. doi:10.1016/j.agrformet.2006.03.009

    Article  Google Scholar 

  • Jones CA, Kiniry JR (1986) CERES-Maize: a simulation model of maize growth and development. Texas A&M University Press, College Station

    Google Scholar 

  • Jones PD, Harpham C, Goodess CM, Kilsby CG (2011) Perturbing a weather generator using change factors derived from regional climate model simulations. Nonlin Process Geophys 18:503–511

    Article  Google Scholar 

  • Kilsby CG, Jones PD, Burton A, Ford AC, Fowler HJ, Harpham C, James P, Smith A, Wilby RL (2007) A daily weather generator for use in climate change studies. Environ Model Softw 22:1705–1719

    Article  Google Scholar 

  • Kjellström E, Boberg F, Castro M, Christensen JH, Nikulin G, Sánchez E (2010) Daily and monthly temperature and precipitation statistics as performance indicators for regional climate models. Clim Res 44:135–150

    Article  Google Scholar 

  • Liu M, Chung SH, Rajagopalan K, Jiang X, Harrison J, Nergui T, Guenther A, Miller C, Reyes J, Tague C, Choate J, Salathé EP, Stöckle CO, Adam JC (2014) What is the importance of climate model bias when projecting the impacts of climate change on land surface processes? Biogeosciences 11(10):2601–2622. doi:10.5194/bg-11-2601-2014

    Article  Google Scholar 

  • Maraun D (2012) Non stationarities of regional climate model biases in European seasonal mean temperature and precipitation sums. Geophys Res Lett 39, L06706

    Article  Google Scholar 

  • Mearns LO, Giorgi F, Mcdaniel L, Shields C (2003) Climate scenarios for the southeast U.S. based on GCM and regional model simulations. Clim Chang 60:7–35

    Article  Google Scholar 

  • Michelangeli PA, Vrac M, Loukos H (2009) Probabilistic downscaling approaches: application to wind cumulative distribution functions. Geophys Res Lett 36, L11708

    Article  Google Scholar 

  • Mínguez MI, Ruiz-Ramos M, Díaz-Ambrona CH, Quemada M, Sau F (2007) First-order impacts on winter and summer crops assessed with various high-resolution climate models in the Iberian Peninsula. Clim Chang 81(SI):343–355

    Article  Google Scholar 

  • Nakicenovic N, Swart R (eds) (2000) Emissions scenarios. Special report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge

    Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models, part I - a discussion of principles. J Hydrol 10:282–290

    Article  Google Scholar 

  • Nikulin G, Kjellström E, Hansson U, Jones C, Strandberg G, Ullerstig A (2011) Evaluation and future projections of temperature, precipitation and wind extremes over Europe in an ensemble of regional climate simulations. Tellus A 63:41–55. doi:10.1111/j.1600-0870.2010.00466.x

    Article  Google Scholar 

  • Oettli P, Sultan B, Baron C, Vrac M (2011) Are regional climate models relevant for crop yield prediction in West Africa? Environ Res Lett 6:014008

    Article  Google Scholar 

  • Piani C, Haerter JO (2012) Two dimensional bias correction of temperature and precipitation copulas in climate models. Geophys Res Lett 39, L20401

    Article  Google Scholar 

  • Piani C, Haerter JO, Coppola E (2010a) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 99:187–192. doi:10.1007/s00704-009-0134-9

    Article  Google Scholar 

  • Piani C, Weedon GP, Best M, Gomes SM, Viterbo P, Hagemann S, Haerter JO (2010b) Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. J Hydrol 395:199–215. doi:10.1016/j.jhydrol.2010.10.024

    Article  Google Scholar 

  • Räisänen J, Räty O (2013) Projections of daily mean temperature variability in the future: cross-validation tests with ENSEMBLES regional climate simulations. In: Climate dynamics: observational, theoretical and computational research on the climate system, 41:5–6, p. 1553–1568

  • Räty O, Räisänen J, Ylhäisi J (2014) Evaluation of delta change and bias correction methods for future daily precipitation: intermodel cross-validation using ENSEMBLES simulations. In: Climate dynamics: observational, theoretical and computational research on the climate system, 42:9–10, p. 2287–2303

  • Rötter RP, Höhn J, Trnka M, Fronzek S, Carter TR, Kahiluoto H (2013) Modelling shifts in agroclimate and crop cultivar response under climate change. Ecol Evol 3(12):4197–4214. doi:10.1002/ece3.782

    Article  Google Scholar 

  • Ruffault J, Martin-StPaul NK, Duffet C, Goge F, Mouillot F (2014) Projecting future drought in Mediterranean forests: bias correction of climate models matters! Theor Appl Climatol 117(1–2):113–122. doi:10.1007/s00704-013-0992-z

    Article  Google Scholar 

  • Ruiz-Ramos M, Sánchez E, Gallardo C, Mínguez MI (2011) Impacts of projected maximum temperature extremes for C21 by an ensemble of regional climate models on cereal cropping systems in the Iberian Peninsula. Nat Hazards Earth Syst Sci 11:3275–3291

    Article  Google Scholar 

  • Sánchez E, Gallardo C, Gaertner MA, Arribas A, Castro M (2004) Future climate extreme events in the Mediterranean simulated by a regional climate model: a first approach. Glob Planet Chang 44:163–180

    Article  Google Scholar 

  • Sánchez E, Domínguez M, Romera R, López de la Franca N, Gaertner MA, Gallardo C, Castro M (2011) Regional modeling of dry spells over the Iberian Peninsula for present climate and climate change conditions. Clim Chang 107:625–634. doi:10.1007/s10584-011-0114-9

    Article  Google Scholar 

  • Stéfanon M, Martin-StPaul NK, Leadley P, Bastin S, Dell’Aquila A, Drobinski P, Gallardo C (2015) Testing climate models using an impact model: what are the advantages? Clim Chang 131(4):649–661. doi:10.1007/s10584-015-1412-4

    Article  Google Scholar 

  • Teutschbein C, Seibert J (2010) Regional climate models for hydrological impact studies at the catchment scale: a review of recent model strategies. Geogr Compass 4(7):834–860. doi:10.1111/j.1749-8198.2010.00357.x

    Article  Google Scholar 

  • 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. doi:10.1016/j.jhydrol.2012.05.052

  • van der Linden P, Mitchell JFB (eds) (2009) ENSEMBLES: climate change and its impacts: summary of research and results from the ENSEMBLES Project. Met Off Hadley Cent, Exeter

    Google Scholar 

  • Wang Y, Leung LR, McGregor JL, Lee DK, Wang WC, Ding Y, Kimura F (2004) Regional climate modeling: progress, challenges, and prospects. J Meteorol Soc Jpn Ser II 82:1599–1628

    Article  Google Scholar 

  • Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Chang 62:189–216. doi:10.1023/B:CLIM.0000013685.99609.9e

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by MACSUR-Modelling European Agriculture with Climate Change for Food Security, FACCE JPI, and by MULCLIVAR, from the Spanish Ministerio de Economía y Competitividad (CGL2012-38923-C02-02).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. Ruiz-Ramos.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(DOCX 436 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ruiz-Ramos, M., Rodríguez, A., Dosio, A. et al. Comparing correction methods of RCM outputs for improving crop impact projections in the Iberian Peninsula for 21st century. Climatic Change 134, 283–297 (2016). https://doi.org/10.1007/s10584-015-1518-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10584-015-1518-8

Keywords

Navigation