Theoretical and Applied Climatology

, Volume 117, Issue 1–2, pp 113–122 | Cite as

Projecting future drought in Mediterranean forests: bias correction of climate models matters!

  • Julien Ruffault
  • Nicolas K Martin-StPaul
  • Carole Duffet
  • Fabien Goge
  • Florent Mouillot
Original Paper


Global and regional climate models (GCM and RCM) are generally biased and cannot be used as forcing variables in ecological impact models without some form of prior bias correction. In this study, we investigated the influence of the bias correction method on drought projections in Mediterranean forests in southern France for the end of the twenty-first century (2071–2100). We used a water balance model with two different atmospheric climate forcings built from the same RCM simulations but using two different correction methods (quantile mapping or anomaly method). Drought, defined here as periods when vegetation functioning is affected by water deficit, was described in terms of intensity, duration and timing. Our results showed that the choice of the bias correction method had little effects on temperature and global radiation projections. However, although both methods led to similar predictions of precipitation amount, they induced strong differences in their temporal distribution, especially during summer. These differences were amplified when the climatic data were used to force the water balance model. On average, the choice of bias correction leads to 45 % uncertainty in the predicted anomalies in drought intensity along with discrepancies in the spatial pattern of the predicted changes and changes in the year-to-year variability in drought characteristics. We conclude that the choice of a bias correction method might have a significant impact on the projections of forest response to climate change.


Regional Climate Model Bias Correction Drought Index Global Radiation Soil Water Potential 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is a contribution to the UE 7th FP Env. FUME “Forest fires under climate, social and economic changes in Europe, the Mediterranean and other fire-affected areas of the world”, grant agreement no. 243888. A doctoral research grant was provided to JR by the Languedoc-Roussillon region, the Centre National de la Recherche Scientifique, and the ANR project SCION (no. ANR-09-PEXT-006) and a postdoctoral grant to CD by the ANR project MESOEROS (grant agreement no. ANR-06-VULN-012). A postdoctoral grant to NKM-SP was provided by the HUMBOLDT project, which is part of the GIS Climate-Environment-Society consortium.

Supplementary material

704_2013_992_MOESM1_ESM.pdf (3.6 mb)
ESM 1 (PDF 3665 kb)


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Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  1. 1.Centre d’écologie fonctionnelle et évolutive (CEFE)CNRSMontpellier cedex 5France
  2. 2.IRD, UMR CEFE (Centre d’écologie fonctionnelle et évolutive)Montpellier cedex 5France
  3. 3.Laboratoire Ecologie Systématique et Evolution (ESE) (UMR8079)CNRS, Université Paris SudOrsayFrance

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