Original Paper

Theoretical and Applied Climatology

, Volume 117, Issue 1, pp 113-122

First online:

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

  • Julien RuffaultAffiliated withCentre d’écologie fonctionnelle et évolutive (CEFE), CNRSIRD, UMR CEFE (Centre d’écologie fonctionnelle et évolutive) Email author 
  • , Nicolas K Martin-StPaulAffiliated withLaboratoire Ecologie Systématique et Evolution (ESE) (UMR8079), CNRS, Université Paris Sud
  • , Carole DuffetAffiliated withIRD, UMR CEFE (Centre d’écologie fonctionnelle et évolutive)
  • , Fabien GogeAffiliated withIRD, UMR CEFE (Centre d’écologie fonctionnelle et évolutive)
  • , Florent MouillotAffiliated withIRD, UMR CEFE (Centre d’écologie fonctionnelle et évolutive) Email author 

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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.