Réf. Najac & al. 2011 - A

Référence bibliographique complète

NAJAC, J., LAC, C., TERRAY, L. 2011. Impact of climate change on surface winds in France using a statistical-dynamical downscaling method with mesoscale modelling. International Journal of Climatology, Vol. 31, 415–430. DOI PDF

Abstract: A statistical-dynamical downscaling method is presented to estimate 10 m wind speed and direction distributions at high spatial resolutions using a weather type based approach combined with a mesoscale model. Daily 850 hPa wind fields (predictors) from ERA40 reanalysis and daily 10 m wind speeds and directions (predictands) measured at 78 meteorological stations over France are used to build and validate the downscaling algorithm over the period 1974–2002. First of all, the daily 850 hPa wind fields are classified into a large number of wind classes and one day is randomly chosen inside each wind class. Simulations with a non-hydrostatic mesoscale atmospheric model are then performed for the selected days over three interactively nested domains over France, with finest horizontal mesh size of 3 km over the Mediterranean area. The initial and coupling fields are derived from the ERA40 reanalysis. Finally, the 10 m wind distributions are reconstructed by weighting each simulation by the corresponding wind class frequency. Evaluation and uncertainty assessment of each step of the procedure is performed. This method is then applied for a climate change impact study: daily 850 hPa wind fields from 14 general circulation models of the CMIP3 multimodel dataset are used to determine evolutions in the frequency of occurrence of the wind classes and to assess the potential evolution of the wind resources in France. Two time periods are focused on: a historical period (1971–2000) from the climate of the twentieth century experiment and a future period (2046–2065) from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) experiment. Evolution of the 10 m winds in France and associated uncertainties are discussed. Significant changes are depicted, in particular a decrease of the wind speed in the Mediterranean area. Copyright © 2010 Royal Meteorological Society

Mots-clés

Downscaling - Mesoscale modelling - Surface winds - Climate change - Wind energy

 

Organismes / Contact

• Climate Modelling and Global Change Team, CERFACS/CNRS, SUC URA 1875, 42 Avenue Gaspard Coriolis, 31057, Toulouse Cedex 1, France - Email: Laurent Terray (terray@cerfacs.fr)
• CNRM/GMME/Meso-NH, 42 Avenue Gaspard Coriolis, 31057 Toulouse, France

 

(1) - Paramètre(s) atmosphérique(s) modifié(s)

(2) - Elément(s) du milieu impacté(s)

(3) - Type(s) d'aléa impacté(s)

(3) - Sous-type(s) d'aléa

 

 

 

 

 

Pays / Zone

Massif / Secteur

Site(s) d'étude

Exposition

Altitude

Période(s) d'observation

 

 

 

 

 

 

 

(1) - Modifications des paramètres atmosphériques

Reconstitutions

 

Observations

 

Modélisations

 

Hypothèses

 

 

Informations complémentaires (données utilisées, méthode, scénarios, etc.)

 

 

(2) - Effets du changement climatique sur le milieu naturel

Reconstitutions

 

Observations

 

Modélisations

Climate scenario

Downscaled winds

We now focus on mean changes in the downscaled 10 m winds for the 2046–2065 period relative to the 1971–2000-historical period (Figure 15 for ONDJFM and Figure 16 for AMJJAS). The mean changes are simply estimated as the difference between the climatology of the two periods.

In ONDJFM, the northwest experiences a low increase (maximum of 2.6%) while the Mediterranean area experiences a stronger decrease of the mean 10 m wind speed (maximum of 5.8%). This is associated with a decrease of the westerly, north-westerly and northerly winds over the southeast (decrease of the Mistral and Tramontana events), and an increase of the south-westerly winds over the northwest. Decrease of the wind speeds in the southeast is particularly pronounced in areas where the Mistral and Tramontana winds are usually the strongest: the Rhone valley between the Alps and the Massif Central with an extension over the sea, and the most southern part of France where the Pyrenees reach the Mediterranean sea and which extends far away over the sea. In the northwest, changes are more uniform. Differences between the 9- and 3-km domains are weak, except that results from the 3-km domain obviously provide more details. Most models are in agreement concerning the sign of the changes in those areas (80% of the models agree) while the sign of the changes is unclear in the Centre and the southwest of France. The dispersion of the models is of the same order of magnitude as the amplitude of the changes all over France. This means that although there is high confidence in the sign of the changes in the southeast and the northwest, the amplitude of the changes remains uncertain.

In AMJJAS, the southern part of France (but the Rhone valley) experiences a decrease of the mean wind speed. This is associated with an increase of the northerly winds all over France. Increase of the northerly winds only leads to a very small increase of the mean wind speed in a very limited area in the Rhone valley, where the complex topography accelerates the wind flows. However, uncertainties are higher than in ONDJFM, implying that the sign and the amplitude of the changes are unclear all over France, except in the Centre (maximum decrease of 4.8%). However, even in the Centre, the dispersion of the models is of the same order of magnitude as the changes. Differences between the 9- and 3-km domains are also weak.

Those results are in agreement with the 10 m wind changes found by Najac et al. (2009), with regard to both the sign and the amplitude of the changes.

Changes in the weather type occurrences

The changes in the 10 m winds that have been highlighted previously may be linked to changes in weather type occurrences. The relevance of this approach relies on recent studies which suggested that anthropogenic climate change may manifest itself as a projection onto the preexisting natural modes of variability of the climate system (Corti et al., 1999; Stone et al., 2001).

As illustrated in Figure 17, multimodel mean changes, reflecting a biased estimator of the response to the anthropogenic forcing, occur in ONDJFM: WT1cold occurrence increases by 8% and WT4cold occurrence by 11%, while WT2cold occurrence decreases by 13% and WT5cold occurrence by 10% (percentages of increase/decrease relative to the frequency of occurrence in the historical simulation). Note that those results agree with previous studies concerning changes in the residence frequency of the climate system in the wintertime North Atlantic–European atmospheric circulation regimes (Terray et al., 2004; Stephenson et al., 2006). These 10 m wind changes have amplitude which is usually weaker than the multimodel projection spread, making them hard to detect. Nevertheless, those changes in the weather type occurrences may have additive effects and give rise to larger changes in the wind speed distribution. Indeed, according to Section 4.1 and Figure 3,WT4cold is associated with strong south-westerly winds in northern France, WT1cold with weak anticyclonic winds over France, WT5cold with weak northerly winds in northern France and strong wind events in the Mediterranean area (Mistral and Tramontana), and WT2cold with weak north-easterly winds all over France. As a consequence, in ONDJFM, changes in the weather type occurrence are expected to lead to a decrease of the wind speed in the Mediterranean area and an increase in north-western France. This is in good agreement with the 10 m wind speed changes highlighted in Section 4.3.1.

In AMJJAS, WT1warm occurrence increases by 14%, while WT2warm occurrences decrease by 11% and WT5warm occurrences decrease by 9%. According to Section 4.1 and Figure 4, WT1warm is associated with weak anticyclonic winds over France, WT2warm with southerly winds all over France and WT5warm with strong south-westerly winds in northern France. As a result, changes in the weather type occurrences in AMJJAS are expected to lead to a low decrease of the wind speed all over France. This is also in agreement with the 10 m wind speed changes highlighted in Section 4.3.1.

However, Najac et al. (2009) showed that changes in the weather type occurrences are only a part of the climate change signal and are not sufficient to explain the whole change in the 10 m winds. Indeed changes in the distribution of the days within the weather types may be as much important. Note that, in our method, those changes are accounted for by changes in the occurrences of the wind classes, which have been defined within the weather types.

Hypothèses

 

 

Sensibilité du milieu à des paramètres climatiques

Informations complémentaires (données utilisées, méthode, scénarios, etc.)

The surface winds are mainly driven by the large scale circulation (LSC). However, several local features such as the surface roughness and the orography modify the spatial and temporal features of the surface winds. Because of their coarse resolution, general circulation models (GCMs), cannot represent the small spatial scale variability of the near surface winds (Pryor and Schoof, 2005). However, they show reasonable skill in simulating the global climate and the LSC. To bridge this scale gap, different downscaling strategies have been developed (Wilby et al., 2004). They consist in deriving the local climate state from the GCM’s coarse resolution climate state (Giorgi and Mearns, 1991). (…)

In this paper the authors follow the main ideas of the methodology described by Frey-Buness et al. (1995) and apply a similar approach for France. They use a non-hydrostatic mesoscale model with three nested domains with the middle one covering the whole of France at a 9-km horizontal resolution. The inner one covers the southeast of France at 3-km resolution. This method is applied to estimate the impact of climate change on the near-surface winds in France with a multimodel approach. The main advantage of this hybrid method is to combine a multimodel approach in terms of the large-scale predictors with a complete and high spatial resolution for the predictands which are provided by the mesoscale model.

A statistical-dynamical downscaling method is presented to estimate 10 m wind speed and direction distributions at high spatial resolutions using a weather type based approach combined with a mesoscale model. Daily 850 hPa wind fields (predictors) from ERA40 reanalysis and daily 10 m wind speeds and directions (predictands) measured at 78 meteorological stations over France are used to build and validate the downscaling algorithm over the period 1974–2002. First of all, the daily 850 hPa wind fields are classified into a large number of wind classes and one day is randomly chosen inside each wind class. Simulations with a non-hydrostatic mesoscale atmospheric model are then performed for the selected days over three interactively nested domains over France, with finest horizontal mesh size of 3 km over the Mediterranean area. The initial and coupling fields are derived from the ERA40 reanalysis. Finally, the 10 m wind distributions are reconstructed by weighting each simulation by the corresponding wind class frequency. Evaluation and uncertainty assessment of each step of the procedure is performed. This method is then applied for a climate change impact study: daily 850 hPa wind fields from 14 general circulation models of the CMIP3 multimodel dataset are used to determine evolutions in the frequency of occurrence of the wind classes and to assess the potential evolution of the wind resources in France. Two time periods are focused on: a historical period (1971–2000) from the climate of the twentieth century experiment and a future period (2046–2065) from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) experiment. Evolution of the 10 m winds in France and associated uncertainties are discussed. [See details in the study]

 

(3) - Effets du changement climatique sur l'aléa

Reconstitutions

 

Observations

 

Modélisations

Hypothèses

 

 

Paramètre de l'aléa

Sensibilité des paramètres de l'aléa à des paramètres climatiques

Informations complémentaires (données utilisées, méthode, scénarios, etc.)

 

 

 

 

(4) - Remarques générales

 

 

(5) - Synthèses et préconisations

Conclusion

In this paper we have presented a statistical–dynamical downscaling method to estimate the 10 m wind speed and direction distributions at high spatial resolution. Relatively good agreements between the observed and the reconstructed wind roses were found, justifying the use of the method in studies of the impacts due to future climate change.

The multimodel study of the impact of climate change on the wind resources over France was carried out for the 2046–2065 period with the 1971–2000 period as a reference. Concerning the mean 10 m wind speeds, although there is confidence in the sign of the changes in some areas (increase in the northwest and decrease in the southeast in ONDJFM, decrease in the Centre in AMJJAS), there is a large uncertainty with regard to the amplitude of those changes. Furthermore, the changes that have been highlighted remain low (maximum of 5.8%). Those results are in good agreement with previous studies (Najac et al., 2009).

In this work no attempt has been made to constrain the source of uncertainty linked to the climate models. Many possibilities exist such as to weigh the models according to their ability in reproducing the present climate distributions (Tebaldi and Knutti, 2007) or the key physical processes responsible for the spread of future projections (Boé and Terray, 2008). These different options will be explored in future work.

We have also analysed the various sources of errors from the method itself. The main drawbacks of the statistical–dynamical downscaling method are the addition of two sources of errors (errors that originate from the day sampling and errors that originate from the mesoscale simulations) and the assumption that the climate change signal may be entirely captured by changes in the wind class frequency of occurrence. Furthermore, while a sample of 200 days appeared to be satisfactory to represent the 10 m wind speed and direction distributions, the current version of the method is not adapted for extreme wind studies. The main advantage of this method is that it can provide crucial information at the scale of interest for policymakers. Moreover, given the mesoscale simulations, it can be easily applied to a wide range of GCMs for different time periods, which is essential to carry out relevant impact studies.

Références citées :

Bée J, Terray L. 2008. Uncertainties in summer evapotranspiration changes over Europe and implications for regional climate change. Geophysical Research Letters 35: L05702, DOI:10.1029/2007GL032417.

Corti S, Molteni F, Palmer TN. 1999. Signature of recent climate change in frequencies of natural atmospheric circulation regimes. Nature 398: 799–802.

Frey-Buness F, Heimann D, Sausen R. 1995. A statistical-dynamical downscaling procedure for global climate simulations. Theoretical and Applied Climatolgy 50: 117–131.

Giorgi F, Mearns LO. 1991. Approaches to the simulation of regional climate change: a review. Reviews of Geophysics 29(2): 191–216.

Najac J, Bo`e J, Terray L. 2009. A multi-model ensemble approach for assessment of climate change impact on surface winds in France. Climate Dynamics 32(5): 615–634.

Pryor SC, Schoof JT. 2005. Empirical downscaling of wind speed probability distributions. Journal of Geophysical Research 110: D19109.

Stephenson DB, Pavan V, Collins M, Junge MM, Quadrelli R, and participating CMIP2 modelling groups. 2006. North Atlantic oscillation response to transient greenhouse gas forcing and the impact on european winter climate: a CMIP2 multi-model assessment. Climate Dynamics 27: 401–420.

Stone DA, Weaver AJ, Stouffer RJ. 2001. Projection of climate change onto modes of atmospheric variability. Journal of Climate 14: 3551–3565.

Taylor KE. 2001. Summarizing multiple aspects of model performance in single diagram. Journal of Geophysical Research 106(D7): 7183–7192.

Tebaldi C, Knutti R. 2007. The use of the multimodel ensemble in probabilistic climate projections. Philosophical Transactions of the Royal Society of London, Series A 365: 2053–2075, DOI:10.1098/rsta.2007.2076.

Terray L, Demory ME, D´equ´e M, Coetlogon G, Maisonnave E. 2004. Simulation of late-twenty-first-century changes in wintertime atmospheric circulation over Europe due to anthropogenic causes. Journal of Climate 17(24): 4630–4635.

Wilby RL, Charles SP, Zorita E, Timbal B, Whetton P, Mearns LO. 2004. Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods. Data Distribution Centre of the Intergovernmental Panel on Climate Change.