Bias correction of the ENSEMBLES high resolution climate change projections for use by impact...

Bias correction of the ENSEMBLES high resolution climatechange projections for use by impact models: Analysisof the climate change signal

A. Dosio,1 P. Paruolo,2 and R. Rojas1

Received 19 April 2012; revised 4 July 2012; accepted 6 July 2012; published 6 September 2012.

[1] A statistical bias correction technique is applied to twelve high-resolution climatechange simulations of temperature and precipitation over Europe, under the SRES A1Bscenario, produced for the EU project ENSEMBLES. The bias correction technique isbased on a transfer function, estimated on current climate, which affects the wholeProbability Distribution Function (PDF) of variables, and which is assumed constantbetween the current and future climate. The impact of bias correction on 21st Centuryprojections, their inter-model variability, and the climate change signal is investigated, withfocus being on discrepancies between the original and the bias-corrected results. Asassessing the impact of climate change is significantly dependent on the frequency ofextreme events, we also analyze the evolution of the shape of the PDFs, and extreme eventsindices. Results show that the ensemble mean climate change signal and its inter-modelvariability are generally conserved. However, the impact of the bias correction variesamongst regions, seasons and models, and differences up to 0.5�C for the summertemperature climate change signal are found in Southern Europe. Finally the biascorrection is found to influence the probability of extreme events like extremely hot or frostdays, which also impacts the climate change signal.

Citation: Dosio, A., P. Paruolo, and R. Rojas (2012), Bias correction of the ENSEMBLES high resolution climate changeprojections for use by impact models: Analysis of the climate change signal, J. Geophys. Res., 117, D17110,doi:10.1029/2012JD017968.

1. Introduction

[2] High-resolution climate change projections are beingincreasingly used as an input for process-based models toassess the impact of climate change on e.g. the hydrologicalcycle or crop production. By employing limited-area climatemodels (regional climate models: RCMs) driven by theoutputs of global climate models (GCMs), information onthe evolution of, e.g., temperature and precipitation can beobtained at high spatial and temporal resolutions. Thistechnique is usually referred to as dynamical downscaling[e.g., Giorgi, 1990] and it is particularly useful in regionsof complex topography, or with highly heterogeneousland-cover, where GCMs are unable to resolve small scalefeatures [e.g., [Wilby et al., 2004].[3] RCMs, however, are not able to improve the simula-

tion skills of large-scale fields over those simulated by theGCMs [Rockel et al., 2008]; as a consequence, the

dynamically downscaled climate may still retain large errors,inherited from the driving GCM, in addition to those intro-duced by the RCM by means of model errors and para-meterizations [e.g., Fowler et al., 2007; Beniston et al.,2007; Kjellström et al., 2010; Nikulin et al., 2011]. Forinstance, several studies show that modeled summer tem-perature in Southern Europe is usually overestimated [e.g.,Christensen et al., 2008], whereas large biases exist forprecipitation especially at the higher tail of the probabilitydistribution function (PDF) [Kjellström et al., 2010]. Theuse of the outputs of climate models for impact assessmentmay therefore lead to unrealistic results, unless these biasesare corrected [e.g.,Wood et al., 2004; Baigorria et al., 2007;Christensen et al., 2008; Teutschbein and Seibert, 2010;Boberg and Christensen, 2012].[4] Bias correction techniques are post-processing, statis-

tical methods that usually employ a transfer function tomatch the cumulative distribution functions (CDFs) ofmodeled and observed data [e.g., Wood et al., 2004; Inesand Hansen, 2006; Li et al., 2010; Piani et al., 2010a,2010b; Themeßl et al., 2011a, 2011b; Heinrich and Gobiet,2011]. In a previous work, Dosio and Paruolo [2011]applied the bias correction technique originally developedby Piani et al. [2010b] to a set of high-resolution climatechange projections for Europe developed in the frameworkof the European Union 6th Framework Programme project

1Institute for Environment and Sustainability, European CommissionJoint Research Centre, Ispra, Italy.

2Department of Economics, University of Insubria, Varese, Italy.

Corresponding author: A. Dosio, Institute for Environment andSustainability, European Commission Joint Research Centre, Via E. Fermi2749, IT-21027 Ispra, Italy. ([email protected])

©2012. American Geophysical Union. All Rights Reserved.0148-0227/12/2012JD017968

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117, D17110, doi:10.1029/2012JD017968, 2012

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ENSEMBLES [Van der Linden and Mitchell, 2009]. Theyevaluated the results on the present climate, showing that themethodology performed satisfactorily not only for meanquantities but even for time dependent statistical properties,such as the number of consecutive dry days and the cumu-lative amount of rainfall for consecutive heavy precipitationdays. By using a large ensembles of model runs, the GCM-RCM inter-model variability was also taken into account, asit can have a large impact on, e.g., hydrological impactstudies, as pointed out by Wilby et al. [2004].[5] Recent works have investigated the application of a

bias correction method to climate models’ outputs; inparticular, Hagemann et al. [2011] applied the same biascorrection technique employed here on the projectedhydrological changes obtained from three GCMs and twohydrological models; Heinrich and Gobiet [2011] evalu-ated the future of dry and wet spell in Europe as modeledby 8 GCM-RCMs ENSEMBLES runs, but no comparisonbetween bias-corrected and original results was shown;finally, Themeßl et al. [2011b] investigated the differencesbetween original and bias-corrected climate change signal,but for a single model only, thus not taking into accountinter-model variability. Recently, Boberg and Christensen[2012] applied a temperature-dependent bias-correction tothe ENSEMBLES runs, and they found that the uncor-rected climate change signal for southern Europe in sum-mer may be overestimated.[6] This study builds on the work by Dosio and Paruolo

[2011] and focuses on the analysis of the impact of biascorrection on the climate change signal for temperature andprecipitation as modeled by a large ensemble of GCM-RCMruns. In particular, as impact models may be significantlydependent on the occurrence and frequency of extremeevents (i.e. the tails of the PDF), we analyze the impact ofthe bias correction on the shape of the PDF, and, in turn, onthe value of extreme climatic indices.[7] The paper is structured as follows: Section 2 describes

very shortly the bias correction technique and the models’runs analyzed in this study; in Section 3 the results are dis-cussed for temperature and precipitation, with focus beingon both mean values and extreme events. In Section 4 wediscuss the bivariate temperature and precipitation PDF,we analyze the CDFs of the climate change signal, and wepropose a method to select a sub-set of runs that conservesthe main statistical properties of the whole ensemble.Finally, a summary of the results and concluding remarks arepresented in Section 5.

2. Methods and Data

2.1. The Bias Correction Technique

[8] In this section we briefly describe the bias correctiontechnique employed in this work, which was originallydeveloped by Piani et al. [2010b]. A more detaileddescription of the technique, and the evaluation of the resultson the present climate can be found in Dosio and Paruolo[2011], to which we refer for details.[9] Let xt denote the time series of an observed climate

variable, where t is time (here, days), and let xmod,t the pre-diction of a climate model for the same variable. The biascorrection is based on the calculation of a transfer function G(henceforth referred to as: TF) which, when applied to model

predictions xmod,t delivers the corrected prediction xcor,t =G(xmod,t), such the one of the PDF of xcor,t matches that ofthe observed measurement xt. G is allowed to depend on t,but we report this dependence only when needed forclarity. The TF G is estimated parametrically as a functionof up to 4 parameters, using the following functionalforms:

xcor;t ¼ aþ bxmod;t ð1Þ

xcor;t ¼ aþ bxmod;t� �

1� exp � xmod;t � x0� �

=t� �� ð2Þ

Here, a, b, x0 and t are parameters. The linear functionalform (equation (1)) is used for temperature. FollowingPiani et al. [2010b], we also used equation (1) for pre-cipitation, unless it gave unsatisfactory results, in the formof estimated parameters a and b in equation (1) of the typea > 0, b < 0.2 or b > 5. In this case we reverted to themore complex four-parameter TF (equation (2)). A morecomplete evaluation of the impact of using different TFsfor precipitation can be found in Piani et al. [2010b].[10] The parameters of the TF (1)–(2) are estimated by

least squares on sorted values of xt and xmod,t in a givenmonth m, so that monthly TF are produced, here indicated asG(m). In order to interpolate the monthly TF into daily ones,Piani et al. [2010b] suggested the following smoothingtechnique. Let tm indicate the value of t at the center ofmonth m, and consider a generic day t with tm < t ≤ tm+1; thedaily TF, denoted by Gt, were then defined as

Gt ¼ atG mð Þ þ ð1� atÞG mþ1ð Þ; at :¼ t � tmtmþ1 � tm

;

tm < t ≤ tmþ1 ð3Þ

For the case of temperature, mean, minimum and maximumdaily temperature (Tmean, Tmin, and Tmax, respectively), arebias-corrected after a preliminary transformation to (Tmean,Trng, Tsk), where Trng: = Tmax � Tmin (daily temperaturerange) and Tsk: = (Tmean � Tmin)/Trng (daily temperatureskewness). After correcting (Tmean, Trng, Tsk), the correctedmeasures were transformed back to Tmean, Tmin, and Tmax, asexplained by Piani et al. [2010b].[11] Bias correction parameters are calculated by

employing the E-OBS dataset [Haylock et al., 2008], whichincludes daily observations of temperature and precipitationcovering the whole European land area, and spatially aver-aged on the same grid as the ENSEMBLES RCMs.Although there may be some potentially important limita-tions to the observational dataset, such as heterogeneities(both spatial and temporal) and large absolute and relativedifferences over regions where dense station networks exist[e.g., Hofstra et al., 2009] or under-catching in mountainareas [Lenderink, 2010], however, the climate statistics forthe E-OBS dataset are very similar to e.g. the CRU dataset[e.g., Hofstra et al., 2009].[12] A 30 year period (1961–1990) was chosen for the

construction of the TF in order to take longer climate vari-ability into account. As pointed out by Piani et al. [2010b]and Berg et al. [2012], if the bias correction is constructedover shorter periods of time (e.g. a single decade) the com-ponent of the error linked to decadal variability may not beremoved. However, Chen et al. [2011] showed, using the

DOSIO ET AL.: BIAS CORRECTING THE ENSEMBLES A1B RUNS D17110D17110

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very same technique adopted here, that the contribution tothe uncertainty in hydrological projections from choice ofdecade used as construction period, was small compared toother sources such as the choice of GCM, the hydrologicalmodel, or the Green-House Gases (GHG) scenario.

2.2. Data

[13] The bias correction technique was applied to a set ofhigh resolution climate change projections performed bystate-of-the-art GCMs and RCMs in the framework of theFP6 project ENSEMBLES [Van der Linden and Mitchell,2009], listed in Table 1. The simulations cover the period1961–2100 under the SRES A1B climate change scenario[Nakicenovic and Swart, 2000], with a domain covering theentire European continent at a resolution of about 25 km. Asexplained, the evaluation of the results on the present climatehas already been described by Dosio and Paruolo [2011]. Itis important to note that the bias correction technique wasapplied to the GCM driven RCM data, and not, as is alsocommon, to a reanalysis driven simulations, thus correctingboth the GCM and RCM biases. Here we analyze the tem-poral evolution over the 21st Century of temperature andprecipitation, and the climate change signal (i.e., the differ-ence between future and reference periods), with focus beingon the analysis of discrepancies between the original and thebias-corrected results. In addition, as impact models may besignificantly dependent on the occurrence and frequency ofextreme events (i.e. the tails of the PDF), we analyze theevolution of the shape of the PDF, and we calculate extremeevents indices, which are in turn compared with the original(uncorrected) ones.[14] In the following analysis, results have sometimes been

spatially averaged over geographical regions defined as inChristensen and Christensen [2007]: Alps (AL); British Isles(BI); Eastern Europe (EA); France (FR); Iberian Peninsula(IP); Mid-Europe (ME); Mediterranean (MD); Scandinavia(SC). Two additional macro-regions have also been used:Northern Europe (NEU) and Southern Europe (SEU), definedas all the land points above or below 50�N, respectively.

3. Evaluation of the Results

3.1. Surface Temperature

3.1.1. Annual and Seasonal Means[15] Figure 1 shows the temporal evolution of the (5-year

running) mean surface temperature from 1961 to the end of

the 21st Century, averaged over several geographic areas,according to the original ENSEMBLES models (upper rows)and after bias correction (lower rows). In the same figure, theobserved temperature (E-OBS) is shown for the period1961–2010.[16] From the analysis of the original results, it is clear

that most of the models present a significant bias, whosesign depends strongly on the model and the region: forexample, in general, models tend to underestimate temper-ature in Northern Europe (British Isles, and Scandinavia),with DMI-HIRHAM5-BCM showing a cold bias up to 5�C.In Southern and Eastern Europe, on the contrary, most ofthe models tend to overestimate the mean temperature.Finally, in some areas such as France and the IberianPeninsula, most of the models simulate the 20-th centurymean temperature relatively well (with biases around 1�C),although DMI-HIRHAM5-ARPEGE shows a constantwarm bias and both DMI-HIRHAM5-BCM and SMHI-RCA-HadCM3Q3 show a cold bias of around 3�C. Suchbiases are a result of the combined errors in both the generalcirculation as simulated by the driving GCMs and small scalefeatures generated by the RCMs; they have been extensivelyanalyzed in the past [e.g.,Christensen et al., 2008;Kjellströmet al., 2010, 2011], and therefore they will not be discussedhere. Over the course of the 21st Century, all the modelsshow a positive trend in the evolution of temperature,although the slope of the increase depends strongly on themodel.[17] After bias correction, all models show a very similar

behavior in the simulation of the mean temperature of the20th Century, and they start to diverge from the middle ofthe 21st Century. The improvement of the results over the1961–1990 period is expected as it is a direct consequenceof the TF being built over the same period.[18] It is interesting to compare the climate change signal

as simulated by the models before and after bias correction;this analysis is shown in Figures 2 and 3.[19] Figure 2 shows the ensemble mean of winter (DJF)

mean temperature for the reference period (1961–1990), atthe end of the 21st Century (2071–2100), and the conse-quent climate change signal. Inter-model variability (stan-dard deviation) for every period is also shown. Over thereference period, the ensemble mean of the original modelsis relatively similar to the observations (E-OBS), althoughtemperature in South–Eastern Europe is generally slightlyoverestimated. However, single models can have very dif-ferent behaviors, as shown by the inter-model standarddeviation, which reaches values of around 3�C in EasternEurope and up to more than 5�C in Scandinavia.[20] As explained, the TF used to bias correct the model

runs has been built over the 1961–1990 construction period;as a result, the bias-corrected ensemble mean temperatureover the reference period is very close to the observed one,and the inter-model standard deviation stays generally below0.25�C.[21] At the end of the 21st Century, both the original and

the bias-corrected ensemble mean show a general increase inthe mean temperature, especially over Northern Europe.However, the inter-model variability for the bias-correctedruns is greatly reduced compared to the original runs, withvalues ranging between 0.75 and 1.5�C for the bias-correctedruns compared to values up to 5�C for the original ones. The

Table 1. List of Model Runs Analyzed in This Studya

Institute RCM Driving GCM

METO-HC HadRM3Q0 HadCM3Q0MPI-M REMO ECHAM5C4I RCA3 HadCM3Q16ETHZ CLM HadCM3Q0KNMI RACMO2 ECHAM5-r3SMHI RCA BCMSMHI RCA HadCM3Q3SMHI RCA ECHAM5-r3DMI HIRHAM5 BCMDMI HIRHAM5 ARPEGEDMI HIRHAM5 ECHAM5CNRM RM5.1 ARPEGE

aFor a full description of the Institutes’ acronyms see, e.g., J. H.Christensen et al. [2010].


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Figure 1. Time evolution of 5-year running mean surface temperature for some geographic areas (BIBritish Isles; SC Scandinavia; IP Iberian Peninsula; FR France; EA Eastern Europe; NEU NorthernEurope; SEU Southern Europe for the original (top two rows) and bias-corrected (B.C.) models’ runs(bottom two rows). The black line represents the E-OBS measurements for the period 1961–2010.Temperature in �C.


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Figure 2. Winter mean surface temperature as simulated by the original ENSEMBLES (top two rows)and bias-corrected (B.C.) runs (bottom two rows). From left to right: E-OBS measurements for the presentreference climate (1961–1990), models’ ensemble average for the present climate; models’ ensemble aver-age at the end of the century (2071–2100); ensemble averaged climate change signal. Inter-model standarddeviations of the mean values and the climate change signal are also shown (second and fourth rows).


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Figure 3. Same as Figure 2 but for the summer mean temperature.


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value of the bias-corrected variability can be therefore inter-preted as the uncertainty related solely to the choice of themodel (GCM and RCM), as the one related to the initial errorin the temperature projection has been removed. It is inter-esting to note that the original and the bias-correctedensemble mean climate change signals (and the relativestandard deviations) are generally similar, thus confirmingthat the robustness of the mean climate change signal asmodeled by the original set of models is conserved by thebias correction technique. However, some differences existover Portugal, Ireland and the Alps, where the bias-correctedclimate change signal is slightly smaller, and over Scandi-navia, where the bias-corrected signal is slightly larger, asalso observed by Themeßl et al. [2011b].[22] Figure 3 shows the ensemble mean summer (JJA)

temperature over the reference period and at the end of the21st Century, together with the climate change signal andthe relative inter-model variability. As in winter the originalensemble mean results are satisfactorily close to the obser-vations, although a general warm bias is present over South–Eastern Europe and a cold bias exists over Finland, aspreviously discussed [e.g., Christensen et al., 2008]. Theinter-model variability shows values up to 3�C over allthe Southern and Mediterranean regions, whereas the bias-corrected results are very close to the observed ones and thestandard deviation is very small. At the end of the 21stCentury, differences between the original and bias-correctedresults are larger than in winter, with Southern Europeshowing a less marked warming in the bias-corrected casescompared to the original results. As we can see from theanalysis of the climate change signal, the bias-correctedresults show a smaller temperature increase especially overSpain, France and Eastern Europe. This is also confirmed byThemeßl et al. [2011b] and Boberg and Christensen [2012].In particular, Boberg and Christensen [2012] claim thatRCMs share systematic temperature-dependent biases. Byemploying a bias-correction conditioned on temperature,they found differences up to 0.8�C between the original andbias-corrected summer temperature in the Mediterranean.This behavior will be discussed more in detail in Section 4.2below.3.1.2. Probability Distribution Functions and ExtremeEvents[23] PDFs of summer temperatures over selected regions

are shown in Figure 4. In this figure, the black line is thePDF of the observed E-OBS temperature, the red curve themodels’ ensemble averaged PDF in the reference period(1961–1990) and the blue line the ensemble averaged PDFat the end of the Century (2071–2100). The green and blueshaded areas represent the range of variability of the singlemodels’ PDFs in the reference and future periods, respec-tively. The two top rows show the results for the originalENSEMBLES models, whereas the bias-corrected resultsare shown in the bottom rows.[24] First, we note that generally in Northern Europe the

shape of the ensemble averaged PDF is relatively close tothe observed one, although the averaged value of the tem-perature is slightly underestimated. On the contrary, in theSouthern areas (IP, FR, MD and EA) the shape of themodels’ PDF is significantly different from the observedone, although in general the average temperature is relativelywell reproduced, as shown in Figure 3. Usually, the PDF is

too wide, which leads to an overestimation of the probabilityof extreme temperature events. At the end of the 21st Cen-tury, whereas in the Northern Countries (SC and NEU) thePDF generally maintains the original shape, showing only aslight increment in the mean temperature, in Southern Europe(IP, FR, MD, and EA) the PDF tends to become significantlywider. In addition, large differences exist amongst differentmodels, as shown by the inter-model variability.[25] The bias-corrected results for the reference period are

close to the observed PDF, and the inter-model variability issmall, as a consequence of the choice of the constructionperiod of the TF. At the end of the 21st Century, the biascorrection technique greatly influences the shape of thetemperature PDF; for Northern Europe the bias-correctedPDF has generally retained its shape, compared to the ref-erence period, but it is shifted toward higher temperaturesthan the original ones; for Southern Europe, on the otherhand, the PDF is generally wider than in the referenceperiod, but it is significantly narrower than the non correctedone. In addition, we note that the inter-model variability isgreatly reduced by the bias correction, especially for theSouthern regions.[26] The shape of the PDF is strictly related to the proba-

bility of extreme events, as illustrated in Figure 5, where theoccurrence of extremely hot days (T max > 35�C) is shown.The original ENSEMBLES runs tend to overestimate thenumber of extremely hot days in the reference period,especially in Spain, Italy and Eastern Europe: this is a directconsequence of the PDF being too wide with respect toobservations. In addition, the inter-model standard deviationis very large, with values that may be of the same order ofmagnitude as the ensemble mean. At the end of the 21stCentury, the original models project a marked increase in thenumber of extremely hot days, not only for the Mediterra-nean regions, but also in France and part of Central andEastern Europe, although the uncertainty (standard devia-tion) for both the absolute value and the climate changesignal remain very large.[27] The bias-corrected results show a general reduction in

the climate change signal compared to the original runs,especially over Central Europe (where almost no change isforeseen), Southern Italy, Greece and Turkey, in accordancewith the reduction of the mean climate change signal.However, the number of extremely hot days over the IberianPeninsula (spatially averaged) does not significantly changebetween the original and bias corrected results. Other works[e.g., Themeßl et al., 2011b] found, on the other hand, asignificant increase of the Txn25 index (i.e. the number ofsummer days with Tmax > 25�C) over Spain after bias cor-rection. As the mean temperature climate change signal isreduced by the bias correction, this may be explained by thefact that the PDF of the corrected climate change signal ismore skewed than the original one.[28] The analysis of extreme events related to the lower

end of the temperature PDF is shown in Figure 6, wherethe number of frost days (Tmin < 0�C) is shown. Theoriginal ensemble mean result for the reference period isrelatively close to the observations, an exception being theBalkans, where the number of frost days is generallyunderestimated. At the end of the 21st Century, the dif-ference between the original and bias-corrected results arelarger, especially over Central and Eastern Europe. Both


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Figure 4. PDF of summer daily temperature for some geographic areas. The first two rows show originalnon corrected models; the bottom two rows show bias-corrected RCMs. The red line is the ensemble aver-aged PDF for the present climate (1961–1990). The green area represents the range of the 12 models’ PDFs.The black line is the PDF calculated from the E-OBS dataset. The blue line is the ensemble average PDF forthe future climate (2071–2100). The light blue area represents the range of the 12 models’ PDFs. The blackline is the PDF calculated from the E-OBS dataset. The blue line is the average of the different models’PDFs for the future climate (2071–2100). The light blue area represents the range of the 12 models’ PDFs.


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Figure 5. Same as Figure 2 but for the average number of extremely hot summer days (Tmax > 35 C).


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Figure 6. Same as Figure 2 but for the average number of winter frost days (Tmin < 0 C).


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the original and the bias-corrected runs show a significantreduction (climate change signal) in the number of frost daysat the end of the 21st Century, especially over Central Europeand southern Sweden, although the bias-corrected ensemblemean change is slightly smaller over Eastern Europe andScandinavia.[29] Summarizing, we note that after bias correction the

ensemble mean temperature (and the relative inter-modelstandard deviation) in both the reference period and at theend of the 21st Century may differ from the original one.The climate change signal and its inter-model standarddeviation are generally conserved by the bias correction,although, locally, discrepancies exist: in particular, the bias-corrected climate change signal is smaller than the originalone for Southern Europe, especially in summer, and slightlylarger for Scandinavia in winter. As the TF acts on the entirePDF, changes in its shape (e.g., skewness) may alter also theprobability of future extreme events like extremely hot orfrost days. This is particularly evident in regions where thenon corrected PDF in the reference period was significantlydifferent from the observed one.

3.2. Daily Precipitation

3.2.1. Annual and Seasonal Means[30] Figure 7 shows the temporal evolution of the (5-year

running) mean annual precipitation from 1961 to the end ofthe 21st Century, averaged over several geographic areasaccording to the original ENSEMBLES models (top rows)and after bias correction (bottom rows).[31] Despite the uncertainties in the observational data set,

the general overestimation by the original ENSEMBLESmodels is evident, with some models (e.g. DMI-HIRHAM5-ECHAM5) predicting for some areas amounts of annualprecipitation more than twice the observed one. This is due toboth the excessive moist air advected by the driving GCMsand small scale errors generated by the RCMs [e.g., Van derLinden and Mitchell, 2009; Dosio and Paruolo, 2011]. ForNorthern Europe all the models project an increase in pre-cipitation during the course of the 21st century, and adecrease in the Mediterranean and Iberian Peninsula. How-ever, for other areas such as the British Isles and MiddleEurope the trend does not show a clear signal, with somemodels (e.g., DMI-HIRHAM5-ECHAM5) projecting anincrease in precipitation and others (e.g., DMI-HIRHAM5-ARPEGE) showing no change in the precipitation amountover the century.[32] The bias-corrected results are obviously closer to

observations over the reference period, and the inter-modelvariability is in general small. At the end of the 21st Centurythe spread between different models becomes larger; as forthe original results, whereas the trend for Northern andSouthern Europe is generally consistent amongst the models,for Middle Europe no clear signal is visible, with somemodels (e.g., DMI-HIRHAM5-ARPEGE) projecting a slightdecrease in the annual precipitation and others (e.g., CNRM-RM5.1-ARPEGE) an increase.[33] This behavior may be explained by analyzing the

seasonal variations in mean daily precipitation, as shown inFigures 8 and 9. Figure 8 shows the ensemble averaged dailyprecipitation for winter over the reference period, at the endof the 21st Century and the climate change signal (in per-centage), together with the relative inter-model standard

deviation and the observed values (E-OBS) for the period1961–1990. Upper rows show the results for the originalENSEMBLES models, whereas the lower rows refer to thebias-corrected results. The general overestimation of winterprecipitation over all Europe is clear from the figure, withbiases reaching values larger than 1 mm/day over large partsof France, Italy and Central and Eastern Europe. Inter-modelstandard deviation is also very large, showing valuesreaching 50% over Spain, the Alps and Greece. Theensemble mean change at the end of the 21st Century showsa general positive trend (with the exception of the IberianPeninsula), where the increase in daily precipitation overNorthern Europe and Scandinavia ranges between 20%and 45%, similarly to the findings by [Van der Linden andMitchell, 2009]. Over Middle, Southern, and EasternEurope, however, the value of the change is similar orsmaller than the inter-model standard deviation (around15%), meaning that the value (and in some cases also thesign) of the change depends strongly on the model.[34] Unsurprisingly, bias-corrected results are very close

to the observations over the reference period, with values ofthe inter-model standard deviation usually smaller than 5%.At the end of the 21st Century, the absolute value of theensemble mean daily precipitation is significantly lower thanthe original results, however, the value of the climate changesignal and the relative standard deviation are very similar touncorrected ones.[35] Results for the summer daily precipitation are shown

in Figure 9. Original results show a general overestimationof precipitation over Scandinavia, and an underestimationover the Balkans, as shown by, e.g., J. H. Christensen et al.[2010].In addition, the inter-model variability is very largeall over Southern Europe, reaching values between 50% and100%. Also the bias-corrected inter-model standard devia-tion over the reference period is relatively large over south-ern Europe (between 10% and 25%), although we note thatthe absolute value of daily precipitation over those areas isvery small (less than 1 mm/day). As for winter, the bias-corrected climate mean change signal is very close to theoriginal uncorrected one, as is the standard deviation. Also,we note that over great part of Central and Eastern Europe,the value of the mean change is very small (less than 15%)and comparable to the value of the inter-model variability.[36] From the analysis of the seasonal mean precipitation

we can conclude that in general Europe can be divided intotwo separate zones, with projected increased precipitation inthe North in winter (although for Scandinavia a clear signalis visible for both summer and winter) and decreased pre-cipitation in the South in summer. In the areas in betweenthese two zones, precipitation is expected to increase duringwinter and decrease in summer; as a consequence the annualmean precipitation signal is very small or nearly zero(Figure 7), although its sign depends strongly on the model.[37] Kendon et al. [2009] showed that the winter precipi-

tation increase in Northern Europe is related to the increasein moisture in a warming atmosphere, whereas the decreasein summer precipitation in Southern Europe is causedmainly by reduction in soil moisture. In addition, Déqué etal. [2011] showed that the major cause of inter-modelspread for summer precipitation is the choice of the RCM,whereas in winter the choice of the driving GCM dominates,especially in Western Europe. From our results, we observe


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that the bias correction technique maintains both the meanseasonal climate change signal, caused by modeled chan-ges in the atmospheric and soil moisture content, and theinter-model variability, related to the construction of theGCM-RCM matrix, thus confirming the robustness ofclimate change information as modeled by the originalENSEMBLES runs.

3.2.2. Probability Distribution Functions and ExtremeEvents[38] As discussed, mean precipitation is usually over-

estimated in winter mainly due to the errors in the circulationsimulated by the driving GM, whereas in summer SouthernEurope is usually drier than the observations [e.g., J. H.Christensen et al., 2010]. However, this bias is non

Figure 7. Same as Figure 1 but for the cumulated yearly precipitation.


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Figure 8. Same as Figure 2 but for the winter daily precipitation.


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Figure 9. Same as Figure 2 but for the summer daily precipitation.


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Figure 10. Same as Figure 2 but for the consecutive number of dry days in summer.


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uniformly distributed along the precipitation PDF, andwhereas the lower end distribution is usually overestimateddue to the tendency of the models to simulate too many epi-sodes of drizzle rain, the central part of the PDF is generallyunderestimated [e.g., Lenderink, 2010; Kjellström et al.,2010]. Finally, models tend to overestimate the higher-endof the precipitation distribution, resulting in bias up to morethan 100% for the value of the 99.9th percentile in summerfor the Mediterranean regions [Dosio and Paruolo, 2011].[39] As impact models, such as hydrological and crop

models, may be affected by the values of both ends of thePDF, here we analyze the probability of extreme precipita-tion events by calculating the average number of consecutivedry days (precipitation <1 mm/day), which may be related tothe occurrence of droughts, and the Pr99 climatic index (i.e.the cumulated precipitation amount over the days when theprecipitation is greater than the 99th percentile of the refer-ence period), which may be related to flooding events [see,e.g., Rojas et al., 2011].[40] Figure 10 shows the analysis of the average number

of consecutive dry days (drought index) in summer asmodeled before and after bias correction. Generally, theensemble mean of the original ENSEMBLES runs is satis-factorily close to the observations over the reference period,although the drought index is generally overestimated overItaly and the Balkans, and underestimated over England,Denmark, Northern Germany and Sweden. However, asshown by Dosio and Paruolo [2011] individual models canbehave very differently, as confirmed by the inter-modelstandard deviation, which reaches values as large as themean drought index, especially over the MediterraneanCountries. As pointed out by Piani et al. [2010a], thedrought index is directly dependent on the autocorrelationof the precipitation time series, whereas the TF used tocorrect the models’ result is not. As a consequence, thebias-corrected results are on average relatively similar tothe original ones, both in the reference period and at theend of the 21st Century, although some improvements arevisible for the reference period over Italy and the Balkans.However, the inter-model standard deviation of the bias-corrected results is generally reduced, both for the presentand the future climate. Finally, the mean climate changesignal is generally unchanged by the bias correction,although over Southern–Eastern Europe the change induration of the dry spells is somehow reduced in the bias-corrected cases.[41] Results for the Pr99 climatic index for winter are

shown in Figure 11. From the analysis of the observations(E-OBS) we note a significant difference in the geographicaldistribution of the value of Pr99, with Southern Europe andthe Mediterranean showing values of the index much largerthan Eastern and Northern Europe. This means that, forinstance, regions where the mean precipitation is usuallysmall (e.g., the Mediterranean), may experience very intenseextreme precipitation events. As expected, the originalENSEMBLES models tend to greatly overestimate theamount of extreme precipitation, although large differencesexist amongst different models, especially over the Alps, andalong the coasts of the Iberian Peninsula and the Balkans, asshown by the inter-model standard deviation. Bias-correctedresults are very close to the observed one, for the reference

period, as the precipitation PDFmatches closely the observedone.[42] At the end of the 21st Century, results show a sig-

nificant increase in the value of the Pr99 index, especiallyover Western and Southern Europe. In general the climatechange signal for the bias-corrected results is smaller thanthe original one, ranging respectively between 5 and 10 mmfor the bias-corrected results and between 10 and 35 mm inthe original cases. Similarly to temperature, the bias correc-tion can alter significantly the shape of the PDF, andalthough the mean climate signal of the mean variable canremain unchanged (or very slightly modified, see forinstance Figure 9), the probability of occurrence of extremeevents may be altered.

4. Analysis of the Climate Change Signal

4.1. Bivariate Temperature-Precipitation ProbabilityDistribution Functions

[43] Figure 12 shows the bivariate temperature-precipitationPDFs for the seasonal (summer and winter) climate changesignal for Northern and Southern Europe. The PDFs are cal-culated by using information from every land point and everymodel. The 30 values of the seasonal change for each singleyear in the 2071–2100 period compared to the mean referenceperiod 1961–1990 have been used; no kernel density estima-tion has therefore been employed. In the figure, the mean cli-mate change signal for all the models is also shown, togetherwith the ensemble mean value (the numerical values of theensemble mean climate change signal and the inter-modelstandard deviation are also reported in Table 2).[44] The shape of the bivariate PDF is generally conserved

by the bias correction, for both regions and seasons. Forinstance, in Northern Europe winter temperatures are pro-jected to increase on average of 4.52�C for the original runsand 4.57�C for the bias-corrected ones, whereas summertemperatures are projected to increase on average of 2.60�Cfor the original runs and 2.72�C for the bias-corrected ones.For Southern Europe, winter temperatures are projected toincrease on average of 3.05�C for the original runs and2.96�C for the bias-corrected ones. On the contrary, largerdifferences exist between original and bias-corrected sum-mer temperature change, whit values of 4.01�C for theoriginal runs and 3.53�C for the bias-corrected ones.[45] However, the bias-corrected signal for individual

models can be largely different than the original one, as forC4I-RCA3-HadCM3Q16 in Northern Europe in winter. TheC4I-RCA3-HadCM3Q16 model clearly stands out as themodel predicting the warmest signal in nearly every seasonand region. This may be due to the specific parameterizationof the driving GCM (HadCM3Q16), which was designed tohave a high sensitivity to the changes in the radiative forcing[Collins et al., 2006].[46] Winter precipitation in Northern Europe are projected

to increase generally between 0.1 and 0.8 mm/day (i.e., 10%and 40% with an average of +21%), with very little differ-ence between original and bias-corrected runs. For southernEurope, summer precipitation are projected to decreasebetween 0.1 and 0.5 mm/day (with an average of �27.8%).The average precipitation signal in winter in NorthernEurope and the summer in Southern Europe are modeled


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Figure 11. Same as Figure 2 but for the Pr99 index (i.e., the accumulated precipitation amount when thedaily precipitation is greater than the 99th percentile of precipitation in the reference 1961–1990 period)for winter.


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Figure 12. Bivariate PDF of seasonal temperature and precipitation change for the original and bias cor-rected (BC) runs. Colored symbols represent the mean change for each model.


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consistently by all the models. However, the PDF shows asignificant probability (around 20%) of increasing summerprecipitation in Southern Europe and decreasing winterprecipitation in Northern Europe. This may be explainedby noticing from Figures 8 and 9 that the precipitationclimate signal in large areas of Central Europe is compa-rable or even smaller than the inter-model standarddeviation.[47] On the other hand, the PDFs for summer in Northern

Europe and winter in Southern Europe show a nearly equalprobability of having either a positive or negative precipi-tation signal, and also the mean climate signal is close tozero (Table 2). This analysis depends strongly on the choiceof the region, and it would certainly differ if smaller areaswere considered: for instance, the precipitation climate sig-nal for Scandinavia is expected to be always positive, both inwinter and summer, and relatively larger than the inter-model standard deviation (Figures 8 and 9). In addition, asthe PDF is calculated from the 30 values of the yearly meanchange compared to the reference period, the uncertainty inthe sign of the signal is therefore also a measure of the interannual variability, at the end of the 21st Century.[48] From this analysis, we note that the ensemble mean

climate change signal is generally conserved by the biascorrection, exception being Southern Europe in summerwhere a difference of around 0.5�C between the original andthe bias-corrected climate change signal is found. However,the impact of the bias correction varies amongst models; thisbehavior is investigated in the next Section.

4.2. CDF of Climate Change Signal

[49] Figure 13 shows, for each model, the CDF of summertemperature climate change signal for the Iberian Peninsula,following Brekke et al. [2008]. Black lines represent theCDF calculated from the original runs, and red lines the bias-corrected ones. In the same figure box-plots of mean, stan-dard deviation, 5th, and 95th percentiles of climate changesignal are also shown. They are calculated from the values ofthe 12 models, following Tapiador et al. [2009].[50] We note how the bias-correction acts non uniformly

amongst different models: for instance, for DMI-

HIRHAM5-BCM and SMHI-RCA-HadCM3Q3 the originaland bias-corrected CDF are very similar, whereas forMETO-HC-HadRM3Q0-HadCM3Q0 the bias-correctedCDF shows a nearly constant reduction of about 1�C acrossall the temperature range. In other cases, the slope of thecorrected CDF is different from the original one (e.g., C4I-RCA3-HadCM3Q16 and SMHI-RCA-BCM).[51] For each model, the TF generates different projec-

tions compared to the original ones. For example, the linearTF in (equation (1)) implies that the bias-corrected climatechange signal xcor,t2 � xcor,t1 is related to the original onexmod,t2 � xmod,t1, with factor of proportionality given by theparameter b. This implies that the original and bias-correctedclimate change signals have in general different CDFs.[52] The box-plots show that the bias-corrected ensemble

mean signal is reduced compared to the original one ofaround 0.5�C, as noted earlier (e.g., Figures 3 and 12), as itis for the value of the 5th percentile. However, the standarddeviation is slightly increased, and the value of the 95thpercentile is reduced by only 0.2�C: this means that the bias-corrected PDF of the climate change signal is more skewedthan the original one (0.2 for the original PDF and 0.55 forthe bias-corrected, not shown). This explains the fact thatdespite a decrease in the mean value of the temperaturesignal, the value of extreme climate indices can remainconstant or even increase locally.[53] Similar considerations are valid for the precipitation

signal CDF, shown in Figure 14. We note that in general thebias correction tends to increase the value of the lower per-centiles of the precipitation signal, whereas the higher-endvalues remain nearly unchanged. This is consistent withFigure 11, which shows very little differences between theoriginal and bias-corrected climate change signal for thePr99 index, for Southern Europe.

4.3. Normalized Climate Change Signal and Selectionof Runs

[54] Finally, as a useful tool to investigate the perfor-mances of the different models in simulating the response tothe GHG forcing, we mirror an analysis previously per-formed by O. B. Christensen et al. [2010] for the original

Table 2. Normalized Temperature and Precipitation Climate Change Signals and Total RMSE for Each Bias-Corrected Runa

Model

NEU SEU

RMSEDJF T, P JJA T, P DJF T, P JJA T, P

METO-HC-HadRM3Q0-HadCM3Q0 0.14, 0.38 1.53, �0.77 0.47, 0.28 0.98, �0.55 2.17MPI-M-REMO-ECHAM5 0.68, �1.04 0.09, �0.33 0.65, �0.01 0.23, �0.57 1.57C4I-RCA3-HadCM3Q16 2.50, 1.91 2.03, 1.35 2.49, 0.91 1.77, 0.49 5.13ETHZ-CLM-HadCM3Q0 0.07, 0.64 0.12, �0.36 0.19, 0.51 0.11, �1.26 1.58KNMI-RACMO2-ECHAM5-r3 �0.21, �0.44 �0.04, 0.24 �0.09, 0.71 0.37, �0.86 1.31SMHI-RCA-BCM �1.37, 0.14 �0.54, 0.81 �0.89, 0.17 �0.44, 1.58 2.52SMHI-RCA-HadCM3Q3 �0.67, �0.54 �0.59, �0.24 �0.54, �0.61 �0.51, 0.98 1.75SMHI-RCA-ECHAM5-r3 0.76, 0.53 0.24, 0.39 0.51, 1.12 0.51, �0.25 1.70DMI-HIRHAM5-BCM �0.48, 0.07 �1.63, 0.36 �0.24, �0.10 �1.77, 1.14 2.74DMI-HIRHAM5-ARPEGE �0.77, �1.25 �0.49, �1.70 �1.31, �1.88 �0.69, �0.85 3.43DMI-HIRHAM5-ECHAM5 0.04, 1.06 �0.92, 1.61 �0.31, 0.78 �1.32, 1.11 2.83CNRM-RM5.1-ARPEGE �0.71, �1.47 0.18, �1.33 �0.93, �1.88 0.74, �0.96 3.23Ens. mean change BC 4.57, 21.3 2.72, 3.4 2.96, 1.44 3.53, �27.8Ens. mean std. dev. BC 0.70, 6.42 1.03, 7.5 0.75, 6.7 0.94, 7.1Ens. mean change OR 4.52, 19.7 2.60, 3.4 3.05, 9.0 4.01, �22.8Ens. mean std. dev. OR 0.38, 7.11 0.86, 7.7 0.73, 7.7 1.04, 9.1

aMaximum values are highlighted in bold. Also in the table the values of the ensemble mean temperature and precipitation changes (in �C and %,respectively) are reported, together with the inter-model standard deviations, for both the original (OR) and bias-corrected (BC) runs.


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Figure 13. CDF of summer temperature climate change signal for the Iberian Peninsula. Black linesrepresent the CDF calculated from the original runs, whereas red lines the bias-corrected ones. Box-plots of mean, standard deviation, 5th and 95th percentiles are also shown, calculated from the valuesof the 12 models.


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ENSEMBLES runs; based on the values of the seasonal(summer and winter) change of mean temperature and pre-cipitation, for each model a normalized climate change iscalculated, defined as the difference between the climatechange signal for each model and the ensemble averagedone, normalized by the ensemble averaged standard devia-tion of the change. A ‘ranking score’ is then calculated based

on the total RMSE (Root Mean Square Error) of the nor-malized climate change signals. Results are reported inTable 2 where the most extreme cases (i.e. those that divergemore from the ensemble average change) are emphasized inbold.[55] Although a ranking score may somehow be subjec-

tive, and it is by no means meant to be a representation of the

Figure 14. Same as Figure 13 but for precipitation.


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quality of the simulation, this procedure may be useful tohighlight the simulations that are most similar to ensembleaveraged results, and those that diverge the most from theensemble averaged results, i.e. those that maximize thevariability. Process-based impact models are often tooexpensive to be run by using a large ensemble of climateruns as an input; however, the main statistical properties ofthe whole ensemble may be conserved by selecting a subsetof simulations that represent both the mean climate changesignal and the most extreme deviations from it.[56] From Table 2, we first note that KNMI-RACMO2-

ECHAM5 has the smallest total RMSE; in addition, thevalues of the normalized signals for all seasons and regionsare always smaller than 1 (i.e., smaller than the standarddeviation). As a result, we may consider the KNMI-RACMO2-ECHAM5 run the closest to the ensemble aver-aged results.[57] Identifying the simulations that stand out from the

mean values is not trivial, as this may depend on the seasonand the variable to be considered. For example, SMHI-RCA-BCM shows a signal that is always colder and wetter than theaverage; DMI-HIRHAM5-ECHAM5 shows a very similarbehavior (with the only difference being that it is onlyslightly warmer than average in winter in Northern Europe)but it is usually wetter and colder than SMHI-RCA-BCM. Onthe contrary, MPI-M-REMO-ECHAM5 shows a signalalways drier and warmer than average, although summertemperature in Northern Europe and winter precipitation inSouthern Europe are very close to the ensemble mean values.On the other hand, METO-HC-HadRM3Q0-HadCM3Q0shows a very warm and dry signal in summer, both forNorthern and Southern Europe. In addition, it shows awarmer than average signal in winter, although slightlywetter. Finally, C4I-RCA3-HadCM3Q16 is the warmestmodel in both winter and summer, but also one of the wettest,whereas DMI-HIRHAM5-ARPEGE is always colder anddrier than average.

5. Summary and Concluding Remarks

[58] This work extends the analysis by Dosio and Paruolo[2011], in which a statistical bias correction technique wasapplied to a set of high resolution climate change simula-tions for Europe, where results were evaluated for the pres-ent climate. Here, we studied the impact of bias correctionon the temporal evolution over the 21st Century of meantemperature and precipitation and their inter-model vari-ability. In particular, we focused on the analysis of dis-crepancies between the original and the bias-correctedensemble mean climate change signal.[59] Results showed that the ensemble mean climate

change signal and its inter-model standard deviation aregenerally conserved by the bias correction, thus confirmingthe robustness of climate change information as modeled bythe original ENSEMBLES runs.[60] However, the impact of the bias correction varies

largely amongst models, seasons and regions. For instance,the bias-corrected results show a general reduction in thesummer temperature climate change signal compared to theoriginal runs, especially over Central Europe (where almostno change is foreseen), Southern Italy, Greece, Turkey, and

Spain, with differences up to 0.5�C between the original andbias-corrected results.[61] As impact models are significantly dependent on the

occurrence and frequency of extreme events, we analyzedthe impact of the bias correction on the present and futureshape of PDF. The TF affects directly the shape of the PDF,especially for Southern Europe in summer, where the origi-nal PDF was significantly different from the observed oneover the present climate. As a consequence, the resultingPDF of bias-corrected climate change signal is different (e.g.more skewed) than the original one. As a result, despite adecrease in the mean value of the temperature signal, thevalue of extreme climate indices can remain constant, oreven increase locally.[62] About precipitation, from our results, we observe that

the bias correction technique maintains both the mean sea-sonal climate change signal, caused by modeled changes inthe atmospheric and soil moisture content, and the inter-model variability, related to the construction of the GCM-RCM matrix. As pointed out by Kendon et al. [2009], winterprecipitation increase in Northern Europe is related to theincrease in moisture in a warming atmosphere, whereas thedecrease in summer precipitation in Southern Europe iscaused mainly by reduction in soil moisture. In addition,inter-model variability for summer precipitation is stronglyrelated to the choice of the RCM, whereas in winter thechoice of the driving GCM dominates, especially in WesternEurope [Déqué et al., 2011].[63] The original ENSEMBLES models tend to greatly

overestimate the amount of extreme precipitation, althoughlarge differences exist amongst different models. At the endof the 21st Century, bias-corrected results show a significantincrease in the value of the Pr99 index, especially overWestern and Southern Europe. However, the bias-correctedclimate change signal is smaller than the original one,ranging respectively between 5 and 10 mm for the bias-corrected results and between 10 and 35 mm for the originalcases. Similarly to temperature, the bias correction can altersignificantly the shape of the PDF, and although the climatesignal of the mean variable can remain unchanged (or veryslightly modified) after bias correction, the probability ofoccurrence of extreme events may be altered.[64] It must be noted that the bias correction technique

used here assumes that the TF remains constant between thepresent and future climate. This assumption reflects the ideathat each GCM-RCM combination may display systematicdistortions which do not depend on the level of forcing (e.g.CO2 increase). While this is a simple and relatively strongassumption, it appears worth considering because of itssimplicity. The validity of this assumption has been evalu-ated by Piani et al. [2010a], who calculated the TF usingdata from 1961 to 1970, and applied it to data from 1991 to2000. This choice of time periods was made to maximize thelag between calibration and validation within the ERA40reanalysis period. Results show that the method performswell, thus encouraging the application of this technique tomultidecadal climate projections.[65] Several other bias-correction techniques exist, based

on different assumptions [e.g., Themeßl et al., 2011b;Boberg and Christensen, 2012]. In particular, Boberg andChristensen [2012] claim that RCMs share systematic tem-perature-dependent biases, which affect their ability to


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capture accurately certain observable climate conditions.They therefore apply a (simple) bias correction conditionedon temperature; they find that their bias-correction reducesthe Mediterranean summer temperature projections by up toone degree, on average by 10–20%. Also Hagemann et al.[2011] state that the bias correction has an impact on theclimate change signal for specific locations and months.[66] As pointed out by Haerter et al. [2011] every statis-

tical bias correction makes assumptions on the applicabilityof statistical TFs from today’s climate to the future climate.A bias correction is inherently assuming specific modelcharacteristics that cause the discrepancy between simulatedand observational data. As stated by Haerter et al. [2011],whether a bias correction should impact on the climatechange signal or not is still an open question. The presentwork provides evidence on this question for the simpleassumption that the TF is constant between present andfuture climate.[67] One of the main outcomes of our work is the devel-

opment of a dataset of multimodel bias-corrected runs foruse by the impact community, as impact assessment basedon multimodel runs is becoming more and more frequent[e.g., Rojas et al., 2012]. As stated by Hagemann et al.[2011], when using climate change predictions for impactassessment studies, bias correction adds another level ofuncertainty, which may be of the same order of magnitude asthat related to the choice of the climate or impact model.[68] Finally, from the analysis of the bias-corrected

bivariate PDF of temperature and precipitation signal, wecalculated a ‘ranking score’ for each model based on thetotal RMSE of the normalized climate change signals. Thisprocedure may be useful to highlight the simulations that aremore similar to ensemble averaged results, and those thatdiverge most from the ensemble averaged results, i.e. thosethat maximize the variability. As many impact models areoften too expensive to be run by using as an input a largeensemble of climate runs, by selecting a subset of modelsthat represent both the mean climate change signal and themost extreme deviations from it, one assumes that the mainstatistical properties of the whole ensemble of simulationsare conserved.

[69] Acknowledgments. This work was partially supported by theEuropean Commission Joint Research Centre PESETA II project. Wewould like to thank C. Piani (ICTP Trieste) and J. O. Haerter (MPI Ham-burg) for providing the bias correction code, which was developed underthe European Union FP6 integrated Project WATCH (contract 036946).The ENSEMBLES data used in this work was funded by the EU FP6 Inte-grated Project ENSEMBLES (contract 505539) whose support is gratefullyacknowledged. We would like to thank Luc Feyen and Alessandra Bianchi(both JRC) for helping with the preparation of the data, and Mirco Miglia-vacca (JRC) for useful discussion.

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