GEOLOGICAL SURVEY OF DENMARK AND GREENLANDMINISTRY OF CLIMATE, ENERGY AND BUILDING G E U S

DA N M A R K S O G G R Ø N L A N D S G E O L O G I S K E U N D E R S Ø G E L S E R A P P O RT 2 0 1 3 / 5 8

Uncertainty in Hydrological Change Modelling

Ph.D. Thesis 2013Lauren Paige Seaby

DEPARTMENT OF GEOSCIENCES AND NATURAL RESOUCE MANAGEMENTUNIVERSITY OF COPENHAGEN

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Contents

Preface 5

Abstract 6

Resume (in Danish) 8

Introduction 10

Motivation of PhD Study ........................................................................................................ 10

Objectives of Ph.D. Study ....................................................................................................... 11

Climate Change and Hydrology 13

Climate models ....................................................................................................................... 13

Bias correction methods ........................................................................................................ 13

Delta Change (DC) ............................................................................................................. 13

Bias Removal (BR) ............................................................................................................. 14

Distribution Based Scaling (DBS) ....................................................................................... 14

Projected change for Denmark .............................................................................................. 15

Ph.D. Research 17

Models, methods, and data used in this study ...................................................................... 17

DMI Climate Grid – Denmark ............................................................................................ 17

ENSEMBLES RCMs ............................................................................................................. 19

Danish National Water Resources Model (DK-model) ...................................................... 22

Bias corrected climate inputs ................................................................................................. 25

Delta change factors and values ....................................................................................... 25

Bias removal factors and values........................................................................................ 26

Distribution based scaling precipitation ........................................................................... 27

Variance Decomposition ................................................................................................... 29

Paper I .................................................................................................................................... 30

4 G E U S

Paper II ................................................................................................................................... 31

Technical Note ........................................................................................................................ 33

Paper III and IV ....................................................................................................................... 33

Conclusions and Perspectives 36

References 38

Appendix 1: Delta Change Factors and Values 43

Appendix 2: Paper I 79

Appendix 3: Paper II 97

Appendix 4: Technical Note 131

Appendix 5: Paper III 147

Appendix 6: Paper IV 167

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Preface

This thesis has been submitted in partial fulfilment of the requirements for the Ph.D. degree at

the University of Copenhagen, Denmark. It is the result of a three-year Ph.D. program, which

was carried out at the Department of Geography and Geology (IGG) from August 2009 to Au-

gust 2013.

The Ph.D. study was funded in part by the Faculty of Science (University of Copenhagen), the

International Research School of Water Resources in Denmark (FIVA), and the Geological Sur-

vey of Denmark and Greenland (GEUS) by a grant from the Danish Strategic Research Council

for the project HYdrological Modelling for Assessing Climate Change Impacts at differeNT

Scales (HYACINTS – www.hyacints.dk) under Contract No: DSF-EnMi 2104-07-0008. The Danish

Meteorological Institute (DMI) contributed by providing climate data and supervision. The

study was supervised by Jens Christian Refsgaard (GEUS), Torben Obel Sonnenborg (GEUS),

Karsten Høgh Jensen (IGG), and Jens Hesselbjerg Christensen (DMI).

In accordance with the guidelines of the Faculty of Science, the thesis comprises an introduc-

tion, a review of relevant literature, an introduction to the research carried out, and the fol-

lowing four papers and technical note:

Paper I: Seaby, L.P., Refsgaard, J.C., Sonnenborg, T.O., Stisen, S., Christensen, J.H., and

Jensen, K.H., (2013). Assessment of robustness and significance of climate change sig-

nals for an ensemble of distribution-based scaled climate projections. Journal of Hy-

drology 486, 479-493, doi: 10.1016/j.jhydrol.2013.02.015.

Paper II: Seaby, L.P., Refsgaard, J.C., Sonnenborg, T.O., Højberg, A.L., (submitted). Spa-

tial uncertainty in bias corrected climate change projections and hydrogeological im-

pacts. Hydrological Processes.

Technical Note: Seaby, L.P., (2013). Climate model and bias correction uncertainty in

hydrological modelling of projected future conditions for Denmark.

Paper III: Rasmussen, J., Sonnenborg, T.O., Stisen, S., Seaby, .LP., Christensen, B.S.B,

Hinsby, K., (2012). Climate change effects on irrigation demands and minimum stream

discharge: impact of bias-correction method. Hydrol. Earth Syst. Sci., 16, 4675–4691,

doi:10.5194/hess-16-4675-2012.

Paper IV: Kidmose, J., Refsgaard J.C., Troldborg, L., Seaby, L.P., Escriva, MM., (2013).

Climate change impact on groundwater levels: ensemble modelling of extreme values.

Hydrol. Earth Syst. Sci. 17, 1619–1634, doi:10.5194/hess-17-1619-2013.

6 G E U S

Abstract

Hydrological change modelling methodologies generally use climate models outputs to force

hydrological simulations under changed conditions. There are nested sources of uncertainty

throughout this methodology, including choice of climate model and subsequent bias correc-

tion methods. Uncertainty cascades through this methodology, potentially accumulating and

increasing or balancing out.

This Ph.D. study aims to evaluate the uncertainty of the impact of climate change in hydrologi-

cal simulations given multiple climate models and bias correction methods of varying complex-

ity.

Three distribution based scaling methods (DBS) were developed and benchmarked against a

more simplistic and commonly used delta change (DC) approach. Initial climate model bias in

the historical period and the temporal evolution of climate change in future periods were ana-

lysed given 11 climate models and multiple bias correction methods. These climate model pro-

jections were then used to force hydrological simulations under climate change for the island

Sjælland in Denmark to analyse the contribution of different climate models and bias correc-

tion methods to overall uncertainty in the hydrological change modelling methodology for

basin discharge and groundwater heads.

The analysis of multiple climate models, bias correction methods, and time periods elucidated

the nature of climate model uncertainty. The ensemble of 11 climate models varied in

strength, significance, and sometimes in direction of the climate change signal. Generally,

climate change signals in the near future (2011-2040) are hidden by natural variability and

were not significant, in the mid future (2041-2070) the significance of climate change signals

depended on the choice of climate model, and in the far future (2071-2100) climate change

signals were strong across all models and variables.

It was demonstrated that both DC and DBS methods equally retain mean monthly climate

change characteristics. The simplistic monthly DC approach was adequate for capturing the

smooth temporal characteristics of temperature changes, but less sufficient at recreating pro-

jected precipitation regimes, which vary day to day and grid to grid. The more complex daily

DBS correction methods were more accurate at transferring changes in the mean as well as the

variance, and improving the characterisation of day to day variation as well as heavy precipita-

tion events. However, the most highly parameterised of the DBS methods were less robust

under change conditions.

The spatial characteristics of groundwater head and stream discharge were best represented

by DBS methods applied at the grid scale. The magnitude of spatial bias seen in precipitation

inputs did not necessarily correspond to the magnitude of biases seen in hydrological outputs.

Flux and state hydrological outputs which integrate responses over time and space showed

G E U S 7

more sensitivity to mean spatial biases and less so on extremes . In the investigated catch-

ments, the projected change of groundwater levels and basin discharge between current and

future conditions was found to be modest across multiple climate models and bias correction

methods.

The choice of RCM contributes almost all of the uncertainty across precipitation inputs, ex-

plaining 99% of uncertainty in total annual precipitation, while choice of bias correction meth-

od contributes far less to overall uncertainty, contributing to 7% of variance in total annual

precipitation values. Similarly choice of climate model contributes to almost all of the variance

in stream discharge, 97% in total annual, while choice of bias correction method contributes to

10% of uncertainty in total annual discharge.

Given the importance of climate model uncertainty, multiple models of future climate condi-

tions should be incorporated into studies and planning for climate change adaptation.

8 G E U S

Resume (in Danish)

Hydrologisk modellering af klima forandringer anvender generelt klimamodel output til at drive

den hydrologiske simulering under ændrede forhold. Der er indlejrede kilder til usikkerhed i

denne metode, herunder valg af klimamodel og efterfølgende bias korrektionsmetoder. Usik-

kerheder videreføres gennem denne metode, og kan akkumuleres og øges eller udjævnes.

Dette ph.d.-studie har til formål at vurdere usikkerheden på konsekvenserne af klimaændrin-

ger i de hydrologiske simuleringer ved brug af flere klimamodeller og bias korrektionsmetoder

af forskellig kompleksitet.

Tre avancerede metoder (Distribution Based Scaling (DBS)) er udviklet og testet imod en mere

simpel og ofte anvendt metode (Delta Change (DC)). Klima model bias for en historisk referen-

ce periode og den tidslige udvikling i klima ændringer i tre fremtidige perioder er analyseret for

11 klimamodeller og forskellige bias korrektions metoder. Disse klimamodelfremskrivninger er

anvendt til at drive hydrologiske simuleringer for det fremtidige klima for Sjælland i Danmark

og analysere bidraget fra forskellige klimamodeller og bias korrektionsmetoder til den samlede

usikkerhed på vandføring og grundvandstand.

Analysen af flere klimamodeller, bias korrektionsmetoder og perioder belyste beskaffenheden

af klimamodellernes usikkerhed. Ensemblet af 11 klimamodeller varierede i styrke, signifikans,

og undertiden i retning af klimaforandringernes signal. Generelt er klimaændringernes signal i

den nærmeste fremtid (2011-2040), skjult af naturlig variabilitet og ikke signifikante, i middel-

nær fremtid (2041-2070) afhang signifikansen af klimaforandringernes signal af valget af kli-

mamodel, og i den fjerne fremtid (2071-2100) var klimaændringernes signal stærke på tværs af

alle modeller og variabler.

Det blev påvist, at både DC og DBS metoderne bevarede klimaændringernes karakteristika for

månedlige gennemsnit. Den forsimplede månedlige DC metode var tilstrækkelig til at opfange

den gradvise og tidsafhængige ændringer, der gør sig gældende for temperaturændringer, men

mindre velegnet til at genskabe fremskrevne nedbørsregimer, der varierer fra dag til dag og

gridcelle til gridcelle. De mere komplekse dagsbaserede DBS korrektionsmetoder kunne mere

eksakt overfører ændringer i middelværdi og varians, og forbedrer karakterisering af dag til dag

variationer samt ekstreme nedbørshændelser. Dog var den mest parameteriserede DBS meto-

de mindre robust under forandrede forhold.

De rumlige fordelingaf grundvands trykniveau og vandløbsafstrømning var bedst repræsente-

ret ved DBS metoderne, der anvendes på celleniveau. Størrelsesordenen af den rumlige fejl

registreret for nedbøren svarerede ikke nødvendigvis til størrelsesordnen af fejl set i de hydro-

logiske simuleringer. De simulerede hydrologiske variable som integrerer variationer over tid

og rum, viste mere følsomhed overfor rumlige afvigelser og mindre overfor ekstremer. I dette

opland blev den forventede ændring af ekstrem grundvandsstanden og oplandsafstrømning

G E U S 9

mellem de nuværende og fremtidige klimaforhold fundet at være beskeden på tværs af klima-

modeller og bias korrektionsmetoder.

Valget af regional klimamodel (RCM) bidrager med næsten al usikkerheden på nedbørsinput-

tet, og forklarer 99% af usikkerheden på den samlede årlige nedbør , mens valget af bias kor-

rektionsmetoden bidrager langt mindre til den samlede usikkerhed, og bidrager med 7% af

variansen i de samlede årlige nedbørsværdier. Tilsvarende bidrager valget af klimamodel til

næsten al variansen i afstrømningen, med 97% i de samlede årlige værdier, mens valget af bias

korrektionsmetode bidrager med 10% af usikkerheden i den samlede årlige afstrømning .

I betragtning af betydningen af klimamodellers usikkerhed bør flere modeller for fremtidens

klimaforhold indarbejdes i undersøgelser og planlægning af klimatilpasning.

10 G E U S

Introduction

Motivation of PhD Study

Hydrological change modelling methodologies generally use climate models outputs to force

hydrological simulations under changed conditions. There are nested sources of uncertainty

throughout this methodology, from the emissions scenario, the global circulation model

(GCM), the regional climate models (RCM), subsequent climate downscaling and bias correc-

tion methods, and hydrological model structural and prediction uncertainty. Uncertainty cas-

cades through this methodology, potentially accumulating and increasing or balancing out.

Historical data are often analysed for significance of climate change in the 20th century, where-

as projections of future climates are often discussed in terms of magnitude and timing but not

in terms of statistical significance (IPCC, 2007). Significance testing of climate change over the

21st century would enhance model comparison and/or selection for impact modelling.

With the availability of ensembles of RCM projections, it is now possible and recommended to

incorporate multiple climate models into hydrological change studies (Minville et al., 2008;

Olsson et al., 2011; Stoll et al., 2011). If uncertainty is considered as spread, this can account

for climate model uncertainty in hydrological change modelling methodologies by introducing

a range of responses. RCM outputs are still subject to systematic errors and biases and require

additional bias correction to prepare them for hydrological forcing (Fowler et al., 2007). Sim-

plistic methods have known limitations in their abilities to capture the full characteristics of

future climate regimes, often focusing on mean changes and not on extremes, while more

complex methods are potentially over parameterised and not robust in changed conditions.

Uncertainty from choice of bias correction method can be accounted for by incorporating mul-

tiple approaches.

RCM simulated precipitation shows significant natural variability both on interannual and de-

cadal time scales, and the resultant signal vs. noise issue is often discussed because it can

overwhelm long term trends of climate change (Bates et al., 2008). Compared to temperature,

which exhibits smooth temporal and spatial characteristics, precipitation regimes are tempo-

rally and spatially variable, and sensitive to local controls on climate. Therefore, precipitation

may require more complex bias correction methods to a) remove initial climate model bias and

b) retain the projected regime characteristics. A variety of bias correction methods have been

developed to estimate or scale future climate variables, but few studies have compared these

methods over an ensemble of climate models. It is important to apply these methods in differ-

ent regimes and to benchmark them against other methods.

Hydrological change is typically characterised by comparing outputs from a reference period to

a future period, both forced by inputs from a climate model, effectively capturing relative cli-

mate changes and assuming model bias is consistent in both periods. Successful applications

G E U S 11

of bias correction methods rely on the assumption of stationarity of the RCM climate within

the control and future periods, and on the balance between climate change and climate varia-

bility (signal to noise ratio) projected by the RCMs, yet few studies have systematically ana-

lysed the robustness of these periods within RCMs. There is a need to characterise the impact

of natural variability in RCM variables and the ramifications of non-stationary control and fu-

ture time periods in the context of these bias correction method.

The spatio-temporal scale of observational data used in calibrating hydrological models often

forms the baseline for RCM bias correction, though a RCM’s performance (i.e. amount of bias)

in this reference period is not indicative of its ability to project future climate changes (Chris-

tensen et al. , 2010). Depending on the process representation and parameterisation of a giv-

en RCM, the spatial scale of processes affecting climate change projections might differ from

the scale of processes affecting local climate variables. The spatial scale at which climate

change signals are robust is different, and likely larger, than the spatial scale necessary to bias

correct RCMs. Therefore, when designing bias correction methods there is a need to differen-

tiate between the scale at which the climate change signal in an RCM varies and the scale at

which the spatial biases in the RCM vary.

Bias correction can in principle be carried out using different bias correction parameter values

for each observational location/grid, but this would result in a huge number of parameter val-

ues with the risk of over parameterisation and lack of robustness when used outside the train-

ing period (e.g. future climate projections). There is a need to assess the appropriate spatial

resolution of bias correction methods, and to adopt methods that are not over parameterised

but are still accurate enough to be meaningful in impact studies. No study has yet compared

the importance and scale of RCM spatial bias compared to projected climate change signals, or

attempted to recommend a bias correction approach based on the nature of RCM biases and

scales relevant to hydrogeological responses.

Objectives of Ph.D. Study

The objectives of this Ph.D. study were to:

1. Analyse the temporal and spatial significance of projected climate change signals.

2. Assess the relationship between complexity and robustness in bias correction meth-

ods.

3. Assess the accuracy and robustness of distribution based scaling bias correction meth-

ods, and benchmark them against the delta change method.

4. Quantify and compare initial climate mode bias and the amount of bias remaining after

bias correction methods have been applied.

12 G E U S

5. Evaluate the hydrological outputs groundwater head and stream discharge as forced

by climate model inputs in historical and future conditions.

6. Quantify uncertainty as the amount of variability contributed by choice of climate

model and bias correction method in the hydrological change modelling methodology.

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Climate Change and Hydrology

Climate models

Assessing hypotheses about climate change ultimately rests on the coupled processes in the

atmosphere, ocean, biosphere, and on land. Global Circulation Models (GCMs) are numerical

coupled models representing ocean-atmosphere circulation and can be used to project chang-

es in atmospheric variables under scenarios of climate change by imposing scenarios of in-

creasing CO2 emissions (IPCC, 2007). GCMs are run at coarse spatial resolutions, and therefore

only effectively capture large-scale climate features and sub-continental patterns (i.e. temper-

ature and precipitation). At such scales (e.g. 250 km) the hydrological cycle is greatly simplified

(i.e. parameterised); this inability of GCMs to resolve sub-grid processes makes their outputs

unable to represent local climate dynamics (Rummukainen, 2010) and therefore not suitable

for direct forcing of hydrological models. Regional climate models (RCMs) are run using lateral

boundary conditions from GCMs over a limited area to produce higher resolution outputs (i.e.

dynamically downscaled). Because of the GCM-RCM nesting, the overall quality of RCM output

is tied to the realistic large-scale forcing of the underlying GCM and likewise affected by its

biases (Xu et al., 2005), and are subject to their systematic errors and biases (Fowler et al.,

2007; Jones et al., 2004). Previous studies associated with the PRUDENCE and ENSEMBLES

climate modelling projects have found that uncertainty introduced by the driving GCM to be

greater than from the RCM or emissions scenario (Christensen and Christensen, 2007; Déqué

et al., 2007).

Bias correction methods

RCM outputs, especially precipitation, require further downscaling and bias correction prior to

use in hydrological simulations (Hansen et al., 2006; Sharma et al., 2007). Bias correction

methods can be classified as direct or indirect in terms of their use of RCM outputs and linear

or nonlinear in terms of their scaling procedures, where the nonlinear direct methods are sub-

classified as parametric or nonparametric.

Delta Change (DC)

A relatively straightforward and therefore commonly applied bias correction approach is the

delta change (DC) method, where change factors/values are derived by comparing RCM past

and future climate and perturbed onto a reference climate series (Fowler et al., 2007; Graham

et al., 2007; 2000; IPCC, 2007). DC methods can be implemented at various temporal (e.g.

daily, monthly, seasonal, annual) and spatial (e.g. grid, basin, national) scales, can be formulat-

ed to transfer relative or absolute changes (e.g. additive, multiplicative factors), and can be

14 G E U S

calculated as a single factor per variable or multiple magnitude dependent factors (Anandhi et

al., 2011; Hay et al., 2000).

DC methods are indirect since RCM outputs are not used directly and the perturbed change is

usually linear, an exception being e.g. magnitude dependent factors which perturb change in a

nonlinear way. By definition, DC methods preserve the climate dynamics of the observed ref-

erence period, producing locally realistic climate variables reflecting mean changes simulated

by the climate models, but do not utilise or retain information on changes in precipitation dy-

namics simulated by RCMs in the future period (Fowler et al., 2007; Lenderink et al., 2007).

Since changes are reduced to mean factors perturbed evenly over daily data, changes in varia-

bility and regime are not necessarily captured, like frequency of wet and dry days, more fre-

quent precipitation events of high intensities, and extreme events.

Bias Removal (BR)

Bias removal (BR), or linear scaling (Teutschbein and Seibert, 2012b), operates similar to DC

methods except mean correction factors are found between observed and simulated values in

a reference period and are perturbed onto future RCM simulated climate outputs, making it a

direct, linear method. BR methods can be expanded to correct for variance bias with nonlinear

power transformations (Leander and Buishand, 2007; Sunyer et al., 2012), in this case making a

direct, nonlinear method. By definition, BR methods will generate corrected RCM outputs in

the reference period that match the mean (e.g. monthly, seasonal, annual) values in the obser-

vations, and this relationship is assumed to hold under future conditions

Distribution Based Scaling (DBS)

Given that climate change is likely to impact the hydrological cycle most significantly by accel-

erating extremes (Bates et al., 2008; IPCC, 2007), and that simple statistical downscaling meth-

ods like the DC approach cannot capture these crucial characteristics, more sophisticated

downscaling methods have been developed that enable the use of RCM outputs directly (e.g.

weather generators, weather typing schemes, regression models) (Fowler et al., 2007). Distri-

bution-based scaling (DBS) has emerged as a promising method that fully utilises the RCM sim-

ulation’s projected changes in precipitation regimes (i.e. mean, variability, frequency, and in-

tensity) by bias correcting based on daily precipitation intensity, producing internally con-

sistent time series that have the same statistical intensity distribution as the observations (Pia-

ni et al., 2010; Yang et al., 2010). The DBS method was developed and documented for precipi-

tation over Europe (Piani et al., 2010), Sweden (Yang et al., 2010), and in Denmark previous

work has made limited comparisons between the DBS and DC methods (van Roosmalen et al.,

2011).

G E U S 15

Given the physical constraint of precipitation to be nonnegative, the statistical distribution of

daily intensities is asymmetric and positively skewed. Gamma distributions are commonly

used to theoretically represent precipitation intensity distributions as they are bound on the

left by zero and skewed to the right (Haylock et al., 2006; Wilks, 2006). In DBS, gamma proba-

bility distributions are fitted to observational and RCM daily precipitation data, and RCM pre-

cipitation is then scaled such that the statistical distribution of observed precipitation in the

reference period is preserved.

The DBS method can be applied at various temporal scales (e.g. annually, seasonally, monthly)

and spatial scales (e.g. nationwide, regional, single grid) which decide how the data should be

grouped for fitting distribution parameters and the subsequent scaling. Ideally, the temporal

scale should be short enough to preserve intra-annual climate characteristics (e.g. seasonality)

and long enough to allow for potential shifts in future regimes (e.g. monthly changes). Like-

wise, the spatial scale should be fine enough to retain spatial heterogeneities in the RCM pro-

jections (e.g. local climate changes) but large enough to be robust and meaningful when fit to a

distribution (e.g. more grids pooled).

Projected change for Denmark

Globally, future changes in climate are expected to result in an intensification of hydrological

conditions, meaning an increase in extremes (Huntington, 2006; IPCC, 2007; Loaciga et al.,

1996). Precipitation regimes are tied to changes in atmospheric temperature and radiation

balance, with the future dynamics lending to increased climate variability, namely more in-

tense precipitation and more droughts (Trenberth et al., 2003). Temperature increases should

increase the availability of atmospheric water vapour content from surface evaporation (in-

creased specific humidity), which can decrease water availability but increase heavy precipita-

tion events (Bates et al., 2008). Climate variables simulated by GCMs or RCMs, especially pre-

cipitation, show significant natural variability both on interannual and decadal time scales, and

the resultant signal vs. noise issue is often discussed because it can overwhelm long term

trends of climate change .

For Denmark, there is variability between climate models on the direction and strength of cli-

mate change signals and characteristics of future climate dynamics. However, most climate

models predict increases in annual precipitation and temperatures for Denmark. Projections

generally show winter precipitation increasing in mean and frequency, and reduced mean but

higher intensity precipitation and increased evapotranspiration rates in summer (van der Lin-

den and Mitchell, 2009; IPCC, 2007). For Denmark, these changes in the quantity, timing, and

delivery of precipitation are expected to result in higher rates of groundwater recharge in the

winter months, as well as flooding and water logging in low lying areas, and decreased water

tables, dry root zones, and reduced low flows in the summer months (van Roosmalen et al.,

2007). This likely results in more groundwater recharge available, however, changes to intensi-

ty and frequency of precipitation events could result in increased discharge rather than

16 G E U S

groundwater recharge, and longer dry periods with base flow stream conditions. Projections of

future climate change are needed at spatial scales relevant to groundwater systems in order to

quantify the effects of climate change (Dragoni and Sukhija, 2008; Green et al., 2011; Loaiciga,

2003).

G E U S 17

Ph.D. Research

Models, methods, and data used in this study

DMI Climate Grid – Denmark

The Danish Meteorological Institute (DMI) provided daily gridded climate variables precipita-

tion (P) [mm/day], temperature (T) [°C], and reference evapotranspiration (ETref) [mm/day] for

the 20 year reference period 1991-2010 from their Climate Grid – Denmark (Kern-Hansen,

2012). Observational precipitation station data was gridded to 10 km through an interpolation

method of the approximately 500 rain gauges distributed evenly throughout Denmark, while T

and ETref was gridded to 20 km based on a sparser network of climate stations (Scharling,

2000). An additional correction was made to precipitation variables to compensate for gauge

under catch due to aerodynamic effects and wetting losses. Stisen et al. (2012) corrected P

with daily factors for each 10 km grid based on daily observations of wind speed and air tem-

perature (Allerup et al., 1998). To achieve a uniform grid for all climate variables in this study,

T and ETref were interpolated from their 20 km grid to the 10 km grid of P using an inverse

weighted distance interpolation method (Shepard, 1967). From here onward, this common

grid is referred to as the DMI 10 km grid shown in Figure 1.

Reference Evapotranspiration Calculation (Climate Grid)

DMI previously calculated daily ETref using a modified Penman formula proposed by the Danish

Agricultural Institute (DFJ). The formula is built around two main components: radiation levels

(Si) and wind speed (u), where the radiation levels are assumed to be of greatest importance.

The formulation also requires the climate variables temperature (T) and relative humidity (rh).

Computation of most of the daily climate variables needed for Penman is a straightforward

averaging or summing of the 1-hour or 3-hour values recorded at each station. However, for

rh values must be calculated for the day (09:00-15:00) and night (16:00-08:00) periods,

weighted following the so-called RH7-17 scheme (7 night hours and 17 day hours), then an

average daily rh value is computed. An effect of the RH7-17 weighting was that values were

generally too high for stations that only measured every third hour because they omit critical

values in the evening and morning period where humidity is likely to be lower. For consistency

between station data in calculating ETref for the Climate Grid, it was decided by DMI to use the

more simplistic Makkink method (Eqn. 1), which only relies on the climatic variables T and Si as

follows:

(Eqn. 1)

18 G E U S

where is reference evapotranspiration [mm d-1], and are empirical constants ,

is the latent heat of vaporization [2.465 MJ kg-1], is saturation vapour pressure [hPa °C-1],

is global radiation [MJ m-2 d-1], and is the psychrometric constant [kPa °C-1] (Makkink, 1957).

Compared to Penman, the Makkink formulation is more robust across stations with various

methods of data collection and data quality, and was shown to achieve good monthly and an-

nual sums for Denmark (Scharling, 2001). It is noted that negative values (i.e. dew) in

the Climate Grid were adjusted to zero by DMI.

Figure 1: The study area (Denmark) as divided by the DK-model into six hydrological domains; the 10 km DMI grid and centroids from the 25 km ENSEMBELS RCM grid are shown.

G E U S 19

ENSEMBLES RCMs

The European Commission funded ENSEMBLES project ran multiple climate models consisting

of different pairings of GCMs and RCMs to generate a matrix of simulations (van der Linden

and Mitchell, 2009). The main objective was to generate outputs representing a range of fu-

ture projections allowing the uncertainty to be measured. Compared to previous regional cli-

mate modelling projects, ENSEMBLES had a more extensive set of GCM-RCM pairings, longer

transient runs to the middle or end of the 21st century, and they are resolved at a higher reso-

lution from 25 to 50 km. A common European RCM domain, model resolution, and set of out-

put variables were defined making the results between climate models convenient to compare

and ideal for use in impact studies (Christensen et al., 2009; Kjellström and Giorgi, 2009). For

this study, a subset of 11 climate models (GCM-RCM pairings) from the ENSEMBLES matrix

where selected based on the following criteria for maximum comparability: highest resolution

(25 km), longest simulations to the end of the 21st century covering approximately 1951-2100

(some start in 1961 and/or end in 2099), and consistent climate sensitivity to atmospheric CO2

(excludes the low and high sensitivity Hadley GCMs). The resultant subset of the ENSEMBLES

matrix is depicted in Table 1, comprised of four GCMs and eight RCMs from the institutions

GCM

RCM

HadRM3 X

REMO X

RM5.1 X

HIRHAM5 X X X

CLM X

RACMO2 X

RegCM3 X

RCA3 X X

HadCM3 ECHAM5 ARPEGE BCM2

Table 1: Matrix of ENSEMBLES climate models shown as GCM–RCM pairings

Table 2: Climate models from the ENSEMBLES project for which projections have been used in the present study.

20 G E U S

listed in Table 2.

The climate models were all forced with the A1B emissions scenario as formulated by the UN

Intergovernmental Panel on Climate Change (IPCC) in their fourth assessment report (IPCC,

2007b). This scenario is considered to be mid-severity in that it projects a moderate increase in

the emission of global greenhouse gasses and thus positions itself in between the other scenar-

ios described in IPCC. The A1B scenario reflects an integrated world, rapid economic growth,

quick spreading of new technology, and a convergence in income and way of life between re-

gions. The global population and thus global anthropogenic emission of greenhouse gasses are

expected to peak mid-century after which they will decline.

Direct RCM output variables were downloaded from the ENSEMBLES data portal, including

precipitation (P) [mm/day] and temperature (T) [K] at 2 m above ground, and the variables

needed for calculating ETref (i.e. temperature minimum and maximum, incoming long and short

wave solar radiation, relative humidity, and wind speed) at the 25 km grid scale. Post-

processing of RCM climate outputs was as follows: (1) estimate ETref from RCM outputs, (2)

correct P for RCM wet day bias, and (3) interpolate P, T, and ETref to the 10 km DMI grid.

(1) Reference Evapotranspiration Calculation (RCM)

Actual evapotranspiration (ETact) is a direct RCM output, but the simplified representation of

land-surface processes makes ETact values inadequate as hydrological modelling inputs. The

processes effecting ETact (i.e. relative humidity, temperature, solar radiation, wind speed) are

simulated at more realistic scales, therefore, it is common practice to estimate ETref using em-

pirical formulas and output variables from the RCMs (van Roosmalen, 2009; Ekström et al.,

2007). Though observational ETref was calculated following the more simplistic Makkink formu-

lation for the Climate Grid due to inconsistencies in the data quality of rh variables across sta-

tions, ETref was calculated following a Penman formulation (Eqn. 2) because the climate varia-

bles needed for its calculation were available across all RCMs in consistent form. An adapted

Penman-Monteith formulation developed by the Food and Agriculture Organization of the

United Nations was applied as follows:

(Eqn. 2)

Where ETref is reference evapotranspiration [mm d-1], Rn is net radiation at the crop surface [MJ

m-2 d-1], G is soil heat flux density [MJ m-2 d-1], u2 is wind speed at 2 m height [m s-1], es – ea is

saturation vapour pressure deficit [kPa], Δ is slope vapour pressure curve [kPa °C-1], and γ is the

psychrometric constant [kPa °C-1] (Allen at al., 2004). All meteorological variables are assumed

G E U S 21

to be measured at 2 m, which is the approximate height of RCM variables with the exception of

wind speed at 10 m. A conversion factor based on the logarithmic wind profile was applied to

adjust wind speed to 2 m (Allen et al., 2004). Rn is calculated using RCM predictions of net

incoming short and long wave radiation. The mean saturation vapour pressure (es) and the

slope of the vapour pressure curve are calculated using daily mean temperature, which is cal-

culated as the average of daily maximum and minimum temperature. The actual vapour pres-

sure (ea) is derived from relative humidity. Soil heat flux (G) is small compared to Rn (Allen et

al., 1998) and is therefore set to zero. For consistency with DMI’s Climate Grid, all negative

values (i.e. dew) of RCM estimated ETref were set to zero.

(2) Precipitation Dry Day Correction

It is well established that RCMs have a systematic wet day bias originating from numerical

overflow, or a so-called drizzle effect, resulting in excessive low intensity precipitation on a

high number of days (e.g. Gutowski et al., 2007). In the reference period 1991-2010, annually

46% of days are dry in Denmark, with winter having the lowest frequency of 39% and spring

having the highest frequency at 54%. The 11 ENSEMBLE climate models average 15% dry days

annually. While these small daily amounts of drizzle may have negligible effects on mean

monthly and total annual precipitation, the distribution and frequency of wet days is important

for distribution based scaling bias correction methods, and this climate model error would

impact such methods and therefore should be adjusted. It is common practice to correct this

wet bias so the frequency of dry days in the climate model control period is equivalent to the

frequency in the observations (Yang et al., 2010). Data outside of the control period (e.g. fu-

ture data) are corrected using the cut-off value obtained in the control period, considered to

be the value above which modelled precipitation is realistic, and below which values are erro-

neous drizzle and therefore set to zero. Apply a cut-off value compared to simply adjusting the

frequency allows for correcting the wet bias while preserving potential changes in the frequen-

cy of dry days in modelled future precipitation regimes. Across all climate models, domains,

and seasons, dry day cut-off values ranged from 0.08 – 1.63 mm, with an ensemble average of

0.67 mm. The correction was applied at a temporal scale (i.e. seasonal) and spatial scale (i.e.

regional) consistent with the subsequent bias correction methods to ensure they train properly

in the reference period.

(3) Grid Interpolation

It is essential to have all climate variables on a uniform grid for bias correction. All RCM cli-

mate variables were interpolated from 25 km down to the 10 km DMI grid to preserve the

higher resolution of observational data. The same inverse weighted distance interpolation

method was applied to the Climate Grid variables T, and ETref which were initially at 20 km.

This method effectively subsets the RCM European dataset to the grids over Denmark, elimi-

22 G E U S

nating the usual step of applying a land-sea mask to determine which RCM grids 1) cover your

study area and 2) are composed of more than 50% land compared to ocean. The inverse

weight distance interpolation method ensures that values in ocean dominated grids are

weighted less than land dominated grids, which should be closer to the DMI grid centroids.

Figure 1 depicts the 10 km DMI grid with the 25 km ENSEMBLES grid centroids over Denmark.

Danish National Water Resources Model (DK-model)

Groundwater and surface water modelling done is this study has utilised the Danish National

Water Resources Model (DK-model) developed by the Geological Survey of Denmark and

Greenland (GEUS) (Henriksen et al., 2003). The DK-model was first finalised in 2001 and has

since been applied to assess the exploitable groundwater resources for Denmark (Henriksen et

al., 2012; Henriksen et al., 2008). It has been continuously updated to include more recent and

detailed local data (Højberg et al., 2013), including an enhanced interpretation of the hydroge-

ological model, the hydrological model resolution was reduced to 500 m, and the unsaturated

zone module was dynamically coupled to the saturated zone modules and the irrigation zone

module (Højberg et al., 2013).

The DK-model divides Denmark (approx. 43,000 km2) into seven hydrologically distinct model

domains (DK1-7) with boundaries defined by the sea or topographical divides coinciding with

natural hydraulic conditions (Figure 1) (Højberg et al., 2013). DK1 is a single domain over the

island Sjælland (7,163 km2), DK2 covers the Southern Islands (2,042 km2), DK3 is the island Fyn

(3,473 km2), and DK4-6 covers the Jylland peninsula divided into South (7,897 km2), Central

(11,578 km2), and North (9,934 km2), respectively. The 590 km2 island Bornholm (DK7) is ex-

cluded from this study due to the high proportion of ocean to land in the grid cells covering the

small island, making projected climate changes more representative for the surrounding ocean

than for the island itself. These DK-model domains form the basis of the subsequent “domain-

scale” bias correction methods for mainland Denmark (DK1-6), while hydrological modelling

was focused in DK1. Five catchements of various size (54 – 611 km2) and covering different

parts of the DK1 were selected -model domain (Figure 2). Daily stream discharge for these five

catchments and mean and maximun daily groundwater head for the entire DK1 domain is

output under simulations forced with the nine climate projections. Analysis of groundwater

heads is focused on the uppermost sand aquifer (DK-model layer 9) at 1-5 meters depth, and

the deeper regional sand aquifer (DK-model layer 3) at 30-50 meters depth and where the

exploitable water resource is found.

G E U S 23

Model setup

The DK-model is setup in the physically based and fully distributed terrestrial hydrological

modelling system MIKE SHE/MIKE 11 (Abbott et al., 1986; Graham and Butts, 2005). The upper

boundary is defined by climate input for precipitation (P) [mm/day], temperature (T) [°C], and

reference evapotranspiration (ETref) [mm/day] applied as daily values. Saturated groundwater

Figure 2: The study area is DK-model domain 1 (DK1) covering the island Sjælland (7,163 km2) with the

five catchments areas and discharge stations shown along with the 10 km DMI grid relevant to the cli-mate data.

24 G E U S

flow is described by a fully 3D finite difference formulation coupled with a simplified two-layer

unsaturated zone model. Evapotranspiration from the unsaturated zone is described by a sim-

ple water balance module based on a formulation presented in Yan and Smith (1994), which

only considers average conditions for the unsaturated zone. Overland flow is simulated in 2D

using the diffuse wave approximation. Stream flow is simulated as a 1D process by MIKE 11

using the kinematic routing approach. MIKE SHE/MIKE 11 is considered a coupled surface-

subsurface model, as it solves the saturated, unsaturated, and overland flow in separate mod-

ules with two-way coupling for each time step (Højberg et al., 2013).

Hydrogeology

Hydrogeology in the DK1 domain is characterised by a pre-Quaternary sequence of

chalk/limestone overlaid by a Quaternary sequence of low permeable deposits with sand lay-

ers which is quite variable across the domain. The model is constructed with up to four Qua-

ternary sand and gravel aquifers of variable thickness from < 1 to 80 meters and uniform hy-

draulic parameters and one chalk/limestone aquifer in the pre-Quaternary. The lower bounda-

ry of the model is considered impermeable, defined as 50 meters below the top of the lowest

hydrogeological layer (layer 1), which is the chalk layer in all DK-domains. The top layer (layer

9) is constructed based on information from a top soil map from the Geological Survey of

Denmark and Greenland (Nielsen et al., 2000). Drainage levels are uniformly set to a half me-

ter below the soil surface, where simulated groundwater levels above a half meter depth drain

to the nearest stream controlled by a uniformly defined drainage time coefficient.

Land-use and groundwater extraction

There are 21 classes of land use which are further described in Stisen et al. (2012). Agricultural

farmland is subdivided by crop type as well as soil type, such the control soil type has on root-

ing depth is reflected. The distribution of crops reflects 2005 regional statistics. Forests are

divided in deciduous and coniferous (dominant in DK1). Groundwater extraction rates for in-

dustrial and domestic use reflect the rates in 2010. For future simulations in this study, the

land use description and groundwater extraction rates reflect those in the reference period so

as not to introduce additional variables in the change simulations beyond climate change.

However, irrigation amounts are calculated internally in MIKE SHE based on the root zone soil

water deficit and therefore respond to climate changes.

Calibration

The DK-model has been calibrated on groundwater levels and stream discharge with the PEST

optimization tool (Doherty, 2010) per model domain as described in Stisen et al. (2012). The

G E U S 25

model parameters included in the calibration were selected based on sensitivity analysis, and

include hydrological properties of the subsurface, stream-aquifer interactions, drainage, and

the available water content for evapotranspiration.

Bias corrected climate inputs

Delta change factors and values

The delta change (DC) method consists of altering an observed (control) climate series with

change factors or values to obtain a new series representative of future change. For state vari-

ables (e.g. T) absolute change values are applied, whereas for flux variables (e.g. P and ETref)

relative change factors are applied. Monthly change factors/values were derived and per-

turbed as follows for day and month (i,j), where i = 1,2, ...,31 and j = 1,2, ...,12:

; (Eqn. 3)

; (Eqn. 4)

; (Eqn. 5)

where are DC perturbed daily climate change variables, are

observed climate variables in the control period, are the changes in climate as

simulated by the RCMs, (j) are daily climate means by month, and the index ctrl

stands for the control period (1991-2010), and fut stands for future periods near (2011-2040),

middle (2041-2070), and far (2071-2100). DC values for P, T, and ETref were determined from

11 RCM simulation’s output, for three future time periods, with unique sets of monthly (12)

factors spatially averaged for all grids in each of the six DK-model domains. The tables in Ap-

pendix 1 fully report, for each climate model and per DK-model domain, mean daily values per

month in the reference and near, mid, and far future periods , and the resultant change factor

or value. These factors and values were then used to adjust the observational Climate Grid

daily variables within the individual months to an input representing mean climate change of

the respective future period (near, middle, far).

26 G E U S

Bias removal factors and values

A bias removal (BR) approach used on T and ETref to correct daily RCM values with seasonal

bias removal factors calculated between the RCM reference period and the observational data.

Seasonal BR factors are used rather than e.g. monthly factors for consistency with the seasonal

approach used in deriving distribution parameters for the DBS methods. Likewise, BR factors

are calculated at both the domain (BR-domain) and grid (BR-grid) scale for consistency with the

two spatial scales relevant to the various DBS approaches (discussed below). For the state

variable T, factors are additive, and for flux variable ETref, factors are multiplicative, adjusting

daily values according to the seasonal mean bias. BR factors were calculated between the RCM

control period (1991-2010) per DK-domain or per grid and the observed data for each day in

season :

(Eqn. 6)

(Eqn. 7)

where and are mean daily RCM simulated outputs and and are

mean daily observed values, each calculated as the mean of all days per season and per DK-

domain or grid, and is the seasonal bias, which is then applied to the RCM direct outputs to

remove mean seasonal biases:

(Eqn. 8)

(Eqn. 9)

where and are corrected RCM outputs and and are simulated

RCM outputs, and is the seasonal bias factor representing relative change for the flux

variable ETref and while ƐT is the absolute change for state variables T. BR-domain scale tem-

perature and reference evapotranspiration are paired with DBS-domain precipitation for hy-

drological modelling, and BR-grid scale temperature and reference evapotranspiration are

paired with DBS-spatial and DBS-grid precipitation for hydrological modelling.

G E U S 27

Distribution based scaling precipitation

The distribution based scaling (DBS) bias correction method directly operates on RCM output

in a manner that preserves the statistical distribution of the observed precipitation in the ref-

erence period. To explore spatial variation in the RCM outputs, DBS parameters are fit for

each DK-model domain covering Denmark (DK1-6) as well as to each grid in DK1. The gamma

distribution is defined by two parameters, the shape parameter alpha ( ) and the scale param-

eter beta ( ). The probability density function (PDF) of daily P (mm/day) is as follows:

(Eqn. 10)

where is the gamma function:

(Eqn.11)

The parameters (α,β) were fit using the method of maximum likelihood for the shape parame-

ter (β) after which the scale parameter (α) is obtained (Wilks, 2006). The parameter set that

maximises the log likelihood function is found through an iterative process using the multidi-

mensional generalisation of the Newton-Raphson method. Precipitation regimes are charac-

terised by a high number of low intensity events which can dominate the fitting of the distribu-

tion parameters. After single gamma distribution fitting, the high error in variance suggested

that extreme high values (i.e. the upper tail) could not be represented within a single gamma

distribution. The DBS approach was expanded to a double gamma distribution partitioned at

the 95th percentile, effectively isolating normal precipitation from extreme precipitation. With

a double gamma approach, distributions are fit to the 95upper and 95lower groups separately,

resulting in two parameter sets per season and per DK-domain or grid. This fitting is applied to

observational and climate model precipitation values in the reference period 1991-2010.

For the scaling (=correcting) of precipitation values simulated by a RCM, the probability of the

precipitation not exceeding a given value of the RCM simulation in the reference period is

found from the cumulative distribution function (CDF) for the RCM in the reference period.

Locating this probability in the CDF for the observations provides the corresponding (i.e.

scaled) P value following this method:

28 G E U S

(Eqn. 12)

where Pcorr is the bias corrected RCM daily P, F is the CDF of the gamma distribution (Eqn. 11),

and F(αctrl, βctrl, PRCM) is the probability of not exceeding the value PRCM. Model biases were as-

sumed to be the same in the control, past, and future periods, therefore, Pcorr stands for any

value in the entire transient RCM outputs. Figure 3 graphically depicts the correction in Eqn.

12 with y1 = Pcorr and x1 = PRCM.

To account for both between and within-domain heterogeneities, and to evaluate DBS meth-

ods of varying complexity, three approaches were developed:

DBS-domain: domain scale dry day correction + domain scale DBS correction (DK1-6)

DBS-spatial: domain scale dry day correction + domain scale DBS correction + grid

scale BR (DK1-6)

DBS-grid: grid scale dry day correction + grid scale DBS correction (DK1)

In the approach denoted DBS-domain, parameters were fitted according to the precipitation

characteristics of an entire domain on a seasonal basis (i.e. pooled grids and daily values) and

each daily value per grid (10km) was scaled according to the seasonal domain distribution pa-

Figure 3: Graphical illustration of the DBS correction method on daily precipitation

G E U S 29

rameters. In the approach denoted DBS-grid, parameters were fitted according to precipita-

tion characteristics of a single grid (10 km) on a seasonal basis and each daily value per grid

was scaled according to the seasonal grid distribution parameters. In the approach denoted

DBS-spatial, DBS-domain scaled precipitation undergoes an additional BR correction at the grid

scale.

Variance Decomposition

Methodological choices, like which climate model(s) and/or bias correction method(s) to use,

generate uncertainty which can be quantified. Uncertainty in choosing one over another can

be measured by standard deviation and uncertainty from multiple outcomes can be measured

in spread. Uncertainty in bias correction methods are nested in climate model uncertainty,

therefore, they are not independent from one another. The contribution of choice of climate

model and choice of bias correction method to overall uncertainty was quantified for precipita-

tion inputs and hydrological outputs following the variance decomposition method from Dé-

qué et al., (2007). The amount of variability attributed to different climate models and bias

correction methods was quantified as percentages that explain the variance in spread across a

response. For a given variable response (X), in this case precipitation, discharge, and ground-

water head, the individual part of variance due to RCM (R) from 11 climate models and bias

correction (B) from four methods (three DBS and one DC) is calculated as follows:

(Eqn. 13)

where is the average response considering the indices i = 1 - 11 according (R) and j = 1 – 4

according to (B). The joint variance contribution (RB) is found by calculating all possible com-

binations of variance from R and B as follows:

(Eqn. 14)

The terms R and B can be explained as percentages of total variance as follows:

30 G E U S

(Eqn. 15)

where the sum of these is less than 100%. Finally, variance terms (V) are calculated which in-

corporate the interaction term RB as follows:

(Eqn. 16)

where the sum of V(R) and V(B) could be over 100%, but the magnitude of each term repre-

sents the contribution of each source in the overall uncertainty (Déqué et al., 2007).

Paper I

Assessment of robustness and significance of climate change signals for an ensemble of dis-

tribution-based scaled climate projections

Introduction and Objectives

There is high variability between climate models on the direction and strength of the climate

change signals and characteristics of future climate dynamics for Denmark. In this paper, an

ensemble of 11 regional climate model (RCM) projections are analysed from a hydrological

modelling inputs perspective. Two downscaling approaches were applied: a relatively simple

monthly delta change (DC) method and a more complex daily distribution-based scaling (DBS)

method. Differences in the strength and direction of climate change signals were compared

across models and between downscaling methods, the statistical significance of climate change

was tested as it evolves over the 21st century. The impact of choice of control period was ana-

lysed as it relates to assumptions of stationary current climate and change signals. The objec-

tives this paper were (1) to assess the accuracy and robustness of the DBS bias correction

method by evaluating it for an ensemble of climate models over multiple model domains; (2)

to analyse to which extent projected climate changes are actually significantly different from

the current climate; and (3) to evaluate reference and change period lengths to ensure distin-

guishing climate change from natural variability.

Major findings

G E U S 31

It was demonstrated that both DC and DBS methods equally retain mean monthly change

characteristics. The simplistic monthly DC approach was adequate for capturing the smooth

temporal characteristics of temperature changes, but insufficient at recreating projected pre-

cipitation regimes, which vary day to day and grid to grid. The more complex daily DBS correc-

tion method was accurate and robust, transferring changes in the mean as well as the variance,

and improving the characterisation of day to day variation as well as heavy precipitation

events. Though, compared to the DC method, the DBS method could carry possible spatial

biases in the RCM on to the impact model.

This analysis of multiple climate models, downscaling methods, and time periods elucidated

the nature of climate model uncertainty. The ensemble of 11 climate models varied in

strength, significance, and sometimes in direction of the climate change signal. Generally,

climate change signals in the near future are hidden by natural variability and are not signifi-

cant, in the mid future the significance of climate change signals depend on the choice of cli-

mate model, and in the far future climate change signals are strong across all models and vari-

ables.

Some models already display significant differences in climate variables within the past

timeframe. Current climate characteristics were not necessarily stationary and the temporal

positioning of a control period might impact the magnitude of relative climate change. A dras-

tic decrease in the standard deviations of DC factors was seen in periods over 15 years, with

periods over 20 years continuing to decrease and level off. Control period lengths over 15

years are adequate in size to overcome natural variability and still have stationarity in the cli-

mate change signal. The temporal positioning of a control period (e.g. middle or late 20th cen-

tury) could impact the magnitude of relative climate change (e.g. DC factors).

Paper II

Spatial uncertainty in bias corrected climate projections and hydrogeological impacts for

Denmark

Introduction and Objectives

The relationship between groundwater and climate variables is more complicated than with

surface water due to the slower response and long residence times. Bias correction methods

are often analysed in their ability to remove initial climate model bias, but the question of

which methods and spatial scales are best suited for retaining projected future changes has so

far received little attention. In this study, 11 ENSEMBLE outputs of daily precipitation, temper-

ature, and reference evapotranspiration are bias corrected with three distribution based scal-

ing (DBS) methods of varying complexity and spatial scales for all of Denmark and used to force

hydrological simulations in a reference and future change period in a Danish basin. The Danish

32 G E U S

National Water Resources Model (DK-Model) is used for hydrological simulations of mean and

maximum groundwater heads and catchment discharge. Metrics were devised to quantify and

compare spatial variability in climate model bias and climate change signals. The main objec-

tives of this study were to (1) Quantify the spatial bias for uncorrected RCM and DBS corrected

precipitation at the domain scale, (2) Analyse the spatial characteristics of groundwater re-

charge and stream discharge given multiple climate models and three DBS bias correction

methods under current conditions, (3) Assess the relationship between complexity and robust-

ness in bias correction methods, and (4) Assess the spatial relationship between climate

change signal and climate model bias.

Major findings

The spatial characteristics of groundwater recharge and stream discharge are best represented

by DBS methods applied at the grid scale (DBS-spatial, DBS-grid). The magnitude of spatial bias

seen in precipitation inputs does not necessarily correspond to the magnitude of biases seen in

hydrological outputs. Flux and state hydrological outputs which integrate responses over time

and space showed more sensitivity to mean spatial biases and less so on extremes. High spa-

tial bias in mean and maximum groundwater head values are apparent in the uppermost (1-5

meters) shallow sand aquifer compared to the deeper (30-50 meters) regional sand aquifer

under all DBS corrected precipitation. Hydrological simulations forced by the least parameter-

ised DBS approach showed the highest bias in mean and maximum groundwater heads, over

+/- 3 meters in some grids.

In the reference period, higher parameterised DBS bias correction methods can reproduce the

characteristics of observational precipitation, but become less robust in climate conditions

different from the reference period they were fitted. This relationship is more severe in cli-

mate models with high initial bias. Of the DBS bias correction methods applied, no method is

superior in all respects. The DBS-spatial and DBS-grid methods are more highly parameterised,

and therefore more effective across all climate models, including those with high spatial bias.

However, the highly parameterised DBS-grid method may begin to break down for heavy pre-

cipitation and show less robustness of climate change signals when compared to the DBS-

spatial method. The highly (possibly over) parameterised methods should be used with cau-

tion, and checked for robustness in their projections.

The spatial variability of climate model precipitation bias is at a finer scale than the spatial var-

iability in the climate change signal. The spatial pattern (or scale) of uniformity in RCM initial

bias is not related to the scale of the uniformity in the climate change signal. While the scale of

spatial variability in climate change signal is adequately captured by the domain scale DBS ap-

proach, a lot of initial model bias remains at the spatial scale of this method, which may over-

shadow the impact of climate change in hydrological simulations. However, the most highly

parameterised DBS approach showed less robustness in future change periods compared to

the reference period it was trained in.

G E U S 33

Technical Note

Climate model and bias correction uncertainty in hydrological modelling of projected future

conditions for Denmark

There are multiple sources of uncertainty associated with the use of future climate change

projections in hydrological change modelling methodologies. For example, uncertainty in how

anthropogenic emissions will evolve is inherent with climate model projections, where “true

values” cannot be known. There exists a prediction uncertainty with all climate and hydrologi-

cal models from e.g. calibration and process representation, but in addition, there is uncertain-

ty in how the crucial controls, feedbacks, and physical relationships will hold under future con-

ditions. Different climate models often display different characteristics in terms of initial bias

and future change signals, yet it is not possible to determine which projections are most prob-

able of future conditions. Some work has been done on weighting climate models in terms of

their performance in a reference period (e.g. Christensen et al., 2010), but the ability of a cli-

mate model to replicate historical conditions is not indicative of its ability to simulate future

changes.

In this technical note, hydrological change simulations are made in the reference period 1991-

2010 and the far future period 2071-2100 forced with combinations of 11 climate models with

four bias correction methods in a Danish catchment. Uncertainty was considered in terms of

spread across precipitation inputs and across hydrological outputs. The objective is to quantify

the amount of uncertainty which can be attributed to choice of climate model and choice of

bias correction to overall uncertainty. Overwhelmingly, choice of climate model contributes to

the majority of overall uncertainty (99%) and choice of bias correction method explains less

than 10% of uncertainty, but was of more importance on precipitation inputs compared to

hydrological responses.

Paper III and IV

Climate change effects on irrigation demands and minimum stream discharge: impact of bias

correction method

Introduction and Objectives

The agricultural demand for irrigation depends heavily on precipitation. With climate change,

increasing temperatures and associated evapotranspiration rates will likely increase the agri-

cultural demand for irrigation. In heavily irrigated catchments, changes in the amount and

timing of irrigation can impact stream discharge. In the summer, this impact is pronounced

due to high irrigation demands and base flow conditions in streams. This study assessed the

impacts of projected warmer climate and increased inter-annual variability in precipitation on

34 G E U S

irrigation demand and low flow in streams. The main objectives were to (1) analyse the effects

of two bias correction methods on the impact of climate changes; (2) analyse the effect of two

methods applied to account for the effect of increasing CO2-levels on transpiration; and (3)

compare the hydrological outputs of minimum stream flow and yearly irrigation volumes to

quantify the extreme low flow situations

Main contributions to findings

This study utilised one climate model (ECHAM5-RACMO2) with two bias correction methods

(DC and DBS). This climate model was found to be the median model in ENSEMBLES in terms

of projected climate change and demonstrated the least initial bias over Denmark. Mean cli-

mate and discharge variables were comparable between the two methods of bias correction.

While the annual frequency of dry days was nearly equivalent between methods, there were

large differences in the summer months. The DBS methods retained the projected dry spells

seen in future conditions, while the DC method underestimated the frequency of dry days in

the summer. Irrigation was significantly underestimated with the DC method due to its inability

to account for changes in inter-annual variability in precipitation and reference evapotranspi-

ration. The underestimation of irrigation demand had little impact on summer stream flow

since the studied catchment is controlled by winter and spring recharge, rather than summer

precipitation. For this study, the choice of bias correction method had the largest impact in

the driest periods of the year.

Climate change impact on groundwater levels: ensemble modelling of extreme values

Introduction and Objectives

Climate change adaptation is increasingly becoming part of planning of infrastructure devel-

opment, namely, that infrastructure should be able to withstand the extreme hydrological

events projected under future climate conditions. Analyses of groundwater head extremes are

highly relevant for infrastructure, such as roads, which are close to groundwater tables, making

them vulnerable to flooding and drainage issues. In this study, local-scale groundwater levels

were simulated for a sensitive area given a planned motorway, utilising 18 climate change pro-

jections from nine RCMs and two bias correction methods. Extreme value statistics (EVA) were

applied to model predictions of future groundwater levels in the period 2081-2100. Three

uncertainty sources were evaluated: climate models, bias correction method, and extreme

value statistics. The main objectives were to (1) investigate the impacts of climate change on

extreme groundwater levels in relation to infrastructure design, and (2) assess the uncertainty

of extreme groundwater level estimates considering the key sources of uncertainties on the

future climate.

G E U S 35

Main contributions to findings

Climate scenarios were generated specifically for this study, considering the local scale and

main objectives. The DC method was adjusted to a 20 year future period of 2081-2100 to high-

light the most pronounced climate changes seen at the end of the century in the transient RCM

simulations (1951-2100). The DBS-basin method had exhibited potentially high spatial bias

within DK-model domains on certain grids. As this study was a highly localised and concerned

with extreme values, grid-scale biases might have had a pronounced impact on the findings

(e.g. compared to regional studies, mean values). Therefore, a grid-based DBS method was

applied to the study area to ensure initial climate model bias was removed. The ensemble of

18 climate change projections facilitated the uncertainty analysis. In the investigated aquifer,

the projected change of extreme groundwater levels between current and future conditions

was found to be very modest. The largest source of uncertainty for the extreme groundwater

levels was the extreme value analysis, accounting for 46%. Climate models accounted for 23%,

and bias correction methods accounted for just 4% of uncertainty. Given the importance of

climate model uncertainty, multiple models of future climate conditions should be incorpo-

rated into studies and planning for climate change adaptation.

36 G E U S

Conclusions and Perspectives

This research focused on bias and change signals at regional scales and seasonal and annual

temporal scales. This large scale approach was adopted to enable multiple climate models for

the entire Denmark to be processed, and to facilitate climate change impacts studies. The bias

correction methods were developed at various spatial scales, but evaluated primarily at re-

gional scales based in their ability to characterise observed climate and hydrological outputs

scales larger than their implementation (Paper 1 and II). In other studies, such as in Paper III

and IV, the datasets were evaluated at smaller temporal and spatial scales, which reveal some

information which is not as evident at the regional scale. At the regional scale, choice of cli-

mate model causes the majority of variability in the ensemble of inputs and outputs, and

therefore contributes the most uncertainty to the methodology.

For RCMs with high spatial bias, the scale and choice of bias correction methods might dilute,

mischaracterise, or overshadow the impact of climate change, especially on hydrological varia-

bles exhibiting heterogeneity within a domain (e.g. groundwater recharge). For variables that

are accumulated over the domain (e.g. stream discharge), RCM spatial bias may be of less con-

sequence if the spatial biases balance out when results are aggregated.

If regional climate models begin to align for a given region in terms of signal and strength of

change signals, including multiple climate models might not be as important in future studies.

Currently, climate model uncertainty is the most critical part of overall uncertainty, so it is rec-

ommended to include multiple climate models. Ideally, climate models from a variety of GCMs

and RCMs should be utilised given the trends in bias and change signals seen in the 11 climate

models used in this research. Generally, GCMs dominate climate change and RCMs dominate

bias. For regional studies, simplistic bias correction methods like DC seem adequate. At this

scale, the most complex method applied, DBS-grid, did not result in significantly different hy-

drological change variables. At smaller scales and for sensitive variables, choice of bias correc-

tion method is more important.

The simplistic DC method remains a highly attractive option due to its ease of implementation

and since it completely removes the issues of initial climate model bias from the methodology.

The application in this research was the most simplistic DC approach, where only mean chang-

es were transferred. This was done for benchmarking purposes, with the intention of evaluat-

ing the more complex methods against the most simplistic and commonly applied approach.

DC methods can be expanded to account for changes in variability, or factors can be generated

for normal and heavy precipitation separately. Theses more complex DC methods might not

be as effective as the DBS methods in terms of fully characterising projected changes, but given

1) the lack of climate model bias in DC methods and 2) the knowledge that choice of climate

model still contributes nearly all uncertainty, it could be sufficient and efficient to use multiple

climate models and sophisticated DC methods.

G E U S 37

This study performed climate model bias correction at spatial division relevant to the National

Water Resources Model (DK-model). The seven domains are logical given hydrological divides,

but do not seem relevant to climate variables. Future work should instead focus on grouping

the climate grids covering Denmark into based on known local controls on climate, such as

elevation, proximity to the coast, north-south and/or east-west divides. This research found

that the spatial scales relevant to removing initial bias and preserving climate change signals

were not the same, with bias more spatially variable than change signals. If DBS scaling was

applied to grids grouped by e.g. elevation, a similar analysis should be done to ensure that

climate change signals are stable within the groups. Checks should be made to ensure that the

bias correction methods to not interfere with the relative climate change signals.

In the present study, it was attempted to fit reference evapotranspiration to a gamma distribu-

tion for DBS scaling. Numerous attempts were made using single gamma distributions and

double gamma distributions split at the 50th, 75th and 95th percentiles, and at annual, seasonal,

and monthly scales, but a good fit could not be achieved. Temperature follows a normal dis-

tribution and DBS methods could have been applied, but since evapotranspiration could but

undergo DBS scaling, and given their daily covariation, both variables had a simple bias remov-

al factor method applied. For regimes that are more sensitive to evapotranspiration, scaling

this variable in a more sophisticated way would be important, and should continue to be inves-

tigated.

If possible and with the availability of more GCM-RCM pairings, a three dimensional variance

decomposition which includes RCM and GCM as separate variance terms would enhance the

uncertainty analysis. The conclusions in this research regarding the influence of GCMs vs.

RCMs are strictly qualitative and based on knowledge of climate model process representation.

It would not be expected that such a variance decomposition analysis would contradict these

findings, but quantitatively characterising the contribution of choice of GCM and RCM would

be informative.

38 G E U S

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G E U S 43

Appendix 1: Delta Change Factors and Values

Monthly delta change factors (precipitation and reference evapotranspiration) and values

(temperature) for 11 ENSEMBLES climate models for each DK-model domain (DK1-6) are re-

ported for three future periods: near (2011-2040), mid (2041-2070), and far (2071-2100).

Mean daily absolute values per month in the reference period (1991-2010) and three future

periods are also reported, which form the basis of the delta change calculation.

44 G E U S

G E U S 45

ref near mid far near mid far

Jan 2.52 2.03 2.35 2.41 0.81 0.93 0.96

Feb 2.22 2.26 2.02 2.31 1.02 0.91 1.04

Mar 1.95 1.71 1.59 1.76 0.88 0.82 0.90

Apr 1.89 1.72 1.83 1.58 0.91 0.97 0.84

May 2.19 2.43 1.96 2.12 1.11 0.89 0.97

Jun 2.45 2.77 2.66 2.22 1.13 1.09 0.91

Jul 1.93 1.90 1.86 1.49 0.98 0.97 0.77

Aug 1.71 1.34 1.07 1.00 0.79 0.63 0.58

Sep 2.23 1.60 1.75 1.18 0.72 0.78 0.53

Oct 2.37 2.38 2.55 2.15 1.00 1.07 0.91

Nov 2.47 2.28 2.33 2.16 0.92 0.94 0.87

Dec 1.98 2.23 2.43 2.45 1.13 1.23 1.24

Jan 2.55 1.95 2.35 2.33 0.77 0.92 0.92

Feb 2.14 2.12 1.97 2.17 0.99 0.92 1.02

Mar 1.80 1.63 1.59 1.62 0.90 0.88 0.90

Apr 1.67 1.59 1.57 1.49 0.95 0.94 0.89

May 1.75 2.06 1.82 1.89 1.17 1.04 1.08

Jun 2.14 2.63 2.39 2.07 1.23 1.11 0.97

Jul 1.73 1.72 1.66 1.33 0.99 0.96 0.77

Aug 1.64 1.32 0.96 0.89 0.81 0.58 0.54

Sep 2.22 1.57 1.74 1.30 0.71 0.78 0.58

Oct 2.54 2.35 2.53 2.19 0.93 1.00 0.86

Nov 2.43 2.28 2.30 2.25 0.94 0.95 0.93

Dec 1.97 2.23 2.45 2.39 1.13 1.24 1.22

Jan 2.57 2.02 2.37 2.45 0.79 0.92 0.95

Feb 2.21 2.20 2.03 2.30 0.99 0.92 1.04

Mar 1.89 1.73 1.67 1.76 0.91 0.88 0.93

Apr 1.89 1.76 1.84 1.59 0.93 0.97 0.84

May 2.10 2.22 2.06 2.11 1.06 0.98 1.00

Jun 2.34 2.78 2.51 2.32 1.19 1.07 0.99

Jul 1.79 1.75 1.65 1.31 0.98 0.92 0.73

Aug 1.72 1.22 0.93 0.90 0.71 0.54 0.52

Sep 2.03 1.46 1.66 1.26 0.72 0.82 0.62

Oct 2.30 2.35 2.34 1.93 1.02 1.02 0.84

Nov 2.43 2.23 2.20 2.07 0.92 0.91 0.85

Dec 1.96 2.26 2.43 2.43 1.15 1.24 1.24

Jan 3.11 2.55 2.78 3.02 0.82 0.89 0.97

Feb 2.62 2.60 2.51 2.73 0.99 0.96 1.04

Mar 2.33 2.04 1.91 2.07 0.87 0.82 0.89

Apr 2.30 2.09 2.28 1.90 0.91 0.99 0.82

May 2.41 2.62 2.39 2.42 1.08 0.99 1.00

Jun 2.56 2.91 2.69 2.68 1.14 1.05 1.04

Jul 2.08 1.99 1.90 1.58 0.96 0.92 0.76

Aug 2.02 1.40 1.10 1.03 0.69 0.55 0.51

Sep 2.22 1.77 1.78 1.36 0.80 0.80 0.61

Oct 2.49 2.68 2.49 2.08 1.07 1.00 0.83

Nov 2.69 2.54 2.56 2.43 0.95 0.95 0.90

Dec 2.25 2.64 2.90 3.02 1.18 1.29 1.34

Jan 2.85 2.43 2.55 2.75 0.85 0.90 0.97

Feb 2.54 2.51 2.40 2.70 0.99 0.95 1.06

Mar 2.24 1.97 1.86 1.98 0.88 0.83 0.88

Apr 2.30 2.09 2.30 1.94 0.91 1.00 0.84

May 2.54 2.64 2.42 2.47 1.04 0.95 0.97

Jun 2.66 2.89 2.78 2.69 1.09 1.05 1.01

Jul 2.16 2.05 2.01 1.62 0.95 0.93 0.75

Aug 1.97 1.57 1.20 1.09 0.80 0.61 0.55

Sep 2.26 1.70 1.65 1.34 0.75 0.73 0.59

Oct 2.28 2.49 2.45 2.01 1.09 1.07 0.88

Nov 2.58 2.36 2.37 2.24 0.91 0.92 0.87

Dec 2.10 2.50 2.70 2.77 1.19 1.28 1.32

Jan 3.03 2.51 2.56 2.82 0.83 0.85 0.93

Feb 2.65 2.54 2.51 2.86 0.96 0.95 1.08

Mar 2.31 2.02 1.86 2.01 0.88 0.81 0.87

Apr 2.17 1.98 2.18 1.80 0.91 1.00 0.83

May 2.33 2.47 2.21 2.32 1.06 0.95 1.00

Jun 2.46 2.58 2.45 2.42 1.05 1.00 0.98

Jul 2.03 1.98 1.89 1.61 0.98 0.93 0.80

Aug 1.85 1.65 1.36 1.14 0.89 0.74 0.62

Sep 2.54 1.80 1.76 1.46 0.71 0.69 0.58

Oct 2.54 2.77 2.81 2.26 1.09 1.11 0.89

Nov 2.81 2.57 2.59 2.60 0.91 0.92 0.92

Dec 2.32 2.61 2.88 3.00 1.12 1.24 1.29

DK2

DK3

DK4

DK5

DK6

Mean (mm/day) DC Factor (-)Precipitation ARPEGE-RM5.1

DK1

46 G E U S

ref near mid far near mid far

Jan 2.96 2.68 2.58 3.02 0.90 0.87 1.02

Feb 2.49 2.63 2.44 2.55 1.05 0.98 1.02

Mar 2.23 1.98 1.78 1.82 0.88 0.80 0.82

Apr 1.53 1.64 1.56 1.20 1.07 1.02 0.78

May 1.82 1.83 1.63 1.44 1.01 0.89 0.79

Jun 1.89 1.83 1.57 1.28 0.97 0.83 0.68

Jul 1.06 1.00 1.09 0.81 0.94 1.02 0.77

Aug 0.79 0.77 0.66 0.53 0.97 0.83 0.67

Sep 2.06 1.63 1.75 1.17 0.79 0.85 0.57

Oct 3.16 3.10 2.86 2.62 0.98 0.90 0.83

Nov 3.49 3.19 2.92 2.91 0.91 0.83 0.83

Dec 2.60 2.90 3.04 3.15 1.12 1.17 1.21

Jan 3.03 2.48 2.47 2.88 0.82 0.81 0.95

Feb 2.45 2.50 2.14 2.35 1.02 0.87 0.96

Mar 2.14 1.86 1.64 1.75 0.87 0.77 0.82

Apr 1.34 1.55 1.50 1.20 1.16 1.12 0.90

May 1.71 1.66 1.58 1.36 0.97 0.92 0.79

Jun 1.75 1.68 1.43 1.20 0.96 0.82 0.69

Jul 1.12 0.82 0.91 0.68 0.73 0.81 0.61

Aug 0.79 0.67 0.55 0.47 0.84 0.69 0.59

Sep 2.02 1.48 1.62 1.15 0.73 0.80 0.57

Oct 3.07 2.91 2.79 2.57 0.95 0.91 0.84

Nov 3.31 3.15 2.92 2.84 0.95 0.88 0.86

Dec 2.50 2.67 2.72 3.04 1.07 1.09 1.22

Jan 3.15 2.68 2.61 3.18 0.85 0.83 1.01

Feb 2.58 2.56 2.49 2.56 0.99 0.96 0.99

Mar 2.31 2.01 1.75 1.94 0.87 0.76 0.84

Apr 1.49 1.57 1.63 1.34 1.05 1.09 0.90

May 1.55 1.88 1.73 1.37 1.21 1.12 0.89

Jun 1.92 1.69 1.56 1.34 0.88 0.81 0.70

Jul 1.09 1.03 1.12 0.87 0.94 1.02 0.79

Aug 0.89 0.85 0.72 0.53 0.95 0.81 0.60

Sep 2.25 1.74 1.66 1.26 0.78 0.74 0.56

Oct 3.32 3.30 3.14 2.83 1.00 0.95 0.85

Nov 3.65 3.44 3.08 3.07 0.94 0.84 0.84

Dec 2.71 3.01 3.09 3.28 1.11 1.14 1.21

Jan 3.67 3.30 3.13 3.79 0.90 0.85 1.03

Feb 3.10 3.06 3.07 3.01 0.99 0.99 0.97

Mar 2.70 2.31 2.00 2.13 0.86 0.74 0.79

Apr 1.64 1.67 1.77 1.49 1.01 1.08 0.91

May 1.47 1.87 1.73 1.36 1.27 1.18 0.92

Jun 1.81 1.58 1.47 1.23 0.87 0.82 0.68

Jul 1.04 0.98 1.03 0.76 0.95 0.99 0.73

Aug 0.88 0.83 0.66 0.45 0.95 0.76 0.51

Sep 2.20 1.70 1.67 1.30 0.77 0.76 0.59

Oct 3.24 3.28 3.11 2.80 1.01 0.96 0.86

Nov 3.93 3.75 3.36 3.32 0.95 0.85 0.84

Dec 3.07 3.49 3.51 3.72 1.14 1.14 1.21

Jan 3.08 2.93 2.85 3.24 0.95 0.93 1.05

Feb 2.72 2.81 2.78 2.77 1.03 1.02 1.02

Mar 2.47 2.15 1.92 1.99 0.87 0.78 0.80

Apr 1.67 1.58 1.70 1.38 0.95 1.02 0.82

May 1.58 1.78 1.70 1.37 1.13 1.07 0.86

Jun 1.82 1.49 1.47 1.21 0.82 0.81 0.67

Jul 1.04 1.08 1.10 0.78 1.03 1.06 0.75

Aug 1.00 0.94 0.74 0.52 0.94 0.74 0.52

Sep 1.93 1.50 1.55 1.22 0.78 0.81 0.63

Oct 2.61 2.94 2.71 2.47 1.13 1.04 0.95

Nov 3.43 3.21 2.88 2.79 0.94 0.84 0.81

Dec 2.60 3.08 3.24 3.25 1.18 1.25 1.25

Jan 3.14 3.08 3.06 3.46 0.98 0.98 1.10

Feb 2.80 2.84 2.91 2.90 1.01 1.04 1.04

Mar 2.45 2.22 1.94 2.04 0.91 0.79 0.83

Apr 1.79 1.66 1.72 1.46 0.93 0.96 0.82

May 1.74 1.88 1.80 1.54 1.08 1.03 0.89

Jun 1.92 1.57 1.59 1.32 0.82 0.83 0.69

Jul 1.23 1.37 1.36 0.95 1.12 1.10 0.78

Aug 1.24 1.14 0.87 0.72 0.92 0.70 0.58

Sep 2.13 1.73 1.84 1.44 0.81 0.86 0.68

Oct 2.92 3.30 3.11 2.85 1.13 1.07 0.98

Nov 3.63 3.46 3.22 3.21 0.95 0.89 0.88

Dec 2.67 3.25 3.56 3.63 1.22 1.34 1.36

ARPEGE-HIRHAM5Mean (mm/day) DC Factor (-)

DK1

DK2

DK3

DK4

DK5

DK6

Precipitation

G E U S 47

ref near mid far near mid far

Jan 2.50 2.82 2.82 2.94 1.13 1.13 1.18

Feb 2.25 2.21 2.28 2.33 0.98 1.01 1.03

Mar 1.96 2.43 2.35 2.87 1.24 1.20 1.47

Apr 1.72 1.62 1.91 2.02 0.94 1.11 1.17

May 1.65 1.46 1.92 1.77 0.88 1.17 1.08

Jun 1.32 1.53 1.58 1.74 1.16 1.19 1.31

Jul 1.48 1.52 1.80 1.94 1.02 1.21 1.31

Aug 2.39 2.64 2.39 2.52 1.10 1.00 1.06

Sep 3.00 2.81 2.85 3.18 0.94 0.95 1.06

Oct 3.29 2.84 3.17 2.94 0.86 0.96 0.90

Nov 2.80 3.02 3.18 2.98 1.08 1.14 1.07

Dec 2.77 2.94 3.28 3.03 1.06 1.18 1.09

Jan 2.45 2.95 2.91 3.13 1.21 1.19 1.28

Feb 2.26 2.18 2.27 2.26 0.97 1.00 1.00

Mar 1.93 2.35 2.21 2.88 1.22 1.15 1.49

Apr 1.62 1.50 1.74 1.98 0.93 1.08 1.23

May 1.45 1.37 1.79 1.76 0.94 1.23 1.21

Jun 1.31 1.54 1.47 1.55 1.17 1.12 1.18

Jul 1.37 1.41 1.71 1.70 1.03 1.25 1.24

Aug 2.30 2.39 2.36 2.35 1.04 1.02 1.02

Sep 2.87 2.71 2.79 3.10 0.94 0.97 1.08

Oct 3.11 2.78 2.98 2.75 0.90 0.96 0.89

Nov 2.91 2.91 3.12 3.10 1.00 1.07 1.07

Dec 2.76 3.16 3.30 3.07 1.14 1.19 1.11

Jan 2.66 2.97 3.03 3.26 1.11 1.14 1.22

Feb 2.37 2.26 2.45 2.42 0.95 1.03 1.02

Mar 2.05 2.48 2.36 2.93 1.21 1.15 1.43

Apr 1.74 1.68 2.04 2.15 0.97 1.17 1.24

May 1.60 1.54 2.01 1.76 0.96 1.25 1.10

Jun 1.45 1.69 1.49 1.75 1.16 1.02 1.20

Jul 1.59 1.73 1.93 2.02 1.09 1.22 1.27

Aug 2.51 2.83 2.64 2.73 1.13 1.05 1.09

Sep 3.39 2.91 3.16 3.43 0.86 0.93 1.01

Oct 3.42 3.09 3.29 3.12 0.90 0.96 0.91

Nov 3.00 3.31 3.32 3.16 1.11 1.11 1.06

Dec 2.91 3.16 3.51 3.23 1.08 1.21 1.11

Jan 2.85 3.21 3.26 3.54 1.13 1.14 1.24

Feb 2.64 2.49 2.69 2.77 0.94 1.02 1.05

Mar 2.29 2.77 2.59 3.18 1.21 1.13 1.39

Apr 1.83 1.74 2.12 2.28 0.95 1.16 1.25

May 1.65 1.52 1.91 1.69 0.92 1.15 1.02

Jun 1.50 1.63 1.45 1.80 1.08 0.97 1.20

Jul 1.85 1.72 1.94 2.13 0.93 1.05 1.15

Aug 2.33 2.70 2.51 2.46 1.16 1.08 1.05

Sep 3.21 2.76 2.96 3.21 0.86 0.92 1.00

Oct 3.44 3.18 3.50 3.14 0.92 1.02 0.91

Nov 3.08 3.47 3.50 3.19 1.13 1.14 1.04

Dec 3.08 3.29 3.70 3.47 1.07 1.20 1.13

Jan 2.57 2.77 2.78 2.96 1.08 1.08 1.15

Feb 2.37 2.22 2.32 2.44 0.94 0.98 1.03

Mar 1.91 2.53 2.39 2.80 1.32 1.25 1.46

Apr 1.92 1.75 2.14 2.15 0.91 1.12 1.12

May 1.68 1.54 1.98 1.65 0.92 1.18 0.98

Jun 1.37 1.58 1.42 1.84 1.15 1.04 1.34

Jul 1.80 1.70 1.88 2.01 0.95 1.04 1.12

Aug 2.35 2.65 2.44 2.32 1.13 1.04 0.99

Sep 2.88 2.65 2.68 2.87 0.92 0.93 1.00

Oct 3.06 2.88 3.12 2.70 0.94 1.02 0.88

Nov 2.76 3.07 2.99 2.80 1.11 1.08 1.02

Dec 2.73 2.85 3.22 2.92 1.05 1.18 1.07

Jan 2.63 2.76 2.86 2.96 1.05 1.09 1.13

Feb 2.38 2.08 2.33 2.44 0.87 0.98 1.02

Mar 1.85 2.42 2.37 2.82 1.31 1.28 1.52

Apr 2.07 1.70 2.21 2.26 0.82 1.07 1.09

May 1.80 1.65 2.03 1.68 0.91 1.13 0.93

Jun 1.40 1.61 1.49 1.86 1.15 1.06 1.33

Jul 1.96 1.83 1.93 2.15 0.94 0.99 1.10

Aug 2.49 2.78 2.69 2.53 1.12 1.08 1.02

Sep 2.99 3.00 2.90 3.13 1.00 0.97 1.05

Oct 3.31 3.10 3.42 3.07 0.94 1.03 0.93

Nov 2.95 3.26 3.40 3.12 1.11 1.15 1.06

Dec 2.74 2.98 3.40 3.07 1.09 1.24 1.12

BCM2-HIRHAM5Mean (mm/day) DC Factor (-)

DK1

DK2

DK3

DK4

DK5

DK6

Precipitation

48 G E U S

ref near mid far near mid far

Jan 2.35 2.80 2.87 3.15 1.19 1.22 1.34

Feb 2.24 2.31 2.24 2.07 1.03 1.00 0.92

Mar 2.23 2.35 2.61 2.77 1.06 1.17 1.24

Apr 1.77 1.58 1.79 2.33 0.89 1.01 1.31

May 1.70 1.86 1.80 2.03 1.09 1.06 1.19

Jun 1.51 1.93 1.96 1.67 1.27 1.29 1.10

Jul 2.10 2.21 2.39 2.31 1.06 1.14 1.10

Aug 2.37 2.69 2.77 2.61 1.13 1.17 1.10

Sep 2.53 2.62 2.62 2.89 1.04 1.04 1.14

Oct 2.60 2.96 2.66 2.74 1.14 1.03 1.05

Nov 2.80 2.73 3.07 3.18 0.97 1.10 1.14

Dec 2.68 2.99 3.23 3.37 1.12 1.21 1.26

Jan 2.20 2.80 2.83 3.14 1.27 1.29 1.43

Feb 2.05 2.13 2.14 1.92 1.04 1.05 0.94

Mar 2.01 2.07 2.36 2.46 1.03 1.17 1.22

Apr 1.54 1.42 1.63 2.07 0.92 1.06 1.34

May 1.57 1.87 1.69 2.02 1.19 1.08 1.29

Jun 1.58 1.88 1.96 1.71 1.19 1.24 1.08

Jul 2.18 2.19 2.28 2.20 1.00 1.04 1.01

Aug 2.00 2.45 2.72 2.36 1.22 1.35 1.18

Sep 2.17 2.22 2.45 2.60 1.02 1.13 1.19

Oct 2.39 2.61 2.52 2.52 1.09 1.06 1.05

Nov 2.67 2.48 2.96 3.09 0.93 1.11 1.16

Dec 2.76 2.93 3.20 3.30 1.06 1.16 1.20

Jan 2.31 2.79 2.86 3.19 1.21 1.24 1.38

Feb 2.29 2.27 2.24 2.09 0.99 0.98 0.91

Mar 2.16 2.26 2.58 2.65 1.05 1.19 1.23

Apr 1.78 1.59 1.83 2.33 0.89 1.03 1.31

May 1.81 1.89 1.73 1.95 1.04 0.95 1.07

Jun 1.69 1.85 1.93 1.80 1.09 1.14 1.06

Jul 2.20 2.22 2.33 2.29 1.01 1.06 1.04

Aug 2.24 2.80 2.50 2.42 1.25 1.11 1.08

Sep 2.43 2.49 2.66 2.91 1.02 1.10 1.19

Oct 2.58 2.90 2.58 2.56 1.12 1.00 0.99

Nov 2.78 2.66 3.08 3.10 0.95 1.10 1.11

Dec 2.72 2.94 3.23 3.30 1.08 1.19 1.21

Jan 2.88 3.45 3.55 4.09 1.20 1.23 1.42

Feb 2.80 2.85 2.84 2.70 1.02 1.01 0.96

Mar 2.54 2.80 3.17 3.38 1.10 1.25 1.33

Apr 2.04 1.84 2.20 2.68 0.90 1.08 1.32

May 1.98 1.81 1.84 1.98 0.91 0.93 1.00

Jun 1.72 1.87 2.06 1.84 1.09 1.20 1.07

Jul 2.15 2.20 2.19 2.44 1.02 1.02 1.13

Aug 2.85 3.02 2.58 2.44 1.06 0.90 0.86

Sep 2.70 2.93 3.17 3.32 1.08 1.17 1.23

Oct 3.25 3.45 3.01 3.09 1.06 0.92 0.95

Nov 3.34 3.35 3.73 3.78 1.00 1.12 1.13

Dec 3.21 3.79 4.07 3.94 1.18 1.27 1.23

Jan 2.82 3.16 3.25 3.64 1.12 1.15 1.29

Feb 2.67 2.86 2.65 2.58 1.07 0.99 0.97

Mar 2.48 2.78 3.01 3.37 1.12 1.21 1.36

Apr 2.15 1.89 2.27 2.73 0.88 1.05 1.27

May 2.05 1.88 1.99 1.98 0.92 0.97 0.96

Jun 1.75 1.85 2.10 1.83 1.06 1.20 1.04

Jul 2.44 2.28 2.36 2.64 0.93 0.97 1.08

Aug 2.89 3.00 2.82 2.58 1.04 0.98 0.89

Sep 2.94 3.18 3.30 3.37 1.08 1.12 1.15

Oct 3.32 3.46 3.09 3.12 1.04 0.93 0.94

Nov 3.29 3.37 3.52 3.47 1.02 1.07 1.05

Dec 3.05 3.50 3.65 3.55 1.15 1.20 1.17

Jan 2.78 3.07 3.13 3.53 1.11 1.13 1.27

Feb 2.57 2.70 2.53 2.52 1.05 0.98 0.98

Mar 2.27 2.57 2.71 3.15 1.13 1.19 1.39

Apr 2.11 1.82 2.21 2.59 0.86 1.05 1.23

May 2.04 1.91 2.03 1.96 0.93 1.00 0.96

Jun 1.69 1.82 2.08 1.77 1.08 1.23 1.05

Jul 2.46 2.38 2.36 2.66 0.97 0.96 1.08

Aug 2.94 2.96 2.89 2.70 1.01 0.98 0.92

Sep 3.18 3.50 3.48 3.41 1.10 1.09 1.07

Oct 3.51 3.66 3.26 3.22 1.04 0.93 0.92

Nov 3.34 3.37 3.48 3.29 1.01 1.04 0.99

Dec 2.99 3.44 3.44 3.37 1.15 1.15 1.12

BCM2-RCA3Mean (mm/day) DC Factor (-)

DK1

DK2

DK3

DK4

DK5

DK6

Precipitation

G E U S 49

ref near mid far near mid far

Jan 2.68 3.00 3.23 3.81 1.12 1.20 1.42

Feb 3.06 2.56 3.14 2.80 0.84 1.03 0.92

Mar 2.18 2.53 2.97 2.66 1.16 1.36 1.22

Apr 2.03 2.26 2.53 2.78 1.12 1.25 1.37

May 2.29 2.80 2.65 2.93 1.22 1.16 1.28

Jun 2.98 2.96 2.98 3.16 0.99 1.00 1.06

Jul 3.12 3.08 3.33 3.61 0.99 1.07 1.16

Aug 3.59 3.55 4.05 4.23 0.99 1.13 1.18

Sep 4.44 4.32 4.28 4.47 0.97 0.96 1.01

Oct 4.30 3.75 4.34 4.36 0.87 1.01 1.01

Nov 3.30 3.91 3.95 4.82 1.19 1.20 1.46

Dec 3.01 3.19 3.72 4.17 1.06 1.24 1.38

Jan 2.56 2.86 3.15 3.56 1.12 1.23 1.39

Feb 2.90 2.62 2.96 2.68 0.90 1.02 0.93

Mar 1.98 2.47 2.73 2.46 1.25 1.38 1.24

Apr 1.97 2.16 2.43 2.66 1.10 1.23 1.35

May 2.05 2.61 2.39 2.56 1.28 1.17 1.25

Jun 2.77 2.67 2.81 2.78 0.97 1.02 1.00

Jul 3.02 2.96 3.15 3.45 0.98 1.04 1.15

Aug 3.73 3.54 3.98 3.99 0.95 1.07 1.07

Sep 4.31 4.09 4.15 4.31 0.95 0.96 1.00

Oct 4.22 3.55 4.03 4.22 0.84 0.96 1.00

Nov 3.35 3.77 3.80 4.51 1.12 1.13 1.35

Dec 2.83 3.00 3.77 3.99 1.06 1.33 1.41

Jan 2.79 3.00 3.31 3.82 1.07 1.19 1.37

Feb 3.07 2.74 3.21 2.91 0.89 1.05 0.95

Mar 2.31 2.64 2.82 2.59 1.14 1.22 1.12

Apr 2.12 2.36 2.71 2.81 1.11 1.28 1.33

May 2.17 2.79 2.80 2.96 1.28 1.29 1.37

Jun 2.93 2.97 3.24 3.18 1.01 1.11 1.08

Jul 3.31 3.16 3.50 4.08 0.95 1.06 1.23

Aug 3.89 3.87 4.21 4.46 0.99 1.08 1.15

Sep 4.62 4.64 4.46 4.85 1.00 0.97 1.05

Oct 4.50 3.88 4.50 4.59 0.86 1.00 1.02

Nov 3.50 4.18 4.08 4.91 1.20 1.17 1.40

Dec 3.23 3.36 3.96 4.34 1.04 1.23 1.34

Jan 3.25 3.30 3.59 4.06 1.01 1.10 1.25

Feb 3.31 2.99 3.49 3.17 0.90 1.05 0.96

Mar 2.70 2.86 2.98 2.94 1.06 1.10 1.09

Apr 2.21 2.43 2.91 2.99 1.10 1.32 1.35

May 2.38 2.89 3.02 3.25 1.22 1.27 1.37

Jun 3.09 3.21 3.43 3.41 1.04 1.11 1.10

Jul 3.20 3.26 3.62 3.92 1.02 1.13 1.23

Aug 4.03 3.97 4.09 4.48 0.98 1.01 1.11

Sep 4.85 4.90 4.60 5.08 1.01 0.95 1.05

Oct 4.88 4.30 5.05 5.01 0.88 1.03 1.03

Nov 3.78 4.50 4.36 5.24 1.19 1.16 1.39

Dec 3.68 3.67 4.06 4.49 1.00 1.10 1.22

Jan 3.07 3.09 3.40 3.84 1.01 1.11 1.25

Feb 3.00 2.66 3.31 3.11 0.89 1.10 1.04

Mar 2.62 2.75 2.97 2.91 1.05 1.13 1.11

Apr 2.19 2.34 2.87 2.89 1.07 1.31 1.32

May 2.43 2.87 2.98 3.12 1.18 1.23 1.29

Jun 3.06 3.24 3.38 3.35 1.06 1.11 1.10

Jul 3.01 3.07 3.73 3.97 1.02 1.24 1.32

Aug 3.62 3.72 3.72 4.16 1.03 1.03 1.15

Sep 4.34 4.34 4.22 4.49 1.00 0.97 1.03

Oct 4.19 3.84 4.42 4.49 0.92 1.05 1.07

Nov 3.34 3.93 3.93 4.72 1.18 1.18 1.41

Dec 3.41 3.38 3.59 4.03 0.99 1.05 1.18

Jan 3.10 3.12 3.32 3.92 1.01 1.07 1.26

Feb 2.93 2.64 3.37 3.35 0.90 1.15 1.14

Mar 2.73 2.83 3.00 2.99 1.04 1.10 1.10

Apr 2.35 2.38 3.11 3.01 1.01 1.32 1.28

May 2.57 3.02 3.14 3.31 1.18 1.22 1.29

Jun 3.25 3.27 3.46 3.51 1.01 1.06 1.08

Jul 3.21 3.31 3.98 4.08 1.03 1.24 1.27

Aug 4.11 3.99 4.18 4.57 0.97 1.02 1.11

Sep 4.66 4.75 4.54 5.02 1.02 0.97 1.08

Oct 4.37 4.25 4.79 4.82 0.97 1.09 1.10

Nov 3.43 4.04 4.03 5.05 1.18 1.18 1.47

Dec 3.45 3.35 3.48 4.07 0.97 1.01 1.18

ECHAM5-HIRHAM4Mean (mm/day) DC Factor (-)

DK1

DK2

DK3

DK4

DK5

DK6

Precipitation

50 G E U S

ref near mid far near mid far

Jan 2.42 2.49 2.66 3.16 1.03 1.10 1.30

Feb 2.40 2.53 2.58 2.43 1.06 1.08 1.01

Mar 2.14 1.89 2.06 2.22 0.88 0.96 1.04

Apr 1.92 1.72 1.94 2.03 0.90 1.01 1.06

May 2.22 2.24 2.17 2.30 1.01 0.98 1.04

Jun 2.42 2.44 2.23 2.37 1.01 0.92 0.98

Jul 2.65 2.12 2.81 2.51 0.80 1.06 0.95

Aug 2.99 2.61 2.53 2.59 0.87 0.85 0.87

Sep 2.85 2.97 2.81 2.59 1.04 0.99 0.91

Oct 3.09 2.87 3.07 3.04 0.93 0.99 0.98

Nov 2.60 3.17 2.82 3.42 1.22 1.09 1.32

Dec 2.64 2.80 2.75 3.24 1.06 1.04 1.23

Jan 2.32 2.46 2.61 2.99 1.06 1.13 1.29

Feb 2.35 2.50 2.42 2.50 1.06 1.03 1.06

Mar 2.01 1.83 2.05 2.13 0.91 1.02 1.06

Apr 1.86 1.62 1.90 2.02 0.87 1.02 1.09

May 2.01 2.18 2.19 2.21 1.08 1.09 1.10

Jun 2.45 2.39 2.08 2.06 0.97 0.85 0.84

Jul 2.45 1.99 2.53 2.36 0.81 1.03 0.96

Aug 2.60 2.52 2.42 2.48 0.97 0.93 0.95

Sep 2.63 2.96 2.81 2.38 1.13 1.07 0.91

Oct 2.93 2.75 2.96 2.90 0.94 1.01 0.99

Nov 2.61 3.06 2.57 3.29 1.17 0.98 1.26

Dec 2.54 2.71 2.81 3.16 1.07 1.11 1.25

Jan 2.45 2.59 2.79 3.18 1.06 1.14 1.30

Feb 2.43 2.55 2.55 2.56 1.05 1.05 1.06

Mar 2.14 1.85 2.20 2.35 0.87 1.03 1.10

Apr 1.87 1.72 1.91 2.06 0.92 1.02 1.10

May 2.03 2.27 2.25 2.16 1.12 1.11 1.06

Jun 2.34 2.44 2.01 2.23 1.04 0.86 0.95

Jul 2.49 2.02 2.58 2.58 0.81 1.04 1.04

Aug 2.63 2.71 2.47 2.64 1.03 0.94 1.00

Sep 2.95 3.09 2.99 2.64 1.05 1.01 0.89

Oct 3.25 2.99 3.35 3.20 0.92 1.03 0.98

Nov 2.63 3.17 2.84 3.56 1.20 1.08 1.35

Dec 2.75 2.89 2.81 3.24 1.05 1.02 1.18

Jan 2.88 3.07 3.22 3.83 1.07 1.12 1.33

Feb 2.87 2.86 3.07 2.91 1.00 1.07 1.02

Mar 2.50 2.16 2.57 2.76 0.86 1.03 1.10

Apr 2.26 1.98 2.22 2.26 0.88 0.98 1.00

May 2.26 2.60 2.41 2.41 1.15 1.07 1.07

Jun 2.67 2.63 2.27 2.54 0.99 0.85 0.95

Jul 2.81 2.42 2.83 2.99 0.86 1.00 1.06

Aug 3.39 3.32 3.04 3.17 0.98 0.90 0.94

Sep 3.71 3.79 3.69 3.41 1.02 1.00 0.92

Oct 4.03 3.69 4.15 3.95 0.91 1.03 0.98

Nov 3.10 3.86 3.57 4.38 1.25 1.15 1.41

Dec 3.29 3.42 3.48 3.89 1.04 1.06 1.18

Jan 2.77 2.82 3.05 3.52 1.02 1.10 1.27

Feb 2.71 2.65 2.94 2.70 0.98 1.08 1.00

Mar 2.34 2.06 2.36 2.51 0.88 1.01 1.07

Apr 2.13 1.90 2.20 2.25 0.89 1.04 1.05

May 2.22 2.42 2.41 2.30 1.09 1.09 1.04

Jun 2.67 2.63 2.32 2.55 0.98 0.87 0.95

Jul 2.74 2.33 2.77 2.82 0.85 1.01 1.03

Aug 3.32 3.32 2.95 2.92 1.00 0.89 0.88

Sep 3.44 3.41 3.46 3.10 0.99 1.01 0.90

Oct 3.92 3.45 3.86 3.69 0.88 0.99 0.94

Nov 2.87 3.58 3.42 4.11 1.25 1.19 1.43

Dec 3.15 3.20 3.24 3.65 1.02 1.03 1.16

Jan 2.70 2.76 2.94 3.46 1.02 1.09 1.28

Feb 2.71 2.60 2.96 2.70 0.96 1.09 1.00

Mar 2.32 2.16 2.14 2.48 0.93 0.92 1.06

Apr 2.10 1.91 2.20 2.28 0.91 1.04 1.08

May 2.18 2.36 2.33 2.37 1.08 1.07 1.09

Jun 2.60 2.45 2.25 2.36 0.94 0.86 0.90

Jul 2.58 2.31 2.71 2.60 0.90 1.05 1.01

Aug 3.07 3.14 2.90 2.92 1.02 0.94 0.95

Sep 3.44 3.36 3.44 3.00 0.98 1.00 0.87

Oct 3.67 3.25 3.88 3.45 0.89 1.06 0.94

Nov 2.80 3.58 3.35 4.08 1.28 1.20 1.46

Dec 3.06 3.15 3.13 3.64 1.03 1.02 1.19

ECHAM5-RegCM3Mean (mm/day) DC Factor (-)

DK1

DK2

DK3

DK4

DK5

DK6

Precipitation

G E U S 51

ref near mid far near mid far

Jan 2.36 2.63 2.66 3.13 1.12 1.13 1.33

Feb 2.12 2.33 2.46 2.29 1.10 1.16 1.08

Mar 1.76 1.63 1.86 2.07 0.93 1.06 1.18

Apr 1.63 1.58 1.79 1.78 0.97 1.10 1.09

May 1.59 1.74 1.95 2.11 1.09 1.23 1.33

Jun 2.15 2.06 2.15 1.91 0.96 1.00 0.89

Jul 2.24 1.84 2.42 2.41 0.82 1.08 1.08

Aug 3.00 2.68 2.40 2.68 0.89 0.80 0.89

Sep 2.99 3.15 3.01 2.90 1.05 1.01 0.97

Oct 3.23 2.77 3.15 3.05 0.86 0.98 0.94

Nov 2.68 2.99 2.54 3.46 1.12 0.95 1.29

Dec 2.42 2.58 3.06 3.24 1.06 1.26 1.34

Jan 2.28 2.63 2.52 3.07 1.16 1.11 1.35

Feb 2.11 2.26 2.32 2.37 1.07 1.10 1.12

Mar 1.68 1.56 1.83 2.05 0.93 1.09 1.22

Apr 1.52 1.55 1.86 1.69 1.02 1.23 1.12

May 1.53 1.71 1.93 2.01 1.12 1.26 1.32

Jun 2.10 1.92 2.12 1.85 0.92 1.01 0.88

Jul 2.15 1.82 2.50 2.32 0.85 1.16 1.08

Aug 2.83 2.55 2.29 2.59 0.90 0.81 0.91

Sep 2.76 3.15 2.98 2.74 1.14 1.08 0.99

Oct 3.14 2.61 2.96 3.05 0.83 0.94 0.97

Nov 2.73 2.97 2.49 3.47 1.09 0.91 1.27

Dec 2.38 2.55 3.13 3.11 1.08 1.32 1.31

Jan 2.40 2.73 2.65 3.16 1.14 1.10 1.32

Feb 2.16 2.33 2.51 2.46 1.08 1.16 1.14

Mar 1.77 1.65 1.85 2.08 0.93 1.05 1.17

Apr 1.64 1.65 1.86 1.70 1.01 1.14 1.04

May 1.65 1.79 1.94 2.07 1.09 1.18 1.26

Jun 2.13 1.87 2.02 1.83 0.88 0.95 0.86

Jul 2.15 1.83 2.34 2.17 0.85 1.08 1.01

Aug 2.73 2.49 2.21 2.34 0.91 0.81 0.85

Sep 2.86 2.94 2.91 2.76 1.03 1.02 0.96

Oct 3.05 2.73 2.94 3.05 0.89 0.97 1.00

Nov 2.75 2.94 2.58 3.47 1.07 0.94 1.26

Dec 2.54 2.58 3.13 3.19 1.02 1.23 1.25

Jan 2.99 3.33 3.25 3.96 1.12 1.09 1.33

Feb 2.66 2.85 3.03 2.97 1.07 1.14 1.11

Mar 2.15 1.97 2.29 2.46 0.91 1.06 1.14

Apr 1.93 1.81 2.08 2.00 0.93 1.08 1.03

May 1.99 2.00 2.06 2.38 1.00 1.03 1.20

Jun 2.52 2.24 2.32 2.05 0.89 0.92 0.81

Jul 2.90 2.40 2.93 2.73 0.83 1.01 0.94

Aug 3.81 3.53 2.90 3.01 0.93 0.76 0.79

Sep 3.85 3.91 3.80 3.70 1.02 0.99 0.96

Oct 4.04 3.61 4.07 3.87 0.89 1.01 0.96

Nov 3.40 3.59 3.45 4.30 1.06 1.01 1.26

Dec 3.25 3.22 3.74 4.05 0.99 1.15 1.25

Jan 2.84 3.03 3.08 3.68 1.07 1.08 1.30

Feb 2.50 2.61 2.90 2.68 1.04 1.16 1.07

Mar 2.06 1.91 2.23 2.32 0.93 1.08 1.12

Apr 2.01 1.80 2.04 1.91 0.90 1.01 0.95

May 1.90 1.98 2.08 2.28 1.04 1.10 1.20

Jun 2.51 2.22 2.25 2.09 0.89 0.90 0.83

Jul 2.72 2.23 2.88 2.66 0.82 1.06 0.98

Aug 3.39 3.25 2.79 2.76 0.96 0.82 0.82

Sep 3.42 3.38 3.39 3.29 0.99 0.99 0.96

Oct 3.64 3.28 3.78 3.42 0.90 1.04 0.94

Nov 3.08 3.36 3.18 3.97 1.09 1.03 1.29

Dec 3.00 3.05 3.38 3.67 1.02 1.13 1.22

Jan 2.70 2.95 3.05 3.61 1.09 1.13 1.33

Feb 2.49 2.61 2.82 2.61 1.05 1.13 1.05

Mar 1.95 1.94 2.21 2.23 1.00 1.14 1.15

Apr 2.07 1.76 2.08 1.84 0.85 1.01 0.89

May 1.87 2.02 2.06 2.27 1.08 1.10 1.21

Jun 2.29 2.18 2.15 2.06 0.95 0.94 0.90

Jul 2.58 2.15 2.70 2.56 0.83 1.04 0.99

Aug 3.31 3.15 2.84 2.81 0.95 0.86 0.85

Sep 3.44 3.28 3.36 3.18 0.95 0.98 0.92

Oct 3.59 3.14 3.79 3.28 0.87 1.06 0.91

Nov 2.96 3.44 3.17 3.98 1.16 1.07 1.34

Dec 2.97 2.97 3.25 3.49 1.00 1.10 1.18

ECHAM5-RACMO2Mean (mm/day) DC Factor (-)

DK1

DK2

DK3

DK4

DK5

DK6

Precipitation

52 G E U S

ref near mid far near mid far

Jan 2.35 2.37 2.58 2.92 1.01 1.10 1.24

Feb 2.17 2.18 2.21 2.17 1.01 1.02 1.00

Mar 1.69 1.43 1.74 1.94 0.85 1.03 1.14

Apr 1.34 1.53 1.55 1.39 1.14 1.16 1.04

May 1.55 1.76 1.70 1.85 1.14 1.09 1.19

Jun 2.20 1.94 1.84 1.86 0.88 0.84 0.85

Jul 2.57 2.20 2.58 2.64 0.85 1.00 1.03

Aug 3.73 3.53 2.89 3.19 0.95 0.78 0.85

Sep 3.72 3.69 3.54 3.23 0.99 0.95 0.87

Oct 3.67 3.27 3.60 3.21 0.89 0.98 0.88

Nov 2.87 3.25 2.84 3.66 1.13 0.99 1.27

Dec 2.41 2.74 3.03 3.08 1.14 1.26 1.28

Jan 2.26 2.29 2.48 2.76 1.01 1.10 1.22

Feb 2.03 2.08 2.01 2.18 1.02 0.99 1.07

Mar 1.61 1.42 1.69 1.88 0.89 1.05 1.17

Apr 1.18 1.35 1.49 1.33 1.15 1.26 1.13

May 1.46 1.54 1.60 1.64 1.05 1.09 1.12

Jun 1.78 1.75 1.68 1.77 0.98 0.95 1.00

Jul 2.25 2.10 2.44 2.55 0.93 1.08 1.13

Aug 3.59 3.41 2.95 3.16 0.95 0.82 0.88

Sep 3.79 3.80 3.87 3.39 1.00 1.02 0.90

Oct 3.77 3.32 3.87 3.37 0.88 1.03 0.89

Nov 2.85 3.20 2.82 3.99 1.13 0.99 1.40

Dec 2.32 2.74 2.97 3.09 1.18 1.28 1.33

Jan 2.38 2.45 2.63 2.93 1.03 1.11 1.23

Feb 2.30 2.21 2.22 2.25 0.96 0.97 0.98

Mar 1.73 1.46 1.89 2.04 0.84 1.10 1.18

Apr 1.32 1.50 1.67 1.49 1.14 1.27 1.13

May 1.66 1.64 1.75 1.97 0.99 1.05 1.19

Jun 2.18 1.86 1.80 1.80 0.85 0.83 0.82

Jul 2.51 2.18 2.48 2.56 0.87 0.99 1.02

Aug 3.59 3.31 2.71 2.90 0.92 0.76 0.81

Sep 3.55 3.58 3.38 3.11 1.01 0.95 0.88

Oct 3.57 3.30 3.68 3.29 0.92 1.03 0.92

Nov 2.85 3.29 2.91 3.82 1.16 1.02 1.34

Dec 2.47 2.81 3.01 3.12 1.14 1.22 1.26

Jan 2.84 2.90 3.10 3.56 1.02 1.09 1.26

Feb 2.72 2.67 2.84 2.58 0.98 1.04 0.95

Mar 1.94 1.72 2.26 2.38 0.89 1.16 1.23

Apr 1.77 1.75 1.94 1.88 0.99 1.10 1.07

May 1.97 1.94 2.12 2.20 0.98 1.08 1.12

Jun 2.61 2.28 2.21 2.09 0.87 0.85 0.80

Jul 2.93 2.55 2.84 2.68 0.87 0.97 0.91

Aug 4.00 3.68 2.83 3.17 0.92 0.71 0.79

Sep 3.94 4.12 3.71 3.51 1.04 0.94 0.89

Oct 3.98 3.49 4.20 3.59 0.88 1.06 0.90

Nov 3.13 3.76 3.37 4.32 1.20 1.07 1.38

Dec 2.98 3.26 3.43 3.69 1.10 1.15 1.24

Jan 2.59 2.58 2.85 3.28 1.00 1.10 1.27

Feb 2.58 2.44 2.62 2.33 0.95 1.02 0.90

Mar 1.78 1.60 2.07 2.12 0.90 1.16 1.19

Apr 1.81 1.67 1.83 1.76 0.93 1.02 0.97

May 1.96 1.97 2.06 2.23 1.01 1.05 1.14

Jun 2.50 2.36 2.11 2.06 0.94 0.84 0.82

Jul 2.75 2.42 2.72 2.46 0.88 0.99 0.89

Aug 3.49 3.33 2.58 2.77 0.95 0.74 0.79

Sep 3.44 3.35 3.08 2.85 0.97 0.89 0.83

Oct 3.50 3.10 3.62 3.17 0.89 1.03 0.90

Nov 2.64 3.39 3.06 3.70 1.28 1.16 1.40

Dec 2.70 2.93 3.10 3.29 1.09 1.15 1.22

Jan 2.75 2.81 3.09 3.54 1.02 1.12 1.28

Feb 2.65 2.56 2.79 2.50 0.97 1.05 0.94

Mar 1.80 1.74 2.09 2.22 0.97 1.16 1.23

Apr 1.83 1.61 1.82 1.83 0.88 1.00 1.00

May 1.90 1.94 1.94 2.10 1.02 1.02 1.11

Jun 2.07 2.05 1.88 1.95 0.99 0.91 0.94

Jul 2.34 2.26 2.53 2.22 0.96 1.08 0.95

Aug 3.26 3.22 2.47 2.80 0.99 0.76 0.86

Sep 3.66 3.47 3.17 3.07 0.95 0.87 0.84

Oct 4.00 3.54 3.99 3.34 0.88 1.00 0.84

Nov 2.83 3.83 3.48 4.16 1.35 1.23 1.47

Dec 2.91 3.06 3.27 3.46 1.05 1.13 1.19

ECHAM5-REMOMean (mm/day) DC Factor (-)

DK1

DK2

DK3

DK4

DK5

DK6

Precipitation

G E U S 53

ref near mid far near mid far

Jan 2.35 2.26 2.60 2.93 0.96 1.10 1.25

Feb 2.17 2.23 2.44 2.32 1.03 1.12 1.07

Mar 1.77 1.59 1.89 2.13 0.90 1.07 1.20

Apr 1.66 1.54 1.96 2.10 0.93 1.18 1.27

May 2.06 1.92 2.24 2.32 0.93 1.09 1.13

Jun 2.35 2.26 2.18 2.40 0.96 0.93 1.02

Jul 2.75 2.48 2.62 2.58 0.90 0.95 0.94

Aug 3.40 3.28 2.93 2.92 0.96 0.86 0.86

Sep 3.24 3.44 3.08 3.01 1.06 0.95 0.93

Oct 3.41 3.04 3.41 3.12 0.89 1.00 0.91

Nov 2.56 2.99 2.84 3.56 1.17 1.11 1.39

Dec 2.49 2.65 2.91 3.17 1.06 1.17 1.27

Jan 2.29 2.32 2.65 2.78 1.01 1.15 1.21

Feb 2.06 2.10 2.25 2.27 1.02 1.09 1.11

Mar 1.69 1.48 1.82 2.07 0.88 1.08 1.23

Apr 1.57 1.50 1.88 1.96 0.95 1.20 1.24

May 1.94 1.77 2.17 2.20 0.91 1.12 1.13

Jun 2.16 2.06 2.12 2.18 0.96 0.99 1.01

Jul 2.41 2.25 2.47 2.39 0.93 1.03 0.99

Aug 3.05 2.87 2.78 2.68 0.94 0.91 0.88

Sep 3.10 3.33 3.00 2.98 1.07 0.97 0.96

Oct 3.27 2.95 3.19 3.05 0.90 0.98 0.93

Nov 2.65 2.82 2.67 3.45 1.06 1.01 1.30

Dec 2.42 2.61 2.99 3.16 1.08 1.23 1.31

Jan 2.48 2.44 2.76 3.08 0.98 1.11 1.24

Feb 2.26 2.32 2.48 2.48 1.03 1.10 1.10

Mar 1.85 1.63 1.99 2.28 0.88 1.08 1.23

Apr 1.70 1.64 2.06 2.02 0.96 1.21 1.18

May 2.18 1.96 2.18 2.42 0.90 1.00 1.11

Jun 2.41 2.13 2.21 2.29 0.89 0.92 0.95

Jul 2.46 2.33 2.52 2.50 0.95 1.03 1.02

Aug 3.28 3.07 2.69 2.75 0.94 0.82 0.84

Sep 2.97 3.30 3.04 3.09 1.11 1.02 1.04

Oct 3.23 3.13 3.36 3.15 0.97 1.04 0.97

Nov 2.58 2.90 2.75 3.59 1.12 1.07 1.39

Dec 2.61 2.70 3.02 3.24 1.03 1.16 1.24

Jan 2.89 2.84 3.06 3.68 0.98 1.06 1.28

Feb 2.60 2.71 2.85 2.74 1.04 1.10 1.05

Mar 2.11 1.85 2.32 2.61 0.88 1.10 1.24

Apr 2.03 1.86 2.28 2.19 0.92 1.12 1.08

May 2.34 2.19 2.32 2.57 0.94 0.99 1.10

Jun 2.94 2.46 2.33 2.36 0.84 0.79 0.80

Jul 3.01 2.59 2.85 2.74 0.86 0.95 0.91

Aug 3.70 3.57 2.95 3.06 0.96 0.80 0.83

Sep 3.38 3.73 3.52 3.51 1.10 1.04 1.04

Oct 3.67 3.67 4.00 3.66 1.00 1.09 1.00

Nov 2.94 3.42 3.30 4.23 1.16 1.12 1.44

Dec 3.16 3.15 3.50 3.77 1.00 1.11 1.19

Jan 2.76 2.63 2.94 3.54 0.95 1.06 1.28

Feb 2.59 2.67 2.87 2.67 1.03 1.11 1.03

Mar 2.02 1.93 2.25 2.51 0.95 1.12 1.24

Apr 2.04 1.82 2.34 2.30 0.89 1.14 1.13

May 2.33 2.22 2.37 2.55 0.95 1.02 1.10

Jun 2.92 2.56 2.43 2.49 0.87 0.83 0.85

Jul 3.05 2.58 2.88 2.75 0.85 0.94 0.90

Aug 3.67 3.49 3.01 3.06 0.95 0.82 0.83

Sep 3.37 3.51 3.45 3.31 1.04 1.02 0.98

Oct 3.48 3.45 3.87 3.64 0.99 1.11 1.05

Nov 2.74 3.28 3.30 3.99 1.20 1.20 1.45

Dec 3.06 2.97 3.24 3.56 0.97 1.06 1.16

Jan 2.63 2.49 2.84 3.46 0.95 1.08 1.31

Feb 2.51 2.50 2.78 2.61 1.00 1.11 1.04

Mar 1.97 1.90 2.02 2.40 0.96 1.03 1.22

Apr 1.89 1.74 2.23 2.30 0.92 1.18 1.21

May 2.26 2.10 2.34 2.49 0.93 1.04 1.10

Jun 2.64 2.40 2.31 2.29 0.91 0.87 0.87

Jul 2.95 2.58 2.98 2.71 0.87 1.01 0.92

Aug 3.79 3.58 3.19 3.07 0.95 0.84 0.81

Sep 3.70 3.67 3.58 3.35 0.99 0.97 0.91

Oct 3.52 3.38 3.94 3.71 0.96 1.12 1.05

Nov 2.77 3.32 3.28 3.94 1.20 1.18 1.42

Dec 2.94 2.82 3.08 3.39 0.96 1.05 1.15

ECHAM5-RCA3Mean (mm/day) DC Factor (-)

DK1

DK2

DK3

DK4

DK5

DK6

Precipitation

54 G E U S

ref near mid far near mid far

Jan 1.94 2.11 2.31 2.30 1.09 1.19 1.19

Feb 1.97 1.92 1.77 2.13 0.97 0.89 1.08

Mar 1.72 1.69 1.82 1.61 0.98 1.06 0.94

Apr 1.39 1.63 1.70 1.59 1.17 1.22 1.14

May 1.87 2.11 1.76 2.02 1.13 0.94 1.08

Jun 2.30 2.07 2.26 2.12 0.90 0.99 0.92

Jul 1.90 2.13 2.17 1.78 1.12 1.14 0.94

Aug 2.06 1.67 1.31 1.54 0.81 0.63 0.75

Sep 1.69 1.90 1.94 2.03 1.13 1.15 1.20

Oct 2.02 1.84 1.95 1.49 0.91 0.97 0.74

Nov 2.17 2.10 2.45 2.38 0.97 1.13 1.09

Dec 1.81 1.95 2.58 2.36 1.08 1.43 1.31

Jan 2.09 2.23 2.51 2.46 1.07 1.20 1.18

Feb 1.93 2.01 1.92 2.19 1.04 1.00 1.14

Mar 1.71 1.65 1.86 1.62 0.96 1.09 0.95

Apr 1.29 1.60 1.53 1.62 1.24 1.19 1.26

May 1.70 1.99 1.57 1.87 1.17 0.92 1.10

Jun 2.13 1.90 1.91 1.90 0.89 0.89 0.89

Jul 1.79 1.79 1.99 1.68 1.00 1.11 0.94

Aug 2.07 1.57 1.35 1.38 0.76 0.65 0.66

Sep 1.72 1.84 2.04 2.08 1.07 1.19 1.21

Oct 2.21 2.02 2.32 1.58 0.91 1.05 0.72

Nov 2.28 2.34 2.48 2.72 1.03 1.09 1.19

Dec 2.00 2.20 2.85 2.63 1.10 1.42 1.31

Jan 2.21 2.38 2.66 2.56 1.08 1.21 1.16

Feb 2.17 2.15 1.95 2.37 0.99 0.90 1.09

Mar 1.93 1.89 1.98 1.75 0.98 1.02 0.91

Apr 1.46 1.80 1.66 1.74 1.23 1.13 1.19

May 1.85 2.07 1.74 1.94 1.12 0.94 1.05

Jun 2.26 1.99 2.26 2.06 0.88 1.00 0.91

Jul 1.80 2.20 2.01 1.54 1.23 1.12 0.86

Aug 2.01 1.60 1.20 1.52 0.80 0.60 0.75

Sep 1.75 2.01 1.98 2.04 1.14 1.13 1.17

Oct 2.42 2.03 2.23 1.70 0.84 0.92 0.70

Nov 2.28 2.48 2.56 2.63 1.09 1.12 1.15

Dec 2.02 2.22 2.84 2.64 1.10 1.41 1.31

Jan 2.35 2.67 2.97 2.87 1.14 1.27 1.22

Feb 2.48 2.46 2.24 2.71 0.99 0.90 1.09

Mar 2.31 2.16 2.22 1.98 0.94 0.96 0.86

Apr 1.61 2.00 1.87 1.91 1.24 1.17 1.19

May 2.06 2.23 2.07 2.19 1.08 1.00 1.06

Jun 2.56 2.44 2.50 2.52 0.95 0.98 0.98

Jul 2.14 2.40 2.28 1.80 1.12 1.07 0.84

Aug 2.13 1.89 1.43 1.61 0.89 0.67 0.76

Sep 1.94 2.18 2.30 2.09 1.12 1.18 1.07

Oct 2.41 2.12 2.23 1.91 0.88 0.93 0.79

Nov 2.35 2.55 2.63 2.69 1.08 1.12 1.14

Dec 2.07 2.35 2.99 2.82 1.14 1.44 1.36

Jan 2.27 2.49 2.72 2.69 1.10 1.20 1.19

Feb 2.35 2.32 2.11 2.59 0.98 0.90 1.10

Mar 2.13 2.08 2.09 2.03 0.97 0.98 0.95

Apr 1.62 1.99 1.95 1.90 1.23 1.20 1.17

May 2.15 2.19 2.11 2.21 1.02 0.98 1.03

Jun 2.63 2.48 2.56 2.41 0.94 0.97 0.92

Jul 2.06 2.40 2.38 1.80 1.17 1.16 0.88

Aug 2.20 1.97 1.47 1.70 0.89 0.67 0.77

Sep 1.96 2.21 2.20 1.94 1.13 1.12 0.99

Oct 2.24 2.04 2.20 1.93 0.91 0.98 0.86

Nov 2.32 2.44 2.50 2.62 1.05 1.08 1.13

Dec 1.97 2.18 2.83 2.55 1.11 1.44 1.29

Jan 2.28 2.49 2.64 2.68 1.09 1.15 1.17

Feb 2.28 2.28 2.10 2.60 1.00 0.92 1.14

Mar 2.09 1.99 2.08 2.12 0.95 1.00 1.01

Apr 1.63 1.90 2.03 1.87 1.17 1.25 1.15

May 2.13 2.10 1.98 2.15 0.98 0.93 1.01

Jun 2.39 2.27 2.52 2.37 0.95 1.06 0.99

Jul 1.89 2.37 2.38 1.82 1.25 1.26 0.96

Aug 2.22 2.01 1.57 1.80 0.91 0.71 0.81

Sep 2.05 2.25 2.10 1.99 1.10 1.02 0.97

Oct 2.13 2.07 2.26 2.05 0.97 1.06 0.96

Nov 2.52 2.51 2.53 2.78 0.99 1.00 1.10

Dec 2.13 2.21 2.81 2.48 1.04 1.32 1.16

HadCM3-CLMMean (mm/day) DC Factor (-)

DK1

DK2

DK3

DK4

DK5

DK6

Precipitation

G E U S 55

ref near mid far near mid far

Jan 2.57 3.01 2.93 3.12 1.17 1.14 1.21

Feb 2.10 2.40 2.33 2.75 1.14 1.11 1.31

Mar 2.15 1.80 1.98 1.90 0.84 0.92 0.88

Apr 1.10 1.57 1.67 1.61 1.44 1.53 1.47

May 1.53 1.16 1.69 1.51 0.76 1.10 0.99

Jun 1.93 1.95 1.78 1.96 1.01 0.92 1.02

Jul 1.54 1.74 1.87 1.34 1.14 1.22 0.87

Aug 2.22 1.53 1.95 1.74 0.69 0.88 0.78

Sep 2.91 2.61 2.98 2.65 0.90 1.02 0.91

Oct 2.38 2.82 2.60 2.30 1.19 1.09 0.97

Nov 2.43 2.89 2.77 3.43 1.19 1.14 1.41

Dec 2.30 2.62 3.28 3.38 1.14 1.43 1.47

Jan 2.58 3.07 2.97 3.18 1.19 1.15 1.23

Feb 2.11 2.43 2.44 2.75 1.15 1.15 1.30

Mar 2.12 1.74 2.11 1.83 0.82 0.99 0.86

Apr 1.16 1.58 1.62 1.49 1.36 1.40 1.29

May 1.42 1.14 1.59 1.43 0.80 1.12 1.01

Jun 1.74 1.78 1.68 1.66 1.02 0.97 0.96

Jul 1.60 1.66 1.60 1.39 1.04 1.00 0.87

Aug 2.09 1.47 1.80 1.70 0.70 0.86 0.81

Sep 2.62 2.65 3.07 2.33 1.01 1.17 0.89

Oct 2.77 2.97 2.54 2.14 1.07 0.92 0.77

Nov 2.50 2.81 2.73 3.31 1.12 1.09 1.33

Dec 2.31 2.70 3.33 3.51 1.17 1.44 1.52

Jan 2.57 2.94 2.79 2.96 1.14 1.09 1.15

Feb 2.13 2.25 2.32 2.73 1.06 1.09 1.29

Mar 2.19 1.80 2.09 1.80 0.82 0.96 0.82

Apr 1.17 1.66 1.61 1.56 1.42 1.38 1.33

May 1.42 1.28 1.72 1.43 0.90 1.21 1.01

Jun 1.94 2.05 1.79 1.71 1.06 0.92 0.88

Jul 1.20 1.77 1.70 1.31 1.47 1.42 1.09

Aug 2.09 1.46 1.75 1.68 0.70 0.84 0.80

Sep 2.56 2.62 2.78 2.22 1.02 1.09 0.87

Oct 2.57 2.83 2.56 2.28 1.10 0.99 0.89

Nov 2.56 2.73 2.54 3.20 1.07 0.99 1.25

Dec 2.26 2.64 3.18 3.25 1.17 1.41 1.44

Jan 2.80 3.46 3.35 3.53 1.24 1.20 1.26

Feb 2.60 2.77 2.57 3.13 1.06 0.99 1.20

Mar 2.45 2.05 2.19 1.99 0.84 0.89 0.81

Apr 1.28 1.81 1.83 1.56 1.42 1.43 1.22

May 1.61 1.48 1.97 1.65 0.92 1.22 1.03

Jun 2.19 2.06 1.91 2.02 0.94 0.87 0.92

Jul 1.63 2.01 1.87 1.71 1.23 1.15 1.05

Aug 2.35 1.78 1.69 1.96 0.75 0.72 0.83

Sep 2.71 2.70 3.11 2.67 1.00 1.15 0.99

Oct 2.81 2.95 2.82 2.65 1.05 1.00 0.94

Nov 2.79 2.91 3.11 3.87 1.04 1.11 1.39

Dec 2.38 2.99 3.64 3.52 1.26 1.53 1.48

Jan 2.61 2.99 2.89 3.19 1.14 1.11 1.22

Feb 2.40 2.46 2.35 2.87 1.03 0.98 1.20

Mar 2.33 1.88 2.05 1.95 0.80 0.88 0.84

Apr 1.25 1.81 1.80 1.70 1.45 1.44 1.36

May 1.66 1.51 2.08 1.73 0.91 1.26 1.04

Jun 2.18 2.06 1.97 2.20 0.95 0.90 1.01

Jul 1.77 2.20 2.06 1.88 1.25 1.16 1.06

Aug 2.33 1.84 1.86 1.84 0.79 0.80 0.79

Sep 2.68 2.54 2.81 2.63 0.95 1.05 0.98

Oct 2.48 2.67 2.48 2.32 1.08 1.00 0.94

Nov 2.59 2.74 2.79 3.44 1.06 1.08 1.33

Dec 2.21 2.70 3.20 3.09 1.22 1.45 1.40

Jan 2.74 2.96 2.97 3.29 1.08 1.08 1.20

Feb 2.53 2.50 2.47 2.91 0.99 0.98 1.15

Mar 2.35 1.90 2.12 2.12 0.81 0.90 0.90

Apr 1.31 1.83 1.79 1.76 1.39 1.36 1.34

May 1.72 1.55 2.02 1.72 0.90 1.17 1.00

Jun 2.03 1.86 1.95 2.15 0.92 0.96 1.06

Jul 1.71 2.34 2.07 1.81 1.37 1.21 1.06

Aug 2.52 1.95 2.00 1.92 0.78 0.79 0.76

Sep 2.82 2.87 2.88 2.80 1.01 1.02 0.99

Oct 2.51 2.97 2.64 2.62 1.19 1.05 1.05

Nov 2.71 2.84 3.11 3.80 1.05 1.15 1.40

Dec 2.32 2.93 3.39 3.27 1.27 1.46 1.41

HadCM3-HadRM3Mean (mm/day) DC Factor (-)

DK1

DK2

DK3

DK4

DK5

DK6

Precipitation

56 G E U S

ref near mid far near mid far

Jan 0.14 0.17 0.15 0.17 1.21 1.11 1.27

Feb 0.26 0.28 0.32 0.33 1.06 1.23 1.25

Mar 0.64 0.66 0.70 0.72 1.04 1.10 1.13

Apr 1.21 1.25 1.28 1.38 1.04 1.06 1.14

May 1.75 1.81 1.89 1.86 1.03 1.08 1.06

Jun 2.24 2.23 2.37 2.48 1.00 1.06 1.11

Jul 2.66 2.70 2.84 3.10 1.01 1.07 1.17

Aug 2.45 2.51 2.81 3.11 1.02 1.15 1.27

Sep 1.41 1.56 1.69 1.93 1.11 1.20 1.37

Oct 0.68 0.70 0.75 0.79 1.02 1.09 1.16

Nov 0.23 0.24 0.29 0.33 1.05 1.26 1.43

Dec 0.12 0.14 0.18 0.19 1.13 1.42 1.54

Jan 0.20 0.22 0.22 0.24 1.12 1.10 1.20

Feb 0.33 0.34 0.39 0.40 1.04 1.18 1.21

Mar 0.69 0.72 0.76 0.77 1.04 1.10 1.12

Apr 1.22 1.27 1.30 1.40 1.04 1.06 1.14

May 1.77 1.80 1.88 1.87 1.02 1.07 1.06

Jun 2.28 2.24 2.41 2.49 0.98 1.06 1.09

Jul 2.73 2.75 2.95 3.15 1.01 1.08 1.15

Aug 2.56 2.61 2.91 3.17 1.02 1.14 1.24

Sep 1.57 1.72 1.83 2.05 1.09 1.17 1.31

Oct 0.82 0.83 0.89 0.94 1.02 1.08 1.14

Nov 0.32 0.33 0.39 0.44 1.04 1.21 1.35

Dec 0.17 0.21 0.25 0.26 1.18 1.41 1.49

Jan 0.16 0.19 0.18 0.20 1.16 1.11 1.24

Feb 0.29 0.31 0.35 0.36 1.07 1.21 1.24

Mar 0.66 0.69 0.72 0.73 1.05 1.09 1.11

Apr 1.20 1.26 1.29 1.38 1.05 1.07 1.15

May 1.74 1.80 1.87 1.89 1.03 1.07 1.08

Jun 2.24 2.23 2.37 2.47 0.99 1.05 1.10

Jul 2.69 2.73 2.92 3.12 1.01 1.09 1.16

Aug 2.48 2.60 2.92 3.22 1.05 1.18 1.30

Sep 1.46 1.64 1.77 1.98 1.13 1.21 1.36

Oct 0.72 0.73 0.78 0.84 1.02 1.09 1.16

Nov 0.28 0.28 0.33 0.37 1.03 1.20 1.33

Dec 0.14 0.17 0.21 0.23 1.15 1.45 1.56

Jan 0.14 0.17 0.15 0.18 1.19 1.08 1.32

Feb 0.26 0.28 0.31 0.32 1.08 1.23 1.25

Mar 0.62 0.66 0.69 0.69 1.06 1.11 1.11

Apr 1.15 1.23 1.26 1.36 1.07 1.10 1.19

May 1.72 1.77 1.83 1.87 1.03 1.07 1.09

Jun 2.22 2.17 2.29 2.45 0.98 1.03 1.11

Jul 2.70 2.67 2.87 3.02 0.99 1.06 1.12

Aug 2.42 2.59 2.97 3.36 1.07 1.23 1.39

Sep 1.36 1.59 1.76 1.96 1.17 1.30 1.44

Oct 0.63 0.65 0.72 0.76 1.04 1.14 1.20

Nov 0.23 0.24 0.29 0.33 1.04 1.23 1.42

Dec 0.12 0.14 0.18 0.21 1.22 1.57 1.78

Jan 0.11 0.13 0.12 0.15 1.25 1.13 1.41

Feb 0.22 0.24 0.28 0.29 1.10 1.29 1.31

Mar 0.59 0.62 0.66 0.66 1.05 1.11 1.12

Apr 1.14 1.22 1.23 1.33 1.07 1.08 1.17

May 1.70 1.78 1.84 1.84 1.05 1.09 1.08

Jun 2.19 2.18 2.26 2.43 0.99 1.03 1.11

Jul 2.52 2.58 2.72 2.89 1.02 1.08 1.15

Aug 2.27 2.40 2.72 3.07 1.06 1.20 1.35

Sep 1.25 1.43 1.61 1.78 1.15 1.29 1.43

Oct 0.55 0.57 0.64 0.67 1.04 1.15 1.20

Nov 0.18 0.19 0.23 0.26 1.05 1.26 1.47

Dec 0.09 0.11 0.14 0.16 1.16 1.56 1.74

Jan 0.15 0.19 0.17 0.22 1.22 1.12 1.43

Feb 0.26 0.30 0.35 0.34 1.13 1.31 1.30

Mar 0.62 0.64 0.69 0.70 1.04 1.12 1.13

Apr 1.14 1.23 1.23 1.33 1.08 1.08 1.17

May 1.67 1.75 1.85 1.81 1.05 1.10 1.08

Jun 2.17 2.16 2.27 2.41 0.99 1.04 1.11

Jul 2.41 2.50 2.61 2.74 1.04 1.09 1.14

Aug 2.23 2.29 2.52 2.78 1.03 1.13 1.25

Sep 1.29 1.44 1.58 1.71 1.12 1.23 1.33

Oct 0.66 0.66 0.74 0.76 1.01 1.13 1.16

Nov 0.25 0.27 0.33 0.38 1.04 1.30 1.50

Dec 0.15 0.16 0.22 0.24 1.03 1.42 1.57

Mean (mm/day) DC Factor (-)Reference ET ARPEGE-RM5.1

DK1

DK2

DK3

DK4

DK5

DK6

G E U S 57

ref near mid far near mid far

Jan 0.39 0.46 0.47 0.52 1.18 1.21 1.34

Feb 0.43 0.49 0.58 0.52 1.13 1.35 1.20

Mar 0.75 0.76 0.88 0.80 1.02 1.17 1.08

Apr 1.30 1.27 1.45 1.41 0.98 1.12 1.09

May 1.78 1.79 1.87 1.85 1.01 1.05 1.04

Jun 2.05 2.05 2.13 2.16 1.00 1.04 1.06

Jul 2.28 2.36 2.35 2.43 1.03 1.03 1.06

Aug 2.17 2.20 2.37 2.26 1.02 1.09 1.04

Sep 1.84 1.81 2.01 1.97 0.98 1.09 1.07

Oct 1.29 1.33 1.36 1.25 1.03 1.05 0.97

Nov 0.82 0.80 0.95 0.90 0.97 1.15 1.10

Dec 0.44 0.51 0.71 0.66 1.14 1.59 1.48

Jan 0.43 0.50 0.52 0.55 1.17 1.21 1.29

Feb 0.47 0.53 0.64 0.57 1.12 1.36 1.21

Mar 0.79 0.79 0.92 0.85 1.00 1.17 1.08

Apr 1.35 1.29 1.50 1.44 0.96 1.11 1.07

May 1.83 1.82 1.93 1.91 1.00 1.06 1.05

Jun 2.13 2.12 2.23 2.28 0.99 1.05 1.07

Jul 2.46 2.59 2.58 2.69 1.06 1.05 1.10

Aug 2.43 2.44 2.69 2.56 1.00 1.11 1.05

Sep 2.06 2.05 2.28 2.26 1.00 1.11 1.10

Oct 1.40 1.48 1.51 1.40 1.05 1.07 1.00

Nov 0.89 0.88 1.02 0.96 1.00 1.15 1.08

Dec 0.49 0.57 0.76 0.69 1.14 1.54 1.40

Jan 0.45 0.53 0.56 0.60 1.17 1.23 1.32

Feb 0.50 0.56 0.67 0.60 1.12 1.35 1.20

Mar 0.81 0.81 0.95 0.86 1.00 1.17 1.07

Apr 1.34 1.31 1.49 1.44 0.98 1.11 1.08

May 1.74 1.76 1.84 1.84 1.01 1.06 1.05

Jun 1.99 1.99 2.09 2.12 1.00 1.05 1.07

Jul 2.21 2.28 2.29 2.36 1.03 1.04 1.07

Aug 2.13 2.16 2.35 2.26 1.01 1.10 1.06

Sep 1.85 1.85 2.06 2.02 1.00 1.11 1.09

Oct 1.37 1.40 1.45 1.35 1.03 1.06 0.99

Nov 0.92 0.90 1.04 0.98 0.98 1.13 1.07

Dec 0.52 0.60 0.80 0.74 1.16 1.54 1.43

Jan 0.39 0.46 0.48 0.54 1.19 1.24 1.40

Feb 0.41 0.47 0.56 0.51 1.14 1.36 1.23

Mar 0.70 0.70 0.84 0.76 1.01 1.20 1.09

Apr 1.25 1.25 1.42 1.39 1.00 1.14 1.11

May 1.66 1.66 1.75 1.74 1.01 1.06 1.05

Jun 1.84 1.84 1.92 1.97 1.00 1.04 1.07

Jul 1.99 2.03 2.06 2.11 1.02 1.03 1.06

Aug 1.85 1.88 2.06 2.02 1.02 1.11 1.09

Sep 1.51 1.54 1.70 1.67 1.02 1.13 1.11

Oct 1.07 1.11 1.15 1.06 1.03 1.07 0.99

Nov 0.71 0.71 0.84 0.81 1.00 1.18 1.14

Dec 0.41 0.51 0.71 0.65 1.24 1.72 1.59

Jan 0.33 0.39 0.39 0.44 1.18 1.19 1.35

Feb 0.37 0.41 0.49 0.43 1.10 1.32 1.16

Mar 0.65 0.65 0.77 0.70 1.00 1.18 1.07

Apr 1.19 1.21 1.37 1.33 1.02 1.15 1.12

May 1.67 1.69 1.77 1.77 1.02 1.06 1.06

Jun 1.88 1.92 1.98 2.05 1.02 1.06 1.09

Jul 2.06 2.07 2.11 2.16 1.00 1.02 1.05

Aug 1.86 1.90 2.03 2.02 1.02 1.09 1.08

Sep 1.48 1.49 1.64 1.60 1.00 1.11 1.08

Oct 1.02 1.02 1.07 0.99 1.00 1.05 0.98

Nov 0.61 0.61 0.73 0.71 1.00 1.19 1.16

Dec 0.34 0.41 0.58 0.54 1.19 1.69 1.58

Jan 0.38 0.46 0.45 0.52 1.20 1.18 1.36

Feb 0.43 0.47 0.54 0.49 1.10 1.28 1.14

Mar 0.69 0.67 0.79 0.74 0.99 1.15 1.08

Apr 1.15 1.19 1.32 1.31 1.04 1.15 1.14

May 1.59 1.63 1.70 1.70 1.03 1.07 1.07

Jun 1.82 1.84 1.91 1.95 1.01 1.05 1.07

Jul 1.99 1.96 2.03 2.06 0.98 1.02 1.03

Aug 1.81 1.90 1.98 1.95 1.05 1.09 1.08

Sep 1.54 1.52 1.68 1.63 0.99 1.09 1.06

Oct 1.12 1.09 1.15 1.10 0.98 1.03 0.98

Nov 0.70 0.69 0.81 0.81 0.98 1.16 1.15

Dec 0.42 0.47 0.65 0.64 1.12 1.54 1.50

DK3

DK4

DK5

DK6

Reference ET ARPEGE-HIRHAM5Mean (mm/day) DC Factor (-)

DK1

DK2

58 G E U S

G E U S 59

ref near mid far near mid far

Jan 0.24 0.30 0.33 0.36 1.25 1.36 1.48

Feb 0.40 0.41 0.44 0.44 1.03 1.10 1.11

Mar 0.74 0.79 0.84 0.85 1.06 1.13 1.15

Apr 1.25 1.28 1.24 1.22 1.03 0.99 0.98

May 1.64 1.61 1.69 1.63 0.98 1.03 0.99

Jun 1.95 1.89 1.96 1.99 0.97 1.00 1.02

Jul 1.89 1.86 1.88 1.88 0.99 1.00 1.00

Aug 1.51 1.45 1.51 1.52 0.96 1.00 1.01

Sep 1.07 1.03 1.09 1.17 0.96 1.02 1.09

Oct 0.68 0.68 0.71 0.76 1.00 1.05 1.12

Nov 0.37 0.40 0.43 0.44 1.11 1.19 1.22

Dec 0.26 0.31 0.34 0.35 1.20 1.29 1.33

Jan 0.31 0.38 0.42 0.47 1.22 1.35 1.48

Feb 0.49 0.50 0.53 0.53 1.02 1.10 1.08

Mar 0.82 0.86 0.91 0.92 1.05 1.11 1.12

Apr 1.26 1.27 1.24 1.23 1.01 0.99 0.98

May 1.57 1.57 1.64 1.56 1.00 1.04 0.99

Jun 1.83 1.80 1.86 1.88 0.98 1.01 1.03

Jul 1.77 1.77 1.80 1.80 1.00 1.02 1.02

Aug 1.50 1.43 1.53 1.54 0.95 1.02 1.03

Sep 1.16 1.13 1.20 1.30 0.97 1.03 1.11

Oct 0.80 0.81 0.86 0.92 1.02 1.08 1.15

Nov 0.46 0.51 0.57 0.58 1.10 1.22 1.24

Dec 0.34 0.40 0.45 0.46 1.17 1.30 1.34

Jan 0.27 0.34 0.37 0.40 1.25 1.34 1.47

Feb 0.45 0.46 0.48 0.48 1.03 1.09 1.08

Mar 0.80 0.84 0.87 0.89 1.06 1.10 1.12

Apr 1.24 1.27 1.23 1.21 1.02 0.99 0.97

May 1.57 1.56 1.61 1.58 0.99 1.02 1.01

Jun 1.86 1.82 1.88 1.89 0.97 1.01 1.01

Jul 1.79 1.76 1.80 1.80 0.99 1.01 1.00

Aug 1.48 1.42 1.51 1.51 0.96 1.02 1.02

Sep 1.10 1.06 1.12 1.19 0.96 1.02 1.09

Oct 0.72 0.73 0.76 0.80 1.01 1.06 1.12

Nov 0.40 0.44 0.47 0.48 1.11 1.18 1.21

Dec 0.28 0.34 0.37 0.38 1.22 1.33 1.37

Jan 0.23 0.29 0.31 0.33 1.30 1.36 1.46

Feb 0.41 0.42 0.44 0.43 1.01 1.05 1.05

Mar 0.77 0.84 0.84 0.86 1.09 1.09 1.11

Apr 1.27 1.30 1.25 1.21 1.02 0.98 0.95

May 1.64 1.61 1.65 1.65 0.98 1.01 1.01

Jun 1.97 1.90 1.95 1.97 0.97 0.99 1.00

Jul 1.88 1.85 1.89 1.88 0.98 1.00 1.00

Aug 1.45 1.44 1.52 1.52 1.00 1.05 1.05

Sep 1.01 0.98 1.00 1.07 0.97 0.99 1.07

Oct 0.62 0.61 0.64 0.66 1.00 1.04 1.08

Nov 0.31 0.35 0.36 0.38 1.12 1.16 1.21

Dec 0.23 0.29 0.30 0.32 1.26 1.31 1.39

Jan 0.20 0.26 0.28 0.29 1.34 1.39 1.48

Feb 0.37 0.37 0.40 0.40 1.02 1.10 1.10

Mar 0.72 0.78 0.79 0.82 1.09 1.11 1.14

Apr 1.24 1.29 1.23 1.19 1.04 0.99 0.96

May 1.65 1.60 1.63 1.65 0.97 0.99 1.00

Jun 1.97 1.91 1.95 1.98 0.97 0.99 1.01

Jul 1.85 1.84 1.88 1.87 0.99 1.02 1.01

Aug 1.44 1.41 1.47 1.47 0.98 1.03 1.02

Sep 0.95 0.93 0.95 1.02 0.98 1.00 1.07

Oct 0.58 0.58 0.60 0.63 1.00 1.04 1.09

Nov 0.30 0.33 0.33 0.35 1.09 1.11 1.17

Dec 0.22 0.26 0.27 0.29 1.20 1.26 1.35

Jan 0.19 0.25 0.26 0.28 1.32 1.40 1.50

Feb 0.34 0.35 0.39 0.39 1.03 1.15 1.16

Mar 0.68 0.72 0.77 0.79 1.07 1.14 1.17

Apr 1.20 1.26 1.19 1.18 1.05 0.99 0.98

May 1.64 1.59 1.62 1.65 0.97 0.99 1.01

Jun 1.97 1.91 1.95 2.00 0.97 0.99 1.01

Jul 1.85 1.83 1.88 1.88 0.99 1.02 1.02

Aug 1.46 1.42 1.49 1.48 0.97 1.02 1.02

Sep 0.99 0.95 1.00 1.06 0.96 1.01 1.07

Oct 0.61 0.62 0.64 0.68 1.02 1.05 1.11

Nov 0.32 0.34 0.35 0.38 1.05 1.08 1.17

Dec 0.22 0.26 0.28 0.30 1.16 1.24 1.36

DK3

DK4

DK5

DK6

Reference ET BCM2-RCA3Mean (mm/day) DC Factor (-)

DK1

DK2

60 G E U S

ref near mid far near mid far

Jan 0.24 0.23 0.27 0.33 0.95 1.11 1.35

Feb 0.31 0.31 0.36 0.37 1.00 1.14 1.21

Mar 0.61 0.59 0.57 0.65 0.96 0.92 1.06

Apr 1.25 1.22 1.12 1.19 0.97 0.90 0.95

May 1.76 1.83 1.80 1.75 1.04 1.02 0.99

Jun 2.11 2.25 2.16 2.20 1.07 1.02 1.05

Jul 2.51 2.42 2.30 2.33 0.96 0.92 0.93

Aug 2.32 2.28 2.19 2.22 0.98 0.94 0.96

Sep 1.77 1.80 1.82 1.86 1.01 1.02 1.05

Oct 1.06 1.16 1.20 1.19 1.09 1.13 1.12

Nov 0.53 0.66 0.66 0.70 1.24 1.26 1.32

Dec 0.31 0.30 0.35 0.44 0.97 1.12 1.39

Jan 0.29 0.28 0.32 0.38 0.97 1.09 1.32

Feb 0.35 0.35 0.42 0.43 1.01 1.18 1.23

Mar 0.67 0.64 0.62 0.70 0.95 0.93 1.04

Apr 1.28 1.25 1.17 1.24 0.98 0.92 0.97

May 1.83 1.90 1.89 1.84 1.04 1.03 1.00

Jun 2.24 2.39 2.30 2.38 1.06 1.03 1.06

Jul 2.74 2.63 2.50 2.54 0.96 0.91 0.93

Aug 2.57 2.53 2.42 2.47 0.98 0.94 0.96

Sep 1.94 2.02 2.03 2.06 1.04 1.04 1.06

Oct 1.22 1.33 1.38 1.35 1.09 1.13 1.10

Nov 0.62 0.77 0.78 0.82 1.25 1.26 1.31

Dec 0.37 0.37 0.42 0.52 1.00 1.12 1.39

Jan 0.29 0.27 0.31 0.37 0.94 1.07 1.31

Feb 0.35 0.35 0.40 0.43 1.01 1.15 1.22

Mar 0.65 0.61 0.62 0.67 0.93 0.95 1.03

Apr 1.25 1.23 1.13 1.20 0.99 0.91 0.96

May 1.74 1.81 1.78 1.72 1.04 1.03 0.99

Jun 2.11 2.21 2.10 2.16 1.04 0.99 1.02

Jul 2.47 2.39 2.27 2.29 0.97 0.92 0.93

Aug 2.35 2.30 2.24 2.25 0.98 0.95 0.96

Sep 1.84 1.88 1.91 1.95 1.02 1.04 1.06

Oct 1.17 1.26 1.32 1.29 1.08 1.13 1.10

Nov 0.60 0.74 0.75 0.79 1.23 1.25 1.31

Dec 0.36 0.36 0.41 0.50 0.98 1.13 1.37

Jan 0.21 0.19 0.22 0.27 0.92 1.05 1.31

Feb 0.27 0.27 0.32 0.32 0.97 1.15 1.18

Mar 0.55 0.52 0.53 0.56 0.94 0.96 1.02

Apr 1.16 1.14 1.03 1.07 0.98 0.88 0.92

May 1.64 1.70 1.64 1.57 1.03 1.00 0.95

Jun 1.91 2.02 1.87 1.91 1.06 0.98 1.00

Jul 2.21 2.10 1.99 1.99 0.95 0.90 0.90

Aug 2.01 1.96 1.90 1.90 0.98 0.95 0.94

Sep 1.53 1.56 1.56 1.58 1.02 1.02 1.03

Oct 0.87 0.97 1.02 0.97 1.11 1.16 1.12

Nov 0.40 0.52 0.53 0.56 1.31 1.34 1.40

Dec 0.25 0.24 0.28 0.35 0.95 1.12 1.39

Jan 0.19 0.17 0.20 0.24 0.89 1.06 1.29

Feb 0.25 0.25 0.28 0.29 0.99 1.10 1.15

Mar 0.50 0.48 0.47 0.51 0.97 0.95 1.03

Apr 1.08 1.06 0.95 1.01 0.99 0.88 0.93

May 1.61 1.66 1.58 1.52 1.03 0.98 0.94

Jun 1.87 2.01 1.85 1.89 1.08 0.99 1.01

Jul 2.18 2.05 1.90 1.91 0.94 0.88 0.88

Aug 1.90 1.86 1.79 1.75 0.98 0.94 0.92

Sep 1.38 1.37 1.38 1.39 0.99 0.99 1.01

Oct 0.75 0.83 0.86 0.84 1.10 1.14 1.12

Nov 0.35 0.44 0.45 0.47 1.27 1.31 1.34

Dec 0.23 0.21 0.25 0.31 0.93 1.08 1.36

Jan 0.22 0.19 0.23 0.28 0.90 1.08 1.29

Feb 0.28 0.28 0.30 0.31 0.99 1.07 1.10

Mar 0.51 0.49 0.49 0.54 0.97 0.97 1.06

Apr 1.03 1.05 0.93 1.01 1.02 0.91 0.98

May 1.56 1.60 1.50 1.48 1.02 0.96 0.95

Jun 1.78 1.94 1.78 1.82 1.09 1.00 1.02

Jul 2.09 1.98 1.82 1.86 0.95 0.87 0.89

Aug 1.85 1.80 1.75 1.72 0.97 0.95 0.93

Sep 1.40 1.36 1.38 1.41 0.97 0.99 1.01

Oct 0.78 0.84 0.87 0.89 1.08 1.11 1.13

Nov 0.40 0.48 0.50 0.51 1.21 1.25 1.27

Dec 0.26 0.25 0.30 0.36 0.94 1.13 1.37

DK3

DK4

DK5

DK6

Reference ET ECHAM5-HIRHAM4Mean (mm/day) DC Factor (-)

DK1

DK2

G E U S 61

ref near mid far near mid far

Jan 0.16 0.17 0.18 0.18 1.04 1.08 1.13

Feb 0.29 0.30 0.31 0.30 1.00 1.04 1.03

Mar 0.58 0.61 0.60 0.59 1.05 1.04 1.02

Apr 1.03 1.17 1.03 1.17 1.14 1.01 1.13

May 1.55 1.64 1.70 1.74 1.06 1.10 1.12

Jun 1.83 1.86 1.95 2.10 1.02 1.06 1.14

Jul 1.76 1.96 1.82 1.94 1.12 1.04 1.11

Aug 1.38 1.52 1.62 1.63 1.10 1.17 1.18

Sep 1.00 1.02 1.04 1.11 1.02 1.04 1.10

Oct 0.48 0.52 0.54 0.55 1.08 1.11 1.14

Nov 0.22 0.22 0.25 0.26 1.03 1.14 1.19

Dec 0.15 0.15 0.16 0.16 0.98 1.05 1.05

Jan 0.17 0.19 0.19 0.20 1.08 1.10 1.17

Feb 0.31 0.31 0.33 0.32 0.99 1.07 1.04

Mar 0.59 0.63 0.61 0.60 1.06 1.03 1.02

Apr 1.07 1.19 1.06 1.19 1.11 0.99 1.11

May 1.59 1.68 1.71 1.77 1.05 1.08 1.11

Jun 1.83 1.88 1.96 2.11 1.03 1.07 1.15

Jul 1.78 1.99 1.87 1.98 1.12 1.05 1.11

Aug 1.43 1.58 1.67 1.68 1.10 1.17 1.17

Sep 1.06 1.07 1.10 1.19 1.01 1.03 1.12

Oct 0.52 0.57 0.59 0.60 1.09 1.14 1.15

Nov 0.24 0.25 0.28 0.30 1.02 1.16 1.22

Dec 0.17 0.16 0.17 0.19 0.95 1.03 1.12

Jan 0.17 0.18 0.18 0.19 1.06 1.07 1.13

Feb 0.30 0.30 0.31 0.30 1.00 1.05 1.02

Mar 0.58 0.60 0.59 0.58 1.04 1.02 1.01

Apr 1.03 1.16 1.02 1.15 1.13 0.99 1.12

May 1.53 1.62 1.65 1.70 1.06 1.08 1.11

Jun 1.78 1.82 1.89 2.02 1.02 1.06 1.13

Jul 1.71 1.91 1.79 1.87 1.12 1.05 1.09

Aug 1.36 1.49 1.58 1.59 1.10 1.16 1.18

Sep 1.00 1.01 1.03 1.11 1.01 1.03 1.11

Oct 0.49 0.52 0.55 0.56 1.07 1.13 1.13

Nov 0.23 0.23 0.26 0.27 1.02 1.14 1.18

Dec 0.15 0.15 0.17 0.17 0.97 1.07 1.12

Jan 0.18 0.19 0.19 0.20 1.05 1.05 1.13

Feb 0.31 0.30 0.32 0.31 0.97 1.01 0.99

Mar 0.57 0.59 0.58 0.58 1.05 1.03 1.02

Apr 1.00 1.13 0.98 1.10 1.13 0.98 1.10

May 1.46 1.54 1.57 1.60 1.06 1.08 1.10

Jun 1.70 1.72 1.78 1.89 1.01 1.04 1.11

Jul 1.58 1.78 1.67 1.73 1.12 1.05 1.09

Aug 1.28 1.39 1.46 1.48 1.09 1.14 1.16

Sep 0.93 0.94 0.97 1.04 1.01 1.04 1.12

Oct 0.48 0.50 0.54 0.53 1.04 1.12 1.10

Nov 0.24 0.24 0.26 0.28 1.03 1.09 1.17

Dec 0.17 0.16 0.19 0.19 0.97 1.10 1.12

Jan 0.16 0.17 0.17 0.18 1.04 1.06 1.12

Feb 0.29 0.29 0.29 0.29 0.98 1.00 0.99

Mar 0.56 0.57 0.57 0.57 1.02 1.03 1.02

Apr 0.98 1.10 0.96 1.09 1.12 0.98 1.11

May 1.43 1.51 1.57 1.59 1.05 1.10 1.11

Jun 1.71 1.72 1.79 1.88 1.00 1.04 1.10

Jul 1.60 1.78 1.67 1.72 1.11 1.04 1.07

Aug 1.24 1.36 1.43 1.43 1.09 1.15 1.15

Sep 0.89 0.90 0.92 0.99 1.01 1.03 1.11

Oct 0.44 0.46 0.49 0.49 1.04 1.11 1.10

Nov 0.21 0.21 0.23 0.24 1.02 1.10 1.13

Dec 0.15 0.15 0.16 0.16 0.97 1.09 1.07

Jan 0.16 0.17 0.18 0.18 1.03 1.08 1.13

Feb 0.30 0.29 0.30 0.30 0.97 1.01 1.01

Mar 0.57 0.56 0.59 0.59 0.98 1.02 1.02

Apr 1.00 1.13 0.99 1.11 1.13 0.99 1.11

May 1.47 1.54 1.62 1.64 1.05 1.10 1.11

Jun 1.81 1.82 1.88 1.99 1.01 1.04 1.10

Jul 1.72 1.86 1.77 1.82 1.08 1.03 1.06

Aug 1.31 1.42 1.47 1.48 1.09 1.12 1.13

Sep 0.91 0.92 0.94 1.01 1.02 1.03 1.11

Oct 0.45 0.46 0.49 0.50 1.02 1.08 1.11

Nov 0.22 0.22 0.24 0.23 1.00 1.11 1.08

Dec 0.15 0.15 0.17 0.17 0.97 1.12 1.08

DK3

DK4

DK5

DK6

Reference ET ECHAM5-RegCM3Mean (mm/day) DC Factor (-)

DK1

DK2

62 G E U S

ref near mid far near mid far

Jan 0.27 0.27 0.30 0.37 1.00 1.12 1.36

Feb 0.39 0.38 0.41 0.45 0.98 1.07 1.16

Mar 0.71 0.73 0.75 0.75 1.02 1.05 1.05

Apr 1.27 1.34 1.30 1.39 1.05 1.02 1.09

May 1.88 1.96 1.97 1.96 1.04 1.05 1.04

Jun 2.25 2.28 2.35 2.52 1.01 1.04 1.12

Jul 2.34 2.46 2.33 2.51 1.05 1.00 1.07

Aug 2.01 2.13 2.10 2.15 1.06 1.04 1.07

Sep 1.44 1.49 1.57 1.65 1.04 1.09 1.15

Oct 0.89 0.85 0.95 0.96 0.96 1.07 1.08

Nov 0.47 0.50 0.53 0.57 1.07 1.14 1.20

Dec 0.33 0.32 0.37 0.38 0.97 1.10 1.14

Jan 0.29 0.30 0.33 0.39 1.04 1.15 1.38

Feb 0.41 0.40 0.44 0.47 0.98 1.07 1.15

Mar 0.72 0.72 0.75 0.76 1.01 1.05 1.05

Apr 1.28 1.33 1.28 1.40 1.04 1.00 1.09

May 1.88 1.95 1.95 1.97 1.04 1.04 1.05

Jun 2.29 2.33 2.39 2.58 1.02 1.04 1.12

Jul 2.42 2.54 2.38 2.61 1.05 0.99 1.08

Aug 2.12 2.23 2.20 2.27 1.05 1.04 1.07

Sep 1.54 1.59 1.65 1.76 1.04 1.08 1.15

Oct 0.97 0.93 1.04 1.03 0.96 1.07 1.06

Nov 0.51 0.55 0.58 0.61 1.07 1.13 1.20

Dec 0.35 0.35 0.40 0.42 1.00 1.13 1.18

Jan 0.30 0.31 0.34 0.40 1.03 1.12 1.33

Feb 0.42 0.42 0.45 0.49 0.98 1.06 1.15

Mar 0.73 0.74 0.76 0.77 1.01 1.04 1.05

Apr 1.26 1.32 1.27 1.38 1.05 1.01 1.09

May 1.85 1.91 1.93 1.93 1.03 1.04 1.04

Jun 2.21 2.25 2.31 2.47 1.02 1.04 1.12

Jul 2.30 2.41 2.28 2.44 1.05 0.99 1.06

Aug 1.96 2.06 2.06 2.10 1.05 1.05 1.07

Sep 1.42 1.46 1.53 1.62 1.02 1.07 1.14

Oct 0.91 0.86 0.96 0.96 0.95 1.06 1.06

Nov 0.50 0.53 0.56 0.59 1.06 1.13 1.17

Dec 0.36 0.36 0.40 0.41 0.99 1.12 1.15

Jan 0.24 0.24 0.27 0.32 1.01 1.10 1.33

Feb 0.36 0.35 0.39 0.42 0.97 1.06 1.15

Mar 0.70 0.71 0.72 0.73 1.02 1.03 1.05

Apr 1.26 1.36 1.29 1.39 1.08 1.02 1.10

May 1.87 1.97 1.98 1.99 1.05 1.06 1.06

Jun 2.18 2.25 2.31 2.44 1.03 1.06 1.12

Jul 2.21 2.31 2.17 2.32 1.04 0.98 1.05

Aug 1.76 1.86 1.91 1.89 1.06 1.09 1.07

Sep 1.22 1.23 1.30 1.38 1.01 1.07 1.14

Oct 0.71 0.69 0.78 0.78 0.97 1.10 1.09

Nov 0.38 0.40 0.41 0.45 1.07 1.10 1.20

Dec 0.27 0.27 0.30 0.32 1.00 1.13 1.20

Jan 0.23 0.22 0.25 0.29 0.96 1.06 1.27

Feb 0.35 0.33 0.36 0.40 0.95 1.04 1.13

Mar 0.66 0.69 0.70 0.71 1.04 1.05 1.07

Apr 1.23 1.34 1.27 1.38 1.09 1.04 1.12

May 1.87 1.96 1.99 1.97 1.04 1.06 1.05

Jun 2.17 2.21 2.30 2.44 1.02 1.06 1.12

Jul 2.18 2.29 2.17 2.28 1.05 0.99 1.05

Aug 1.72 1.81 1.85 1.84 1.05 1.08 1.07

Sep 1.18 1.19 1.26 1.33 1.01 1.07 1.13

Oct 0.65 0.65 0.71 0.74 1.00 1.09 1.13

Nov 0.34 0.37 0.38 0.41 1.09 1.11 1.20

Dec 0.25 0.25 0.27 0.29 0.98 1.10 1.15

Jan 0.26 0.24 0.26 0.32 0.95 1.03 1.24

Feb 0.37 0.34 0.38 0.41 0.91 1.02 1.11

Mar 0.69 0.69 0.72 0.73 1.00 1.05 1.07

Apr 1.21 1.31 1.27 1.35 1.09 1.05 1.12

May 1.83 1.89 1.95 1.91 1.03 1.07 1.05

Jun 2.16 2.17 2.27 2.38 1.01 1.05 1.10

Jul 2.15 2.27 2.18 2.25 1.05 1.01 1.05

Aug 1.75 1.83 1.87 1.85 1.04 1.07 1.06

Sep 1.24 1.25 1.33 1.38 1.01 1.07 1.11

Oct 0.70 0.71 0.75 0.80 1.01 1.07 1.14

Nov 0.39 0.41 0.43 0.46 1.07 1.11 1.19

Dec 0.29 0.28 0.32 0.32 0.96 1.10 1.09

DK3

DK4

DK5

DK6

Reference ET ECHAM5-RACMO2Mean (mm/day) DC Factor (-)

DK1

DK2

G E U S 63

ref near mid far near mid far

Jan 0.25 0.25 0.29 0.38 0.99 1.16 1.52

Feb 0.30 0.29 0.31 0.37 0.95 1.01 1.21

Mar 0.60 0.60 0.64 0.62 0.99 1.06 1.03

Apr 1.24 1.26 1.19 1.24 1.01 0.96 1.00

May 1.72 1.70 1.67 1.64 0.99 0.97 0.96

Jun 1.89 1.95 1.93 2.02 1.03 1.02 1.07

Jul 2.08 2.12 2.05 2.13 1.02 0.99 1.02

Aug 1.95 2.05 1.99 2.06 1.05 1.02 1.05

Sep 1.56 1.64 1.65 1.72 1.05 1.06 1.10

Oct 1.09 1.03 1.12 1.13 0.95 1.03 1.04

Nov 0.62 0.68 0.69 0.75 1.11 1.12 1.22

Dec 0.38 0.39 0.44 0.44 1.01 1.15 1.14

Jan 0.31 0.32 0.37 0.48 1.04 1.18 1.53

Feb 0.36 0.35 0.37 0.43 0.96 1.00 1.17

Mar 0.65 0.63 0.69 0.67 0.98 1.06 1.04

Apr 1.20 1.21 1.13 1.22 1.01 0.94 1.01

May 1.63 1.62 1.59 1.60 0.99 0.97 0.98

Jun 1.86 1.92 1.91 1.99 1.03 1.02 1.07

Jul 2.13 2.16 2.09 2.20 1.01 0.98 1.03

Aug 2.10 2.19 2.13 2.21 1.04 1.01 1.05

Sep 1.76 1.85 1.86 1.94 1.05 1.06 1.10

Oct 1.30 1.23 1.33 1.35 0.94 1.02 1.04

Nov 0.78 0.85 0.87 0.94 1.09 1.11 1.21

Dec 0.47 0.50 0.56 0.55 1.05 1.18 1.17

Jan 0.30 0.31 0.36 0.46 1.02 1.18 1.52

Feb 0.36 0.35 0.37 0.43 0.98 1.02 1.20

Mar 0.65 0.64 0.68 0.68 0.98 1.03 1.04

Apr 1.24 1.25 1.16 1.25 1.01 0.94 1.01

May 1.65 1.67 1.64 1.64 1.01 0.99 0.99

Jun 1.92 1.95 1.93 2.01 1.02 1.01 1.05

Jul 2.10 2.14 2.07 2.14 1.02 0.99 1.02

Aug 2.00 2.06 2.01 2.07 1.03 1.01 1.04

Sep 1.58 1.68 1.67 1.77 1.06 1.06 1.12

Oct 1.16 1.10 1.19 1.21 0.95 1.03 1.05

Nov 0.70 0.77 0.79 0.84 1.10 1.12 1.20

Dec 0.45 0.47 0.52 0.51 1.04 1.16 1.13

Jan 0.23 0.24 0.27 0.37 1.04 1.17 1.60

Feb 0.30 0.30 0.32 0.37 0.99 1.08 1.23

Mar 0.65 0.63 0.65 0.66 0.97 1.00 1.02

Apr 1.26 1.31 1.19 1.27 1.04 0.95 1.01

May 1.65 1.69 1.65 1.66 1.02 1.00 1.00

Jun 1.86 1.89 1.84 1.92 1.02 0.99 1.03

Jul 2.00 2.02 1.92 1.94 1.01 0.96 0.97

Aug 1.81 1.87 1.80 1.84 1.03 0.99 1.01

Sep 1.34 1.43 1.42 1.50 1.06 1.06 1.11

Oct 0.92 0.88 0.96 0.97 0.95 1.05 1.06

Nov 0.51 0.59 0.60 0.65 1.17 1.18 1.27

Dec 0.34 0.35 0.40 0.41 1.03 1.18 1.21

Jan 0.22 0.22 0.25 0.34 0.99 1.13 1.53

Feb 0.28 0.27 0.30 0.35 0.97 1.07 1.23

Mar 0.60 0.60 0.62 0.63 1.00 1.03 1.05

Apr 1.24 1.29 1.19 1.24 1.04 0.96 1.00

May 1.64 1.68 1.64 1.62 1.02 1.00 0.99

Jun 1.81 1.85 1.83 1.89 1.02 1.01 1.04

Jul 1.90 1.94 1.85 1.87 1.02 0.97 0.98

Aug 1.69 1.76 1.68 1.72 1.04 1.00 1.02

Sep 1.25 1.32 1.31 1.38 1.05 1.05 1.10

Oct 0.83 0.80 0.86 0.88 0.96 1.03 1.06

Nov 0.46 0.52 0.52 0.57 1.13 1.15 1.25

Dec 0.31 0.31 0.36 0.36 1.02 1.16 1.17

Jan 0.30 0.29 0.33 0.42 0.97 1.11 1.42

Feb 0.35 0.32 0.35 0.41 0.92 1.02 1.17

Mar 0.61 0.60 0.64 0.66 0.98 1.03 1.08

Apr 1.17 1.22 1.15 1.17 1.04 0.99 1.00

May 1.57 1.56 1.53 1.50 1.00 0.98 0.96

Jun 1.68 1.74 1.71 1.77 1.03 1.02 1.05

Jul 1.79 1.81 1.72 1.73 1.01 0.96 0.97

Aug 1.64 1.68 1.61 1.65 1.03 0.98 1.00

Sep 1.29 1.34 1.32 1.37 1.03 1.02 1.06

Oct 0.92 0.87 0.91 0.92 0.94 0.98 1.00

Nov 0.56 0.61 0.61 0.67 1.10 1.09 1.19

Dec 0.41 0.41 0.46 0.45 1.00 1.14 1.10

DK3

DK4

DK5

DK6

Reference ET ECHAM5-REMOMean (mm/day) DC Factor (-)

DK1

DK2

64 G E U S

ref near mid far near mid far

Jan 0.27 0.27 0.31 0.36 1.02 1.15 1.36

Feb 0.42 0.39 0.42 0.45 0.94 1.00 1.08

Mar 0.80 0.77 0.79 0.75 0.96 0.99 0.94

Apr 1.27 1.29 1.20 1.19 1.02 0.95 0.93

May 1.64 1.65 1.65 1.60 1.00 1.00 0.97

Jun 1.80 1.81 1.83 1.90 1.01 1.02 1.06

Jul 1.83 1.91 1.85 1.92 1.04 1.01 1.05

Aug 1.65 1.71 1.71 1.71 1.04 1.04 1.04

Sep 1.28 1.33 1.34 1.38 1.04 1.05 1.08

Oct 0.79 0.79 0.84 0.83 1.00 1.06 1.04

Nov 0.44 0.47 0.47 0.51 1.07 1.07 1.17

Dec 0.32 0.30 0.34 0.34 0.94 1.05 1.06

Jan 0.31 0.32 0.35 0.41 1.05 1.13 1.34

Feb 0.46 0.44 0.45 0.49 0.96 0.98 1.07

Mar 0.80 0.78 0.81 0.78 0.98 1.01 0.97

Apr 1.21 1.23 1.15 1.16 1.02 0.95 0.96

May 1.55 1.56 1.57 1.54 1.00 1.01 0.99

Jun 1.75 1.78 1.77 1.86 1.02 1.02 1.06

Jul 1.87 1.93 1.86 1.95 1.03 0.99 1.04

Aug 1.81 1.86 1.84 1.86 1.03 1.02 1.03

Sep 1.45 1.51 1.53 1.57 1.04 1.05 1.09

Oct 0.95 0.92 1.01 0.98 0.97 1.06 1.03

Nov 0.54 0.58 0.58 0.63 1.08 1.07 1.16

Dec 0.36 0.36 0.41 0.41 1.01 1.14 1.14

Jan 0.28 0.29 0.32 0.37 1.04 1.13 1.32

Feb 0.44 0.41 0.42 0.46 0.93 0.96 1.03

Mar 0.79 0.76 0.79 0.75 0.97 1.01 0.95

Apr 1.22 1.24 1.15 1.16 1.01 0.94 0.95

May 1.55 1.56 1.56 1.52 1.01 1.01 0.98

Jun 1.70 1.74 1.75 1.81 1.02 1.03 1.06

Jul 1.81 1.86 1.80 1.86 1.03 0.99 1.03

Aug 1.67 1.73 1.73 1.73 1.04 1.03 1.03

Sep 1.33 1.37 1.39 1.41 1.03 1.05 1.06

Oct 0.84 0.81 0.89 0.87 0.97 1.06 1.04

Nov 0.47 0.50 0.50 0.54 1.06 1.08 1.17

Dec 0.33 0.32 0.36 0.36 0.98 1.09 1.09

Jan 0.27 0.28 0.31 0.36 1.04 1.12 1.31

Feb 0.45 0.41 0.44 0.45 0.91 0.97 0.99

Mar 0.84 0.81 0.82 0.78 0.96 0.98 0.93

Apr 1.30 1.31 1.20 1.20 1.01 0.92 0.92

May 1.61 1.62 1.61 1.58 1.01 1.00 0.98

Jun 1.71 1.75 1.76 1.82 1.02 1.03 1.06

Jul 1.75 1.78 1.73 1.79 1.02 0.99 1.02

Aug 1.51 1.56 1.57 1.56 1.03 1.04 1.03

Sep 1.16 1.19 1.22 1.23 1.03 1.05 1.06

Oct 0.71 0.69 0.75 0.74 0.96 1.06 1.04

Nov 0.39 0.42 0.43 0.47 1.08 1.10 1.19

Dec 0.30 0.30 0.33 0.34 0.99 1.11 1.12

Jan 0.27 0.26 0.29 0.34 0.99 1.09 1.26

Feb 0.42 0.38 0.41 0.42 0.90 0.98 1.02

Mar 0.82 0.77 0.79 0.75 0.94 0.96 0.92

Apr 1.31 1.33 1.21 1.19 1.01 0.92 0.91

May 1.65 1.65 1.64 1.59 1.00 0.99 0.97

Jun 1.75 1.77 1.78 1.84 1.01 1.02 1.05

Jul 1.74 1.81 1.75 1.80 1.04 1.00 1.03

Aug 1.47 1.54 1.56 1.53 1.05 1.07 1.04

Sep 1.12 1.14 1.19 1.20 1.02 1.06 1.08

Oct 0.69 0.67 0.70 0.72 0.98 1.03 1.05

Nov 0.38 0.40 0.40 0.44 1.06 1.06 1.17

Dec 0.29 0.28 0.32 0.30 0.94 1.08 1.04

Jan 0.26 0.25 0.28 0.33 0.96 1.08 1.25

Feb 0.39 0.35 0.39 0.40 0.90 1.01 1.05

Mar 0.78 0.73 0.76 0.73 0.93 0.98 0.93

Apr 1.28 1.30 1.20 1.18 1.02 0.94 0.92

May 1.67 1.67 1.65 1.62 1.00 0.99 0.97

Jun 1.85 1.86 1.85 1.92 1.01 1.00 1.04

Jul 1.85 1.90 1.82 1.88 1.03 0.98 1.02

Aug 1.56 1.64 1.65 1.62 1.05 1.06 1.04

Sep 1.19 1.22 1.27 1.29 1.02 1.06 1.08

Oct 0.74 0.73 0.75 0.78 0.99 1.02 1.05

Nov 0.41 0.42 0.43 0.46 1.04 1.07 1.13

Dec 0.29 0.28 0.32 0.30 0.94 1.10 1.03

DK3

DK4

DK5

DK6

Reference ET ECHAM5-RCA3Mean (mm/day) DC Factor (-)

DK1

DK2

G E U S 65

ref near mid far near mid far

Jan 0.17 0.21 0.23 0.24 1.26 1.37 1.45

Feb 0.26 0.31 0.32 0.32 1.16 1.20 1.20

Mar 0.53 0.55 0.55 0.57 1.03 1.04 1.07

Apr 1.05 0.96 0.97 1.01 0.92 0.92 0.96

May 1.72 1.73 1.55 1.65 1.01 0.90 0.96

Jun 2.10 2.17 2.15 2.15 1.03 1.02 1.03

Jul 2.33 2.30 2.33 2.43 0.99 1.00 1.04

Aug 1.75 2.16 2.06 2.31 1.23 1.18 1.32

Sep 1.07 1.22 1.29 1.35 1.14 1.20 1.26

Oct 0.61 0.63 0.68 0.73 1.03 1.11 1.19

Nov 0.28 0.30 0.35 0.39 1.10 1.25 1.42

Dec 0.17 0.20 0.25 0.27 1.16 1.43 1.53

Jan 0.22 0.25 0.27 0.28 1.15 1.24 1.29

Feb 0.31 0.35 0.37 0.37 1.13 1.17 1.18

Mar 0.57 0.61 0.60 0.62 1.07 1.04 1.08

Apr 1.08 1.00 1.01 1.05 0.92 0.93 0.97

May 1.72 1.74 1.60 1.70 1.01 0.93 0.99

Jun 2.14 2.13 2.18 2.23 1.00 1.02 1.04

Jul 2.38 2.35 2.43 2.56 0.99 1.02 1.07

Aug 1.84 2.27 2.20 2.46 1.23 1.19 1.34

Sep 1.16 1.34 1.40 1.49 1.15 1.21 1.29

Oct 0.70 0.73 0.78 0.86 1.04 1.12 1.23

Nov 0.34 0.38 0.42 0.46 1.13 1.24 1.38

Dec 0.21 0.24 0.29 0.32 1.13 1.38 1.49

Jan 0.25 0.28 0.31 0.32 1.11 1.21 1.25

Feb 0.34 0.39 0.41 0.41 1.15 1.20 1.22

Mar 0.60 0.64 0.63 0.66 1.07 1.05 1.10

Apr 1.09 1.02 1.02 1.07 0.93 0.94 0.98

May 1.66 1.66 1.55 1.62 1.00 0.93 0.98

Jun 1.99 2.03 2.03 2.07 1.02 1.02 1.04

Jul 2.23 2.14 2.23 2.34 0.96 1.00 1.05

Aug 1.76 2.08 2.03 2.24 1.18 1.15 1.27

Sep 1.19 1.31 1.36 1.42 1.10 1.15 1.19

Oct 0.74 0.76 0.82 0.87 1.03 1.11 1.17

Nov 0.39 0.43 0.47 0.50 1.11 1.22 1.30

Dec 0.24 0.28 0.34 0.35 1.16 1.42 1.45

Jan 0.14 0.18 0.19 0.20 1.26 1.38 1.40

Feb 0.23 0.27 0.26 0.27 1.17 1.14 1.19

Mar 0.49 0.51 0.51 0.53 1.05 1.04 1.07

Apr 0.98 0.90 0.91 0.94 0.92 0.93 0.97

May 1.57 1.55 1.43 1.51 0.99 0.91 0.96

Jun 1.93 2.02 1.96 2.03 1.05 1.02 1.06

Jul 2.20 2.06 2.18 2.30 0.94 0.99 1.05

Aug 1.61 1.97 1.94 2.19 1.22 1.21 1.37

Sep 0.95 1.05 1.09 1.14 1.11 1.14 1.19

Oct 0.49 0.51 0.56 0.57 1.03 1.14 1.17

Nov 0.21 0.24 0.28 0.31 1.15 1.37 1.48

Dec 0.13 0.16 0.20 0.20 1.19 1.54 1.53

Jan 0.10 0.11 0.12 0.12 1.17 1.26 1.26

Feb 0.17 0.20 0.19 0.20 1.18 1.15 1.22

Mar 0.43 0.44 0.45 0.46 1.03 1.03 1.07

Apr 0.92 0.85 0.85 0.88 0.92 0.91 0.95

May 1.56 1.48 1.37 1.45 0.95 0.88 0.93

Jun 1.89 2.02 1.91 1.97 1.06 1.01 1.04

Jul 2.10 1.97 2.06 2.17 0.94 0.98 1.03

Aug 1.49 1.79 1.80 2.04 1.20 1.21 1.37

Sep 0.81 0.93 0.96 1.01 1.15 1.19 1.25

Oct 0.39 0.40 0.45 0.45 1.02 1.13 1.15

Nov 0.15 0.16 0.19 0.21 1.09 1.27 1.41

Dec 0.09 0.11 0.13 0.13 1.18 1.41 1.47

Jan 0.14 0.17 0.18 0.19 1.22 1.31 1.39

Feb 0.21 0.26 0.25 0.26 1.24 1.19 1.24

Mar 0.47 0.48 0.49 0.50 1.04 1.06 1.07

Apr 0.93 0.88 0.89 0.91 0.95 0.95 0.98

May 1.51 1.45 1.38 1.44 0.96 0.92 0.95

Jun 1.85 1.98 1.87 1.90 1.07 1.01 1.03

Jul 2.05 1.94 1.99 2.08 0.95 0.97 1.01

Aug 1.48 1.72 1.75 1.92 1.16 1.18 1.30

Sep 0.89 0.98 1.06 1.11 1.11 1.19 1.25

Oct 0.48 0.49 0.53 0.54 1.02 1.09 1.13

Nov 0.20 0.22 0.26 0.29 1.10 1.27 1.44

Dec 0.14 0.16 0.19 0.21 1.12 1.36 1.44

DK3

DK4

DK5

DK6

Reference ET HadCM3-CLMMean (mm/day) DC Factor (-)

DK1

DK2

66 G E U S

ref near mid far near mid far

Jan 0.15 0.23 0.26 0.29 1.51 1.72 1.88

Feb 0.30 0.42 0.43 0.46 1.40 1.47 1.55

Mar 0.70 0.79 0.83 0.92 1.13 1.19 1.31

Apr 1.31 1.39 1.49 1.58 1.06 1.14 1.20

May 1.92 1.96 2.17 2.23 1.02 1.13 1.16

Jun 2.31 2.40 2.61 2.71 1.04 1.13 1.17

Jul 2.66 2.64 2.91 3.15 0.99 1.10 1.19

Aug 2.34 2.47 2.80 2.99 1.06 1.20 1.28

Sep 1.34 1.57 1.69 1.75 1.18 1.26 1.31

Oct 0.68 0.76 0.84 0.95 1.13 1.23 1.40

Nov 0.28 0.34 0.36 0.46 1.21 1.31 1.65

Dec 0.15 0.19 0.27 0.28 1.28 1.80 1.86

Jan 0.20 0.29 0.33 0.36 1.44 1.63 1.80

Feb 0.36 0.47 0.48 0.52 1.29 1.34 1.43

Mar 0.74 0.82 0.86 0.93 1.11 1.15 1.25

Apr 1.30 1.34 1.44 1.50 1.04 1.11 1.16

May 1.86 1.86 2.02 2.05 1.00 1.09 1.10

Jun 2.17 2.26 2.46 2.52 1.04 1.14 1.16

Jul 2.47 2.54 2.78 2.98 1.03 1.13 1.20

Aug 2.24 2.40 2.71 2.84 1.07 1.21 1.27

Sep 1.39 1.61 1.75 1.81 1.16 1.26 1.30

Oct 0.77 0.87 0.95 1.07 1.13 1.23 1.39

Nov 0.36 0.44 0.48 0.57 1.22 1.32 1.57

Dec 0.20 0.25 0.34 0.36 1.27 1.72 1.80

Jan 0.13 0.20 0.22 0.24 1.53 1.70 1.89

Feb 0.29 0.38 0.39 0.41 1.30 1.34 1.41

Mar 0.68 0.76 0.80 0.87 1.12 1.18 1.28

Apr 1.31 1.34 1.46 1.51 1.03 1.11 1.15

May 1.91 1.91 2.07 2.12 1.00 1.08 1.11

Jun 2.24 2.32 2.49 2.56 1.04 1.11 1.15

Jul 2.54 2.49 2.72 2.97 0.98 1.07 1.17

Aug 2.20 2.31 2.58 2.76 1.05 1.17 1.25

Sep 1.28 1.45 1.57 1.62 1.13 1.22 1.26

Oct 0.61 0.68 0.74 0.87 1.11 1.23 1.43

Nov 0.23 0.29 0.32 0.39 1.27 1.42 1.72

Dec 0.12 0.15 0.22 0.22 1.30 1.83 1.88

Jan 0.10 0.15 0.16 0.19 1.57 1.73 1.97

Feb 0.24 0.31 0.32 0.34 1.30 1.35 1.42

Mar 0.62 0.70 0.72 0.80 1.12 1.16 1.30

Apr 1.29 1.31 1.40 1.47 1.02 1.09 1.14

May 1.86 1.87 2.03 2.05 1.01 1.09 1.10

Jun 2.15 2.22 2.37 2.44 1.03 1.10 1.13

Jul 2.47 2.33 2.54 2.84 0.95 1.03 1.15

Aug 2.06 2.19 2.38 2.63 1.06 1.15 1.27

Sep 1.17 1.30 1.42 1.47 1.11 1.21 1.26

Oct 0.52 0.58 0.63 0.75 1.12 1.22 1.44

Nov 0.17 0.22 0.25 0.31 1.27 1.44 1.79

Dec 0.09 0.11 0.16 0.16 1.25 1.83 1.82

Jan 0.08 0.13 0.15 0.16 1.59 1.83 2.01

Feb 0.20 0.28 0.30 0.31 1.37 1.47 1.51

Mar 0.58 0.65 0.69 0.77 1.13 1.19 1.33

Apr 1.25 1.27 1.37 1.44 1.02 1.10 1.15

May 1.82 1.85 2.03 2.07 1.02 1.11 1.14

Jun 2.14 2.21 2.35 2.42 1.03 1.10 1.13

Jul 2.39 2.28 2.49 2.75 0.95 1.04 1.15

Aug 1.95 2.08 2.22 2.50 1.07 1.14 1.28

Sep 1.11 1.21 1.34 1.39 1.09 1.21 1.26

Oct 0.49 0.53 0.57 0.68 1.10 1.18 1.40

Nov 0.15 0.19 0.21 0.27 1.25 1.43 1.80

Dec 0.08 0.10 0.15 0.15 1.32 1.92 1.90

Jan 0.09 0.14 0.16 0.18 1.51 1.78 1.93

Feb 0.21 0.29 0.31 0.32 1.40 1.49 1.49

Mar 0.58 0.64 0.68 0.77 1.12 1.18 1.34

Apr 1.22 1.26 1.34 1.42 1.03 1.10 1.16

May 1.80 1.85 2.01 2.06 1.03 1.12 1.14

Jun 2.12 2.17 2.33 2.38 1.02 1.10 1.12

Jul 2.30 2.22 2.40 2.63 0.97 1.04 1.15

Aug 1.83 2.02 2.11 2.35 1.10 1.15 1.28

Sep 1.08 1.19 1.32 1.37 1.10 1.22 1.27

Oct 0.52 0.57 0.60 0.70 1.09 1.15 1.35

Nov 0.17 0.20 0.24 0.29 1.22 1.43 1.75

Dec 0.09 0.12 0.17 0.18 1.25 1.80 1.89

DK3

DK4

DK5

DK6

Reference ET HadCM3-HadRM3Mean (mm/day) DC Factor (-)

DK1

DK2

G E U S 67

68 G E U S

ref near mid far near mid far

Jan 3.02 4.18 4.06 5.24 1.16 1.04 2.22

Feb 1.83 2.23 3.96 4.27 0.40 2.13 2.44

Mar 2.74 2.45 3.77 4.67 -0.29 1.03 1.94

Apr 4.94 4.90 6.71 6.76 -0.04 1.77 1.82

May 8.37 8.86 9.88 10.23 0.49 1.51 1.86

Jun 12.06 12.48 13.14 14.04 0.42 1.08 1.98

Jul 15.50 16.14 16.69 17.36 0.64 1.19 1.86

Aug 16.65 16.90 18.10 18.59 0.25 1.45 1.94

Sep 15.21 15.60 16.24 17.01 0.39 1.02 1.79

Oct 11.77 12.21 12.80 13.08 0.44 1.04 1.32

Nov 7.94 8.21 9.22 9.58 0.27 1.28 1.64

Dec 4.48 5.68 7.02 7.37 1.20 2.54 2.89

Jan 3.31 4.32 4.29 5.46 1.01 0.98 2.15

Feb 2.08 2.52 4.18 4.51 0.44 2.10 2.43

Mar 2.95 2.70 4.04 4.92 -0.25 1.09 1.96

Apr 5.08 5.06 6.84 6.89 -0.02 1.77 1.81

May 8.38 8.93 9.97 10.32 0.55 1.59 1.94

Jun 12.16 12.60 13.33 14.19 0.44 1.18 2.04

Jul 15.86 16.62 17.16 17.89 0.76 1.30 2.02

Aug 17.26 17.50 18.71 19.15 0.24 1.45 1.89

Sep 15.78 16.25 16.89 17.70 0.47 1.10 1.91

Oct 12.24 12.68 13.27 13.52 0.44 1.03 1.27

Nov 8.29 8.58 9.60 9.87 0.30 1.31 1.58

Dec 4.86 5.92 7.27 7.55 1.06 2.41 2.69

Jan 3.78 4.78 4.86 5.92 1.00 1.08 2.14

Feb 2.42 2.89 4.55 4.85 0.47 2.13 2.43

Mar 3.05 2.85 4.22 5.02 -0.19 1.17 1.97

Apr 4.78 4.95 6.60 6.72 0.17 1.82 1.94

May 7.84 8.43 9.44 9.83 0.59 1.60 1.99

Jun 11.52 11.98 12.70 13.50 0.47 1.18 1.99

Jul 15.09 15.69 16.26 16.87 0.60 1.17 1.78

Aug 16.45 16.73 17.84 18.31 0.28 1.39 1.86

Sep 15.42 15.81 16.47 17.23 0.39 1.05 1.81

Oct 12.37 12.75 13.35 13.65 0.38 0.98 1.28

Nov 8.75 9.07 10.00 10.32 0.32 1.26 1.57

Dec 5.40 6.50 7.75 8.09 1.10 2.35 2.69

Jan 3.52 4.69 4.27 5.56 1.17 0.75 2.05

Feb 2.39 2.81 4.52 4.84 0.42 2.13 2.45

Mar 3.54 3.16 4.49 5.39 -0.38 0.96 1.86

Apr 5.72 5.74 7.58 7.57 0.02 1.86 1.85

May 8.89 9.35 10.42 10.73 0.46 1.54 1.84

Jun 12.23 12.60 13.12 14.20 0.37 0.89 1.97

Jul 15.20 15.83 16.37 17.02 0.62 1.17 1.81

Aug 16.23 16.46 17.72 18.31 0.23 1.49 2.08

Sep 14.58 15.14 15.69 16.58 0.56 1.11 1.99

Oct 11.27 11.73 12.34 12.62 0.46 1.07 1.35

Nov 7.58 8.03 8.98 9.30 0.45 1.40 1.71

Dec 4.58 5.86 7.18 7.41 1.28 2.60 2.83

Jan 2.74 4.12 3.58 4.87 1.38 0.83 2.12

Feb 1.73 2.15 3.93 4.21 0.42 2.20 2.48

Mar 3.05 2.55 3.89 4.83 -0.49 0.85 1.78

Apr 5.44 5.45 7.27 7.24 0.01 1.83 1.80

May 8.97 9.38 10.46 10.80 0.41 1.49 1.83

Jun 12.36 12.78 13.25 14.42 0.42 0.88 2.05

Jul 15.23 15.77 16.35 17.00 0.53 1.11 1.77

Aug 16.02 16.25 17.51 18.16 0.23 1.49 2.14

Sep 14.15 14.68 15.23 16.08 0.53 1.08 1.93

Oct 10.61 11.08 11.71 12.07 0.47 1.10 1.46

Nov 6.81 7.28 8.19 8.58 0.47 1.38 1.77

Dec 3.82 5.21 6.39 6.71 1.38 2.57 2.89

Jan 2.88 4.39 3.91 5.10 1.51 1.03 2.22

Feb 1.82 2.25 4.09 4.34 0.43 2.27 2.52

Mar 2.98 2.46 3.84 4.79 -0.52 0.86 1.81

Apr 5.08 5.17 6.92 6.96 0.09 1.85 1.88

May 8.42 8.88 9.95 10.33 0.46 1.53 1.91

Jun 11.81 12.21 12.81 13.85 0.40 1.00 2.04

Jul 14.77 15.14 15.82 16.44 0.38 1.05 1.67

Aug 15.66 15.91 17.07 17.73 0.26 1.42 2.07

Sep 14.08 14.55 15.14 15.90 0.47 1.06 1.82

Oct 10.73 11.16 11.83 12.26 0.43 1.10 1.53

Nov 7.10 7.52 8.41 8.92 0.42 1.31 1.82

Dec 4.08 5.49 6.63 7.05 1.40 2.55 2.97

ARPEGE-HIRHAM5Mean (°C/day) DC Value (+°C/day)

DK1

DK2

DK3

DK4

DK5

DK6

Temperature

G E U S 69

ref near mid far near mid far

Jan -0.69 -0.32 1.32 2.01 0.38 2.01 2.70

Feb -0.55 -0.93 1.02 2.29 -0.38 1.57 2.84

Mar -0.09 0.75 1.98 3.57 0.83 2.07 3.66

Apr 3.15 3.27 5.25 5.91 0.11 2.10 2.75

May 7.69 7.36 8.63 9.29 -0.34 0.94 1.60

Jun 10.87 10.68 11.87 12.90 -0.19 1.00 2.03

Jul 12.97 12.99 14.23 14.78 0.02 1.27 1.82

Aug 13.15 13.28 14.41 14.97 0.13 1.26 1.82

Sep 11.62 11.38 12.60 13.29 -0.23 0.99 1.68

Oct 8.32 8.34 9.56 10.11 0.02 1.24 1.78

Nov 4.37 5.03 6.08 6.71 0.67 1.72 2.34

Dec 1.02 1.88 3.10 3.80 0.85 2.08 2.77

Jan -0.01 0.44 2.03 2.65 0.45 2.04 2.66

Feb 0.04 -0.27 1.57 2.71 -0.30 1.54 2.67

Mar 0.46 1.29 2.55 4.08 0.83 2.09 3.62

Apr 3.55 3.65 5.66 6.21 0.10 2.11 2.67

May 7.76 7.47 8.85 9.40 -0.29 1.09 1.64

Jun 10.95 10.79 12.09 13.09 -0.15 1.14 2.15

Jul 13.19 13.20 14.58 15.17 0.01 1.38 1.98

Aug 13.63 13.72 14.93 15.50 0.09 1.30 1.87

Sep 12.16 11.94 13.18 13.80 -0.22 1.02 1.64

Oct 8.83 8.85 10.04 10.56 0.02 1.21 1.73

Nov 4.87 5.56 6.58 7.15 0.69 1.70 2.28

Dec 1.57 2.53 3.71 4.34 0.96 2.14 2.77

Jan 0.59 0.94 2.49 3.11 0.35 1.90 2.52

Feb 0.49 0.25 1.99 3.09 -0.24 1.50 2.61

Mar 0.75 1.48 2.74 4.19 0.73 1.99 3.44

Apr 3.49 3.57 5.46 6.10 0.08 1.97 2.61

May 7.46 7.12 8.44 9.09 -0.35 0.98 1.63

Jun 10.46 10.30 11.55 12.51 -0.17 1.09 2.04

Jul 12.84 12.84 14.15 14.66 0.01 1.31 1.83

Aug 13.42 13.51 14.66 15.23 0.09 1.24 1.81

Sep 12.08 11.93 13.10 13.68 -0.14 1.02 1.60

Oct 8.99 9.05 10.13 10.67 0.06 1.14 1.68

Nov 5.27 5.92 6.85 7.47 0.65 1.58 2.20

Dec 2.21 3.07 4.11 4.78 0.86 1.91 2.57

Jan -0.97 -0.73 1.12 1.65 0.24 2.10 2.63

Feb -0.39 -0.93 1.00 2.28 -0.54 1.39 2.67

Mar 0.25 1.02 2.22 3.78 0.78 1.97 3.53

Apr 3.78 3.66 5.66 6.17 -0.12 1.88 2.39

May 8.59 8.10 9.14 9.81 -0.49 0.55 1.22

Jun 11.67 11.42 12.57 13.63 -0.25 0.89 1.96

Jul 13.71 13.66 14.79 15.14 -0.05 1.08 1.43

Aug 13.46 13.51 14.54 15.04 0.05 1.08 1.58

Sep 11.52 11.33 12.35 12.94 -0.20 0.83 1.42

Oct 7.96 8.02 9.12 9.65 0.06 1.16 1.69

Nov 3.77 4.52 5.49 6.10 0.75 1.72 2.33

Dec 0.49 1.34 2.51 3.24 0.86 2.02 2.75

Jan -1.91 -1.73 0.16 0.78 0.18 2.07 2.69

Feb -1.23 -1.78 0.23 1.66 -0.56 1.46 2.88

Mar -0.44 0.28 1.51 3.11 0.72 1.94 3.55

Apr 3.27 3.19 5.15 5.76 -0.08 1.88 2.49

May 8.47 7.93 8.97 9.71 -0.54 0.50 1.24

Jun 11.78 11.45 12.54 13.63 -0.33 0.76 1.85

Jul 13.59 13.57 14.70 15.03 -0.03 1.11 1.43

Aug 13.09 13.17 14.18 14.70 0.09 1.09 1.61

Sep 10.96 10.76 11.82 12.44 -0.20 0.87 1.49

Oct 7.22 7.28 8.45 9.01 0.06 1.23 1.79

Nov 3.01 3.75 4.75 5.44 0.74 1.74 2.42

Dec -0.34 0.43 1.61 2.46 0.76 1.94 2.79

Jan -1.69 -1.52 0.35 1.08 0.18 2.05 2.77

Feb -1.12 -1.53 0.44 1.87 -0.41 1.56 2.99

Mar -0.38 0.33 1.55 3.06 0.71 1.93 3.44

Apr 3.07 3.04 4.91 5.59 -0.03 1.85 2.52

May 7.94 7.44 8.60 9.38 -0.51 0.66 1.44

Jun 11.25 10.95 12.04 13.09 -0.31 0.79 1.84

Jul 13.16 13.16 14.36 14.71 0.00 1.19 1.55

Aug 12.91 13.03 14.02 14.55 0.11 1.10 1.64

Sep 10.90 10.70 11.79 12.43 -0.20 0.89 1.53

Oct 7.31 7.31 8.57 9.15 0.00 1.26 1.84

Nov 3.29 3.92 4.98 5.74 0.63 1.69 2.45

Dec 0.01 0.74 1.85 2.79 0.72 1.84 2.77

BCM2-HIRHAM5Mean (°C/day) DC Value (+°C/day)

DK1

DK2

DK3

DK4

DK5

DK6

Temperature

70 G E U S

ref near mid far near mid far

Jan 1.57 1.87 3.31 4.05 0.30 1.74 2.47

Feb 1.01 1.21 2.67 3.73 0.20 1.65 2.72

Mar 2.33 2.91 4.11 5.29 0.58 1.79 2.96

Apr 5.52 5.56 7.10 7.59 0.04 1.58 2.07

May 9.56 9.37 10.63 11.14 -0.19 1.07 1.58

Jun 12.45 12.42 13.83 14.22 -0.04 1.38 1.76

Jul 14.99 15.08 15.88 16.33 0.09 0.89 1.34

Aug 14.62 14.85 15.85 16.56 0.22 1.23 1.93

Sep 13.20 13.30 14.12 14.66 0.10 0.92 1.46

Oct 10.10 10.10 10.95 11.30 -0.01 0.84 1.20

Nov 6.47 7.02 7.82 8.29 0.55 1.35 1.83

Dec 3.28 4.03 5.18 5.57 0.75 1.91 2.29

Jan 2.57 3.02 4.34 4.97 0.45 1.77 2.40

Feb 2.14 2.29 3.62 4.52 0.15 1.48 2.38

Mar 3.12 3.59 4.79 5.81 0.47 1.67 2.69

Apr 5.46 5.50 7.03 7.60 0.04 1.57 2.15

May 8.59 8.48 9.85 10.37 -0.10 1.26 1.78

Jun 11.26 11.25 12.76 13.21 0.00 1.50 1.95

Jul 13.88 14.05 15.10 15.61 0.17 1.22 1.73

Aug 14.45 14.63 15.76 16.42 0.17 1.30 1.96

Sep 13.52 13.57 14.52 15.10 0.05 1.00 1.57

Oct 10.69 10.72 11.56 11.97 0.02 0.87 1.27

Nov 7.18 7.66 8.56 9.05 0.47 1.38 1.87

Dec 4.16 4.90 6.08 6.48 0.74 1.92 2.32

Jan 2.40 2.81 4.12 4.81 0.41 1.72 2.42

Feb 2.04 2.21 3.51 4.48 0.17 1.47 2.44

Mar 3.11 3.59 4.73 5.79 0.48 1.62 2.67

Apr 5.68 5.70 7.18 7.68 0.02 1.49 2.00

May 9.07 8.88 10.14 10.69 -0.19 1.07 1.62

Jun 11.81 11.75 13.16 13.61 -0.06 1.34 1.80

Jul 14.34 14.43 15.37 15.85 0.10 1.03 1.51

Aug 14.57 14.71 15.75 16.41 0.15 1.19 1.85

Sep 13.33 13.41 14.23 14.75 0.08 0.90 1.42

Oct 10.41 10.44 11.19 11.58 0.03 0.79 1.17

Nov 6.91 7.42 8.18 8.69 0.51 1.27 1.78

Dec 3.86 4.67 5.77 6.16 0.81 1.91 2.30

Jan 1.64 2.14 3.50 4.19 0.50 1.86 2.55

Feb 1.74 1.83 3.15 4.17 0.09 1.41 2.43

Mar 3.14 3.70 4.72 5.82 0.56 1.59 2.68

Apr 6.38 6.26 7.71 8.08 -0.12 1.34 1.70

May 10.37 10.08 11.12 11.69 -0.29 0.75 1.32

Jun 13.14 13.04 14.34 14.80 -0.10 1.20 1.65

Jul 15.48 15.60 16.26 16.66 0.12 0.78 1.18

Aug 14.80 15.00 15.90 16.62 0.21 1.11 1.82

Sep 13.06 13.18 13.78 14.33 0.13 0.73 1.27

Oct 9.91 9.92 10.58 10.93 0.01 0.67 1.02

Nov 6.16 6.78 7.42 7.93 0.62 1.27 1.78

Dec 3.00 3.98 5.03 5.37 0.98 2.03 2.37

Jan 0.81 1.19 2.69 3.43 0.38 1.88 2.61

Feb 0.80 0.94 2.43 3.55 0.15 1.63 2.75

Mar 2.25 2.85 3.96 5.21 0.60 1.71 2.96

Apr 5.90 5.84 7.31 7.66 -0.06 1.41 1.76

May 10.29 9.97 10.99 11.55 -0.32 0.70 1.26

Jun 13.14 13.05 14.31 14.77 -0.10 1.17 1.63

Jul 15.47 15.54 16.16 16.52 0.07 0.68 1.05

Aug 14.57 14.75 15.63 16.33 0.18 1.06 1.76

Sep 12.67 12.83 13.42 13.92 0.16 0.75 1.24

Oct 9.44 9.44 10.13 10.49 0.00 0.69 1.05

Nov 5.62 6.33 6.89 7.45 0.71 1.27 1.82

Dec 2.38 3.28 4.33 4.73 0.89 1.94 2.35

Jan 0.25 0.52 2.18 2.96 0.27 1.93 2.71

Feb 0.02 0.20 1.84 3.06 0.19 1.82 3.05

Mar 1.53 2.06 3.35 4.69 0.53 1.82 3.16

Apr 5.35 5.34 6.82 7.26 -0.01 1.46 1.90

May 9.92 9.59 10.68 11.26 -0.33 0.76 1.34

Jun 12.98 12.87 14.10 14.58 -0.11 1.12 1.61

Jul 15.45 15.40 16.03 16.43 -0.05 0.59 0.98

Aug 14.66 14.79 15.72 16.36 0.13 1.06 1.70

Sep 12.81 12.94 13.59 14.03 0.13 0.77 1.21

Oct 9.49 9.51 10.23 10.61 0.03 0.75 1.13

Nov 5.57 6.28 6.86 7.46 0.71 1.29 1.89

Dec 2.21 2.98 4.08 4.53 0.77 1.87 2.32

BCM2-RCA3Mean (°C/day) DC Value (+°C/day)

DK1

DK2

DK3

DK4

DK5

DK6

Temperature

G E U S 71

ref near mid far near mid far

Jan 1.76 1.55 2.81 4.37 -0.21 1.05 2.60

Feb 1.11 0.91 2.94 3.50 -0.20 1.83 2.39

Mar 3.34 3.29 4.16 4.99 -0.05 0.81 1.64

Apr 6.48 6.40 7.16 7.98 -0.08 0.67 1.50

May 10.61 10.84 11.33 12.12 0.23 0.72 1.51

Jun 13.71 14.64 14.75 15.35 0.93 1.04 1.64

Jul 16.79 16.67 17.42 17.89 -0.12 0.63 1.10

Aug 16.74 16.92 17.52 18.13 0.18 0.78 1.39

Sep 14.19 14.75 15.63 16.10 0.56 1.45 1.91

Oct 10.37 11.55 12.57 12.97 1.18 2.20 2.60

Nov 6.09 7.09 8.39 9.36 0.99 2.29 3.26

Dec 3.36 3.51 4.35 6.39 0.15 0.99 3.04

Jan 2.01 1.89 3.17 4.65 -0.12 1.16 2.64

Feb 1.46 1.29 3.27 3.81 -0.17 1.81 2.36

Mar 3.66 3.56 4.47 5.26 -0.11 0.81 1.60

Apr 6.69 6.64 7.41 8.19 -0.05 0.72 1.50

May 10.89 11.17 11.65 12.43 0.28 0.75 1.54

Jun 14.16 15.10 15.21 15.84 0.94 1.04 1.68

Jul 17.37 17.27 18.00 18.47 -0.10 0.63 1.10

Aug 17.40 17.58 18.16 18.78 0.18 0.76 1.39

Sep 14.77 15.38 16.28 16.73 0.61 1.51 1.96

Oct 10.97 12.10 13.10 13.51 1.13 2.14 2.54

Nov 6.62 7.61 8.82 9.74 0.98 2.19 3.12

Dec 3.67 3.91 4.80 6.73 0.24 1.14 3.06

Jan 2.24 2.12 3.33 4.81 -0.12 1.09 2.57

Feb 1.59 1.47 3.33 3.90 -0.12 1.74 2.30

Mar 3.52 3.42 4.35 5.12 -0.10 0.83 1.60

Apr 6.41 6.37 7.13 7.91 -0.04 0.71 1.49

May 10.41 10.68 11.13 11.90 0.27 0.72 1.49

Jun 13.70 14.46 14.60 15.23 0.76 0.89 1.53

Jul 16.72 16.63 17.36 17.83 -0.09 0.64 1.10

Aug 16.88 17.05 17.71 18.27 0.17 0.83 1.39

Sep 14.57 15.12 16.06 16.51 0.55 1.49 1.94

Oct 11.01 12.06 13.11 13.49 1.05 2.10 2.48

Nov 6.83 7.80 9.01 9.86 0.97 2.18 3.03

Dec 3.96 4.16 5.08 6.90 0.20 1.12 2.94

Jan 1.45 1.19 2.42 4.02 -0.26 0.97 2.56

Feb 1.00 0.77 2.82 3.30 -0.23 1.82 2.30

Mar 3.43 3.31 4.14 4.94 -0.12 0.71 1.51

Apr 6.77 6.64 7.34 8.00 -0.13 0.57 1.24

May 10.98 11.13 11.54 12.17 0.15 0.56 1.19

Jun 13.73 14.59 14.57 15.07 0.86 0.84 1.34

Jul 16.54 16.27 17.08 17.42 -0.26 0.55 0.88

Aug 16.19 16.34 17.00 17.53 0.15 0.81 1.34

Sep 13.60 14.17 15.04 15.45 0.58 1.45 1.85

Oct 9.80 10.95 12.00 12.32 1.15 2.19 2.52

Nov 5.52 6.53 7.84 8.73 1.01 2.32 3.21

Dec 2.88 3.04 3.84 5.99 0.15 0.96 3.11

Jan 1.06 0.72 1.95 3.58 -0.34 0.89 2.52

Feb 0.48 0.23 2.35 2.88 -0.25 1.87 2.40

Mar 2.99 2.93 3.74 4.58 -0.06 0.75 1.59

Apr 6.46 6.32 7.00 7.73 -0.14 0.54 1.27

May 10.84 10.93 11.34 11.96 0.09 0.50 1.12

Jun 13.50 14.41 14.38 14.87 0.91 0.88 1.37

Jul 16.26 15.93 16.71 17.02 -0.33 0.45 0.76

Aug 15.69 15.84 16.46 16.96 0.15 0.78 1.27

Sep 12.96 13.49 14.34 14.75 0.53 1.38 1.79

Oct 9.12 10.29 11.35 11.68 1.17 2.23 2.57

Nov 4.92 5.88 7.30 8.20 0.96 2.38 3.28

Dec 2.47 2.50 3.26 5.49 0.03 0.79 3.02

Jan 1.30 1.00 2.18 3.76 -0.30 0.88 2.47

Feb 0.62 0.40 2.45 3.01 -0.21 1.84 2.39

Mar 2.91 2.87 3.73 4.56 -0.04 0.82 1.65

Apr 6.13 6.01 6.74 7.54 -0.12 0.62 1.41

May 10.36 10.46 10.89 11.58 0.10 0.53 1.22

Jun 13.08 13.96 13.99 14.52 0.88 0.91 1.44

Jul 15.85 15.55 16.30 16.70 -0.30 0.45 0.85

Aug 15.43 15.56 16.21 16.71 0.13 0.77 1.28

Sep 12.85 13.34 14.20 14.64 0.48 1.34 1.78

Oct 9.11 10.26 11.32 11.68 1.15 2.21 2.57

Nov 5.14 6.02 7.45 8.33 0.88 2.32 3.19

Dec 2.77 2.74 3.50 5.67 -0.03 0.73 2.90

ECHAM5-HIRHAM4Mean (°C/day) DC Value (+°C/day)

DK1

DK2

DK3

DK4

DK5

DK6

Temperature

72 G E U S

ref near mid far near mid far

Jan 0.50 0.62 1.78 3.21 0.12 1.28 2.72

Feb 0.79 0.93 2.85 3.25 0.13 2.06 2.46

Mar 3.04 3.01 3.43 4.56 -0.03 0.40 1.53

Apr 6.48 7.21 7.39 8.51 0.73 0.91 2.03

May 10.56 11.59 12.04 12.86 1.03 1.48 2.30

Jun 13.71 14.20 15.04 15.93 0.48 1.32 2.22

Jul 15.47 15.89 16.54 17.31 0.42 1.07 1.84

Aug 14.65 15.12 16.39 16.98 0.47 1.75 2.34

Sep 12.39 12.76 13.85 14.53 0.37 1.46 2.14

Oct 8.34 9.40 10.41 11.10 1.07 2.08 2.76

Nov 4.37 5.07 6.45 7.23 0.70 2.09 2.87

Dec 2.22 2.03 3.12 5.17 -0.19 0.90 2.95

Jan 0.91 1.08 2.22 3.63 0.17 1.31 2.72

Feb 1.23 1.45 3.29 3.61 0.22 2.06 2.38

Mar 3.52 3.42 3.83 4.94 -0.10 0.31 1.42

Apr 6.91 7.61 7.81 8.84 0.70 0.91 1.93

May 10.90 11.90 12.32 13.15 1.00 1.42 2.25

Jun 13.91 14.37 15.21 16.12 0.46 1.29 2.21

Jul 15.65 16.11 16.75 17.56 0.46 1.10 1.90

Aug 14.98 15.47 16.73 17.37 0.49 1.75 2.40

Sep 12.85 13.24 14.34 15.03 0.39 1.49 2.18

Oct 8.88 9.91 10.86 11.59 1.03 1.98 2.71

Nov 4.83 5.54 6.89 7.75 0.70 2.06 2.92

Dec 2.51 2.46 3.64 5.64 -0.06 1.12 3.12

Jan 0.86 1.12 2.17 3.62 0.26 1.31 2.76

Feb 1.21 1.41 3.19 3.53 0.20 1.98 2.32

Mar 3.34 3.30 3.65 4.79 -0.05 0.31 1.44

Apr 6.73 7.45 7.60 8.69 0.72 0.87 1.96

May 10.66 11.62 12.02 12.87 0.96 1.36 2.21

Jun 13.62 14.02 14.87 15.75 0.40 1.25 2.13

Jul 15.33 15.78 16.43 17.14 0.45 1.10 1.81

Aug 14.72 15.19 16.41 17.02 0.47 1.70 2.30

Sep 12.62 12.97 14.11 14.75 0.35 1.49 2.13

Oct 8.74 9.74 10.77 11.42 1.00 2.03 2.68

Nov 4.85 5.54 6.88 7.71 0.69 2.03 2.86

Dec 2.58 2.53 3.65 5.65 -0.05 1.08 3.08

Jan 1.05 1.40 2.35 3.84 0.35 1.30 2.79

Feb 1.44 1.60 3.32 3.73 0.16 1.88 2.30

Mar 3.36 3.37 3.73 4.83 0.01 0.37 1.47

Apr 6.67 7.33 7.45 8.56 0.67 0.79 1.89

May 10.41 11.34 11.71 12.54 0.93 1.30 2.13

Jun 13.36 13.69 14.53 15.34 0.32 1.17 1.98

Jul 15.06 15.47 16.13 16.76 0.41 1.07 1.70

Aug 14.51 14.98 16.14 16.71 0.47 1.64 2.20

Sep 12.45 12.78 13.93 14.51 0.33 1.48 2.06

Oct 8.67 9.66 10.73 11.33 0.99 2.06 2.66

Nov 5.03 5.75 7.00 7.81 0.71 1.97 2.77

Dec 2.89 2.84 3.93 5.82 -0.05 1.04 2.93

Jan 0.59 0.83 1.83 3.31 0.24 1.24 2.72

Feb 0.92 1.04 2.85 3.28 0.12 1.93 2.36

Mar 2.88 2.90 3.31 4.47 0.03 0.43 1.59

Apr 6.25 6.94 7.06 8.27 0.70 0.81 2.02

May 10.11 11.07 11.49 12.31 0.96 1.38 2.20

Jun 13.17 13.51 14.37 15.17 0.34 1.21 2.00

Jul 14.91 15.23 15.94 16.52 0.32 1.02 1.60

Aug 14.17 14.62 15.81 16.33 0.44 1.63 2.15

Sep 12.01 12.31 13.45 14.06 0.30 1.43 2.05

Oct 8.13 9.14 10.29 10.87 1.01 2.16 2.74

Nov 4.53 5.20 6.55 7.24 0.67 2.02 2.71

Dec 2.48 2.32 3.35 5.31 -0.16 0.87 2.83

Jan 0.29 0.50 1.52 3.00 0.20 1.23 2.71

Feb 0.57 0.68 2.58 3.00 0.11 2.01 2.43

Mar 2.58 2.59 3.04 4.26 0.01 0.47 1.69

Apr 5.98 6.72 6.83 8.09 0.74 0.85 2.11

May 9.93 10.92 11.39 12.18 0.99 1.46 2.25

Jun 13.12 13.50 14.35 15.15 0.38 1.23 2.04

Jul 14.93 15.17 15.93 16.49 0.24 1.00 1.56

Aug 14.10 14.51 15.67 16.20 0.41 1.57 2.10

Sep 11.81 12.11 13.22 13.87 0.30 1.41 2.06

Oct 7.88 8.87 10.06 10.66 0.98 2.17 2.78

Nov 4.31 4.92 6.36 6.92 0.61 2.05 2.61

Dec 2.31 2.07 3.06 5.09 -0.24 0.75 2.78

ECHAM5-RegCM3Mean (°C/day) DC Value (+°C/day)

DK1

DK2

DK3

DK4

DK5

DK6

Temperature

G E U S 73

ref near mid far near mid far

Jan 1.79 1.94 2.96 4.30 0.15 1.17 2.51

Feb 1.51 1.66 3.34 3.81 0.16 1.83 2.30

Mar 2.98 3.13 3.89 4.89 0.14 0.90 1.91

Apr 6.09 6.35 6.90 8.00 0.26 0.81 1.91

May 9.90 10.67 11.20 12.07 0.77 1.30 2.17

Jun 13.59 14.08 14.83 15.68 0.49 1.24 2.09

Jul 16.14 16.53 17.11 18.00 0.40 0.97 1.86

Aug 16.07 16.57 17.63 18.13 0.50 1.56 2.06

Sep 13.96 14.40 15.46 15.98 0.45 1.50 2.03

Oct 10.16 11.11 12.21 12.71 0.95 2.05 2.55

Nov 6.35 6.97 8.09 8.86 0.63 1.74 2.51

Dec 3.54 3.57 4.66 6.39 0.03 1.12 2.86

Jan 1.98 2.25 3.27 4.60 0.26 1.29 2.62

Feb 1.74 1.94 3.59 4.07 0.20 1.84 2.32

Mar 3.19 3.27 4.11 5.09 0.08 0.92 1.90

Apr 6.18 6.41 7.04 8.10 0.22 0.86 1.92

May 10.02 10.76 11.27 12.18 0.74 1.25 2.16

Jun 13.81 14.30 15.00 15.92 0.49 1.19 2.11

Jul 16.41 16.86 17.37 18.32 0.45 0.96 1.92

Aug 16.45 16.98 17.99 18.55 0.53 1.54 2.10

Sep 14.39 14.87 15.91 16.46 0.48 1.52 2.07

Oct 10.62 11.56 12.59 13.16 0.94 1.96 2.54

Nov 6.75 7.30 8.40 9.21 0.55 1.65 2.46

Dec 3.72 3.87 5.09 6.68 0.15 1.38 2.96

Jan 2.31 2.55 3.55 4.82 0.24 1.24 2.51

Feb 2.07 2.23 3.82 4.27 0.16 1.74 2.20

Mar 3.39 3.43 4.21 5.19 0.04 0.82 1.79

Apr 6.21 6.38 7.00 8.06 0.17 0.79 1.85

May 9.82 10.51 11.06 11.93 0.69 1.24 2.11

Jun 13.48 13.91 14.61 15.49 0.43 1.13 2.01

Jul 15.99 16.35 16.91 17.75 0.36 0.92 1.76

Aug 16.02 16.47 17.50 17.98 0.45 1.48 1.97

Sep 14.02 14.44 15.51 16.01 0.42 1.49 2.00

Oct 10.45 11.33 12.38 12.91 0.88 1.93 2.45

Nov 6.87 7.37 8.47 9.21 0.50 1.60 2.34

Dec 4.03 4.13 5.28 6.86 0.10 1.24 2.82

Jan 1.81 2.09 3.04 4.32 0.28 1.24 2.52

Feb 1.92 2.10 3.74 4.06 0.18 1.82 2.14

Mar 3.68 3.76 4.36 5.30 0.09 0.69 1.63

Apr 6.97 7.30 7.68 8.74 0.32 0.70 1.77

May 10.68 11.46 11.94 12.74 0.77 1.26 2.05

Jun 13.88 14.33 15.10 15.87 0.45 1.21 1.98

Jul 15.97 16.28 16.87 17.62 0.32 0.90 1.65

Aug 15.54 16.00 17.11 17.51 0.46 1.58 1.97

Sep 13.33 13.74 14.82 15.30 0.41 1.49 1.97

Oct 9.64 10.56 11.70 12.17 0.93 2.06 2.53

Nov 6.14 6.58 7.66 8.46 0.45 1.53 2.32

Dec 3.36 3.42 4.52 6.30 0.06 1.16 2.94

Jan 1.47 1.63 2.62 3.86 0.16 1.15 2.38

Feb 1.55 1.62 3.35 3.66 0.07 1.80 2.10

Mar 3.24 3.42 3.96 4.95 0.18 0.72 1.71

Apr 6.65 7.04 7.36 8.52 0.39 0.70 1.86

May 10.49 11.28 11.79 12.56 0.79 1.30 2.07

Jun 13.69 14.10 14.92 15.65 0.42 1.23 1.97

Jul 15.71 15.99 16.63 17.32 0.28 0.92 1.61

Aug 15.16 15.56 16.73 17.10 0.40 1.57 1.95

Sep 12.85 13.24 14.31 14.84 0.39 1.46 1.99

Oct 9.07 10.09 11.24 11.71 1.02 2.17 2.64

Nov 5.59 6.15 7.27 8.01 0.56 1.68 2.42

Dec 3.05 3.01 3.95 5.88 -0.04 0.91 2.83

Jan 1.56 1.66 2.63 3.87 0.10 1.08 2.31

Feb 1.51 1.46 3.28 3.63 -0.04 1.78 2.13

Mar 3.15 3.27 3.86 4.90 0.13 0.71 1.76

Apr 6.35 6.71 7.11 8.26 0.36 0.76 1.91

May 10.13 10.88 11.45 12.18 0.75 1.32 2.06

Jun 13.40 13.80 14.61 15.35 0.40 1.21 1.94

Jul 15.58 15.79 16.49 17.11 0.21 0.91 1.54

Aug 15.16 15.47 16.65 17.02 0.32 1.49 1.87

Sep 12.88 13.25 14.33 14.88 0.37 1.44 2.00

Oct 9.14 10.14 11.28 11.79 1.00 2.14 2.65

Nov 5.68 6.27 7.44 8.09 0.59 1.76 2.41

Dec 3.25 3.12 4.03 5.94 -0.13 0.78 2.69

ECHAM5-RACMO2Mean (°C/day) DC Value (+°C/day)

DK1

DK2

DK3

DK4

DK5

DK6

Temperature

74 G E U S

ref near mid far near mid far

Jan 2.03 2.10 3.23 4.74 0.07 1.20 2.71

Feb 1.24 1.42 3.11 3.84 0.18 1.88 2.60

Mar 2.67 2.56 3.69 4.58 -0.11 1.02 1.91

Apr 5.71 5.93 6.57 7.64 0.22 0.86 1.94

May 9.63 10.19 10.72 11.60 0.55 1.08 1.97

Jun 13.35 13.78 14.37 15.27 0.44 1.02 1.92

Jul 16.15 16.39 17.05 17.82 0.23 0.89 1.67

Aug 16.31 16.78 17.71 18.30 0.47 1.40 1.99

Sep 14.33 14.77 15.85 16.47 0.44 1.52 2.14

Oct 10.92 11.68 12.64 13.31 0.76 1.72 2.39

Nov 6.96 7.83 8.95 9.56 0.87 1.99 2.60

Dec 4.06 4.21 5.33 6.84 0.15 1.27 2.78

Jan 2.32 2.52 3.65 5.13 0.20 1.33 2.81

Feb 1.54 1.79 3.36 4.12 0.26 1.83 2.59

Mar 2.78 2.64 3.88 4.73 -0.15 1.10 1.95

Apr 5.49 5.69 6.36 7.46 0.21 0.87 1.98

May 9.26 9.79 10.37 11.30 0.53 1.11 2.05

Jun 13.24 13.67 14.24 15.15 0.42 1.00 1.91

Jul 16.24 16.51 17.15 17.97 0.27 0.91 1.74

Aug 16.70 17.16 18.05 18.69 0.46 1.36 1.99

Sep 14.89 15.33 16.44 17.03 0.44 1.55 2.14

Oct 11.55 12.29 13.23 13.93 0.73 1.67 2.38

Nov 7.60 8.41 9.49 10.15 0.81 1.88 2.55

Dec 4.46 4.74 5.91 7.30 0.28 1.45 2.84

Jan 2.53 2.70 3.82 5.27 0.17 1.28 2.74

Feb 1.90 2.07 3.64 4.34 0.18 1.74 2.44

Mar 3.18 3.03 4.12 5.00 -0.15 0.94 1.82

Apr 5.97 6.16 6.73 7.82 0.20 0.76 1.85

May 9.64 10.22 10.73 11.62 0.57 1.09 1.97

Jun 13.43 13.77 14.34 15.22 0.34 0.90 1.79

Jul 16.13 16.37 17.01 17.72 0.24 0.88 1.60

Aug 16.39 16.81 17.70 18.27 0.41 1.31 1.88

Sep 14.47 14.93 16.00 16.60 0.45 1.53 2.12

Oct 11.25 11.99 12.94 13.61 0.74 1.68 2.35

Nov 7.49 8.29 9.36 9.98 0.80 1.87 2.49

Dec 4.57 4.79 5.91 7.30 0.22 1.34 2.73

Jan 2.36 2.55 3.62 5.04 0.19 1.26 2.68

Feb 2.10 2.22 3.84 4.40 0.12 1.74 2.30

Mar 3.72 3.52 4.45 5.29 -0.19 0.74 1.58

Apr 6.82 7.07 7.44 8.48 0.25 0.62 1.66

May 10.39 11.01 11.46 12.26 0.62 1.07 1.86

Jun 13.68 13.98 14.55 15.39 0.31 0.87 1.71

Jul 16.01 16.18 16.82 17.43 0.17 0.81 1.42

Aug 15.91 16.32 17.22 17.77 0.41 1.31 1.86

Sep 13.81 14.28 15.35 15.93 0.47 1.54 2.12

Oct 10.50 11.32 12.27 12.94 0.82 1.77 2.44

Nov 6.78 7.66 8.71 9.32 0.89 1.94 2.54

Dec 4.15 4.31 5.39 6.85 0.16 1.24 2.70

Jan 2.14 2.16 3.27 4.69 0.03 1.13 2.55

Feb 1.71 1.80 3.53 4.09 0.09 1.82 2.38

Mar 3.32 3.21 4.06 5.01 -0.10 0.75 1.70

Apr 6.47 6.74 7.14 8.21 0.27 0.67 1.74

May 10.06 10.72 11.16 11.96 0.66 1.11 1.90

Jun 13.29 13.62 14.24 15.05 0.33 0.94 1.76

Jul 15.63 15.77 16.45 17.04 0.15 0.82 1.41

Aug 15.49 15.85 16.75 17.30 0.36 1.26 1.81

Sep 13.36 13.78 14.82 15.45 0.42 1.46 2.09

Oct 10.05 10.84 11.81 12.49 0.79 1.76 2.45

Nov 6.37 7.23 8.37 8.90 0.86 2.00 2.53

Dec 3.89 3.94 4.96 6.52 0.05 1.07 2.63

Jan 2.59 2.60 3.72 5.05 0.01 1.13 2.47

Feb 1.99 2.01 3.76 4.36 0.03 1.77 2.37

Mar 3.29 3.21 4.11 5.12 -0.08 0.82 1.83

Apr 6.14 6.38 6.89 7.93 0.24 0.75 1.79

May 9.49 10.07 10.58 11.39 0.58 1.09 1.90

Jun 12.72 13.05 13.66 14.48 0.33 0.94 1.76

Jul 15.22 15.30 15.98 16.56 0.08 0.75 1.34

Aug 15.31 15.55 16.43 16.99 0.24 1.13 1.68

Sep 13.35 13.69 14.68 15.31 0.34 1.33 1.96

Oct 10.21 10.89 11.88 12.53 0.68 1.66 2.32

Nov 6.77 7.51 8.68 9.15 0.74 1.91 2.39

Dec 4.44 4.40 5.41 6.93 -0.04 0.98 2.49

ECHAM5-REMOMean (°C/day) DC Value (+°C/day)

DK1

DK2

DK3

DK4

DK5

DK6

Temperature

G E U S 75

ref near mid far near mid far

Jan 1.53 1.46 2.88 4.36 -0.07 1.34 2.82

Feb 1.29 1.37 3.16 3.84 0.08 1.87 2.54

Mar 3.10 2.95 3.96 4.96 -0.15 0.86 1.86

Apr 6.11 6.30 7.07 7.90 0.19 0.96 1.79

May 9.92 10.41 11.04 11.69 0.50 1.12 1.77

Jun 13.23 13.64 14.26 15.10 0.41 1.03 1.87

Jul 15.68 15.89 16.61 17.36 0.21 0.93 1.68

Aug 15.71 16.01 17.04 17.62 0.30 1.33 1.90

Sep 13.61 14.05 15.13 15.65 0.44 1.52 2.05

Oct 10.17 10.90 12.00 12.65 0.73 1.83 2.48

Nov 6.21 6.94 8.18 8.96 0.73 1.97 2.75

Dec 3.47 3.55 4.72 6.35 0.08 1.26 2.88

Jan 2.10 2.21 3.43 4.86 0.11 1.33 2.76

Feb 1.74 1.88 3.47 4.19 0.14 1.74 2.45

Mar 3.13 3.03 4.08 5.03 -0.10 0.95 1.90

Apr 5.69 5.85 6.66 7.54 0.16 0.97 1.85

May 9.27 9.72 10.40 11.14 0.45 1.13 1.88

Jun 12.88 13.31 13.90 14.79 0.43 1.01 1.90

Jul 15.67 15.91 16.57 17.42 0.24 0.90 1.75

Aug 16.07 16.38 17.36 17.97 0.31 1.29 1.90

Sep 14.14 14.59 15.68 16.21 0.45 1.54 2.07

Oct 10.81 11.49 12.57 13.24 0.68 1.76 2.42

Nov 6.96 7.56 8.74 9.57 0.61 1.79 2.61

Dec 3.95 4.18 5.48 6.91 0.23 1.53 2.96

Jan 2.08 2.14 3.33 4.80 0.06 1.25 2.72

Feb 1.82 1.85 3.46 4.15 0.04 1.65 2.34

Mar 3.19 3.03 4.04 5.02 -0.16 0.85 1.83

Apr 5.77 5.92 6.71 7.57 0.15 0.94 1.80

May 9.30 9.77 10.41 11.14 0.47 1.11 1.84

Jun 12.78 13.16 13.78 14.64 0.38 0.99 1.86

Jul 15.46 15.66 16.35 17.11 0.20 0.89 1.65

Aug 15.73 16.04 17.03 17.58 0.31 1.30 1.85

Sep 13.76 14.22 15.32 15.80 0.45 1.55 2.04

Oct 10.48 11.19 12.27 12.93 0.71 1.79 2.46

Nov 6.74 7.35 8.55 9.33 0.61 1.81 2.58

Dec 3.92 4.08 5.31 6.76 0.16 1.39 2.84

Jan 1.98 2.06 3.24 4.74 0.07 1.25 2.75

Feb 2.17 2.16 3.82 4.36 0.00 1.65 2.20

Mar 3.93 3.79 4.64 5.55 -0.14 0.71 1.61

Apr 6.88 7.05 7.73 8.46 0.17 0.85 1.58

May 10.30 10.82 11.36 11.95 0.52 1.06 1.65

Jun 13.20 13.54 14.15 14.97 0.35 0.95 1.77

Jul 15.38 15.54 16.23 16.94 0.15 0.85 1.56

Aug 15.28 15.58 16.59 17.13 0.30 1.31 1.85

Sep 13.21 13.65 14.78 15.23 0.45 1.57 2.02

Oct 9.91 10.69 11.77 12.44 0.78 1.86 2.53

Nov 6.27 6.92 8.08 8.91 0.65 1.81 2.64

Dec 3.66 3.75 4.97 6.49 0.10 1.31 2.83

Jan 1.55 1.41 2.73 4.26 -0.14 1.18 2.71

Feb 1.61 1.57 3.38 3.94 -0.04 1.77 2.33

Mar 3.58 3.37 4.22 5.22 -0.21 0.64 1.64

Apr 6.72 6.92 7.56 8.31 0.20 0.84 1.59

May 10.28 10.84 11.37 11.94 0.56 1.09 1.66

Jun 13.16 13.50 14.12 14.93 0.34 0.96 1.77

Jul 15.26 15.40 16.15 16.77 0.14 0.89 1.51

Aug 15.01 15.29 16.32 16.83 0.28 1.31 1.82

Sep 12.84 13.26 14.38 14.86 0.42 1.54 2.02

Oct 9.50 10.27 11.37 12.06 0.77 1.86 2.56

Nov 5.80 6.48 7.72 8.47 0.67 1.92 2.67

Dec 3.31 3.30 4.40 6.06 -0.01 1.08 2.74

Jan 1.33 1.02 2.48 4.06 -0.30 1.16 2.73

Feb 1.15 1.06 2.96 3.61 -0.09 1.81 2.45

Mar 3.10 2.87 3.80 4.86 -0.23 0.70 1.75

Apr 6.35 6.56 7.24 8.03 0.21 0.89 1.67

May 10.17 10.72 11.27 11.87 0.55 1.09 1.70

Jun 13.28 13.61 14.21 15.03 0.33 0.93 1.75

Jul 15.47 15.56 16.33 16.90 0.09 0.86 1.43

Aug 15.25 15.49 16.51 17.01 0.24 1.26 1.76

Sep 13.04 13.44 14.56 15.07 0.40 1.51 2.02

Oct 9.65 10.39 11.51 12.20 0.74 1.86 2.55

Nov 5.92 6.57 7.86 8.53 0.65 1.94 2.61

Dec 3.33 3.28 4.34 6.01 -0.05 1.01 2.68

ECHAM5-RCA3Mean (°C/day) DC Value (+°C/day)

DK1

DK2

DK3

DK4

DK5

DK6

Temperature

76 G E U S

ref near mid far near mid far

Jan 0.16 2.87 4.01 4.63 2.71 3.84 4.47

Feb 0.99 2.16 3.07 3.79 1.17 2.08 2.80

Mar 1.54 2.59 4.06 4.75 1.05 2.52 3.22

Apr 4.16 5.41 6.78 7.10 1.25 2.62 2.94

May 9.01 10.15 10.72 11.42 1.14 1.71 2.41

Jun 13.36 14.23 15.28 15.77 0.87 1.93 2.41

Jul 16.24 16.96 18.14 18.76 0.71 1.90 2.51

Aug 15.73 17.40 18.45 19.66 1.67 2.72 3.93

Sep 13.12 14.53 15.41 16.66 1.41 2.29 3.54

Oct 9.18 10.68 12.05 13.27 1.50 2.87 4.08

Nov 5.18 6.76 8.58 9.36 1.57 3.39 4.17

Dec 1.61 3.43 5.28 5.99 1.82 3.68 4.39

Jan 0.83 3.41 4.46 5.10 2.57 3.63 4.27

Feb 1.46 2.61 3.51 4.30 1.16 2.05 2.84

Mar 2.04 3.10 4.50 5.19 1.06 2.45 3.15

Apr 4.43 5.68 7.00 7.34 1.25 2.57 2.90

May 8.98 10.07 10.71 11.44 1.09 1.72 2.45

Jun 13.22 14.01 15.17 15.71 0.79 1.95 2.50

Jul 16.24 17.02 18.31 18.91 0.78 2.08 2.68

Aug 16.01 17.63 18.82 20.02 1.62 2.81 4.01

Sep 13.52 14.93 15.86 17.09 1.41 2.34 3.56

Oct 9.66 11.12 12.45 13.65 1.45 2.79 3.99

Nov 5.71 7.23 8.97 9.76 1.52 3.26 4.05

Dec 2.22 4.00 5.74 6.47 1.78 3.52 4.25

Jan 1.44 3.89 4.94 5.49 2.45 3.50 4.05

Feb 2.03 3.08 3.95 4.66 1.05 1.92 2.64

Mar 2.41 3.39 4.74 5.40 0.99 2.33 2.99

Apr 4.49 5.74 7.07 7.40 1.25 2.58 2.90

May 8.84 9.91 10.62 11.30 1.07 1.78 2.45

Jun 12.95 13.77 14.82 15.35 0.82 1.87 2.40

Jul 15.88 16.49 17.68 18.24 0.60 1.79 2.36

Aug 15.64 17.10 18.19 19.29 1.46 2.55 3.65

Sep 13.46 14.78 15.61 16.82 1.32 2.15 3.37

Oct 9.85 11.29 12.56 13.76 1.44 2.71 3.90

Nov 6.11 7.67 9.34 10.06 1.55 3.23 3.95

Dec 2.73 4.52 6.20 6.88 1.79 3.47 4.15

Jan 0.31 3.08 4.02 4.51 2.77 3.71 4.21

Feb 1.44 2.42 3.17 3.91 0.98 1.73 2.48

Mar 2.15 2.98 4.32 4.99 0.84 2.17 2.84

Apr 4.64 5.77 7.12 7.44 1.13 2.49 2.80

May 9.46 10.51 11.02 11.73 1.05 1.56 2.27

Jun 13.61 14.41 15.29 15.81 0.80 1.67 2.20

Jul 16.36 16.82 18.00 18.49 0.46 1.64 2.13

Aug 15.43 17.07 18.11 19.36 1.64 2.69 3.94

Sep 12.84 14.16 14.92 16.22 1.32 2.09 3.38

Oct 8.94 10.32 11.64 12.83 1.38 2.70 3.89

Nov 4.83 6.46 8.30 9.06 1.63 3.46 4.22

Dec 1.28 3.25 5.04 5.72 1.96 3.75 4.44

Jan -0.53 2.20 3.26 3.79 2.73 3.79 4.32

Feb 0.69 1.72 2.50 3.22 1.03 1.81 2.53

Mar 1.53 2.40 3.75 4.47 0.87 2.22 2.94

Apr 4.32 5.47 6.83 7.13 1.15 2.50 2.80

May 9.47 10.47 10.93 11.65 1.00 1.46 2.19

Jun 13.76 14.52 15.35 15.84 0.76 1.59 2.08

Jul 16.27 16.69 17.82 18.35 0.43 1.56 2.09

Aug 15.12 16.72 17.76 19.05 1.60 2.65 3.93

Sep 12.28 13.69 14.38 15.72 1.40 2.10 3.44

Oct 8.33 9.73 11.10 12.35 1.40 2.77 4.02

Nov 4.15 5.83 7.67 8.42 1.68 3.52 4.26

Dec 0.55 2.47 4.34 5.01 1.93 3.80 4.47

Jan -0.30 2.35 3.49 4.07 2.64 3.79 4.37

Feb 0.74 1.86 2.71 3.34 1.13 1.97 2.61

Mar 1.35 2.37 3.74 4.45 1.02 2.39 3.10

Apr 4.09 5.31 6.71 6.98 1.22 2.62 2.89

May 8.99 10.03 10.65 11.32 1.05 1.67 2.33

Jun 13.35 14.08 14.97 15.43 0.74 1.62 2.08

Jul 15.86 16.33 17.42 17.95 0.47 1.56 2.09

Aug 14.94 16.48 17.51 18.67 1.54 2.57 3.73

Sep 12.37 13.76 14.45 15.77 1.38 2.08 3.40

Oct 8.51 9.94 11.33 12.56 1.43 2.82 4.06

Nov 4.44 6.13 7.94 8.62 1.69 3.50 4.18

Dec 1.05 2.83 4.69 5.36 1.79 3.65 4.31

HadCM3-CLMMean (°C/day) DC Value (+°C/day)

DK1

DK2

DK3

DK4

DK5

DK6

Temperature

G E U S 77

ref near mid far near mid far

Jan -0.95 1.98 3.22 4.11 2.93 4.17 5.06

Feb 0.46 2.21 3.13 3.86 1.75 2.67 3.40

Mar 1.98 3.14 4.62 5.12 1.16 2.64 3.14

Apr 5.32 6.71 8.08 8.54 1.39 2.76 3.22

May 10.26 11.33 12.56 13.32 1.07 2.30 3.06

Jun 14.72 15.42 16.79 17.48 0.70 2.07 2.76

Jul 17.16 17.82 19.10 19.97 0.66 1.94 2.80

Aug 16.58 17.62 19.08 20.28 1.03 2.49 3.69

Sep 12.97 14.42 15.38 16.64 1.44 2.41 3.66

Oct 8.63 9.71 11.32 12.61 1.08 2.69 3.98

Nov 4.24 5.31 7.49 8.16 1.06 3.25 3.92

Dec 0.78 2.21 4.35 4.61 1.43 3.57 3.84

Jan 0.44 3.18 4.41 5.28 2.75 3.98 4.84

Feb 1.46 3.17 4.06 4.83 1.71 2.61 3.37

Mar 2.59 3.80 5.17 5.75 1.21 2.58 3.17

Apr 5.28 6.59 7.93 8.42 1.31 2.65 3.14

May 9.53 10.59 11.80 12.53 1.06 2.27 3.00

Jun 13.76 14.58 16.06 16.69 0.82 2.30 2.94

Jul 16.50 17.41 18.81 19.59 0.91 2.32 3.09

Aug 16.56 17.69 19.21 20.28 1.13 2.65 3.72

Sep 13.66 15.08 16.11 17.30 1.42 2.45 3.64

Oct 9.69 10.78 12.35 13.56 1.09 2.66 3.87

Nov 5.47 6.60 8.67 9.40 1.13 3.20 3.93

Dec 2.24 3.67 5.64 5.99 1.43 3.40 3.76

Jan 0.19 2.91 4.02 4.84 2.72 3.82 4.65

Feb 1.42 3.03 3.88 4.53 1.61 2.46 3.11

Mar 2.62 3.75 5.14 5.68 1.13 2.52 3.05

Apr 5.62 6.88 8.30 8.70 1.26 2.68 3.08

May 10.28 11.23 12.42 13.12 0.95 2.14 2.84

Jun 14.45 15.06 16.38 17.01 0.61 1.93 2.56

Jul 16.80 17.39 18.61 19.40 0.58 1.80 2.59

Aug 16.39 17.40 18.77 19.88 1.01 2.38 3.50

Sep 13.22 14.57 15.51 16.73 1.34 2.29 3.51

Oct 9.19 10.18 11.74 13.01 0.98 2.55 3.82

Nov 4.96 6.04 8.15 8.80 1.09 3.20 3.84

Dec 1.76 3.11 5.08 5.40 1.35 3.32 3.64

Jan -0.57 2.34 3.39 4.32 2.91 3.96 4.89

Feb 1.11 2.69 3.46 4.04 1.59 2.35 2.93

Mar 2.47 3.51 4.87 5.41 1.04 2.40 2.94

Apr 5.66 6.86 8.22 8.61 1.20 2.56 2.95

May 10.41 11.34 12.48 13.13 0.93 2.07 2.72

Jun 14.45 15.04 16.31 16.92 0.59 1.86 2.47

Jul 16.70 17.14 18.30 19.13 0.43 1.59 2.43

Aug 16.01 17.08 18.34 19.54 1.07 2.32 3.53

Sep 12.69 13.99 14.91 16.19 1.30 2.22 3.50

Oct 8.54 9.45 11.07 12.34 0.91 2.52 3.79

Nov 4.21 5.19 7.48 8.06 0.98 3.26 3.85

Dec 0.88 2.24 4.28 4.55 1.36 3.40 3.67

Jan -1.41 1.53 2.69 3.56 2.93 4.09 4.97

Feb 0.28 1.95 2.85 3.39 1.67 2.58 3.11

Mar 1.80 2.85 4.31 4.86 1.05 2.51 3.06

Apr 5.26 6.46 7.88 8.25 1.21 2.62 2.99

May 10.17 11.14 12.35 12.95 0.97 2.17 2.78

Jun 14.31 14.95 16.20 16.81 0.64 1.89 2.50

Jul 16.41 16.86 18.08 18.87 0.44 1.66 2.46

Aug 15.52 16.68 17.85 19.13 1.15 2.33 3.61

Sep 12.16 13.43 14.33 15.62 1.27 2.17 3.46

Oct 7.93 8.84 10.47 11.78 0.91 2.53 3.85

Nov 3.60 4.56 6.87 7.40 0.97 3.27 3.81

Dec 0.19 1.57 3.60 3.89 1.38 3.41 3.70

Jan -1.58 1.34 2.59 3.42 2.93 4.17 5.01

Feb 0.07 1.75 2.69 3.20 1.68 2.61 3.12

Mar 1.49 2.56 4.05 4.66 1.07 2.56 3.17

Apr 5.01 6.24 7.67 8.04 1.23 2.66 3.03

May 9.91 10.94 12.18 12.73 1.04 2.27 2.82

Jun 14.09 14.71 16.02 16.62 0.62 1.93 2.53

Jul 16.09 16.60 17.84 18.59 0.51 1.75 2.50

Aug 15.18 16.46 17.59 18.81 1.28 2.40 3.62

Sep 12.01 13.30 14.16 15.43 1.29 2.15 3.42

Oct 7.86 8.81 10.41 11.70 0.96 2.56 3.84

Nov 3.49 4.49 6.83 7.32 1.00 3.34 3.82

Dec 0.16 1.53 3.55 3.88 1.37 3.39 3.72

HadCM3-HadRM3Mean (°C/day) DC Value (+°C/day)

DK1

DK2

DK3

DK4

DK5

DK6

Temperature

78 G E U S

G E U S 79

Appendix 2: Paper I

Assessment of robustness and significance of climate change signals for

an ensemble of distribution-based scaled climate projections.

Seaby, L.P., Refsgaard, J.C., Sonnenborg, T.O., Stisen, S., Christensen, J.H., and Jensen, K.H.,

(2013). Journal of Hydrology 486, 479-493, doi: 10.1016/j.jhydrol.2013.02.015.

G E U S 97

Appendix 3: Paper II

Spatial uncertainty in bias corrected climate change projections and

hydrogeological impacts.

Seaby, L.P., Refsgaard, J.C., Sonnenborg, T.O., Højberg, A.L., (submitted). Hydrol. Earth Syst. Sci.

G E U S 131

Appendix 4: Technical Note

Climate model and bias correction uncertainty in hydrological modelling

of projected future conditions for Denmark.

Seaby, L.P., (2013)

132 G E U S

G E U S 133

Climate model and bias correction uncertainty in hydrological mod-

elling of projected future conditions for Denmark

Seaby, L.P. (2013)

Geological Survey of Denmark and Greenland, Øster Voldgade 10, 1350 Copenhagen K, Denmark

1. Introduction

There are multiple sources of uncertainty associated with the use of future climate change projec-

tions in hydrological change modelling methodologies. For example, uncertainty in how anthropo-

genic emissions will evolve is inherent with climate model projections, where “true values” cannot be

known. There exists a prediction uncertainty with all climate and hydrological models from e.g. cali-

bration and process representation, but in addition, there is uncertainty in how the crucial controls,

feedbacks, and physical relationships will hold under future conditions. Different climate models

often display different characteristics in terms of initial bias and future change signals, yet it is not

possible to determine which projections are most probable of future conditions. Some work has

been done on weighting climate models in terms of their performance in a reference period (e.g.

Christensen et al., 2010), but the ability of a climate model to replicate historical conditions is not

indicative of its ability to simulate future changes.

Regional climate models (RCMs) represent the dynamical downscaling of the parent global circula-

tion model (GCM) to a regional scale (e.g. 25 km), but their outputs are still subject to systematic

errors and biases (e.g. Fowler et al., 2007) and require additional bias correction to prepare them for

hydrological forcing (e.g. Sharma et al., 2007). Compared to temperature, which exhibits smooth

temporal and spatial characteristics, precipitation regimes are temporally and spatially variable, and

sensitive to local controls on climate. Therefore, precipitation may require more complex bias cor-

rection methods to a) remove initial climate model bias and b) retain the projected regime character-

istics. A variety of bias correction methods have been developed to estimate or scale future climate

variables. Methodological choices, like which climate model(s) and/or bias correction method(s) to

use, generate uncertainty which can be quantified. Uncertainty in choosing one over another can be

measured by

Déqué et al., 2007).

134 G E U S

Along with projects such as ENSEMBLES (van der Linden and Mitchell, 2009), which paired multiple

GCMs with multiple RCMs, there are now numerous RCM projections of future climate change over

common areas, making it possible (and recommended) to incorporate multiple climate models into

hydrological impacts studies. Using multiple RCM projections account for climate model uncertainty

by introducing uncertainty (spread), likewise, the subsequent application of multiple bias correction

methods accounts for these uncertainties by introducing variance in ensemble responses. Uncertain-

ty in bias correction methods are nested in climate model uncertainty, therefore, they are not inde-

pendent from one another.

In this study, hydrological change simulations are made in the reference period 1991-2010 and the

far future period 2071-2100 forced with combinations of 11 climate models with four bias correction

methods in a Danish catchment. Uncertainty is considered in terms of spread across precipitation

inputs and across hydrological outputs. The objective is to quantify the amount of uncertainty which

can be attributed to choice of climate model and choice of bias correction to overall uncertainty.

2. Data and Methods

2.1 DK-Model

The Danish National Water Resources Model (DK-model) was finalised in 2003 by the Geological Sur-

vey of Denmark and Greenland (GEUS) for the purpose of assessing the exploitable groundwater

resources for Denmark (Henriksen et al., 2003; Henriksen et al., 2008). It is set up in the physically

based and fully distributed terrestrial hydrological modelling system MIKE SHE/MIKE 11 (Abbott et

al., 1986; Graham and Butts, 2005). The most current description of the DK-model can be found in

Højberg et al., (2013) and details on calibration in Stisen et al., (2012). The DK-model divides Den-

mark (approx. 43,000 km2) into seven hydrologically distinct model domains (DK1-7). This study is

focused in DK1 (7,163 km2), a single model domain over the island Sjælland (Figure 1). Stream dis-

charge (mm/day) is analysed from catchments of various size (54 – 611 km2) and covering different

parts of the DK1-model domain (Figure 1). Daily groundwater head values (meters depth to top of

head) are output and averaged across the entire DK1 domain, focused on the uppermost sand aqui-

fer (DK-model layer 9) at 1-5 meters depth, and the deeper regional sand aquifer (DK-model layer 3)

at 30-50 meters depth, where the exploitable water resource is found.

G E U S 135

2.2 Climate Data

From the ENSEMBLES Project, 11 GCM/RCM projections are selected, all of which are transient simu-

lations (1951-2071) at the 25 km grid scale and forced with the A1B (i.e. moderate) emissions scenar-

io (IPCC, 2007). The resultant subset of the ENSEMBLES matrix is depicted in Table 1, comprised of

four GCMs and eight RCMs from the institu-

tions listed in Table 2. Observational climate

data used for training or perturbing the sub-

sequent bias correction methods is provided

by the Danish Meteorological Institute (DMI)

for the 20 year reference period 1991-2010

(Scharling, 2000) at the 10km grid scale, with

105 grids covering DK1 (Figure 1). Four bias

correction methods are applied to the 11

climate models, totalling 44 climate forcings

for the far future period 2071-2100.

GCM

RCM

HadRM3 X

REMO X

RM5.1 X

HIRHAM5 X X X

CLM X

RACMO2 X

RegCM3 X

RCA3 X X

HadCM3 ECHAM5 ARPEGE BCM2

Table 1: Matrix of ENSEMBLES climate models shown as GCM–RCM pairings.

l 1

Figure 1: The study area is DK-model domain 1 (DK1) covering the island Sjælland (7,163 km

2) with the five catchments areas and

discharge stations shown along with the 10 km DMI grid relevant to the climate data.

136 G E U S

The four bias correction methods have different approaches in terms of direct and indirect use of

RCM outputs, spatial scale of application, number of free parameters, and thus amount of complexi-

ty. The first and most simplistic method applied, delta change (DC), is an indirect method that con-

sists of altering an observed (reference) climate series with change factors to obtain a new series

representative of future conditions (Graham et al., 2007). The present DC approach is outlined in

Seaby et al. (2013b). The other three methods are variations of a distribution-based scaling (DBS)

approach, a direct method in which gamma probability distributions are fitted to observational and

RCM daily precipitation data, and RCM precipitation is then scaled such that the statistical distribu-

tion of observed precipitation in the reference period is preserved (Piani et al., 2010; Yang et al.,

2010). The DBS approaches are all carried out on the same temporal scale (seasonal distributions,

daily scaling) but differ in their spatial scales of application, ranging from the domain scale (DBS-

domain) to the 10 km grid scale (DBS-grid), with an intermediate approach using domain-wide scaling

and grid scale bias removal (DBS-spatial). The development and application of these DBS methods

are fully explained in Seaby et al., (2013a).

2.3 Variance Contribution

The contribution of choice of climate model and choice of bias correction method to overall uncer-

tainty is quantified for precipitation inputs and hydrological outputs following the variance decompo-

sition method from Déqué et al., (2007). The amount of variability attributed to different climate

models and bias correction methods is quantified as percentages that explain the variance in spread

Table 2: Climate models from the ENSEMBLES project for which projections have been used in the present study.

G E U S 137

across a response. For a given variable response (X), in this case precipitation, discharge, and

groundwater head, the individual part of variance due to RCM (R) and bias corrected (B) is calculated

as follows:

where is the average response considering the indices i = 1 - 11 according (R) and j = 1 – 4 accord-

ing to (B). The joint variance contribution (RB) is found by calculating all possible combinations of

variance from R and B as follows:

The terms R and B can be explained as percentages of total variance as follows:

where the sum of these terms is less than 100%. Finally, variance terms (V) are calculated which

incorporate the interaction term RB as follows:

where the sum of V(R) and V(B) does not equal 100%, but the magnitude of each term represents the

contribution of each source in the overall uncertainty (Déqué et al., 2007).

3. Results and Discussion

For precipitation inputs, total annual and 99th percentile daily precipitation values in the far future

period 2071-2100 are reported Table 3. Total annual values reflect the temporal average of 30 annu-

al values per grid in DK1, then the spatial average of the 105 grid values in DK1. The 99th percentile

precipitation values reflect the probability from daily accumulated values over the 30 year period,

and then the spatial average of the 105 grid values in DK1. Mean and standard deviation across the

ensemble of precipitation values from multiple climate models and bias correction methods are also

reported in Table 3.

138 G E U S

For the far future period, the ensemble average total annual precipitation is 803 mm/year, an overall

increase from the ensemble average of 752 mm/year in the reference period. Some RCMs project an

opposite signal (i.e. 626 mm/year) or a stronger wet signal (i.e. 945 mm/year) (Table 3a). The en-

semble average for 99th percentile precipitation is 21.81 mm/day in the far future period, a slight

increase from the ensemble average of 20.11 mm/day in the reference period. There is spread across

the ensemble, with opposite signals (i.e. 16.63 mm/day) and higher intensity signals (i.e. 26.64

mm/day).

a) Total Annual Precip.

DK1 mm/yr

ARPEGE-

RM5.1

ARPEGE-

HIRHAM5

BCM2-

HIRHAM5

BCM2-

RCA3

ECHAM5-

HIRHAM5

ECHAM5-

RegCM3

ECHAM5-

RACMO2

ECHAM5-

REMO

ECHAM5-

RCA3

HadCM3-

CLM

HadCM3-

HadRM3

Mean

Climate

Model

Std. Dev.

Climate

Model

DC 655 625 855 867 904 794 835 788 822 825 769 794 81

DBS-domain 662 631 861 883 941 829 868 807 831 833 776 811 88

DBS-spatial 663 629 859 888 945 831 863 811 835 833 776 812 89

DBS-grid 662 627 858 875 780 831 868 809 832 833 777 796 78

Mean Bias Correction 661 628 858 878 893 821 858 803 830 831 775 803

Std. Dev Bias Correction 3 2 2 8 67 15 14 9 5 4 3

b) 99th Percentile

Precip. DK1 mm/day

ARPEGE-

RM51

ARPEGE-

HIRHAM5

BCM2-

HIRHAM5

BCM2-

RCA3

ECHAM5-

HIRHAM5

ECHAM5-

RegCM3

ECHAM5-

RACMO2

ECHAM5-

REMO

ECHAM5-

RCA3

HadCM3-

CLM

HadCM3-

HadRM3

Mean

Climate

Model

Std. Dev.

Climate

Model

DC 17.3 16.6 22.6 22.8 23.7 20.7 21.8 20.6 21.3 21.7 20.3 20.9 2.1

DBS-domain 19.7 18.5 22.2 22.2 24.0 23.4 24.5 22.8 22.4 21.8 20.7 22.0 1.7

DBS-spatial 20.1 18.8 22.6 22.4 24.3 23.4 24.4 22.9 22.6 21.8 20.8 22.2 1.6

DBS-grid 19.8 18.6 22.6 22.3 23.9 23.5 24.6 23.2 22.5 21.9 20.8 22.2 1.7

Mean Bias Correction 19.2 18.1 22.5 22.4 24.0 22.7 23.8 22.4 22.2 21.8 20.7 21.8

Std. Dev Bias Correction 1.1 0.9 0.2 0.2 0.2 1.2 1.2 1.0 0.5 0.1 0.2

Choice of RCM contributes almost all of the uncertainty across precipitation inputs, explaining 99% of

uncertainty in total annual precipitation and 91.5% in 99th percentile precipitation (Table 7a). Choice

of bias correction method contributes far less to overall uncertainty, contributing to 6.6% of variance

in total annual values. However, for 99th percentile precipitation, the choice of bias correction meth-

od contributes to 16.1% of overall uncertainty. This result is to be expected, as the bias correction

methods considered are differently suited at characterising high intensity precipitation, whereas all

methods are similarly trained on mean values and thus total annual values.

Under hydrological change simulations in the far future period 2071-2100, the ensemble average

total annual discharge from the five catchments selected in DK1 range between 187 – 240 mm/year,

99th percentile discharge ranges between 1.16 – 1.72 mm/day, and 1st percentile discharge ranges

between 0.03 – 0.06 reflecting base flow conditions. All ensemble values, including mean and stand-

ard deviations, are reported in Table 4a-c. Table 7b reports all the variance terms V(B) and V(R) for

Table 3: Spread across precipitation inputs used to force hydrological change simulations in the far future period 2071-2100: a) total annual precipitation and b) 99

th percentile, with mean and standard deviation across the en-

semble of climate models and bias correction methods reported.

G E U S 139

each catchment. Averaged across all catchments, and similar to the effect on precipitation inputs,

choice of RCM contributes to almost all of the variance in stream discharge: 97.2% in total annual,

96.9% in 99th percentile, and 92.8% in 1st percentile. Choice of bias correction method contributes to

9.5% of uncertainty in total annual discharge, and just 10.6% in 99th percentile discharge. The im-

portance of bias correction methods in upper percentile precipitation is apparently filtered by the

hydrological model, such that less contribution to variance is seen in 99th percentile discharge. Inter-

estingly, 14.6% of variance in base flows can be attributed to bias correction methods. This could

reflect the difference between methods in determining the number of dry days, where DC by design

retains the same frequency of dry days as seen in the observed values, and the DBS methods are able

to adjust the number and distribution of dry days.

Ensemble spread across groundwater head values in the far future period 2071-2100 is reported for

the uppermost layer 9 (1-5 meters depth) in Table 5a-c and for the deeper layer 3 (30-50 meters

depth) in Table 6a-c. Groundwater head values are found by first calculating the temporal

mean/min/max value per grid over the 30 year period, then taking the spatial average across the

grids in DK1. Mean and standard deviation of hydrological outputs across the ensemble of climate

models and bias correction methods are also reported (Table 6). In layer 9, the average ensemble

mean head depth for the entire period is 17.24 m and in layer 3 the ensemble mean head depth is

12.05 m, with both layers seeing spread of approximately +/- 1 m in the ensemble. Choice of RCM

contributes to nearly all (99%) of overall variance in mean head values in both layers 9 and 3, and

choice of bias correction method contributes to just 2% (Table 7c). In the deeper layer 3, the same

effect is seen on minimum and maximum groundwater heads in terms of RCM variance contribution.

Choice of bias correction method contributes slightly more (3.1%) to variance in maximum head val-

ues, and less than 1% in minimum head values. In the uppermost layer, which should respond more

directly to precipitation inputs, bias correction methods contribute significantly more to overall vari-

ance: 12.2% in maximum heads and 15.6% in minimum heads.

In this uncertainty analysis, overall variance in precipitation inputs and hydrological outputs is ex-

plained by decomposing the variance and quantifying the contribution of different sources. Different

RCMs by far create the most uncertainty and therefore explain the majority of variance throughout

the hydrological change modelling methodology. In the present ensemble, choice of bias correction

method does not contribute as much variance as choice of RCM. While bias corrected methods are

both critical for and highly variable in removing initial RCM bias (e.g. Seaby et al., 2013a), this analysis

suggests bias correction methods do not impact climate change projections at the regional scale. In

140 G E U S

other words, the choice of RCM dominates variance in the climate change signals, and the subse-

quent bias correction methods do not change these projections. If the RCMs were in tighter agree-

ment in terms of direction and strength of their change signals, it is likely that choice of bias correc-

tion method would contribute more to overall variance, and therefore become a more important

source of uncertainty. The higher contribution of bias correction method to uncertainty in ground-

water heads in the top layer 9 as opposed to the discharge data suggests that it might matter more

at smaller spatial scales. If variance decomposition analyses were done at single grids instead of

averaging results from DK1 (105 grids), more uncertainty contribution from bias correction methods

might have been seen.

Table 4: Mean annual discharge for the five selected catchments in DK1 under hydrological change

simulations in the far future period 2071-2100 for a) mean annual discharge, b) 99th percentile dis-

charge, and c) 1st percentile discharge.

a) Mean Annual Discharge

per Station

ARPEGE-

RM51

ARPEGE-

HIRHAM5

BCM2-

HIRHAM5

BCM2-

RCA3

ECHAM5-

HIRHAM5

ECHAM5-

RegCM3

ECHAM5-

RACMO2

ECHAM5-

REMO

ECHAM5-

RCA3

HadCM3-

CLM

HadCM3-

HadRM3

Mean

climate

Model

Std. Dev.

Climate

Model

DC 52.08 81 82 229 263 306 187 224 198 235 223 127 196 72

DBS-basin 52.08 105 49 134 219 231 197 222 157 195 292 141 176 68

DBS-spatial 52.08 91 86 200 265 332 210 242 195 226 251 143 204 74

DBS-grid 52.08 93 83 195 260 315 211 246 194 222 251 144 201 71

Mean Climate Models 92 75 190 252 296 201 233 186 220 254 139 194

Std. Dev. Climate Models 10 17 40 22 45 11 12 19 17 29 8

DC 55.01 110 114 253 292 333 222 257 233 271 259 157 227 72

DBS-basin 55.01 154 57 152 306 259 240 298 148 279 289 167 214 82

DBS-spatial 55.01 138 109 214 293 355 265 280 246 272 291 162 239 75

DBS-grid 55.01 135 101 208 292 337 263 281 234 269 285 161 233 74

Mean Climate Models 134 95 207 296 321 248 279 215 273 281 162 228

Std. Dev. Climate Models 18 26 41 7 42 21 17 45 4 15 4

DC 57.04 121 127 269 309 351 238 274 249 286 274 174 243 73

DBS-basin 57.04 148 67 186 271 280 251 326 150 258 283 183 218 77

DBS-spatial 57.04 144 112 223 314 351 283 306 277 291 291 173 251 77

DBS-grid 57.04 142 106 215 308 340 281 307 262 285 284 173 246 76

Mean Climate Models 139 103 223 300 330 263 303 234 280 283 176 240

Std. Dev. Climate Models 12 26 34 20 34 22 22 58 15 7 5

DC 59.01 114 121 256 296 336 224 260 237 272 260 166 231 70

DBS-basin 59.01 129 122 250 242 350 222 285 159 233 223 159 216 69

DBS-spatial 59.01 132 116 224 302 340 265 301 270 278 252 167 241 73

DBS-grid 59.01 137 117 225 297 341 263 309 262 274 251 168 240 72

Mean Climate Models 128 119 239 284 342 243 289 232 264 246 165 232

Std. Dev. Climate Models 10 3 17 28 6 24 22 50 21 16 4

DC 60.03 93 97 208 241 275 183 214 193 223 214 134 189 58

DBS-basin 60.03 76 54 144 191 205 183 260 183 188 202 140 166 59

DBS-spatial 60.03 119 87 178 244 266 217 250 234 228 214 132 197 60

DBS-grid 60.03 118 86 173 240 256 218 254 227 225 214 134 195 58

Mean Climate Models 101 81 176 229 251 200 244 209 216 211 135 187

Std. Dev. Climate Models 21 19 27 25 31 20 21 25 19 6 4

G E U S 141

b) 99th Percentile

Discharge per Station

ARPEGE-

RM51

ARPEGE-

HIRHAM5

BCM2-

HIRHAM5

BCM2-

RCA3

ECHAM5-

HIRHAM5

ECHAM5-

RegCM3

ECHAM5-

RACMO2

ECHAM5-

REMO

ECHAM5-

RCA3

HadCM3-

CLM

HadCM3-

HadRM3

Mean

climate

Model

Std. Dev.

Climate

Model

DC 52.08 0.53 0.53 1.45 1.71 2.03 1.28 1.52 1.33 1.56 1.52 0.85 1.30 0.48

DBS-basin 52.08 0.70 0.33 0.84 1.43 1.48 1.39 1.47 1.06 1.31 1.95 0.96 1.17 0.45

DBS-spatial 52.08 0.61 0.58 1.25 1.68 2.03 1.46 1.58 1.31 1.48 1.68 0.97 1.33 0.45

DBS-grid 52.08 0.63 0.56 1.20 1.61 1.95 1.46 1.60 1.30 1.45 1.66 0.97 1.31 0.44

Mean Climate Models 0.62 0.50 1.18 1.61 1.87 1.40 1.54 1.25 1.45 1.70 0.94 1.28

Std. Dev. Climate Models 0.07 0.11 0.25 0.13 0.27 0.09 0.06 0.13 0.11 0.18 0.06

DC 55.01 0.80 0.83 1.75 2.06 2.41 1.66 1.90 1.70 1.96 1.93 1.15 1.65 0.51

DBS-basin 55.01 1.07 0.39 0.96 2.08 1.77 1.74 2.09 1.09 1.95 2.07 1.20 1.49 0.57

DBS-spatial 55.01 0.96 0.79 1.37 1.98 2.37 1.96 1.99 1.83 1.94 2.10 1.17 1.68 0.52

DBS-grid 55.01 0.95 0.73 1.32 1.93 2.26 1.94 2.00 1.72 1.92 2.07 1.17 1.64 0.51

Mean Climate Models 0.95 0.69 1.35 2.01 2.20 1.82 2.00 1.59 1.94 2.04 1.17 1.61

Std. Dev. Climate Models 0.11 0.20 0.32 0.07 0.30 0.15 0.07 0.33 0.02 0.08 0.02

DC 57.04 0.82 0.86 1.83 2.12 2.45 1.70 1.94 1.74 2.00 1.97 1.21 1.69 0.52

DBS-basin 57.04 0.99 0.42 1.14 1.76 1.89 1.75 2.21 1.01 1.70 1.95 1.24 1.46 0.54

DBS-spatial 57.04 0.97 0.75 1.39 2.07 2.30 2.00 2.10 2.01 1.97 2.03 1.18 1.71 0.53

DBS-grid 57.04 0.96 0.71 1.33 2.03 2.23 1.98 2.11 1.89 1.95 1.99 1.18 1.67 0.53

Mean Climate Models 0.93 0.68 1.42 1.99 2.22 1.86 2.09 1.66 1.91 1.99 1.20 1.63

Std. Dev. Climate Models 0.08 0.19 0.29 0.16 0.24 0.16 0.11 0.45 0.14 0.04 0.03

DC 59.01 0.76 0.80 1.91 2.24 2.53 1.72 2.01 1.77 2.08 2.03 1.20 1.73 0.58

DBS-basin 59.01 0.86 0.89 1.75 1.69 2.56 1.68 2.17 1.19 1.66 1.66 1.14 1.57 0.52

DBS-spatial 59.01 0.88 0.81 1.54 2.20 2.48 2.08 2.31 2.09 2.07 1.94 1.22 1.78 0.58

DBS-grid 59.01 0.92 0.81 1.56 2.18 2.48 2.11 2.36 2.02 2.04 1.94 1.23 1.79 0.57

Mean Climate Models 0.86 0.83 1.69 2.08 2.51 1.90 2.21 1.77 1.96 1.89 1.19 1.72

Std. Dev. Climate Models 0.07 0.04 0.17 0.26 0.04 0.23 0.16 0.41 0.20 0.16 0.04

DC 60.03 0.57 0.59 1.26 1.48 1.76 1.14 1.33 1.17 1.38 1.35 0.81 1.17 0.37

DBS-basin 60.03 0.47 0.35 0.81 1.10 1.27 1.17 1.68 1.12 1.11 1.27 0.85 1.02 0.38

DBS-spatial 60.03 0.71 0.55 1.01 1.47 1.67 1.40 1.63 1.52 1.41 1.37 0.80 1.23 0.39

DBS-grid 60.03 0.71 0.54 0.98 1.46 1.58 1.40 1.66 1.46 1.38 1.38 0.81 1.22 0.38

Mean Climate Models 0.62 0.51 1.01 1.38 1.57 1.28 1.58 1.32 1.32 1.34 0.82 1.16

Std. Dev. Climate Models 0.12 0.11 0.18 0.18 0.21 0.14 0.17 0.20 0.14 0.05 0.02

c) 1st Percentile Discharge

per Station

ARPEGE-

RM51

ARPEGE-

HIRHAM5

BCM2-

HIRHAM5

BCM2-

RCA3

ECHAM5-

HIRHAM5

ECHAM5-

RegCM3

ECHAM5-

RACMO2

ECHAM5-

REMO

ECHAM5-

RCA3

HadCM3-

CLM

HadCM3-

HadRM3

Mean

climate

Model

Std. Dev.

Climate

Model

DC 52.08 0.01 0.01 0.04 0.05 0.05 0.03 0.04 0.03 0.04 0.04 0.02 0.03 0.02

DBS-basin 52.08 0.01 0.01 0.01 0.02 0.04 0.02 0.02 0.02 0.03 0.03 0.01 0.02 0.01

DBS-spatial 52.08 0.01 0.01 0.02 0.03 0.05 0.02 0.02 0.02 0.03 0.02 0.01 0.02 0.01

DBS-grid 52.08 0.01 0.01 0.02 0.03 0.05 0.02 0.02 0.02 0.03 0.02 0.01 0.02 0.01

Mean Climate Models 0.01 0.01 0.02 0.04 0.05 0.02 0.02 0.03 0.03 0.03 0.01 0.03

Std. Dev. Climate Models 0.00 0.00 0.01 0.01 0.01 0.00 0.01 0.01 0.01 0.01 0.00

DC 55.01 0.03 0.03 0.03 0.04 0.04 0.03 0.03 0.03 0.04 0.03 0.03 0.03 0.00

DBS-basin 55.01 0.03 0.02 0.03 0.04 0.04 0.03 0.03 0.03 0.04 0.03 0.03 0.03 0.01

DBS-spatial 55.01 0.02 0.03 0.03 0.04 0.05 0.03 0.03 0.03 0.04 0.03 0.03 0.03 0.01

DBS-grid 55.01 0.02 0.03 0.03 0.04 0.05 0.03 0.03 0.03 0.04 0.03 0.03 0.03 0.01

Mean Climate Models 0.02 0.02 0.03 0.04 0.04 0.03 0.03 0.03 0.04 0.03 0.03 0.03

Std. Dev. Climate Models 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

DC 57.04 0.04 0.04 0.07 0.08 0.08 0.06 0.07 0.07 0.07 0.07 0.05 0.06 0.01

DBS-basin 57.04 0.04 0.04 0.05 0.07 0.07 0.06 0.06 0.05 0.07 0.06 0.05 0.06 0.01

DBS-spatial 57.04 0.04 0.05 0.06 0.07 0.08 0.06 0.06 0.06 0.07 0.06 0.04 0.06 0.01

DBS-grid 57.04 0.04 0.04 0.06 0.07 0.08 0.06 0.06 0.06 0.07 0.06 0.04 0.06 0.01

Mean Climate Models 0.04 0.04 0.06 0.07 0.08 0.06 0.06 0.06 0.07 0.06 0.05 0.06

Std. Dev. Climate Models 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.00

DC 59.01 0.03 0.03 0.06 0.06 0.06 0.04 0.05 0.05 0.05 0.05 0.04 0.05 0.01

DBS-basin 59.01 0.03 0.04 0.05 0.05 0.06 0.04 0.04 0.04 0.05 0.04 0.03 0.04 0.01

DBS-spatial 59.01 0.03 0.04 0.04 0.06 0.06 0.04 0.04 0.05 0.06 0.04 0.03 0.04 0.01

DBS-grid 59.01 0.03 0.04 0.04 0.06 0.06 0.04 0.04 0.05 0.06 0.04 0.03 0.04 0.01

Mean Climate Models 0.03 0.04 0.05 0.06 0.06 0.04 0.04 0.05 0.06 0.04 0.03 0.04

Std. Dev. Climate Models 0.00 0.00 0.01 0.00 0.00 0.00 0.01 0.01 0.00 0.01 0.00

DC 60.03 0.03 0.03 0.05 0.06 0.06 0.04 0.05 0.05 0.05 0.05 0.03 0.04 0.01

DBS-basin 60.03 0.02 0.02 0.03 0.05 0.04 0.03 0.03 0.04 0.05 0.03 0.03 0.03 0.01

DBS-spatial 60.03 0.03 0.02 0.03 0.06 0.06 0.03 0.03 0.05 0.06 0.03 0.03 0.04 0.01

DBS-grid 60.03 0.03 0.02 0.03 0.06 0.06 0.03 0.03 0.05 0.06 0.03 0.03 0.04 0.01

Mean Climate Models 0.02 0.02 0.04 0.06 0.06 0.03 0.04 0.04 0.06 0.04 0.03 0.04

Std. Dev. Climate Models 0.00 0.00 0.01 0.00 0.01 0.00 0.01 0.00 0.00 0.01 0.00

142 G E U S

Table 5: Spread across groundwater head values in the far future period 2071-2100. For the upper-most DK-model Layer 9 (1-5 meters depth). Show is a) maximum, b) mean, and c) minimum values, found by first calculating the temporal mean/min/max value per grid over the 30 year period, then taking the spatial average across the grids in DK1. Mean and standard deviation across the ensemble of climate models and bias correction methods reported.

a) Max GW Head DK1

Layer 9 (m)

ARPEGE-

RM51

ARPEGE-

HIRHAM5

BCM2-

HIRHAM5

BCM2-

RCA3

ECHAM5-

HIRHAM5

ECHAM5-

RegCM3

ECHAM5-

RACMO2

ECHAM5-

REMO

ECHAM5-

RCA3

HadCM3-

CLM

HadCM3-

HadRM3

Mean

Climate

Model

Std. Dev.

Climate

Model

DC 104.83 105.22 108.91 109.43 109.80 108.85 109.23 108.76 109.16 109.24 108.19 108.33 1.61

DBS-domain 104.44 102.46 108.42 108.55 108.18 107.89 109.39 105.91 107.74 108.50 106.53 107.09 1.99

DBS-spatial 105.91 103.58 108.68 109.81 108.87 108.63 109.24 108.59 108.66 108.76 105.93 107.88 1.81

DBS-grid 105.83 103.44 108.52 109.64 108.80 108.59 109.27 108.30 108.48 108.62 105.92 107.77 1.80

Mean Bias Correction 105.25 103.68 108.63 109.36 108.92 108.49 109.29 107.89 108.51 108.78 106.64 107.77

Std. Dev Bias Correction 0.63 0.99 0.18 0.49 0.58 0.36 0.06 1.15 0.51 0.28 0.93

b) Mean GW Head DK1

Layer 9 (m)

ARPEGE-

RM51

ARPEGE-

HIRHAM5

BCM2-

HIRHAM5

BCM2-

RCA3

ECHAM5-

HIRHAM5

ECHAM5-

RegCM3

ECHAM5-

RACMO2

ECHAM5-

REMO

ECHAM5-

RCA3

HadCM3-

CLM

HadCM3-

HadRM3

Mean

Climate

Model

Std. Dev.

Climate

Model

DC 16.39 16.42 17.42 17.57 17.71 17.27 17.43 17.31 17.48 17.43 16.86 17.21 0.43

DBS-domain 16.60 16.21 17.18 17.55 17.66 17.41 17.58 17.23 17.47 17.50 16.94 17.21 0.43

DBS-spatial 16.58 16.48 17.31 17.58 17.78 17.45 17.56 17.40 17.50 17.51 16.95 17.28 0.40

DBS-grid 16.59 16.46 17.30 17.56 17.76 17.45 17.58 17.39 17.48 17.51 16.95 17.28 0.40

Mean Bias Correction 16.54 16.39 17.30 17.56 17.73 17.40 17.53 17.33 17.48 17.49 16.93 17.24

Std. Dev Bias Correction 0.09 0.11 0.08 0.01 0.05 0.08 0.06 0.07 0.01 0.03 0.04

c) Min GW Head DK1

Layer 9 (m)

ARPEGE-

RM51

ARPEGE-

HIRHAM5

BCM2-

HIRHAM5

BCM2-

RCA3

ECHAM5-

HIRHAM5

ECHAM5-

RegCM3

ECHAM5-

RACMO2

ECHAM5-

REMO

ECHAM5-

RCA3

HadCM3-

CLM

HadCM3-

HadRM3

Mean

Climate

Model

Std. Dev.

Climate

Model

DC 8.38 7.79 7.71 7.72 7.70 7.79 7.76 7.74 7.74 7.75 7.93 7.82 0.19

DBS-domain 8.38 7.75 7.68 7.70 7.59 8.19 7.79 7.79 7.68 7.79 7.87 7.84 0.23

DBS-spatial 8.38 7.78 7.69 7.71 7.60 7.79 7.79 7.77 7.71 7.79 8.38 7.86 0.25

DBS-grid 8.38 7.79 7.70 7.72 7.61 7.79 7.79 7.77 7.73 7.79 8.38 7.86 0.25

Mean Bias Correction 8.38 7.78 7.70 7.71 7.63 7.89 7.78 7.77 7.72 7.78 8.14 7.84

Std. Dev Bias Correction 0.00 0.02 0.01 0.01 0.05 0.17 0.01 0.02 0.02 0.02 0.24

G E U S 143

Table 6: Spread across groundwater head values in the far future period 2071-2100. For the deeper sand aquifer in DK-model Layer 3 (30-50 meters depth). Show is a) maximum, b) mean, and c) mini-mum values, found by first calculating the temporal mean/min/max value per grid over the 30 year period, then taking the spatial average across the grids in DK1. Mean and standard deviation across the ensemble of climate models and bias correction methods reported.

a) Max GW Head DK1

Layer 3 (m)

ARPEGE-

RM51

ARPEGE-

HIRHAM5

BCM2-

HIRHAM5

BCM2-

RCA3

ECHAM5-

HIRHAM5

ECHAM5-

RegCM3

ECHAM5-

RACMO2

ECHAM5-

REMO

ECHAM5-

RCA3

HadCM3-

CLM

HadCM3-

HadRM3

Mean

Climate

Model

Std. Dev.

Climate

Model

DC 63.36 63.47 65.59 65.90 66.17 65.37 65.67 65.42 65.75 65.66 64.56 65.18 0.91

DBS-domain 63.13 63.21 65.43 65.64 65.88 65.38 66.27 65.06 65.41 65.81 64.41 65.06 1.00

DBS-spatial 63.61 63.21 65.47 65.98 66.25 65.77 66.16 65.91 65.77 65.81 64.25 65.29 1.03

DBS-grid 63.60 63.21 65.46 65.91 66.17 65.75 66.17 65.78 65.74 65.77 64.24 65.25 1.00

Mean Bias Correction 63.43 63.28 65.49 65.86 66.12 65.57 66.07 65.55 65.67 65.76 64.36 65.19

Std. Dev Bias Correction 0.20 0.11 0.06 0.13 0.14 0.19 0.23 0.33 0.15 0.06 0.13

b) Mean GW Head DK1

Layer 3 (m)

ARPEGE-

RM51

ARPEGE-

HIRHAM5

BCM2-

HIRHAM5

BCM2-

RCA3

ECHAM5-

HIRHAM5

ECHAM5-

RegCM3

ECHAM5-

RACMO2

ECHAM5-

REMO

ECHAM5-

RCA3

HadCM3-

CLM

HadCM3-

HadRM3

Mean

Climate

Model

Std. Dev.

Climate

Model

DC 11.38 11.40 12.20 12.31 12.41 12.08 12.20 12.11 12.24 12.20 11.77 12.03 0.34

DBS-domain 11.55 11.23 12.02 12.28 12.38 12.19 12.31 12.03 12.22 12.24 11.82 12.02 0.34

DBS-spatial 11.52 11.44 12.11 12.32 12.46 12.21 12.30 12.18 12.25 12.25 11.83 12.08 0.32

DBS-grid 11.53 11.43 12.10 12.30 12.45 12.22 12.31 12.17 12.25 12.25 11.83 12.08 0.32

Mean Bias Correction 11.49 11.38 12.11 12.30 12.42 12.17 12.28 12.12 12.24 12.24 11.81 12.05

Std. Dev Bias Correction 0.07 0.09 0.06 0.02 0.03 0.06 0.04 0.06 0.01 0.02 0.03

c) Min GW Head DK1

Layer 3 (m)

ARPEGE-

RM51

ARPEGE-

HIRHAM5

BCM2-

HIRHAM5

BCM2-

RCA3

ECHAM5-

HIRHAM5

ECHAM5-

RegCM3

ECHAM5-

RACMO2

ECHAM5-

REMO

ECHAM5-

RCA3

HadCM3-

CLM

HadCM3-

HadRM3

Mean

Climate

Model

Std. Dev.

Climate

Model

DC 28.24 28.24 27.94 27.90 27.63 27.97 27.93 27.97 27.92 27.93 28.09 27.98 0.16

DBS-domain 28.19 28.17 27.93 27.89 27.57 27.96 27.92 27.96 27.91 27.92 28.05 27.95 0.16

DBS-spatial 28.24 28.25 27.93 27.89 27.57 27.96 27.92 27.96 27.91 27.92 28.08 27.97 0.18

DBS-grid 28.24 28.25 27.93 27.89 27.57 27.96 27.92 27.96 27.91 27.92 28.07 27.97 0.18

Mean Bias Correction 28.23 28.23 27.94 27.89 27.58 27.96 27.93 27.96 27.91 27.92 28.07 27.97

Std. Dev Bias Correction 0.02 0.04 0.00 0.01 0.03 0.00 0.00 0.00 0.00 0.00 0.02

144 G E U S

Table 7: Variance contributions from bias correction methods V(B) and RCMs V(R) for the a) precipita-tion inputs and the hydrological responses b) discharge and c) groundwater head.

Total Annual 99th percentile

V(B) 6.6% 16.1%

V(R) 99.0% 91.5%

Mean Annual 99th percentile 1st percentile

V(B) 9.1% 8.7% 25.8%

V(R) 97.6% 98.1% 84.1%

V(B) 9.7% 10.4% 10.8%

V(C) 98.3% 98.0% 98.1%

V(B) 10.3% 12.0% 10.5%

V(R) 97.0% 96.1% 95.2%

V(B) 8.0% 9.7% 9.7%

V(R) 97.8% 97.3% 95.7%

V(B) 10.2% 12.2% 16.2%

V(R) 95.4% 94.9% 91.0%

V(B) 9.5% 10.6% 14.6%

V(R) 97.2% 96.9% 92.8%

Mean Max Min

V(B) 2.3% 12.2% 15.6%

V(R) 99.3% 94.3% 99.5%

V(B) 2.2% 3.1% 0.9%

V(R) 99.4% 99.2% 99.7%

DK1 Layer 9

DK1 Layer 3

Station 52.08

Station 55.01

Station 57.04

Station 59.01

Station 60.03

b) Discharge

a) Precipitation

DK1

Average

c) Groundwater Head

G E U S 145

4. References

Abbott, M.B., Bathurst, J.C., Cunge, J.A., O’Connell, P.E., and Rasmussen, J.: An introduction to the European hydrological system – systeme hydrologique European, She .2. Structure of a physically-based, distributed modelling system, J. Hydrol., 87, 61-77, 1986.

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Graham, L.P., Andréasson, J., and Carlsson, B.: Assessing climate change impacts on hydrology from an ensemble of regional climate models, model scales and linking methods – a case study on the Lule River Basin, Clim. Change, 81, 293-307, 2007.

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Appendix 5: Paper III

Climate change effects on irrigation demands and minimum stream dis-

charge: impact of bias-correction method.

Rasmussen, J., Sonnenborg, T.O., Stisen, S., Seaby, .LP., Christensen, B.S.B, and Hinsby,

K.,(2012). Hydrol. Earth Syst. Sci., 16, 4675–4691, doi:10.5194/hess-16-4675-2012.

G E U S 167

Appendix 6: Paper IV

Climate change impact on groundwater levels: ensemble modelling of

extreme values.

Kidmose, J., Refsgaard J.C., Troldborg, L., Seaby, L.P., Escriva, MM., (2013). Hydrol. Earth Syst.

Sci. 17, 1619–1634, doi:10.5194/hess-17-1619-2013.

Paper I: 

Seaby LP, Refsgaard JC, Sonnenborg TO, Stisen S, Christensen JH, Jensen KH (2013) Assessment of robustness and significance of climate change signals for an ensemble of distribution‐based scaled climate projections. Journal of Hydrology 486, 479‐493, doi: 10.1016/j.jhydrol.2013.02.015. 

Paper II: 

Seaby LP, Refsgaard JC, Sonnenborg TO, Højberg AL. Spatial uncertainty in bias corrected climate change projections and hydrogeological impacts. Submitted to Hydrol. Earth Syst. Sci. 

Paper III: 

Rasmussen J, Sonnenborg TO, Stisen S, Seaby LP, Christensen BSB, Hinsby K (2012) Climate change effects on irrigation demands and minimum stream discharge: impact of bias‐correction method. Hydrol. Earth Syst. Sci., 16, 4675–4691, doi:10.5194/hess‐16‐4675‐2012. Open Access 

Paper IV: 

Kidmose J, Refsgaard JC, Troldborg L, Seaby LF, Escriva MM (2013) Climate change impact on groundwater 

levels: ensemble modelling of extreme values. Hydrol. Earth Syst. Sci. 17, 1619‐1634, doi:10.5194/hess‐17‐

1619‐2013. Open Access