CHR-workshop june 2003

Regional climate modelling for the Rhine basin

Regional climate modelling for the Rhine basin

Bart van den Hurk (KNMI)

With contributions fromGeert Lenderink, Erik van Meijgaard,

Jules Beersma (KNMI)Hendrik Buiteveld (RIZA)

CHR-workshop june 2003

ContentsContents

• Regional climate modelling at KNMI• Verification and improvement of the

RCM• Precipitation statistics over the Rhine

basin (present-day climate)• Conclusions

CHR-workshop june 2003

Regional climate modellingat KNMI

Regional climate modellingat KNMI

• Regional climate scenarios– Statistical downscaling (Buishand, Beersma et al)– Weather generators– Since late 2001: RACMO = HIRLAM + ECMWF

physical package• RegioKlim project

– Verification and improvement of RACMO– Production of scenarios– Participation in (inter)national projects

(PRUDENCE, new FP6-projects, ICES/KIS)

CHR-workshop june 2003

Verification andimprovement of RACMO

Verification andimprovement of RACMO

• Free interior,atmospheric forcing atlateral boundaries

• 15yr simulation with“observed” boundaries(ERA15, 1979-1993)

• Verification using synops data, ECA-data and meteorologicalanalyses

CHR-workshop june 2003

Basic problems of control version of RACMO (1)

Basic problems of control version of RACMO (1)

• Summertime dry warm bias

Bias compared to all ±220 ECA-stations

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SolutionSolution

• Increase hydrological memory of thesoil– Deeper soil reservoir– Smaller sensitivity of transpiration to soil

water content

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Basic problems of control version of RACMO (2)

Basic problems of control version of RACMO (2)

• Too much precipitation overmountains

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SolutionSolution

• Filter orography and reducehorizontal diffusion

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Basic problems of control version of RACMO (3)

Basic problems of control version of RACMO (3)

• Different synoptic patterns compared to driving meteorological fields

ERA15 RACMO

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SolutionSolution

• Optimize procedure to importinformation in lateral boundary

ERA15RACMO

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Precipitation over the Rhine basin (present-day climate)Precipitation over the Rhine basin (present-day climate)

gredients (1)CHR daily precipitation data1961-1995 over 134 sub-catchmentsAggregated to RACMO model grid (69gridpoints)

102

107

109 104 103

105106

115113

110

108112

111

114

66

34

50171849 47

24

1648 12

7

8 6

2

3

1

7675

72

7174

73

100

97

70

69

68

96

92

67 6564

33

35

37 36

93

94

9

10

78

79

8081

82

84

83

4

5

11

13

1415

19

20

21

22

23

25

28

29

26

27

8886

85

87

3231

30

45

44

39

38

40

46

51

52

55

5453

56

57

60 6263

42

41

59

43

61

58

90

9899

10195

91

77

89

116117

121

129

130

126

131133

132

118

119

120

128

127

123124

125

122

134

100 0 100 Kilometer

N

HBV DistrictsErftLahnLippeLower RhineMainMiddle RhineMoselleNaheNeckarRuhrSchweizSiegUpper RhineUpper Rhine 2

GewässerStaatsgrenze

• In–

–

CHR-workshop june 2003

Precipitation over the Rhine basin (present-day climate)Precipitation over the Rhine basin (present-day climate)

• Ingredients (2)– ERA15: RACMO

reference simulation(1979-1993)

– HadAM3: RACMOsimulation with modifications control climate (1961-1990, only first 10yrsanalysed) (included in PRUDENCE project)

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Average annual cycle aggregated over domain

Average annual cycle aggregated over domain

CHR-workshop june 2003

Average annual cycle aggregated over domain

Average annual cycle aggregated over domain

Effect of soil drying

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Pattern of average bias inHadAM run

Pattern of average bias inHadAM run

Lower areas surrounded by mountains too dry, mountains too wet

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Autocorrelation of timeseries

Autocorrelation of timeseries

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Frequency distribution ofareal averages

Frequency distribution ofareal averages

1 day sum

5 day sum

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Individual eventsIndividual events

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Pattern correlationPattern correlation

• Correlate daily field on day t with day t+lag

• For each lag, average correlations for all events

• Expresses temporal consistency ofspatial patterns

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Pattern correlationPattern correlation

Strong persistence ofprecipitation patterns,partially by (too) strongorographic control in themodels

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Pattern correlationPattern correlation

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ConclusionsConclusions

• Model modifications improved hydrological cycle and its seasonal time scale

• Average annual cycle of precipitation well simulated, but systematic spatial errors

• Small overestimation of light events on 1-day timescale

• Small underestimation of extreme events on5-day timescale