Verification of clustering properties of extreme daily temperatures in winter and summer using the...

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Verification of clustering properties of extreme daily temperatures in winter and summer using the extremal index in five downscaled climate models José .A. LÓPEZ Climatological Techniques Unit AEMET Spain EMMS & ECAM 2011, Berlin

Transcript of Verification of clustering properties of extreme daily temperatures in winter and summer using the...

Page 1: Verification of clustering properties of extreme daily temperatures in winter and summer using the extremal index in five downscaled climate models José.A.

Verification of clustering properties of extreme daily temperatures in winter and summer using the extremal

index in five downscaled climate models

José .A. LÓPEZClimatological Techniques Unit

AEMETSpain

EMMS & ECAM 2011, Berlin

Page 2: Verification of clustering properties of extreme daily temperatures in winter and summer using the extremal index in five downscaled climate models José.A.

Outline • Methodology

– Extremal Index θ: definition, example– Estimation of θ – Declustering procedure– Bootstrapping technique for C.I.– Data, deviation index

• Verification for Dec-Jan lowest daily temperatures – Observed θ values– Statistics of verification of θ for AR4 models– Some results including AR3 models

• Verification for Jul-Aug highest daily temperatures – ....

– ...

EMMS & ECAM 2011, Berlin

Page 3: Verification of clustering properties of extreme daily temperatures in winter and summer using the extremal index in five downscaled climate models José.A.

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The Extremal Index: Definition • The Extremal Index θ is a statistical measure of the clustering in a

stationary series. It varies between 0 and 1, with 1 corresponding to absence of clustering (Poisson process)

Formal definition:

• Let X(i), i=1,..,n be a stationary series of r.v. with cdf F (with F*= 1-F); define M(n)= max(X(i): 1 ≤ i ≤ n). We say that the process X(i) has extremal index θ ε [0, 1] if for each τ > 0 there is a succession u(n) such that for n -> ∞,

• a) n F* (u(n)) -> τ (mean nº of exceedances = τ)

• b) P ( M(n) ≤ u(n) ) -> exp (- θ τ)

• If θ = 1 the exceedances of progressively higher thresholds u(n) occur independently, i.e. They for a Poisson process (this is the case of independent r.v X(i)

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The Extremal Index : interexceedance times

The extremal index is the proportion of interexceedance times that may be regarded as intercluster times.

This fact is used for declustering.

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The Extremal Index : simulation with an ARMAX process

The ARMAX process is defined by:

where de Z’s are standard independent Fechet variables,

i.e. prob(Z < x) = Exp (-1/x)

This process has an extremal index:

Θ = 1 - α

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The Extremal Index : simulation for θ = 1 (Poisson)

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The Extremal Index : simulation for θ = 0.8

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The Extremal Index : simulation for θ = 0.5

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The Extremal Index : simulation for θ = 0.2

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If the Ti are the successive times between exceedances of the high threshold u the Extremal Index is estimates by:

(Ferro, C.A.T. “Inference for cluster of extreme values”, J.R.Statist.Soc. B(2003), 65, Part 2, 545-556)

.

Estimation of the Extremal Index

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Objective: Define the clusters in a series of exceedances

The times between exceedances are classified as inter-cluster times or intra-cluster (belonging to the same cluster) ones according to their length.

The criterion used is “objective” and simple, it depends only the Extremal Index θ.

More specifically the longest θ N inter-exceedance times are assigned an inter-cluster character, the rest are assigned an intra-cluster character. Between two successive inter-cluster times there is a set (which may be void) of intra-cluster times

Declustering procedure

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In order to build confidence intervals for the θ of a series, a “bootstrapping” technique was used:

a) Sample with replacement successively from the set of inter-cluster times, and then from the set of sets of intra-cluster times to build a fictitious process

b) Compute the θ of this fictitious process

c) Repeat the above steps the desired nº of times to build the confidence interval

Bootstrapping technique

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Data and models used

Period: 1961-1990

Data used: observed and dowscaled daily temperature at 16 observatories of Spain

Models AR4: cccma-cgcm3 (CA), gfdl-cm2 (US), inmcm3 (RU), mpi-echam5 (AL), mri-cgcm2 (JA)

Models AR3: ECHAM4, HadAM3, CGCM2

The statistical downscaling technique was analog-based

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• The thresholds used to build the exceedances (on 15-day moving windows)

– 90th percentile for Jul-Aug – 10th percentile for Dic-Jan (in this case the values below the

threshold are found)

• In order to assess the differences in θ between observations and downscaled data the following deviation index was used

where 1000 bootstrap samples where used to compute the medians and the IQR

Verification of the Extremal Index in extreme temperature for downscaled climate models

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Dec-Jan (occurrances below the 10th percentile of

daily temperature)

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Observed values of θ Dec-Jan (in percent)

2938

26304138

4223

41

37

41

34

27 344842

3742

52

3740

35 57

4938

35

3733

45

3428

3031

34

36

Median= 37

Max = 57

Min = 23

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Observed values of θ Dec-Jan: values above (1) and below (-1) the median

11

1111

11 1

0

1

1

1 111

01

1

01

1 1

11

1

01

1

11

11

1

1

Page 18: Verification of clustering properties of extreme daily temperatures in winter and summer using the extremal index in five downscaled climate models José.A.

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Observed values of θ Dec-Jan : spatial distribution

Lowest values of θ (more clustering) in the NE and interior

Highest values of θ (less clustering) in the western half

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Verification of θ for AR4 downscaled models Dec-Jan

Histogram of absolute deviation index of θ (on the y-axis nº of observatories, on the x-axis accumulated frequencies)

Aver. absol. dev. Index: CA (1.3), US(2.3), RU(1.3) AL(0.8) JA (1.2)

Aver. dev. Index: CA (0.9), US(2.3), RU(0.2) AL(-0.3) JA (-0.2)

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Verification of θ for AR4 downscaled models Dec-Jan: leading models

At each observatory the downscaled model that leads the others in terms of absolute deviation index (in no case by more than 1.0)

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Verification of θ for downscaled models in Dec-Jan: leading models AR4+ 3 AR3 models

Aver. dev. Index AR3 : EC (-2.3) HA ( -1.5) CG (-0.5)

Aver. dev. Index AR4: CA (0.9), US(2.3), RU(0.2) AL(-0.3) JA (-0.2)

Four models of AR4 show little or moderate global bias in θ, whereas with AR3 only one shows little bias (the rest show more clustering)

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Jul-Aug (occurrances above the 90th percentile of

daily temperature)

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Observed values of θ Jul-Aug (in percent)

4237

65415561

3640 56

60

48

49

44 515142

4244

47

4446

57 81

5448

52

5657

57

5650

4039

42

47

Median = 48

Max = 81

Min = 36

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Observed values of θ Jul-Aug: values above (1) and below (-1) the median

11

1111

11 1

1

0

1

1 111

11

1

11

1 1

10

1

11

1

11

11

1

1

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Observed values of θ Jul-Aug : spatial distribution

It is more difficult than in the Dic-Jan case to discern spatial patterns of the θ index

The northern coast and obsevatories on the Iberian mountain range show above average θ values (less clustering)

The contrary (more clustering) happens at the NE extreme (Catalonia)

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Verification of θ for AR4 downcaled models Jul-Aug

Histogram of absolute deviation index of θ (on the y-axis nº of observatories, on the x-axis accumulated frequencies)

Aver. absol. dev. Index: CA (1.6), US(1.8), RU(3.0) AL(1.6) JA (1.3)Aver. dev. Index: CA (-1.4), US(-1.6), RU(-2.9) AL(-1.2) JA (-0.5)

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Verification of θ for AR4 downcaled models Jul-Aug

All the downscaled AR4 models show a bias towards excessive clustering (the Japanese little) in Jul-Aug (though less than in the three AR3 models)

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Verification of θ for AR4 downscaled models Jul-Aug: leading models

At each observatory the downscaled model that leads the others in terms of absolute deviation index (with an asterisk when the difference to the others is >1.0)

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Verification of θ for downscaled models in Jul-Aug: leading models AR4+ 3 AR3 models

Aver. dev. Index AR3: EC (-2.9) HA ( -4.1) CG (-2.4)

Aver. dev. Index AR4: CA (-1.4), US(-1.6), RU(-2.9) AL(-1.2) JA (-0.5)

There is a clear decrease in the amount of bias (excess clustering) in AR4 models with respect to AR3·

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