0.25cmresults from the value perfect predictor experiment€¦ · results from the value perfect...

Results from the VALUE perfect predictor experimentD. Maraun, J. Gutierrez, M. Widmann, S. Kotlarski, E. Hertig,J. Wibig and R. Huth

EU COST Action ES1102

www.value-cost.eu

Maraun VALUE May 18, 2016 1 / 63

Outline

Background

VALUE Framework

Results


Background

Background

VALUE Framework

Results


Background

I “Researchers are still struggling to develop tools to accuratelyforecast climate changes for the twenty-first century at the localand regional level.” Nature Editorial, 2010

I “Certainty is what current-generation regional studies cannot yetprovide.” Nature Editorial, 2010


Background

Downscaling and robust adaptation debateFrom naivety to throwing out the baby with the bathtub


Background

The question is......not, whether downscaling is required or not, but...

...for wich variable, region, season lead time and aspect, i.e.,for which user problem is downscaling skillful and/or essential?


Background

Aim

The main objective of the Action is to establish anetwork to systematically validate and improve

downscaling methods for climate change research.


Background

A certain validation tendency


VALUE Framework

Background

VALUE Framework

Results


VALUE Framework

Framework paper


VALUE Framework

Key questionsfor regional climate change researcg

I How well do GCMs simulate the input for regional climate changeprojections?

I How well do downscaling methods work, in particular underclimate change?

I How strong is the signal-to-noise ratio between climate changetrends and internal climate variability at regional scales?


Maraun et al., Earth’s Future, 2015

VALUE Framework

Validation tree

Marginal Temporal Spatial Inter-Variable

Phenomena

Indices

Performance Measures

Aspects

Which climatic phenomena arerelevant for my problem?

Which aspects of the model output make up these phenomena?

What indices should be used to quantify these aspects?

How do I measure the performance to simulate these indices?

User Problem

Q1

Q2

Q3

Q4


VALUE Framework

Examples of Indices and Performance Measureswww.value-cost.eu/reports

Marginal DistributionsIndex Performance MeasureMean, Variance, 98% Percentile (relative) bias

Temporal DependenceIndex Performance MeasureSpell statistics Bias

Spatial DependenceIndex Performance MeasureDecay lengths of correlation/tail dependence (relative) bias

Multivariate DependenceIndex Performance MeasureJoint threshold exceedances (relative) biasVariable conditioned on large-scale circulation (relative) bias


VALUE Framework

Validation experimentswww.value-cost.eu/validation

I Perfect PredictorPredictors/boundary conditions from ERA-Interim Reanalysis

I Pseudo RealityPredictands from regional climate models (Present and future)

I GCM-PredictorsPredictors/boundary conditions from global climate models


Maraun et al., Earth’s Future, 2015

VALUE Framework

Reference datawww.value-cost.eu/data

� gridded data without registration

� gridded data with registration

� sub-daily data

� gridded data only for VALUE members

Data

I 85 ECA-D stations;

I corresponding E-OBS gridboxes;

I high quality gridded data forselected regions;

I hourly data for EmiliaRomagna.

Variables

I temperature, precipitation; atselected stations: wind,humidity, radiation. radiation.


VALUE Framework

VALUE Portal (open upload!)www.value-cost.eu/validationportal


Many thanks to Jose, Sixto, Dani et al.

VALUE Framework

Method classes

Perfect prog (PP) vs Model Output Statistics (MOS)I PP: classical empirical statistical downscalingI MOS: bias correction methods

Stochastic vs. deterministicI Stochastic: weather generator (with or without predictors)I Deterministic: same predictor value, same predicted value

Transfer functionI Regression, weather typing, scaling, distribution mapping etc.

Single/multi-site, Markov-chain/white noise


following Maraun et al., Rev. Geophys., 2010

Results

Background

VALUE Framework

Results


Results Marginal aspects - temperature

ResultsMarginal aspects - temperature



Tmax - mean, winter (DJF)Observed climate

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Tmax - variance, winter (DJF)Observed climate

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Tmax - variance, winter (DJF)Relative errors across all stations

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Tmax - 20 season return level, summer (JJA)Observed climate

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Tmax - 20 season return level, summer (JJA)Biases across all stations

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Results Marginal aspects - precipitation

ResultsMarginal aspects - precipitation



Precipitation - mean, summer (JJA)Observed climate

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Precipitation - mean, summer (JJA)Biases across all stations

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tal−

M8−

RR

atye

tal−

M9−

ER

atye

tal−

M9−

RB

C−

EB

C−

RM

OS

−A

N−

EM

OS

−A

N−

RM

OS

−G

LM−

EM

OS

−G

LM−

RV

GLM

GA

MM

A−

EF

IC02

P−

EF

IC04

P−

EA

NA

LOG

−A

NO

MA

NA

LOG

AN

ALO

G−

MP

AN

ALO

G−

SP

MO

−G

PM

LR−

SD

SM

MLR

−R

AN

MLR

−A

SN

MLR

−R

SN

MLR

−A

SW

GLM

−D

ET

GLM

GLM

−W

TF

IC01

PF

IC03

PD

SC

LIM

DS

CLI

M−

DC

QR

−90

CQ

R−

95C

QR

−99

SS

−W

GM

AR

FI−

BA

SIC

MA

RF

I−TA

DM

AR

FI−

M3

GO

ME

Z−

BA

SIC

GO

ME

Z−

TAD

WT

−W

GS

WG

−0.5

0.0

0.5

1.0





Precipitation - wet day probability, winter (DJF)Observed climate

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0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0.65



Precipitation - wet day probability, winter (DJF)Relative errors across all stations

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raw MOS PP WG

ER

AIN

T−

200

ER

AIN

T−

075

RA

CM

O22

EIS

I−M

IP−

EIS

I−M

IP−

RE

QM

−E

EQ

M−

RE

QM

−W

T−

EE

QM

−W

T−

RG

QM

−E

GQ

M−

RG

QM

−W

T−

EG

QM

−W

T−

RG

PQ

M−

EG

PQ

M−

Rgp

QM

−W

T−

Egp

QM

−W

T−

RQ

Mm

−E

QM

m−

RC

DF

t−E

CD

Ft−

RQ

M−

DA

P−

EQ

M−

DA

P−

RE

QM

−W

IC65

8−E

EQ

M−

WIC

658−

RD

BS

−E

DB

S−

RQ

MB

C−

BJ−

PR

−E

QM

BC

−B

J−P

R−

RR

atye

tal−

M6−

ER

atye

tal−

M6−

RR

atye

tal−

M7−

ER

atye

tal−

M7−

RR

atye

tal−

M8−

ER

atye

tal−

M8−

RR

atye

tal−

M9−

ER

atye

tal−

M9−

RB

C−

EB

C−

RM

OS

−A

N−

EM

OS

−A

N−

RM

OS

−G

LM−

EM

OS

−G

LM−

RV

GLM

GA

MM

A−

EF

IC02

P−

EF

IC04

P−

EA

NA

LOG

−A

NO

MA

NA

LOG

AN

ALO

G−

MP

AN

ALO

G−

SP

MO

−G

PM

LR−

SD

SM

MLR

−R

AN

MLR

−A

SN

MLR

−R

SN

MLR

−A

SW

GLM

−D

ET

GLM

GLM

−W

TF

IC01

PF

IC03

PD

SC

LIM

DS

CLI

M−

DC

QR

−90

CQ

R−

95C

QR

−99

SS

−W

GM

AR

FI−

BA

SIC

MA

RF

I−TA

DM

AR

FI−

M3

GO

ME

Z−

BA

SIC

GO

ME

Z−

TAD

WT

−W

GS

WG

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4





Precipitation - 20 season return level, summer (JJA)Observed climate

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25

36

47

58

69

80

91

102

113

124

135



Precipitation - 20 season return level, summer (JJA)Biases across all stations

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raw MOS PP WG

ER

AIN

T−

200

ER

AIN

T−

075

RA

CM

O22

EIS

I−M

IP−

EIS

I−M

IP−

RE

QM

−E

EQ

M−

RE

QM

−W

T−

EE

QM

−W

T−

RG

QM

−E

GQ

M−

RG

QM

−W

T−

EG

QM

−W

T−

RG

PQ

M−

EG

PQ

M−

Rgp

QM

−W

T−

Egp

QM

−W

T−

RQ

Mm

−E

QM

m−

RC

DF

t−E

CD

Ft−

RQ

M−

DA

P−

EQ

M−

DA

P−

RE

QM

−W

IC65

8−E

EQ

M−

WIC

658−

RD

BS

−E

DB

S−

RQ

MB

C−

BJ−

PR

−E

QM

BC

−B

J−P

R−

RR

atye

tal−

M6−

ER

atye

tal−

M6−

RR

atye

tal−

M7−

ER

atye

tal−

M7−

RR

atye

tal−

M8−

ER

atye

tal−

M8−

RR

atye

tal−

M9−

ER

atye

tal−

M9−

RB

C−

EB

C−

RM

OS

−A

N−

EM

OS

−A

N−

RM

OS

−G

LM−

EM

OS

−G

LM−

RV

GLM

GA

MM

A−

EF

IC02

P−

EF

IC04

P−

EA

NA

LOG

−A

NO

MA

NA

LOG

AN

ALO

G−

MP

AN

ALO

G−

SP

MO

−G

PM

LR−

SD

SM

MLR

−R

AN

MLR

−A

SN

MLR

−R

SN

MLR

−A

SW

GLM

−D

ET

GLM

GLM

−W

TF

IC01

PF

IC03

PD

SC

LIM

DS

CLI

M−

DC

QR

−90

CQ

R−

95C

QR

−99

SS

−W

GM

AR

FI−

BA

SIC

MA

RF

I−TA

DM

AR

FI−

M3

GO

ME

Z−

BA

SIC

GO

ME

Z−

TAD

WT

−W

GS

WG

−40

−20

0

20

40




Results Temporal Aspects - Temperature

ResultsTemporal Aspects - Temperature



Tmax - ACF lag 1, summer (JJA)Observed climate

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0.766666666666667

0.777777777777778

0.788888888888889

0.8

0.811111111111111

0.822222222222222

0.833333333333333

0.844444444444445

0.855555555555556

0.866666666666667

0.877777777777778

0.888888888888889



Tmax - ACF lag 1, summer (JJA)Biases across all stations

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raw MOS PP WG

ER

AIN

T−

200

ER

AIN

T−

075

RA

CM

O22

EIS

I−M

IP−

EIS

I−M

IP−

RE

QM

−E

EQ

M−

RE

QM

−W

T−

EE

QM

−W

T−

RG

PQ

M−

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PQ

M−

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QM

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T−

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QM

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Mm

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QM

m−

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DF

t−E

CD

Ft−

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M−

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P−

EQ

M−

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P−

RE

QM

−W

IC65

8−E

EQ

M−

WIC

658−

RD

BS

−E

DB

S−

RQ

MB

C−

BJ−

PR

−E

QM

BC

−B

J−P

R−

RR

aiR

at−

M6−

ER

aiR

at−

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aiR

at−

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ER

aiR

at−

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RR

aiR

at−

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ER

aiR

at−

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aiR

at−

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ER

aiR

at−

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RS

B−

ES

B−

RD

BB

C−

ED

BB

C−

RD

BD

−E

DB

D−

RM

OS

−R

EG

−E

MO

S−

RE

G−

RF

IC02

T−

EF

IC01

T−

EA

NA

LOG

−A

NO

MA

NA

LOG

AN

ALO

G−

MP

AN

ALO

G−

SP

MO

−G

PM

LR−

SD

SM

MLR

−R

AN

MLR

−A

SN

MLR

−R

SN

MLR

−A

SW

MLR

−A

AN

MLR

−A

AN

_V1

MLR

−A

AW

MLR

−P

CA

−Z

TR

ES

D−

EO

FS

LPE

SD

−E

OF

T2

ES

D−

SLP

ES

D−

T2

MLR

MLR

−W

TD

SC

LIM

−D

SS

−W

GM

AR

FI−

BA

SIC

MA

RF

I−TA

DM

AR

FI−

M3

GO

ME

Z−

BA

SIC

GO

ME

Z−

TAD

WT

−W

GS

WG

QR

−90

QR

−95

QR

−99

−0.6

−0.4

−0.2

0.0

0.2





Tmax - mean annual maximum warm spellObserved climate

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4

5

6

7

8

9

10

11

12

13



Tmax - mean annual maximum warm spellBiases across all stations

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raw MOS PP WG

ER

AIN

T−

200

ER

AIN

T−

075

RA

CM

O22

EIS

I−M

IP−

EIS

I−M

IP−

RE

QM

−E

EQ

M−

RE

QM

−W

T−

EE

QM

−W

T−

RG

PQ

M−

EG

PQ

M−

Rgp

QM

−W

T−

Egp

QM

−W

T−

RQ

Mm

−E

QM

m−

RC

DF

t−E

CD

Ft−

RQ

M−

DA

P−

EQ

M−

DA

P−

RE

QM

−W

IC65

8−E

EQ

M−

WIC

658−

RD

BS

−E

DB

S−

RQ

MB

C−

BJ−

PR

−E

QM

BC

−B

J−P

R−

RR

aiR

at−

M6−

ER

aiR

at−

M6−

RR

aiR

at−

M7−

ER

aiR

at−

M7−

RR

aiR

at−

M8−

ER

aiR

at−

M8−

RR

aiR

at−

M9−

ER

aiR

at−

M9−

RS

B−

ES

B−

RD

BB

C−

ED

BB

C−

RD

BD

−E

DB

D−

RM

OS

−R

EG

−E

MO

S−

RE

G−

RF

IC02

T−

EF

IC01

T−

EA

NA

LOG

−A

NO

MA

NA

LOG

AN

ALO

G−

MP

AN

ALO

G−

SP

MO

−G

PM

LR−

SD

SM

MLR

−R

AN

MLR

−A

SN

MLR

−R

SN

MLR

−A

SW

MLR

−A

AN

MLR

−A

AN

_V1

MLR

−A

AW

MLR

−P

CA

−Z

TR

ES

D−

EO

FS

LPE

SD

−E

OF

T2

ES

D−

SLP

ES

D−

T2

MLR

MLR

−W

TD

SC

LIM

−D

SS

−W

GM

AR

FI−

BA

SIC

MA

RF

I−TA

DM

AR

FI−

M3

GO

ME

Z−

BA

SIC

GO

ME

Z−

TAD

WT

−W

GS

WG

QR

−90

QR

−95

QR

−99

−4

−2

0

2

4




Results Temporal Aspects - Precipitation

ResultsTemporal Aspects - Precipitation



Precipitation - wet-wet probability, winter (DJF)Observed climate

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0.0333333333333333

0.0666666666666667

0.1

0.133333333333333

0.166666666666667

0.2

0.233333333333333

0.266666666666667

0.3

0.333333333333333

0.366666666666667

0.4



Precipitation - wet-wet probability, winter (DJF)Relative errors across all stations

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Precipitation - mean annual maximum dry spellObserved climate

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20

28

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44

52

60

68

76

84

92



Precipitation - mean annual maximum dry spellBiases across all stations

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IC65

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658−

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5





Precipitation - interannual variability, summerObserved climate (proportion of variance beyond one season)

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0.00125

0.0025

0.00375

0.005

0.00625

0.0075

0.00875

0.01

0.01125

0.0125

0.01375

0.015



Precipitation - interannual variability, summerRelative errors across all stations

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ER

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O22

EIS

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IC65

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658−

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BS

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S−

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MB

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QM

BC

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OS

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OS

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MM

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NA

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AN

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G−

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AN

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G−

SP

MO

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SM

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AN

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ET

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IC01

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SC

LIM

DS

CLI

M−

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QR

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CQ

R−

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QR

−99

SS

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AR

FI−

BA

SIC

MA

RF

I−TA

DM

AR

FI−

M3

GO

ME

Z−

BA

SIC

GO

ME

Z−

TAD

WT

−W

GS

WG

0.5

1.0

1.5

2.0




Results Spatial Aspects - Temperature

ResultsSpatial Aspects - Temperature



Temperature - correlation (distance)Observed climate and selected methods



Temperature - de-correlation lengthBiases across all stations


Results Spatial Aspects - Precipitation

ResultsSpatial Aspects - Precipitation



Precipitation - correlation (distance)Observed climate and selected methods



Precipitation - de-correlation lengthBiases across all stations


Results Process Aspects - Temperature

ResultsProcess Aspects - Temperature



Tmin - NAO (pos - neg), winterObserved climate

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6



Tmin - NAO (pos - neg), winterBias differences across all stations

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ISI−

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CD

Ft

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AP

EQ

M−

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658

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SQ

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C−

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Rai

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−M

6R

aiR

at−

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8R

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at−

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DB

BC

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DM

OS

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EG

FIC

02T

FIC

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AN

ALO

G−

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OM

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ALO

GA

NA

LOG

−M

PA

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LOG

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PM

O−

GP

MLR

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MM

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NM

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NM

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WM

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1M

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CLI

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OM

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SW

G

−4

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0

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2

* values outside plotted rangeMaraun VALUE May 18, 2016 53 / 63


Tmax - Blocking (yes - no), summerObserved climate

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Tmax - Blocking (yes - no), summerBias differences across all stations

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ER

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ISI−

MIP

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ME

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CD

Ft

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AP

EQ

M−

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658

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SQ

MB

C−

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Rai

Rat

−M

6R

aiR

at−

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−M

8R

aiR

at−

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SB

DB

BC

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DM

OS

−R

EG

FIC

02T

FIC

01T

AN

ALO

G−

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OM

AN

ALO

GA

NA

LOG

−M

PA

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LOG

−S

PM

O−

GP

MLR

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DS

MM

LR−

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NM

LR−

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NM

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NM

LR−

AS

WM

LR−

AA

NM

LR−

AA

N_V

1M

LR−

AA

WM

LR−

PC

A−

ZT

RE

SD

−E

OF

SLP

ES

D−

EO

FT

2E

SD

−S

LPE

SD

−T

2M

LRM

LR−

WT

DS

CLI

M−

DQ

R−

90Q

R−

95Q

R−

99S

S−

WG

MA

RF

I−B

AS

ICM

AR

FI−

TAD

MA

RF

I−M

3G

OM

EZ

−B

AS

ICG

OM

EZ

−TA

DW

T−

WG

SW

G

−2

−1

0

1

2


Results Process Aspects - Precipitation

ResultsProcess Aspects - Precipitation



Precipitation - NAO (pos - neg), winterObserved climate

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6



Precipitation - NAO (pos - neg), winterBias differences across all stations

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Precipitation - Blocking (yes - no), summerObserved climate

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Precipitation - Blocking (yes - no), summerBias differences across all stations

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Temperature and precipitation biasesCMIP5, multi-model mean

Impact modellers often desire to “correct” these biases.


Flato et al., IPCC AR5, 2013


But cross validation is not sufficient to show skillMap Buenos Aires daily temperature on Cambridge daily precipitation (DJF)Calibration: 1961-1980; validation: 1981-2000

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●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

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●● ●●●● ●●

0 10 20 30 40

0

10

20

30

40

Observation: Precipitation[mm]

Pre

dict

ion:

Tem

pera

ture

[°C

]/Pre

cipi

tatio

n[m

m]

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●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

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●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

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calibration, uncorrectedvalidation, uncorrectedcalibration, correctedvalidation, corrected



SummaryI Quite sobering, need time to digestI Still experiments 2 and 3 needed!I Please, no premature applications anymore!I When doing statistical post-processing, please evaluate it properly

(e.g., use the VALUE portal)!

I Special issue in preparation (to appear in Int. J. Climatol.)

[email protected]

Thank you for your attention!Maraun VALUE May 18, 2016 63 / 63

0.25cmresults from the value perfect predictor experiment€¦ · results from the value perfect...

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