The STARDEX project - background, challenges and successes A project within the EC 5th Framework Programme 1 February 2002 to 31 July 2005

The STARDEX project - background, challenges and successes

A project within the EC 5th Framework Programme

1 February 2002 to 31 July 2005

http://www.cru.uea.ac.uk/projects/stardex/http://www.cru.uea.ac.uk/projects/mps/

Clare GoodessClimatic Research Unit, UEA, Norwich, UK

The STARDEX consortium

Organisation name Key personsUniversity of East Anglia, UK Clare Goodess

Malcolm HaylockGavin CawleyPhil Jones

King’s College London, UK Rob WilbyColin Harpham

Fundación para la Investigación del Clima, Spain Jaime RibalayguaRafael BorénManuel Blanco

University of Bern, Switzerland Evi SchuepbachCentre National de la Recherche Scientifique, France Guy Plaut

Eric SimonnetServizio Meteorologico Regional, ARPA-Emilia Romagna,Italy

Carlo CacciamaniValentina PavanRodica Tomozeiu

Atmospheric Dynamics Group, University of Bologna, Italy Ennio TosiDanish Meteorological Institute, Denmark Torben SchmithEidgenössische Technische Hochschule, Switzerland Christoph Frei

Juerg SchmidliFachhochschule Stuttgart – Hochschule für Technik,Germany

Hans Caspary

Institut für Wasserbau, Germany András BárdossyYeshewatesfa Hundecha

University of Thessaloniki, Greece Panagiotis MaherasChristina Anagnostopoulou

http://www.cru.uea.ac.uk/projects/stardex/

STARDEX general objectives

• To rigorously & systematically inter-compare & evaluate statistical and dynamical downscaling methods for the reconstruction of observed extremes & the construction of scenarios of extremes for selected European regions & Europe as a whole

• To identify the more robust downscaling techniques & to apply them to provide reliable & plausible future scenarios of temperature & precipitation-based extremes


Consistent approach:

e.g., indices of extremes


STARDEX Diagnostic extremes indices software

• Fortran subroutine:– 19 temperature indices– 35 precipitation indices– least squares linear regression to fit linear trends & Kendall-

Tau significance test

• Program that uses subroutine to process standard format station data

• User information document

• All available from public web site


STARDEX core indices

• 90th percentile of rainday amounts (mm/day)

• greatest 5-day total rainfall

• simple daily intensity (rain per rainday)

• max no. consecutive dry days

• % of total rainfall from events > long-term P90

• no. events > long-term 90th percentile of raindays

• Tmax 90th percentile

• Tmin 10th percentile

• number of frost days Tmin < 0 degC

• heat wave duration


1958-2000 trend in frost days

Days per yearBlue is increasing

Malcolm Haylock, UEA

1958-2000 trend in summer rainevents > long-term 90th percentile

Scale is days/yearBlue is increasing

Malcolm Haylock, UEA

Local scale trends in extreme heavy precipitation indices

+variablevariablevariableFrench Alps

++++++Switzerland

--Greece

++--N. Italy

+--+++Germany

--++England

AutumnSummerSpringWinterRegion

Andras Bardossy, USTUTT-IWS

Investigation of causes, focusing onpotential predictor variables

e.g., SLP, 500 hPa GP, RH, SST, NAO/blocking/cyclone indices, regional circulation indices


Winter R90N relationships with MSLP& NAO, Malcolm Haylock

-3

-2

-1

0

1

2

3

4

1955 1965 1975 1985 1995

NAO -R90N PC2


R = 0.64

MSLP Canonical Pattern 1. Variance = 44.4%.

R90N Canonical Pattern 1. Variance = 11.3%.

Winter R90N relationships with MSLP, Malcolm Haylock


Analysis of GCM/RCM output & their ability to simulate extremes and predictor variables

and their relationships


Annual Cycle

RCMs: HadAM3H control (1961-1990). ERA15-drivenDomain: 2.25-17.25 °E, 42.25-48.75 °N, All Alps

Christoph Frei, ETH

SON Wet-day 90% Quantile (mm/day)

RCMs: HadAM3H control (1961-1990).Christoph Frei, ETH

Approach

• Use high-resolution observations to evaluate model at its grid scale

• „How well can a GCM represent regional climate anomalies in response to changes in large-scale forcings?“ Use interannual variations as a surrogate forcing.

• Use Reanalysis as a quasi-perfect surrogate GCM.

• Distinguish between resolved (GCM grid-point) and unresolved (single station) scales.

Christoph Frei, ETH

Study Regions

England (UEA)P: 13-27 per gpT: 8-30 per gp

German Rhine (USTUTT)P: ~500 per gpT: ~150 per gp

Greece (AUTH)P: 5-10 per gpT: 5-10 per gp

Emilia-Rom. (ARPA)P: 10-20 per gpT: 5-10 per gp

Europe (FIC)481 stations in total

Alps (ETH)P: ~500 per gp

Christoph Frei, ETH

Example: German Rhine Basin

DJF JJA

GCM scaleStation scale

Precipitation Indices

Christoph Frei, ETH

Inter-comparison of improved downscaling methods with emphasis on extremes


Iberia Greece Alps Germany UK Europe ItalyUEA x x xKCL x xARPA-SMR x xAUTH x xUSTUTT-IWS & FTS

x x

ETH x xFIC xDMI xUNIBE xCNRS x x

Downscaling methods • canonical correlation analysis• neural networks• conditional resampling• regression• conditional weather generator• “potential precipitation circulation index”/”critical circulation patterns”

Study regions

Predictor selection methods

• Correlation• Stepwise multiple regression• PCA/CCA• Compositing• Neural networks• Genetic algorithm• “Weather typing”• Trend analysis


PREDICTAND

Time series of 90th percentile of maximum temperature (Tmax90p);

30 stations from Emilia-Romagna (1958-2000) that were clusterised in 3 regions (Fig.2)

PREDICTORS

Exp 1: Seasonal mean (DJF) of first 4 PCs of Z500 over the area: 90°W-60°E, 20°N-90°N )

Exp 2: Seasonal mean (DJF) of WA, EA, EB, SCA, over the area: 90°W-60°E, 20°N-90°N

Model is constructed on the period 1958-1978/1994-2000 and validated on 1979-1993

Clusters for Tmax90p (DJF)

Downscaling of Tmax90p

Tomozeiu et al., ARPA-SMR

Interannual variability of downscaled, Observed and NCEP Tmax90p (DJF), 1979-1993


Downscaling of 692R90N – 2 exp.

PREDICTAND

Time series of observed no.of events greater than 90th percentile of raindays (692R90N);44 stations

from Emilia-Romagna (1958-2000) that were clusterised in 5 regions (Fig.1)

PREDICTORS

Seasonal mean (DJF) of first 4 PCs of Z500 that covers the area: 90°W-60°E, 20°N-90°N (NCEP

reanalysis,2.5°x2.5°)

Downscaling of 692R90N

11

2 3

4

5

Fig.1 Clusters for 692R90N (DJF)

Model is constructed on the period 1958-1978/1994-2000 and validated on 1979-1993


Skill of the statistical downscaling model 1979-1993 expressed as correlation coefficient between the observed

and estimated 692R90N (bold-5%significance)

Zone 1_forecast

2_forecast

3_forecast

4_forecast

5_forecast

Emilia Romagna region forecast

1_observed

0.21

2_observed

0.30

3_observed

0.37

4_observed

0.27

5_observed

0.26

Emilia Romagna

region obs

0.25


0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

-0.09 -0.04 0.01 0.06 0.11 0.16

Moisture flux

Pro

ba

bil

ity

of

pre

cip

ita

tio

n

CP02

CP11

CP09

CP01

CP07

CP02 without moisture flux



Probability of precipitation at station 75103 conditioned to wet and dry CPs

Andras Bardossy, USTUTT-IWS

At the end of the project (July 2005) we will have:

• Recommendations on the most robust downscaling methods for scenarios of extremes

• Downscaled scenarios of extremes for the end of the 21st century

• Summary of changes in extremes and comparison with past changes

• Assessment of uncertainties associated with the scenarios


Dissemination & communication

• internal web site (with MICE and PRUDENCE)• public web site• scientific reports and papers• scientific conferences• information sheets, e.g., 2002 floods, 2003 heat wave• powerpoint presentations• external experts• within-country contacts


http://www.cru.uea.ac.uk/projects/stardex/http://www.cru.uea.ac.uk/projects/mps/

[email protected]

The STARDEX project - background, challenges and successes A project within the EC 5th Framework Programme 1 February 2002 to 31 July 2005

Documents

eth slide

uea slide

indices of extremes

arpasmr slide

ustuttiws slide

relationships http

malcolm haylock http

gp christoph frei