The STARDEX project - background, challenges and successes A project within the EC 5th Framework...
-
Upload
aiden-combs -
Category
Documents
-
view
216 -
download
2
Transcript of The STARDEX project - background, challenges and successes A project within the EC 5th Framework...
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
http://www.cru.uea.ac.uk/projects/stardex/
Consistent approach:
e.g., indices of extremes
http://www.cru.uea.ac.uk/projects/stardex/
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
http://www.cru.uea.ac.uk/projects/stardex/
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
http://www.cru.uea.ac.uk/projects/stardex/
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
http://www.cru.uea.ac.uk/projects/stardex/
Winter R90N relationships with MSLP& NAO, Malcolm Haylock
-3
-2
-1
0
1
2
3
4
1955 1965 1975 1985 1995
NAO -R90N PC2
http://www.cru.uea.ac.uk/projects/stardex/
R = 0.64
MSLP Canonical Pattern 1. Variance = 44.4%.
R90N Canonical Pattern 1. Variance = 11.3%.
Winter R90N relationships with MSLP, Malcolm Haylock
http://www.cru.uea.ac.uk/projects/stardex/
Analysis of GCM/RCM output & their ability to simulate extremes and predictor variables
and their relationships
http://www.cru.uea.ac.uk/projects/stardex/
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
http://www.cru.uea.ac.uk/projects/stardex/
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
http://www.cru.uea.ac.uk/projects/stardex/
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
Tomozeiu et al., ARPA-SMR
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
Tomozeiu et al., ARPA-SMR
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
Tomozeiu et al., ARPA-SMR
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
CP11 without moisture flux
CP09 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
http://www.cru.uea.ac.uk/projects/stardex/
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/stardex/http://www.cru.uea.ac.uk/projects/mps/