The STARDEX project background, challenges and successes

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The STARDEX project - background, challenges
and successes
Clare Goodess Climatic Research Unit, UEA,
Norwich, UK

• 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/

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The STARDEX consortium
http//www.cru.uea.ac.uk/projects/stardex/
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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/
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Consistent approach
e.g., indices of extremes
http//www.cru.uea.ac.uk/projects/stardex/
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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/
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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 gt long-term P90
• no. events gt long-term 90th percentile of
  raindays
• Tmax 90th percentile
• Tmin 10th percentile
• number of frost days Tmin lt 0 degC
• heat wave duration

http//www.cru.uea.ac.uk/projects/stardex/
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1958-2000 trend in frost days
Days per year Blue is increasing
Malcolm Haylock, UEA
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1958-2000 trend in summer rain events gt long-term
90th percentile
Scale is days/year Blue is increasing
Malcolm Haylock, UEA
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Local scale trends in extreme heavy precipitation
indices
Andras Bardossy, USTUTT-IWS
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Investigation of causes, focusing on potential
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/
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Winter R90N relationships with MSLP NAO, Malcolm
Haylock
R 0.64
http//www.cru.uea.ac.uk/projects/stardex/
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Winter R90N relationships with MSLP, Malcolm
Haylock
MSLP Canonical Pattern 1. Variance 44.4.
R90N Canonical Pattern 1. Variance 11.3.
http//www.cru.uea.ac.uk/projects/stardex/
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Analysis of GCM/RCM output their ability to
simulate extremes and predictor variables and
their relationships
http//www.cru.uea.ac.uk/projects/stardex/
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Annual Cycle
RCMs HadAM3H control (1961-1990).
ERA15-driven Domain 2.25-17.25 E, 42.25-48.75
N, All Alps
Christoph Frei, ETH
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SON Wet-day 90 Quantile (mm/day)
RCMs HadAM3H control (1961-1990).
Christoph Frei, ETH
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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
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Study Regions
Europe (FIC) 481 stations in total
England (UEA) P 13-27 per gp T 8-30 per gp
German Rhine (USTUTT) P 500 per gp T 150 per
gp
Alps (ETH) P 500 per gp
Greece (AUTH) P 5-10 per gp T 5-10 per gp
Emilia-Rom. (ARPA) P 10-20 per gp T 5-10 per gp
Christoph Frei, ETH
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Example German Rhine Basin
Precipitation Indices
DJF
JJA
GCM scale Station scale
Christoph Frei, ETH
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Inter-comparison of improved downscaling methods
with emphasis on extremes
http//www.cru.uea.ac.uk/projects/stardex/
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• Downscaling methods
• canonical correlation analysis
• neural networks
• conditional resampling
• regression
• conditional weather generator
• potential precipitation circulation
  index/critical circulation patterns

Study regions
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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/
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Downscaling of Tmax90p
Model is constructed on the period
1958-1978/1994-2000 and validated on 1979-1993
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 90W-60E, 20N-90N ) Exp 2
Seasonal mean (DJF) of WA, EA, EB, SCA, over the
area 90W-60E, 20N-90N
Clusters for Tmax90p (DJF)
Tomozeiu et al., ARPA-SMR
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Interannual variability of downscaled, Observed
and NCEP Tmax90p (DJF), 1979-1993
Tomozeiu et al., ARPA-SMR
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Downscaling of 692R90N 2 exp.
Downscaling of 692R90N
Model is constructed on the period
1958-1978/1994-2000 and validated on 1979-1993
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
90W-60E, 20N-90N (NCEP reanalysis,2.5x2.5)
Fig.1 Clusters for 692R90N (DJF)
Tomozeiu et al., ARPA-SMR
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Skill of the statistical downscaling model
1979-1993 expressed as correlation coefficient
between the observed and estimated 692R90N
(bold-5significance)
Tomozeiu et al., ARPA-SMR
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Probability of precipitation at station 75103
conditioned to wet and dry CPs
Andras Bardossy, USTUTT-IWS
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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/
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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/
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• http//www.cru.uea.ac.uk/projects/stardex/
• http//www.cru.uea.ac.uk/projects/mps/

c.goodess_at_uea.ac.uk