STARDEX STAtistical and Regional dynamical

Downscaling of EXtremes for European regions

A project within the EC 5th Framework Programme

EVK2-CT-2001-00115

1 February 2002 to 31 July 2005

Co-ordinator: Dr Clare Goodess Climatic Research Unit, UK c.goodess@uea.ac.uk

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

The STARDEX consortium

Partner no. Organisation name Short name Key persons involved1 University of East Anglia, UK UEA Clare Goodess

Phil JonesSteve DorlingGavin Cawley

2 King’s College London, UK KCL Rob Wilby3 Fundación para la Investigación del Clima, Spain FIC Jaime Ribalaygua

Rafael Borén4 University of Bern, Switzerland UNIBE Evi Schuepbach

5 Centre National de la Recherche Scientifique, France CNRS Guy PlautEric Simonnet

6 Servizio Meteorologico Regional, ARPA-Emilia Romagna, Italy ARPA-SMR Carlo CacciamaniValentina PavanStefano Tibaldi

7 Atmospheric Dynamics Group, University of Bologna, Italy ADGB Ennio Tosi8 Danish Meteorological Institute, Denmark DMI Torben Schmith

9 Eidgenössische Technische Hochschule, Switzerland ETH Christoph FreiChristoph Schär

10 Fachhochschule Stuttgart – Hochschule für Technik, Germany FTS Hans Caspary11 Institut für Wasserbau, Germany USTUTT-IWS András Bárdossy

12 University of Thessaloniki, Greece AUTH Panagiotis MaherasTheodore KaracostasMargaritis VafiadisFotini Kolyva

Subcontractor Norwegian Meteorological Institute, Norway DNMI Eirik FørlandSubcontractor Swedish Meteorological and Hydrological Institute SMHI Hans Alexandersson

The STARDEX description of work

42 months 12 partners 2 general objectives 5 measurable objectives 12 specific objectives 5 workpackages 7 milestones 20 deliverables

STARDEX general objectives

To rigorously and systematically inter-compare and evaluate statistical, dynamical and statistical-dynamical downscaling methods for the reconstruction of observed extremes and the construction of scenarios of extremes for selected European regions.

To identify the more robust downscaling techniques and to apply them to provide reliable and plausible future scenarios of temperature and precipitation-based extremes for selected European regions.

STARDEX measurable objectives

1. Development of standard observed and climate model simulated data sets, and a diagnostic software tool for calculating a standard set of extreme event statistics, for use by all partners.

2. Analysis of recent trends in extremes, and their causes and impacts, over a wide variety of European regions.

3. Validation of HadCM3 and ECHAM4/OPYC3 climate model integrations, particularly for extremes.

4. Inter-comparison of improved statistical, dynamical and statistical-dynamical downscaling methods using data from the second half of the 20 th century and identification of the more robust methods.

5. Development of scenarios, particularly for extremes, for the late 21st century using the more robust statistical, dynamical and/or statistical-dynamical downscaling methods.

The STARDEX workpackages

WP1: Data set development, co-ordination and dissemination (UEA Goodess/Jones)

WP2: Observational analysis of changes in extremes, their causes and impacts (USTUTT-IWS Bardossy)

WP3: Analysis of GCM/RCM output and their ability to simulate extremes and predictor variables (ETH Frei)

WP4: Inter-comparison of improved downscaling methods with emphasis on extremes (DMI Schmith)

WP5: Application of the more robust downscaling techniques to provide scenarios of extremes for European regions for the end of the 21st century (UEA Goodess/Jones)

Month 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42

WP1

WP2

WP3

WP4

WP5

Milestones M1 M2 M3 M4M5

M6M7

Deliverables D1D2

D8D3-D7

D9 D10 D11 D12 D13 D14D15D16

D17 D18D19D20

Project meetings

Annual/final reports

Short EU progress reports

Technology Implementation Plan

Draft Final

Schedule of STARDEX work

Temperature extremes Precipitation extremes Daily measures of Tmax/Tmin/Tday

using 10th/90th percentiles Diurnal temperature range Intra-annual extreme temperature

range Number of frost days (Tmin < 0°C) Frost severity index (percentage of

time with Tmin < 0°C) Growing season length (period

between Tday > 5°C for > 5 days andTday < 5°C for > 5 days)

Heat wave duration index (maximumperiod > 5 consecutive days withTmax > 5°C above long-term mean)

Magnitude of the 90th percentile Frequency of exceeding 90th

percentile (number of days) Simple daily intensity index

(total precipitation/number of raindays)

Maximum length of wet/dry spell Percentage of precipitation falling on

days with rainfall above 90th percentile No. of days with precipitation 10 mm Maximum 5 day precipitation total

STARDEX proposed standard indicators

Region Focus of case studyIberian Peninsula and Greece UEA, FIC, AUTH

Thermal discomfort index Fire hazard index Low river flow (robust hydrologic model). Regional drought (200-300 km by 200-300 km).

Alps UNIBE, CNRS, ARPA-SMR, ADGB, ETH

Extreme precipitation. Identification of homogeneous sub-regions. Detailed comparison of a 14 km RCM coupled to a

distributed catchment model with statisticaldownscaling coupled to a robust hydrologic model.

Scandinavia DMI

Extreme precipitation. Temperature – frost/growing season duration.

Germany FTS, USTUTT-IWS

Extreme precipitation, including storm events. Flooding (extreme diagnostics/hydrological models).

British Isles UEA, KCL

Extreme precipitation. Regional flooding (e.g. joint probabilities). Highly-resolved daily precipitation data 1961-. Temperature – frost/growing season duration.

Proposed STARDEX regional case studies

STARDEX WP1: Data set development, co-ordination and dissemination

To ensure that a consistent approach (in terms of regions, observed and climate model data inputs, variables and statistics studied and time periods) is used for all analyses

To ensure that the needs of the climate impacts community for scenarios of extremes are taken into account, that output from the most recent climate model simulations is available for use in the project and that the work is subject to continuous assessment by the expert advisory panel.

To ensure wide dissemination of the project results to stakeholders, the scientific community and the public.

STARDEX WP1 deliverables

Members’ web site D1(1) Public web site D2 (1)

Standard data sets of:– station temperature/precipitation data D3 (3)– NCEP reanalysis data D4 (3)– Objective circulation patterns & indices D5 (3)– HadCM3, ECHAM4/OPYC3 & RCM data D6 (3)– damages arising from extreme events D7 (3)

Statistical diagnostic software tool D8 (4)

Final project report D20 (42)

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

STARDEX – Workpackage 3Analysis of GCM/RCM output and their

ability to simulate extremes and predictor variables

WP3 objectives(i) Identify downscaling needs. Reliability of GCMs simulation of

extremes.

(ii) Evaluate the GCMs‘ simulation of parameters, used as potential predictors in statistical downscaling methods.

(iii) Evaluate the RCMs simulation of extreme events for inter-comparison of different downscaling techniques.

(iv) Exploit empirical scale-relationships as a framework for advanced climate model evaluation.

(v) Use dynamical downscaling results to assess basic assumptions of statistical downscaling methods. (Stationarity.)

• GCMs: – HadAM3H (1.25°x1.875°) 150 km– ECHAM4.5 (T106)

• RCMs:– 50 km RCMs

• several models: HadRM, HIRHAM, CHRM, ...

• for all study regions.

– 20 km RCMs • for some of the study regions

– GCMs/RCMs from PRUDENCE project– Standard emission scenario, where possible full range of

SRES marker scenarios (uncertainty analysis in WP5)– 30 year integrations (1960–1990, 2071-2100)– Ensemble members where available (scenario

uncertainty, robustness of extremes).– RCM integrations from MERCURE

• ERA-driven RCMs: 1979-1993 (15 years)

• fair comparison of downscaling techniques:– avoid effects from GCM biases

– reproduction of observed natural variations as measure of downscaling skill

WP5: Application of the more robust downscaling techniques to provide scenarios of extremes for European regions for the end of the 21st century

To apply the more robust statistical, dynamical and/or statistical-dynamical downscaling methods to HadCM3 and ECHAM4/OPYC3 integrations for the end of the 21st century to provide scenarios of extreme events for European regions.

To use these scenarios to identify changes in extremes.

To investigate whether these changes are in accordance with recent observed changes.

To consider their potential impacts in terms of losses of life and financial costs.

To assess the uncertainties associated with the scenarios.

The scenarios of extremes will incorporate three forms of uncertainty

uncertainty related to downscaling methods

uncertainty related to possible future emission paths

uncertainty due to intra- and inter-model variability

STARDEX WP5 deliverables

Downscaled scenarios based on HadCM3 and ECHAM4/OPYC3 output for the end of the 21st century for regions of Europe and 400-500 locations across Europe D17 (36)

Summary of changes in extremes, comparison with

past changes and consideration of impacts/damages D18 (42)

Assessment of uncertainties associated with the scenarios D19 (42)