Statistical downscaling of extreme precipitation and temperature – a systematic and rigorous inter-comparison of methods

T. Schmith (1), C.M. Goodess (2) and the STARDEX team1. Danish Meteorological Institute, Copenhagen Ø, Denmark

2. Climatic Research Unit, University of East Anglia, Norwich, UK

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

This co-operative cluster of projects brings together European expertise in the fields of climate modelling, regional downscaling, statistics, and impacts analysis to explore future changes in extreme events in response to global warming.•PRUDENCE will provide high-resolution climate change scenarios for 2071-2100 for Europe using regional climate models.   PRUDENCE project summary•STARDEX will provide improved downscaling methodologies for the construction of scenarios of changes in the frequency and intensity of extreme events.   STARDEX project summary•MICE uses information from climate models to explore future changes in extreme events across Europe in response to global warming.   MICE project summary     Last modified:16 August 2002 MICE     STARDEX     PRUDENCEProject Web Sites:

Contact InformationCopyright information: the above photo montage was created in XaraX using copyright pictures from: © Collier County Florida Emergency Management and © Environment Agency.The three projects are supported by the European Commission under the Framework  V Thematic Programme ”Energy, Environment and Sustainable Development” (EESD), 2002-2005.

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PRUDENCE

STARDEX

MICE

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

Dynamical vs. statistical downscaling: the STARDEX objectives

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

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

STARDEX downscaling methods

• Canonical correlation analysis• Neural networks• Two-stage analogue technique• Conditional resampling• Regression analysis• Conditional weather generator• Potential precipitation circulation index (cluster analysis)• Critical circulation patterns (fuzzy rules)• Local rescaling of GCM simulated precipitation

EGU 2004 presentations: UEA, KCL, CNRS, ARPA-SMR, USTUTT/IWS, AUTH

Principles of verification

• Predictor dataset : NCEP reanalysis• Predictand datasets: “FIC dataset” and regional sets

Principles of verification

• Predictor dataset : NCEP reanalysis• Predictand datasets: “FIC dataset” and regional sets• Regions• Stations within regions

Study RegionsThe ‘FIC dataset’

Study Regions

UK: 6 stations

Iberia: 16 stationsGreece: 8 stations

Italy: 7 stations

Alps: 10 stations

Germany: 10 stations

The ‘FIC dataset’

Partners/regions

Iberia Greece Alps Germany UK Italy

UEA x x x x x x

KCL x x

ARPA-SMR x x

ADGB x

AUTH x x

USTUTT-IWS & FTS x x

ETH x x

FIC x x x x x x

DMI x x x x x x

UNIBE x

CNRS x x

Principles of verification

• Predictor dataset : NCEP reanalysis• Predictand datasets: “FIC dataset” and regional sets• Regions• Stations within regions• Core indices

Core indices(downscaled directly or calculated from downscaled daily series)

Principles of verification

• Predictor dataset : NCEP reanalysis• Predictand datasets: “FIC dataset” and regional sets• Regions• Stations within regions• Core indices• Verification period: 1979-1994 (for compatibility with

ECMWF-driven regional models)• Training period: 1958-1978 & 1995-2000• Statistics: RMSE, SPEARMAN-RANK-CORR for each

station/index

Preliminary comparison of statistical downscaling results:

UK – 90th percentile rainday amounts

Iberia – 90th percentile rainday amounts

Greece – 90th percentile rainday amounts

Greece – Tmax 90th percentile

Concluding remarks

• Large scatter in performance between stations for a given method, region and index

• Major signal is winter/summer difference in some regions, and some indices are more robust

• A ’best’ method can therefore not be selected at this stage

• The reason(s) for this might be:– inhomogeneity of station data– short verification period– inherent limitations/variability in predictability

Future work

• Completion of these intercomparisons (D12)

• Detailed regional comparisons (all stations)

• Comparison with RCM output (upscaling)

• Application using validated GCM predictors

• Recommendations to users/decision makers

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