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 team 1. Danish Meteorological Institute, Copenhagen Ø, Denmark 2. Climatic Research Unit, University of East Anglia, Norwich, UK

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 for Europe using regional climate models. PRUDENCE project summaryPRUDENCE project summarySTARDEX will provide improved downscaling methodologies for the construction of scenarios of changes in the frequency and intensity of extreme events. STARDEX project summarySTARDEX project summaryMICE uses information from climate models to explore future changes in extreme events across Europe in response to global warming. MICE project summary MICE project summary Last modified: 16 August 2002 MICE STARDEX PRUDENCE Project Web Sites: Contact Information Copyright 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), Scroll down for Project Summaries: follow the links above to Project Web Sites. Hit Counter Web Site designed and implemented by Tom Holt, © 2002 Comments and suggestions welcome: PRUDENCE STARDEX MICE

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 Regions The FIC dataset

Study Regions UK: 6 stations Iberia: 16 stations Greece: 8 stations Italy: 7 stations Alps: 10 stations Germany: 10 stations The FIC dataset

Partners/regions IberiaGreeceAlpsGermanyUKItaly UEAxxxxxx KCLxx ARPA-SMRxx ADGBx AUTHxx USTUTT-IWS & FTSxx ETHxx FICxxxxxx DMIxxxxxx UNIBEx CNRSxx

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: (for compatibility with ECMWF-driven regional models) Training period: & Statistics: RMSE, SPEARMAN-RANK-CORR for each station/index

Preliminary comparison of statistical downscaling results:

UK – 90 th percentile rainday amounts

Iberia – 90 th percentile rainday amounts

Greece – 90 th percentile rainday amounts

Greece – Tmax 90 th 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