The new German project KLIWEX-MED: Changes in weather and climate extremes in the Mediterranean basin Andreas Paxian, University of Würzburg MedCLIVAR Workshop 2008, Trieste

Group members University of Würzburg: –Heiko Paeth –Andreas Paxian –Gernot Vogt University of Augsburg: –Jucundus Jacobeit augsburg.de) –Elke Hertig –Stefanie Seubert

Project overview Main goal: Detection of climate change and extreme events in the Mediterranean basin and probabilistic quantification of uncertainties Broad spectrum of different methods: Comparison of different scenarios, methods and models evaluation with observations - Global model simulations (direct) Regional model simulations (direct) Statistical down- scaling (indirect) Weather generator (indirect)

Project overview

Observations

Analysis of observation data for evaluation of present-day results of different methods Station time series: –EMULATE [MOBERG et al. 2006] –assistance of MedCLIVAR and CIRCE community Aggregated grid box data: –CRU [NEW et al. 2000, MITCHELL & JONES 2005] –VasCLIMO [BECK et al. 2007]

Observations Analysis: –Quality control (no inhomogeneous time series) –Mean value statistics, seasonal cycle, trends –Extreme value statistics: appropriate extreme value distribution (General Pareto) return times and values uncertainty of assessment and significance of variability (Monte Carlo sampling approach) 0 1 RV RT RV f present-day climate forced climate

Global model simulations

IPCC 2007 AR4 multi-model ensembles ( observed GHG and A1b, A2, B1 scenarios) Variables: temperature, precipitation, wind Analysis: –Mean value statistics, seasonal cycle and trends (analogue to observations) –Extreme value statistics (analogue to observations) –Probabilistic assessment of climate change: statistical confidence intervals due to different ensemble members 1% 10% 90% 99% x=50% s + =84% s - =16% Global model simulations

–Analysis of variance: detection of climate change signals in multi-model ensemble results - Temperature internal model variability differences between ensemble members external model variability trend of ensemble mean model uncertainty Global model simulations

Regional model simulations

REMO from MPI Hamburg (JACOB et al., 2001) dynamical downscaling: –high spatial resolution (0.5°) detailed climate change fingerprints (impact modelling) –orography, land-sea contrast and synoptic processes improved extreme value statistics REMO simulations for IMPETUS project: – observed GHG – A1b, B1 and FAO land degradation scenarios – simulations successfully validated Regional model simulations

REMO precipitation post-processing with MOS (correction of systematic model errors): –extraction of orthogonal predictors (stepwise multiple regression, principal component analysis) –statistical transfer functions between predictors and target variable (training over past 25 years) –implementation of transfer functions on simulations of Analysis (analogue to global model simulations) Regional model simulations

Statistical downscaling

Transfer functions from observations: –classification of Mediterranean large-scale circulation and meso-scale weather types in ERA40/ NCEP reanalysis data –extreme values from daily EMULATE temperature and precipitation time series –statistical transfer functions between circulation types and extreme values (canonical correlation analysis, multiple regressions) –cross validation to test robustness of scale transfer large-scale circulation patterns statistical transfer functions local extreme values Statistical downscaling

Implementation of transfer functions to models: –classification of large-scale circulation and meso- scale weather types in global and regional models –implementation of transfer functions to models local station extreme values (past and future) probabilistic assessment of extreme value uncertainty (model ensembles) Linear statistical downscaling underestimation of climate system complexity Statistical downscaling

Weather generator

Creation of virtual precipitation station time series from global and regional model grid box data improved comparison to observations: virtual station precipitation Physical part: coastal distance, sub-scale synoptic processes, orography (DEM, station meta data) Dynamical part: REMO or AR4 model grid box data Stochastic part: stochastic spread within model grid box (station time series) climate models: area-mean precipitation observations: local station data problems of comparison

Weather generator Correction algorithm: final adjustment of simulated precipitation sums or daily distribution functions to observation data in training period Cross validation: evaluation of results Probabilistic assessment of uncertainty (model ensembles) Good results of weather generator for analysis of IMPETUS simulations in tropical Africa (input for hydrological impact modeling)

Evaluation and Comparison

Summary