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Regional Dynamical Downscaling of Mediterranean Climate – Climate Change Perspectives Heiko Paeth, Institute of Geography, University of Würzburg, I.Introduction II.Dynamical downscaling III.Extreme value statistics IV.Simulated extreme events V.Simulated changes VI.Postprocessing of model data VII.Conclusions MedCLIVAR Workshop 2007, La Londe les Maures

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I. Introduction industrial emissions traffic emissions biomass burning over- grazing heat stress flood wind extremes drought

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I. Introduction How can we infer future changes in the frequency and intensity of extreme events? dynamical aspect (climate modelling) statististical aspect (assessment of uncertainty)

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II. Dynamical downscaling low latitudes are dominated by convective rain events the spatial heterogeneity of individual rain events is high regional rainfall estimates are subject to large sampling errors

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station data global model regional model statist. interpol. II. Dynamical downscaling day-to-day variability annual precipitation station data are often too sparse to represent regional rainfall global models are too coarse-grid for regional details statistically interpolated data sets fail in mountainous areas dynamic nonlinear regional models account for the effect of orography

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II. Dynamical downscaling the rainfall trends predicted by the global model are barely relevant to political plannings and measures the rainfall trends predicted by the regional model are much more detailed and of higher amplitude more detailed fingerprint or spatial noise added value ??? 3 x CO 2

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II. Dynamical downscaling consideration of various ensemble members enables the statistical quantification of the human impact on climate in the climate model Temperature Precipitation different initial conditions (stochastic) differences between ensemble members at certain time scales measure of internal variability variance of the ensemble mean measure of external variability statistical comparison

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II. Dynamical downscaling REMO: observed GHG constant LC ECHAM5/MPI-OM: observed GHG constant LC REMO: A1B (GHG+LC) REMO: B1 (GHG+LC) ECHAM5/MPI-OM: A1B (GHG) constant LC ECHAM5/MPI-OM: B1 (GHG) constant LC Land degradation: FAO original Land degradation: FAO reduced dynamics: hydrostatic physics: ECHAM4 sector: 30°W-60°E ; 15°S-45°N resolution: 0,5° ; 20 hybrid levels validation: good results

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II. Dynamical downscaling The main features of Mediterranean climate are well reproduced by REMO.

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III. Extreme value statistics The processes, which cause climate extremes, are not necessarily the same as for weak climate variations. Hence, they usually do not obey a normally distributed random process. f climate parameter

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III. Extreme value statistics The Generalized Pareto Distribution (GPD) is a useful statistical distribution, since it is a parent distribution for other extreme value distributions (Gumbel, Exponential, Pareto). The quantile function x(F) is given by: = location parameter (expectation) = scale parameter (dispersion) = shape parameter (skewness) The parameters of the GPD can be estimated by the method of L- moments. Estimation of T-year return values (RVs): dispersion parameter: threshold quantile T=5a q=99% RV=43mm cumulative GPDs

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III. Extreme value statistics uncertainty of the RV estimate is inferred from bootstrap sampling: 1)from fitted GPD b random samples of size N generated 2)from random samples b indi- vidual RVs estimated 3)these b RVs are normal distri- buted such that STD is a mea- sure of the standard error of the RV estimate 4)signal-to-noise ratio is given by MEAN/STD over b RVs f cGPD 0 1 mm new samples of size N change in RV is significant at the 1% level, if 90% confidence inter- vals of two PDFs of RVs over b bootstrap samples do not overlap: RV N random numbers STD 90% conf. interv. f RV present-day climate forced climate

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III. Extreme value statistics 100-year RV in mm The 100-year RV estimate ranges between 200 mm and 800 mm, depending on the random sample.

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III. Extreme value statistics probabilistic forecast of future rainfall changes provides a reasonable scientific basis for political plannings and measures one predicted value without uncertainty range: pretended precision probabilistic forecast with mean and uncertainty range: more objective basis for decision makers security costs single estimate / simulation Monte Carlo approach 1% 10% 90% 99% x=50% s + =84% s - =16% RV

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IV. Simulated extreme events The occurrence of extreme rain events is a function of the land-sea contrast, orography, geographical latitude and seasonal cycle. 1-year return values of heavy daily rainfall

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IV. Simulated extreme events 1-year return values of high daily temperature The occurrence of high temperature is also a function of the land-sea contrast, orography, geographical latitude and seasonal cycle.

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IV. Simulated extreme events The estimate of extrem values is more robust in regions and seasons with large- scale rather than convective precipitation. The choice of long return times in the pre- sence of short time series is unappropriate. S/N ratio for 1-year RVs of heavy daily rainfall

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seasonal means extremes (1y-RV) α = 5% V. Simulated changes PRECIPITATION 2025 minus present-day

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α = 5% extremes (1y-RV) seasonal means V. Simulated changes TEMPERATURE 2025 minus present-day

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VI. Postprocessing of model data The assessment of changes in weather extremes is very sensitive to inhomogeneities in observational data. No problem with model data daily precipitation discontinuity assessed variability

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VI. Postprocessing of model data precipitation is the end product of a complex causal chain each step imposes addititional uncertainty, particularly if it is based on a physical parameterization in the model different initial conditions (stochastic) radiation budget and energy fluxes atmospheric and oceanic circulation instability and convection cloud micro- physics precipitation time error nonlinear error growth

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VI. Postprocessing of model data climate models: area-mean precipitation observations: local station data comparison ? REMO grid box (50km x 50km) observed station time series (local information) model datastation data

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VI. Postprocessing of model data virtual station rainfall (result) simulated grid-box precipitation (dynamical part) local topography (physical part) random distribution in space (stochastical part) Weather Generator

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VI. Postprocessing of model data REMO rainfall: - wrong seasonal cycle - underestimated extremes - hardly any dry spells Weather Generator: - statistical distribution as observed - individual events not in phase with observations model data station data model data postprocessed original REMO rainfall rainfall from weather generator station time series

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VII. Conclusions Regional climate models are required in order to account for the spatial heterogeneity of Mediterranean climate. The estimate of extreme values and their changes requires appropriate statistical distributions and a probabilistic approach. When estimating EVs from short time series, it is necessary to restrict the analysis to short return periods. The occurrence of climate extremes is a function of land-sea contrast, orography, geographical latitude and seasonal cycle. REMO projects no coherent changes in heavy rainfall whereas warm temperature extremes clearly tend to increase. Systematic model deficiencies and the grid-box problem can be overcome by use of a weather generator. The model results now need to be corroborated by available homogeneized long-term observational time series.

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