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270 Climate change impact assessment in central and eastern Europe: The CLAVIER project Susanne Pfeifer (1) , Daniela Jacob (1) , Gabor Balint (2) , Dan Balteanu (3) , Andreas Gobiet (4) , Andras Horanyi (5) , Laurent Li (6) , Franz Prettenthaler (8) , Tamas Palvolgyi (7) and Gabriella Szepszo (5) (1) Max Planck Institute for Meteorology Hamburg, Germany (susanne.pfeifer@zmaw.de) (2) Environmental Protection and Water Management Research Institute (VITUKI), Budapest, Hungary (3) Institute of Geography, Romanian Academy, Bucharest, Romania (4) Wegener Center for Climate and Global Change, University of Graz, Austria (5) Hungarian Meteorological Service (HMS), Budapest, Hungary (6) Institut Pierre Simon Laplace, Centre National de la Recherche Scientifique, Paris, France (7) Env-in-Cent Ltd. (EiC), Budapest, Hungary (8) Institute of Technology and Regional Policy, JOANNEUM RESEARCH Forschungsgesellschaft mbH, Graz, Austria 1. The CLAVIER project The CLAVIER project (Climate Change and Variability: Impact on Central and Eastern Europe) is supported by the European Commission's 6th Framework Programme as a 3 year Specific Targeted Research Project from 2006 to 2009 under the Thematic Sub-Priority "Global Change and Ecosystems". The CLAVIER consortium consists of 13 partners from 6 countries, most of them coming from the CLAVIER target countries Hungary, Romania and Bulgaria. Based on a set of climate change simulations from different regional climate models, linkages between climate change and its impact on weather patterns, air pollution, extreme events, and on water resources are investigated. Furthermore, an evaluation of the economic impact on agriculture, tourism, energy supply and the public sector is conducted. Figure 1 shows the case study regions for economic impact assessment which have been defined based on the expert knowledge of the local CLAVIER partners. figure 1: CLAVIER study regions for impact assessment. Green: hydrology, yellow: tourism, grey: energy, cyan: agriculture. 2. The CLAVIER model ensemble The CLAVIER model ensemble consists of three regional climate models, namely REMO from the Max Planck Institute for Meteorology (MPI-M) in Hamburg, used in Version 5.7 by MPI-M and REMO5.0 used by the Hungarian Meteorological Service in Budapest, and the LMDZ model developed at CNRS in Paris. From all models, A1B scenario simulations driven by the global climate model ECHAM5 are available for the period of 1950 to 2050 with a horizontal resolution of about 25 to 30 km. For LMDZ, additional simulations driven by the IPSL global model are available for the A1B scenario. 3. Interface climate simulations – impact studies One main challenge of conducting climate change impact assessment is the multidisciplinary knowledge which is needed. CLAVIER joins disciplines such as climate modelers, hydrologists, geographers, engineers and economists. To facilitate the communication and the data flow, an interfacing work package has been established which bridges the gap between the model simulation data and the needed input data for the impact studies. Among others, this work package is carrying out a bias correction procedure to account for the known biases of the regional climate models. 4. Physical Impact Assessment The impact of climate change on different sectors has been studied. Among others, analyzes have been and will be carried out on: the water levels of the Lake Balaton, the hydrological regime of the Tisza river basin, possible changes in weather extremes, landslide and mud flow risks in the Sub-Carpathian regions, the risk of road damages in Hungary, and possible changes in typical weather regimes for Central and Eastern Europe and their implications to air pollution levels. 5. Economic Impact Assessment The economic impact assessment concentrates on the four economic sectors with the highest expected impacts, agriculture, tourism, energy supply and the public sector. Based on the climate change information and on economic data for the respective sector (e.g. temperatures, precipitation from the climate simulations, sectoral economic data e.g. on tourism revenues, overnight stays, number and types of hotels, tourism infrastructure, restaurants, shops, beaches etc. for the tourism studies), the sensitivity and the exposure to a changing climate is assessed in 10 case study regions on the NUTS II and NUTS III level and finally translated into expected changes in regional macroeconomic indicators, such as the sectoral Climate Change impact on Gross Regional Product (GRP) allowing also for first estimates on the impact on Gross Domestic Product (GDP) on the national level respectively. This workshop contribution will give an overview on the results of the CLAVIER project. References http://www.clavier-eu.org
271 Climate change impacts on extreme wind speeds S.C. Pryor 1,2 , R.J. Barthelmie 1,2 , N.E. Claussen 2 , N.M. Nielsen 2 , E. Kjellström 3 and M. Drews 4 1 Indiana University, Bloomington, IN 47405 USA spryor@indiana.edu 2 Risø DTU National Laboratory for Sustainable Energy, Roskilde, Denmark 3 Rossby Centre, SMHI, Norrköping, Sweden 4 Danish Meteorological Institute, Copenhagen, Denmark 1. Introduction and objectives Our objective is to quantify potential changes in extreme wind speeds across northern Europe under a variety of climate change scenarios. For wind energy applications we conform to the Wind Turbine design criteria, which define the suitable class of wind turbines based on the 50-year return period wind speed (U 50yr ). We determine extreme winds based on the Gumbel distribution so: −1 ⎡ ⎛ T ⎞⎤ (1) U T = ln⎢ln⎜ ⎟⎥ + β α ⎣ ⎝ T −1⎠⎦ U T wind speed for a given return period (T), and α and β are the distribution parameters. 2. Methods Two downscaling tools are applied to AOGCM output to generate higher resolution realizations of surface winds from which we calculate the extreme wind speeds: 1. Dynamically downscaled time series of grid-cell averaged wind speeds are used from two applications of Regional Climate Models (RCMs): (a) Simulations conducted using the Rossby Centre RCAO lateral boundary conditions from two General Circulation Models (ECHAM4/OPYC3 and HadAM3H) and two emission scenarios (SRES A2 and B2) (Pryor et al. 2005). (b) Simulations conducted using the HIRHAM RCM and lateral boundary conditions from ECHAM5/MPI-OM for the A1B SRES. Simulated time series of annual maximum wind speeds are used to compute U 50yr using the method of moments to determine α and β: ln 2 (2) α = max 2b1 −U γ β = max (3) U − α 1 n i −1 (4) max b1 = ∑ Ui n i= 1n − 1 The uncertainty on U T is given by: 2 (5) π 1+ 1.14kT + 1.10kT σ ( UT ) = α 6n Where n is the sample size, the frequency factor (k T ) is: 6 ⎛ ⎡ ⎞ ⎜ ⎛ T ⎞⎤ (6) k T = − ln − ⎟ ⎢ln⎜ ⎟⎥ γ π ⎝ ⎣ ⎝ T −1⎠⎦ ⎠ Where γ = Euler’s constant (0.577216) Assuming a Gaussian distribution of U T , then 95% of all realizations will lie with ±1.96σ of the mean, and thus σ can be used to provide 95% confidence intervals on the estimates of extreme winds with any return period. 2. Empirical downscaling is used to develop site specific estimates of extreme wind speeds using output from eight AOGCMs; BCCR-BCM2.0, CGCM3.1, CNRM-CM3, ECHAM5/MPI-OM, GFDL-CM2.0, GISS- ModelE20/Russell, IPSL-CM4, and MRI-CGCM2.3.2. AOGCM output for two historical periods (1982-2000 and 1961-1990) are taken from climate simulations of the twentieth century. AOGCM output for 2046-2065 and 2081-2100 are from simulations conducted using the A2 SRES. Wind speed observations (1982-2000) at 10-m for 43 stations used to condition the transfer functions are as in Pryor et al. (2006). This empirical downscaling technique develops a probability distribution of wind speeds during a specific time window rather than a time series of wind speeds. Thus the downscaled parameters are the scale (A) and shape (k) of the Weibull distribution: ⎡ k ⎤ (7) ⎛ U ⎞ P( U ) = 1− exp⎢− ⎜ ⎟ ⎥ ⎢ ⎣ ⎝ A ⎠ ⎥ ⎦ This approach is advantageous in the current context because it avoids a focus on mean conditions, underestimation of variance, and difficulties associated with reproducing the time structure of wind speeds. Downscaled Weibull A and k for each time period, AOGCM and station are used to compute U 50yr using eq. (1) and: ⎛ ( ) ⎟ ⎞ = k ⎜ − n 1 (8) 1 α ln( ) k A ⎝ ⎠ 1/ β = A( ln( n) ) k (9) Where n is the number of independent observations For the 8 AOGCMs presented and the 43 stations, the range of downscaled U 50yr for 1961-1990 lie within ±4% of the mean U 50yr and 95% lie within ±2%, so the downscaling results for extreme winds from the Weibull A and k parameters are relatively consistent for the historical period. Estimates from the future time periods are defined as being different from the historical period (1961-1990) if they lie beyond the 95% confidence intervals derived based on propagation of the uncertainty in A and k. 3. Dynamically downscaled extreme wind speeds RCAO simulations for the end of the C21st conducted using boundary conditions from HadAM3 imply little change in U 50yr (Table 1). Estimated U 50yr for the future period (and both SRES) generally lie within the 95% confidence intervals derived for the control period simulations (O(4-15%) of the U 50yr in 1961-1990). This is also the case for simulations conducted using lateral boundary conditions from ECHAM4/OPYC3, although in those simulations U 50yr for the future period under either SRES show increases particularly in the southwest of the study domain (Figure 1). The choice of SRES appears to have little influence on the d(U 50yr ) in either set of runs. Table 1. Fraction of grid cells (in %) that exhibit a significant increase, decrease or no change for the 4 future simulations (2071-2100) relative to 1961-1990. Declines No change Increases ECHAM4: A2 0.1 73.2 26.7 ECHAM4: B2 0.1 72.9 27.0 HadAM3: A2 6.0 90.1 3.9 HadAM3: B2 1.8 95.8 2.4
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2 nd International Lund RCM Worksho
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Associated Organisations ENSEMBLES
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Preface This Second Lund Regional-s
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II High-resolution dynamical downsc
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IV Investigation of added value at
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VI Soil organic layer: Implications
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VIII Session 4: Regional Observatio
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X Predicting water resources in Wes
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XII The impact of atmospheric therm
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XIV Observation and simulation with
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XVI Favot F .......................
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2 5. Influence of SN on the Ensembl
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4 (+/- 5 days) were used to increas
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6 Dynamical downscaling: Assessment
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8 Improvement of long-term integrat
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10 air temperature annual cycle (Fi
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12 Sensitivity study of CRCM-simula
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14 Regional precipitation anomalies
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16 Assessment of precipitation as s
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18 The Asian summer monsoon in ERA4
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20 Added value of limited area mode
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22 Sensitivity of CRCM basin annual
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24 High-resolution dynamical downsc
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28 technique, as it is based on the
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30 4. Heating mechanism In order to
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32 The geographical distribution of
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34 The CCM scheme reveals a distinc
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36 Performance of pattern scaling i
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38 Evaluation of the analyzed large
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40 Sensitivity studies with a stati
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42 5SL, the results suggest that 5S
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48 Figure 1. Precipitation rate (mm
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50 References Biau. G., E. Zorita,
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52 Investigation of regional climat
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7.5 8.5 9.5 10.5 60 For the climato
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72 Will, A., M. Baldauf, B. Rockel,
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76 Investigation of precipitation o
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82 Large-scale skill in regional cl
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86 The models capture this pattern
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88 of the simulations due to the di
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91 Examining the relative roles con
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93 Dynamical coupling of the HIRHAM
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95 A parameterization of aircraft i
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97 Stratospheric variability and it
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99 Effects of numerical methods on
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101 Development of a climate model
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103 Study of the capability of the
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105 Evaluation of the land surface
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107 Simulation of the precipitation
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109 Climate simulations over North
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111 Soil organic layer: Implication
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113 4. Results The method how to do
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115 Figure 1. Observed (a) and simu
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117 Evaluation of the Rossby Centre
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119 Simulating aerosols in the regi
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121 Moisture availability and the r
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123 Temperature and precipitation s
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125 4. Preliminary results for down
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127 a) Knutson, T.R., J.J. Sirutis,
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129 Effects of variations in climat
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131 3.2. Precipitation The ensemble
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133 knowledge is essential to asses
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135 pronounced biases in the simula
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137 variability was concentrated at
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139 Investigation of ‘Hurricane K
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141 Evaluation of seasonal forecast
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143 NASH J. E., SUTCLIFFE J. V., 19
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145 Climate change assessment over
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147 For relative operating characte
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149 responsible for the excessive s
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151 and 30km). Domains D1 (90 and 6
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153 can be seen in Fig. 2, where th
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155 High-resolution simulation of a
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157 MesoClim - A mesoscale alpine c
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159 Observed and modeled extremes i
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161 analyzed the surface wind behav
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163 summer particularly in the Paci
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165 since 01.01.2004. For the Meteo
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167 Wind, m/s 12 10 8 6 4 2 Vilsand
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169 heterogeneity. For example, lar
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171 Analysis of surface air tempera
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173 Environmental database and the
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Climate change and water pollution
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177 During summer, winds over the M
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179 followed nearly the correct pat
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181 Verification of simulated near
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183 The North American Regional Cli
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185 An atmosphere-ocean regional cl
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187 different rainfall characterist
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189 with increasing temperature. Mo
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191 Inter-comparison of Asian monso
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193 On the validation of RCMs in te
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195 AMMA-Model Intercomparison Proj
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197 parameterization are used in th
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199 Evaluation of European snow cov
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201 Projections of eastern Mediterr
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203 Atmospheric river induced heavy
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205 CLARIS project: Towards climate
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207 Influence of soil moisture init
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209 Projection of the Changes in th
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211 Regional climate model studies
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213 ENSEMBLES regional climate mode
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215 Assessing the performance of se
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217 Applying the Rossby Centre Regi
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Page 291 and 292: 265 Future challenges for regional
Page 293 and 294: 267 Linking climate factors and ada
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Page 323: No. 34: BALTEX Phase II 2003 - 2012
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