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84 Figure 3: As Figure 1 but for Winter (DJF). References: Winterfeldt, J. and R. Weisse, Assessment of value added for surface marine wind obtained from two Regional Climate Models (RCMs), submitted to Mon. Wea. Rev., 2009 Winterfeldt, J., Andersson, A., Klepp, C., Bakan, S. and R. Weisse, Assessment of value added for surface marine wind obtained from two Regional Climate Models (RCMs), submitted to IEEE Trans. Geosciences and Rem. Sens., 2009
85 Present climate simulations (1982-2003) of precipitation and surface temperature using the RegCM3, RSM, and WRF Yoo-Bin Yhang, Kyo-Sun Lim, E-Hyung Park and Song-You Hong, ybyhang@yonsei.ac.kr 1. Introduction Interannual variations of precipitation and surface air temperature are integral parts of the climate system, where remote controls by planetary circulation and global surface anomalies act together with local influences by regional mesoscale surface characteristics. These two broad factors (remote and local) can be distinguished by using a regional climate model (RCM), in which planetary signals are integrated through lateral boundary conditions while mesoscale impacts are internally resolved (e.g., Giorgi et al., 1993; Liang et al., 2001). Given the inadequacies of general circulation models (GCMs) to simulate regional climate variability, the RCM downscaling has become a powerful alterative and is widely applied in studies of seasonalinterannual climate prediction and future climate change projection. It is imperative that any RCM must be rigorously validated in reproducing historical observations, including both mean climate and temporal variability, before credible application for climate change projection. In the published literature, numerous studies have demonstrated the RCM skill enhancement for downscaling regional characteristics, especially of precipitation and surface air temperature, focusing on mean climate (long-term averaged) biases such as in the annual cycles (Pan et al., 2001; Roads et al., 2003). In this study, three different RCMs, the Weather Research and Forecasting model (WRF), National Centers for the Environmental Prediction (NCEP) Regional Spectral Model (Juang et al., 1997), and Regional Climate Model version 3 (RegCM3, Pal et al., 2007), are chosen to examine downscaling skill. The downscaling sckills of RCMs are compared with the driving reanalysis, against observation of interannual variations of precipitation and surface temperature during 1982-2003 over East Asia. This is facilitated by using empirical orthogonal function (EOF) and correlation analyses. 2. Model and experimental design Three regional climate models used in this study are the RSM, WRF, and RegCM3. The RSM is a primitive equation model using the sigma-vertical coordinate. The model includes parameterizations of surface, boundary layer (BL), and moist processes that account for the physical exchanges between the land surface, the boundary layer, and the free atmosphere. The RegCM3 is a primitive equation, compressible, sigma-vertical coordinate, and limited area model of which the dynamical core is based on the hydrostatic version of the fifth-generation Penn State University-National Center for Atmospheric Research (PSU- NCAR) Mesoscale Model (MM5; Grell et al. 1994). The Advanced Research WRF (ARW; Skamarock et al. 2005) is a community model suitable for both research and forecasting. Three-month-long simulations for the summer season (June- July-August; JJA) were performed for 22 years from 1982 to 2003. The model grids consist of 109 (west-east) by 86 (north-south) grid lines at 60 km horizontal separation with the polar stereographic map projection. The model configurations used for each model are summarized in Table 1. The simulations are performed with reanalyses-derived boundary forcing. RegCM3 RSM WRF vertical σ-23 layers σ-28 layers σ-28 layers levels dynamics hydrostatic hydrostatic nonhydrostatic numerics finite difference spectral computation finite difference CPS Grell SAS 2005 Kain-Fritsch (Byun and Hong, (Kain and Fritsch, (Grell, 1993) 2007) 1993) PBL LSM RAD Holtslag (Holtslag ,1990) BATS (Dickinson et al., 1986) CCM3 (Kiehl, 1998) YSUPBL (Hong et al., 2006) OSU (Mahrt and Pan, 1984) GSFC (Chou and Suarez, 1999) YSUPBL (Hong et al., 2006) Noah (Chen and Dudhia, 2001) simple cloudinteractive (Dudhia, 1989), RRTM (Mlawer et al., 1997) 3. Dominant interannual variation patterns Figure 1. The first EOF mode and corresponding principal component of surface temperature. Figure 1 shows the first EOF mode of summer temperature and the corresponding principal component (PC) reanalysis data and simulated by the RegCM3, RSM, and WRF, where summer is defined as the average of JJA. The first dominant mode of the reanalysis explains 36% of the total variance in the model domain, which is characterized by positive values over the whole domain.
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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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XVIII Ruoho-Airola T...............
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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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26 There is nevertheless a large ag
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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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Page 62 and 63: 36 Performance of pattern scaling i
Page 64 and 65: 38 Evaluation of the analyzed large
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Page 70 and 71: 44 are however occasional episodes
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Page 74 and 75: 48 Figure 1. Precipitation rate (mm
Page 76 and 77: 50 References Biau. G., E. Zorita,
Page 78 and 79: 52 Investigation of regional climat
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Page 82 and 83: 56 The climate change in Europe sim
Page 84 and 85: 58 Simulation of South Asian summer
Page 86 and 87: 7.5 8.5 9.5 10.5 60 For the climato
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Page 90 and 91: 64 3. Results Fig. 2 shows the anal
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Page 96 and 97: Figure 2. Same as in Fig.1 but for
Page 98 and 99: 72 Will, A., M. Baldauf, B. Rockel,
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Page 102 and 103: 76 Investigation of precipitation o
Page 104 and 105: 78 Impacts of the spectral nudging
Page 106 and 107: 80 Extremes and predictability in t
Page 108 and 109: 82 Large-scale skill in regional cl
Page 112 and 113: 86 The models capture this pattern
Page 114 and 115: 88 of the simulations due to the di
Page 117 and 118: 91 Examining the relative roles con
Page 119 and 120: 93 Dynamical coupling of the HIRHAM
Page 121 and 122: 95 A parameterization of aircraft i
Page 123 and 124: 97 Stratospheric variability and it
Page 125 and 126: 99 Effects of numerical methods on
Page 127 and 128: 101 Development of a climate model
Page 129 and 130: 103 Study of the capability of the
Page 131 and 132: 105 Evaluation of the land surface
Page 133 and 134: 107 Simulation of the precipitation
Page 135 and 136: 109 Climate simulations over North
Page 137 and 138: 111 Soil organic layer: Implication
Page 139 and 140: 113 4. Results The method how to do
Page 141 and 142: 115 Figure 1. Observed (a) and simu
Page 143 and 144: 117 Evaluation of the Rossby Centre
Page 145 and 146: 119 Simulating aerosols in the regi
Page 147 and 148: 121 Moisture availability and the r
Page 149 and 150: 123 Temperature and precipitation s
Page 151 and 152: 125 4. Preliminary results for down
Page 153 and 154: 127 a) Knutson, T.R., J.J. Sirutis,
Page 155 and 156: 129 Effects of variations in climat
Page 157 and 158: 131 3.2. Precipitation The ensemble
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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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219 Interactions between European s
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221 ALADIN-Climate/CZ simulation of
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223 experiment of recreating the pr
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225 Figure 2. the 10-year averaged
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227 Use of regional climate models
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229 Wave estimations using winds fr
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231 Figure 1. Model domain for the
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233 A coupled regional climate mode
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235 Arctic regional coupled downsca
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237 Convection-resolving regional c
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239 4. Outlook Currently a coupled
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241 distribution within the Netherl
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243 Very high-resolution regional c
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245 Impact of convective parameteri
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247 Development of a regional ocean
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249 Regional climate change impact
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251 River runoff projection of futu
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253 Application of regional-scale c
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255 Local air mass dependence of ex
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257 Impact of vegetation on the sim
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259 Climate change impacts on the w
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261 Influence of soil moisture-near
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263 Forest damage in a changing cli
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265 Future challenges for regional
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267 Linking climate factors and ada
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269 GCMs. In the end of the 21 st c
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271 Climate change impacts on extre
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273 Winter storms with high loss po
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275 The impact of land use change a
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277 6. Analysis of Results (Rain) T
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279 (a) (b) Figure 3. Impact of veg
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281 that cold (Fig. 5). Winter temp
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283 Streamflow in the upper Mississ
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285 Dynamical downscaling of urban
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287 Souma, K., K. Tanaka, E. Nakaki
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289 In April 2000, the fire emissio
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291 Impacts of vegetation on global
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International BALTEX Secretariat Pu
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No. 34: BALTEX Phase II 2003 - 2012
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