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62 precipitation field was more closer to observations than simulated by the model driven with other boundary conditions (see Figure 5). Comparison between hydrostatic model RegCM3 and non-hydrostatic model MM5 will also be carried out. Figure 4. Same as Figure 3 but without the additional changes in the NOAH LSM model. Figure 2. 72 h sum of the heavy flood event of 20 May 1999 to 22 May 1999, based on data of Frei and Schär (1998). Figure 3. Total precipitation in the past 72 h of the May event of run L1 with the additional changes made by Zängl (Mauser and Strasser, 2007) in the NOAH LSM. Figure 5. Same as Figure 3 but of one of the RegCM simulations. References Auer, I., R. Böhm, A. Jurković, W. Lipa, A. Orlik, R. Potzmann, W. Schöner, M. Ungersböck, C. Matulla, K. Briffa, P. Jones, D. Efthymiadis, M. Brunetti, T. Nanni, M. Maugeri, L. Mercalli, O. Mestre, J.-M. Moisselin, M. Begert, G. Müller-Westermeier, V. Kveton, O. Bochnicek, P. Stastny, M. Lapin, S. Szalai, T. Szentimrey, T. Cegnar, M. Dolinar, M. Gajic- Capka, K. Zaninovic, Z. Majstorovic, and E. Nieplova, HISTALP - historical instrumental climatological surface time series of the Greater Alpine Region, International Journal of Climatology, 27, 1, 17-4166. (doi:10.1002/joc.1377), 2007 Frei, C. and C. Schär. A precipitation climatology of the Alps from high-resolution rain-gauge observations, Int. J. Climatol., 18, 873 – 900, 1998 Grell G., Dudhia J. and Stauffer D, A Description of the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5), NCAR/TN-398+STR, (http://www.mmm.ucar.edu/mm5/documents/mm5- desc-doc.html), 1994 Mauser, W. and U. Strasser (Eds.), GLOWA-Danube: Integrative Techniken, Szenarien und Strategien zur Zukunft des Wassers im Einzugsgebiet der Oberen Donau, Department für Geographie, Lehrstuhl für Geographie und Geographische Fernerkundung Ludwig-Maximilians-Universität München, (http://www.glowadanube.de/PDF/reports/abschlussbericht_phase2.pdf, accessed 06.02.2009), 2007 Pal, J.S., F. Giorgi, X. Bi, N. Elguindi, F. Solmon, X. Gao, S.A. Rauscher, R. Francisco, A. Zakey, J. Winter, M. Ashfaq, F.S. Syed, J.L. Bell, N.S. Diffenbaugh, J. Karmacharya, A. Konaré, D. Martinez, R.P. da Rocha, L.C. Sloan, and A.L. Steiner, Regional Climate Modeling for the Developing World: The ICTP RegCM3 and RegCNET. Bull. Amer. Meteor. Soc., 88, 1395–1409. 2007
63 Parameter perturbation study with the GEM-LAM: the issue of domain size Leo Separovic 1,3,4 , Ramon de Elia 2,3,4 and René Laprise 1,3,4 1 Université du Québec à Montréal (UQÀM), Montréal (Québec), Canada, separovi@sca.uqam.ca; 2 Consortium Ouranos, Montréal (Québec), Canada; 3 Canadian Network for Regional Climate Modelling and Diagnostics (CRCMD), Montréal (Québec), Canada; 4 Centre pour l’Étude et la Simulation du Climat à l’Échelle Régionale (ESCER), Montréal (Québec), Canada. 1. Introduction Fine-scale processes that are not resolved explicitly in models are parameterized with bulk formulae that depend on large-scale variables. A number of poorly constrained parameters employed in these parameterizations rely on a mixture of theoretical understanding and empirical fitting. The skill level of a model is achieved through the process of tuning in which parameters are adjusted on the trial-anderror basis. However, due to the large number of parameters present in the model, it is impossible to run the model for every combination of their plausible value and examine all possible skill scores. Hence, tuning does not guarantee neither an optimal skill level nor that this skill is not achieved as a compensation of model errors. This study aims at quantifying uncertainty in dynamical downscaling originating in poorly constrained Regional Climate Model (RCM) parameters. For this purpose we are currently designing a Perturbed-Parameters Ensemble (PPE) of RCM simulations. Currently underway is the exploratory phase that should allow for an optimized trade-off between the available computational/human resources and ensemble size necessary to formally quantify parameter uncertainty. Important decisions in the PPE design are related to the domain size and position as well as integration period. The domain (integration period) should be as small (short) as possible due to the enormous computational cost of the PPE, but sufficiently large to allow extrapolation of results to the typical operational RCM simulations. Here we discuss the sensitivity of 3-month averages of model’s variables to parameter perturbations obtained using two domains of different size, focusing on the issue of detection of model response to perturbations against internal variability noise. Currently underway is the study of the nonlinearity of the model output in the parameter space in order to verify whether summing up the responses to perturbations of individual parameters can approximate the response to simultaneous multiple-parameter perturbations. 2. Methodology The model is the limited-area version of the Global Environmental Multi-scale (GEM) model, described in Yeh et al. (2002). It is a non-hydrostatic grid-point model based on the semi-Lagrangian, semi-implicit time discretization scheme. It includes a terrain-following vertical coordinate based on hydrostatic pressure with 58 levels in the vertical and the horizontal discretization on an Arakawa C grid. The ensemble integrations are performed over a single year at two domains shown in Fig 1. The large (small) domain consists of 120x120 (72x50) grid points in the zonal and latitudinal direction, respectively, both at the resolution of ~55 km. The domains shown in Fig 1 include a 10-point relaxation zone at the perimeter of their boundaries. The initial and Lateral Boundary Conditions (LBC) are provided by ERA40 reanalyses and ocean surface is from AMIP2. Figure 1. Two domains (large – 120x120 and small, denoted by the rectangle – 72x50 grid points). For each domain, the PPE consist of the standard (unperturbed) version of the model and single-parameter perturbations. Regardless of its origin and magnitude, perturbations also excite internal variability noise. In order to distinguish the model sensitivity to perturbations from this noise, each member of the PPE consists of 5 identical integrations initialized from November 01 trough 05, 1992, each at 00UTC, 24 hours apart. All simulations end on December 01, 1993 at 00UTC, thus providing a 12- month integration period (November 1992 being excluded from consideration). For each model run, time averages are computed for the four distinct seasons. The only exception is the standard model version for which 10 lagged integrations with perturbed initial conditions are performed in order to provide a more robust estimate of noise statistics. The noise standard deviation is estimated by the RMSD of 10 lagged seasonal averages obtained by the standard model version. The model response to a parameter perturbation is quantified by the difference between the two means: that computed from 5 integrations given a parameter perturbation and that from 10 runs of the standard version. This is further referred to as the signal. The statistical significance of the signal is quantified by the rejection level of the null hypothesis (H 0 ) that the two means are drawn from the same distribution, under the two-tailed t-distribution test. Finally, results obtained using the two domains (Fig. 1) are compared over the area of the smaller domain, excluding its lateral boundary relaxation zone. The somewhat unusual shape of the small domain (Fig. 1) is chosen to shorten the residence time within the domain of air parcels carried by the climatological zonal flow, while keeping a large latitudinal span for comparison with the results obtained using the large domain. It is shown in Lucas-Picher et al. (2008) that the internal variability noise decreases with decreasing average residence time of air parcels within the 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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Page 44 and 45: 18 The Asian summer monsoon in ERA4
Page 46 and 47: 20 Added value of limited area mode
Page 48 and 49: 22 Sensitivity of CRCM basin annual
Page 50 and 51: 24 High-resolution dynamical downsc
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Page 54 and 55: 28 technique, as it is based on the
Page 56 and 57: 30 4. Heating mechanism In order to
Page 58 and 59: 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 102 and 103: 76 Investigation of precipitation o
Page 104 and 105: 78 Impacts of the spectral nudging
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Page 110 and 111: 84 Figure 3: As Figure 1 but for Wi
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Page 123 and 124: 97 Stratospheric variability and it
Page 125 and 126: 99 Effects of numerical methods on
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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
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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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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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