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64 3. Results Fig. 2 shows the analysis of sensitivity of summer (JJA) time-averaged precipitation to a moderate perturbation of the threshold vertical velocity in the trigger function of the Kain-Fritch deep convection scheme. This parameter is perturbed from its standard value of 3.4 cm/s to 4.8 cm/s. In Fig. 2 the noise standard deviation (a), the signal (b) and the level of rejection of H 0 (c), all computed for summer precipitation are shown. Results obtained with the large domain are on the left and those with the small domain on the right. It can be seen that internal variability noise standard deviation in the large domain (Fig. 2a, left) exhibit very large values that locally reach 2 mm/day. In the same time, in response to the parameter perturbation (Fig. 2b, left), there is a decrease of precipitation over the continent and the southeast portion of the domain, comparable to the noise level. The signal is scarcely distinguishable from internal variability since the level of rejection of H 0 is predominantly below 90% (Fig. 2c, left). Clearly, more simulations with perturbed initial conditions are necessary to provide statistical significance in the large domain. Some more confidence can be attributed to the signal pattern over the continent, since a perturbation of the same parameter and magnitude but of the opposite sign produces a similar pattern of model response but with the opposite sign (not shown). This indicates a strong linear component of the sensitivity to the given parameter. It is also worth noting that the internal variability exhibits a pronounced annual cycle, with maximum in summer and minimum in winter. Hence, the rejection level of H 0 is larger in the remaining seasons. However, the magnitude and the pattern of the signal also considerably vary through seasons. Reduction of domain size decreases the noise magnitude roughly by factor of 3 (Fig. 2a, right). In the same time, comparison of Figs. 2b left and right shows no evidence that the sensitivity of summer precipitation is reduced. One exception is the area of the positive sensitivity off the coast of Virginia. Over land the absolute change in precipitation is locally even larger in the smaller domain. This is reflected in a substantial increase in statistical significance of the signal: the level of rejection of H 0 becomes larger than 99% over a considerable part of the area of comparison (Fig. 2c, right). 4. Discussion and concluding remarks It is clear that reduction in domain size decreases the computational cost of the PPE, both directly through reduction of the number of computational points and indirectly – reducing the number of identical simulations with perturbed initial conditions needed to provide the statistical significance of the signal. Given that the computational resources are usually limited by external funding and thus non-negotiable, the lowering of noise is an important advantage of the small domain because it allows allocation of resources to the PPE size (e.g., more parameters and perturbations can be included in the study). Furthermore, noise can render very difficult the quantification of non-linear components of the model response to simultaneous multiple-parameter perturbations. There are, however, two important disadvantages of the small domain to be noted. Firstly, the results obtained in the small domain are representative for a small region and are of little value for typical RCM domains to which this study is addressed. Secondly, it can be argued that in a too small domain, control of the large-scale interior flow by the LBC may be excessive and suppress the signal. Suppression of the model sensitivity to parameter perturbations would decrease spread among the members of the PPE and yield an underestimation of the uncertainty range with respect to that in typical RCM simulations. Our results provide no evidence that this happens with seasonal precipitation but the concern still remains when less smallscale dominated variables are considered. References Figure 2. Summer seasonal-average precipitation (left – large domain, right – small domain): (a) internal variability noise standard deviation (mm/day); (b) change in response to positive perturbation of the threshold vertical velocity in the Kain-Fritch deep convection (mm/day), and (c) the statistical significance of the change (%). Lucas-Picher, P., D. Caya, S. Biner, and R. Laprise, Quantification of the Lateral Boundary Forcing of a Regional Climate Model Using an Aging Tracer. Mon. Wea. Rev., 136, 4980–4996, 2008. Yeh, K.-S., J. Côté, S. Gravel, A. Méthot, A. Patoine, M. Roch, and A. Staniforth, The CMC–MRB Global Environmental Multiscale (GEM) model. Part III: Nonhydrostatic formulation. Mon. Wea. Rev., 130, 339–356, 2002.
65 Modeling climate over Russian regions: RCM validation and projections Igor Shkolnik, S. Efimov, T. Pavlova, E. Nadyozhina Voeikov Main Geophysical Observatory, 7, Karbyshev str., 194021, St.-Petersburg, Russia, igor@main.mgo.rssi.ru Increased levels of atmospheric greenhouse gases will have bigger effects on climate in northern Eurasia than in most of other regions of the Earth. In this study the estimates of climate and its change potential are obtained using Voeikov Main geophysical observatory regional climate model (RCM) applied to the two major domains in the northern Eurasia (western Russia and Siberia) within the framework of the Northern Eurasia Earth Science Partnership Initiative (NEESPI). The RCM demonstrated satisfactory performance skill in reproducing 20 th century climate and its variability over the northern Eurasia. The presented analysis, in particular, addresses aspects of model validation strategies over the regions with sparce observations (e.g. Siberia and the Russian Far East) at 50 and 25 km resolution. The discretization of RCM output at daily time intervals makes it feasible to investigate statistics of high frequency climate variability and associated rare events. These events at limited modeling resolution are manifested in large-scale slowly evolving climate (weather) anomalies that can be utilized to feed different impact models for more practical assessment. Considered are some changes in the extreme indices of temperature and precipitation in 21 st century over Russia. Additionally, changes in cryosphere characteristics over parts of northern Eurasia are shown. Owing to the largest climate variability the northern Eurasia exhibits significant uncertainties in climate projections. In order to decrease the uncertainties (including those due to natural variability, model sensitivity to prescribed forcings and due to forcings themselves), large samples of RCM simulations are apparently required. Estimates of extreme events and their frequencies of occurrence also require massive RCM ensemble simulations over the region.
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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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VI Soil organic layer: Implications
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X Predicting water resources in Wes
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XII The impact of atmospheric therm
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2 5. Influence of SN on the Ensembl
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12 Sensitivity study of CRCM-simula
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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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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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149 responsible for the excessive s
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151 and 30km). Domains D1 (90 and 6
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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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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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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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181 Verification of simulated near
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183 The North American Regional Cli
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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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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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217 Applying the Rossby Centre Regi
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221 ALADIN-Climate/CZ simulation of
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235 Arctic regional coupled downsca
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245 Impact of convective parameteri
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249 Regional climate change impact
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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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