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74 ( ) with i = 1,K,n; j = 1,K,m; ψ i, j =ψ r i ,λ j r i ∈ [ 0,ar]; λ j ∈ [ 0, 2π[., then the convolution formula is written as follows: ψ = r , λ ( r , λ ) i ∑ ∑ = ∑ k l r λ d i − k ≤dmax d j − l ≤d max where j ∑ k r d i − k ≤dmax ψ r r ( r , λ ) ⋅ w( d ) ⋅ s( d ) r r ( ) ⋅ s( d ) r i − k max λ r r λ ( r , λ ) ⋅ w( d ) ⋅ w( d ) ⋅ s( d ) ⋅ s( d ) ∑ k l r λ d i − k ≤dmax d j − l ≤d max r d i-k and d k ψ λ j-l d λ l k ∑ k ≤d λ r r λ ( ) ⋅ w( d ) ⋅ s( d ) ⋅ s( d ) w d j−l between two points r i ,λ j λ ( ) d j l j w d i−k j−l i−k i−k i−k i−k i−k i−k i−k j−l j−l are the radial and azimuthally distances r ( ) and ( rk , λ l ), and ( d i k ) s − are the surface areas around the ( ) s − and rk , λ l point. The convolution is calculated up to a user prescribed distance d max between the application point and all other points necessary for the convolution. When the convolution is applied for the components of a vector (e.g. the horizontal winds), we must consider the representation of the vector components relative to the same referential centred on the application point. The polar grid was used in this project as an intermediate step to the application of the filter on a spherical latitudelongitude stretched grid. In both cases, at the origin of a polar coordinate system or at the north and south poles of the sphere, the lines of constant polar angle or constant longitude converge in a single point. This convergence has consequence a severe time-stepping limit (the so-called «pole problem»). One way to stabilize the solution of a numerical differential equation near the pole is to damp the waves that are unstable for a chosen timestep at a rate that exceeds the exponential growth (Williamson and Laprise 2000). 4. Results We present in this section two sets of tests performed with this smoothing operator. In both cases we used a large-scale cosine signal represented into a polar domain. An artificial noise was added to represent the small-scale noise to be removed. We first verified the efficiency of the convolution operator to act as polar filter using a uniform polar grid. In figure 1 is presented the total signal (a) and the filtered signal (b). We find that the amplitude of the large-scale scale signal is unchanged and that the short-scale noise is removed. In the second case we used a stretched polar grid and the filter was applied outside the uniform high-resolution area to remove the anisotropy. When the filter was built, we had decreased the resolution with 8% in the radial direction and with 3.8% in azimuthally direction for every grid point in the stretching zones. The total stretching factor is almost 6 in both directions. The large-scale signal was represented on the entire domain and the noise was added in the stretching zones. The initial signal is represented in figure 2a and the filtered signal is represented in figure 2b. One thing to remark is that the filtering operator conserves the quantities and keeps unchanged the amplitude of the long waves. Figure 1: The initial signal composed from a large-scale signal and a small-scale noise in (a) and the filtered signal in b). Figure 2: The initial signal composed of a long wave and the noise added in the stretching zones in a) and the filtered signal in b). References Fox-Rabinovitz, M., J. Côté, B. Dugas, M. Déqué and J.L. McGregor, Variable resolution general circulation models: Stretched-grid model intercomparison project (SGMIP), J. Geophys. Res., 111, D16104, doi:10.1029/2005JD006520, 2006. M. Fox-Rabinovitz, J. Côté, B. Dugas, M. Déqué, J.L. McGregor and A. Belochitski, Stretched-grid Model Intercomparison Project: decadal regional climate simulations with enhanced variable and uniformresolution GCMs, Meteorology and Atmospheric Physics, Volume 100, Numbers 1-4, pp. 159-178, 2008. R. Laprise, Regional climate modelling, J. Comput. Phys., doi:10.1016/j.jcp.2006.10.024, 2006. Surcel, D., Filtres universels pour les modèles numériques à résolution variable, Mémoire de maîtrise en Sciences de l'atmosphère, Université du Québec à Montréal, 104 pp., 2005. Williamson, D., and R. Laprise: Numerical approximations for global atmospheric General Circulation Model, P. Mote and A. O’Neil (eds.) Numerical modeling of global atmosphere in the climate system, Kluwer Academic Publishers, 127- 219, 2000.
75 What details can the regional climate models add to the global projections in the Carpathian Basin? Gabriella Szépszó, Gabriella Csima and András Horányi Hungarian Meteorological Service, Budapest, Hungary (szepszo.g@met.hu) 1. Motivation Recently the continuously improving global climate models are providing solid basis and realistic projections for the synoptic scale characteristics of the climate, however they are at the moment largely insufficient for detailed regional scale estimations. The use of regional climate models ensures a dynamics-based opportunity to interpret and enhance the global results for regional scale. A couple of years ago two regional climate models were adapted at the Hungarian Meteorological Service (HMS): the ALADIN- Climate model developed by Météo France on the basis of the internationally developed ALADIN modelling system and the REMO model developed by the Max Planck Institute for Meteorology in Hamburg. It is anticipated in Hungary that these models are able to give realistic regional climate estimations not only for the next few decades but also for the end of 21st century, particularly for the area of the Carpathian Basin. This area of interest is especially important considering the fact that one of the largest uncertainties in climate projections can be found over the Carpathian Basin as it had already been identified by former large international projects. 2. Model experiments Firstly, the models were integrated for a past period (1961– 1990) with the use of ERA-40 re-analyses as lateral boundary conditions in order to explore the main characteristics of the models’ behaviour in case of “quasiperfect” forcing. The model domains include continental Europe with approximately 25 km horizontal resolution. The models were also integrated with lateral boundary conditions provided by global atmosphere-ocean general circulation models: by ARPEGE/OPA in the case of ALADIN-Climate and ECHAM5/MPI-OM in the case of REMO. With the latter regional climate model a hundred-year transient run was accomplished for the period of 1951–2050, while the ALADIN-Climate simulations covered three thirty-year time slices in 1961–1990, 2021–2050, 2071–2100 over a smaller domain in its focus with Hungary on 10 km horizontal resolution. The regional models were forced with the A1B SRES emission scenario, which is considered as a “realistic” estimate for the evolution of the greenhouse gas concentrations until the end of the 21st century. 3. Results Naturally, the validation of regional climate models for the past climate is an indispensable ingredient in understanding the behaviour of RCMs. This is originating from the considerations that on the one hand successful climate projections can be only expected if the models are already capable to reasonably simulate the past climate and on the other hand the biases of the model for the past might indicate and anticipate biases for the future. The model experiments for the past were validated against the CRUdataset focusing on two main variables: the mean temperature and precipitation. Regarding the future projections the change of the same parameters was investigated for the 2021–2050 period with respect to the reference 1961–1990 one. The analysis was concentrating not only on the regional results but also taking into account the driving global models’ results, in order to carefully scrutinize the possible added value of the regional climate models with respect to the global ones and to draw reliable and robust conclusions in terms of quantitative uncertainties in the projections. Figure 1 shows the change of the annual and winter precipitation over Hungary for 2021–2050 with respect to the model means in 1961–1990 on the basis of the ALADIN-Climate and REMO results. It can be concluded, that the annual change is a non-significant decrease projected quite similarly by both RCMs, however in the case of seasonal changes large differences can be experienced, which is pointing towards large uncertainties. Figure 1. The annual (top) and the winter (bottom: DJF) precipitation change over Hungary for 2021– 2050 with respect to the model means in 1961–1990 based on two RCMs (left: ALADIN-Climate; right: REMO). This workshop contribution will give an overview about the results of regional climate models applied at the HMS with special emphasis on their inter-comparison and evaluation with respect to the global models. References Csima, G. and Horányi, A.: Validation of the ALADIN- Climate regional climate model at the Hungarian Meteorological Service, Időjárás, 112., 3-4, pp. 155- 177, 2008 Szépszó, G. and Horányi, A.: Transient simulation of the REMO regional climate model and its evaluation over Hungary, Időjárás, 112., 3-4, pp. 203-231, 2008
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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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12 Sensitivity study of CRCM-simula
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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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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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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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151 and 30km). Domains D1 (90 and 6
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157 MesoClim - A mesoscale alpine c
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161 analyzed the surface wind behav
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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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181 Verification of simulated near
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183 The North American Regional Cli
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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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225 Figure 2. the 10-year averaged
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227 Use of regional climate models
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235 Arctic regional coupled downsca
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237 Convection-resolving regional c
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241 distribution within the Netherl
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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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