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94 Acknowledgements This work is part of the “HYACINTS” project, www.hyacints.dk, and has been funded by the Danish Council for Strategic Research under the Programme Commision on “Sustainable Energy and Environment”. References Butts MB, Payne JT, Kristensen M and Madsen H. An evaluation of the impact of model structure on hydrological modelling uncertainty for streamflow prediction. Journal of Hydrology, 298, pp 242-266, 2004. Christensen JH, Christensen OB, Lopez P, van Meijgaard E, Botzet M. The HIRHAM4 Regional Atmospheric Climate Model; DMI Scientific Report 96-4. Danish Meteorological Institute, 1996. Christensen OB, Drews M, Christensen JH, Dethloff K, Ketelsen K, Hebestadt I, Rinke A. The HIRHAM Regional Climate Model Version 5β; DMI Technical Report 06-17. Danish Meteorological Institute, 2006. Graham DN and Butts MB, Flexible, integrated watershed modelling with MIKE SHE. In Watershed Models, Eds. V.P. Singh & D.K. Frevert, pp 245-272, CRC Press. ISBN: 0849336090, 2005. Graham LP, Hagemann S, Jaun S, Beniston M. On interpreting hydrological change from regional climate models, 81 Suppl. 1, 97-122. doi: 10.1007/s10584-006- 9217-0, 2007. Gregersen JB, Gijsbers PJA, Westen SJP. OpenMI: Open modelling interface. Journal of Hydroinformatics, 09.3, 175-191. doi: 10.2166/hydro, 2007. Overgaard J, Butts MB, Rosbjerg D. Improved scenario prediction by using coupled hydrological and atmospheric models. In: Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management (Eds: Boegh E, Kunstmann H, Wagner T, Hall A, Bastidas L, Franks S, Gupta H, Rosbjerg D Schaake J), IAHS Publ. 313, pp 242-248, 2007. Overgaard J, Butts MB, Rosbjerg D, Gregersen J. Coupling hydrological and meteorological models using OpenMI to investigate land-use and climate change. Proceedings of the 7th International Conference on Hydroinformatics Editted by P. Gourbesville, J. Cunge, V. Guinot, S-Y Liong., HIC 2006, Nice, France, September 2006, Vol III, 2040-2047, 2006. Roeckner E, Bäuml G, Bonaventura L, Brokopf R, Esch M, Giorgetta M, Hagemann S, Kirchner I, Kornblueh L, Manzini E, Rhodin A, Schlese U, Schulzweida U, and Tompkins A. The atmospheric general circulation model ECHAM5. Part 1. Model description. Report no. 349, Max-Planck-Institut für Meteorologie (MPI-M), 2003.
95 A parameterization of aircraft induced cloudiness in the CLM regional climate model Andrew Ferrone, Philippe Marbaix, Ben Matthews, Ralph Lescroart and Jean-Pascal van Ypersele Institut d’astronomie et de géophysique G. Lemaître (ASTR), Université catholique de Louvain (UCL) 2, chemin du cyclotron 1348 Louvain-la-Neuve, Belgium 1. Introduction Under well defined conditions (see Schumann, 2000) the hot and moist engine exhaust gases of airplanes at cruise level trigger the formation of condensation trails (contrails) in the wake or the aircraft. If the surrounding air is icesupersaturated these contrails become persistent and transform into cirrus clouds and can persist for several hours (IPCC,1999). It has been shown (IPCC, 1999, IPCC, 2007) that this socalled aircraft induced cloudiness (AIC) has a warming impact on climate, although the magnitude of the impact is not well determined. This study aims at modeling the contrail formation and their evolution into cirrus clouds with the regional climate model CLM over Europe. A regional model has been chosen as its cloud microphysics are generally more detailed than in global climate models. The higher resolution of the model (20km x 20km) permits a more detailed representation of small-scale structures like contrails. The global contrail coverage is spatially very inhomogeneous. Therefore Europe has been chosen as a model domain as it presents very high coverage due to the high traffic density over this region. 2. Model and contrail parameterisation The regional climate model CLM (Climate version of the Lokal Model) (Will et al., 2008) has been chosen for this study. This regional climate model is derived from the operational non-hydrostatic weather-forecast model COSMO used among others by the German Weather Service (DWD). The cloud microphysical scheme is a one moment, two category ice-scheme based on (Lin et al., 1983). To this scheme a parameterization was added (figure 1) that creates supplementary ice clouds when the meteorological conditions permit the creation of contrails (Schmidt- Appleman criterion, see Schumann, 2000) and the air is icesupersaturated. The subgridscale growth of contrails in the model is based on results by Lewellen and Lewellen (2001) and depends on the ambient supersaturation level. The supplementary ice created is then fully integrated into the microphysics of the climate model, which allows the simulation of the growth of linear contrails into cirrus clouds. 3. Some preliminary results For these preliminary runs a homogeneous flight distribution has been assumed (i.e. a plane is continuously flying in every grid-cell). In all grid boxes the flown distance has been set to the average over Europe derived from the AERO2k database (Eyers et al., 2005) Figure 2 shows the difference of ice mass between the run with the homogeneous contrail forcing and a reference run, without contrails averaged over January 2005 and all model levels. This tells us the location of regions which are more prone than others to form persistent contrails This experiment shows that especially over the northern Iberian peninsula, but also over the English Channel and the North-Sea, conditions are present to form many persistent contrails, whereas the region west of the Alps shows a rather low potential. These findings are in accordance with satellite based observations of linear contrails (Meyer et al., 2002). Figure 1. Schematic representation of the contrail parameterization in the model, with q v the specific humidity calculated by CLM, q v si the specific ice saturation humidity, α a parameter to account for the sub-grid scale growth of contrails and RH the relative humidity Figure 3 shows a vertical slice through the model domain at the black line indicated in figure 2. Even though in this theoretical experiment planes are flying at every altitude (including the stratosphere) it can be seen that contrails form only between 8 and 12 km, which are the heights where most ice-supersaturation is observed (Gettelman et al., 2006). Figure 2. Relative difference (in %) of the mass of ice given by model, averaged over all model levels between the reference run and a run including the contrail parameterisation, for January 2005. The darker the red aera the more ice has been formed by aircrafts in this idealised case (see text)
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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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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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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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Page 133 and 134: 107 Simulation of the precipitation
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Page 139 and 140: 113 4. Results The method how to do
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Page 153 and 154: 127 a) Knutson, T.R., J.J. Sirutis,
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Page 165 and 166: 139 Investigation of ‘Hurricane K
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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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