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214 new and more comprehensive climate, climate change and climate impacts for the region. The simulations will also serve as a learning platform for future co-ordinated RCM experiments for other world regions, and not least those that so far are less extensively studied. 7. Acknowledgement We acknowledge the ENSEMBLES project, funded by the European Commission's 6th Framework Programme through contract GOCE-CT-2003-505539. The AMMA participants and project are acknowledged for provision of various observed and modeled data. References Boko, M., I. Niang, A. Nyong, C. Vogel, A. Githeko, M. Medany, B. Osman-Elasha, R. Tabo and P. Yanda, 2007: Africa. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University Press, Cambridge UK, 433-467. Christensen, J.H., B. Hewitson, A. Busuioc, A. Chen, X. Gao, I. Held, R. Jones, R.K. Kolli, W.-T. Kwon, R. Laprise, V. Magaña Rueda, L. Mearns, C.G. Menéndez, J. Räisänen, A. Rinke, A. Sarr and P. Whetton, 2007: Regional Climate Projections. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Hewitt, C. and Griggs, D. Ensembles-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) Eos, Vol. 85, No. 52, 566, 2004. Nakićenović, N. et al. Emission scenarios. A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, 599 pp, 2000. Redelsperger, J.L., Thorncroft, C., Diedhiou, A., Lebel, T., Parker, D.J. and Polcher, J. African Monsoon Multidisciplinary Analysis (AMMA): An International Research Project and Field Campaign. BAMS, Vol 87, Issue 12, pp. 1739–1746, 2006. Rockel, B., Meinke, I., Roads, J., Gutowski, W. J. Jr. Arritt, R., Takle, W E. S. and Jones, C. The Inter-CSE Transferability Study. CEOP Newsletter No.8, 2005. Uppala S. M. et al. The ERA-40 Re-analysis. Q. J. Roy. Meteorol. Soc., Vol 131, 2961—3012, 2006.
215 Assessing the performance of seasonal snow in the NARCCAP RCMs – An analyses for the Upper Colorado River Basin Nadine Salzmann (1,2,3) and Linda Mearns (3) (1) University of Zurich, Department of Geography, Switzerland, nadine.salzmann@geo.uzh.ch (2) University of Fribourg, Department of Geosciences, Switzerland (3) National Center for Atmospheric Research, Institute for the Study of Society and Environment (NCAR/ISSE), Boulder, CO, USA 1. Introduction Seasonal snow is a major component of the hydrological cycle and thus of the climate system Cohen and Rind (1991). The dynamics of the snow cover depends on the atmospheric circulation, which in turn is thermodynamically influenced by the snow pack. In mountain regions, the dynamics of the seasonal snow cover is a key factor for hydrological run off and consequent availability of water for human consumption, irrigation and power generation e.g. Barnett et al. (2005). Due to the proximity to the melting point, snow is particular sensitive to changes in the atmospheric conditions with significant effects on the timing and magnitude of hydrological run off. Among the most promising tools for assessing future changes in the hydrological run off in mountain regions is Regional Climate Models (RCMs). They are able to simulate and provide climate input variables on a regional scale and for heterogeneous landscapes such as mountain topography Leung and Qian (2003). However, so far only a few studies analyzed the performance of mountain snow in RCMs. There are many reasons for that, including foremost the general difficulties associated with process-modeling in complex topography and the lack of ample snow observations in most mountain regions worldwide for validation purposes. Despite these limitations, here, we aim at assessing the performance of the seasonal snow in the RCM simulations calculated for the international RCM program NARCCAP (North American Climate Change Program, www.narccap.ucar.edu) Mearns et al. (2005). 2. Study site and data The following analyses are conducted for the region of the Upper Colorado River Basin (UCRB) in the U.S. Rocky Mountains. Here, the seasonal snow pack of the highest peaks contributes about 70 % of the Colorado River’s total annual run off Christensen et al. (2004). Within the perimeter of the UCRB, 45 SNOTEL observation stations exist, most of them measuring Snow Water Equivalent (SWE), 2m Temperature (2mT) and precipitation (Precip), since 1981 and before. In addition to these point measurements, a second source of ‘observation’ is used in this study; the gridded NARR data (North American Regional Re-analyses), with a horizontal resolution of 32 km. SNOTEL and NARR are compared in the following with results from NARCCAP simulations. Only NCEPdriven NARCCAP runs are used in a first step. The comparison includes the following RCMs, listed with the respective Landsurface Scheme (LSS) used in each RCM. It is in the LSS, where the processes concerning the snow dynamics are defined. ECPC (Experimental Climate Prediction Center) from Scripps Institution of Oceanography, La Jolla, CA, USA. LSS: NOAH MRCC (Modèle Régional Canadien du Climat) from Ouranos Consortium, Montreal (Quebec), Canada. LSS: CLASS RegCM3 (REGional Climate Model) from UC Santa Cruz, ITCP, USA. LSS: BATS WRF (Weather Research and Forecasting Model) from Pacific Northwest National Lab, Washington, USA. LSS: NOAH MM5 (Mesoscale Meteorologic Model) from Iowa State University, Iowa, USA. LSS: NOAH All RCMs are run with a grid spacing of 50 km and cover the UCRB by 10 grid boxes. 3. Analyses and some results ‘SWE’ and the two related variables ‘2mT’ and ‘Precip’ were compared and analyzed in various spatial and temporal terms. It was found that NARCCAP RCMs generally simulate higher air temperatures than NARR and particularly than SNOTEL. However, the departures are relatively constant within different elevation levels, indicating that theoretically a lapse rate correction could be applied for adjusting the NARCCAP results towards the SNOTEL measurements. Though, based on the relatively small elevation difference between the SNOTEL and NARCCAP topography, the lapse-rate factor only explains a minor part of the deviation. Regarding precipitation the NARCCAP models are in general too dry compared to SNOTEL. A large-scale factor that influences the climate and particularly precipitation on the Colorado Plateau is the ENSO. Major El Niño events as e.g. in 1982-83, 1992-93 and 1994-95 produce during winter exceptionally wet weather in the region. It is thus of interest, if these events are reflected in the datasets. However, no clear evidence is found in any of the datasets used here, pointing towards ENSO events. Finally, SWE, shows significant departures between the different RCMs, and also compared to SNOTEL and NARR. While the annual cycle is captured relatively well, the amount of snow as well as the duration and the time of maximum vertical snow depth are significantly underestimated by the NARCCAP RCMs and NARR
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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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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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46 this season, as well as the high
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48 Figure 1. Precipitation rate (mm
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50 References Biau. G., E. Zorita,
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52 Investigation of regional climat
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54 Selected examples of the added v
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56 The climate change in Europe sim
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58 Simulation of South Asian summer
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7.5 8.5 9.5 10.5 60 For the climato
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62 precipitation field was more clo
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64 3. Results Fig. 2 shows the anal
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66 Is the position of the model dom
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68 question E. Finally a 10-member
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Figure 2. Same as in Fig.1 but for
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72 Will, A., M. Baldauf, B. Rockel,
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74 ( ) with i = 1,K,n; j = 1,K,m;
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76 Investigation of precipitation o
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80 Extremes and predictability in t
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82 Large-scale skill in regional cl
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84 Figure 3: As Figure 1 but for Wi
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86 The models capture this pattern
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88 of the simulations due to the di
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91 Examining the relative roles con
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93 Dynamical coupling of the HIRHAM
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95 A parameterization of aircraft i
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97 Stratospheric variability and it
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99 Effects of numerical methods on
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101 Development of a climate model
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103 Study of the capability of the
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105 Evaluation of the land surface
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107 Simulation of the precipitation
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109 Climate simulations over North
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111 Soil organic layer: Implication
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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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Page 243 and 244: 217 Applying the Rossby Centre Regi
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Page 279 and 280: 253 Application of regional-scale c
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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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