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230 Development of a Regional Arctic Climate System Model (RACM) John J. Cassano, Wieslaw Maslowski, William Gutowski and Dennis Lettenmaier John J. Cassano, University of Colorado, Cooperative Institute for Research in Environmental Sciences, Boulder, Co. USA john.cassano@colorado.edu 1. Introduction Observations and global climate system model projections indicate significant changes in the state of the Arctic climate (IPCC, 2007). These changes are impacting many aspects of the climate system (atmosphere, ocean, sea ice, land) and changes in one component of the climate system are intimately linked to changes in other components of the climate system. In order to understand the forcing for observed change in the Arctic and to increase confidence in future projections of Arctic climate change it is necessary to consider coupled changes in the Arctic climate system. Global climate system models include many, but not all, components of the Arctic climate system, yet errors remain in their simulation of the current and past state of the Arctic. These errors arise from many sources including errors propagating into the Arctic from lower latitudes, inadequate representation of polar climate processes, and coarse model resolution. One way to address these shortcomings is through the use of an Arctic regional climate system model. An Arctic regional climate system model allows increased horizontal and vertical resolution and improved model physics that are optimized for polar regions, as well as the use of “perfect” lateral boundary conditions for retrospective simulations. This extend abstract, and associated presentation at the 2 nd Lund Regional-Scale Climate Modeling workshop, discusses plans for and the status of a collaborative project to develop a state-of-the-art Regional Arctic Climate system Model (RACM) including high-resolution atmosphere, land, ocean, sea ice and land hydrology components. The ultimate goal of this project is to perform multi-decadal numerical experiments with RACM using high performance computers to minimize uncertainties and fundamentally improve current predictions of climate change in the northern polar regions. The project involves PIs from four institutions: Naval Postgraduate School (lead institution, PD/PI - Wieslaw Maslowski), University of Colorado in Boulder (co-PI - John J. Cassano), Iowa State University (co-PI - William J. Gutowski) and the University of Washington (co-PI - Dennis P. Lettenmaier) funded by the United States Department of Energy. In addition, collaborators from the University of Alaska – Fairbanks Arctic Regions Supercomputing Center and International Arctic Research Center (Andrew Roberts, Juanxiong He, and Greg Newby) are actively involved in this effort, while several other international experts in Arctic climate modeling are contributing to this project. 2. Regional Arctic Climate System Model The regional Arctic climate system model (RACM) that is currently under development couples atmosphere, ocean, sea ice, and land component models. The atmospheric model used in RACM is a version of the National Center for Atmospheric Research (NCAR) Weather Research and Forecasting (WRF) model that has been optimized for the polar regions. The ocean and sea ice models are basically the same as those used in the NCAR Community Climate System Model (CCSM3), although used on a regional domain: the Los Alamos National Laboratory POP ocean model and CICE sea ice model. Land surface processes and hydrology will be represented by the Variable Infiltration Capacity (VIC) model. These four climate system component models are being coupled using the NCAR CCSM coupler CPL7. The simulation domain of RACM covers the entire pan- Arctic region and includes all sea ice covered regions in the Northern Hemisphere as well as all terrestrial drainage basins that drain to the Arctic Ocean. The simulation domain is shown in Figure 1, with the red line indicating the extent of the atmosphere and land model domains and the blue line indicating the extent of the ocean and sea ice model domains. The ocean and sea ice model will use a horizontal grid spacing of less than 10 km, while the atmosphere and land component models will use a horizontal grid spacing of 50 km or less. 3. Benefits and Outcomes A major product of this effort, and a principal community benefit, will be the development of a high-resolution pan- Arctic climate system model that combines all major climate elements in an internally consistent framework for focused studies. The coupling framework will allow straightforward implementation of additional climatesystem components, thereby extending its capacity to cover a range of natural and human impacts. A primary focus of the project is to improve our understanding of processes leading to reductions in Arctic sea ice cover. One outcome from this effort will be improved prediction of ice-free Arctic Ocean for use in policy planning. This project will facilitate synthesis and integration of historical and new observations with model results. It will involve undergraduate, graduate, and postdoctoral students, who will receive practical training in coupled climate system modeling and/or analysis of model output. References IPCC, 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, 996 pp, 2007
231 Figure 1. Model domain for the regional Arctic climate system model. The extent of the atmosphere and land model domains is shown by the red line and the extent of the ocean and sea ice model domains is shown by the solid blue line. Land topography and ocean bathymetry are given by color shading.
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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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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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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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42 5SL, the results suggest that 5S
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44 are however occasional episodes
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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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78 Impacts of the spectral nudging
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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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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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Page 219 and 220: 193 On the validation of RCMs in te
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Page 231 and 232: 205 CLARIS project: Towards climate
Page 233 and 234: 207 Influence of soil moisture init
Page 235 and 236: 209 Projection of the Changes in th
Page 237 and 238: 211 Regional climate model studies
Page 239 and 240: 213 ENSEMBLES regional climate mode
Page 241 and 242: 215 Assessing the performance of se
Page 243 and 244: 217 Applying the Rossby Centre Regi
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Page 253 and 254: 227 Use of regional climate models
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Page 267 and 268: 241 distribution within the Netherl
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Page 279 and 280: 253 Application of regional-scale c
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Page 293 and 294: 267 Linking climate factors and ada
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Page 303 and 304: 277 6. Analysis of Results (Rain) T
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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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