Climate Dynamics

, Volume 40, Issue 11–12, pp 2903–2918 | Cite as

On the role of domain size and resolution in the simulations with the HIRHAM region climate model

  • Morten A. D. Larsen
  • Peter Thejll
  • Jens H. Christensen
  • Jens C. Refsgaard
  • Karsten H. Jensen


We investigate the simulated temperature and precipitation of the HIRHAM regional climate model using systematic variations in domain size, resolution and detailed location in a total of eight simulations. HIRHAM was forced by ERA-Interim boundary data and the simulations focused on higher resolutions in the range of 5.5–12 km. HIRHAM outputs of seasonal precipitation and temperature were assessed by calculating distributed model errors against a higher resolution data set covering Denmark and a 0.25° resolution data set covering Europe. Furthermore the simulations were statistically tested against the Danish data set using bootstrap statistics. The results from the distributed validation of precipitation showed lower errors for the winter (DJF) season compared to the spring (MAM), fall (SON) and, in particular, summer (JJA) seasons for both validation data sets. For temperature, the pattern was in the opposite direction, with the lowest errors occurring for the JJA season. These seasonal patterns between precipitation and temperature are seen in the bootstrap analysis. It also showed that using a 4,000 × 2,800 km simulation with an 11 km resolution produced the highest significance levels. Also, the temperature errors were more highly significant than precipitation. In similarly sized domains, 12 of 16 combinations of variables, observation validation data and seasons showed better results for the highest resolution domain, but generally the most significant improvements were seen when varying the domain size.


HIRHAM RCM Climate model Domain Temperature Precipitation 



The present study was funded by a grant from the Danish Strategic Research Council for the project HYdrological Modelling for Assessing Climate Change Impacts at differeNT Scales (HYACINTS– under contract no: DSF-EnMi 2104-07-0008. We acknowledge the E-OBS dataset from the EU-FP6 project ENSEMBLES (, the data providers in the ECA&D project (, the HOBE project (Jensen and Illangasekare 2011) and the CRES project ( Also we would like to thank, Simon Stisen, Philippe Lucas-Picher, Søren Højmark Rasmussen, Ole Bøssing Christensen, Frederik Boberg, Martin Drews, Flemming Vejen and Michael Scharling for assistance and comments during the process.


  1. Achberger C, Linderson ML, Chen D (2003) Performance of the Rossby Centre regional atmospheric model in Southern Sweden: comparison of simulated and observed precipitation. Theor Appl Climatol 76:219–234. doi: 10.1007/s00704-003-0015-6 CrossRefGoogle Scholar
  2. Adam JC, Lettenmaier DP (2003) Adjustment of global gridded precipitation for systematic bias. J Geophys Res 108:D9 4257. doi: 10.1029/2002JD002499
  3. Alexandru A, De Elía R, Laprise R (2007) Internal variability in regional climate downscaling at the seasonal time scale. Mon Weather Rev 135:3221–3238. doi: 10.1175/MWR3456.1 CrossRefGoogle Scholar
  4. Allerup P, Madsen H (1980) Accuracy of point precipitation measurements. Nord Hydrol 11:57–70Google Scholar
  5. Antic S, Laprise R, Denis B, De Elía R (2006) Testing the downscaling ability of a one-way nested regional climate model in regions of complex topography. Clim Dyn 26:305–325. doi: 10.1007/s00382-005-0046-z CrossRefGoogle Scholar
  6. Berg P, Christensen JH (2008) Poor man’s reanalysis over Europe. WATCH Technical 5 Report No. 2Google Scholar
  7. Brasseur O, Gallée H, Creutin JD, Lebel T, Marbaix P (2002) High resolution simulations of precipitation over the Alps with the perspective of coupling to hydrological models. Climatic change: implications for the hydrological cycle and for water management. Adv Global Chang Res 10:75–99CrossRefGoogle Scholar
  8. Cappelen J, Jørgensen BV (2011) Dansk vejr siden 1874—måned for måned med temperatur, nedbør og soltimer samt beskrivelser af vejret—with English translations (Danish Weather since 1874—month by month with Temperature, Precipitation and Hours of Sun Light and Weather Descriptions—with English Translations). Danish Meteorological Institute Technical Report 11-02Google Scholar
  9. Caya D, Biner S (2004) Internal variability of RCM simulations over an annual cycle. Clim Dyn 22:33–46. doi: 10.1007/s00382-003-0360-2 CrossRefGoogle Scholar
  10. Christensen JH, Carter TR, Giorgi F (2002) PRUDENCE employs new methods to assess European climate change. EOS 83:147. doi: 10.1029/2002EO000094 CrossRefGoogle Scholar
  11. Christensen OB, Drews M, Christensen JH, Dethloff K, Ketelsen K, Hebestadt I, Rinke A (2007) The HIRHAM regional climate model version 5 (β). Danish Meteorological Institute Technical Report 06-17Google Scholar
  12. Christensen JH, Boberg F, Christensen OB, Lucas-Picher P (2008) On the need for bias correction of regional climate change projections of temperature and precipitation. Geophys Res Lett 35:L20709. doi: 10.1029/2008GL035694 CrossRefGoogle Scholar
  13. Clemens M, Bumke K (2002) Precipitation fields over the Baltic Sea derived from ship rain gauge measurements on merchant ships. Boreal Environ Res 7:425–436Google Scholar
  14. CRES (2012) Centre for Regional Change in the Earth System. Accessed 1 March 2012
  15. De Castro M, Gallardo C, Jylha K, Tuomenvirta H (2007) The use of a climate-type classification for assessing climate change effects in Europe from an ensemble of nine regional climate models. Clim Chang 81:329–341. doi: 10.1007/s10584-006-9224-1 CrossRefGoogle Scholar
  16. Denis B, Laprise R, Caya D (2003) Sensitivity of a regional climate model to the resolution of the lateral boundary conditions. Clim Dyn 20:107–126. doi: 10.1007/s00382-002-0264-6 Google Scholar
  17. Dimitrijevic M, Laprise R (2005) Validation of the nesting technique in a regional climate model and sensitivity tests to the resolution of the lateral boundary conditions during summer. Clim Dyn 25:555–580. doi: 10.1007/s00382-005-0023-6 CrossRefGoogle Scholar
  18. Efron B (1987) Better bootstrap confidence intervals. J Am Stat Assoc 82:171–182CrossRefGoogle Scholar
  19. Elía RD, Côté H (2010) Climate and climate change sensitivity to model configuration in the Canadian RCM over North America. Meteorol Z 19:325–339. doi: 10.1127/0941-2948/2010/0469 CrossRefGoogle Scholar
  20. Frich P, Rosenørn S, Madsen H, Jensen JJ (1997) Observed Precipitation in Denmark, 1961-90. Danish Meteorological Institute Technical Report 97-8Google Scholar
  21. Giorgi F, Bi X (2000) A study of internal variability of a regional climate model. J Geophys Res 105(D24):29503–29521. doi: 10.1029/2000JD900269 CrossRefGoogle Scholar
  22. Giorgi F, Marinucci MR (1996) An investigation of the sensitivity of simulated precipitation to model resolution and its implications for climate studies. Mon Weather Rev 124:148–166CrossRefGoogle Scholar
  23. Graham DN, Butts MB (2006) Flexible, integrated watershed modelling with MIKE SHE. In: Singh VP, Frevert DK (eds) Watershed models. CRC Press, Boca Raton, pp 245–272, ISBN: 0849336090Google Scholar
  24. Haylock MR, Cawley GC, Harpham C, Wilby RL, Goodess CM (2006) Downscaling heavy precipitation over the United Kingdom: a comparison of dynamical and statistical methods and their future scenarios. Int J Climatol 26:1397–1415. doi: 10.1002/joc.1318 CrossRefGoogle Scholar
  25. Haylock MR, Hofstra N, Klein Tank AMG, Klok EJ, Jones PD, New M (2008) A European daily high-resolution gridded dataset of surface temperature and precipitation. J Geophys Res 113:D20119. doi: 10.1029/2008JD10201 CrossRefGoogle Scholar
  26. Hofstra N, New M, McSweeney C (2010) The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data. Clim Dyn 35:841–858. doi: 10.1007/s00382-009-0698-1 CrossRefGoogle Scholar
  27. Ikeda K, Rasmussen R, Liu C, Gochis D, Yates D, Chen F, Tewari M, Barlage M, Dudhia J, Miller K, Arsenault K, Grubišić V, Thompson G, Guttman E (2010) Simulation of seasonal snowfall over Colorado. Atmos Res 97:462–477. doi: 10.1016/j.atmosres.2010.04.010 CrossRefGoogle Scholar
  28. IPCC (2007) Summary for Policymakers. 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, USAGoogle Scholar
  29. Jacob D, Bärring L, Christensen OB, Christensen JH, De Castro M, Déqué M, Giorgi F, Hagemann S, Hirschi M, Jones R, Kjellström E, Lenderink G, Rockel B, Sánchez E, Schär C, Seneviratne SL, Somot S, Van Ulden AP, Van Den Hurk BJJM (2007) An inter-comparison of regional climate models for Europe: model performance in present-day climate. Clim Chang 81:31–52. doi: 10.1007/s10584-006-9213-4 CrossRefGoogle Scholar
  30. Jensen KH, Illangasekare TH (2011) HOBE: a Hydrological Observatory. Vadose Zone J. 10:1–7. doi: 10.2136/vzj2011.0006 CrossRefGoogle Scholar
  31. Jones RG, Murphy JM, Noguer M (1995) Simulation of climate change over Europe using a nested regional-climate model. I:assessment of control climate, including sensitivity to location of lateral boundaries. Q J R Meteorol Soc 121:1413–1449Google Scholar
  32. Juang HMH, Hong SY (2001) Sensitivity of the NCEP regional spectral model to domain size and nesting strategy. Mon Weather Rev 129:2904–2922CrossRefGoogle Scholar
  33. Kendon EJ, Jones RG, Kjellström E, Murphy JM (2010) Using and designing GCM–RCM ensemble regional climate projections. J Clim 23:6485–6503. doi: 10.1175/2010JCLI3502.1 CrossRefGoogle Scholar
  34. Kiktev D, Sexton DMH, Alexander L, Folland CK (2003) Comparison of Modeled an Observed trends in indices of daily climate extremes. J Climate 16:3560–3571CrossRefGoogle Scholar
  35. Kjellström E, Bärring L, Jacob D, Jones R, Lenderink G, Schär C (2007) Modelling daily temperature extremes: recent climate and future changes over Europe. Climatic Change 81(249–265 Supplement):1. doi: 10.1007/s10584-006-9220-5 Google Scholar
  36. Køltzow MAØ, Iversen T, Haugen JE (2011) The Importance of Lateral Boundaries, Surface Forcing and Choice of Domain Size for Dynamical Downscaling of Global Climate Simulations. Atmosphere 2:67–95. doi: 10.3390/atmos2020067 CrossRefGoogle Scholar
  37. Laursen EV, Thomsen RS, Cappelen J (1999) Observed air temperature, humidity, pressure, cloud cover and weather in Denmark—with climatological standard normals, 1961-90. Danish Meteorological Institute Technical Report 99-5Google Scholar
  38. Leduc M, Laprise R (2009) Regional climate model sensitivity to domain size. Clim Dyn 32:833–854. doi: 10.1007/s00382-008-0400-z CrossRefGoogle Scholar
  39. Li F, Collins WD, Wehner MF, Williamson DL, Olson JG, Algieri C (2011) Impact of horizontal resolution on simulation of precipitation extremes in an aqua-planet version of Community Atmospheric Model (CAM3). Tellus 63A:884–892. doi: 10.1111/j.1600-0870.2011.00544.x Google Scholar
  40. Lind P, Kjellström E (2008) Temperature and precipitation changes in Sweden, a wide range of model-based projections for the 21st century. SMHI Reports meteorology and Climatology, No 113Google Scholar
  41. Lucas-Picher P, Boberg F, Christensen JH, Berg P (2012) Dynamical downscaling with reinitializations: a method to generate fine-scale climate data sets suitable for impact studies. Revised version submitted to J HydrometeorolGoogle Scholar
  42. May W (2007) The simulation of the variability and extremes of daily precipitation over Europe by the HIRHAM regional climate model. Global Planet Change 57:59–82. doi: 10.1016/j.gloplacha.2006.11.026 CrossRefGoogle Scholar
  43. Mearns LO, Gutowski WJ, Jones R, Leung LY, McGinnis S, Nunes AMB, Qian Y (2009) A regional climate change assessment program for North America. EOS 90:311–312. doi: 10.1029/2009EO360002 CrossRefGoogle Scholar
  44. Murphy J (1999) An Evaluation of Statistical and Dynamical Techniques for Downscaling Local Climate. J Climate 12:2256–2284CrossRefGoogle Scholar
  45. Pryor SC, Nikulin G, Jones CG (2012) Influence of spatial resolution on Regional Climate Model derived wind climates. J Geophys Res (in press). doi: 10.1029/2011JD016822
  46. Rapaić M, Leduc M, Laprise R (2010) Evaluation of the internal variability and estimation of the downscaling ability of the Canadian Regional Climate Model for different domain sizes over the north Atlantic region using the Big-Brother experimental approach. Clim Dyn 36:1979–2001. doi: 10.1007/s00382-010-0845-8 CrossRefGoogle Scholar
  47. Rauscher SA, Seth A, Qian JH, Camargo SJ (2006) Domain choice in an experimental nested modeling prediction system for South America. Theor Appl Climatol 86:229–246CrossRefGoogle Scholar
  48. Rauscher SA, Coppola E, Piani C, Giorgi F (2010) Resolution effects on regional climate model simulations of seasonal precipitation over Europe. Clim dyn 35:685–711. doi: 10.1007/s00382-009-0607-7 CrossRefGoogle Scholar
  49. Rinke A, Marbaix P, Dethloff K (2004) Internal variability in Arctic regional climate. Clim Res 27:197–209CrossRefGoogle Scholar
  50. Roosmalen LV, Christensen JH, Butts MB, Jensen KH, Refsgaard JC (2010) An intercomparison of regional climate model data for hydrological impact studies in Denmark. J Hydrol 380:406–419. doi: 10.1016/j.jhydrol.2009.11.014 CrossRefGoogle Scholar
  51. Samuelsson P, Jones CG, Willén U, Ullerstig A, Gollvik S, Hansson U, Jansson C, Kjellström E, Nikulin G, Wyser K (2011) The rossby centre regional climate model RCA3: model description and performance. Tellus 63A:4–23Google Scholar
  52. Scharling M (1999a) Klimagrid—Danmark—Nedbør 10*10 Km (ver.2) (Climate grid—Denmark—Precipitation 10*10 Km (Ver. 2)). Danish Meteorological Institute Technical Report 99-15Google Scholar
  53. Scharling M (1999b) Klimagrid–Danmark–Nedbør, lufttemperatur og potentiel fordampning—20*20 & 40*40 Km (Climate grid—Denmark—Precipitation, Air Temperature and Potential Evapotranspiration—20*20 and 40*40 Km). Danish Meteorological Institute Technical Report 99-12Google Scholar
  54. Scharling M, Kern-Hansen C (2000) Praktisk anvendelse af nedbørkorrektion på gridværdier (Practical use of Correction of Precipitation). Danish Meteorological Institute Technical Report 00-21Google Scholar
  55. Seth A, Giorgi F (1998) The effect of domain choice on summer precipitation simulation and sensitivity in a regional climate model. J Clim 11:2698–2712CrossRefGoogle Scholar
  56. Stahl K, Tallaksen LM, Gudmundsson L, Christensen JH (2011) Streamflow data from small basins: a challenging test to high resolution regional climate modeling. J Hydrometeorol 12:900–912. doi: 10.1175/2011JHM1356.1 CrossRefGoogle Scholar
  57. Stisen S, Sonnenborg TO, Højbjerg AL, Troldborg L, Refsgaard JC (2010) Evaluation of climate input biases and water balance issues using a coupled surface-subsurface model. Vadose Zone J 10:37–53. doi: 10.2136/vzj2010.0001 CrossRefGoogle Scholar
  58. Teutschbein C, Seibert J (2010) Regional climate models for hydrological impact studies at the catchment scale: a review of recent modeling strategies. Geography Compass 4:834–860. doi: 10.1111/j.1749-8198.2010.00357.x CrossRefGoogle Scholar
  59. Torma C, Coppola E, Giorgi F, Bartholdy J, Pongrácz R (2011) Validation of a high-resolution version of the regional climate model RegCM3 over the carpathian basin. J Hydrometeorol 12:4–23. doi: 10.1175/2010JHM1234.1.84-100 CrossRefGoogle Scholar
  60. Uppala S, Dee D, Kobayashi S, Berrisford P, Simmons A (2008) Towards a climate data assimilation system: status update of ERA-Interim. ECMWF Newsletter No. 115, 12–18Google Scholar
  61. Van de Beek CZ, Leijnse H, Torfs PJJF, Uijlenhoet R (2011) Climatology of daily rainfall semi-variance. Hydrol Earth Syst Sci 15:171–183. doi: 10.5194/hess-15-171-2011 CrossRefGoogle Scholar
  62. Van Der Linden P, Mitchell JFB (eds) (2009) ENSEMBLES: Climate Change and its Impacts: Summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3 PB, UK. pp 160Google Scholar
  63. Vejen F (2002) Korrektion for fejlkilder på måling af nedbør—Korrektionsprocenter ved udvalgte stationer i 2001 (Correction of Sources of Error in the Measurement of Precipitation—Correction percentages on chosen stations in 2002). Danish Meteorological Institute Technical Report 02-08Google Scholar
  64. Vejen F, Madsen H, Allerup P (2000) Korrektion for fejlkilder på måling af nedbør (Correction of Sources of Error in the Measurement of Precipitation). Danish Meteorological Institute Technical Report 00-20Google Scholar
  65. Wang B, Yang H (2008) Hydrological issues in lateral boundary conditions for regional climate modelling: simulation of east asian summer monsoon in 1998. Clim Dyn 31:477–490. doi: 10.1007/s00382-008-0385-7 CrossRefGoogle Scholar
  66. Yang D, Elomaa E, Tuominen A, Aaltonen A, Goodison B, Gunther T, Golubev V, Sevruk B, Madsen H, Milkovic J (1999) Wind-induced precipitation undercatch of the Hellmann Gauges. Nord Hydrol 30:57–80Google Scholar
  67. Yang W, Andréasson J, Graham LP, Olsson J, Rosberg J, Wetterhall F (2010) Distribution based scaling to improve usability of regional climate model projections for hydrological climate change impacts studies. Hydrol Res 41:211–229CrossRefGoogle Scholar
  68. Zwiers FW (1990) The effect of serial correlation on statistical inferences made with resampling procedures. J Clim 3:1452–1461CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Morten A. D. Larsen
    • 1
  • Peter Thejll
    • 2
  • Jens H. Christensen
    • 2
  • Jens C. Refsgaard
    • 3
  • Karsten H. Jensen
    • 1
  1. 1.Department of Geography and GeologyUniversity of CopenhagenCopenhagen KDenmark
  2. 2.Danish Meteorological InstituteCopenhagenDenmark
  3. 3.Geological Survey of Denmark and GreenlandCopenhagenDenmark

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