Climate Dynamics

, Volume 43, Issue 7–8, pp 1753–1772 | Cite as

Sensitivity of the regional climate model RegCM4.2 to planetary boundary layer parameterisation

  • Ivan GüttlerEmail author
  • Čedo Branković
  • Travis A. O’Brien
  • Erika Coppola
  • Branko Grisogono
  • Filippo Giorgi


This study investigates the performance of two planetary boundary layer (PBL) parameterisations in the regional climate model RegCM4.2 with specific focus on the recently implemented prognostic turbulent kinetic energy parameterisation scheme: the University of Washington (UW) scheme. When compared with the default Holtslag scheme, the UW scheme, in the 10-year experiments over the European domain, shows a substantial cooling. It reduces winter warm bias over the north-eastern Europe by 2 °C and reduces summer warm bias over central Europe by 3 °C. A part of the detected cooling is ascribed to a general reduction in lower tropospheric eddy heat diffusivity with the UW scheme. While differences in temperature tendency due to PBL schemes are mostly localized to the lower troposphere, the schemes show a much higher diversity in how vertical turbulent mixing of the water vapour mixing ratio is governed. Differences in the water vapour mixing ratio tendency due to the PBL scheme are present almost throughout the troposphere. However, they alone cannot explain the overall water vapour mixing ratio profiles, suggesting strong interaction between the PBL and other model parameterisations. An additional 18-member ensemble with the UW scheme is made, where two formulations of the master turbulent length scale in unstable conditions are tested and unconstrained parameters associated with (a) the evaporative enhancement of the cloud-top entrainment and (b) the formulation of the master turbulent length scale in stable conditions are systematically perturbed. These experiments suggest that the master turbulent length scale in the UW scheme could be further refined in the current implementation in the RegCM model. It was also found that the UW scheme is less sensitive to the variations of the other two selected unconstrained parameters, supporting the choice of these parameters in the default formulation of the UW scheme.


Eddy heat diffusivity Structural uncertainty RegCM Systematic errors 



ECMWF ERA-Interim data used in this study have been obtained from the ECMWF data server. University of East Anglia CRU data used in this study have been obtained from Surface flux measurements from the EUMETNET organized C-SRNWP Project have been obtained from the COSMO consortium database ( and provided by the FMI, KNMI, DWD and Meteo-France. Computations and visualizations in this study have been performed using cdo (, GrADS ( and R ( software. Branko Grisogono is supported by the Croatian Ministry of Science, Education and Sports (MZOS) and Croatian Science Foundation through projects BORA-MZOS 119-1193086-1311 and CATURBO-HRZZ 09/151. Ivan Güttler and Čedo Branković are supported by the MZOS project 004-1193086-3035. The contribution by T.A. O’Brien was supported by the Director, Office of Science, Office of Biological and Environmental Research of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 as part of the Regional and Global Climate Modeling Program (RGCM). We thank to two anonymous reviewers for their constructive criticism, comments and suggestions that greatly improved the original manuscript.

Supplementary material

382_2013_2003_MOESM1_ESM.doc (726 kb)
Supplementary material 1 (DOC 726 kb)
382_2013_2003_MOESM2_ESM.doc (4.2 mb)
Supplementary material 2 (DOC 4,324 kb)


  1. Baklanov A, Grisogono B, Bornstein R, Mahrt L, Zilitinkevich S, Taylor P, Larsen S, Rotach M, Fernando HJS (2011) On the nature, theory, and modeling of atmospheric planetary boundary layers. Bull Am Meteor Soc 92:123–128CrossRefGoogle Scholar
  2. Bellprat O, Kotlarski S, Lüthi D, Schär C (2012) Exploring perturbed physics ensembles in a regional climate model. J Clim 25:4582–4599CrossRefGoogle Scholar
  3. Blackadar AK (1962) The vertical distribution of wind and turbulent exchange in a neutral atmosphere. J Geophys Res 67:3095–3102CrossRefGoogle Scholar
  4. Bretherton CS, Park S (2009) A new moist turbulence parameterization in the community atmosphere model. J Clim 22:3422–3448CrossRefGoogle Scholar
  5. Coppola E, Giorgi F, Mariotti L, Bi X (2012) RegT-Band: a tropical band version of RegCM4. Clim Res 52:115–133CrossRefGoogle Scholar
  6. Curry JA, Webster PJ (2011) Climate science and the uncertainty monster. Bull Am Meteor Soc 92:1667–1682CrossRefGoogle Scholar
  7. Cuxart J et al (2006) Single-column model intercomparison for a stably stratified atmospheric boundary layer. Bound-Layer Meteor 118:273–303CrossRefGoogle Scholar
  8. Davis N, Bowden J, Semazzi F, Xie L, Önol B (2009) Customization of RegCM3 regional climate model for eastern Africa and a tropical Indian Ocean domain. J Clim 22:3595–3616CrossRefGoogle Scholar
  9. Dee DP et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597CrossRefGoogle Scholar
  10. Dethloff K, Abegg C, Rinke A, Hebestadt I, Romanov VF (2001) Sensitivity of Arctic climate simulations to different boundary-layer parameterizations in a regional climate model. Tellus 53A:1–26CrossRefGoogle Scholar
  11. Dickinson RE, Henderson-Sellers A, Kennedy PJ (1993) Biosphere-atmosphere transfer scheme (BATS) version 1e as coupled to the NCAR community climate model. NCAR Tech. Note NCAR/TN-387 + STR, NCAR, Boulder, Colorado, USA, 72 ppGoogle Scholar
  12. Emanuel KA (1991) A scheme for representing cumulus convection in large-scale models. J Atmos Sci 48:2313–2335CrossRefGoogle Scholar
  13. Esau I, Zilitinkevich S (2010) On the role of the planetary boundary layer depth in the climate system. Adv Sci Res 4:63–69CrossRefGoogle Scholar
  14. Galperin B, Kantha LH, Hassid S, Rosati A (1988) A quasi-equilibrium turbulent energy model for geophysical flows. J Atmos Sci 45:55–62CrossRefGoogle Scholar
  15. García-Díez M, Fernández J, Fita L, Yagüe C (2013) Seasonal dependence of WRF bias and sensitivity to PBL schemes over Europe. Q J R Meteorol Soc 139:501–514CrossRefGoogle Scholar
  16. Gianotti RL, Zhang D, Eltahir EAB (2012) Assessment of the regional climate model version 3 over the maritime continent using different cumulus parameterization and land surface schemes. J Clim 25:638–656CrossRefGoogle Scholar
  17. Giorgi F, Marinucci MR, Bates GT (1993) Development of a second-generation regional climate model (RegCM2). Part I: boundary-layer and radiative transfer processes. Mon Weather Rev 121:2794–2813CrossRefGoogle Scholar
  18. Giorgi F et al (2012) RegCM4: model description and preliminary tests over multiple CORDEX domains. Clim Res 52:7–29CrossRefGoogle Scholar
  19. Grenier H, Bretherton CS (2001) A moist PBL parameterization for large-scale models and its application to subtropical cloud-topped marine boundary layers. Mon Weather Rev 129:357–377CrossRefGoogle Scholar
  20. Grisogono B (2010) Generalizing ‘z-less’ mixing length for stable boundary layers. Q J R Meteorol Soc 136:213–221CrossRefGoogle Scholar
  21. Güttler I (2011) Reducing warm bias over the north-eastern Europe in a regional climate model. Croatian Meteorol J 44(45):19–29Google Scholar
  22. Güttler I, Branković Č, Srnec L, Patarčić M (2013) The impact of boundary forcing on RegCM4.2 surface energy budget. Clim Change. doi: 10.1007/s10584-013-0995-x
  23. Holtslag AAM, Boville B (1993) Local versus nonlocal boundary-layer diffusion in a global climate model. J Clim 6:1825–1842CrossRefGoogle Scholar
  24. Holtslag AAM, Moeng C-H (1991) Eddy diffusivity and countergradient transport in the convective atmospheric boundary layer. J Atmos Sci 48:1690–1698CrossRefGoogle Scholar
  25. Holtslag AAM, de Bruijn EIF, Pan HL (1990) A high resolution air mass transformation model for short-range weather forecasting. Mon Weather Rev 118:1561–1575CrossRefGoogle Scholar
  26. Hu X-M, Nielsen-Gammon JW, Zhang F (2010) Evaluation of three planetary boundary-layer schemes in the WRF model. J Appl Meteor Climatol 49:1831–1844CrossRefGoogle Scholar
  27. Jaeger EB, Stöckli R, Seneviratne SI (2009) Analysis of planetary boundary layer fluxes and land-atmosphere coupling in the regional climate model CLM. J Geophys Res 114:D17106. doi: 10.1029/2008JD011658 CrossRefGoogle Scholar
  28. Kiehl J, Hack J, Bonan G, Boville B, Breigleb B, Williamson D, Rasch P (1996) Description of the NCAR community climate model (CCM3). NCAR Tech. Note NCAR/TN-420 + STR. NCAR, Boulder, Colorado, USA, 152 ppGoogle Scholar
  29. Kim J, Waliser DE, Mattmann CA, Goodale CE, Hart AF, Zimdars PA, Crichton DJ, Jones C, Nikulin G, Hewitson B, Jack C, Lennard C, Favre A (2013) Evaluation of the CORDEX-Africa multi-RCM hindcast: systematic model errors. Clim Dyn. doi: 10.1007/s00382-013-1751-7
  30. Knight CG, Knight SHE, Massey N, Aina T, Christensen C, Frame DJ, Kettleborough JA, Martin A, Pascoe S, Sanderson B, Stainforth DA, Allen MR (2007) Association of parameter, software, and hardware variation with large-scale behaviour across 57,000 climate models. Proc Natl Acad Sci USA 104:12259–12264CrossRefGoogle Scholar
  31. Kothe S, Ahrens B (2010) On the radiation budget in regional climate simulations for West Africa. J Geophys Res 115:D23120. doi: 10.1029/2010JD014331 CrossRefGoogle Scholar
  32. Mahrt L, Vickers D (2003) Formulation of turbulent fluxes in the stable boundary layer. J Atmos Sci 60:2538–2548CrossRefGoogle Scholar
  33. Mauritsen T, Svensson G, Zilitinkevich SS, Esau I, Enger L, Grisogono B (2007) A total turbulent energy closure model for neutrally and stably stratified atmospheric boundary layers. J Atmos Sci 64:4113–4126CrossRefGoogle Scholar
  34. Mearns LO, Arritt R, Biner S, Bukovsky MS, McGinnis S, Sain S, Caya D, Correia J Jr, Flory D, Gutowski W, Takle ES, Jones R, Leung R, Muofouma-Okia W, McDaniel L, Nunes AMB, Qian Y, Roads J, Sloan L, Snyder M (2012) The North American Regional Climate Change Assessment Program: overview of phase I results. Bull Am Meteor Soc 93:1337–1362CrossRefGoogle Scholar
  35. Medeiros B, Hall A, Stevens B (2005) What controls the mean depth of the PBL? J Clim 18:3157–3172CrossRefGoogle Scholar
  36. Mellor G, Yamada T (1982) Development of a turbulence closure model for geophysical fluid problems. Rev Astrophys Space Phys 20:851–875CrossRefGoogle Scholar
  37. Mitchell TD, Jones PD (2005) An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int J Climatol 25:693–712CrossRefGoogle Scholar
  38. Murphy JM, Sexton DMH, Barnett DN, Jones GS, Webb MJ, Collins M, Stainforth DA (2004) Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430:768–772CrossRefGoogle Scholar
  39. Nicholls S, Turton JD (1986) Observational study of the structure of stratiform cloud layers. Part II: entrainment. Q J R Meteorol Soc 112:461–480CrossRefGoogle Scholar
  40. Nieuwstadt FTM (1984) The turbulent structure of the stable, nocturnal boundary layer. J Atmos Sci 41:2202–2216CrossRefGoogle Scholar
  41. O’Brien TA, Sloan LC, Snyder MA (2011) Can ensembles of regional climate model simulations improve results from sensitivity studies? Clim Dyn 37:1111–1118CrossRefGoogle Scholar
  42. O’Brien TA, Chuang PY, Sloan LC, Faloona IC, Rossiter DL (2012) Coupling a new turbulence parametrization to RegCM adds realistic stratocumulus clouds. Geosci Model Dev Discuss 5:989–1008CrossRefGoogle Scholar
  43. Ozturk T, Altinsoy H, Türkeş M, Kuranz ML (2012) Simulation of temperature and precipitation climatology for the Central Asia CORDEX domain using RegCM 4.0. Clim Res 52:63–76CrossRefGoogle Scholar
  44. Pal JS, Small EE, Eltahir EA (2000) Simulation of regional-scale water and energy budgets: representation of subgrid cloud and precipitation processes within RegCM. J Geophys Res 105:D24, 29579–29594Google Scholar
  45. Pielke RA Sr (2002) Mesoscale meteorological modeling, 2nd edn. Academic Press, San Diego, p 676Google Scholar
  46. Sánchez E, Yagüe C, Gaertner MA (2007) Planetary boundary layer energetics simulated from a regional climate model over Europe for present climate and climate change conditions. Geophys Res Lett 34:L01709. doi: 10.1029/2006GL028340 Google Scholar
  47. Shin S-H, Ha K-J (2007) Effects of spatial and temporal variations in PBL depth on a GCM. J Clim 20:4717–4732CrossRefGoogle Scholar
  48. Solmon F, Elguindi N, Mallet M (2012) Radiative and climatic effects of dust over West Africa, as simulated by a regional climate model. Clim Res 52:97–113CrossRefGoogle Scholar
  49. Stainforth DA et al (2005) Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433:403–406CrossRefGoogle Scholar
  50. Stainforth DA, Allen MR, Tredger ER, Smith LA (2007) Confidence, uncertainty and decision-support relevance in climate predictions. Philos Trans R Soc Lond A365:2145–2161CrossRefGoogle Scholar
  51. Steiner AL, Pal JS, Giorgi F, Dickinson RE, Chameides WL (2005) The coupling of the common land model (CLM0) to a regional climate model (RegCM). Theor Appl Climatol 82:225–243CrossRefGoogle Scholar
  52. Steiner AL, Pal JS, Rauscher SA, Bell JL, Diffenbaugh NS, Boone A, Sloan LC, Giorgi F (2009) Land surface coupling in regional climate simulations of the West African monsoon. Clim Dyn 33:869–892CrossRefGoogle Scholar
  53. Stensrud D (2007) Parameterization schemes: keys to understanding numerical weather prediction models. Cambridge University Press, Cambridge, p 459CrossRefGoogle Scholar
  54. Stewart RW (1979) The atmospheric boundary layer, WMO no 523. World Meteorological Organization, Geneva, p 44Google Scholar
  55. Stull RB (1988) An introduction to boundary layer meteorology. Kluwer, Dordrecht, p 666CrossRefGoogle Scholar
  56. Suklitsch M, Gobiet A, Truhetz H, Awan NK, Göttel H, Jacob D (2011) Error characteristics of high resolution regional climate models over the Alpine region. Clim Dyn 37:377–390CrossRefGoogle Scholar
  57. Sylla MB, Coppola E, Mariotti L, Giorgi F, Ruti PM, Dell’Aquila A, Bi X (2010) Multiyear simulation of the African climate using a regional climate model (RegCM3) with the high-resolution ERA-Interim reanalysis. Clim Dyn 35:231–247CrossRefGoogle Scholar
  58. Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Philos Trans R Soc Lond A365:2053–2075CrossRefGoogle Scholar
  59. Trenberth KE, Fasullo JT, Kiehl J (2009) Earth’s global energy budget. Bull Am Meteor Soc 90:311–323CrossRefGoogle Scholar
  60. Troen IB, Mahrt L (1986) A simple model of the atmospheric boundary layer: sensitivity to surface evaporation. Bound-Layer Meteor 37:129–148CrossRefGoogle Scholar
  61. Van de Berg WJ, van den Broeke MR, van Meijgaard F (2007) Heat budget of the East Antarctic lower atmosphere derived from a regional atmospheric climate model. J Geophys Res 112:D23101. doi: 10.1029/2007JD008613 CrossRefGoogle Scholar
  62. Vautard R et al (2013) The simulation of European heat waves from an ensemble of regional climate models within the EURO-CORDEX project. Clim Dyn 41:2555–2575CrossRefGoogle Scholar
  63. Walter KM, Zimov SA, Chanton JP, Verbyla D, Chapin FS III (2006) Methane bubbling from Siberian thaw lakes as a positive feedback to climate warming. Nature 443:71–75CrossRefGoogle Scholar
  64. Winter JM, Pal JS, Eltahir EAB (2009) Coupling of integrated biosphere simulator to regional climate model version 3. J Clim 22:2743–2757CrossRefGoogle Scholar
  65. Winton M (2006) Surface albedo feedback estimates for the AR4 climate models. J Clim 19:359–365CrossRefGoogle Scholar
  66. Wyngaard JC (1985) Structure of the planetary boundary layer and implications for its modeling. J Clim Appl Meteorol 24:1131–1142CrossRefGoogle Scholar
  67. Xie B, Fung JCH, Chan A, Lau A (2012) Evaluation of nonlocal and local planetary boundary layer schemes in the WRF model. J Geophys Res 117:D12103. doi: 10.1029/2011JD017080 Google Scholar
  68. Yang Z, Arritt RW (2002) Test of a perturbed physics ensemble approach for regional climate modeling. J Clim 15:2881–2986CrossRefGoogle Scholar
  69. Zhang Y, Xie S, Covey C, Lucas DD, Gleckler P, Klein SA, Tannahill J, Doutriaux C, Klein R (2012) Regional assessment of the parameter-dependent performance of CAM4 in simulating tropical clouds. Geophys Res Lett 39:L14708. doi: 10.1029/2012GL052184 Google Scholar
  70. Zhu P et al (2005) Intercomparison and interpretation of single-column model simulations of a nocturnal stratocumulus-topped marine boundary layer. Mon Weather Rev 133:2741–2758CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ivan Güttler
    • 1
    Email author
  • Čedo Branković
    • 1
  • Travis A. O’Brien
    • 2
  • Erika Coppola
    • 3
  • Branko Grisogono
    • 4
  • Filippo Giorgi
    • 3
  1. 1.Croatian Meteorological and Hydrological Service (DHMZ)ZagrebCroatia
  2. 2.Earth Sciences DivisionLawrence Berkeley National LabBerkeleyUSA
  3. 3.Earth System Physics SectionInternational Centre for Theoretical Physics (ICTP)TriesteItaly
  4. 4.Department of Geophysics, Faculty of ScienceUniversity of ZagrebZagrebCroatia

Personalised recommendations