Evaluating the performance of a WRF physics ensemble over South-East Australia

Abstract

When using the Weather Research and Forecasting (WRF) modelling system it is necessary to choose between many parametrisations for each physics option. This study examines the performance of various physics scheme combinations on the simulation of a series of rainfall events near the south-east coast of Australia known as East Coast Lows. A thirty-six member multi-physics ensemble was created such that each member had a unique set of physics parametrisations. No single ensemble member was found to perform best for all events, variables and metrics. This is reflected in the fact that different climate variables are found to be sensitive to different physical parametrisations. While a standardised super-metric can be used to identify best performers, a step-wise decision approach described here, allows explicit recognition of the “robustness” of choosing one parameterisation over another, allowing the identification of a group of “equally robustly” performing physics combinations. These results suggest that the Mellor-Yamada-Janjic planetary boundary layer scheme and the Betts-Miller-Janjic cumulus scheme can be chosen with some robustness. Possibly with greater confidence, the results also suggest that the Yonsei University planetary boundary layer scheme, Kain-Fritsch cumulus scheme and RRTMG radiation scheme should not be used in combination in this region. Results further indicate that the selection of physics scheme options has larger impact on model performance during the more intensive rainfall events.

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References

  1. Betts AK (1986) A new convective adjustment scheme. Part I: observational and theoretical basis. Quart J Roy Meteor Soc 121:255–270

    Google Scholar 

  2. Betts AK, Miller MJ (1986) A new convective adjustment scheme. Part II: single column tests using GATE wave, BOMEX, and arctic air-mass data sets. Quart J Roy Meteor Soc 121:693–709

    Google Scholar 

  3. Bukovsky M, Karoly D (2009) Precipitation simulations using WRF as a nested regional climate model. J Appl Meteorol Climatol 48(10):2152–2159

    Article  Google Scholar 

  4. Charles S, Bates B, Smith I, Hughes J (2004) Statistical downscaling of daily precipitation from observed and modelled atmospheric fields. Hydrol Process 18(8):1373–1394

    Article  Google Scholar 

  5. Christensen J, Kjellstrom E, Giorgi F, Lenderink G, Rummukainen M (2010) Weight assignment in regional climate models. Clim Res 44:179–194

    Article  Google Scholar 

  6. Collins WD, Rash PJ, Boville BA, Hack JJ, McCaa JR, Williamson DL, Kiehl JT, Briegleb B (2004) Description of the NCAR community atmosphere model (CAM 3.0), NCAR technical note, NCAR/TN-464+STR

  7. Deque M et al (2005) Global high resolution versus limited area model climate change projections over Europe: quantifying confidence level from PRUDENCE results. Clim Dyn 25(6):653–670. doi:10.1007/s00382-005-0052-1

    Article  Google Scholar 

  8. Dudhia J (1989) Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46:3077–3107

    Article  Google Scholar 

  9. Dyer AJ, Hicks BB (1970) Flux-gradient relationships in the constant flux layer. Quart J Roy Meteor Soc 96:715–721

    Article  Google Scholar 

  10. Evans J, McCabe MF (2010). Regional climate simulation over Australia’s Murray-Darling basin: a multitemporal assessment. J Geophys Res 115. doi:10.1029/2010JD013816

  11. Evans J, Zaitchik B (2008) Modeling the large-scale water balance impact of different irrigation systems. Water Resour Res 44. doi:10.1029/2007WR006671

  12. Flaounas E, Bastin S, Janicot S (2011) Regional climate modelling of the 2006 West African monsoon: sensitivity to convection and planetary boundary layer parameterisation using WRF. Clim Dynam 36(5–6):1083–1105. doi:10.1007/s00382-010-0785-3

    Article  Google Scholar 

  13. Fowler H, Blenkinsop S, Tebaldi C (2007) Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int J Climatol 27(12):1547–1578

    Article  Google Scholar 

  14. Frei C, Christensen JH, Déqué M, Jacob D, Jones RG, Vidale PL (2003) Daily precipitation statistics in regional climate models: evaluation and intercomparison for the European Alps. J Geophys Res 108:4124. doi:10.1029/2002JD002287

    Google Scholar 

  15. Frei C, Schöll R, Fukutome S, Schmidli J, Vidale PL (2006) Future change of precipitation extremes in Europe: intercomparison of scenarios from regional climate models. J Geophys Res 111:D06105. doi:10.1029/2005JD005965

  16. Giorgi F, Lionello P (2008) Climate change projections for the Mediterranean region. Global Planet Change 63(2–3):90–104. doi:10.1016/j.gloplacha.2007.09.005

    Article  Google Scholar 

  17. Hong SY, Dudhia J, Chen SH (2004) A revised approach to ice microphysical processes for the bulk parameterizaion of clouds and precipitation. Mon Weather Rev 132:103–120

    Article  Google Scholar 

  18. Hong SY, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev 134:2318–2341

    Article  Google Scholar 

  19. Janjic ZI (1994) The step-mountain eta coordinate model: further developments of the convection, viscous sublayer and turbulence closure schemes. Mon Weather Rev 122:927–945

    Article  Google Scholar 

  20. Janjic ZI (2000) Comments on “development and evaluation of a convection scheme for use in climate models”. J Atmos Sci 57:3686

    Article  Google Scholar 

  21. Jankov I, Gallus W Jr, Segal M, Shaw B, Koch S (2005) The impact of different WRF model physical parameterizations and their interactions on warm season MCS rainfall. Weather Forecast 20:1048–1060

    Article  Google Scholar 

  22. Jankov I, Schultz P, Anderson C, Koch S (2007) The impact of different physical parameterizations and their interactions on cold season QPF in the American River basin. J Hydrometeorol 8(5):1141–1151

    Article  Google Scholar 

  23. Jones D, Wang W, Fawcett R (2009) High-quality spatial climate data-sets for Australia. Aust Meteorol Mag 58:233–248

    Google Scholar 

  24. Kain JS (2004) The Kain-Fritsch convective parameterization: an update. J Appl Meteor 43:170–181

    Article  Google Scholar 

  25. Kain JS, Fritsch JM (1990) A one-dimensional entraining/detraining plume model and its application in convective parameterization. J Atmos Sci 47:2784–2802

    Article  Google Scholar 

  26. Kain JS, Fritsch JM (1993) Convective parameterization for mesoscale models: the Kain-Fritsch scheme. In: Emanuel KA, Raymond DJ (eds) The representation of cumulus convection in numerical models. Amer Meteor Soc

  27. Keeling CD, Piper SC, Bacastow RB, Wahlen M, Whorf TP, Heimann M, Meijer HA (2005) Atmospheric CO2 and 13CO2 exchange with the terrestrial biosphere and oceans from 1978 to 2000: observations and carbon cycle implications. In: Ehleringer JR, Cerling TE, Dearing MD (eds) History of atmospheric CO2 and its effects on plants, animals, and ecosystems. Springer, New York, pp 83–113

    Chapter  Google Scholar 

  28. Kjellström E, Boberg F, Castro M, Christensen JH, Nikulin, Sánchez E (2010) Daily and monthly temperature and precipitation statistics as performance indicators for regional climate models. Clim Res 44:135–150

  29. Li Y, Smith I (2009) A statistical downscaling model for Southern Australia winter rainfall. J Clim 22(5):1142–1158. doi:10.1175/2008JCLI2160.1

    Article  Google Scholar 

  30. Lim KSS, Hong SY (2010) Development of an effective double-moment cloud microphysics scheme with prognostic Cloud Condensation Nuclei (CCN) for weather and climate models. Mon Weather Rev 138:1587–1612

    Article  Google Scholar 

  31. Mills GA, Webb R, Davidson N, Kepert J, Seed A, Abbs D (2010) The Pasha Bulker east coast low of 8 June 2007. CAWCR technical report no. 23. Centre for Australian Weather and Climate Research, Melbourne, Australia

  32. Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the long-wave. J Geophys Res 102:16663–16682

    Article  Google Scholar 

  33. Paulson CA (1970) The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. J Appl Meteor 9:857–861

    Article  Google Scholar 

  34. Roberts NM, Lean HW (2008) Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon Weather Rev 136:78–97

    Article  Google Scholar 

  35. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda M, Huang XY, Wang W, Powers JG (2008) A description of the advanced research WRF version 3. NCAR technical note, NCAR, Boulder

  36. Speer M, Wiles P, Pepler A (2009) Low pressure systems off the New South Wales coast and associated hazardous weather: establishment of a database. Aust Meteorol Oceanogr J 58:29–39

    Google Scholar 

  37. Vidal J, Wade S (2008) Multimodel projections of catchment-scale precipitation regime. J Hydrol 353(1–2):143–158. doi:10.1016/j.jhydrol.2008.02.003

    Article  Google Scholar 

  38. Webb EK (1970) Profile relationships: the log-linear range, and extension to strong stability. Quart J Roy Meteor Soc 96:67–90

    Article  Google Scholar 

  39. Weinzierl B, Smith RK, Reeder MJ, Jackson GE (2008) MesoLAPS predictions of low-level convergence lines over Northeastern Australia. Weather Forecast 22:910–927

    Article  Google Scholar 

  40. Wilby R, Tomlinson O, Dawson C (2003) Multi-site simulation of precipitation by conditional resampling. Clim Res 23(3):183–194

    Article  Google Scholar 

Download references

Acknowledgments

This work is made possible by funding from the NSW Environmental Trust for the ESCCI-ECL project, the NSW Office of Environment and Heritage backed NSW/ACT Regional Climate Modelling Project (NARCliM), and the Australian Research Council as part of the Discovery Project DP0772665. Thanks to the Australian Climate Change Science Program (ACCSP) and the South Eastern Australian Climate Initiative (SEACI) for funding the CSIRO contribution to this study. This work was supported by an award under the Merit Allocation Scheme on the NCI National Facility at the Australian National University.

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Correspondence to Jason P. Evans.

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Evans, J.P., Ekström, M. & Ji, F. Evaluating the performance of a WRF physics ensemble over South-East Australia. Clim Dyn 39, 1241–1258 (2012). https://doi.org/10.1007/s00382-011-1244-5

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Keywords

  • Australia
  • WRF
  • Physics parametrisation
  • Regional climate modelling
  • Ensemble