Climate Dynamics

, Volume 44, Issue 7–8, pp 2195–2214 | Cite as

Simulating weather regimes: impact of stochastic and perturbed parameter schemes in a simple atmospheric model

  • H. M. Christensen
  • I. M. Moroz
  • T. N. Palmer


Representing model uncertainty is important for both numerical weather and climate prediction. Stochastic parametrisation schemes are commonly used for this purpose in weather prediction, while perturbed parameter approaches are widely used in the climate community. The performance of these two representations of model uncertainty is considered in the context of the idealised Lorenz ’96 system, in terms of their ability to capture the observed regime behaviour of the system. These results are applicable to the atmosphere, where evidence points to the existence of persistent weather regimes, and where it is desirable that climate models capture this regime behaviour. The stochastic parametrisation schemes considerably improve the representation of regimes when compared to a deterministic model: both the structure and persistence of the regimes are found to improve. The stochastic parametrisation scheme represents the small scale variability present in the full system, which enables the system to explore a larger portion of the system’s attractor, improving the simulated regime behaviour. It is important that temporally correlated noise is used in the stochastic parametrisation—white noise schemes performed similarly to the deterministic model. In contrast, the perturbed parameter ensemble was unable to capture the regime structure of the attractor, with many individual members exploring only one regime. This poor performance was not evident in other climate diagnostics. Finally, a ‘climate change’ experiment was performed, where a change in external forcing resulted in changes to the regime structure of the attractor. The temporally correlated stochastic schemes captured these changes well.


Weather regimes Stochastic physics Perturbed parameter schemes Model uncertainty Lorenz ’96 system Climate change 



The authors would like to thank Andrew Dawson and Susanna Corti for helpful discussions regarding atmospheric regimes. The research of H.M.C. was supported by a Natural Environment Research Council studentship, and the research of T.N.P. was supported by European Research Council grant number 291406.


  1. Arnold HM, Moroz IM, Palmer TN (2013) Stochastic parameterizations and model uncertainty in the Lorenz’96 system. Philos Trans R Soc A 371(1991) Google Scholar
  2. Berner J, Branstator G (2007) Linear and nonlinear signatures in the planetary wave dynamics of an AGCM: probability density functions. J Atmos Sci 64(1):117–136CrossRefGoogle Scholar
  3. Berner J, Shutts GJ, Leutbecher M, Palmer TN (2009) A spectral stochastic kinetic energy backscatter scheme and its impact on flow dependent predictability in the ECMWF ensemble prediction system. J Atmos Sci 66(3):603–626CrossRefGoogle Scholar
  4. Branstator G, Berner J (2005) Linear and nonlinear signatures in the planetary wave dynamics of an AGCM: phase space tendencies. J Atmos Sci 62(6):1792–1811CrossRefGoogle Scholar
  5. Branstator G, Selten F (2009) “Modes of variability” and climate change. J Clim 22(10):2639–2658CrossRefGoogle Scholar
  6. Charney J, DeVore J (1979) Multiple flow equilibria in the atmosphere and blocking. J Atmos Sci 36(7):1205–1216CrossRefGoogle Scholar
  7. Corti S, Molteni F, Palmer TN (1999) Signature of recent climate change in frequencies of natural atmospheric circulation regimes. Nature 398(6730):799–802CrossRefGoogle Scholar
  8. Crommelin D, Vanden-Eijnden E (2008) Subgrid-scale parametrisation with conditional markov chains. J Atmos Sci 65(8):2661–2675CrossRefGoogle Scholar
  9. Dawson A, Palmer TN (2014) Simulating weather regimes: impact of model resolution and stochastic parametrisation. Clim Dyn. doi: 10.1007/s00382-014-2238-x
  10. Dawson A, Palmer TN, Corti S (2012) Simulating regime structures in weather and climate prediction models. Geophys Res Lett 39(21):?L21805Google Scholar
  11. Dorrestijn J, Crommelin DT, Siebesma AP, Jonker HJJ (2012) Stochastic parameterization of shallow cumulus convection estimated from high-resolution model data. Theor Comp Fluid Dyn 27(1-2):133–148Google Scholar
  12. Ehrendorfer M (1997) Predicting the uncertainty of numerical weather forecasts: a review. Meteorol Z 6(4):147–183Google Scholar
  13. Frame THA, Methven J, Gray SL, Ambaum MHP (2013) Flow-dependent predictability of the North-Atlantic jet. Geophys Res Lett 40(10):2411–2416CrossRefGoogle Scholar
  14. Frenkel Y, Majda AJ, Khouider B (2012) Using the stochastic multicloud model to improve tropical convective parametrisation: a paradigm example. J Atmos Sci 69(3):1080–1105CrossRefGoogle Scholar
  15. Hasselmann K (1999) Climate change—linear and nonlinear signatures. Nature 398(6730):755–756CrossRefGoogle Scholar
  16. Houtekamer PL, Lefaivre L, Derome J (1996) A system simulation approach to ensemble prediction. Mon Weather Rev 124(6):1225–1242CrossRefGoogle Scholar
  17. Itoh H, Kimoto M (1996) Multiple attractors and chaotic itinerancy in a quasigeostrophic model with realistic topography: implications for weather regimes and low-frequency variability. J Atmos Sci 53(15):2217–2231CrossRefGoogle Scholar
  18. Itoh H, Kimoto M (1997) Chaotic itinerancy with preferred transition routes appearing in an atmospheric model. Physica D 109(3–4):274–292CrossRefGoogle Scholar
  19. Kimoto M, Ghil M (1993a) Multiple flow regimes in the northern hemisphere winter. Part I: methodology and hemispheric regimes. J Atmos Sci 50(16):2625–2644CrossRefGoogle Scholar
  20. Kimoto M, Ghil M (1993b) Multiple flow regimes in the northern hemisphere winter. Part II: sectorial regimes and preferred transitions. J Atmos Sci 50(16):2645–2673CrossRefGoogle Scholar
  21. Kwasniok F (2012) Data-based stochastic subgrid-scale parametrisation: an approach using cluster-weighted modelling. Philos Trans R Soc A 370(1962):1061–1086CrossRefGoogle Scholar
  22. Lorenz EN (1963) Deterministic nonperiodic flow. J Atmos Sci 20(2):130–141CrossRefGoogle Scholar
  23. Lorenz EN (1996) Predictability—a problem partly solved. In: Proceedings of seminar on predictability, 4–8 September 1995, vol 1. ECMWF, Shinfield Park, Reading, pp 1–18Google Scholar
  24. Lorenz EN (2006) Regimes in simple systems. J Atmos Sci 63(8):2056–2073CrossRefGoogle Scholar
  25. 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(7001):768–772CrossRefGoogle Scholar
  26. Palmer TN (1993) A nonlinear dynamical perpective on climate change. Weather 48(10):314–326CrossRefGoogle Scholar
  27. Palmer TN (1999) A nonlinear dynamical perpective on climate prediction. J Clim 12(2):575–591CrossRefGoogle Scholar
  28. Palmer TN, Alessandri A, Andersen U, Cantelaube P, Davey M, Délécluse P, Déqué M, Díez E, Doblas-Reyes FJ, Feddersen H, Graham R, Gualdi S, Guérémy J-F, Hagedorn R, Hoshen M, Keenlyside N, Latif M, Lazar A, Maisonnave E, Marletto V, Morse AP, Orfila B, Rogel P, Terres J-M, Thomson MC (2004) Development of a European multimodel ensemble system for seasonal-to-interannual prediction (DEMETER). Bull Am Meteorol Soc 85(6):853–872CrossRefGoogle Scholar
  29. Palmer TN, Buizza R, Doblas-Reyes F, Jung T, Leutbecher M, Shutts GJ, Steinheimer M, Weisheimer A (2009) Stochastic parametrization and model uncertainty, Tech. Rep. 598, European Centre for Medium-Range Weather Forecasts, Shinfield park, ReadingGoogle Scholar
  30. Pohl B, Fauchereau N (2012) The southern annular mode seen through weather regimes. J Clim 25(9):3336–3354CrossRefGoogle Scholar
  31. Rougier J, Sexton DMH, Murphy JM, Stainforth D (2009) Analyzing the climate sensitivity of the HadSM3 climate model using ensembles from different but related experiments. J Clim 22:3540–3557CrossRefGoogle Scholar
  32. Selten F, Branstator G (2004) Preferred regime transition routes and evidence for an unstable periodic orbit in a baroclinic model. J Atmos Sci 61(18):2267–2282CrossRefGoogle Scholar
  33. Stainforth DA, Aina T, Christensen C, Collins M, Faull N, Frame DJ, Kettleborough JA, Knight S, Martin A, Murphy JM, Piani C, Sexton D, Smith LA, Spicer RA, Thorpe AJ, Allen MR (2005) Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433(7024):403–406CrossRefGoogle Scholar
  34. Stephenson DB, Hannachi A, O’Neill A (2004) On the existence of multiple climate regimes. Q J R Meteorol Soc 130(597):583–605CrossRefGoogle Scholar
  35. Straus DM, Corti S, Molteni F (2007) Circulation regimes: chaotic variability versus sst-forced predictability. J Clim 20(10):2251–2272CrossRefGoogle Scholar
  36. Wilks DS (2005) Effects of stochastic parametrizations in the Lorenz ’96 system. Q J R Meteorol Soc 131(606):389–407CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • H. M. Christensen
    • 1
  • I. M. Moroz
    • 2
  • T. N. Palmer
    • 1
  1. 1.Atmospheric, Oceanic and Planetary Physics, Department of PhysicsUniversity of OxfordOxfordUK
  2. 2.Oxford Centre for Industrial and Applied MathematicsUniversity of OxfordOxfordUK

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