Skip to main content

Advertisement

SpringerLink
Log in

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

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Notes

  1. One model time unit corresponds to approximately 5 atmospheric days (Lorenz 1996).

  2. The time series must not be too heavily smoothed as this will cause the pdf to tend towards a Gaussian distribution (following the central limit theorem).

  3. This is equivalent to considering complex EOFs, which are used to capture propagating modes. In this study, EOF1 and EOF2 together represent the first complex EOF, and EOF3 and EOF4 represent the second complex EOF, and therefore \(||[PC1,PC2]|| = ||CPC1||\), where CPC1 is the PC of the first complex EOF, etc.

References

  • Arnold HM, Moroz IM, Palmer TN (2013) Stochastic parameterizations and model uncertainty in the Lorenz’96 system. Philos Trans R Soc A 371(1991)

  • 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):117–136

    Article  Google Scholar 

  • 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):603–626

    Article  Google Scholar 

  • 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):1792–1811

    Article  Google Scholar 

  • Branstator G, Selten F (2009) “Modes of variability” and climate change. J Clim 22(10):2639–2658

    Article  Google Scholar 

  • Charney J, DeVore J (1979) Multiple flow equilibria in the atmosphere and blocking. J Atmos Sci 36(7):1205–1216

    Article  Google Scholar 

  • Corti S, Molteni F, Palmer TN (1999) Signature of recent climate change in frequencies of natural atmospheric circulation regimes. Nature 398(6730):799–802

    Article  Google Scholar 

  • Crommelin D, Vanden-Eijnden E (2008) Subgrid-scale parametrisation with conditional markov chains. J Atmos Sci 65(8):2661–2675

    Article  Google Scholar 

  • Dawson A, Palmer TN (2014) Simulating weather regimes: impact of model resolution and stochastic parametrisation. Clim Dyn. doi:10.1007/s00382-014-2238-x

  • Dawson A, Palmer TN, Corti S (2012) Simulating regime structures in weather and climate prediction models. Geophys Res Lett 39(21):?L21805

  • 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):133–148

    Google Scholar 

  • Ehrendorfer M (1997) Predicting the uncertainty of numerical weather forecasts: a review. Meteorol Z 6(4):147–183

    Google Scholar 

  • Frame THA, Methven J, Gray SL, Ambaum MHP (2013) Flow-dependent predictability of the North-Atlantic jet. Geophys Res Lett 40(10):2411–2416

    Article  Google Scholar 

  • 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):1080–1105

    Article  Google Scholar 

  • Hasselmann K (1999) Climate change—linear and nonlinear signatures. Nature 398(6730):755–756

    Article  Google Scholar 

  • Houtekamer PL, Lefaivre L, Derome J (1996) A system simulation approach to ensemble prediction. Mon Weather Rev 124(6):1225–1242

    Article  Google Scholar 

  • 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):2217–2231

    Article  Google Scholar 

  • Itoh H, Kimoto M (1997) Chaotic itinerancy with preferred transition routes appearing in an atmospheric model. Physica D 109(3–4):274–292

    Article  Google Scholar 

  • Kimoto M, Ghil M (1993a) Multiple flow regimes in the northern hemisphere winter. Part I: methodology and hemispheric regimes. J Atmos Sci 50(16):2625–2644

    Article  Google Scholar 

  • 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):2645–2673

    Article  Google Scholar 

  • Kwasniok F (2012) Data-based stochastic subgrid-scale parametrisation: an approach using cluster-weighted modelling. Philos Trans R Soc A 370(1962):1061–1086

    Article  Google Scholar 

  • Lorenz EN (1963) Deterministic nonperiodic flow. J Atmos Sci 20(2):130–141

    Article  Google Scholar 

  • 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 1–18

  • Lorenz EN (2006) Regimes in simple systems. J Atmos Sci 63(8):2056–2073

    Article  Google Scholar 

  • 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):768–772

    Article  Google Scholar 

  • Palmer TN (1993) A nonlinear dynamical perpective on climate change. Weather 48(10):314–326

    Article  Google Scholar 

  • Palmer TN (1999) A nonlinear dynamical perpective on climate prediction. J Clim 12(2):575–591

    Article  Google Scholar 

  • 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):853–872

    Article  Google Scholar 

  • 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, Reading

  • Pohl B, Fauchereau N (2012) The southern annular mode seen through weather regimes. J Clim 25(9):3336–3354

    Article  Google Scholar 

  • 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 22:3540–3557

    Article  Google Scholar 

  • 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):2267–2282

    Article  Google Scholar 

  • 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):403–406

    Article  Google Scholar 

  • Stephenson DB, Hannachi A, O’Neill A (2004) On the existence of multiple climate regimes. Q J R Meteorol Soc 130(597):583–605

    Article  Google Scholar 

  • Straus DM, Corti S, Molteni F (2007) Circulation regimes: chaotic variability versus sst-forced predictability. J Clim 20(10):2251–2272

    Article  Google Scholar 

  • Wilks DS (2005) Effects of stochastic parametrizations in the Lorenz ’96 system. Q J R Meteorol Soc 131(606):389–407

    Article  Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

  1. Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Oxford, UK

    H. M. Christensen & T. N. Palmer

  2. Oxford Centre for Industrial and Applied Mathematics, University of Oxford, Oxford, UK

    I. M. Moroz

Authors
  1. H. M. Christensen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. I. M. Moroz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. T. N. Palmer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to H. M. Christensen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Christensen, H.M., Moroz, I.M. & Palmer, T.N. Simulating weather regimes: impact of stochastic and perturbed parameter schemes in a simple atmospheric model. Clim Dyn 44, 2195–2214 (2015). https://doi.org/10.1007/s00382-014-2239-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-014-2239-9

Keywords

Search

Navigation