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Climatic Change

, Volume 136, Issue 2, pp 155–174 | Cite as

DADA: data assimilation for the detection and attribution of weather and climate-related events

  • A. Hannart
  • A. Carrassi
  • M. Bocquet
  • M. Ghil
  • P. Naveau
  • M. Pulido
  • J. Ruiz
  • P. Tandeo
Article

Abstract

We describe a new approach that allows for systematic causal attribution of weather and climate-related events, in near-real time. The method is designed so as to facilitate its implementation at meteorological centers by relying on data and methods that are routinely available when numerically forecasting the weather. We thus show that causal attribution can be obtained as a by-product of data assimilation procedures run on a daily basis to update numerical weather prediction (NWP) models with new atmospheric observations; hence, the proposed methodology can take advantage of the powerful computational and observational capacity of weather forecasting centers. We explain the theoretical rationale of this approach and sketch the most prominent features of a “data assimilation–based detection and attribution” (DADA) procedure. The proposal is illustrated in the context of the classical three-variable Lorenz model with additional forcing. The paper concludes by raising several theoretical and practical questions that need to be addressed to make the proposal operational within NWP centers.

Keywords

Event attribution Data assimilation Causality theory Modified Lorenz model 

Notes

Acknowledgements

It is a pleasure to thank Fredi Otto and Dáithí Stone, who provided careful and constructive reviews of the original paper. This work has been supported by grant DADA from the Agence Nationale de la Recherche (ANR, France: AH and all co-authors) and by the Multi-University Research Initiative (MURI) N00014-12-1-0911 from the the U.S. Office of Naval Research (MG).

References

  1. Allen MR (2003) Liability for climate change. Nature 421:891–892CrossRefGoogle Scholar
  2. Baum LE, Petrie T, Soules G, Weiss N (1970) A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Ann Math Stat 41(1):164–171CrossRefGoogle Scholar
  3. Balmaseda MA, Alves OJ, Arribas A, Awaji T, Behringer DW, Ferry N, Fujii Y, Lee T, Rienecker M, Rosati T, Stammer D (2009) Ocean initialization for seasonal forecasts. Oceanography Special Issue 22(3)Google Scholar
  4. Bengtsson L, Ghil M, Källén E (1981) Dynamic meteorology: Data assimilation methods. Springer-Verlag, New YorkCrossRefGoogle Scholar
  5. Bhend J, Franke J, Folini D, Wild M, Brönnimann S (2012) An ensemble-based approach to climate reconstructions. Clim Past 8:963–976CrossRefGoogle Scholar
  6. Bocquet M, Pires CA, Wu L (2010) Beyond Gaussian statistical modeling in geophysical data assimilation. Mon Wea Rev 138:2997–3023CrossRefGoogle Scholar
  7. Bocquet M (2012) Parameter-field estimation for atmospheric dispersion: application to the Chernobyl accident using 4D-Var. Quart J Roy Meteor Soc 138:664–681CrossRefGoogle Scholar
  8. Bucklew JA (2004) Introduction to rare event simulation. SpringerGoogle Scholar
  9. Carrassi A, Vannitsem S (2010) Model error and variational data assimilation: A deterministic formulation. Mon Wea Rev 138:3369–3386CrossRefGoogle Scholar
  10. Carrassi A, Ghil M, Trevisan A, Uboldi F (2008) Data assimilation as a nonlinear dynamical systems problem: Stability and convergence of the prediction-assimilation system. Chaos: An Interdisciplinary Journal of Nonlinear Science 18(2):023–112CrossRefGoogle Scholar
  11. Chekroun MD, Simonnet E, Ghil M (2011) Stochastic climate dynamics: Random attractors and time-dependent invariant measures. Phys D 240(21):1685–1700. doi: 10.1016/j.physd.2011.06.005 CrossRefGoogle Scholar
  12. Chevallier F (2013) On the parallelization of atmospheric inversions of CO2 surface fluxes within a variational framework. Geosci Model Dev Discuss 6:37–57CrossRefGoogle Scholar
  13. Christidis N, Stott PA, Scaife A A, Arribas A, Jones G S, Copsey D, Knight J R, Tennant W J (2013) A New HadGEM3-A-Based System for Attribution of Weather- and Climate-Related Extreme Events. J Clim 26(9):2756–2783CrossRefGoogle Scholar
  14. Cosme E, Brankart JM, Verron J, Brasseur P, Krysta M (2006) Implementation of a reduced-rank, square-root smoother for ocean data assimilation. Ocean Model 33:87–100CrossRefGoogle Scholar
  15. Dalcher A, Kalnay E, Hoffman RN (1988) Medium-range lagged average forecasts. Mon Wea Rev 116:402–416. doi: 10.1175/1520-0493. 1988116<0402:MRLAF>2.0.CO;2.CrossRefGoogle Scholar
  16. Del Moral P, Garnier J (2005) Genealogical particle analysis of rare events. Ann Appl Probab 15(4):2496–2534CrossRefGoogle Scholar
  17. Evensen G (2003) The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn 53:343–367CrossRefGoogle Scholar
  18. Gardiner C (2004) Handbook of stochastic methods for physics, Chemistry and the natural sciences. Publisher. Pls.; no web toniteGoogle Scholar
  19. Gelb A (1974) Applied optimal estimation. M.I.T. Press, CambridgeGoogle Scholar
  20. Ghil M, Childress S (1987) Topics in geophysical fluid dynamics: Atmospheric dynamics, dynamo theory and climate dynamics. Springer-Verlag, New York, p 485CrossRefGoogle Scholar
  21. Ghil M, Malanotte-Rizzoli P (1991) Data assimilation in meteorology and oceanography. Adv Geophys 33:141–266CrossRefGoogle Scholar
  22. Ghil M, Cohn S, Tavantzis J, Bube K, Isaacson E (1981). In: Bengtsson L, Ghil M, Källén E (eds) Applications of estimation theory to numerical weather prediction. In: Dynamic meteorology: Data assimilation methods. Springer Verlag, pp 139–224Google Scholar
  23. Gini C (1921) Measurement of inequality of incomes. Econ J 31(121):124–126. doi: 10.2307/2223319 CrossRefGoogle Scholar
  24. Greenland S, Rothman KJ (1998) Measures of effect and measures of association, Chapter 4. In: Rothman K J, Greenland S (eds) Modern Epidemiology, 2nd edn.,Lippincott-Raven, Philadelphia, USAGoogle Scholar
  25. Hannart A, Pearl J, Otto FEL, Naveau P, Ghil M (2015) Counterfactual causality theory for the attribution of weather and climate-related events. Bull Am Meteorol Soc. in pressGoogle Scholar
  26. Harris T E, Kahn H (1951) Estimation of particle transmission by random sampling. Natl Bur Stand Appl Math Ser 12:27–30Google Scholar
  27. Heidelberg P (1995) Fast simulation of rare events in queueing and reliability models. ACM Trans Models Comput Simul 5:43–85CrossRefGoogle Scholar
  28. Hewitt C, Mason S, Walland D (2012) The global framework for climate services. Nat Clim Change 2:831–832Google Scholar
  29. Hoffman RN, Kalnay E (1983) Lagged average forecasting, an alternative to Monte Carlo forecasting. Tellus 35A:100–118. doi: 10.1111/j.1600-0870.1983.tb00189.x CrossRefGoogle Scholar
  30. Houtekamer PL, Mitchell HL, Pellerin G, Buehner M, Charron M (2005) Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations. Mon Wea Rev 133:604–620CrossRefGoogle Scholar
  31. Hürzeler M, Künsch HR (2001). In: Doucet A, De Freitas JFG, Gordon NJ (eds) Approximation and maximising the likelihood for a general state-space model. In: Sequential Monte Carlo Methods Practice. Springer-Verlag, New YorkGoogle Scholar
  32. Ide K, Courtier P, Ghil M, Lorenc A (1997) Unified notation for data assimilation: Operational, sequential and variational. J Meteor Soc Japan 75:181–189Google Scholar
  33. Ihler AT, Kirshner S, Ghil M, Robertson AW, Smyth P (2007) Graphical models for statistical inference and data assimilation. Phys D 230:72–87CrossRefGoogle Scholar
  34. Jazwinski AH (1970) Stochastic and filtering theory. Mathematics in sciences and engineering series 64 :376Google Scholar
  35. Kalman RE (1960) A new approach to linear filtering and prediction problems. J Basic Eng 82D:33– 45Google Scholar
  36. Kantas N, Doucet A, Singh SS, Maciejowski JM (2009) An overview of sequential Monte Carlo methods for parameter estimation. In: General state-space models, IFAC System Identification, no. MlGoogle Scholar
  37. Kondrashov D, Sun CJ, Ghil M (2008) Data assimilation for a coupled ocean-atmosphere model. Part II: Parameter estimation. Mon Wea Rev 136:5062–5076. doi: 10.1175/2008MWR2544.1 CrossRefGoogle Scholar
  38. Kondrashov D, Shprits Y, Ghil M (2011) Log-normal Kalman filter for assimilating phase-space density data in the radiation belts. Space Weather 9:S11006. doi: 10.1029/2011SW000726 CrossRefGoogle Scholar
  39. Lee TCK, Zwiers FW, Tsao M (2008) Evaluation of proxy-based millennial reconstruction methods. Climate Dyn 31:263–281CrossRefGoogle Scholar
  40. Lorenz EN (1963) Deterministic non-periodic flow. J Atmos Sci 20:130–141CrossRefGoogle Scholar
  41. Lorenz MO (1905) Methods of measuring the concentration of wealth. Publications of the American Statistical Association 9(70):209–219. doi: 10.2307/2276207 CrossRefGoogle Scholar
  42. Martin MJ, et al. (2014) Status and future of data assimilation in operational oceanography. J of Oper Ocean. in pressGoogle Scholar
  43. Massey N, Jones R, Otto FEL, Aina T, Wilson S, Murphy JM, Hassell D, Yamazaki YH, Allen MR (2014) weather@home — development and validation of a very large ensemble modelling system for probabilistic event attribution. Q J R Meteorol Soc. doi: 10.1002/qj.2455 Google Scholar
  44. Otto FEL, Boyd E, Jones RG, Cornforth RJ, James R, Parker HR, Allen MR (2015) Attribution of extreme weather events in Africa: a preliminary exploration of the science and policy implications. Climatic Change Google Scholar
  45. Palmer TN (1999) A non-linear dynamical perspective on climate prediction. J Clim 12:575–591CrossRefGoogle Scholar
  46. Pearl J (2000) Causality: Models, reasoning and inference. Cambridge University Press, CambridgeGoogle Scholar
  47. Pitt MK (2002) Smooth particle filters for likelihood evaluation and maximisation. Warwick Economic Research Papers, No. 651Google Scholar
  48. Robert C, Blayo E, Verron J (2006) Comparison of reduced-order sequential, variational and hybrid data assimilation methods in the context of a Tropical Pacific ocean model. Ocean Dyn 56:624–633CrossRefGoogle Scholar
  49. Roques L, Chekroun MD, Cristofol M, Soubeyrand S, Ghil M (2014) Parameter estimation for energy balance models with memory. Proc R Soc A 470:20140349CrossRefGoogle Scholar
  50. Ruiz J, Pulido M, Miyoshi T (2013) Estimating model parameters with ensemble-based data assimilation: A review. JMSJ 91(2):79–99CrossRefGoogle Scholar
  51. Sakov P, Counillon F, Bertino L, Lister KA, Oke PR, Korablev A (2012) TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic. Ocean Sci 8:633–656. doi: 10.5194/os-8-633-2012 CrossRefGoogle Scholar
  52. Stone DA, Allen MR (2005) The end-to-end attribution problem: from emissions to impacts. Clim Change 71:303–318CrossRefGoogle Scholar
  53. Stott PA, et al. (2013). In: Asrar GR, Hurrell J W (eds) Attribution of weather and climate-related events, in: Climate Science for Serving Society: Research, Modelling and Prediction Priorities. Springer. in pressGoogle Scholar
  54. Stott PA, Stone DA, Allen MR (2004) Human contribution to the European heatwave of 2003. Nature 432:610–614CrossRefGoogle Scholar
  55. Talagrand O (1997) Assimilation of observations, an introduction. J Meteor Soc Japan 75(1B):191–209Google Scholar
  56. Tandeo P, Pulido M, Lott F (2014) Offline parameter estimation using EnKF and maximum likelihood error covariance estimates: Application to a subgrid-scale orography parametrization. Q J R Meteorol Soc. doi: 10.1002/qj.2357 Google Scholar
  57. Trenberth K E, Fasullo J T, Shepherd T G (2015) Attribution of climate extreme events. Nature Clim Change 5:725–730CrossRefGoogle Scholar
  58. Wiener N (1949) Extrapolation, Interpolation and smoothing of stationary time series, with engineering applications. M.I.T. Press, Cambridge, p 163Google Scholar
  59. Wouters J, Bouchet F (2015) Rare event simulation of the chaotic Lorenz 96 dynamical system. Geophysical Research Abstracts, EGU General Assembly 2015 Vol. 17, EGU2015-10421-1Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • A. Hannart
    • 1
  • A. Carrassi
    • 2
  • M. Bocquet
    • 3
  • M. Ghil
    • 4
    • 5
  • P. Naveau
    • 6
  • M. Pulido
    • 7
  • J. Ruiz
    • 1
  • P. Tandeo
    • 8
  1. 1.IFAECI, CNRS-CONICET-UBABuenos AiresArgentina
  2. 2.Mohn-Sverdrup Center, Nansen Environmental and Remote Sensing CenterBergenNorway
  3. 3.CEREA, joint laboratory École des Ponts ParisTech and EDF R&DUniversité Paris-EstChamps-sur-MarneFrance
  4. 4.Ecole Normale SupérieureParisFrance
  5. 5.University of CaliforniaLos AngelesUSA
  6. 6.LSCE, CNRSGif-sur-YvetteFrance
  7. 7.Department of PhysicsUniversidad Nacional del NordesteCorrientesArgentina
  8. 8.Télécom BretagneBrestFrance

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