Skip to main content
SpringerLink
Log in

Anomaly transform methods based on total energy and ocean heat content norms for generating ocean dynamic disturbances for ensemble climate forecasts

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

In our study we use the anomaly transform, a special case of ensemble transform method, in which a selected set of initial oceanic anomalies in space, time and variables are defined and orthogonalized. The resulting orthogonal perturbation patterns are designed such that they pick up typical balanced anomaly structures in space and time and between variables. The metric used to set up the eigen problem is taken either as the weighted total energy with its zonal, meridional kinetic and available potential energy terms having equal contributions, or the weighted ocean heat content in which a disturbance is applied only to the initial temperature fields. The choices of a reference state for defining the initial anomalies are such that either perturbations on seasonal timescales and or on interannual timescales are constructed. These project a-priori only the slow modes of the ocean physical processes, such that the disturbances grow mainly in the Western Boundary Currents, in the Antarctic Circumpolar Current and the El Nino Southern Oscillation regions. An additional set of initial conditions is designed to fit in a least square sense data from global ocean reanalysis. Applying the AT produced sets of disturbances to oceanic initial conditions initialized by observations of the MPIOM-ESM coupled model on T63L47/GR15 resolution, four ensemble and one hind-cast experiments were performed. The weighted total energy norm is used to monitor the amplitudes and rates of the fastest growing error modes. The results showed minor dependence of the instabilities or error growth on the selected metric but considerable change due to the magnitude of the scaling amplitudes of the perturbation patterns. In contrast to similar atmospheric applications, we find an energy conversion from kinetic to available potential energy, which suggests a different source of uncertainty generation in the ocean than in the atmosphere mainly associated with changes in the density field.

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

References

  • Baehr J, Piontek R (2013) Ensemble initialization of the oceanic component of a coupled model through bred vectors at seasonal-to-interannual timescales. Geosci Model Dev Discuss 6:5189–5214

    Article  Google Scholar 

  • Deng G, Tian H, Li X, Chen J, Gong J, Jiao M (2012) A comparison of breeding and ensemble transform vectors for global ensemble generation. Acta Meteorol Sin 26(1):52–61

    Article  Google Scholar 

  • Diaconescu EP, Laprise R (2012) Singular vectors in atmospheric sciences: a review. Earth-Sci Rev 113:161–175

    Article  Google Scholar 

  • Dommenget D, Latif M (2002) A cautionary note on the interpretation of EOFs. J Clim 15:216–225

    Article  Google Scholar 

  • Doney SC, Yeager S, Danabasoglu G, Large William G, Large WG, James C, McWilliams JC (2007) Mechanisms governing interannual variability of upper-ocean temperature in a global ocean hindcast simulation. J Phys Oceanogr 37:1918–1938. doi:10.1175/JPO3089.1

    Article  Google Scholar 

  • Garca-Serrano J, Doblas-Reyes FJ, Haarsma RJ, Polo I (2013) Decadal prediction of the dominant West African monsoon rainfall modes. J Geophys Res. doi:10.1002/jgrd.50465

  • Goddard L, Kumar A, Solomon A, Smith D, Boer G, Gonzalez P, Kharin V, Merryfield W, Deser C, Mason SJ, Kirtman BP, Msadek R, Sutton R, Hawkins E, Fricker T, Hegerl G, Ferro CAT, Stephenson DB, Meehl GA, Stockdale T, Burgman R, Greene AM, Kushnir Y, Newman M, Carton J, Fukumori I, Delworth T (2012) A verification framework for interannual-to-decadal predictions experiments. Clim Dyn. doi:10.1007/s00382-012-1481-2

  • Hense A, Glowienka-Hense R, Muller M, Braun P (2002) Spatial modelling of phenological observations to analyse their interannual variations in Germany. Agric For Meteorol 3:161–178

    Article  Google Scholar 

  • Jungclaus JH, Botzet M, Haak H, Keenlyside N, Luo JJ, Latif M, Marotzke J, Mikolajewicz U, Roeckner E (2006) Ocean circulation and tropical variability in the coupled model ECHAM5/MPI-OM. J Clim 19:3952–3972

    Article  Google Scholar 

  • Jungclaus JH, Fischer N, Haak H, Lohmann K, Marotzke J, Matei D, Mikolajewicz U, Notz D, von Storch JS (2013) Characteristics of the ocean simulations in MPIOM, the ocean component of the MPI-earth system model. J Adv Model Earth Syst 5:422–446. doi:10.1002/jame.20023

    Article  Google Scholar 

  • Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A, Reynolds R, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Amer Meteor Soc 77:437–471

    Article  Google Scholar 

  • Keller JD, Kornblueh L, Hense A, Rhodin A (2008) Towards a GME ensemble forecasting system: ensemble initialization using the breeding technique. Meteorologische Zeitschrift 17:707–718

    Article  Google Scholar 

  • Keller JD, Hense A, Kornblueh L, Rhodin A (2010) On the orthogonalization of bred vectors. Weather Forecast 25:1219–1234

    Article  Google Scholar 

  • Keller JD, Hense A (2011) A new non-Gaussian evaluation method for ensemble forecasts based on analysis rank histograms. Meteorologische Zeitschrift 20(2):107–117

    Article  Google Scholar 

  • Köhl A (2014) Evaluation of the GECCO2 ocean synthesis: transports of volume. Heat freshwater in the Atlantic. QJR Meteorol Soc. doi:10.1002/qj.2347

  • Marsland SJ, Haak H, Jungclaus JH, Latif M, Röske F (2003) The Max Planck Institute global ocean/sea ice model with orthogonal curvilinear coordinates. Ocean Model 5:91–127

    Article  Google Scholar 

  • Matei D, Pohlmann H, Jungclaus J, Mller W, Haak H, Marotzke J (2012) Two tales of initializing decadal climate prediction experiments with the ECHAM5/MPI-OM model. J Clim 25:8502–8523

    Article  Google Scholar 

  • Matei D, Baehr J, Jungclaus JH, Haak H, Mller WA, Marotzke J (2012) Multiyear prediction of monthly mean Atlantic meridional overturning circulation at 26.5N. Science 335:76–79. doi:10.1126/science.1210299

    Article  Google Scholar 

  • Mochizuki T, Ishii M, Kimoto M, Chikamoto Y, Watanabe M, Nozawa T, Sakamoto TT, Shiogama H, Awaji T, Sugiura N, Toyoda T, Yasunaka S, Tatebe H, Mori M (2010) Pacific decadal oscillation hindcasts relevant to near-term climate prediction. PNAS 107(5):1833–1837

    Article  Google Scholar 

  • Molteni F, Buizza R, Palmer TN, Petroliagis T (1996) The new ECMWF ensemble prediction system: methodology and validation. QJR Meteorol Soc 122:73–119

    Article  Google Scholar 

  • Notz D, Haumann FA, Haak H, Jungclaus JH, Marotzke J (2013) Arctic sea-ice evolution as modeled by Max Planck Institute for meteorology’s Earth system model. J Adv Model Earth Syst 5:173–194. doi:10.1002/jame.20016

    Article  Google Scholar 

  • Olbers D, Willebrand J, Eden C (2012) Ocean dynamics. Springer, Berlin, p 704

    Book  Google Scholar 

  • Polkova I, Köhl A, Stammer D (2013) Impact of initialization procedures on the predictive skill of a coupled ocean–atmosphere model. Clim Dyn. doi:10.1007/s00382-013-1969-4

  • Palmer TN, Gelaro R, Barkmeijer J, Buizza R (1998) Singular vectors, metrics, and adaptive observations. J Atmos Sci 55:633–653

    Article  Google Scholar 

  • Palmer T, Buizza R, Hagedorn R, Lawrence A, Leutbecher M, Smith L (2005) Ensemble prediction: a pedagogical perspective. ECMWF Newsletter, 106

  • Pohlmann H, Jungclaus JH, Köhl A, Stammer D, Marotzke J (2009) Initializing decadal climate predictions with the GECCO oceanic synthesis: effects on the North Atlantic. J Clim 22:3926–3938

    Article  Google Scholar 

  • Pohlmann H, Smith DM, Balmaseda MA, Keenlyside NS, Masina S, Matei D, Mller WA, Rogel P (2013) Predictability of the mid-latitude Atlantic meridional overturning circulation in a multi-model system. Clim Dyn 41:775–785

    Article  Google Scholar 

  • Pohlmann H, Mller WA, Kulkarni K, Kameswarrao M, Matei D, Vamborg FSE, Kadow C, Illing S, Marotzke J (2013) Improved forecast skill in the tropics in the new MiKlip decadal climate predictions. Geophys Res Lett 40:5798–5802

    Article  Google Scholar 

  • Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res D14(108):4407. doi:10.1029/2002JD002670

    Article  Google Scholar 

  • Roeckner E, Bäuml G, Bonaventura L, Brokopf R, Esch M, Giorgetta M, Hagemann S, Kirchner I, Kornblueh L, Manzini E, Rhodin A, Schlese U, Schulzweida U, Tompkins A (2003) The atmospheric general circulation model ECHAM5. Part I: Model description. Max Planck Institute for Meteorology Rep, pp 349:127. [Available from MPI for Meteorology, Bundesstr. 53, 20146 Hamburg, Germany]

  • Schneider EK, Huang B, Zhu Z, DeWitt DG, Kinter JL, Kirtman B, Shukla J (1999) Ocean data assimilation, initialization and predictions of ENSO with a coupled GCM. Mon Weather Rev 127:1187–1207

    Article  Google Scholar 

  • Stolzenberger S, Glowienka-Hense R, Spangehl T, M. Schröder, Mazurkiewicz A and Hense A (2014) Revealing skill of the MiKlip decadal prediction system by three dimensional probabilistic evaluation. Meteorologische Zeitschrift, submitted

  • Toth Z, Kalnay E (1993) Ensemble forecasting at NMC: the generation of perturbations. Bull Am Meteorol Soc 74:2317–2330

    Article  Google Scholar 

  • Toth Z, Kalnay E (1997) Ensemble forecasting at NCEP and the breeding method. Mon Weather Rev 125:3297–3319

    Article  Google Scholar 

  • Vikhliaev Y, Kirtman B, Schopf P (2007) Decadal North Pacific bred vectors in a coupled GCM. J Clim 20:5744–5764

    Article  Google Scholar 

  • von Storch H, Zwiers F (2001) Statistical analysis in climate research. Cambridge University Press, Cambridge, p 484

    Google Scholar 

  • von Storch JS, Eden C, Fast I, Haak H, Hrnandez-Deckers D, Maier-Reimer E, Marotzke J, Stammer D (2012) An estimate of Lorenz energy cycle for the world ocean based on the STORM/NCEP simulation. J Phys Oceanogr 42:2185–2205

    Article  Google Scholar 

  • Wei M, Toth Z, Wobus R, Zhu Y, Bishop CH, Wang X (2006) Ensemble transform Kalman filter-based ensemble perturbations in an operational global prediction system at NCEP. Tellus 58A:28–44

    Article  Google Scholar 

  • Wolf A, Swift JB, Swinney HL, Vastano JA (1985) Determining Lyapunov exponents from a time series. Phys D 16:285–317

    Article  Google Scholar 

  • Zhu J, Huang B, Marx L, Kinter JL III, Balmaseda MA, Zhang RH, Hu ZZ (2012) Ensemble ENSO hindcasts initialized from multiple ocean analyses. Geophys Res Lett 39:L09602

    Article  Google Scholar 

Download references

Acknowledgments

This work was funded by the German Federal Ministry of Research and Higher Education within the MiKlip program Module A through the AODA-PENG Project (FKZ 01LP1157A) and in cooperation with the MiKliP Module E VECAP project. This work is partly supported by SPECS EU project. The calculations were performed at the German Climate Computing Center (DKRZ) in Hamburg and for the evaluation purposes the VECAP standard tool was used.

Author information

Authors and Affiliations

  1. Meteorological Institut, Bonn University, Auf dem Hügel 20, 53121, Bonn, Germany

    Vanya Romanova & Andreas Hense

Authors
  1. Vanya Romanova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andreas Hense
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vanya Romanova.

Additional information

This paper is a contribution to the special issue on Ocean estimation from an ensemble of global ocean reanalyses, consisting of papers from the Ocean Reanalyses Intercomparsion Project (ORAIP), coordinated by CLIVAR-GSOP and GODAE OceanView. The special issue also contains specific studies using single reanalysis systems.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Romanova, V., Hense, A. Anomaly transform methods based on total energy and ocean heat content norms for generating ocean dynamic disturbances for ensemble climate forecasts. Clim Dyn 49, 731–751 (2017). https://doi.org/10.1007/s00382-015-2567-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-015-2567-4

Keywords

Search

Navigation