Correcting circulation biases in a lower-resolution global general circulation model with data assimilation

Bias correction; Data assimilation; Global ocean model; Stochastic forcing; NEMO-LIM2; Ensemble transform Kalman filter; Ensemble smoother; Circulation bias; Parameter estimation

Abstract :

[en] In this study, we aim at developing a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias by directly adding an additional source term into the model equations. This method is presented and tested first with a twin experiment on a fully controlled Lorenz ’96 model. It is then applied to the lower-resolution global circulation NEMO-LIM2 model, with both a twin experiment and a real case experiment. Sea surface height observations are used to create a forcing to correct the poorly located and estimated currents. Validation is then performed throughout the use of other variables such as sea surface temperature and salinity. Results show that the method is able to consistently correct part of the model bias. The bias correction term is presented and is consistent with the limitations of the global circulation model causing bias on the oceanic currents.

Research Center/Unit :

GeoHydrodynamics and Environment Research

Disciplines :

Earth sciences & physical geography

Author, co-author :

Canter, Martin ; Université de Liège > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER)

Barth, Alexander ; Université de Liège > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER)

Beckers, Jean-Marie ; Université de Liège > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER)

Language :

English

Title :

Correcting circulation biases in a lower-resolution global general circulation model with data assimilation

Publication date :

07 December 2016

Journal title :

Ocean Dynamics

ISSN :

1616-7341

eISSN :

1616-7228

Publisher :

Springer Science & Business Media B.V., Heidelberg, Germany

Special issue title :

Topical Collection on the 47th International Liège Colloquium on Ocean Dynamics, Liège, Belgium, 4-8 May 2015

Peer reviewed :

Peer Reviewed verified by ORBi

Tags :

CÉCI : Consortium des Équipements de Calcul Intensif

Additional URL :

http://link.springer.com/article/10.1007%2Fs10236-016-1022-3

European Projects :

FP7 - 283580 - SANGOMA - Stochastic Assimilation for the Next Generation Ocean Model Applications

Name of the research project :

SANGOMA

Funders :

BELSPO - SPP Politique scientifique - Service Public Fédéral de Programmation Politique scientifique
UE - Union Européenne
CE - Commission Européenne

Available on ORBi :

since 08 December 2016

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Bibliography

Anderson JL (2009) Spatially and temporally varying adaptive covariance inflation for ensemble filters. Tellus A 61(1):72–83
Annan J, Lunt D, Hargreaves J, Valdes P (2005) Parameter estimation in an atmospheric GCM using the ensemble Kalman filter. Nonlinear Process Geophys 12:363–371
Baek S.-J., Hunt BR, Kalnay E, Ott E, Szunyogh I (2006) Local ensemble kalman filtering in the presence of model bias. Tellus A 58(3):293–306
Baek S.-J., Szunyogh I, Hunt BR, Ott E (2009) Correcting for surface pressure background bias in ensemble-based analyses. Mon Weather Rev 137(7):2349–2364
Barth A, Alvera-Azcárate A, Beckers J.-M, Rixen M, Vandenbulcke L (2007) Multigrid state vector for data assimilation in a two-way nested model of the Ligurian Sea. J Mar Syst 65(1-4):41–59. http://hdl.handle.net/2268/4260
Barth A, Alvera-Azcárate A., Beckers J.-M., Weisberg RH, Vandenbulcke L, Lenartz F, Rixen M (2009) Dynamically constrained ensemble perturbations - application to tides on the West Florida Shelf. Ocean Sci 5(3):259–270
Barth A, Alvera-Azcárate A., Gurgel K.-W., Staneva J, Port A, Beckers J.-M., Stanev EV (2010) Ensemble perturbation smoother for optimizing tidal boundary conditions by assimilation of High-Frequency radar surface currents - application to the German Bight. Ocean Sci 6(1):161–178
Barth A, Beckers J.-M, Troupin C, Alvera-Azcárate A, Vandenbulcke L (2014) Divand-1.0: n-dimensional variational data analysis for ocean observations. Geosci Model Dev 7(1):225–241. http://www.geosci-model-dev.net/7/225/2014/
Barth A, Canter M, Van Schaeybroeck B, Vannitsem S, Massonnet F, Zunz V, Mathiot P, Alvera-Azcárate A., Beckers J.-M. (2015) Assimilation of sea surface temperature, sea ice concentration and sea ice drift in a model of the southern ocean. Ocean Modell 93:22–39
Bell MJ, Martin M, Nichols N (2004) Assimilation of data into an ocean model with systematic errors near the equator. Q J Royal Meteorol Soc 130(598):873–893
Bishop CH, Etherton BJ, Majumdar SJ (2001) Adaptive Sampling with the Ensemble Transform Kalman Filter. Part I: Theoretical Aspects. Mon Weather Rev 129(3):420–436
Bouillon S, Maqueda MAM, Legat V, Fichefet T (2009) An elastic-viscous-plastic sea ice model formulated on Arakawa B and C grids. Ocean Modell 27:174–184
Broquet G, Moore A, Arango H, Edwards C (2011) Corrections to ocean surface forcing in the california current system using 4d variational data assimilation. Ocean Modell 36(1):116–132
Carton JA, Chepurin G, Cao X, Giese B (2000) A simple ocean data assimilation analysis of the global upper ocean 1950-95. Part I: Methodology. J Phys Oceanogr 30(2):294–309
Chepurin GA, Carton JA, Dee D (2005) Forecast model bias correction in ocean data assimilation. Mon Weather Revi 133(5):1328–1342
Dee DP (2004) Variational bias correction of radiance data in the ECMWF system. In: Proceedings of the ECMWF workshop on assimilation of high spectral resolution sounders in NWP, vol 28, pp 97–112
Dee DP (2005) Bias and data assimilation. Q J Royal Meteorol Soc 131(613):3323–3344
Dee DP, Da Silva A (1998) Data assimilation in the presence of forecast bias. Q J Royal Meteorol Soc 124 (545):269–295
Dee DP, Todling R (2000) Data assimilation in the presence of forecast bias: The geos moisture analysis. Mon Weather Rev 128(9):3268–3282
Derber J, Rosati A (1989) A global oceanic data assimilation system. J Phys Oceanogr 19:1333–1347
Derber JC, Wu W.-S. (1998) The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon Weather Rev 126(8):2287–2299
Evensen G (2007) Data assimilation: the Ensemble Kalman Filter. Springer, p 279
Fertig EJ, BAEK S.-J., Hunt BR, Ott E, Szunyogh I, Aravéquia J. A., Kalnay E, Li H, Liu J (2009) Observation bias correction with an ensemble kalman filter. Tellus A 61(2):210–226
Fichefet T, Maqueda MAM (1997) Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics. J Geophys Res 102:12609–12646
Friedland B (1969) Treatment of bias in recursive filtering. IEEE Trans Autom Control 14(4):359–367
Gelb A (1974) Applied optimal estimation. MIT Press, Cambridge, MA, p 374
Gerbig C, Körner S., Lin J (2008) Vertical mixing in atmospheric tracer transport models: error characterization and propagation. Atmos Chem Phys 8(3):591–602
Hunt BR, Kalnay E, Kostelich EJ, Ott E, Patil DJ, Sauer T, Szunyogh I, Yorke JA, Zimin AV (2004) Four-dimensional ensemble Kalman filtering. Tellus 56A:273–277
Hunt BR, Kostelich EJ, Szunyogh I (2007) Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Phys D 230:112–126
Jazwinski AH (1970) Stochastic processes and filtering theory. Academic, San Diego, California
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. Bullet Amer Meteorol Soc 77:437–471
Keppenne CL, Rienecker MM, Kurkowski NP, Adamec DA (2005) Ensemble Kalman filter assimilation of temperature and altimeter data with bias correction and application to seasonal prediction. Nonlinear Process Geophys 12:491–503
Leeuwenburgh O (2008) Estimation and correction of surface wind-stress bias in the tropical pacific with the ensemble kalman filter. Tellus A 60(4):716–727
Levitus S, Boyer T (1994) World ocean atlas 1994. volume 4. temperature. Technical report., National Environmental Satellite, Data and Information Service, DC, USA
Li H, Kalnay E, Miyoshi T (2009) Simultaneous estimation of covariance inflation and observation errors within an ensemble kalman filter. Q J Royal Meteorol Soc 135(639):523–533
Lorenz EN (1963) Deterministic nonperiodic flow. J Atmos Sci 20:130–141
Lorenz EN (1996) Predictability: A problem partly solved. In: Proceedings Seminar on Predictability, vol 1
Lorenz EN, Emanuel KA (1998) Optimal sites for supplementary weather observations: Simulation with a small model. J Atmos Sci 55:399–414
Madec G (2008) NEMO ocean engine. No. 27 in Note du Pole de modélisation Institut Pierre-Simon Laplace. IPSL, France
Massonnet F, Goosse H, Fichefet T, Counillon F (2014) Calibration of sea ice dynamic parameters in an ocean-sea ice model using an ensemble kalman filter. J Geophys Res: Oceans 119(7):4168–4184
Massonnet F, Mathiot P, Fichefet T, Goosse H, Beatty CK, Vancoppenolle M, Lavergne T (2013) A model reconstruction of the Antarctic sea ice thickness and volume changes over 1980-2008 using data assimilation. Ocean Modell 64:67–75
Mathiot P, Goosse H, Fichefet T, Barnier B, Gallée H. (2011) Modelling the seasonal variability of the Antarctic Slope Current. Ocean Sci 7(4):455–470
Nerger L, Gregg WW (2008) Improving assimilation of seawifs data by the application of bias correction with a local seik filter. J Mar Syst 73(1):87–102
Radakovich JD, Bosilovich MG, Chern J.-D., da Silva A, Todling R, Joiner J, Wu M.-L., Norris P (2004) Implementation of coupled skin temperature analysis and bias correction in the NASA/GMAO finite-volume data assimilation system (FvDAS). In: P1. 3 in Proceedings of the Eighth AMS Symposium on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface, pp 12?-15
Radakovich JD, Houser PR, Da Silva A, Bosilovich MG (2001) Results from global land-surface data assimilation methods. In: AGU Spring Meeting Abstracts, vol 1
Rio M, Guinehut S, Larnicol G (2011) New CNES-CLS09 global mean dynamic topography computed from the combination of GRACE data, altimetry, and in situ measurements. J Geophys Res: Oceans 116(C7):1978–2012
Sakov P, Evensen G, Bertino L (2010) Asynchronous data assimilation with the EnKF. Tellus 62A:24–29
Timmermann R, Goosse H, Madec G, Fichefet T, Ethe C, Dulière V. (2005) On the representation of high latitude processes in the ORCA-LIM global coupled sea ice-ocean model. Ocean Modell 8(1-2):175–201
Van Leeuwen PJ (2001) An Ensemble Smoother with Error Estimates. Mon Weather Rev 129:709–728
Van Leeuwen PJ (2010) Nonlinear Data Assimilation in geosciences: an extremely efficient particle filter. Q J Royal Meteorol Soc 136:1991–1996
Zunz V, Goosse H, Massonnet F (2013) How does internal variability influence the ability of CMIP5 models to reproduce the recent trend in Southern Ocean sea ice extent? Cryosphere 7(2):451–468