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Coupled data assimilation and parameter estimation in coupled ocean–atmosphere models: a review

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Abstract

Recent studies have started to explore coupled data assimilation (CDA) in coupled ocean–atmosphere models because of the great potential of CDA to improve climate analysis and seamless weather–climate prediction on weekly-to-decadal time scales in advanced high-resolution coupled models. In this review article, we briefly introduce the concept of CDA before outlining its potential for producing balanced and coherent weather–climate reanalysis and minimizing initial coupling shocks. We then describe approaches to the implementation of CDA and review progress in the development of various CDA methods, notably weakly and strongly coupled data assimilation. We introduce the method of coupled model parameter estimation (PE) within the CDA framework and summarize recent progress. After summarizing the current status of the research and applications of CDA-PE, we discuss the challenges and opportunities in high-resolution CDA-PE and nonlinear CDA-PE methods. Finally, potential solutions are laid out.

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References

  • Anderson J, Hoar T, Raeder K, Liu H, Collins N, Torn R, Avellano A (2009) The data assimilation research testbed: a community facility. Bull Am Meteorol Soc 90(9):1283–1296. https://doi.org/10.1175/2009bams2618.1(ISSN 0003-0007)

    Article  Google Scholar 

  • Annan JD, Hargreaves JC (2007) Efficient estimation and ensemble generation in climate modelling filter. Philos Trans R Soc A 365:2077–2088

    Article  Google Scholar 

  • Annan JD, Hargreaves JC, Edwards NR, Marsh R (2005) Parameter estimation in an intermediate complexity earth system model using an ensemble Kalman filter. Ocean Model 8:135–154

    Article  Google Scholar 

  • Ballabrera-Poy J, Busalacchi AJ, Murtugudde R (2001) Application of a reduced-order Kalman filter to initialize a coupled atmosphere–ocean model: impact on the prediction of El Niño. J Clim 14:1720–1737

    Article  Google Scholar 

  • Balmaseda M, Anderson D (2009) Impact of initialization strategies and observations on seasonal forecast skill. Res Lett, Geophys. https://doi.org/10.1029/2008GL035561

    Book  Google Scholar 

  • Brassington GB, Martin MJ, Tolman HL, Akella S, Balmeseda M, Chambers CRS, Chassignet E, Cummings JA, Drillet Y, Jansen PAEM, Laloyaux P, Lea D, Mehra A, Mirouze I, Ritchie H, Samson G, Sandery PA, Smith GC, Suarez M, Todling R (2015) Progress and challenges in short- to mediumrange coupled prediction. J Oper Oceanogr 8(S2):s239–s258. https://doi.org/10.1080/1755876X.2015.1049875

    Article  Google Scholar 

  • Brousseau P, Berre L, Bouttier F, Desroziers G (2012) Flow-dependent background error covariances for a convective-scale data assimilation system. Q J R Meteorol Soc 138(663):310–322. https://doi.org/10.1002/qj.920

    Article  Google Scholar 

  • Browne B, van Leeuwen P (2015) Twin experiments with the equivalent weights particle filter and HadCM3. Q J R Meteorol Soc 141(693 Part B):3399–3414. https://doi.org/10.1002/qj.2621(ISSN 00359009)

    Article  Google Scholar 

  • Browne PA, de Rosnay P, Zuo H, Bennett A, Dawson A (2019) Weakly coupled ocean-atmosphere data assimilation in the ECMWF NWP system. Remote Sens 11(234):1–24. https://doi.org/10.3390/rs11030234

    Article  Google Scholar 

  • Brunet G, Jones S, Ruti PM (2015) Seamless prediction of the earth system: from minutes to months. WMO Rep 1156:483

    Google Scholar 

  • Buehner M, Du P, Bedard Joel (2017) A new approach for estimating the observation impact in ensemble-variational data assimilation. Weather Rev, Mon. https://doi.org/10.1175/MWR-D-17-0252.1

    Book  Google Scholar 

  • Chen D, Cane MA (2008) El Niño prediction and predictability. J Comput Phys 227:3625–3640

    Article  Google Scholar 

  • Chen D, Zebiak SE, Busalacchi AJ, Cane MA (1995) An improved procedure for El Niño forecasting. Science 269:1699–1702

    Article  Google Scholar 

  • Chen D, Cane MA, Zebiak SE, Kaplan A (1998) The impact of sea level data assimilation on the Lamont model prediction of the 1997/98 El Niño. Geophys Res Lett 25:2837–2840

    Article  Google Scholar 

  • Courtier P, Thepaut JN, Hollingsworth A (1994) A strategy for operational implementation of 4D-Var using an incremental approach. Q J R Meteorol Soc 120:1367–1387

    Article  Google Scholar 

  • Dee DP, Balmaseda M, Balsamo G, Engelen R, Simmons AJ, Thépaut JN (2014) Toward a consistent reanalysis of the climate system. Bull Am Meteorol Soc 95:1235–1248

    Article  Google Scholar 

  • Delworth TL, Rosati A, Anderson W, Adcroft AJ, Balaji V, Benson R et al (2012) Simulated climate and climate change in the gfdl cm2. 5 high-resolution coupled climate model. J Clim 25(8):2755–2781

    Article  Google Scholar 

  • Derber J, Rosati A (1989) A global oceanic data assimilation system. J Phys Oceanogr 19:1333–1347

    Article  Google Scholar 

  • Du H, Huang S, Cai Q, Cheng L (2009) Studies of variational assimilation for the inversion of the coupled air-sea model. Mar Sci Bull 11(2):13–22

    Google Scholar 

  • Dubinkina S, Goosse H (2013) An assessment of particle filtering methods and nudging for climate state reconstructions. Clim Past 9:1141–1152. https://doi.org/10.5194/cp-9-1141-2013(ISSN 18149324)

    Article  Google Scholar 

  • Dubinkina S, Goosse H, Sallaz-Damaz Y, Crespin E, Crucifix M (2011) Testing a particle filter to reconstruct climate changes over the past centuries. Int J Bifurc Chaos 21(12):3611–3618. https://doi.org/10.2172/1025774

    Article  Google Scholar 

  • Evensen G (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res 99(5):10143–10162

    Article  Google Scholar 

  • Evensen G (2007) Data assimilation: the ensemble Kalman Filter. Springer Press, Verlag New York, p 187

    Google Scholar 

  • Fairbairn D, Pring SR, Lorenc AC, Roulstone I (2014) A comparison of 4dvar with ensemble data assimilation methods. Q J R Meteorol Soc 140(678):281–294. https://doi.org/10.1002/qj.2135

    Article  Google Scholar 

  • Feng XB, Haines K, Boisseson ED (2018) Coupling of surface air and sea surface temperatures in the CERA-20C reanalysis. Q J R Meteorol Soc 144(710):195–207

    Article  Google Scholar 

  • Fowler AM, Lawless AS (2016) An Idealized study of coupled atmosphere-ocean 4D-Var in the presence of model error. Mon Weather Rev 144:4007–4030

    Article  Google Scholar 

  • Frankignoul C, Czaja A, L’Heveder B (1998) Air–sea feedback in the North Atlantic and surface boundary conditions for ocean models. J Clim 11:2310–2324

    Article  Google Scholar 

  • Frolov S, Bishop C, Holt T, Cummings J, Kuhl D (2016) Facilitating strongly-coupled ocean-atmosphere data assimilation with an interface solver. Mon Weather Rev 144(1):3–20

    Article  Google Scholar 

  • Fujii Y, Nakaegawa T, Matsumoto S, Yasuda T, Yamanaka G, Kamachi M (2009) Coupled climate simulation by constraining ocean fields in a coupled model with ocean data. J Clim 22:5541–5557

    Article  Google Scholar 

  • Galanti E, Tziperman E, Harrison M, Rosati A, Sirkes Z (2003) A study of ENSO prediction using a hybrid coupled model and the adjoint method for data assimilation. Mon Weather Rev 131(11):2748–2764

    Article  Google Scholar 

  • Gandin LS (1965) Objective analysis of meteorological fields. U.S. Dept. Commerce and National Science Foundation Washington, D.C. (Original in Russian, ob”ektivnyi analiz meteorologicheskikh polei, 1963)

  • Gao C, Wu X, Zhang RH (2016) Testing a four-dimensional variational data assimilation method using an improved intermediate coupled model for ENSO analysis and prediction. Adv Atmos Sci 33(7):875–888

    Article  Google Scholar 

  • Giering R, Kaminski T, Todling R, Errico R, Gelaro R, Winslow N (2006) Tangent linear and adjoint versions of NASA/GMAO’s fortran 90 global weather forecast model. In: Bücker M, Corliss G, Naumann U, Hovland P, Norris B (eds) Automatic differentiation: applications, theory, and implementations. Lecture notes in computational science and engineering, vol 50. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28438-9_24

    Chapter  Google Scholar 

  • Guiavarc’h C, Roberts-jones J, Harris C, Lea DJ, Ryan A, Ascione I (2019) Assessment of ocean analysis and forecast from an atmosphere ocean coupled data assimilation operational system. Ocean Sci 15(5):1307–1326

    Article  Google Scholar 

  • Han G, Wu X, Zhang S, Liu Z, Li W (2013) Error covariance estimation for coupled data assimilation using a Lorenz atmosphere and a simple pycnocline ocean model. J Clim 26:10218–10231

    Article  Google Scholar 

  • Han G, Zhang X, Zhang S, Wu X, Liu Z (2014) Mitigation of coupled model biases induced by dynamical core misfitting through parameter optimization: simulation with a simple pycnocline prediction model. Nonlinear Process Geophys 21:357–366

    Article  Google Scholar 

  • Han G, Wu X, Zhang S, Liu Z, Navon IM, Li W (2015) A study of coupling parameter estimation implemented by 4D-Var and EnKF with a simple coupled system. In: Advances in meteorology, pp 1–16

    Article  Google Scholar 

  • He Y, Wang B, Liu M, Liu L, Yu Y, Liu J, Li R, Zhang C, Xu S, Huang W, Liu Q, Wang Y, Li F (2017) Reduction of initial shock in decadal predictions using a new initialization strategy. Geophys Res Lett 44:8538–8547

    Article  Google Scholar 

  • Holt T, Cummings J, Bishop C, Doyle J, Hong X, Chen S, Jin Y (2011) Development and testing of a coupled ocean–atmosphere mesoscale ensemble prediction system. Ocean Dyn 61:1937–1954

    Article  Google Scholar 

  • Isaksen L, Fisher M, Berner J (2007) Use of analysis ensembles in estimating flow-dependent background error variance. In Proc. ECMWF workshop on Flow-dependent aspects of data assimilation, pages 65–86, 2007

  • Ito K, Ishikawa Y, Awaji T (2010) Specifying air-sea exchange coefficients in the high-wind regime of a mature tropical cyclone by an adjoint data assimilation method. Sola 6:13–16

    Article  Google Scholar 

  • Ji M, Kumar A, Leetmaa A (1994) An experimental coupled forecast system at the national meteorological center: some early results. Tellus 46A:398–418

    Article  Google Scholar 

  • Jia L, Yang X, Vecchi GA, Gudgel RG, Delworth TL, Rosati A, Stern WF, Wittenberg AT, Krishnamurthy L, Zhang S, Msadek R, Kapnick SB, Underwood SD, Zeng F, Anderson WG, Balaji V, Dixon KW (2015) Improved seasonal prediction of temperature and precipitation over land in a high-resolution GFDL climate model. J Clim 28(5):2044–2062

    Article  Google Scholar 

  • Kang JS, Kalnay E, Liu J, Fung I, Miyoshi T, Ide K (2011) “Variable localization” in an ensemble Kalman filter: application to the carbon cycle data assimilation. J Geophys Res. https://doi.org/10.1029/2010JD014673

    Article  Google Scholar 

  • Kang J, Kalnay E, Liu J, Fung I, Miyoshi T, Ide K (2012) Estimation of surface carbon fluxes with an advanced data assimilation methodology. J Geophys Res 117:D24101. https://doi.org/10.1029/2012JD018259

    Article  Google Scholar 

  • Keenlyside N, Latif M, Jungclaus J, Kornblueh L, Roeckner E (2008) Advancing decadal-scale climate prediction in the North Atlantic sector. Nature 453:84–88

    Article  Google Scholar 

  • Kleeman R, Moore AM, Smith NR (1995) Assimilation of subsurface thermal data into a simple ocean model for the initialization of an intermediate tropical coupled ocean-atmosphere forecast model. Mon Weather Rev 123:3103–3113

    Article  Google Scholar 

  • Kondrashov D, Sun C, Ghil M (2008) Data assimilation for a coupled ocean atmosphere model. Part II: parameter estimation. Mon Weather Rev 136:5062–5076

    Article  Google Scholar 

  • Laloyaux P, Balmaseda M, Dee D, Mogensen K, Janssen P (2016) A coupled data assimilation system for climate reanalysis. Q J R Meteorol Soc 142:65–78

    Article  Google Scholar 

  • Laloyaux P, Boisseson E, Balmaseda M, Bidlot JR, Broennimann S, Buizza R, Dalhgren P, Dee D, Haimberger L, Hersbach H, Kosaka Y, Martin M, Poli P, Rayner N, Rustemeier E, Schepers D (2018) CERA-20C: a coupled reanalysis of the twentieth century. J Adv Model Earth Syst 10:1172–1195. https://doi.org/10.1029/2018MS001273

    Article  Google Scholar 

  • Lea DJ, Mirouze I, Martin MJ, King RR, Hines A, Walters D, Thurlow M (2015) Assessing a new coupled data assimilation system based on the Met Office coupled atmosphere-land-ocean-sea ice model. Mon Weather Rev 143(11):4678–4694

    Article  Google Scholar 

  • Lee T, Boulanger JP, Foo A, Fu LL, Giering R (2000) Data assimilation by an intermediate coupled ocean-atmosphere model: application to the 1997–1998 El Niño. J Geophys Res 105:26063–26087

    Article  Google Scholar 

  • Lerber AV, Moisseev D, Marks DA, Petersen W, Harri A-M, Chandrasekar V (2017) Validation of GMI Snowfall Observations by Using a Combination of Weather Radar and Surface Measurements. J Appl Meterol Climatol 57:797–820. https://doi.org/10.1175/JAMC-D-17-0176.1

    Article  Google Scholar 

  • Li S, Zhang S, Liu Z, Lu L, Zhu J, Zhang X, Wu X, Zhao M, Vecchi GA, Zhang R, Lin X (2018) Estimating convection parameters in the GFDL CM2.1 model using ensemble data assimilation. J Adv Model Earth Syst 10:989–1010. https://doi.org/10.1002/2017ms001222

    Article  Google Scholar 

  • Liu Y, Gupta HV, Sorooshian S, Bastidas LA, Shuttlewort WJ (2005) Constraining land surface and atmospheric parameters of a locally coupled model using observational data. J. Hydrometeorol 6:156–172

    Article  Google Scholar 

  • Liu Z, Wu S, Zhang S, Liu Y, Rong X (2013) Ensemble Data Assimilation in a Simple Coupled Climate Model: the Role of Ocean-Atmosphere Interaction. Adv Atmos Sci 30(5):1235–1248

    Article  Google Scholar 

  • Liu Y, Liu Z, Zhang S, Rong X, Jacob R, Wu S, Lu F (2014a) Ensemble-based parameter estimation in a coupled GCM using the adaptive spatial average method. J Clim 27:4002–4014

    Article  Google Scholar 

  • Liu Y, Liu Z, Zhang S, Jacob R, Lu F, Rong X, Wu S (2014b) Ensemble-Based parameter estimation in a coupled general circulation model. J Clim 27:7151–7162

    Article  Google Scholar 

  • Liu C, Zhang S, Li S, Liu Z (2017a) Impact of the time scale of model sensitivity response on coupled model parameter estimation. Adv Atmos Sci 34:1346–1357

    Article  Google Scholar 

  • Liu X, Köhl A, Stammer D, Masuda S, Ishikawa Y, Mochizuki T (2017b) Impact of in-consistency between the climate model and its initial conditions on climate prediction. Clim Dyn 49:1061–1075

    Article  Google Scholar 

  • Lorenc AC (1986) Analysis methods for numerical weather prediction. Q J R Meteorol Soc 112:1177–1194

    Article  Google Scholar 

  • Lu JX, Hsieh WW (1998) On determining initial conditions and parameters in a simple coupled atmosphere-ocean model by adjoint data assimilation. Tellus 50A:534–544

    Article  Google Scholar 

  • Lu F, Liu Z (2018_ Assessing extratropical influence on the observed El Nino-Southern Osciilation events using regional coupled data assimilation. J Clim (in press)

  • Lu F, Liu Z, Zhang S, Liu Y (2015a) Strongly coupled data assimilation using Leading Averaged Coupled Covariance (LACC). Part I: Simple model study. Mon Weather Rev 143(9):3823–3837

    Article  Google Scholar 

  • Lu F, Liu Z, Zhang S, Liu Y (2015b) Strongly coupled data assimilation using leading averaged coupled covariance (LACC). Part II: CGCM applications. Mon Weather Rev 143(11):4645–4659

    Article  Google Scholar 

  • Lu F, Liu Z, Liu Y, Zhang S, Jacob R (2017a) Understanding the control of extratropical atmospheric variability on ENSO using a coupled data assimilation approach. Clim Dyn 48:3139–3160

    Article  Google Scholar 

  • Lu F, Liu Z, Zhang S, Jacob R (2017b) Assessing extratropical influence on tropical bias in climate models with regional coupled data assimilation. Geophys Res Lett 44:3384–3392

    Article  Google Scholar 

  • Luo X, Hoteit I (2014) Ensemble Kalman filtering with a divided state-space strategy for coupled data assimilation problems. Mon Weather Rev 142:4542–4558

    Article  Google Scholar 

  • Ma X, Jing Z, Chang P, Liu X, Montuoro R, Small RJ, Bryan FO, Greatbatch RJ, Brandt P, Wu D, Lin X, Wu L (2016) Western boundary currents regulated by interaction between ocean eddies and the atmosphere. Nature 535(7613):533–537

    Article  Google Scholar 

  • Mahajan S, Zhang R, Delworth TL (2011) Impact of the Atlantic meridional overturning circulation (AMOC) on Arctic surface air temperature and sea ice variability. J Clim 24:6573–6581. https://doi.org/10.1175/2011JCLI4002.1

    Article  Google Scholar 

  • Meehl GA, Goddard L, Murphy J, et al. (2009) Decadal prediction. Bullet Am Meteorol Soc 90(10):1467–1486

    Article  Google Scholar 

  • Moisseev D, von Lerber A, Tiira J (2017) Quantifying the effect of riming on snowfall using ground-based observations. JGR Atmospheres 122(7):4019–4037. https://doi.org/10.1002/2016JD026272

    Article  Google Scholar 

  • Msadek R, Delworth TL, Rosati A, Anderson WG, Vecchi GA, Chang YS, Dixon KW, Gudgel RG, Stern WF, Wittenberg AT, Yang X, Zeng F, Zhang R, Zhang S (2014) Predicting a decadal shift in North Atlantic climate variability using the GFDL forecast system. J Clim 27(17):6472–6496

    Article  Google Scholar 

  • Mulholland DP, Laloyaux P, Haines K, Balmaseda MA (2015) Origin and impact of initialization shocks in coupled atmosphere-ocean forecasts. Mon Weather Rev 143:4631–4644

    Article  Google Scholar 

  • Navon IM (1997) Practical and theoretical aspects of adjoint parameter estimation and identifiability in meteorology and oceanograph. Dyn Atmos Oceans 27:55–79

    Article  Google Scholar 

  • Nerger L, Hiller W (2013) Software for ensemble-based data assimilation systems—implementation strategies and scalability. Compute Geosci 55:110–118. https://doi.org/10.1016/j.cageo.2012.03.026 (ISSN 00983004)

    Article  Google Scholar 

  • Nerger L, Hiller W, Schröter J (2005) Pdafthe parallel data assimilation framework: experiences with kalman filtering. In: Zwieflhofer W, Mozdzynski G (eds) Proceedings of the Eleventh ECMWF Workshop on the use of high performance computing in meteorology. World Scientific, Reading, pp 63-83

  • Nerger L, Tang Q, Mu L (2019) Efficient ensemble data assimilation for coupled models with the parallel data assimilation framework: Example of awi-cm. Geosci Model Dev Discuss. https://doi.org/10.5194/gmd-2019-167

  • Penny SG, Hamill TM (2017) Coupled data assimilation for integrated earth system analysis and prediction. Bull Am Meteorol Soc 98(7):169–172

    Article  Google Scholar 

  • Penny, SG, Akella S, Alves O, Bishop C, Buehner M, Chevallier M, Counillon F, Draper C, Frolov S, Fujii Y, Karspeck A, Kumar A, Laloyaux P, Mahfouf J-F, Martin M, Peña (NOAA/NCEP) M, Rosnay P, Subramanian A, Tardif R, Wang (NERSC) Y, Wu X (2017) Coupled Data Assimilation for Integrated Earth System Analysis and Prediction: Goals, Challenges and Recommendations. WMO WWRP White paper. https://www.wmo.int/pages/prog/arep/wwrp/new/documents/Final_WWRP_2017_3_27_July.pdf

  • Posselt DJ, Bishop CH (2012) Nonlinear parameter estimation: comparison of an ensemble Kalman smoother with a Markov chain Monte Carlo algorithm. Mon Weather Rev 140:1957–1974

    Article  Google Scholar 

  • Randall DA, Wood RA, Bony S, et al. (2007) Cilmate models and their evaluation. In: Solomon S, Qin D, Manning M, et al, (eds) Climate change 2007: the physical science basis. Contribution Of working Group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, New York, NY (US), pp 597–599

  • Rienecker MM (2003) Report of the coupled data assimilation. Workshop (NOAA/OGP), workshop report, Natl. Oceanogr. and Atmos. Admin., Portland, Oreg., 21–23 Apr

  • Roberts MJ, Vidale PL, Senior C et al (2018) The benefits of global high resolution for climate simulation: process understanding and the enabling of stakeholder decisions at the regional scale. Bull Am Meteorol Soc 99(11):2341–2359

    Article  Google Scholar 

  • Rosati A, Miyakoda K, Gudgel R (1997) The impact of ocean initial conditions on ENSO forecasting with a coupled model. Mon Weather Rev 125:754–772

    Article  Google Scholar 

  • Ruiz JJ, Pulido M, Miyoshi T (2013) Estimating model parameters with ensemble-based data assimilation: a review. J Meteorol Soc Jpn 91(2):79–99

    Article  Google Scholar 

  • Saha S, Nadiga S, Thiaw C, Wang J (2006) The NCEP climate forecast system. J Clim. https://doi.org/10.1175/JCLI3812.1

    Article  Google Scholar 

  • Saha S et al (2010) The NCEP Climate forecast system reanalysis. Bull Am Meteorol Soc 91:1015–1057

    Article  Google Scholar 

  • Singleton T (2011) Data assimilation experiments with a simple coupled ocean-atmosphere model, PhD thesis, Dep. of Atmos. And Oceanic Sci., Univ. of Maryland, College Park, Md

  • Skachko S, Buehner M, Laroche S, Lapalme E, Smith G, Roy F, Surcel-Colan D, Bélanger J-M, Garand L (2019) Weakly coupled atmospheric-ocean data assimilation in the Canadian global prediction system (v1). Geosci Model Dev Discuss 2019:1–30. https://doi.org/10.5194/gmd-2019

    Article  Google Scholar 

  • Sluka TC, Penny SG, Kalnay E, Miyoshi T (2016) Strongly coupled enkf data assimilation in coupled ocean-atmosphere models. In: The 96th AMS Annual Meeting, “Earth System Science in Service to Society,” 10–14 January 2016 in New Orleans, Louisiana

  • Sluka TC, Penny SG, Kalnay E, Miyoshi T (2016b) Assimilating atmospheric observations into the ocean using strongly coupled ensemble data assimilation. Geophys Res Lett 43:752–759

    Article  Google Scholar 

  • Small RJ, Bacmeister J, Bailey D, Baker A, Bishop S, Bryan F, Caron J, Dennis J, Gent P, Hsu HM, Jochum M, Lawrence D, Muñoz E, diNezio P, Scheitlin T, Tomas R, Tribbia J, Tseng YH, Vertenstein M (2014) A new synoptic scale resolving global climate simulation using the Community Earth System Model. JAMES 6(4):1065–1094

    Google Scholar 

  • Smith PJ, Fowler AM, Lawless AS (2015) Exploring strategies for coupled 4DVar data assimilation using an idealised atmosphere–ocean model. Tellus A 67(1):27025

    Article  Google Scholar 

  • Smith PJ, Amos SL, Nancy KN (2017) estimating forecast error covariances for strongly coupled atmosphere-ocean 4D-Var data assimilation. Mon Weather Rev 145:4011–4035

    Article  Google Scholar 

  • Smith PJ, Lawless AS, Nichols NK (2018) Treating sample covariances for use in strongly coupled atmosphere-ocean data assimilation. Geophys Res Lett 45:445–454

    Article  Google Scholar 

  • Song JQ, Cao XQ, Zhang WM, Zhu XQ (2012) Estimating parameters for coupled air-sea model with variational method. Acta Phys. Sin. 61(11):110401 (In Chinese)

    Google Scholar 

  • Stammer D, Wunsch C, Giering R, Eckert C, Heimbach P, Marotzke J, Adcroft A, Hill CN, Marshall J (2002) The global ocean circulation during 1992–1997, estimated from ocean observations and a general circulation model. J Geophys Res 107(C9):3118

    Article  Google Scholar 

  • Sugiura N, Awaji T, Masuda S, Mochizuki T, Toyoda T, Miyama T, Igarashi H, Ishikawa Y (2008) Development of a four-dimensional variational coupled data assimilation system for enhanced analysis and prediction of seasonal to interannual climate variations. J Geophys Res 113:C10017

    Article  Google Scholar 

  • Tardif R, Hakim GJ, Snyder C (2014) Coupled atmosphere-ocean data assimilation experiments with a low order climate model. Clim Dyn 43:1631–1643

    Article  Google Scholar 

  • Wu WS, Purser RJ, Parrish DF (2002) Three-dimensional variational analysis with spatially inhomogeneous covariances. Mon Weather Rev 130:2905–2916

    Article  Google Scholar 

  • Wu X, Zhang S, Liu Z, Rosati A, Delworth TL, Liu Y (2012a) Impact of geographic-dependent parameter optimization on climate estimation and prediction: simulation with an intermediate coupled model. Mon Weather Rev 140:3956–3971

    Article  Google Scholar 

  • Wu X, Zhang S, Liu Z, Rosati A, Delworth TL (2012b) A study of impact of the geographic dependent of observing system on parameter estimation with an intermediate coupled model. Clim Dyn 40:1789–1798

    Article  Google Scholar 

  • Wu X, Han G, Zhang S, Liu Z (2016) A study of the impact of parameter optimization on enso predictability with an intermediate coupled model. Clim Dyn 46:711–727

    Article  Google Scholar 

  • Wu B, Zhou T, Zheng F (2018) EnOI-IAU initialization scheme designed for decadal climate prediction system IAP-DecPreS. J Adv Model Earth Syst 10:342–356

    Article  Google Scholar 

  • Xie Y, Koch S, McGinley J, Albers S, Bieringer PE, Wolfson M, Chan M (2011) A space–time multiscale analysis system: a sequential variational analysis approach. Mon Weather Rev 139:1224–1240

    Article  Google Scholar 

  • Yang X, Rosati A, Zhang S, Delworth TL (2013) A predictable AMO-like pattern in the gfdl fully coupled ensemble initialization and decadal forecasting system. J Clim 26:650–661

    Article  Google Scholar 

  • Yoshida T (2019) Covariance localization in strongly coupled data assimilation. UMD doctoral thesis, https://drum.lib.umd.edu/handle/1903/25170

  • Yoshida T, Kalnay E (2018) Correlation-cutoff method for covariance localization in strongly coupled data assimilation. Weather Rev, Mon. https://doi.org/10.1175/MWR-D-17-0365.1

    Book  Google Scholar 

  • Yu X, Zhang S, Li J, Lu L, Liu Z, Li M, Yu H, Han G, Lin X, Wu L, Chang P (2019) A multi-time scale EnOI-like high-efficiency filter for coupled model data assimilation. J Adv Model Earth Syst 11(1):45–63

  • Zhang S (2011a) Impact of observation-optimized model parameters on decadal predictions: simulation with a simple pycnocline prediction model. Geophys Res Lett 38(2):L02702

    Article  Google Scholar 

  • Zhang S (2011b) A study of impacts of coupled model initial shocks and state-parameter optimization on climate prediction using a simple pycnocline prediction model. J Clim 24:6210–6226

    Article  Google Scholar 

  • Zhang S, Harrison MJ, Wittenberg AT, Rosati A, Anderson JL, Balaji V (2005) Initialization of an ENSO forecast system using a parallelized ensemble filter. Mon Weather Rev 133(11):3176–3201

    Article  Google Scholar 

  • Zhang S, Harrison MJ, Rosati A, Wittenberg A (2007) System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies. Mon Weather Rev 135:3541–3564

    Article  Google Scholar 

  • Zhang S, Liu Z, Rosati A, Delworth TL (2012) A Study of enhancive parameter correction with coupled data assimilation for climate estimation and prediction using a simple coupled model. Tellus 64:10963

    Article  Google Scholar 

  • Zhang S, Chang YS, Yang X, Rosati A (2014a) Balanced and coherent climate estimation by combining data with a biased coupled model. J Clim 27:1302–1314

    Article  Google Scholar 

  • Zhang S, Zhao M, Lin S-J, Yang X, Anderson W (2014b) Retrieval of tropical cyclone statistics with a high-resolution coupled model and data. Geophys Res Lett 41:652–660. https://doi.org/10.1002/2013GL058879

    Article  Google Scholar 

  • Zhang S, Zhao M, Lin S-J, Yang X, Anderson W, Zhang W, Rosati A, Underwood S, Zeng F (2015) Impact of having realistic tropical cyclone frequency on ocean heat content and transport forecasts in a high-resolution coupled model. Geophys Res Lett 42:5966–5973. https://doi.org/10.1002/2015GL064745

    Article  Google Scholar 

  • Zhao Y, Deng X, Zhang S, Liu Z, Liu C, Vecchi G, Han G, Wu X (2017) Impact of an observational time window on coupled data assimilation: simulation with a simple climate model. Nonlinear Process Geophys 24:681–694

    Article  Google Scholar 

  • Zhao Y, Deng X, Zhang S, Liu Z, Liu C (2019) Sensitivity determined simultaneous estimation of multiple parameters in coupled models: part I—based on single model component sensitivities. Clim Dyn. https://doi.org/10.1007/s00382-019-04865-3

    Article  Google Scholar 

  • Zheng F, Zhu J (2010) Coupled assimilation for an intermediated coupled ENSO prediction model. Ocean Dyn 60:1061–1073

    Article  Google Scholar 

  • Zupanski M (2005) Maximum likelihood ensemble filter: theoretical aspects. Mo. Weather Rev 133:1710–1726

    Article  Google Scholar 

  • Županski M (2016) Data assimilation for coupled modeling systems. In: Park SK, Xu L (eds) Data assimilation for atmospheric, oceanic, and hydrologic applications, vol III. Springer International Publishing, Switzerland, pp 55–70

    Google Scholar 

Download references

Acknowledgements

This work is supported by the National Key R&D Program of China (2017YFC1404100, 2017YFC1404103 and 2017YFC1404104) and the National Natural Science Foundation of China (Grant No. 41775100, 41830964).

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Authors and Affiliations

  1. Key Laboratory of Physical Oceanography, .MOE., Institute for Advanced Ocean Study, College of Ocean and Atmosphere, Frontiers Science Center for Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China, Qingdao, China

    Shaoqing Zhang, Xiaolin Yu & Mingkui Li

  2. Ocean Dynamics and Climate Function Lab/Pilot National Laboratory for Marine Science and Technology (QNLM), Qingdao, China

    Shaoqing Zhang, Xiaolin Yu & Mingkui Li

  3. International Laboratory for High-Resolution Earth System Prediction (iHESP), Qingdao, China

    Shaoqing Zhang

  4. Department of Geography, Ohio State University, Columbus, OH, 43210, USA

    Zhengyu Liu

  5. School of Marine Science and Technology, Tianjin University, Tianjin, China

    Xuefeng Zhang & Guijun Han

  6. College of Automation, Harbin Engineer University, Harbin, China

    Xuefeng Zhang, Yuxin Zhao, Chang Liu & Xiong Deng

  7. Ministry of Natural Resources of China, National Marine Data and Information Service, Tianjin, 300171, China

    Xinrong Wu

  8. Department of Oceanography, Texas A & M University, College Station, TX, 77843, USA

    Yun Liu

  9. Nelson Institute Center for Climatic Research, University of Wisconsin-Madison, Madison, WI, 53706, USA

    Shu Wu

  10. Princeton University and NOAA/GFDL, Princeton, NJ, USA

    Feiyu Lu

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  1. Shaoqing Zhang
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  2. Zhengyu Liu
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  3. Xuefeng Zhang
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Zhang, S., Liu, Z., Zhang, X. et al. Coupled data assimilation and parameter estimation in coupled ocean–atmosphere models: a review. Clim Dyn 54, 5127–5144 (2020). https://doi.org/10.1007/s00382-020-05275-6

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