Articles | Volume 15, issue 5
https://doi.org/10.5194/os-15-1307-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-15-1307-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Oct 2019
Research article |
| 07 Oct 2019
| 07 Oct 2019
Assessment of ocean analysis and forecast from an atmosphere–ocean coupled data assimilation operational system
Catherine Guiavarc'h
Met Office, FitzRoy Road, EX1 3PB, Exeter, UK
Jonah Roberts-Jones
Met Office, FitzRoy Road, EX1 3PB, Exeter, UK
Chris Harris
CORRESPONDING AUTHOR
Met Office, FitzRoy Road, EX1 3PB, Exeter, UK
Daniel J. Lea
Met Office, FitzRoy Road, EX1 3PB, Exeter, UK
Andrew Ryan
Met Office, FitzRoy Road, EX1 3PB, Exeter, UK
Isabella Ascione
Met Office, FitzRoy Road, EX1 3PB, Exeter, UK
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Cited articles
Amante, C. and Eakins, B. W.: ETOPO1 1 Arc-Minute Global Relief Model:
Procedures, Data Sources and Analysis, NOAA Technical Memorandum NESDIS
NGDC-24, 2009. a
Apache Software foundation:
Apache Subversion,
available at: http://subversion.apache.org/,
last access: 27 September 2019. a
Blockley, E. W., Martin, M. J., and Hyder, P.: Validation of FOAM near-surface ocean current forecasts using Lagrangian drifting buoys, Ocean Sci., 8, 551–565, https://doi.org/10.5194/os-8-551-2012, 2012. a
Blockley, E. W., Martin, M. J., McLaren, A. J., Ryan, A. G., Waters, J., Lea, D. J., Mirouze, I., Peterson, K. A., Sellar, A., and Storkey, D.: Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts, Geosci. Model Dev., 7, 2613–2638, https://doi.org/10.5194/gmd-7-2613-2014, 2014. a, b, c, d
Brodeau, L., Barnier, B., Gulev, S. K., and Woods, C.: Climatologically significant effects
of some approximations in the bulk parameterizations of turbulent air-sea
fluxes, J. Phys. Oceanogr., 47, 5–28, https://doi.org/10.1175/JPO-D-16-0169.1, 2017. a
Browne, P. A., de Rosnay, P., Zuo, H., Bennett, A., and Dawson, A.: Weakly
Coupled Ocean Atmosphere Data Assimilation in the ECMWF NWP System, Remote
Sens., 11, 234, https://doi.org/10.3390/rs11030234,
2019. a
Burchard, H.: Energy-conserving discretisation of turbulent shear and buoyancy
production, Ocean Model., 4, 347–361, 2002. a
Cameron, J. and Bell, W.: The testing and planned implementation of variational
bias correction (VarBC) at the Met Office, 20th International TOVS study
conference, Madison, WI,
available at: https://cimss.ssec.wisc.edu/itwg/itsc/itsc20/papers/11_01_cameron_paper.pdf (last access: 27 September 2019),
2016. a
Dawe, J. T. and Thompson, L. A.: Effect of ocean surface currents on wind
stress, heat flux, and wind power input to the ocean, Geophys. Res. Lett.,
33, 1–5, 2006. a
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and
Wimmer, W.: The Operational Sea Surface Temperature and Sea Ice Analysis
(OSTIA) system, Remote Sens. Environ., 116, 140–158,
https://doi.org/10.1016/j.rse.2010.10.017,
2012. a
Duhaut, T. H. A. and Straub, D. N.: Wind stress dependence on ocean surface
velocity: implications for mechanical energy input to ocean circulation, J.
Phys. Oceanogr., 36, 202–211, 2006. a
Etienne, H.: Quality Information Document for Global Ocean Delayed Mode in-situ
Observations of Ocean Surface Currents and Temperature from Drifters.
INSITU_GLO_UV_L2_REP_OBSERVATIONS_013_044, CMEMS Quality Information
Document,
available at: http://marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-044.pdf
(last access: 27 September 2019),
2017. a
Fairall, C. W., Bradley, E. F., Hare, J. E., Grachev, A. A., and Edson, J. B.:
Bulk Parameterization of Air–Sea Fluxes: Updates and Verification for the
COARE Algorithm, J. Climate, 16, 571–591,
https://doi.org/10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2,
2003. a
Fiedler, E.: Improvements to feature resolution in the OSTIA sea surface
temperature analysis using the NEMOVAR assimilation scheme,
Q. J. Roy. Meteorol. Soc., online first, https://doi.org/10.1002/qj.3644, 2018. a, b
Gaspar, P., Gregoris, Y., and Lefevre, J. M.: A simple eddy kinetic energy
model for simulations of the ocean vertical mixing: tests at station Papa and
long-term upper ocean study site, J. Geophys. Res., 95, 16179–16193,
https://doi.org/10.1029/JC095iC09p16179, 1990. a
Haney, R. L.: Surface Thermal Boundary Condition for Ocean Circulation Models,
J. Phys. Oceanogr., 1, 241–248,
https://doi.org/10.1175/1520-0485(1971)001<0241:STBCFO>2.0.CO;2,
1971. a
Hollingsworth, A., Arpe, K., Tiedtke, M., Capaldo, M., and Savijrvi, H.: The
Performance of a Medium-Range Forecast Model in Winter: Impact of Physical
Parameterizations, Mon. Weather Rev., 108, 1736–1773,
https://doi.org/10.1175/1520-0493(1980)108<1736:TPOAMR>2.0.CO;2,
1980. a
Hunke, E. C. and Dukowicz, J. K.: The elastic-viscous-plastic sea ice dynamics
model in general orthogonal curvilinear coordinates on a sphere –
incorporation of metric terms, Mon. Weather Rev., 130, 1848–1865, 2002. a
Hunke, E. C. and Lipscomb, W.: CICE: the Los Alamos sea ice model documentation
and software user's manual. Version 4.1, LA-CC-06-012, Technical report,
Los Alamos National Laboratory, N.M., 2010. a
IOC, IHO and BODC: Edition of the GEBCO Digital Atlas, published on CD-ROM on
behalf of the Intergovernmental Oceanographic Commission and the
International Hydrographic Organization as part of the General Bathymetric
Chart of the Oceans, British Oceanographic Data Centre, Liverpool, UK, 2003. a
Kara, A. B., Rochford, P. A., and Hurlburt, H. E.: An optimal definition for
ocean mixed layer depth, J. Geophys. Res.-Oceans, 105,
16803–16821, https://doi.org/10.1029/2000JC900072,
2000. a
King, R. R., While, J., Martin, M. J., Lea, D. J., Lemieux-Dudon, B., Waters,
J., and O'Dea, E.: Improving the initialisation of the Met Office operational
shelf-seas model, Ocean Modell., 130, 1–14,
https://doi.org/10.1016/j.ocemod.2018.07.004,
2018. a, b
Lellouche, J.-M., Greiner, E., Le Galloudec, O., Garric, G., Regnier, C., Drevillon, M., Benkiran, M., Testut, C.-E., Bourdalle-Badie, R., Gasparin, F., Hernandez, O., Levier, B., Drillet, Y., Remy, E., and Le Traon, P.-Y.: Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time 1/12∘ high-resolution system, Ocean Sci., 14, 1093–1126, https://doi.org/10.5194/os-14-1093-2018, 2018. a
Lengaigne, M., Menkes, C., Aumont, O., Gorgues, T., Bopp, L., André, J.-M.,
and Madec, G.: Influence of the oceanic biology on the tropical Pacific
climate in a coupled general circulation model, Clim. Dynam., 5,
503–516, https://doi.org/10.1007/s00382-006-0200-2, 2007. a
Lorenc, A.: VSDP32. VAR documentation, Met Office report, 2013. a
MacLachlan, C., Arribas, A., Peterson, A., Fereday, D., Scaife, A. A., Gordon,
M., Vellinga, M., A., W., Comer, R., Camp, J., and Xavier, P.: Description of
GloSea5: the Met Office high resolution seasonal forecast system, Q. J. Roy.
Meteor. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2014. a
Madec, G. and the NEMO team: NEMO ocean engine, Note du Pôle de modélisation,
Institut Pierre-Simon Laplace (IPSL), No. 27, ISSN 1288-1619, 2008. a
Martin, M. J., Hines, A., and Bell, M. J.: Data assimilation in the FOAM
operational short-range ocean forecasting system: a description of the scheme
and its impact, Q. J. Roy. Meteor. Soc., 133, 981–995, 2007. a
Megann, A., Storkey, D., Aksenov, Y., Alderson, S., Calvert, D., Graham, T., Hyder, P., Siddorn, J., and Sinha, B.: GO5.0: the joint NERC–Met Office NEMO global ocean model for use in coupled and forced applications, Geosci. Model Dev., 7, 1069–1092, https://doi.org/10.5194/gmd-7-1069-2014, 2014. a
Met Office:
Unified Model Met Office,
available at: http://www.metoffice.gov.uk/research/modelling-systems/unified-model,
last access: 27 September 2019a. a
Met Office:
Joint UK Land Environment Simulator (JULES) Documentation,
available at: https://jules-lsm.github.io/,
last access: 27 September 2019b. a
Met Office:
CICE code,
https://code.metoffice.gov.uk/trac/cice/browser,
last access: 27 September 2019c. a
Mirouze, I. and Weaver, A.: Representation of correlation functions in
variational assimilation using an implicit diffusion operator, Q. J. Roy.
Meteor. Soc., 136, 1421–1443, 2010. a
Mirouze, I., Blockley, E. W., Lea, D. J., Martin, M. J., and Bell, M. J.: A
multiple length scale correlation operator for ocean data assimilation,
Tellus A, 68, 29744,
https://doi.org/10.3402/tellusa.v68.29744,
2016. a
Mogensen, K., Balmaseda, M. A., and Weaver, A.: The NEMOVAR ocean data
assimilation system as implemented in the ECMWF ocean analysis for System 4,
European Centre for Medium-Range Weather Forecasts, 2012. a
Mulholland, D. P., Laloyaux, P., Haines, K., and Balmaseda, M. A.: Origin and
Impact of Initialization Shocks in Coupled Atmosphere–Ocean Forecasts,
Mon. Weather Rev., 143, 4631–4644, https://doi.org/10.1175/MWR-D-15-0076.1,
2015. a
Murphy, A. H. and Epstein, E. S.: Skill Scores and Correlation Coefficients in
Model Verification, Mon. Weather Rev., 117, 572–582,
https://doi.org/10.1175/1520-0493(1989)117<0572:SSACCI>2.0.CO;2,
1989. a
NEMO consortium:
NEMO Community ocean model,
available at: http://www.nemo-ocean.eu,
last access: 27 September 2019. a
Penny, S. G. and Hamill, T. M.: Coupled Data Assimilation for Integrated Earth
System Analysis and Prediction, B. Am. Meteorol.
Soc., 98, ES169–ES172, https://doi.org/10.1175/BAMS-D-17-0036.1,
2017. a
Rae, J. G. L., Hewitt, H. T., Keen, A. B., Ridley, J. K., West, A. E., Harris, C. M., Hunke, E. C., and Walters, D. N.: Development of the Global Sea Ice 6.0 CICE configuration for the Met Office Global Coupled model, Geosci. Model Dev., 8, 2221–2230, https://doi.org/10.5194/gmd-8-2221-2015, 2015. a
Rawlins, F., Ballard, S. P., Bovis, K. J., Clayton, A. M., Li, D., Inverarity, G. W., Lorenc, A. C., and Payne, T. J.: The Met Office global four‐dimensional variational data assimilation scheme, Q. J. Roy. Meteor. Soc. 133, 347–362, 2007. a
Renault, L., Molemaker, M. J., McWilliams, J. C., Shchepetkin, A. F., Lemarié,
F., Chelton, D., Illig, S., and Hall, A.: Modulation of Wind Work by Oceanic
Current Interaction with the Atmosphere, J. Phys. Oceanogr.,
46, 1685–1704, https://doi.org/10.1175/JPO-D-15-0232.1,
2016. a
Ridley, J. K., Blockley, E. W., Keen, A. B., Rae, J. G. L., West, A. E., and Schroeder, D.: The sea ice model component of HadGEM3-GC3.1, Geosci. Model Dev., 11, 713–723, https://doi.org/10.5194/gmd-11-713-2018, 2018. a
Rio, M.-H.: Use of Altimeter and Wind Data to Detect the Anomalous Loss of
SVP-Type Drifters Drogue, J. Atmos. Ocean. Tech., 29,
1663–1674, https://doi.org/10.1175/JTECH-D-12-00008.1, 2012. a
Rio, M.-H., Guinehut, S., , and Larnicol, G.: New CNES-CLS09 global mean
dynamic topography computed from the combination of GRACE data, altimetry,
and in situ measurements, J. Geophys. Res., 116, C07018,
https://doi.org/10.1029/2010JC006505, 2011. a
Rio, M.-H., Mulet, S., and Picot, N.: Beyond GOCE for the ocean circulation
estimate: Synergetic use of altimetry, gravimetry, and in situ data provides
new insight into geostrophic and Ekman currents, Geophys. Res.
Lett., 41, 8918–8925, https://doi.org/10.1002/2014GL061773,
2014. a
Roberts-Jones, J., Fiedler, E. K., and Martin, M. J.: Daily, Global,
High-Resolution SST and Sea Ice Reanalysis for 1985-2007 Using the OSTIA
System, J. Climate, 25, 6215–6232, https://doi.org/10.1175/JCLI-D-11-00648.1,
2012. a
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D.,
Hou, Y.-T., Chuang, H.-y., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez,
M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: The
NCEP Climate Forecast System Version 2, J. Climate, 27, 2185–2208,
https://doi.org/10.1175/JCLI-D-12-00823.1, 2014. a
Semtner, A. J.: A model for the thermodynamic growth of sea ice in numerical
investigations of climate, J. Phys. Oceanogr., 6, 379–389, 1976. a
Smith, G. C., Belanger, J.-M., Roy, F., Pellerin, P., Ritchie, H., Onu, K.,
Roch, M., Zadra, A., Colan, D. S., Winter, B., Fontecilla, J.-S., and Deacu,
D.: Impact of Coupling with an Ice-Ocean Model on Global Medium-Range NWP
Forecast Skill, Mon.Weather Rev., 146, 1157–1180,
https://doi.org/10.1175/MWR-D-17-0157.1,
2018. a
Thorndike, A., Rothrock, D., Maykut, G., and Colony, R.: The thickness
distribution of sea ice, J. Geophys. Res., 80, 4501–4513, 1975. a
Valcke, S.: OASIS3 User Guide (prism 2-5), PRISM Support Initiative No 3, 2006. a
Walters, D., Boutle, I., Brooks, M., Melvin, T., Stratton, R., Vosper, S., Wells, H., Williams, K., Wood, N., Allen, T., Bushell, A., Copsey, D., Earnshaw, P., Edwards, J., Gross, M., Hardiman, S., Harris, C., Heming, J., Klingaman, N., Levine, R., Manners, J., Martin, G., Milton, S., Mittermaier, M., Morcrette, C., Riddick, T., Roberts, M., Sanchez, C., Selwood, P., Stirling, A., Smith, C., Suri, D., Tennant, W., Vidale, P. L., Wilkinson, J., Willett, M., Woolnough, S., and Xavier, P.: The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations, Geosci. Model Dev., 10, 1487–1520, https://doi.org/10.5194/gmd-10-1487-2017, 2017. a
Waters, J., Lea, D. J., Martin, M. J., Mirouze, I., Weaver, A., and While, J.:
Implementing a variational data assimilation system in an operational 1/4
degree global ocean model, Q. J. Roy. Meteor.
Soc., 141, 333–349, https://doi.org/10.1002/qj.2388,
2015. a
Weaver, A. T., Deltel, C., Machu, E., Ricci, S., and Daget, N.: A multivariate
balance operator for variational ocean data assimilation, Q. J.
Roy. Meteor. Soc., 131, 3605–3625,
https://doi.org/10.1256/qj.05.119,
2006. a
Wehde, H., V. Schuckmann, K., Pouliquen, S., Grouazel, A., Bartolome, T.,
Tintore, J., De Alfonso Alonso Munoyerro, M., and the INS-TAC team: Quality
Information Document For Near Real Time IN SITU products
INSITU_GLO_NRT_OBSERVATIONS_013_030, CMEMS Quality Information Document, available at:
http://marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-030-036.pdf (last access: 27 September 2019),
2016. a
West, A. E., McLaren, A. J., Hewitt, H. T., and Best, M. J.: The location of the thermodynamic atmosphere–ice interface in fully coupled models – a case study using JULES and CICE, Geosci. Model Dev., 9, 1125–1141, https://doi.org/10.5194/gmd-9-1125-2016, 2016.
a
Zalesak, S. T.: Fully multidimensional flux-corrected transport algorithms for
fluids, J. Computat. Phys., 31, 335–362,
https://doi.org/10.1016/0021-9991(79)90051-2,
1979. a
Short summary
Coupled atmosphere–ocean modelling systems allow changes in the ocean to directly and immediately feed back on the atmosphere and enable improved weather prediction and ocean forecasts. This is particularly true if the coupled feedbacks are also considered in the way real-time observations of the atmospheric and oceanic states are used to obtain the initial conditions for the forecasts. Here we demonstrate promising performance from such a coupled system when used for ocean prediction.
Coupled atmosphere–ocean modelling systems allow changes in the ocean to directly and...
