Ocean information provided through ensemble ocean syntheses

Venice, Italy

Analyzing ocean variability, understanding its importance for the climate system, and quantifying its socio-economic impacts are among the primary motivations for obtaining ongoing global ocean observations. There are several possible approaches to address these tasks. One with much potential for future ocean information services and for climate predictions is called ocean synthesis, and is concerned with merging all available ocean observations with the dynamics embedded in an ocean circulation model to obtain estimates of the changing ocean that are more accurate than either system alone can provide. The field of ocean synthesis has matured over the last decade. Several global ocean syntheses exist today and can be used to investigate key scientific questions, such as changes in sea level, heat content, or transports. This CWP summarizes climate variability as “seen” by several ocean syntheses, describes similarities and differences in these solutions and uses results to highlight developments necessary over the next decade to improve ocean products and services. It appears that multi-model ensemble approaches can be useful to obtain better estimates of the ocean. To make full use of such a system, though, one needs detailed error information not only about data and models, but also about the estimated states. Results show that estimates tend to cluster around methodologies and therefore are not necessarily independent from each other. Results also reveal the impact of a historically under-sampled ocean on estimates of inter-decadal variability in the ocean. To improve future estimates, we need not only to sustain the existing observing system but to extend it to include full-depth ARGO-type measurements, enhanced information about boundary currents and transports through key regions, and to keep all important satellite sensors flying indefinitely, including altimetry, gravimetry and ice thickness, microwave SST observations, wind stress measurements and ocean color. We also need to maintain ocean state estimation as an integral part of the ocean observing and information system.

1. Munk, W. & Wunsch C. (1982). Observing the Ocean in the 1990s. Phil. Trans. R. Soc. Lond., A307, 439-464.
2. Marshall, J., Adcroft A., Hill C., Perelman L. & Heisey C. (1997). A finite-volume, incompressible Navier-Stokes model for studies of the ocean on parallel computers, J. Geophys. Res., 102, 5753-5766.
3. Giering, R. & Kaminski T. (1998). Recipes for adjoint code construction. ACM Trans. on Math. Software, 24, 437-474.
4. Marotzke, J., Giering R., Zhang Q. K., Stammer D., Hill C. N. & Lee T. (1999). Construction of the adjoint MIT ocean general circulation model and application to Atlantic heat transport sensitivity. J. Geophys. Research, 104, 29,529 - 29,548.
5. Stammer, D., Wunsch C., Giering R., Eckert C., Heimbach P., Marotzke J., Adcroft A., Hill C.N. & Marshall J. (2002a). The global ocean circulation during 1992 --1997, estimated from ocean observations and a general circulation model. J. Geophys. Res., 107(C9), 3118, doi:10.1029/2001JC000888.
6. Stammer, D., Wunsch C., Fukumori I. & Marshall J. (2002b). State estimation improves prospects for ocean research. EOS, Transactions, American Geophysical Union, Volume 83, Nr. 27, p. 289, 294--295.
7. Lee, T. & Co-Authors (2010). Ocean State Estimation for Climate Research. In Proceedings of OceanObs’09: Sustained Ocean Observations and Information for Society (Vol. 2), Venice, Italy, 21-25 September 2009, Hall, J., Harrison, D.E. & Stammer, D., Eds., ESA Publication WPP-306
8. Stammer, D., Ueyoshi K., Köhl A., Large W.B., Josey S. & Wunsch C. (2004). Estimating Air-Sea Fluxes of Heat, Freshwater and Momentum Through Global Ocean Data Assimilation. J. Geophys. Res., 109, C05023, doi:10.1029/2003JC002082.
9. Carton, J.A. & Giese B.S. (2008). A reanalysis of ocean climate using Simple Ocean Data Assimilation (SODA). Mon. Wea. Rev., 136, 2999-3017.
10. Wunsch, C., Ponte R.M. & Heimbach P. (2007). Decadal trends in sea level patterns: 1993-2004. J. Clim., 20, 5889-5911.
11. Köhl, A. & Stammer D. (2008a). Variability of the meridional overturning in the North Atlantic from 50-year GECCO state estimation. J. Phys. Oceanogr., 38, 1913 -1930.
12. Köhl, A. & Stammer D. (2008b). Decadal sea level changes in the 50-year GECCO ocean synthesis. J. Clim., 21, 1866-1890.
13. Fukumori, I., Lee T., Cheng B. & Menemenlis D. (2004). The origin, pathway & destination of NINO3 water estimated by a simulated passive tracer and its adjoint. J. Phys. Oceanogr., 34, 582-604.
14. Wang, O., Fukumori I., Lee T. & Cheng B. (2004). On the cause of eastern equatorial Pacific Ocean T-S variations associated with El Nino. Geophys. Res. Lett., 31, L15310, doi:10.1029/2004GL02472.
15. Masuda, S., Awaji T., Sugiura N., Toyoda T., Ishikawa Y. & Horiuchi K. (2006). Interannual variability of temperature inversions in the subarctic North Pacific, Geophys. Res. Lett., 33, doi:10.1029/2006GL027865.
16. Toyoda, T., Awaji T., Masuda S., Sugiura N., Igarashi H., Mochizuki T. & Ishikawa Y. (2009). Interannual variability of North Pacific eastern subtropical mode water formation in the 1990s derived from a 4-dimensional variational ocean data assimilation experiment. (submitted to J. Geophys. Res.).
17. Kim, S.-B., Lee T. & Fukumori I. (2004). The 1997-99 abrupt change of the upper ocean temperature in the northcentral Pacific. Geophys. Res. Lett., 31, L22304, doi:10.1029/2004GL021142.
18 Kim, S.-B., Lee T. & Fukumori I. (2007). Mechanisms controlling the interannual variation of mixed layer temperature averaged over the NINO3 region. J. Clim, 20, 3822-3843.
19. Halkides, D. & T. Lee (2009). Mechanisms controlling seasonal-to-interannual mixed-layer temperature variability in the southeastern tropical Indian Ocean. J. Geophys. Res., in press.
20. Stammer, D., Wunsch C. & Giering R., et al. (2003). Volume, heat, and freshwater transports of the global ocean circulation 1993-2000, estimated from a general circulation model constrained by World Ocean Circulation Experiment (WOCE) data/ J. Geophys. Res.-Oceans, 108,
21. Masina S., Di Pietro P. & Navarra A. (2004). Interannual-to-decadal variability of the North Atlantic from an ocean data assimilation system. Climate Dynamics, 23, 531-546, doi: 10.1007/s00382-004-0453-6.
22. Capotondi, A., Wittenberg A. & Masina S. (2006). Spatial and temporal structure of ENSO in 20th century coupled simulations. Ocean Modelling, 15, (3-4), 274-298.
23. Pierce, D. W., Barnett T. P., Tokmakian R., Semtner A., Maltrud M., Lynsey J. & Craig A. (2004). The ACPI project, element 1: Initializing a coupled climate model from observed initial conditions. Clim. Change, 62, 13-28.
24. Pohlmann, H., Jungclaus J., Marotzke J., Köhl A. & Stammer D. (2009). Improving Predictability through the Initialization of a Coupled Climate Model with Global Oceanic Reanalysis. J. Clim., 22, 10.1175/2009JCLI2535., p. 3926 — 3938.
25. Trenberth, K.E. & Co-Authors (2010). Atmospheric reanalyses: A major resource for ocean product development and modeling. In Proceedings of OceanObs’09: Sustained Ocean Observations and Information for Society (Vol. 2), Venice, Italy, 21-25 September 2009, Hall, J., Harrison, D.E. & Stammer, D., Eds., ESA Publication WPP-306
26. Balmaseda, M. & Co-Authors (2010). Initialization for Seasonal and Decadal Forecasts. In Proceedings of OceanObs’09: Sustained Ocean Observations and Information for Society (Vol. 2), Venice, Italy, 21-25 September 2009, Hall, J., Harrison, D.E. & Stammer, D., Eds., ESA Publication WPP-306
27 Heimbach, P. & Co-Authors (2010). Observational Requirements for Global-scale Ocean Climate Analysis: Lessons from Ocean State Estimation. In Proceedings of OceanObs’09: Sustained Ocean Observations and Information for Society (Vol. 2), Venice, Italy, 21-25 September 2009, Hall, J., Harrison, D.E. & Stammer, D., Eds., ESA Publication WPP-306
28. Domingues, C. M., Church J. A., White N. J., Gleckler P. J., Wijffels S. E., Barker P. M. & Dunn J. R. (2008). Improved estimates of upper-ocean warming and multi-decadal sea-level rise. Nature, 453, 1090–1093.
29. Wijffels, S. E., Willis J., Domingues C. M., Barker P., White N. J., Gronell A., Ridgeway K., Church J. A. (2008). Changing eXpendable BathyThermograph fall-rates and their impact on estimates of thermosteric sea level rise. J. Clim., 21, 5657-5672.
30. Munk W. (2003). Ocean freshening, sea level rising. Science, 300, 2041-2043.
31. Cazenave, A., Dominh K., Guinehut S., Berthier E., Llovel W., Ramillien G., Ablain M. & Larnicol G. (2009). Sea level budget over 2003–2008: A reevaluation from GRACE space gravimetry, satellite altimetry and Argo. Global and Planetary Change.
32.Fisher M. & Courtier P. (1995, Estimating the covariance matrix of analysis and forecast error in variational data assimilation. ECMWF Technical Memorandum No. 220.
33. Powell, B. & Moor A.M. (2009), Estimating the 4DVAR analysis error of GODAE products. Ocean Dynamics (2009) 59:121–138 DOI 10.1007/s10236-008-0172-3
34.Balmaseda, M.A. & Weaver A. (2006). Temperature, salinity, and sea-level changes: climate variability from ocean reanalyses. http://www.clivar.org/organization/gsop/synthesis/synthesis.php.
35. Griffies, S. & Co-Authors (2010). Problems and Prospects in Large-Scale Ocean Circulation Models. In Proceedings of OceanObs’09: Sustained Ocean Observations and Information for Society (Vol. 2), Venice, Italy, 21-25 September 2009, Hall, J., Harrison, D.E. & Stammer, D., Eds., ESA Publication WPP-306.
36. Liu, H (2009). Sensitivity of warming trends in instrumental biases of XBT’s. Diploma Thesis, University of Hamburg, pp. 80.
37. Gaillard, F., Autret E., Thierry V., Galaup P., Coatanoan C. & Loubrieu T. (2009). Quality control of large Argo data sets. J. Atmos. Ocean. Tech., 26, 337-351.
38. Saunders, P. M ., Cunningham S. A.,, de Cuevas B. & Coward A. C. (2009). Decadal changes in the North Atlantic and Pacific meridional overturning circulation and heat flux. J. Phys. Oceanogr., 38, 2104-2107.