OS Ocean Science OS Ocean Sci. 1812-0792 Copernicus Publications Göttingen, Germany 10.5194/os-7-75-2011 Assessment of the three dimensional temperature and salinity observational networks in the Baltic Sea and North Sea Fu W. 1 Høyer J. L. 1 She J. 1 Center for Ocean and Ice (COI), Danish Meteorological Institute (DMI), Denmark 28 01 2011 7 1 75 90 Copyright: © 2011 W. Fu et al. 2011 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://os.copernicus.org/articles/7/75/2011/os-7-75-2011.html The full text article is available as a PDF file from https://os.copernicus.org/articles/7/75/2011/os-7-75-2011.pdf

The spatial averaged correlations are presented in 1.5° × 1.5° bins for the North and Baltic Sea region. The averaged correlations are computed based on the proxy ocean data generated by the operational forecast model of Danish Meteorology Institute (DMI). It is shown that the spatial distribution of the averaged correlations could reflect the overall influence of the local atmospheric forcing, complex topography, coastlines, boundary and bottom effect, etc. Comparisons with the satellite SST data demonstrate that the proxy ocean data reproduce realistic results at the surface. Based on the spatial bin-averaged correlations, a general correlation model is assumed to approximate the spatial and temporal correlation structure. Parameters of the correlation model are obtained on the standard Levitus levels. It is found that the correlation model is not the typical Guaussian-type function. For instance, the exponents of the correlation model vary in the longitudinal direction from 0.75 at the surface to 1.33 at the depth of 250 m for temperature. For salinity, the temporal correlation can be approximated with an exponential function. <br><br> Two complementary quality-indicators, effective coverage rate and "explained" variance, are defined based on the correlation models obtained above. The two indicators are able to identify the "influence area" of the information content in a given observation network and the relative importance of observations at different locations. By these indicators, the 3-D temperature and salinity observational networks are assessed in the Baltic Sea and North Sea for the period 2004–2006. It is found that the surface level is more effectively covered than the deep waters with existing networks. In addition, the Belt Sea and the Baltic Proper also show good coverage for both temperature and salinity. However, more observations are required in the Norwegian Trench and Kattegat. In the vertical, the two indicators show smaller values from 50 m to 125 m in this region, indicating the need for more observations.

 References Barth, N. and Wunsch, C.: Oceanographic experiment design by simulated annealing, J. Phys. Oceanogr., 20, 1249–1263, 1990. Barth, A., Alvera-Azcarate, A., Rixen, M., and Beckers, J.-M.: Two-way nested model of mesoscale circulation features in the Ligurian Sea, Prog. Oceanogr., 66(2–4), 171–189, 2005. Bartello, P. and Mitchell, H. L.: A continuous three-dimensional model of short-range forecast error covariances, Tellus A, 44, 217–235, 1992. Berg, P.: MIKE 3, Estuarine and Coastal Hydraulics and Oceanography, Hydrodynamic Module, Hydrostatic Version, DHI Software, DHI Water & Environment, 2003. Bishop, C. H., Etherton, B. J., and Majumdar, S. J.: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Weather Rev., 129, 420–436, 2001. Buch, E., Elken, J., Gajewsk, J., Håkansson, B., Kahma, K., and Soetje, K.: Present Status of the Baltic Operational Oceanographic System. European Operational Oceanography: Present and Future, EuroGOOS Office, Sweden, and European Commission, Belgium, 276–280, 2006. Chen, D. and Omstedt, A.: climate-induced variability of sea level in Stockholm: Influence of air temperature and atmospheric circulation, Adv. Atmos. Sci., 22(5), 655–664, 2005. Chen, C. S. and Wang, D. P.: Assimilation model study of the Santa Barbara Channel circulation, J. Geophys. Res., 104, 15727–15741, 1999. Canuto, V. M., Howard, A., Cheng, Y., and Dubovikov, M. S.: Ocean Turbulence. Part II: Vertical Diffusivities of Momentum, Heat, Salt, Mass, and Passive Saclars, J. Phys. Oceanogr, 32, 240–264, 2002. Daley, R.: Atmospheric Data Analysis, Cambridge Univ. Press, New York, 45 pp., 1993. Dick, S., Kleine, E., Mueller-Navarra, S. H., Kleine, H., and Komo, H.: The Operational Circulation Model of BSH (BSHcmod) – Model description and validation, Berichte des BSH 29/2001, Bundesamt fur Seeschifffart und Hydrographie, 48 pp., 2001. Fu, W., Zhu, J., and Yan, C.: A comparison between <i>3DVAR</i> and <i>EnOI</i> techniques for satellite altimetry data assimilation, Ocean Model., 26, 206–216, 2009. Hackert, E. C., Miller, R. N., and Busalacchi, A. J.: An optimized design for a moored instrument array in the tropical Atlantic Ocean, J. Geophys. Res., 103, 7491–7509, 1998. Hirschi, J., Baehr, J., Marotzke, J., Stark, J., Cunningham, S., and Beismann, J.-O.: A monitoring design for the Atlantic meridional overturning circulation, Geophys. Res. Lett., 30, 1413, https://doi.org/10.1029/2002GL016776, 2003. Høyer, J. L. and She, J.: Optimal interpolation of SST for the North Sea and Baltic Sea systems, J. Marine Syst., 65, 176–189, 2007. ICES: Baltic Sea Patchiness Experiment (PEX-86). Part I, edited by: Dybern, B. I. and Hansen, H. P., ICES Coop. Res. Rep., 163 pp., 1989. ICES: Baltic Sea Patchiness Experiment PEX-86. Part II. Atlas, edited by: Elken, J., Lips, U., Talpsepp, L., and Väravas, H., Tallinn, 178 pp., 1992. ICES: Patchiness in the Baltic Sea. Selected papers from a symposium held in Mariehamn 3–4 June 1991, edited by: Dybern, B. I., ICES Coop. Res. Rep., 201 pp., 1994. Kelly, G. A.: Influence of observations on the Operational ECMWF System, WMO Bull., 46, 336–342, 1997. Kleine, E.: Das operationelle Modell des BSH feur Nordsee und Ostsee, Konzeption und Uebersicht, Bundesamt fur Seeschifffart und Hydrographie, Technical Report vol. 126S, 1994. Kharne, S. P. and Anderson, J. L.: An examination of ensemble filters based adaptive observation methodologies, Tellus, 58A, 179–195, 2006. Kuo, T. H., Zou, X., and Huang, W.: The impact of global positioniing system data and prediction of and extratropical cyclone: An observing system simulation experimetn. Dynam. Atmos. Oceans, 27, 439–470, 1998. Larsen, J., Høyer, J. L., and She, J.: Validation of a hybrid optimal interpolation and Kalman filter scheme for sea surface temperature assimilation, J. Marine Syst., 65, 122–133, 2007. Langland, R. H.: Issues in targeted observations, Q. J. Roy. Meteor. Soc., 131, 3409–3425, 2005. Leeuwenburgh, O.: Combined Analysis of Sea Surface Height and Temperature for Mapping and Climate Studies, Ph.D. thesis, National Survey and Cadastre, Denmark, 2001. Le Hénaff, M., De Mey, P., and Marsaleix, P.: Assessment <i>of</i> observational networks with the Representer Matrix Spectra method-application to a 3D coastal model of the Bay of Biscay, Ocean Dynam., 59, 3–20, 2009. McIntosh, P. C.: Systematic design of observational arrays, J. Phys. Oceanogr., 17, 885–902, 1987. Meier, H. E. M.: On the parameterization of mixing in three-dimensional Baltic Sea models, J. Geophys. Res., 106(C12), 30997–31016, 2001. Oke, P. R. and Schiller, A.: A model-based assessment and design of a tropical Indian Ocean mooring array, J. Climate, 20, 3269–3283, 2007. Plag, H.-P. and Tsimplis, M. N.: Temporal variability of the seasonal sea-level cycle in the North Sea and Baltic Sea in relation to climate variability, Global Planet. Change, 20(2–3), 173–203, 1999. Raschke, E., Meywerk, J., Warrach, K., Andrea, U., Bergström, S., Beyrich, F., Bosveld, F., Bumke, K., Fortelius, C., Graham, L. P., Gryning, S.-E., Halldin, S., Hasse, L., Heikinheimo, M., Isemer, H.-J., Jacob, D., Jauja, I., Karlsson, K.-G., Keevallik, S., Koistinen, J., van Lammeren, A., Lass, U., Launianen, J., Lehmann, A., Liljebladh, B., Lobmeyr, M., Matthäus, W., Mengelkamp, T., Michelson, D. B., Napiórkowski, J., Omstedt, A., Piechura, J., Rockel, B., Rubel, F., Ruprecht, E., Smedman, A.-S., and Stigebrandtn, A.: BALTEX (Baltic Sea Experiment): A European Contribution to investigate the energy and water cycle over a large drainage basin, B. Am. Meteorol. Soc., 82(11), 2389–2413, 2001. Sakov, P. and Oke, P.R.: Objective array design: Application to the tropical Indian Ocean, J. Atmos. Ocean. Tech., 25, 794–807, 2008. She, J.: Optimal design based on sampling error study for tropical Pacific buoy array, Japan Science and Technology Agency, Tech. Rep., p. 89, 1996. She, J. and Nakamoto, S.: Spatial sampling study for tropical Pafcific with observed sea surface temperature, J. Atmos. Ocean. Tech., 13, 1189–1201, 1996. She, J., Høyer, J. L., and Larsen, J.: Assessment of sea surface temperature observational networks in the Baltic Sea and North Sea, J. Marine Syst., 65, 314–335, 2007. Schiller, A., Wijffels, S. E., and Meyers, G. A.: Design requirements for an Argo float array in the Indian Ocean inferred from observing system simulation experiments, J. Atmos. Ocean. Tech., 21, 1598–1620, 2004. Smith, N. R., and Meyers, G.: An evaluation of expendable bathythermograph and tropical atmosphere ocean array data for monitoring tropical ocean variability, J. Geophys. Res., 101, 28489–28501, 1996. Thiebaux, H. J. and Pedder, M. A.: Spatial Objective Analysis: with applications in atmospheric science, Academic Press, 1987. Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. M., and Whitaker, J. S.: Ensemble square root filters, Mon. Weather Rev., 131, 1485–1490, 2003. Turrell, W. R.: New hypotheses concerning the circulation of the northern North Sea and its relation to North Sea fish stock recruitment, ICES J. Mar. Sci., 49, 107–123, 1992. Umlauf, L., Burchard, H., and Hutter, K.: Extending the k-w turbulence model towards oceanic applications, Ocean Model., 5, 195–218, 2003. Wilcox, D. C.: Reassessment of the scale-determining equation for advanced turbulence models, AIAA J., 26(11), 1299–1310, 1988.