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Assessment of the Black Sea observing system. A focus on 2005-2012 Argo campaigns


An observing system in the Black Sea combining remote sensing data such as sea level anomalies from altimetry, sea surface temperature from satellite radiometer and data from Argo floats has been analyzed with the aim to quantify the contribution of different information sources when reconstructing the ocean state. The main research questions are: (1) do Argo float measurements substantially impact the quality of estimates, (2) what is the dependence of this quality upon the data and sampling used, and (3) are there specific Black Sea issues? Numerical model output and statistical analysis were used for this purpose. It has been demonstrated that the statistical method performs in a consistent way reproducing known geophysical patterns. Maximum footprints of sea level, salinity and temperature were illustrated, most of them clearly connected with specific thermohaline conditions and the general circulation. Reduced analysis capabilities were identified as associated with a low level of dynamical coupling between the shelf and the open ocean, mesoscale dynamics and representation of diapycnic processes in the models. The accuracy of Argo pressure measurements appeared very important to resolve the extremely sharp stratification in the upper layers. The present-day number of Argo floats operating in the Black Sea of about 10, seems optimal for operational purposes.

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  1. ARGO (2000) Argo floats data and metadata from Global Data Assembly Centre (Argo GDAC), Tech. rep., Ifremer

  2. Argo data management (2013) Argo users manual, Tech. rep., Ifremer. doi:10.13155/26387

  3. Gandin LS (1963) Objective analysis of meteorological fields. In: English translation by the Israel Program forScientific Translations, 1965. Gridromet, Leningrad

  4. Grayek S, Stanev E V, Kandilarov R (2010) On the response of Black Sea level to external forcing: altimeter data and numerical modelling. Ocean Dyn 60:123–140

    Article  Google Scholar 

  5. Jaoshvili S (2002) The rivers of the black sea, Tech. Rep. 71. European Environment Agency, Copenhagen, Denmark, in Russian and English

    Google Scholar 

  6. Kalman R E (1960) A new approach to linear Filtering and prediction problems. J Basic Eng 82:35–45

    Article  Google Scholar 

  7. Kara A B, Wallcraft A J, Hurlburt H E (2005) A new solar radiation penetration scheme for use in ocean mixed layer studies: an application to the black sea using a fine-resolution hybrid coordinate ocean model (HYCOM). J Phys Oceanogr 35:12– 32

    Google Scholar 

  8. Kara A B, Wallcraft A J, Hurlburt H E, Stanev E V (2008) Air-sea fluxes and river discharges in the Black Sea with a focus on the Danube and Bosphorus. J Mar Syst 74:74–95

    Article  Google Scholar 

  9. Le Hénaff M, De Mey P, Marsaleix P (2009) Assessment of observational networks with the Representer Matrix Spectra methodapplication to a 3D coastal model of the Bay of Biscay. Ocean Dyn 59:3–20. doi:10.1007/s10236-008-0144-7

    Article  Google Scholar 

  10. Madec G (2008) NEMO ocean engine, Note du Pôle de modélisation No 27, Institut Pierre-Simon Laplace, France. ISSN No:1288–1619

  11. Neumann G (1943) Uber den Aufbau und die Frage der Tiefenzirkulation des Schwarzen Meeres. Ann Hydrogr Berl 71:1– 20

    Google Scholar 

  12. Oke P, Sakov P (2012) Assessing the footprint of a regional ocean observing system. J Mar Syst 105–108:30–51

    Article  Google Scholar 

  13. Oke P, Balmaseda M, Benkiran M, Cummings J, Dombrowsky E, Fujii Y, Guinehut S, Larnicol G, Traon P-Y, Martin M (2009) Observing system evaluations using GODAE systems. Oceanography 22:144–153

    Article  Google Scholar 

  14. Oke P R, Brassington G B, Griffin D A, Schiller A (2008) The Bluelink ocean data assimilation system (BODAS). Ocean Model 41(1):46–70

    Article  Google Scholar 

  15. Peneva E L, Stanev E V, Belokopytov V, Le Traon P Y (2001) Water transport in the Bosphorus straits estimated from hydro-meteorological and altimeter data: seasonal to decadal variability. J Mar Syst 31:21–33

    Article  Google Scholar 

  16. Reynolds R W, Smith T M, Liu C, Chelton D B, Casey K S, Schlax M G (2007) Daily high-resolution-blended analyses for sea surface temperature. J Clim 20:5473–5496

    Article  Google Scholar 

  17. Schulz-Stellenfleth J, Stanev E V (2010) Statistical assessment of ocean observing networks: A study of water level measurements in the German Bight. Ocean Model 33(3–4):270–282

    Article  Google Scholar 

  18. Stanev E V (2005) Understanding Black Sea Dynamics: Overview of Recent Numerical Modelling. Oceanography 18(2):52–71

    Article  Google Scholar 

  19. Stanev E V, Le Traon P Y, Peneva E L (2000) Seasonal and interannual variations of sea level and their dependency on meteorological and hydrological forcing. Analysis of altimeter and surface data for the Black Sea. J Geophys Res 105(c7):17203–17216

    Article  Google Scholar 

  20. Stanev E V, Beckers J M, Lancelot C, Staneva J V, Le Traon P Y, Peneva E L, Gregoire M (2002) Coastal-open Ocean Exchange in the Black Sea: Observations and Modeling, Estuar. Coast Shelf Sci 54:601–620

    Article  Google Scholar 

  21. Stanev E V, Bowman M J, Peneva E L, Staneva J V (2003) Control of Black Sea intermediate water mass formation by dynamics and topography: Comparisons of numerical simulations, survey and satellite data. J Mar Res 61:59–99

    Article  Google Scholar 

  22. Stanev E V, Staneva J V, Bullister J L, Murray J W (2004) Ventilation of the Black Sea pycnocline: Parameterization of convection, numerical simulations and validations against observed chlorofluorocarbon data. Deep-Sea Res 51(12):2137–2169

    Article  Google Scholar 

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We are grateful to P.Y. Le Traon and S. Le Reste for the help to more correctly address the pressure errors. We thank the three anonymous reviewers for their valuable comments. The authors acknowledge the support of the collaborative project ’Euro-Argo Improvements for the GMES Marine Service’ (E-AIMS) funded by the European Union (grant agreement number 312642). The Argo float data were collected and made freely available by the international Argo project and the national programs that contribute to it.

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Corresponding author

Correspondence to Sebastian Grayek.

Additional information

This article is part of the Topical Collection on Topical Collection on Coastal Ocean Forecasting Science supported by the GODAE OceanView Coastal Oceans and Shelf Seas Task Team (COSS-TT)

Responsible Editor: Pierre De Mey


Appendix 1: Altimetry

The standard deviation (STDV) of the altimeter data during 2005-2012 (Fig. 13a), shows well known spatial patterns connected to prominent hydro-dynamical and eddy propagation features of the Black Sea (Grayek et al. 2010). The comparison with the amplitude of oscillations in earlier periods (Stanev et al. 2000) demonstrates that the basic sub-basin scale patterns consisting of the Batumi and Sevastopol eddy and the large meander south of the Kerch Strait are very robust at all multi-year time intervals. The pattern of the formal mapping error (percent of the standard deviation) follows the one presented by (Stanev et al. 2000) therefore it is not shown here. It is below 3 cm (Fig. 13c) with minimum of ∼0.8 cm along the satelite tracks and maximum of ∼4 cm near the coasts. The averaged for the entire basin mapping error varies between 2 cm and 2.4 cm, with only several spikes reaching ∼3 cm, thus it is relatively stable.

Fig. 13

Standard deviation of SLA during 2005-2012 in cm (a) and basin mean formal mapping error of SLA times the standard deviation (b)

Appendix 2: Sea surface temperature

The standard deviation of SST during 2005-2012 (Fig. 14a) states that the largest variations are found in the northern part of the continental shelf region. This is explained by the shallow depths which enhances the amplitude of seasonal signal. This region extends to the south along the western coast and re-directs in the south-western corner of the basin to the interion part. The larger amplitudes in the basin interior in comparison to the ones in the coastal zone of the southern and northern Black Sea are explained by the shallower pycnocline there. In conclusion: maximum amplitudes of SST are either supported by the shallow bottom or by the shallow pycnocline. The relatively smaller SST amplitudes along the coastal zone in the south and north where the abyssal plain almost reaches the coast are explained by the intensive downwelling there. A prominent area with low variability is the region of formation and propagation of the Sevastopol eddy. Its counterpart from the eastern Black Sea, the Batumi eddy is not visible in Fig. 14a because this eddy occurs usually only in summer. The temporal mean error of the SST product (Fig. 14b) is substantially lower than the standard deviation with a minimum of about 0.18 C in the basin interior. Maximum errors of ∼0.25 C are located in a very narrow coastal band. The temporal evolution of spatial mean absolute errors (Fig. 14c) reveals a distinct seasonal characteristic. In summer the mean values are ∼ 1.5 C, while the values in winter are more than twice as high (∼ 3.5 C).

Fig. 14

Standard deviation of SST during 2005-2012 in C (a), absolute error of SST for 2005-2012 in C (b) and basin mean SST error (c)

Appendix 3: Argo floats

All profiles used in the following analysis were real-time quality controlled (QC) and flagged with a QC-level of ’1’. Overall 1724 profiles from fourteen floats were available. Fig. 1a displays trajectories and profile positions of the used floats. Nearly all profiles are positioned within the deeper inner part of the basin. There are only few observations close to the coast and no observations within the shallow shelf region. Profile cycle time, parking and maximum profile depth differed between individual floats. In the first period the maximum profile depth was ∼ 1500 m for every cycle. During the second period fewer profiles with a maximum depth below ∼ 700 m were taken. The Table 6 shows specification of the individual floats programming used in the study. The instrument errors for temperature and salinity are small (0.002 C, 0.01 PSU, see ARGO (2000) and Argo data management (2013)).

Table 6 Inventory of the used floats

The floats presented in Table 6 are different; some older model floats used ARGOS communications technology. The vertical resolution of the data from some of these floats was likely coarser than 5 m, which is probably not quite accurate to resolve adequately vertical stratification. Newer float technology that uses IRIDIUM communications yields much higher vertical resolution, usually 2 m throughout the water column. The possible consequences of inaccuracy in the vertical resolution is addressed in the present study and taken into account in the SEON and SESON.

Appendix 4: Mathematical expressions for observation operators

In the following the exact mathematical expressions used for the observation operator matrix H introduced in Section 2.4 are summarized. The measurement operator \(H_{{\theta _{s}}/{\zeta }}\) for simultaneous SST and SLA measurements is given as

$$\begin{array}{@{}rcl@{}} H_{\theta_{s}/\zeta}=\left( \begin{array}{lll}I_{m} & Z_{m,m} & Z_{m,N-2m}\\ Z_{m,m} & I_{m} & Z_{m,N-2m}\end{array} \right)\;\;\; , \end{array} $$

where I m denotes the m×m identity matrix and Z m, n is the zero matrix of dimension m×n. We then have

$$ H_{{\theta_{s}}/{\zeta}} \; x +\epsilon_{{\theta_{s}}/{\zeta}} = \left( \zeta^{o}, {\theta_{s}^{o}} \right)^{T}\,, $$

with observed (index o) surface temperatures \({\theta _{s}^{o}}\), sea level anomaly ζ o, and respective observation errors \(\epsilon _{{\theta _{s}}/{\zeta }}\). For the vertical profile measurements of salinity and temperature provided by Argo floats, the observation operator H s/θ is given by

$$ H_{s/\theta} = \left( \begin{array}{lll} Z_{m,m} & I_{m} & Z_{m,N-2m}\\ Z_{p,2 m} & Z_{p,r} & Q \\ Z_{p,2 m} & Q & Z_{p,r} \end{array} \right) \;\;\; . $$

Here r denotes the number of bins in the three-dimensional grid below the surface and p is the number of bins below the surface in which salinity and temperature measurements are taken. The matrix Q of dimension p×r depends on the specific trajectory of the floats and is given by

$$\begin{array}{@{}rcl@{}} Q_{i,j} \,=\, \left\{\begin{array}{ll} 1& \text{if measurement number} \;i \; \text{is taken at bin number}\; j\\ 0& \text{else} \end{array}\right.. \end{array} $$

The index j refers to the bin number below the surface with ordering as given in the definition of the state vector (Eq. 1). We then have

$$ H_{s/\theta} \; x + \epsilon_{s/\theta} = \left( \overline{\theta}^{o}_{s} ,s^{o} ,\theta^{o} \right)^{T} , $$

where \(\overline {\theta }^{o}_{s}\) is the surface temperature provided by the profiling instrument, which may differ from the remote sensing measurement \({\theta _{s}^{o}}\), while s o and θ o are the salinity and temperature measurements below the surface. The observation errors of the Argo floats is represented by 𝜖 s/θ .

The observation operator for the combined SST, SLA and Argo measurements can then be written as

$$\begin{array}{@{}rcl@{}} H_{{\theta_{s}}/{\zeta},s/\theta}= \left( \begin{array}{l} H_{{\theta_{s}}/{\zeta}}\\ H_{s/\theta} \end{array}\right) \;\; . \end{array} $$

Appendix 5: Horizontal patterns of the seasonal variability of errors in the SEON

The results from SEON analyzed in the main part of this paper were annual mean. As it has been shown averaging over long periods appeared usefull to derive first order information but gives only an insufficient information of the temporal and spatial dynamics. In Fig. 15 additional seasonal averages for the presented SEONs are given to demonstate that the individual estimates for shorter times could largely differ from the annual mean ones. It is clearly seen from Fig. 15 that the differences exist not only between the individual SEONs but also during the individual seasons.

Fig. 15

Seasonal mean relative reconstruction errors vertically averaged over the upper 70 m (top) and meridionally averaged for all latitudes (bottom) for temperature (left-hand side) and salinity (right-hand side) fields. Estimates for the observation networks (from top to bottom) AT-1, F a -1, ATF a -2 and ATF a -1 (see Table 1 for the nomenclature) are shown

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Grayek, S., Stanev, E.V. & Schulz-Stellenfleth, J. Assessment of the Black Sea observing system. A focus on 2005-2012 Argo campaigns. Ocean Dynamics 65, 1665–1684 (2015).

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  • Observation system evaluation
  • Observation system simulation experiment
  • Profiling float data
  • Ocean state reconstruction