Ocean Dynamics

, Volume 61, Issue 12, pp 2121–2139 | Cite as

Particle tracking in the vicinity of Helgoland, North Sea: a model comparison

  • Ulrich Callies
  • Andreas Plüß
  • Jens Kappenberg
  • Hartmut Kapitza
Article
Part of the following topical collections:
  1. Topical Collection on Maritime Rapid Environmental Assessment

Abstract

Station Helgoland Roads in the south-eastern North Sea (German Bight) hosts one of the richest long-term time series of marine observations. Hydrodynamic transport simulations can help understand variability in the local data brought about by intermittent changes of water masses. The objective of our study is to estimate to which extent the outcome of such transport simulations depends on the choice of a specific hydrodynamic model. Our basic experiment consists of 3,377 Lagrangian simulations in time-reversed mode initialized every 7 h within the period Feb 2002–Oct 2004. Fifty-day backward simulations were performed based on hourly current fields from four different hydrodynamic models that are all well established but differ with regard to spatial resolution, dimensionality (2D or 3D), the origin of atmospheric forcing data, treatment of boundary conditions, presence or absence of baroclinic terms, and the numerical scheme. The particle-tracking algorithm is 2D; fields from 3D models were averaged vertically. Drift simulations were evaluated quantitatively in terms of the fraction of released particles that crossed each cell of a network of receptor regions centred at the island of Helgoland. We found substantial systematic differences between drift simulations based on each of the four hydrodynamic models. Sensitivity studies with regard to spatial resolution and the effects of baroclinic processes suggest that differences in model output cannot unambiguously be assigned to certain model properties or restrictions. Therefore, multi-model simulations are needed for a proper identification of uncertainties in long-term Lagrangian drift simulations.

Keywords

Lagrangian particle tracking Backward trajectories Model comparison North Sea Helgoland Roads 

Notes

Acknowledgements

We gratefully acknowledge the provision of output from the model BSHcmod by our colleagues from the Bundesamt für Seeschifffahrt und Hydrographie (BSH) in Hamburg. Levitus (NODC_WOA98) salinity data were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their web site at http://www.esrl.noaa.gov/psd/. For graphical display, we used the Generic Mapping Tools software (GMT) available from www.soest.hawaii.edu/gmt/. The study was conducted within the framework of the WIMO project (Scientific monitoring concepts for the German Bight), jointly funded by Niedersächsisches Ministerium für Wissenschaft und Kultur (MWK) and Niedersächsisches Ministerium für Umwelt und Klimaschutz (MUK).

References

  1. Brandt G, Wehrmann A, Wirtz KW (2008) Rapid invasion of Crassostrea gigas into the German Wadden Sea dominated by larval supply. J Sea Res 59:279–296CrossRefGoogle Scholar
  2. Burchard H, Bolding K, Villareal MR (2004) Three-dimensional modelling of estuarine turbidity maxima in a tidal estuary. Ocean Dynamics 54:250–265CrossRefGoogle Scholar
  3. Casulli V, Cattani E (1994) Stability, accuracy and efficiency of a semi-implicit method for three-dimensional shallow water flow. Computers Math Appl 27(4):99–112CrossRefGoogle Scholar
  4. Casulli V, Stelling GS (1998) Numerical simulation of 3D quasi-hydrostatic, free-surface flows. J Hydraul Eng 124:678–686CrossRefGoogle Scholar
  5. Casulli V, Walters RA (2000) An unstructured three-dimensional model based on the shallow water equations. Int J Numer Methods Fluids 32:331–348CrossRefGoogle Scholar
  6. Chrastansky A, Callies U (2009) Model-based long-term reconstruction of weather-driven variations in chronic oil pollution along the German North Sea coast. Mar Pollut Bull 58:967–975CrossRefGoogle Scholar
  7. Chrastansky A, Callies U, Fleet DM (2009) Estimation of the impact of prevailing weather conditions on the occurrence of oil-contaminated dead birds on the German North Sea coast. Environ Pollut 157:194–198CrossRefGoogle Scholar
  8. Delhez EJM, Damm P, de Goede E, de Kok JM, Dumas F, Gerritsen H, Jones JE, Ozer J, Pohlmann T, Rasch PS, Skogen M, Proctor R (2004) Variability of shelf-seas hydrodynamic models: lessons from the NOMADS2 project. J Mar Syst 45:39–53CrossRefGoogle Scholar
  9. Dick S, Kleine E, Müller-Navarra SH, Klein H, Komo H (2001) The Operational Circulation Model of BSH (BSHcmod)-Model description and validation. Berichte des Bundesamtes für Seeschifffahrt und Hydrographie 29 (ISSN 0946–6010)Google Scholar
  10. Dippner JW (1993) A frontal-resolving model for the German Bight. Cont Shelf Res 13:49–66CrossRefGoogle Scholar
  11. Gräwe U, Wolff J-O (2010) Suspended particulate matter dynamics in a particle framework. Environ Fluid Mech 10:21–39CrossRefGoogle Scholar
  12. Hainbucher D, Pohlmann T, Backhaus J (1987) Transport of conservative passive tracers in the North Sea: first results of a circulation and transport model. Cont Shelf Res 7:1161–1179CrossRefGoogle Scholar
  13. Heemink AW (1990) Stochastic modelling of dispersion in shallow water. Stochastic Hydrol Hydraul 4:161–174CrossRefGoogle Scholar
  14. Hervouet JM, van Haren L (1996) TELEMAC2D version 3.0 Principle Note. Chatou CEDEX. Rapport EDF HE-4394052BGoogle Scholar
  15. Hickel W (1972) Kurzzeitige Veränderungen hydrographischer Faktoren und der Sestonkomponenten in driftenden Wassermassen in der Helgoländer Bucht. Helgoländer wiss. Meeresunters 23:383–392CrossRefGoogle Scholar
  16. Jones JE (2002) Coastal and shelf-sea modelling in the European context. Oceanogr Marine Biol: an Annual Review 40:37–141Google Scholar
  17. Jones JE, Davies AM (2005) An intercomparison between finite difference and finite element (TELEMAC) approaches to modelling west coast of Britain tides. Ocean Dynamics 55:178–198CrossRefGoogle Scholar
  18. Jones JE, Davies AM (2006) Application of a finite element model (TELEMAC) to computing the wind induced response of the Irish Sea. Cont Shelf Res 26:1519–1541CrossRefGoogle Scholar
  19. Kako S, Isobe A, Magome S, Hinata H, Seino S, Kojima A (2010) Establishment of numerical beach-litter hindcast/forecast models: An application to Goto Islands. Japan Mar Pollut Bull. doi:10.1016/j.marpolbul.2010.10.011
  20. Kistler R, Kalnay E, Collins W, Saha S, White G, Wollen J, Chelliah M, Ebisuzaki W, Kanamitsu M, Kousky V, van den Dool H, Jenne R, Fioriono M (2001) The NCEP-NCAR 50-year reanalysis: monthly means CD-ROM and documentation. Bull Am Meteorol Soc 82:247–268CrossRefGoogle Scholar
  21. Kloeden PE, Platen E (1992) Numerical solution of stochastic differential equations. Springer, HeidelbergGoogle Scholar
  22. Liu Y, Weisberg RH, Hu C (2011) Tracking the Deepwater Horizon oil spill: A modeling perspective. Eos 92:45–52CrossRefGoogle Scholar
  23. Maier-Reimer E, Sündermann J (1982) On tracer methods in computational hydrodynamics. In: Abbot MB, Cunge JA (eds) Engineering Applications of Computational Hydraulics 1. Pitman, London, pp 198–217Google Scholar
  24. Meinke I, von Storch H, Feser F (2004) A validation of the cloud parameterization in the regional model SN-REMO. J Geophys Res 109:D13205. doi:10.1029/2004JD004520 CrossRefGoogle Scholar
  25. Penland C (2003) A stochastic approach to nonlinear dynamics. BAMS 84:ES43–ES52Google Scholar
  26. Plüß A, Heyer H (2007) Morphodynamic multi-model approach for the Elbe estuary. Proceedings of the 5th IAHR Symposium on River, Coastal and Estuarine Morphodynamics (RCEM), Enschede/NL, pp 113–117Google Scholar
  27. Plüß A, Schüttrumpf H (2004) Comparison of numerical tidal models for practical applications. Proceedings of the 29th Int. Conference of Coastal Engineering, pp 1199–1211Google Scholar
  28. Puls W, Pohlmann T, Sündermann J (1997) Suspended particulate matter in the Southern North Sea: Application of a numerical model to extend NERC North Sea project data interpretation. Deutsche Hydrographische Zeitschrift 49:307–327CrossRefGoogle Scholar
  29. Ridderinkhof H, Zimmerman JTF (1992) Chaotic Stirring in a Tidal System. Science 258:1107–1111CrossRefGoogle Scholar
  30. Ridderinkhof H, Zimmerman JTF, Philippart ME (1990) Tidal exchange between the North Sea and Dutch Wadden Sea and mixing time scales of the tidal basins. Neth J Sea Res 25(3):331–350CrossRefGoogle Scholar
  31. Rixen M, Ferreira-Coelho E (2007) Operational surface drift prediction using linear and non-linear hyper-ensemble statistics on atmospheric and ocean models. J Mar Syst 65:105–121CrossRefGoogle Scholar
  32. Rixen M, Ferreira-Coelho E, Signell R (2008) Surface drift prediction in the Adriatic Sea using hyper-ensemble statistics on Atmospheric, ocean and wave models: Uncertainties and probability distributions. J Mar Syst 69:86–98CrossRefGoogle Scholar
  33. Rolinski S (1999) On the dynamics of suspended matter transport in the tidal river Elbe: Description and results of a Lagrangian model. J Geophys Res 104(C11):26043–26057CrossRefGoogle Scholar
  34. Rümelin W (1982) Numerical treatment of stochastic differential equations. SIAM Journ Num Anal 19:604–613CrossRefGoogle Scholar
  35. Schönfeld W (1995) Numerical simulation of the dispersion of artificial radionuclides in the English Channel and the North Sea. J Mar Sys 6:529–544CrossRefGoogle Scholar
  36. Seibert P, Frank A (2004) Source-receptor matrix calculation with a Lagrangian particle dispersion model in backward mode. Atmos Chem Phys 4:51–63CrossRefGoogle Scholar
  37. Smith JA, Damm PE, Skogen MD, Flather RA, Pätsch J (1996) An investigation into the variability of circulation and transport on the north-west European shelf using three hydrodynamic models. Deutsche Hydrographische Zeitschrift 48:325–348CrossRefGoogle Scholar
  38. Stommel H (1949) Horizontal diffusion due to oceanic turbulence. J Mar Res 8:199–225Google Scholar
  39. van der Veer HW, Ruardij P, Van den Berg AJ, Ridderinkhof H (1998) Impact of interannual variability in hydrodynamic circulation on egg and larval transport of plaice Pleuronectes platessa L. in the southern North Sea. J Sea Res 39:29–40CrossRefGoogle Scholar
  40. Vandenbulcke L, Beckers J-M, Lenartz F, Barth A, Poulain P-M, Aidonidis M, Meyrat J, Ardhuin F, Tonani M, Fratianni C, Torrisi L, Pallela D, Chiggiato J, Tudor M, Book JW, Martin P, Peggion G, Rixen M (2009) Super-ensemble techniques: Application to surface drift prediction. Prog Oceanogr 82:149–167CrossRefGoogle Scholar
  41. Weisse R, Plüß A (2006) Storm-related sea level variations along the North Sea coast as simulated by a high-resolution model 1958–2002. Ocean Dynamics 56:16–25CrossRefGoogle Scholar
  42. Weisse R, von Storch H, Callies U, Chrastansky A, Feser F, Grabemann I, Guenther H, Pluess A, Stoye T, Tellkamp J, Winterfeldt J, Woth K (2009) Regional meteo-marine reanalyses and climate change projections: Results for Northern Europe and potentials for coastal and offshore applications. Bull Am Meteorol Soc 90(6):849–860CrossRefGoogle Scholar
  43. Wiltshire KH, Kraberg A, Bartsch I, Boersma M, Franke H-D, Freund J, Gebühr C, Gerdts G, Stockmann K, Wichels A (2010) Helgoland Roads, North Sea: 45 years of change. Estuaries Coasts 33:295–310CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Ulrich Callies
    • 1
    • 3
  • Andreas Plüß
    • 2
  • Jens Kappenberg
    • 1
  • Hartmut Kapitza
    • 1
  1. 1.Helmholtz-Zentrum GeesthachtGeesthachtGermany
  2. 2.Federal Waterways Engineering and Research Institute (BAW)HamburgGermany
  3. 3.Helmholtz-Zentrum GeesthachtInstitute of Coastal ResearchGeesthachtGermany

Personalised recommendations