Supplementary material to "Surface drifters in the German Bight: Model validation considering windage and Stokes drift"

Abstract

Six surface drifters (drogued at about 1 m depth) deployed in the inner German Bight (North Sea) were tracked for between 14 and 54 days. Corresponding simulations were conducted offline based on surface currents from two independent models (BSHcmod and TRIM). Inclusion of a direct wind drag (0.6 % of 10 m wind) was needed for successful simulations based on BSHcmod currents archived for a 5 m depth surface layer. Adding 50 % of surface Stokes drift simulated with the third generation wave model WAM was tested as an alternative approach. Results resembled each other during most of the time. Successful simulations based on TRIM surface currents (1 m depth) suggest that both approaches were mainly needed to compensate insufficient vertical resolution of hydrodynamic currents.The study suggests that main sources of simulation errors were inaccurate Eulerian currents and lacking representation of sub-grid scale processes. Substantial model errors often occurred under low wind conditions. A lower limit of predictability (about 3-5 km per day) was estimated from two drifters that were initially spaced 20 km apart but converged quickly and diverged again after having stayed at a distance of 2 km and less for about 10 days. In most cases, errors in simulated 25 h drifter displacements were of similar order of magnitude. IntroductionLagrangian particle tracking is a natural choice when origins or destinations of drifting objects (or water bodies) need to be known. Such methods have been developed for a wide range of applications (see Mariano et al., 2002). Examples from oceanography are simulations of physical dispersion (Schönfeld, 1995;Sentchev and Korotenko, 2005), possibly augmented by specific source and sink terms (e.g. Puls et al., 1997). In ecosystem modelling, Lagrangian transport models have been employed to better understand the process of non-indigenous species invading an ecosystem (Brandt et al., 2008), the risk of toxic algae blooms (Havens et al., 2010) or larval transport and connectivity being crucial to spatial fishery management (e.g. Nicolle et al., 2013;Robins et al., 2013). Lagrangian transport simulations provide also a basis for more comprehensive individual-based models of fish recruitment (e.g. Daewel et al., 2015), for instance.