Researchers: (alphabetic order) - Garraffo, Zulema1 - Griffa, Annalisa1,2 - Lumpkin, Rick3 - Mariano, Arthur 1 - Poulain, Pierre-Marie 4 - Reynolds, Andy5 - Veneziani, Milena6 - Zambianchi, Enrico7 - 1RSMAS, Miami - 2CNR-ISMAR, Sp, Italy - 3NOAA, AOML, Miami - 4OGS, Italy - 5Rothamsted Res., UK. - 6UCSC, CA - 7Universita' Parthenope, Naples, Italy Sponsors: - ONR - NSF Publications: - Griffa, A., R. Lumpkin, and M. Veneziani , 2008. Cyclonic and anticyclonic motion in the upper ocean, Geophys. Res. Lett., 35, L01608, doi:10.1029/2007GL032100 - Veneziani M., A. Griffa and P.M. Poulain 2006:   Historical drifter data and statistical prediction of particle motion: a case study in the Adriatic Sea, J. Atmos. Ocean Tech, 24, 235-254 (PDF) - Veneziani, M., A. Griffa, A.M. Reynolds, Z.D. Garraffo, and E.P. Chassignet, 2005: Parameterizations of Lagrangian spin statistics and particle dispersion in presence of coherent vortices., J. Mar. Res., 63, 1057-1083, (PDF). - Veneziani, M., A. Griffa, Z.D. Garraffo, and E.P. Chassignet, 2005: Lagrangian spin parameter and coherent structures from trajectories released in a high-resolution ocean model. J. Mar. Res., 63, issue 4, 753-788, (PDF). - Maurizi A., A. Griffa, P.M. Poulain and F. Tampieri, 2004: Lagrangian turbulence in the Adriatic Sea as computed from drifter data: effects of inhomogeneity and nonstationarity. J. Geophys. Res., 109, C04010, doi:10.1029/2003JC002119 - Veneziani M., A. Griffa, A.M. Reynolds and A.J. Mariano, 2004: Oceanic turbulence and stochastic models from subsurface Lagrangian data for the North-West Atlantic Ocean. J. Phys. Oceanogr., 34, (8), 1884-1906. - Reynolds, A.M. and M. Veneziani, 2004: Rotational dynamics of turbulence and Tsallis statistics. Phys. Lett. A, 327, 9-14. - Bauer, S., M.S. Swenson and A. Griffa, 2002: Eddy-mean flow decomposition and eddy diffusivity estimates in the tropical Pacific Ocean. 2: Results. J. Geophys. Res., 107, (C10), 3154-3171. - Garraffo, Z., A. Mariano, A. Griffa, C. Veneziani and E. Chassignet, 2001: Lagrangian data in a high resolution model simulation of the North Atlantic. 1: Comparison with in-situ drifters. J. Mar. Sys., 29, 157-176. - Garraffo, Z., A. Griffa, A. Mariano and E. Chassignet, 2001: Lagrangian data in a high resolution model simulation of the North Atlantic. 2: Mean flow reconstruction and sampling effects. J. Mar. Sys., 29, 177-200. - Falco P., A. Griffa, P.M. Poulain, E. Zambianchi, 2000: Transport properties in the Adriatic Sea as deduced from drifter data. J. Phys. Oceanogr., 30, (8), 2055-2071.

Lagrangian data provide information on ocean currents in terms of velocity and transport. Extensive data sets are available today, both at and below the ocean surface, thanks to a number of extensive field experiments. The data have been analyzed by a number of authors, providing significant contributions to our knowledge of the ocean circulation and transport. 1. Dispersion and Parameterizations 2. Cyclonic and anticyclonic motion in the upper ocean 3. Transport processes in coastal flows 1. Dispersion and Parameterizations We have analyzed data sets of surface drifters in the Adriatic Sea (Maurizi et al.,2004) and the Topical Pacific (Bauer et al.,2002) and a set of subsurface floats in the North Atlantic (Veneziani et al.,2004). Our focus is characterizing dispersion processes and testing suitable transport parameterizations, in particular in terms of Lagrangian Stochastic (LS) models. In the following, we provide some specific information on the North Atlantic study. The historical data set provided by 700 m acoustically-tracked floats has been analyzed in different regions of the north-western Atlantic Ocean. (Fig. 1.1, Fig. 1.2). In the Gulf Stream recirculation and extension regions, the autocovariances and crosscovariances of the Lagrangian velocity exhibit significant oscillatory patterns on time scales comparable with the Lagrangian decorrelation time scale. They are indicative of the presence of significant coherent structures and of sub- and super-diffusive behaviors in the mean spreading of water particles.   Figure 1.1 Trajectory (spaghetti) plot of the acoustically tracked isobaric floats available in the North-Western Atlantic at 700 m depth from the Subsurface Float Data assembly Center (WFDAC) at Woods Hole. >  Figure 1.2 Map of the Eddy Kinetioc Energy obtained averaging the 700 m float data over 1 degree bins. Superimposed are boxes indicating regions of quasi-homogeneous EKE, where Lagrangian statistical analysis has been carried out. Our main result is that the properties of the Lagrangian statistics can be considered as a superposition of two different regimes associated with looping and non-looping trajectories (Fig. 1.3), and that both regimes can be parameterized using a simple first-order Lagrangian stochastic model with spin parameter. The non-looping regime corresponds to an approximately homogeneous "background" flow, while the looping regime is characteristics of the coherent structures. The spin parameter couples the zonal and meridional velocity components, reproducing the effect of rotating vortices. It is considered as a random parameter whose probability distribution is approximately bi-modal, reflecting the distribution of loopers (finite spin) and non-loopers (zero spin). The simple model is found to be very effective in reproducing the statistical properties of the data (Fig. 1.4). Supplementary analysis has been performed using a synthetic data set of trajectories released in a high resolution Miami Isopycnic Model (MICOM) (Fig. 1.5). The goal is to investigate the relationship between Langrangian and Eulerian statistics, and in particular to verify whether the spin parameter can be interpreted as a relative vorticity estimate of the coherent structures (Fig. 1.6).   Figure 1.3 Sample of looping trajectories, "loopers", (left panels), and non looping trajectories, 'non-loopers" (right panel) in a region of approximately homogeneous EKE in the southern Gulf Stream recirculation (region RECW in Fig. 2). Arrows along trajectories are plotted every 5 days.     Figure 1.4 Velocity autocovariance functions computed respectively from 700 m float data (top panels) and from simulated trajectories from a first order Lagrangian Stochastic Model (LSM) with spin (lower panels). First column show statistics from the overall data set, second column from non-loopers only and third column from loopers only. As it can be seen the agreement between data and LSM results is very satisfactory.     Figure 1.5 Snapshot of relative vorticity map from a numerical simulation of a high resolution model in the North Atlantic, showing the presence of a rich population of coherent structures such as rings and vortices. The model is the Miami Isopycnic Model (MICOM) at 1/12 of degree resolution. Positive (negative) vorticity is indicated in red (blue).     Figure 1.6 Video representing the evolution of the velocity field (sqrt EKE) from the MICOM simulation of the North Atlantic at 1/12 of degree resolution. The video spans a period of 1 year, during which high energy ring form and detach from the Gulf Stream, propagating south-eastward. Trajectories of simulated particles seeded in the rings are also shown with 60 day tails. (Click on the above image to play an 8MB animated gif.)   2. Cyclonic and anticyclonic motion in the upper ocean Driven by wind and thermohaline processes, the highly energetic circulation patterns of the upper ocean play important roles in the global distribution of heat and nutrients. Despite this importance, many aspects of upper ocean dynamics are poorly known, especially how its varability is characterized by the interaction of different types of motion and scales. To investigate the properties of upper ocean variability, Griffa, Lumpkin and Veneziani, 2008 (GLV08 in the following) studied the global data set from surface drifters, focusing on the subtropic and subpolar regions (10-60 degrees in latitude) an area well-sampled by drifters. GLV08 computed the distribution of loopers, i.e. trajectories characterized by a well defined sense of rotation, identifying ocean surface cyclonic and anticyclonic motion (Fig. 2.1) for scales ranging from large voritices (R =50-100 km)to smal structures (R less than 10 km). The authors found two zonal bands of small-scale motion: one a known wind-induced anticyclonic band at 30 to 40 degrees latitude and the other an unexpected cyclonic band at 10 to 20 degrees latitude. The latter corresponds to regions of subtropical barrier layer formation (Sato et al., 2006) and might be due to fine-scale processes related to salinity front instabilities. These results provide a first global view of the upper ocean variability at various scales through drifter data. Figure 2.1 Global distribution of loopers from GLV08, for trajectory segments with spin greater than 0.5 day^-1; (upper panel) blue and red dots denote cyclonic and anticyclonic polarity, respectively; (lower panel) estimated loopers radius. Dots represent the mean location of 20 day trajectory segments. The gray shaded equatorial band denotes the region that was excluded from the data analysis. Solid black contours in upper panel show the location of subtropical barrier layer formation (Sato et al., 2006), whereas the dashed contours show the regions with significant correlation between the wind and the ageostrophic drifter velocity (Rio and Hernandez, 2000).   3. Transport processes in coastal flows A study of historical drifters in the Adriatic Sea (Poulain 2001) has been carried out to determine transport properties in coastal areas in presence of topographic constraints.The results (Veneziani et al., 2006) have also been used to determine best launching strategy during a recent experiment, the Dynamics of the Adriatic in Real Time (DART06) experiment, coordinated by M. Rixen (NURC/NATO). The study focuses on an area of the central Adriatic, close to the Gargano Cape. The region is strongly controlled by topography and has significant mesoscale activity arising from the instabilities of the western Adriatic current. The mean flow pattern computed by the drifters (Fig. 3.1) shows the presence of a well defined boundary current along the coast and of a saddle (hyperbolic) point off-shore the cape, separating two recirculating gyres one to the North and the other to the South respectively. Maps of drifter concentration at different times have also been built and interpreted as maps of probability density function (pdf's) of finding a particle at a given time in the neighborhood of a given point in the domain. An example of pdf  for particles released in the region of the hyperbolic point is shown in Fig.3.2, indicating the presence of a well defined two-branch pattern. These results and analogous ones on the boundary current have been used to select launching points during DART06. Figure 3.1 : Maps of mean velocity (upper) and EKE (lower) computed from historical drifter data in the Adriatic close to the DART06 region (red box) (from Veneziani et al., 2006). Notice the hyperbolic point in the mean flow off the Gargano Cape. Figure 3.2: Maps of drifter concentration (pdf's) at different times, computed as percentage of drifters with initial conditions in the region of the hyperbolic point in Fig.5 (from Veneziani et al., 2006 ) . Notice the two branch pattern of the pdf.