Displays

Stirring of passive tracers in the Bay of Bengal driven by altimetry derived daily geostrophic surface currents, is studied on subseasonal timescales. To begin with, Hovmöller plots, wavenumber-frequency diagrams and power spectra confirm the multiscale nature of the flow. Advection of latitudinal and longitudinal bands highlights the chaotic nature of stirring in the Bay via repeated straining and filamentation of the tracer field. An immediate finding is that stirring is local, i.e. of the scale of the eddies, and does not span the entire basin. Further, stirring rates are enhanced along the coast of the Bay and are relatively higher in the pre- and post-monsoonal seasons. Indeed, Finite Time Lyapunov Exponent (FTLE) and Finite Size Lyapunov Exponent (FSLE) maps in all the seasons are patchy with minima scattered through the interior of the Bay. Further, these maps bring out a seasonal cycle wherein rapid stirring progressively moves from the northern to southern Bay during pre- and post-monsoonal periods, respectively. The non-uniform stirring of the Bay is reflected in long tailed probability density functions of FTLEs, that become more stretched for longer time intervals. Quantitatively, advection for a week shows the mean FTLE lies between 0.13±0.07 day^-1, while extremes reach almost 0.6 day^-1 . Averaged over the Bay, Relative dispersion initially grows exponentially, followed by a power-law at scales between approximately 100 and 250 km, which finally transitions to an eddy-diffusive regime. Quantitatively, below 250 km, a scale dependent diffusion coefficient is extracted that behaves as a power-law with cluster size, while above 250 km, eddy-diffusivities range from 6 × 10³ - 1.6 × 10⁴   m²s^-1 in different regions of the Bay. These estimates provide a useful guide for resolution dependent diffusivities in numerical models that hope to properly represent surface stirring in the Bay.

How to cite: Paul, N. and Sukhatme, J.: Seasonality of surface stirring by geostrophic flows in the Bay of Bengal, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1081, https://doi.org/10.5194/egusphere-egu2020-1081, 2020.

The North Atlantic Subtropical Gyre is shown to have a downward spiral flow beneath the mixed layer, where the water slowly gets denser, colder and fresher as it spins around the gyre. This path is traced with Lagrangian trajectories as they enter the Gyre in the Gulf Stream from the south until they exit through the North Atlantic Drift. The preliminary results indicate that these warm, saline waters from the south gradually becomes fresher, colder and denser due to mixing with waters originating from the North Atlantic. There are indications that there is also a diapycnal mixing, in the eastern part of the gyre due to mixing with the saline Mediterranean Waters, which would then be crucial for the Atlantic Meridional Overturning. The mixing in the rest of the gyre is dominated by isopycnic mixing, which transforms gradually the water into colder and fresher water as it spins down the gyre into the abyssal ocean before heading north.

How to cite: Döös, K., Berglund, S., Mcdougall, T., and Groeskamp, S.: The spiralling North Atlantic Subtropical Gyre, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1459, https://doi.org/10.5194/egusphere-egu2020-1459, 2020.

The origin of the atmospheric freshwater fluxes into the Bay of Bengal (BoB) are traced with Lagrangian water trajectories for both present and possible future climates. The water is traced backward from the precipitation at the sea surface to the evaporation regions. In the present-day simulation, the source is mostly from the Western Indian Ocean and near the Western Australian Coast. In the future climate scenario, simulated by EC-Earth, the origin of the moisture will not be the same as the present climate. In addition to it, the Bay of Bengal sourced water is also traced from the evaporation region to the precipitation locations. Most of the BoB originated moisture is precipitating within the neighbouring areas of the drainage basin and some part is transported into the Pacific Ocean. The Lagrangian model TRACMASS is currently running and a detailed analysis and results will be presented in the conference.

How to cite: Dey, D. and Döös, K.: Lagrangian analysis of atmospheric water balance over the Bay of Bengal, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2265, https://doi.org/10.5194/egusphere-egu2020-2265, 2020.

Ocean ensemble data assimilation systems generate ensembles of independent velocity field realizations after every assimilation cycle. Lagrangian tracking of passive tracers within such a framework is challenging due to the exponential growth in the number of particles that arises from describing the behavior of velocity over time as a set of possible combinations of the different realizations. This contribution addresses the problem of efficiently advecting particles, forward and backward in time, in ensemble flow fields, whose statistics are prescribed by an underlying assimilated ensemble. To this end, a parallel adaptive binning procedure that conserves the zeroth, first and second moments of probability is introduced to control the growth in the number of particles. The adaptive binning process offers a tradeoff between speed and accuracy by limiting the number of particles to a desired maximum. To validate the proposed method, we conducted various forward and backward particle tracking experiments within a realistic high-resolution ensemble assimilation setting of the Red Sea, focusing on the effect of the maximum number of particles, the time step, the variance of the ensemble, the travel time, the source location, and history of transport.

How to cite: El Mohtar, S., Hoteit, I., Knio, O., Issa, L., and Lakkis, I.: Forward and Backward Lagrangian Particle Tracking in Ensemble Flow Fields, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2569, https://doi.org/10.5194/egusphere-egu2020-2569, 2020.

How to cite: Berti, S. and Lapeyre, G.: Relative dispersion in a model of stratified upper-ocean turbulence, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2671, https://doi.org/10.5194/egusphere-egu2020-2671, 2020.

Lagrangian methods have been widely used and playing more and more essential roles in the analysis of ocean physical processes, pollution prediction, ecosystem protection and fisheries. Using the Lagrangian methods based on the high resolution coupled ocean model, we report several specific studies. The numerical modelling team from First Institute of Oceanography (FIO), Ministry of Natural Resources (MNR) of China, developed an ocean forecasting system based on the global (1/10°) wave-tide-circulation coupled model, as well as the regional model (1/24°) for China and adjacent seas. Basing on this system and its products, we developed the global ocean radionuclides model to investigate the long-term transport, distribution and evaluation of 137Cs in the ocean both from the Fukushima nuclear accident in March of 2011 and nuclear tests during the past 60 years; established the search and rescue system which has successfully applied in the rescue of the Phuket boat capsizing accident in July 2018; established the Enteromorpha prediction and tracking models for the protection of the marine environmental hazard from Enteromorpha, and to identify the origin area of this harmful green tide; developed the stock enhancement model of edible jellyfish to mimic the distribution of the human-released jellyfish and identify the connectivity between the releasing site and the fishing ground in Liaodong Bay of Bohai Sea, China. With the combination of the statistical methods, we established the near-term forecast and long-term projection system of the oil spill to forecast and evaluate the influence of the oil spill from the “Sanchi” collision accident on the ocean. All of these applications are verified and essential for protecting the oceans.

How to cite: Yin, L., Qiao, F., Zhao, C., and Wang, G.: Application systems of the FIO ocean forecasting system using Lagrangian methods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6507, https://doi.org/10.5194/egusphere-egu2020-6507, 2020.

We present the latest version of the TRACMASS trajectory code, version 7.0. The new version includes new features such as water tracing in the atmosphere, parameterisation scheme for sub-grid scale turbulence, generalisation of the tracer handling, etc. The code has also become more user friendly and easier to get started with. Previous versions of TRACMASS only allowed temperature, salinity and potential density to be calculated along the trajectories, but the new version allows any tracer to be followed e.g. biogeochemical tracers or chemical compounds in the atmosphere. The new parameterisation of sub-grid turbulence will enhance the kinetic energy and dispersion of trajectories in the ocean so that results from eddy-permitting ocean models (dx ∼25km) resemble those from “eddy-resolving” models (dx ∼8km). We will demonstrate some use cases of these new capabilities for atmosphere and ocean sciences. 

TRACMASS calculates Lagrangian trajectories offline for both the ocean and atmosphere by using already stored velocity fields, and optionally tracer fields. The velocity fields may be taken from ocean or atmosphere circulation models (e.g. NEMO, OpenIFS), reanalysis products (e.g. ERA-5) or observations (e.g. geostrophic currents from satellite altimetry). The fact that the numerical scheme in TRACMASS is mass conserving allows us to associate each trajectory with a mass transport and calculate the Lagrangian mass transport between different regions as well as construct Lagrangian stream functions. 

A live demonstration on how to set up, configure and run the TRACMASS code will be given.

How to cite: Aldama Campino, A., Döös, K., Berglund, S., Dey, D., Kjellsson, J., and Jonsson, B.: TRACMASS - A mass conserving trajectory code for ocean and atmosphere general circulation models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6984, https://doi.org/10.5194/egusphere-egu2020-6984, 2020.

    The term "fine scales" is generally used to refer to the ocean processes occuring on horizontal scales smaller than 10 km and
characterized by a short lifetime (days/weeks). Fine scales have been predominantly studied with numerical simulations and
satellite observations which have highlighted their significant role on biological processes. Indeed, their short time scale is the
same as a lot of important processes in phytoplankton dynamics. Model simulations have shown that fine scales such as fronts
and filaments strongly influence the distribution of phytoplankton species. Nowadays, the combination of in situ measurements,
satellite observations and model simulations is a necessity to better understand these mechanisms. However these processes
are particularly challenging to sample in situ because of their size and their ephemeral nature.

    The PROTEVSMED-SWOT cruise was performed in the Western Mediterranean Sea, in the southern region of the Balearic
Islands, onboard BHO Beautemps-Beaupré, between April 30 ^th and May 14 ^th , 2018. In order to study the influence of fine
scales on the distribution of phytoplankton species, a satellite-based adaptive Lagrangian sampling strategy has been deployed
in order to i) identify a fine scale structure of interest, ii) sample it at high spatial resolution the phytoplankton community, and
iii) follow the evolution of this structure and the related distribution of phytoplankton. The SPASSO software package uses
satellite altimetry, SST and surface Chl a concentration data to generate and provide near-real time daily maps of the dynamical
and biogeochemical structures present in the area. The sampling strategy was defined in order to cross a frontal zone separating
different types of water. Multidisciplinary in situ sensors (hull-mounted ADCP, a Seasoar towed fish and an automated flow
cytometer installed on the seawater supply of the Thermosalinograph) were used to sample at high spatial resolution physical
and biological variables. A particular attention was put in adapting the temporal sampling in different water masses to the
biological time scales in order to reconstruct the phytoplankton diurnal cycle.

    Such a strategy was successful in sampling two different water masses separated by a narrow front and characterized by
different aboundances of several phytoplankton species and functional groups. Consequently, our results highlight the role of
the front on the physical and biological coupling confirming previous modelling and remote-sensing studies.

    The new generation of altimetric satellite, SWOT, will provide a 2D sea surface height at an unprecedented resolution and
it will be a unique opportunity to better observe fine scale structures in the global ocean. Our methodology paves the way to
future in situ experiments that are planned in 2022 during the SWOT fast-sampling phase, few months after its launch.

How to cite: Tzortzis, R., Doglioli, A. M., Barrillon, S., Petrenko, A. A., d'Ovidio, F., Izard, L., Thyssen, M., Pascual, A., Cyr, F., Dumas, F., and Gregori, G.: A Lagrangian strategy for in situ sampling the physical-biological coupling at fine scale : the PROTEVSMED-SWOT 2018 cruise, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7357, https://doi.org/10.5194/egusphere-egu2020-7357, 2020.

The Arctic Ocean has been receiving more of the warm and saline Atlantic Water in the past decades. This water mass enters the Arctic Ocean via two Arctic gateways: the Barents Sea Opening and the Fram Strait. Here, we focus on the fractionation of Atlantic Water at these two gateways using a Lagrangian approach based on satellite-derived geostrophic velocities. Simulated particles are released at 70N at the inner and outer branch of the North Atlantic current system in the Nordic Seas. The trajectories toward the Fram Strait and Barents Sea Opening are found to be largely steered by the bottom topography and there is an indication of an anti-phase relationship in the number of particles reaching the gateways. There is, however, a significant cross-over of particles from the outer branch to the inner branch and into the Barents Sea, which is found to be related to high eddy kinetic energy between the branches. This cross-over may be important for Arctic climate variability.

How to cite: Chafik, L. and Broomé, S.: A satellite-based Lagrangian perspective on Atlantic Water fractionation in the Nordic Seas, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8422, https://doi.org/10.5194/egusphere-egu2020-8422, 2020.

This study describes an important pathway of the thermohaline conveyor belt circulation and connects the geographical distribution of water masses with water mass transformation. 
In the Southern Ocean, cold and fresh water up-wells to the surface and returns northward, entering the Pacific, Atlantic and Indian Ocean. This reflects an important part of the thermohaline conveyor belt circulation. As the water flows northward, it changes temperature and salinity, and thus density. These changes can be caused either by internal mixing or air-sea interactions. 

In this study, Lagrangian trajectories are used to follow the pathway from Drake Passage to the warm Pacific Ocean. Trajectories are started in the Drake Passage, and are ended when they either reach 25$^\circ$C or return to the Drake Passage. The trajectories entering the Pacific Ocean follow the Antarctic circumpolar current and separate then into two pathways. The first enters the Pacific Ocean close to the South American coast and flows along the coast until it reaches 25$^\circ$C close to the equator. The second pathway, which corresponds to most of the total volume transport entering the Pacific, are subducted around 40$^\circ$S. The water then moves westward until it reaches Australia where it turns northward and ultimately joins the equatorial undercurrent. 

Along these two pathways, the water changes temperature and salinity, going from cold and fresh to warm and saline. Preliminary results indicate that the water mass transformation for the first pathway are due to air-sea interactions, and internal mixing for the second. 

How to cite: Berglund, S., Döös, K., and Nycander, J.: Tracing the thermohaline Conveyor Belt circulation; from the Drake Passage to the Pacific Ocean, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8690, https://doi.org/10.5194/egusphere-egu2020-8690, 2020.

Wind and waves often have a strong influence on surface drift, especially in the strongly stratified Baltic Sea. However due to the limitations of wave models and analytical solutions, the quantification of the influence of the waves is a complicated problem. In this study we employ a more observational approach by utilizing one of the longest time series of in-situ surface drifters deployed in the Gulf of Finland, Baltic Sea for the period of 2011−2019. Analysis is performed both qualitatively and quantitatively to understand the effects of the wind and waves on surface drift. The forty-seven in-situ surface drifters utilized were designed to follow the uppermost 2 m layer of currents. In addition, a web-based software (DrifterTrack) was specifically developed for real time data monitoring, data collection, storage and access solution. The wind and wave data were obtained by wave buoys and meteorological stations located in the central part of the gulf.  
Several hypothesis tests combined with statistical analysis of drifter trajectories, wind and wave data were utilized for the analysis. Qualitatively the drifter trajectories displayed a variety of shapes and maneuvers, hinting the complexity of the surface drift. Nevertheless, drifter trajectory maps showed for most years a predominance of surface drift towards the east which also coincides with the predominant wind and wave direction. Interestingly the results also suggest that when surface drift towards the west occurred it was generally quicker than the drift to the east. The average current speed was in the range of 0.05−0.15 m/s for approximately 45% of the occurrences. The drifter speed within the range of 0.3−0.5 m/s accounted for approximately 9% of the occurrences. The drifter speed was found to vary between 1.5−2.5 % of the wind speed. Hypothesis tests show that wave heights of >1 m (created by >10 m/s wind speed) have the most significant effect on the drifter speed within the range of 0.15−0.3 m/s. These tests also demonstrated that wind and waves effects are not the only forces influencing strong surface drift in the gulf. Several other processes (e.g. eddies, density gradients, upwellings, downwellings etc.) can substantially contribute to the surface drift.

How to cite: Delpeche-Ellmann, N., Giudici, A., and Soomere, T.: The effects of wind and waves on in-situ surface drift in the Baltic Sea, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11496, https://doi.org/10.5194/egusphere-egu2020-11496, 2020.

The POLEWARD experiment in the Nordic Seas (2007-2009) involved deployment of 150 drifters in the eastern Nordic Seas and has been the first large drifter pair experiment in the ocean (and one of the very few conducted up to date). The experiment yielded nearly 100 drifter pairs with initial separations 2km or less, which allowed us to elucidate several aspects of the relative dispersion (a proxy for tracer spreading and transport) at a basin scale, to quantify the role of mesoscale eddies in surface transport, and to further develop the relevant theoretical and analytical methods through a series of publications. Ten years ago however there were no modeling tools available to carry out a similar numerical Lagrangian study in this region resolving relevant scales of variability.

In this presentation, we will present an update on the relative dispersion of surface drifter pairs in the Nordic Seas, with over 400 pairs available. We will then compare the observed statistics to these derived from Lagrangian simulations (OpenDrift scheme) forced by output from a very high resolution regional ocean model (Regional Ocean Modeling System). The comparison is very favorable pointing to the ability of the ocean model to represent surface eddy stirring processes. We will also show analysis of the regional dispersion regimes using both drifter observations and model simulations, and consider the effect of including vertical motion in the Lagrangian simulations, which impacts their horizontal dispersion. We will also present statistics of the temperature differences on drifters pairs. These are underestimated by the model on daily time scales and deformation scales, which has implications for the model ability to simulate tracer processes on these scales. 

How to cite: Koszalka, I. M. and LaCasce, J.: Relative dispersion in the Nordic Seas - new insights ten years later, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14732, https://doi.org/10.5194/egusphere-egu2020-14732, 2020.

Energetic eddy fields generated by meso and submesoscale dynamics induce tridimensional particle transport pathways, which complicate the interpretation of observed Particulate Organic Carbon (POC) fluxes using sediment traps. It is therefore of importance to understand how horizontal dispersion of particles is structured by these dynamics from surface to depth. In this modelling study, we use a Lagrangian method to backtrack sinking particles collected at various depths ranging from 500 m to 4700 m at the PAP (Porcupine Abyssal Plain) site. Particle trajectories are computed using high-resolution simulations of the Regional Ocean Modelling System (ROMS). Our results show that the horizontal distribution of particles with sinking velocities below 100 m d^-1 presents a large small-scale heterogeneity. Mesoscale eddies act to define the general structure of particle patches while submesoscale features shape particle distributions through convergence/divergence processes. Distribution patterns of particles tracked from different depths suggest regime shifts of particle dispersion between subsurface layers. To identify and quantify these regimes, we perform 2d experiments at specific depths from 100 m to 4000 m and relate the Lagrangian statistics to the characteristics of the different dynamical regimes identified using vertical profiles of eddy energy and Finite Size Lyapunov Exponents (FSLE) approach.                                                                                                                                                               

How to cite: Wang, L., Gula, J., Collin, J., and Memery, L.: Impacts of meso and submesoscale dynamics on the horizontal dispersion of sinking particles from the surface to the deep ocean, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15825, https://doi.org/10.5194/egusphere-egu2020-15825, 2020.

It has long been assumed that young sea turtles drift passively with ocean currents. As a consequence, simple Lagrangian models have often been used to investigate the dispersal of various sea turtle populations during their juvenile stage. However, evidence is growing that juvenile sea turtles do not drift purely passively with ocean currents but also display some swimming activity, generally directed towards favorable habitats.

We have thus developed a new Sea Turtle Active Movement Model (STAMM) in which simulated individuals disperse under the combined influence of oceanic currents and swimming movements triggered by the need to find suitable habitats, that is areas with suitable water temperatures and sufficient food.  Preferred temperatures and food requirements are modeled to vary with the age (or size) of the simulated individuals.

STAMM is used here to investigate the active dispersal of juvenile leatherback turtles (Dermochelys coriacea) born in French Guiana, a major rookery for the Northwest Atlantic population. Our simulations reveal that:

1.  While currents broadly shape the dispersal area, habitat-driven movements profoundly structure the spatio-temporal distribution of juveniles within this area. Passive turtles can drift far North in deadly cold waters or concentrate in oligotrophic waters found at the center of the North Atlantic subtropical gyre. On the contrary, actively swimming juveniles tend to concentrate in favorable habitats along the northern boundary of the subtropical gyre and undertake seasonal north-south migrations allowing them to remain in suitable water temperatures.
2. Active juveniles ultimately target rich areas of the Eastern Atlantic basin, in particular in the Bay of Biscay, off Galicia, Portugal and Mauritania, and in the western Mediterranean Sea where juvenile leatherbacks are actually observed. These zones are inaccessible to passive turtles.
3. Arrival times of the active juveniles in these favorable zones are consistent with the observed sizes of individuals bycaught or stranded in these areas;

All together these results suggest that active habitat-driven swimming movements shall be systematically taken into account to produce realistic simulations of the spatial distribution of sea turtles during their pelagic juvenile stage. This is much needed to help develop effective conservation measures targeting this critical life stage.

How to cite: Gaspar, P., Lalire, M., Giffard, P., and Candela, T.: Lagrangian modeling of the active dispersal of juvenile leatherback turtles in the North Atlantic Ocean, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17793, https://doi.org/10.5194/egusphere-egu2020-17793, 2020.

How to cite: Paredes, M., Abdunabiev, S., Allegretti, M., Perona, G., Tordella, D., Pasero, E., Canavero, F., Merlone, A., and Musacchio, C.: Progress on the development of innovative, floating, biodegradable radio- probes for atmospheric monitoring inside warm clouds, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22374, https://doi.org/10.5194/egusphere-egu2020-22374, 2020.