Surface drifter trajectories in the
Barents Sea applied to validate an
operational oil drift model Challenges related to operational oil drift
modeling in Arctic waters
Lars R. Hole, Göran Broström, Cecilie Wettre & Johannes Röhrs
International R&D workshop on oil spills, Houston, 8 Feb 2011
Meteorologisk institutt met.no
Research activities to improve oil drift
service
• OilWave: Financed by RCN/ENI/Statoil
• BioWave: Financed by RCN
• NorSpill: Financed by RCN + Norwegian
Clean Seas Association for Operation
Companies (NOFO)
• DrivBar: Financed by BarentsWatch
(governmental program)
• MyOcean: Comprehensive metocean
forecast service (EU)
Meteorologisk institutt met.no
Meteorologisk institutt met.no
Cruise with Johan Hjort April 2010
• Shortened due to technical trouble
• Dropped 10 Iridium drifters and 10 Argos
drifters
• Turbulence profiles
• Wave measurements
• Cod egg profiles
Meteorologisk institutt met.no
Cruise with Johan Hjort April 2010
• Shortened due to technical trouble
• Dropped 10 Iridium drifters and 10 Argos
drifters
New cruise in 2011 with
• Turbulence profiles
• novel turbulence measurements
• Wave measurements • wave buoy
• 20 new drifters
• Cod egg profiles
Meteorologisk institutt met.no
Meteorologisk institutt met.no
Meteorologisk institutt met.no
Stokes drift
The waves will set
up a current close
to the surface in
the same direction
as the waves.
9 Important only
close to the
surface.
9
Meteorologisk institutt met.no
Stokes drift
Meteorologisk institutt met.no
Stokes drift
Meteorologisk institutt met.no
Oil drift model at met.no – OD3D
9 Originally
developed by Sintef
9 Current, salinity and temperature in 10 layers
9 Tide
9 Natural dispersion by waves
9 Oil film thickness
9 ~ 90 oil types
Meteorologisk institutt met.no
Oil drift model experiment using
operational models at met.no
9 Wind
field from Hirlam 4km
9 Wave field from WAM 4km
9 Ocean parameters from MIPOM 4km
9 Restart model every 24h at position of drifter
9 Seed 500 oil particles within 30 min
starting at 00:00
9 15 min time step
9 Following mass centre of oil spill
Meteorologisk institutt met.no
Meteorologisk institutt met.no
Oil drift simulation
Meteorologisk institutt met.no
30 min average speed
Meteorologisk institutt met.no
30 min average speed
Meteorologisk institutt met.no
30 min average speed
Meteorologisk institutt met.no
Diurnal average speed
Meteorologisk institutt met.no
Diurnal average speed
Meteorologisk institutt met.no
No Stokes drift and no wind
• 11 % reduction in average speed
Meteorologisk institutt met.no
Further studies
• Find situations where
Stokes drift is important
and analyze these
• Establish whether wind
is important at all
• Try out different ocean
model (ROMS) on finer
scale (800 m)
Meteorologisk institutt met.no
10 days trajectory
for risk assessment
Meteorologisk institutt met.no
BarentsWatch:
Extended area for oil spill service
Meteorologisk institutt met.no
Oil drift simulation for Svalbard
Meteorologisk institutt met.no
Oil drift in ice covered seas: Impact of waves
•
When wave impinge on an ice cover the waves are rapidly damped.
Meteorologisk institutt met.no
Oil drift in ice covered seas: Impact of waves
•
•
When wave impinge on an ice cover the waves are rapidly damped.
However, due to conservation of momentum, wave momentum will force a intense ocean
current just beneath the ice. The waves will also generate a deep reaching roll motion (i.e.,
Langmuir circulation).
Meteorologisk institutt met.no
Oil drift in ice covered seas: Impact of waves
•
•
•
When wave impinge on an ice cover the waves are rapidly damped.
However, due to conservation of momentum, wave momentum will force a intense ocean
current just beneath the ice. The waves will also generate a deep reaching roll motion (i.e.,
Langmuir circulation).
The intensified jet, and the roll motion may be very effective in transporting oil long distances
under the ice (i.e., say up to 5 km).
Meteorologisk institutt met.no
NETMAR
z
Pilot project for integrating integrating satellite, in situ and model data
from ocean and coastal areas in system of systems.
z
E.g. Ice type, ice concentration, ice edge, meteorological models
z
Single point access to data from different providers.
z
Based on OGC, OpeNDAP and W3C standards
z
Uses ontologies and semantic frameworks to enable data discovery,
data service chaining and combining data.
z
z
E.g. All parameters in the datasets are labeled with SeaDataNet
names to ensure a common definition
netmar.nersc.no
Meteorologisk institutt met.no
Meteorologisk institutt met.no