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SEA LION -
SEa ice in the Antarctic
LInked with OceaN-atmosphere
Sea Lion is a joined EU-funded project of
Institute of Marine Research, University of Kiel
British Antarctic Survey, Cambridge
Danish Center of Remote Sensing, Technical University of Denmark
Institute of Environmental Physics, University of Bremen
Objektives and goals
The work packages
Papers and Presentations
Objectives and goals
Presently, sea ice represents the weakest and least sophisticated
part of coupled climate models. The aim of this project is to assess and
improve the performance of coupled global atmosphere-sea ice-ocean models
in reproducing sea ice in the high southern latitudes. This will be achieved
- deriving data sets of sea ice concentration and motion
using remote sensing techniques,
- performing selected runs with a sophisticated high-resolution
dynamic-thermodynamic sea ice model, which will be optimized with the data
sets derived within this project, and
- analyzing the output of coupled global atmosphere-ocean
general circulation model (AOGCM) runs.
State of the Art
On the geophysical scale sea
ice is a thin, broken layer on the polar oceans which is modified in thickness
and concentration by dynamic-thermodynamic processes. Sea ice represents the
boundary between the two much larger geophysical fluids, the atmosphere and
the ocean, and therefore influences their interaction considerably. The
details and consequences of the role of sea ice in mediating between
atmosphere and ocean are partly still unknown.
Due to its high albedo and insulating behaviour sea ice
modifies the heat, salt and momentum exchange between atmosphere and ocean.
Since the sea ice is generally melted far away from the creation area,
the ice drift also strongly modifies the buoyancy flux at the ocean surface.
Because of its low salinity and negative latent heat the sea ice motion
represents a very effective lateral heat and salt flux.
A variety of large-scale sea ice models have been applied
to the Arctic and Antarctic sea ice cover, ranging from very simple thermodynamic
to highly sophisticated dynamic-thermodynamic models. These experiments
suggest that the effects of sea ice dynamics cannot be omitted for realistic
simulations of the sea ice cover, i. e. thickness, concentration and motion.
In order to improve the results of sea ice models and to reduce computing
time, modifications and fine-tuning of process parameterizations within
the surface energy balance, the heat conduction and the dynamic codes seem
to be necessary.
warming experiments with global AOGCMs have shown an enhanced response
in polar regions. This is in line with expectations. However, the details
remain unclear because of an inadequate sea ice model. Coupled general
circulation models (GCM)
have in the past utilized thermodynamic sea ice models and - if at all
- a simplified advection scheme. Presently dynamic-thermodynamic models
(the cavitating fluid model (Flato and Hibler 1992)
or versions of the original Hibler viscous-plastic model) are being implemented
or have already been used at a few modelling centres. The use of the more
sophisticated sea ice model component will allow a better evaluation of
the role of the sea ice in the climate system, especially since these advanced
sea ice models are less sensitive to internal perturbations and to changes
of the boundary conditions in atmosphere and ocean.
In order to understand and predict the interaction of
atmosphere, sea ice and ocean in more detail an optimization of currently
available sea ice models is necessary. Presently modelling activities,
for example within the Sea Ice Modelling Intercomparison Project (SIMIP),
are being coordinated by the Sea Ice-Ocean Modelling Panel (SIOM)
of the Arctic Climate System Study (ACSYS)
within the World Climate Research Programme (WCRP)
to improve the dynamic part of sea ice models using observations of sea
ice drift and the sea ice edge variability in the Arctic.
A similar attempt to improve the thermodynamic part of
sea ice models is still required. Within such a modelling exercise the
following topics should be investigated:
- Which is the optimal albedo parameterization, including
the dependence on surface melting conditions, ice thickness, snow cover,
and snow temperature?
- How is the absorbed insolation disposed, including melting
of the top, bottom or side surface of the ice, heating of the ice, enlargement
of brine pockets, storage in leads and storage in the mixed layer beneath
Due to the lack of sea ice motion data the optimization
procedure has not yet been applied to Antarctic sea ice. An improved parameterization
of the thermodynamic and dynamic processes for the Southern Ocean sea ice
is only feasible with improved sea ice concentration and velocity data.
Therefore, the objectives of this proposal are:
- to develop data sets (time series) of ice drift and concentration
for the Scanning Multichannel Microwave Radiometer- (SMMR)
and Special Sensor Microwave/Imager- (SSM/I)
period (Remote Sensing Part) and
- to derive an optimized dynamic-thermodynamic sea ice
model for the use in coupled atmosphere-ocean general circulation models
for climate research (Modelling Part).
The work packages
The work within this project is subdivided into six tasks,
each containing several work packages. Tasks 1 to 3 belong to the Remote
Sensing Part, tasks 4 to 6 belong to the Modelling Part.
Remote Sensing Part:
- Task 1 (responsible: IUP):
Establishing a surface emissivity model for an ice covered ocean under
variable surface conditions.
To gain insight into the characteristics and processes which determine
the sea ice emissivity. Special emphasis will be attributed to the statistical
horizontal variability for the different ice types.
- Task 2 (responsible: IUP):
Derivation of an improved concentration algorithm for the Southern Ocean
sea ice using all SSM/I-channels, comparison with the data base of in situ
observations (task 1) and sensors with higher spatial resolution (AVHRR,
Integration of the results of task 1 and higher resolving sensors to derive
optimized ice concentration time series over the 20 years of data available
from satellite borne microwave instruments around Antarctica taking into
account the inherent variability of ice types as well as atmospheric contributions.
Show IGS poster: (1.5Mb)
Download ZIPPED IGS poster:(238Kb)
Download ZIPPED WMO/CIWG-Report:(163Kb)
- Task 3 (responsible: DTU):
Ice motion from spaceborne passive microwave observations.
Derive a dataset of ice motion vectors that can be used for validation/verification
of the dynamic part of the ice models.
Have a look at the RESULTS
- Task 4 (responsible: BAS):
Statistical analysis of sea ice concentration and atmospheric data.
To understand the variability of sea ice over the study period in the light
of the atmospheric circulation. This will involve:
Producing indices of the atmospheric circulation from ECMWF
(European Center for Medium-Range Weather Forecasts) surface and upper
air fields and relating these to sea ice variability on the hemispheric
and regional scale.
- Task 5 (responsible: IfM):
Application of a sophisticated dynamic-thermodynamic sea ice model to the
1979-98 period for Antarctica with re-analyzed ECMWF and NCEP
(Numerical Center for Environmental Prediction, USA) atmospheric forcing;
comparison of spatial and temporal characteristics of the observed sea
ice concentration and drift variability with the results of task 2 and
To incorporate improved parameterizations of albedo and heat absorption
and the atmospheric boundary layer structure in a sea ice model for the
Southern Ocean. The model output for annual and interannual sea ice variations
will be compared with observed sea ice concentration and drift data from
task 2 and task 3 in order to optimize the dynamic and thermodynamic parameters of the sea ice model.
Show task report #1:(2.3Mb)
Download ZIPPED report:(144Kb)
Show EGS poster #1:(1.7Mb)
Download ZIPPED EGS poster #1:(575Kb)
Show EGS poster #2:(1.5Mb)
Download ZIPPED EGS poster #2:(564Kb)
- Task 6 (responsible: BAS):
Investigation of the representation of sea ice in the current generation
of GCMs and comparison with the results of tasks 4 and 5.
To gain understanding of the capability of the current state-of-the-art
AOGCMs to represent sea ice around the Antarctic.
Comparing the sea ice extent and concentration in the AOGCM to the fields
produced by the sophisticated regional sea ice model.
Investigate whether sophisticated dynamics schemes are necessary for the
GCM-scale representation or whether simpler schemes suffice.
Investigate use of UK Meteorological Office FOAM (the operational ocean
forecasting system) data to force ocean currents, salinity and temperature.
Institute of Marine Research (IfM)
British Antarctic Survey (BAS)
- Peter Lemke firstname.lastname@example.org
- Markus Harder email@example.com
- Thomas Martin firstname.lastname@example.org
- Holger Pohlmann email@example.com
- Oliver Stenzel firstname.lastname@example.org
Technical University of Denmark,
Danish Center of Remote Sensing (DTU, DCRS)
- William Connolley email@example.com
- Steven Harangozo firstname.lastname@example.org>
- John King email@example.com
- Sandra Schuster firstname.lastname@example.org
- John Turner email@example.com
University of Bremen, Institute
of Remote Sensing (IUP)
- Leif Toudal Pedersen firstname.lastname@example.org
- Roberto Saldo email@example.com
Deutsches Klimarechenzentrum (DKRZ)
- Georg Heygster firstname.lastname@example.org
- Klaus-Peter Johnsen email@example.com
- Stefan Kern firstname.lastname@example.org
Hadley Centre for Climate Prediction and Research (HAD)
- Ulrich Cubasch email@example.com
- Josef Oberhuber firstname.lastname@example.org
- Douglas Cresswell email@example.com
Links to other sites and organisations
- European Union (EU)
- World Meteorological Organisation (World Climate Research Programme WCRP)(WMO)
- Antarctic CRC (Cooperative Research Centre for the Antarctic and Southern Ocean environment)(CRC)
- Antarctic Meteorological Research Center (AMRC)
- Alfred Wegener Institute for Polar and Marine Research (AWI)
Papers and Presentations
Lemke, P., Heygster, G., Toudal, L. Turner, J., 1998:
SEa ice in the Antarctic LInked with OceaN-atmosphere forcing, Proceedings of the European Climate Science Conference, Vienna, 19.-23. Oct. 1998.
Kern, S., Heygster, G., and Miao, J., 1999:
Towards retrieval of Antarctic sea ice using the SSM/I 85.5 GHz polarization difference, Proc.IEEE International Geoscience and Remote Sensing Symposium, Hamburg, Germany, June 28 to July 2, 1999, 1031-1033.
Miao, J., Johnsen, K.-P., and Heygster, G., 1999:
Signature of clouds over sea ice detected by the Special Sensor Microwave/Imager (SSM/I), Proc. IEEE International Geoscience and Remote Sensing Symposium, Hamburg, Germany, June 28 to July 2, 1999, 2075-2077.
Pohlmann, H., Harder, M., Martin, T., and Lemke, P., 1999:
Comparisons of two ice simulations forced with ECMWF and NCEP/NCAR reanalyses data, Conference Proceedings of the Second International Conference on Reanalyses, Reading, UK, 1999, in press.
Harangozo, S. A., 2000:
A search for ENSO teleconnections in the West Antarctic Peninsula Climate in Austral Winter, Int. J. Climatol., 20,663-679.
Kern, S., 2000:
Sea ice concentration derived using SSM/I 85.5 GHz imagery, Proc. Workshop on Mapping and archiving of sea ice data - The expanding role of RADAR, Ottawa, Canada, May 2-4, 2000, 179-184.
Kern, S. and Heygster, G., 2000:
Sea ice concentration retrieval in the Antarctic based on the SSM/I 85 GHz polarization, Symposium of the International Glaciology Society, IGS2000, Fairbanks, Alaska, June 19-23, 2000.
Lemke, P., Heygster, G., Toudal, L., and Turner, J., 2000:
The SEA LION Project: Sea ice in the Antarctic linked with ocean-atmosphere forcing, Symposium of the European Geophysical Society, EGS2000, Nice, France, April, 2000.
Martin, T., Pohlmann, H., Toudal, L, and Saldo, R., 2000:
Comparison of Southern Ocean sea ice drift simulations and satellite derived ice drift data, Symposium of the European Geophysical Society, EGS2000, Nice, France, April, 2000.
Schuster, S., Connolley, W., and Turner, J., 2000:
Assessment of a coupled atmosphere-ocean GCM with a dynamic-thermodynamic sea ice model, Symposium of the International Glaciology Society, IGS2000, Fairbanks, Alaska, June, 2000.
Stenzel, O., Pohlmann, H., Harder, M., Lemke, P., and Martin, T., 2000:
Results from the SEA LION Project: Interannual variability of simulated sea ice properties in the Southern Ocean, Symposium of the European Geophysical Society, EGS2000, Nice, France, April, 2000.
Toudal, L. and Saldo, R., 2000:
A dataset of satellite derived ice motion for the Southern Ocean, Symposium of the European Geophysical Society, EGS2000, Nice, France, April, 2000.
Turner, J., Connolley, W., Cresswell, D., and Harangozo, S., 2000:
The simulation of Antarctic sea ice in the Hadley Centre Climate Model (HadCM3), Symposium of the International Glaciology Society, IGS2000, Fairbanks, Alaska, June 19-23, 2000.
Kern, S. and Heygster, G., 2001:
Sea ice concentration retrieval in the Antarctic based on the SSM/I 85 GHz polarization, Annals of Glaciology, 2001, in press.
Flato, G.M, HiblerIII, W.D., 1992:
Modelling pack ice as a cavitating fluid - J. Phys. Oceanogr., 22,626-651
National Snow and Ice Data Center (NSIDC), 1996:
DMSP SSM/I Brightness temperatures and sea ice concentration grids for Polar Regions, User's guide, University of Colorado, Boulder, Colorado, CO 80309-0449, USA, pp.110.
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Please send comments to Stefan Kern firstname.lastname@example.org