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An Arctic Ocean/Sea Ice Reanalysis
Detlef Stammer, Nikolay Koldunov, Armin Köhl
Center für Erdsystemforschung und NachhaltigkeitUniversität Hamburg
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Preamble
A complete picture of the ocean for the purpose of climate research and applications will only come from a synergy between observations, modeling and data assimilation.
Goal of ocean synthesis (reanalysis) is to obtain such best possible description of the ocean by combining all available data with the dynamics of an ocean circulation model.
“Data assimilation” is mostly least-squares fitting of models to data. Different methods are variant algorithms used to find the minimum of an objective (or cost) function, the extent to which an approximation to that minimum is acceptable, and whether one seeks an error estimate.
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time
Smoothed Estimate: x(t+1)=Ax(t)+Gu(t)
Filtered Estimate: x(t+1)=Ax(t)+Gu(t)+(t+1)x: model state, u: forcing etc, : data increment
Model Physics: A, G
Data increment:
Consistency of Assimilation
Data
The temporal evolution of data-assimilated estimates is physically inconsistent (e.g., budgets do not close) unless the assimilation’s data increments are explicitly ascribed to physical processes (i.e., inverted).
Climate Syntheses need to preserve first principles (Bengtsson et al., 2007)
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The ECCO Effort
• ECCO was established in 1998 as part of the World Ocean Circulation Experiment (WOCE) with the goal to combine a general circulation model with diverse observations to produce a quantitative description of the time-varying global ocean state.
• The dynamically consistent assimilation procedure uses the adjoint method to adjust the temperature and salinity initial conditions and the atmospheric state as well as model parameters to bring the model into consistency with the assimilated data and the prior model-data error weights.
• Various different solutions exist over various time spans. including the German contribution to ECCO, called GECCO.
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• 50+ year long synthesis for global ocean.
• MITgcm dynamic ocean/sea ice model
• Global configuration, 1/3o-1o, 50 layers
• Data sets assimilated include:
• EN3 data base of temperature and salinity profiles from MBT,XBT,CTD and Argo,
• Reynolds and AMSR/E SST,
• AVISO altimetric SLA,
• CNES CLS11 - GOCO02s MDT,
• WHO9 climatological T and S.
GECCO2 Ocean Synthesis
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Observational Coverage
Example: Temperature Profiles
Example: Salinity Profiles
200519951965
1965 1995 2005
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Adjoint Optimization
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Iteration 1-91948 2009
Iteration 9-181948 201
1
1948 1952 1956
2008 2011
SSS from WOA09 added
Iteration 18-23: 5 year windows with one year overlap
(GECCO2)
20132008Annual updates for decadal predictions:
• Simulation 1948-2011 forced with NCEP RA1 6h atmospheric state
• Calculate the model data misfit formulated as a cost function.
• The adjoint calculates gradients of cost function wrs control parameters.
• Control parameters are change iteratively to reduce misfit.
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GECCO2 and Church et al. 2004 SSH Trends
1955 to 2003 sea level trend
total
thermosteric
1950 to 2010 sea level trend
total
thermosteric
mm/yr
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Hierarchy of GECCO Forward and Adjoint Runs
Global 1° x 1/3°
Atl. 32 km Atl. 16 km Atl. 8 km Atl. 4 km
All simulations: Ocean-sea ice coupled simulations (50 vert. levels); Initial T/S conditions from WOA2005; NCEP RA1 6-hourly atmospheric state + bulk formula.
Atl. simulations only: Open bound. cond. at 33°S and Bering St. from global model with imposed barotropic net throughflow of -0.9 Sv; SSS relaxation to WOA2005; SST to monthly ERSST V3.SSH std (cm)
SSH std (cm)
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Mean sea surface height
Farrell et al., 2012 (Knudsen and Andersen 2013) ATL12
Koldunov et. al., 2014, JGR, under revision.
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Coastal sea level
Koldunov et. al., 2014. subm. to JGR
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Model Configurations targeting the Arctic
Model Period Horizontal
resolution
Boundary
conditions
AS forcing Vertical
levels
ATL12 1948-2009 ~8 km GECCO NCEP
reanalysis
50
POL06 2000-2009 ~15 km ATL06 NCEP
reanalysis
50
ARCTIC40 1980-2009 ~40 km Closed NCEP
reanalysis
25
ATL12 POL06 ARCTIC40
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Koldunov et al., 2013
Monthly means of mean September sea ice area sensitivities per grid cell to SAT
Arctic adjoint sensitivity studies.
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Arctic Ocean Data Assimilation Configuration
Medium resolution coupled sea ice-ocean configuration:
-Horizontal resolution ~ 15 km
-Ocean boundary conditions are from the larger Atlantic Ocean setup (ATL06)
-Atmospheric forcing – NCEP Reanalysis
-There are 50 vertical levels
Koldunov et al., 2014, in preparation
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Data Constraints
- Monthly PHC climatology (T, S).
- Mean Dynamic Topography from DTU (GOCO03s).
- Monthly SST from AMSRE.
- Altimetry: TOPEX/Poseidon, ERS-1,2, Envisat.
- Combined EN3 (include NABOS, CABOS, NPEO, Beaufort gyre experiment) and NISE hydrographic data.
- Sea ice concentration:- EUMETSAT OSI-SAF Version 2 (constant uncertainties) - ESA CCI Sea-Ice-ECV project (variable uncertainties).
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Control Paramters
- Initial temperature and salinity
- Atmospheric state: - Surface (2-m) air temperature- Surface (2m) specific humidity- Surface (10-m) zonal and meridional wind velocity- Precipitation- Downward shortwave and longwave radiation
.......
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Misfit changes for different variables wrs initial run
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Sea ice concentration 2005
Satellite 0 iteration Last iteration
March
September
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Sea ice concentration 2007
March
September
Satellite 0 iteration Last iteration
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Model-observations difference for 2007
Satellite 0 iteration Last iteration
March
September
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Spatial distribution of air temperature corrections
June 2005
Short Wave Radiation
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Corrections of wind (2005)
May 2005
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Seasonal cycle of sea ice in 2005
Sea Ice Area
Sea Ice Extent
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Sea ice drift, October 2005
U component V component
Ice velocity
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Sea ice thickness. October-November 2005
ICESat thickness 0 iteration Last iteration
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Sea ice thickness, November 2005
Before assimilation After assimilation
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Summary
• First pilot attempt of a dynamic Arctic ocean/sea ice synthesis.
• First results are promissing and show that sea ice parameters can be used to constrain a coupled ocean/sea ice model.
• Results indicate that rectifying effect seem to happen in that sea ice thickness gets adjusted along with area and concentration.
• Results still need to be evaluated and compared with independent data before mechanisms can be studied.
• More data, esp. thickness, SSH and in situ data sets are required!!