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.

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)

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

Observational Coverage

Example: Temperature Profiles

Example: Salinity Profiles

200519951965

1965 1995 2005

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.

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

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)

Mean sea surface height

Farrell et al., 2012 (Knudsen and Andersen 2013) ATL12

Koldunov et. al., 2014, JGR, under revision.

Coastal sea level

Koldunov et. al., 2014. subm. to JGR

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

Koldunov et al., 2013

Monthly means of mean September sea ice area sensitivities per grid cell to SAT

Arctic adjoint sensitivity studies.

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

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

.......

Misfit changes for different variables wrs initial run

Sea ice concentration 2005

Satellite 0 iteration Last iteration

March

September

Sea ice concentration 2007

March

September

Satellite 0 iteration Last iteration

Model-observations difference for 2007

Satellite 0 iteration Last iteration

March

September

Spatial distribution of air temperature corrections

June 2005

Short Wave Radiation

Corrections of wind (2005)

May 2005

Seasonal cycle of sea ice in 2005

Sea Ice Area

Sea Ice Extent

Sea ice drift, October 2005

U component V component

Ice velocity

Sea ice thickness. October-November 2005

ICESat thickness 0 iteration Last iteration

Sea ice thickness, November 2005

Before assimilation After assimilation

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!!