WWOSC, August 16-21, 2014, Montreal Recent Advances in the Mercator-Ocean reanalysis system : Assimilation of sea-ice concentration in the Arctic sea C.-E

WWOSC, August 16-21, 2014, Montreal

Recent Advances in the Mercator-Ocean reanalysis system : Assimilation of sea-ice

concentration in the Arctic seaC.-E. Testut,

G. Garric, L. Parent, C.Bricaud (Mercator-Ocean, Toulouse, France)G. Smith (CMC, Environnement Canada), Y. Lu (BIO, DFO Canada)

2WWOSC, August 16-21, 2014, Montreal

Assimilation of Sea Ice Concentration in Glorys2v3Main features of the Glorys2v3 simulation

Overview of the results in the Arctic sea

Recent developments in the Sea Ice Analysis- Restart strategies for the sea ice model update

- Use of multivariate analysis

- Use of Gaussian Anamorphosis Approach

Conclusions and Future developments

Outline


GLORYS: GLobal Ocean ReanalYses and Simulations

- French Reanalysis project, supported by GMMC (Mercator, Coriolis). PI: B. Barnier- main partners: Drakkar consortium, CORIOLIS, MERCATOR- project started at national level in 2008 + cooperation with EU funded FP7 MyOcean project

MOTIVATION :The need for a realistic description of the ocean state and variability over the recent decades, at the global scale, and at the scale of the ocean basins and regional seas.

OBJECTIVES :- Produce an eddy permitting global ocean/sea-ice reanalysis spanning the “altimetric + ARGO" era 1992-today- To iterate / produce different reanalysis along the 1992-today time period- Start to design the ERA-Interim reanalysis scenario : 1979-today- Promote the use of reanalysis products in the climate community

Assimilation of Sea Ice Concentration : Main features of the GLORYS2v3 simulation


Assimilation

- Analysis based on a 2D local multivariate SEEK filter

- Weakly-coupled DA system using 2 separate analyses :

- Ocean Analysis (SLA, InSitu Data from CORA3.2, SST) , IAU on (h,T,S,U,V)

- Ice Analysis (SIC), IAU on (SIC)

- SIC Error: 1% open ocean, linear from 25% to 5% for SIC values between 0.01 and 1

- Forecast error covariances are built from a prior ensemble of Sea Ice Concentration anomalies => Fixed basis background error

- Temperature and salinity bias correction using Argo (3DVar method)

Assimilation of Sea Ice Concentration : Main features of the GLORYS2v3 simulation

Model - Nemo 3.1, LIM2-EVP- Global ¼, 75 levels- 1992-2012

Sea Ice Concentration from CERSAT (IFREMER)


SAM2 Data Assimilation System : Pf: Fixed Basis Background error covariances Representation by a prior ensemble of anomalies

We use these anomalies to compute Pf in the analysis

Model trajectory

2001 20022003

20042005

2006

We generate a pseudo-ensemble from a forced simulation

Model trajectory

anomaly

Running mean of the model trajectory

Temporal window

Analysis date 2Analysis date 1


Cer

sat

OR

CA

025

/LIM

2no

EV

P(80N,150W) (80N,0W)

Concentration (SIC) correlation fields from 1 single concentration observation (~280 modes, February)

SAM2 Data Assimilation System : Background Error specification : Correlation fields


Assimilation of Sea Ice Concentration : Impact in Arctic region with GLORYS2V3 System

Sea Ice Concentration on 15th September 1992 (assimilation start in December 1991)

Sea Ice Concentration Misfits to Observation (CERSAT) on 15th September 1992

Sea Ice Concentration RMS misfits

G2V3-NOASSIM/ICE

G2V3-ASSIM/ICE

Jan 1992 Sep 1992 May 1993

Jan 1992 Sep 1992 May 1993

CERSAT GLORYS2V3-NOASSIM/ICEGlobal ¼°, 75 levels

GLORYS2V3-ASSIM/ICEGlobal ¼°, 75 levels


Arctic

September 2001 March 2001

G2V3 - CERSAT

G2V1 - CERSAT



Extreme Events

CERSAT data GLORYS2V3

Sept 1996

Sept 2007

Good behaviour of GLORYS2V3 during extreme events



SIC >85%

Correlation: 0.4 0.9

Correlation: 0.8


Sea Ice extent anomaly

2001

2001



Monthly Volume

Anomalies of Monthly Volume


Recent developments

in the Sea Ice Analysis


Different Restart Strategies: IAU on SIC + …- … nothing using an implicit concept of thickness conservation

SICi, hi = 0, hi = cte <=> Vi = hi . SICi

Restart Strategies for the sea ice model update

hi

SICi

+

SICi

=

Analysis of Sea Ice Concentration (y2006m10d15, 3 days cycle)

InnovationCERSAT- ORCA025/LIM2noEVP

Model updateusing IAU method

Residual

Work in Progress


Different Restart Strategies: IAU on SIC + …- … nothing using an implicit concept of thickness conservation

SICi, hi = 0, hi = cte <=> Vi = hi . SICi

- correction on thickness using a reference thickness h*=1m (Tietsche et al.)

SICi , Vi = h* . SICi <=> hi = ( h* - hi ) .SICi / ( SICi +SICi )

hi

SICi

+

SICi

=

hi

SICi

+

SICi

=h*

G2V3 TEST(G2V3 with h) TEST – G2V3

Restart Strategies for the sea ice model update

Work in Progress

Thickness in March 1994


Development of a multivariate Sea Ice Analysis

SIC Model update Thickness Model update

Test_G2V4 Sea Ice Model update (y2011m11d11)

FREE

G2V4

SIC RMS Misfit

Work in ProgressMonovariate state vector for G2V3 sea ice analysis[SIC] with (SIC) observations

Multivariate state vector for G2V4 sea ice analysis[SST,SIC,Thickness] with (SST,SIC) observationsSST restricted to open ocean close to the marginal zone


Development of a multivariate Sea Ice Analysis

FREE

G2V4

SIC RMS Misfit

Work in ProgressMonovariate state vector for G2V3 sea ice analysis[SIC] with (SIC) observations

Multivariate state vector for G2V4 sea ice analysis[SST,SIC,Thickness] with (SST,SIC) observationsSST restricted to open ocean close to the marginal zone

G2V3 TEST_G2V4 TEST_G2V4 – G2V3

Thickness (y2011m11d11)


The Gaussian anamorphosis method consists to define an appropriate transformation T of the space leading to gaussian distribution of the variables.

1T11T RAHAHRAHIAK nnnn

f0xHyd

Kdxx fa

innovation ][xH[y]d f0TT

Forecast error

iii xxAn

Kalman gain

iii T[x]T[x]An TTf AAAA

1P nnn

Error sub-space

Observational update ]]x[[Kd]]x[[xx f1f1fa TTTT

Analysis step in the physical space in the anamorphosed space

The Gaussian anamorphosis transformation T could be estimated from the ensemble forecast {xf}i

SAM2 Parameterization in progress : The Gaussian Anamorphosis approach


IncrementInnovation

Physical space Anamorphosed space

The Gaussian Anamorphosis approach

Physical space

Local transformation of the sea ice concentration

(SIC) pdf

SIC SIC percentiles Anamorphosed SIC


Conclusions and future developments

Assimilation of Sea Ice Concentration only in G2V3- Good representation of the sea ice extent and the sea ice concentration- Lack in the time evolution of the sea ice thickness

Work in progress on the Sea Ice analysis- Identify of a more efficient restart strategy for the sea ice thickness- Use of multivariate analysis where the state vector is extent to [SST,SIC,thickness]- Use of Gaussian Anamorphosis Approach to improve the sea ice thickness update=> obtain a more robust Sea Ice analysis for the next reanalysis and for the real time

Next steps of the development- Coupling between SAM2 and NEMO3.6/LIM3- Use of Arctic-Northern Atlantic Configurations (CREG4 and

CREG12 with NEMO3.6) which are in development in partnership with Canada (Env. Can. and Fisheries and Oceans)

- CREG4 : research application, dev. of the sea ice analysis and/or an Ensemble approach

- CREG12 : benchmark reanalysis over the last 10 years in order to prepare the Mercator global 1/12°reanalysis

Source: G. Smith, Env. Canada, Montréal


Thank You!!

Documents

WWOSC, August 16-21, 2014, Montreal Recent Advances in the Mercator-Ocean reanalysis system : Assimilation of sea-ice concentration in the Arctic sea C.-E