L. Parent , N. Ferry, B. Barnier, G. Garric, C. Bricaud, C-E Testut, - - PowerPoint PPT Presentation

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L. Parent , N. Ferry, B. Barnier, G. Garric, C. Bricaud, C-E Testut, - - PowerPoint PPT Presentation

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GLOBAL Eddy-Permitting Ocean Reanalyses and Simulations of the period 1992 to Present L. Parent , N. Ferry, B. Barnier, G. Garric, C. Bricaud, C-E Testut, O. Le Galloudec, J-M Lellouche, E. Greiner, M. Drevillon, E. Rmy J-M Molines, Mercator

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GLOBAL Eddy-Permitting Ocean Reanalyses and Simulations of the period 1992 to Present

  • L. Parent, N. Ferry, B. Barnier, G. Garric, C. Bricaud, C-E Testut, O. Le

Galloudec, J-M Lellouche, E. Greiner, M. Drevillon, E. Rémy J-M Molines,

Mercator Océan, LGGE-CNRS, Coriolis, CLS

Sixth WMO Symposium on Data Assimilation NOAA Center for Weather and Climate Prediction / Univ of Maryland, College Park, USA, 7-11 October 2013

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  • 1. Introduction :

The GLORYS project overview and the European context

  • 2. GLORYS2 : an eddy permitting (1/4 ) global ocean

reanalyses of the « altimetric era » System overview and Performances

  • 3. Conclusions & Perspectives

Outline

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  • 1. Introduction
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GLORYS project: National level

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 1992-2009

  • 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
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GLORYS1 GLORYS2 Stream1: 2002-2008 “ARGO” Stream2: 1993-2010 “altimetry”

T/S prof.: < 1000 (+ sea mammals) SST:1 x1 SLA: 2~3 satellites SIC: CERSAT data 25km (12km avail) T/S prof.: 2000~4500 SST: 0,5 x0,5  0,1 x0,1 SLA: 3~4 satellites

Number of Obs. / 7 days

1979 1992 2002 NOW

GLORYS3 Stream3: 1979-2012 “ERAinterim”

T/S prof.: < 800 SST:1 x1 SLA: none SIC: NSIDC data

GLORYS: different Streams

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MyOcean project :

www.myocean.eu.org MyOcean1: 2009-2012, MyOcean2: 2012-2014

Global ocean reanalyses at EU level

Basic ingredients :

  • NEMO Ocean source code, tuned for reanalyses, provided by CNRS
  • ERA Interim forcing + some corrections
  • Reprocessed historical observations provided by Thematic Assembly Center
  • Different data assimilation methods

Ocean reanalyses : Ocean simulations constrained by reprocessed obs.  CMCC, Mercator, U. Reading Ocean free simulation:  CNRS Ocean state estimation based on observations only  CLS

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  • 2. GLORYS2: System Overview and Performances
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Model: DRAKKAR ORCA025 configuration

NEMO OGCM + LIM Sea-Ice model :

Resolution:

  • Global 1/4
  • 75 vertical levels from 1 m at the surface to 200 m at the bottom

Atmospheric forcing:

  • Bulk CORE Formulation (Large&Yeager, 2004)
  • ERA-Interim reanalysis products:

3 hourly for turbulent fluxes Daily for radiation (analytical diurnal cycle for solar) In house corrections :

  • f the radiation based on GEWEX satellites fluxes products
  • f the precipitations based on GPCPv2 observations
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DATA ASSIMILATION SYSTEM: SAM2v1 used in real-time global forecasting systems

SAM2V1 assimilation platform :

  • Reduced order extended Kalman filter family (SEEK kernel)
  • This approach is similar to the Ensemble optimal interpolation (EnOI) developed

by Oke et al., (2008) which is an approximation to the EnKF that uses a stationary ensemble to define background error covariances

  • Innovation is calculated at the First Guess at Appropriate Time (FGAT) approx.
  • Analysis is performed at the middle of the 7-day assimilation cycle

3D-VAR Bias correction : to correct large-scale temperature and salinity biases Incremental Analysis Updates (IAU) :

inserting increments over all model time steps  smooth trajectory, more costly

GLORYS: DATA ASSIMILATION SCHEME

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Delayed time observations for data assimilation

Along track DT SLA (SSLATO/DUACS) : Jason1, Jason2, Envisat, GFO, ERS1, ERS2, Topex/Poseidon + use of an adjusted CNES-CLS09 Mean Dynamic Topography (GOCE obs)

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Delayed time observations for data assimilation

Reynolds AVHRR-only 0.25° SST ftp://eclipse.ncdc.noaa.gov/pub/OI-daily-v2/NetCDF/ assimilated once at the date of the analysis (4th day, 0h)

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Delayed time observations for data assimilation

  • in situ temperature & salinity profiles : CORA3.3 data base

Argo network + Xbts,CTDs, etc… + sea mammal (elephant seals) database (Roquet et al., 2011)

Courtesy from the Sea Mammal Research Unit (SMRU)

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Delayed time observations for data assimilation

  • Sea Ice concentration : IFREMER/CERSAT products (Ezraty et al., 2007).

Assimilated once at the day of the analysis (4th day, 0h), same as SST

March 17, 2003 from QuikSCAT sensor January 5, 2009 from ASCAT sensor

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Quality Control :

Obs. CLIM FCST INNOV

Quality Control on in situ data performed in GLORYS reanalysis : in order to minimise the risk of erroneous observed profiles being assimilated in the model “suspicious” Temperature profiles in 2009

Distrib INNOV

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Some results: assimilated data Altimetry, In-situ

Rms misfit Misfit average Altimetry In-situ Temp Weak warm bias

ARGO is getting in

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Some results: assimilated data SLA

Domain averaged sea level

Obs, gridded data GLORYS2V3 Reference sim

  • Good agreement with SLA CCI gridded data
  • Ref sim: imbalance in the E-P forcing

Imbalance E-P

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Some results: Tide gauges

1993-2011 time period

Good agreement, except along some coasts (no tidal model)

Correlation

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Some results: surface velocity, 1993-2011 mean

NOAA AOML GLORYS2V3 Zonal surface currents

Good agreement but there is a general underestimation: artefact of AOML surface current ? Grodsky et al. 2011, one explanation : undrogued drifters

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Some results: Mean Kinetic Energy,1000m 2002-2009 mean

ANDRO Argo drift data base GLORYS2V3 reference simulation

  • Good agreement with Argo all along the boundary currents and the ACC
  • Deep ocean currents too strong in the equatorial band (+/-10 )
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Some results: Meridional Overturning Circulation 2004-2010 time period

Rapid data GLORYS2V3 Reference sim

  • The Atlantic MOC is stable throughout the reanalysis period
  • 0.8 correlation, underestimation of 3 Sv
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Some results: Meridional Heat Transport 1993-2011 time period

Consistent with Ganachaud and Wunsch (2000), Trenberth and Caron (2001) estimates

Atlantic estimates GLORYS2V3 Reference sim

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Some results: Sea Ice Sea Ice concentration is assimilated

1993-2011 mean Antarctic CERSAT data GLORYS2V3 March Sept

In the Antarctic, Data Assimilation has a positive impact during summer and winter

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Some results: Sea Ice Sea Ice concentration is assimilated

Extreme Events CERSAT data GLORYS2V3 Sept 1996 Sept 2007

Good behaviour of GLORYS2V3 during extreme events

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Some results: Sea Ice Sea Ice concentration is assimilated

Arctic Antarctic CERSAT data GLORYS2V3 Reference sim Sea Ice extent anomaly Sea Ice volume anomaly

not realistic

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  • 3. Conclusions & Perspectives
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Conclusions: GLORYS2 (1992-2011)

  • Ability to produce global meso-scale reanalysis simulations: Unique

collaboration between operational centers (MERCATOR, CORIOLIS) and research Labs (LGGE, …)

  • Stream after stream, there is an improving quality
  • Regional reanalysis are underway: Mediterranean sea, European coasts
  • Difficulties to control deep ocean, unrealistic trends T/S
  • Difficulties to control Sea Ice Volume in the Arctic
  • 40 users, various applications
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Perspectives: GLORYS2-3 . GLORYS2VX (1992-2012) :

  • Ongoing effort to improve products and services
  • MyOcean2 project: production of reanalyses is still a priority

. GLORYS3 (1979-2012) :

  • ERAInterim years
  • First stream in 2014

. Data Assimilation :

  • Data assimilation of surface currents
  • Gaussian Anamorphosis transformation: to improve DA of sea ice conc.
  • Multiple scale analysis

(see poster H-p38, Testut)

  • Use of 4D (+ time) error modes  smoother approach
  • Ensemble approach: open ….