Observing system experiments with ecwmf operational ocean analysis ora s3
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Observing System experiments with ECWMF operational ocean analysis (ORA-S3). The new ECMWF operational ocean analysis system Historical reanalysis and real time The ORA-S3 analysis system Impacts of data assimilation (mean/variability/forecast skill) Results from OSEs

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Observing System experiments with ECWMF operational ocean analysis (ORA-S3)

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Observing system experiments with ecwmf operational ocean analysis ora s3

Observing System experiments with ECWMF operational ocean analysis (ORA-S3)

  • The new ECMWF operational ocean analysis system

    • Historical reanalysis and real time

    • The ORA-S3 analysis system

    • Impacts of data assimilation (mean/variability/forecast skill)

  • Results from OSEs

    - Impact on the ocean state

    - Impact on forecasts

    - Impact on climate variability

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Observing system experiments with ecwmf operational ocean analysis ora s3

ECMWF:

Weather and Climate Dynamical Forecasts

10-Day

Medium-Range

Forecasts

Seasonal

Forecasts

Monthly

Forecasts

Atmospheric model

Atmospheric model

Wave model

Wave model

Ocean model

Real Time Ocean Analysis ~Real time

Delayed Ocean Analysis ~12 days

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Observing system experiments with ecwmf operational ocean analysis ora s3

Real time Probabilistic Coupled Forecast

time

Ocean reanalysis

Consistency between historical and real-time initial initial conditions is required

Quality of reanalysis affects the climatological PDF

Main Objective: to provide ocean Initial conditions for coupled forecasts

Coupled Hindcasts, needed to estimate climatological PDF, require a historical ocean reanalysis

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Ora s3 ocean re analysis system

ORA-S3 Ocean Re-Analysis System

  • Ocean model: HOPE (~1x1, equatorial refinement)

  • Assimilation Method OI (3D OI).

  • ERA-40 fluxes to initialize ocean.

  • Retrospective Ocean Reanalysis back to 1959.

  • Assimilation of T

  • Assimilation of salinity data.

  • Assimilation of altimeter-derived sea level anomalies.

  • Multivariate on-line Bias Correction .

  • Balanced relationships (T-S, ρ-U)

  • 10 days assimilation windows, increment spread in time

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Observing system experiments with ecwmf operational ocean analysis ora s3

Observations used in the S3 ocean analysis

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Observation monitoring

Observation Monitoring

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Altimeter product

Altimeter product

  • Ingredients:

  • Assimilation of detrendend sea level, taking care of removing the spatial average from the altimeter data:

Observed SLA from T/P+ERS+GFO+Jason+ENVISAT

Respect to 7 year mean of measurements.

Weekly anomalies, twice a week.

Global gridded maps

A Mean Sea Level

Choice: MSL from an analysis where no altimeter has been assimilated

There are MSL products derived from GRACE (Rio4/5 from CLS, NASA, …) but the choice of the reference global mean is not trivial and the system can be quite sensitive to this choice. Better assimilation methods are needed to make optimal use of the Gravity product

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Sequential assimilation of data streams

T/S

conserved

T/S

conserved

OI

CH96

T/S

Changed

OI

Sequential Assimilation of data streams

  • Assimilation of Sea level anomalies

  • Assimilation of Subsurface temperature

  • Assimilation of Salinity

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Observing system experiments with ecwmf operational ocean analysis ora s3

Bias evolution vector-equation

¢

=

+

f

f

b

b

b

;

-

k

k

k

1

b

k

prescribed (constant/seasonal)

Some notation

(Temperature,Salinity,Velocity)

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

Balmaseda et al 2007, QJRMS


Effect of the pressure gradient correction

Mean Assimilation Temperature Increment

Without bias correction

Mean Assimilation Temperature Increment

With bias correction

Effect of the pressure-gradient correction

  • The information from the temperature assimilation increment (above left) can be used to estimate a correction to the pressure gradient.

  • The equivalent correction to the wind stress from the bias term appears below right (~5-10%). Units are 10^-2 N/m2.

  • By applying the correction in the pressure gradient the temperature increment is reduced (above right)

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


The assimilation corrects the ocean mean state

Analysis minus Observations

Western Pacific

Equatorial Indian

DATA ASSIM

NO DATA ASSIM

The Assimilation corrects the ocean mean state

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Observing system experiments with ecwmf operational ocean analysis ora s3

…improves the interannual varaibility

No Data Assimilation

Assimilation:T+S

Assimilation:T+S+Alt

Correlation with OSCAR currents

Monthly means, period: 1993-2005

Seasonal cycle removed

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


And the skill of seasonal forecasts of sst

And the skill of Seasonal Forecasts of SST

Data assimilation improves the seasonal forecast of SST

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Observing system experiments with ecwmf operational ocean analysis ora s31

Observing System experiments with ECWMF operational ocean analysis (ORA-S3)

  • The new ECMWF operational ocean analysis system

    • Historical reanalysis and real time

    • The ORA-S3 analysis system

    • Impacts of data assimilation (mean/variability/forecast skill)

  • Results from OSEs

    - Impact on the ocean state

    - Impact on forecasts

    - Impact on climate variability

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Observing system experiments

-

=

ARGO effect (when ALTI)

ALL

NO_ARGO

=

-

ALTI effect (when ARGO)

ALL

NO_ALTI

-

=

ARGO effect (when no ALTI)

NO_ALTI

NEITHER

-

=

NO_ARGO

NEITHER

ALTI effect (when no ARGO)

Observing System Experiments

  • Period 2001-2006:

    ALL NO_ARGO

    NEITHER NO_ALTI

    (no argo/no alti)

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Oses effect on salinity

Effect of ALTI

Effect of ARGO (when alti is present)

Effect of ALTI (when ARGO is not present)

Effect of ARGO (when alti is not present)

OSES: Effect on Salinity

In the Tropical Atlantic/Indian, altimeter data helps ARGO

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Oses effect on sea level

Effect of ALTI

Effect of ARGO (when alti is present)

Effect of ALTI (when ARGO is not present)

Effect of ARGO (when alti is not present)

OSEs:Effect on Sea Level

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Oses effect on t300

OSEs:Effect on T300

Effect of ARGO when Alti is present

Effect of ARGO when Alti is NOT present

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Fit to the observations rms error temperature

Eastern Equatorial Pacific

North Sub Tropical Atlantic

South Pacific

Fit to the observations (rms error)Temperature

ALL NO_ALTI NO_ARGO NEITHER

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Fit to the observations rms error salinity

Fit to the observations (rms error)Salinity

Equatorial Indian

Equatorial Atlantic

ALL NO_ALTI NO_ARGO NEITHER

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Impact on seasonal forecast skill

Impact on Seasonal Forecast skill

  • Moorings: only the effect of anomalies is measured, since the effect of the mean state is included indirectly in the altimeter assimilation.

  • Observing systems are complementary

    • Altimeter has larger effect on Atlantic and Eastern Pacific

    • Argo has larger effect on Indian Ocean and Western Pacific

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


1993 2007

1993-2007

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Impact of observing system in the climate variability

Impact of Observing System in the climate variability

ORA-S3 = Ocean reanalysis using “all” observing system

ORA-nobs= Ocean model forced by surface fluxes

NOARGO = No Argo data 2001-2006

NOSOLO = No SOLO/FSI floats 2001-2006

  • Heat content

  • Attribution of Sea Level Change

  • Salinity

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Ocean heat content at 300 700 3000 m

Ocean Heat Content at 300/700/3000 m

  • Upper 300m, there is a large degree of coherence in ORAS3, ORA-nobs, Lev. The largest signals are in ORAS3 (SYNERGY?)

  • Deeper Ocean: In ORA-nobs the decadal signals do not penetrate deep enough?

  • OSEs indicate that 2002-2003 upper ocean cooling is robust

  • Cooling after 2003 in ORAS3 is a consequence of ARGO in the Southern Oceans.The ARGO SOLO/FSI are not responsible for the post-2004 cooling

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Spatial distribution of trends in heat content

Spatial distribution of trends in heat content

1982-2006 mean minus 1959-1981 mean

SST (deg C)

Taux (x 0.01N/m2)

T300 (deg C)

Tauy (x 0.01N/m2)

How reliable are the trends in ERA40 winds?

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Comparison with ocean observations

Comparison with ocean observations

IPCC-AR4 (LEVITUS)

ORA-S3

CI=0.05 deg/decade

  • Similarities

  • Equatorial cooling

  • Warmer subtropics

  • Cooling at ~60N

  • Comments

  • Trends in ERA40 winds seem robust

  • Stronger features in ORA-S3, more structure

  • Circulation changes as well as mixed layer changes

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Comparison with ocean observations1

Comparison with ocean observations

Atlantic and Indian

IPCC-AR4 (LEVITUS)

ORA-S3

CI=0.05 deg/decade

Largest warming is in the Atlantic

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Attribution of sea level changes

Attribution of Sea level changes

Trends

1961-2003:

SL (IPCC) =1.8 mm/yr

SH (IPCC) =0.5 mm/yr

SH ORA-S3 (1960-2003)=0.9mm/yr

SH ORA-nobs “ =0.5mm/yr

ORAS3 gets closer…..

1993-2003:

SL (IPCC) =3.1 mm/yr

SH (IPCC) =1.6 mm/yr

SH ORA-S3 (1993-2003)=2.1mm/yr

SH ORA-nobs “ =1.1 mm/yr

consistent with others

2002 onwards??

Effect of ARGO?

Altimeter problems?

Sea level changes= Mass + Volume (SH)

Steric Height (SH) can be estimated from ORAS3

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Attribution of sea level change oses

Attribution of Sea Level Change (OSES)

  • Argo is responsible for the decay in SH in ORAS3

  • SOLO/FSI have little impact

  • But even without Argo, the trend in SH stabilizes after 2002

  • While the SL from altimeter keeps increasing…If we believe the altimeter

  • This would imply a mass increase of 2mm/yr (twice as large as the latest IPCC)

  • Worrying: either the estimates are wrong, or a lot of continental ice is melting

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Impact of data assimilation in the moc

ORAS3

ORA-nobs

Bryden05

Cunningham07

ORAS3

ORA-nobs

Impact of data assimilation in the MOC

  • Assimilation improves the estimation of the MOC

  • Downward trend ~4% decade in ORAS3, ~2% decade in ORA-nobs

RMS fit to observations in the NATL

Balmaseda et al, GRL 2007

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Salinity in ora s3

Salinity in ORA-S3

Large spin up/down in the first 2-3 years.

Large effect of ARGO

Large uncertainty in fresh water fluxes

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


Summary

Summary

  • State estimation:

    • Both ARGO temperature and salinity have a large information content.

    • Argo is instrumental in correcting the salinity of the ORA-S3 analysis

    • The ARGO data is best used in combination with the altimeter information.

  • Seasonal forecast skill:

    • Argo/Altimeter/Moorings contribute to the improvement of the skill of seasonal forecast of SST.

    • Their contribution is often complementary: Argo has larger effect in the Western Pacific and Indian Ocean. Altimeter’s impact is larger in Atlantic and Eastern Pacific

  • Climate variability:

    • The profound impact of Argo on the analysis should be taken into account when analysing the climate variability from ORA-S3.

    • OSEs indicate a deceleration in the ocean warming and global SH after 2003.

    • The variability in the ORA-S3 salinity may not be reliable

  • Other comments:

    • A new observing system SHOULD NEVER HAVE a negative impact.

    • In the Seasonal Forecast, the inability to improve predictions in the Equatorial Atlantic is symptomatic of errors in the model/analysis.

    • In future reanalysis, the information provided by Argo could be used in retrospect, for instance via bias-correction algorithms (or improved models).

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


What if the observations have negative impact

What if the Observations have negative impact?

  • In the Analysis?

    • Model error not taken into account

    • Wrong Specification of Background error

    • Wrong Specification of Observation error

  • In the forecast?

    • The analysis error has not been reduced

    • The analysis error has been reduced in total, but the error has increased in the directions of larger error growth.

    • There is model error

Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007


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