observing system experiments with ecwmf operational ocean analysis (ora-s3)

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

Slide 1

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


Slide 2

Delayed Ocean Analysis ~12 days

Real Time Ocean Analysis ~Real time

ECMWF:

Weather and Climate Dynamical Forecasts

ECMWF:

Weather and Climate Dynamical Forecasts

10-Day Medium-Range

Forecasts

10-Day Medium-Range

Forecasts

Seasonal Forecasts

Seasonal Forecasts

Monthly Forecasts

Monthly Forecasts

Atmospheric model

Wave model

Ocean model

Atmospheric model

Wave model


Slide 3

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

Real time Probabilistic Coupled

Forecasttime

Ocean reanalysis

Quality of reanalysis affects the climatological

PDF

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

Main Objective: to provide ocean Initial conditions for coupled forecasts


Slide 5

•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

ORA-S3 Ocean Re-Analysis System


Slide 6

Observations used in the S3 ocean analysis


Slide 7

Observation Monitoring

60°S 60°S

30°S30°S

0° 0°

30°N30°N

60°N 60°N

60°E

60°E

120°E

120°E

180°

180°

120°W

120°W

60°W

60°W

0°

0°

Moorings: 909 profiles

Argo floats: 2520 profiles

XBT probes: 394 profiles

Fully Rejected: 795 profiles

Fully Accepted: 2280 profiles

Partially Accepted: 748 profiles

(at least one per profile)

SuperObs: 1404 profiles

10 days period centered on 20070529

S3 ocean analysis

monitoring (temp)

In situ observation

60°S 60°S

30°S30°S

0° 0°

30°N30°N

60°N 60°N

60°E

60°E

120°E

120°E

180°

180°

120°W

120°W

60°W

60°W

0°

0°

60°S 60°S

30°S30°S

0° 0°

30°N30°N

60°N 60°N

60°E

60°E

120°E

120°E

180°

180°

120°W

120°W

60°W

60°W

0°

0°


Slide 8

Altimeter product1. Ingredients:

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

alt' Observed SLA from T/P+ERS+GFO+Jason+ENVISATRespect to 7 year mean of measurements.Weekly anomalies, twice a week.Global gridded maps

A Mean Sea LevelChoice: MSL from an analysis where no altimeter has been assimilated

''

'''' average spatial ~

; ~

'

altalt

altaltaltalt

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


Slide 9

Sequential Assimilation of data streams

T/S

conserved

alt T/S

Changedinsituinsitu ST ,

aa ST ,

T/S

conservedinsituT

', aa ST

1. Assimilation of Sea level anomalies

2. Assimilation of Subsurface temperature

3. Assimilation of Salinity


Slide 10

Bias evolution vector-equation

Some notation (Temperature,Salinity,Velocity)

T

UTS

TT

TUST

T

T

fkkkk

akk

fk

fk

LLKLbbbb

USTx

xHydbbxx

,, ; ,,

; ,,

~ ~

; ~1

k

U

S

T

a

kU

S

T

kU

S

T

a

kU

S

T

d

L

L

K

b

b

b

b

b

b

b

b

b~

1

prescribed (constant/seasonal)

k

fkk

fk

b

bbb ; 1

Balmaseda et al 2007, QJRMS


Slide 11

Effect of the pressure-gradient correction

50OE 100OE 150OE 160OW 110OW 60OW 10OW

Longitude

500

400

300

200

100

0

Depth

(m

etr

es)

500

400

300

200

100

0Plot resolution is 1.4063 in x and 10 in yZonal section at 0.00 deg NICODE=178 contoured every 0.0002 XXXHOPE gcm:: 0001

Interpolated in y 0 ( 31 day mean)

difference from20020101 ( 31 day mean)

-0.0008

-0.0006

-0.0

00

4

0.0002

0.0012

-0.0024

-0.002

-0.0016

-0.0012

-0.0008

-0.0004

0.0002

0.0006

0.001

0.0014

0.0018

0.0022

MAGICS 6.9.1 hyrokkin - neh Tue Jul 25 19:19:38 2006

Mean Assimilation Temperature Increment

Without bias correction

50OE 100OE 150OE 160OW 110OW 60OW 10OW

Longitude

500

400

300

200

100

0

Depth

(m

etr

es)

500

400

300

200

100

0Plot resolution is 1.4063 in x and 10 in yZonal section at 0.00 deg NICODE=178 contoured every 0.0002 XXXHOPE gcm:: 0001

Interpolated in y

20020101 ( 31 day mean)

-0.0024

-0.002

-0.0016

-0.0012

-0.0008

-0.0004

0.0002

0.0006

0.001

0.0014

0.0018

0.0022

MAGICS 6.9.1 hyrokkin - neh Tue Jul 25 19:19:37 2006

Mean Assimilation Temperature Increment

With bias correction

50OE 100 OE 150 OE 160 OW 110 OW 60OW 10OW

Longitude

8OS

4OS

0O

4ON

8ON

Latit

ude

a Equivalent Taux bias

-10-6-4-2-1-0.8-0.6-0.4-0.20.20.40.60.8124610

•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)


Slide 12

The Assimilation corrects the ocean mean state

-1.5 -1.2 -0.9 -0.6 -0.3 0temperature

-400

-200

Dep

th (m

)

S3-a S3-cMean(199301-200201) of Model minus Observations

eq3-All in situ data

-0.4 -0.2 0 0.2 0.4 0.6temperature

-400

-200

Dept

h (m

)

S3-a S3-cMean(199301-200201) of Model minus Observations

eqind-All in situ dataWestern Pacific Equatorial Indian

Analysis minus Observations

DATA ASSIM

NO DATA ASSIM


Slide 13

Correlation with OSCAR currents

Monthly means, period: 1993-2005

Seasonal cycle removed

No Data Assimilation Assimilation:T+S

Assimilation:T+S+Alt

…improves the interannual varaibility


Slide 14

And the skill of Seasonal Forecasts of SST

0 1 2 3 4 5 6 7Forecast time (months)

0.4

0.5

0.6

0.7

0.8

0.9

1

An

om

aly

co

rre

latio

n

wrt NCEP adjusted OIv2 1971-2000 climatology

NINO4 SST anomaly correlation

0 1 2 3 4 5 6 7Forecast time (months)

0

0.2

0.4

0.6

0.8

Rm

s e

rro

r (d

eg

C)Ensemble sizes are 3 (esj6) and 3 (esj6) 76 start dates from 19870101 to 20050701

NINO4 SST rms errors

Fc S3_assim Fc NOASSIM Persistence Ensemble sd

MAGICS 6.10 hyrokkin - neh Thu Aug 17 11:25:03 2006

Data assimilation improves the seasonal forecast of SST


Slide 15

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


Slide 16

Observing System Experiments

Period 2001-2006:

ALL NO_ARGO

NEITHER NO_ALTI

(no argo/no alti)

ALL NO_ARGO- = ARGO effect (when ALTI)

NO_ALTIALL - ALTI effect (when ARGO)=

=- NEITHERNO_ALTI ARGO effect (when no ALTI)

=- ALTI effect (when no ARGO)NEITHERNO_ARGO


Slide 17

OSES: Effect on Salinity

50OE 100 OE 150 OE 160 OW 110 OW 60OW 10OW

Longitude

80OS

60OS

40OS

20OS

0O

20ON

40ON

60ON

80ON

Latit

ude

ALL-NOALTI: Surface Salinity

-0.7

-0.5

-0.3

-0.2

-0.10.1

0.2

0.3

0.5

0.7

50OE 100 OE 150 OE 160 OW 110 OW 60OW 10OW

Longitude

80OS

60OS

40OS

20OS

0O

20ON

40ON

60ON

80ON

Latit

ude

ALL-NOARGO: Surface Salinity

-0.7

-0.5

-0.3

-0.2

-0.10.1

0.2

0.3

0.5

0.7

50OE 100 OE 150 OE 160 OW 110 OW 60OW 10OW

Longitude

80OS

60OS

40OS

20OS

0O

20ON

40ON

60ON

80ON

Latit

ude

NOARGO-NO_AA: Surface Salinity

-0.7

-0.5

-0.3

-0.2

-0.10.1

0.2

0.3

0.5

0.7

50OE 100 OE 150 OE 160 OW 110 OW 60OW 10OW

Longitude

80OS

60OS

40OS

20OS

0O

20ON

40ON

60ON

80ON

Latit

ude

NOALTI-NO_AA: Surface Salinity

-0.7

-0.5

-0.3

-0.2

-0.10.1

0.2

0.3

0.5

0.7

Effect of ALTI Effect of ARGO (when alti is present)

Effect of ARGO (when alti is not present)Effect of ALTI (when ARGO is not present)

In the Tropical Atlantic/Indian, altimeter data helps ARGO


Slide 18

OSEs:Effect on Sea Level

50OE 100 OE 150 OE 160 OW 110 OW 60OW 10OW

Longitude

80OS

60OS

40OS

20OS

0O

20ON

40ON

60ON

80ON

Latit

ude

ALL-NOALTI: Sea Level

-0.5

-0.2

-0.07

-0.05

-0.03

-0.02

-0.010.01

0.03

0.05

0.07

0.2

0.5

50OE 100OE 150OE 160OW 110OW 60OW 10OW

Longitude

80OS

60OS

40OS

20OS

0O

20ON

40ON

60ON

80ON

Latit

ude

ALL-NOARGO: Sea Level

-0.5

-0.2

-0.07

-0.05

-0.03

-0.02

-0.010.010.030.05

0.070.20.5

50OE 100 OE 150 OE 160 OW 110 OW 60OW 10OW

Longitude

80OS

60OS

40OS

20OS

0O

20ON

40ON

60ON

80ON

Latit

ude

NOARGO-NO_AA: Sea Level

-0.5

-0.2

-0.07

-0.05

-0.03

-0.02

-0.010.01

0.03

0.05

0.07

0.2

0.5

50OE 100 OE 150 OE 160 OW 110 OW 60OW 10OW

Longitude

80OS

60OS

40OS

20OS

0O

20ON

40ON

60ON

80ON

Latit

ude

NOALTI-NO_AA: Sea Level

-0.5

-0.2

-0.07

-0.05

-0.03

-0.02

-0.010.01

0.03

0.05

0.07

0.2

0.5

Effect of ALTI Effect of ARGO (when alti is present)

Effect of ARGO (when alti is not present)Effect of ALTI (when ARGO is not present)


Slide 19

OSEs:Effect on T300

50OE 100 OE 150 OE 160 OW 110 OW 60OW 10OW

Longitude

80OS

60OS

40OS

20OS

0O

20ON

40ON

60ON

80ON

Latit

ude

ALL-NOARGO: Temperature in upper 300m

-3-2-1-0.6-0.4-0.3-0.2-0.10.10.20.30.40.6123

50OE 100 OE 150 OE 160 OW 110 OW 60OW 10OW

Longitude

80OS

60OS

40OS

20OS

0O

20ON

40ON

60ON

80ON

Latit

ude

NOALTI-NO_AA: Temperature in upper 300m

-3-2-1-0.6-0.4-0.3-0.2-0.10.10.20.30.40.6123

Effect of ARGO when Alti is present

Effect of ARGO when Alti is NOT present


Slide 20

Fit to the observations (rms error)Temperature

0.4 0.8 1.2 1.6 2 2.4temperature

-400

-200

Dep

th (m

)

ALL NOALTI NOARGO NO_AA RMS of Model minus Observations

nino12-All in situ dataEastern Equatorial Pacific

0.3 0.6 0.9 1.2 1.5temperature

-400

-200

Dep

th (m

)


nstratl-All in situ dataNorth Sub Tropical Atlantic

ALL NO_ALTI NO_ARGO NEITHER

0.2 0.4 0.6 0.8 1 1.2temperature

-400

-200

Dep

th (m

)

ALL noARG noALT noALT(noARG)RMS(200101-200312) of Model minus Observations

spac-All in situ dataSouth Pacific


Slide 21

ALL NO_ALTI NO_ARGO NEITHER

Fit to the observations (rms error) Salinity

Equatorial Indian Equatorial Atlantic

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7Saliniy (p.s.u.)

-400

-200

De

pth

(m

)


eqind-All in situ data

0 0.1 0.2 0.3 0.4 0.5Saliniy (p.s.u.)

-400

-200

De

pth

(m

)


eqatl-All in situ data


Slide 22

Impact on Seasonal Forecast skill

% Reduction in SST Forecast MAE1-7 Months

Period 2001-2006

0

2

4

6

8

10

12

14

16

18

20

NINO12

NINO3

NINO4

TRAPAC

NSTRATL

EQIND

Regions

%

ALTI

ARGO

MOOR

•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


Slide 23

1993-2007

% Reduction in SST Forecast MAERange 1-7 monthsPeriod 1993-2006

0

2

4

6

8

10

12

14

16

18

20

Regions

%

MOOR

ALTI

ALL


Slide 24

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


Slide 25

Ocean Heat Content at 300/700/3000 mGlobal T300

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005Time

-0.10

-0.05

0.00

0.05

0.10

0.15

ORAS3ORA-nobsLevitus

Global T700

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005Time

-0.05

0.00

0.05

0.10


Global T3000

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005Time

-0.02

0.00

0.02

0.04


•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

Global T300

2001 2002 2003 2004 2005 2006Time

-0.04

-0.02

0.00

0.02

0.04ORAS3ORA-nobsNOSOLONOARGO

Global T700

2001 2002 2003 2004 2005 2006Time

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

ORAS3ORA-nobsNOSOLONOARGO

Global T3000

2001 2002 2003 2004 2005 2006Time

-0.02

-0.01

0.00

0.01

0.02

ORAS3ORA-nobsNOSOLONOARGO


Slide 26

Spatial distribution of trends in heat content

Taux (x 0.01N/m2)

Tauy (x 0.01N/m2)T300 (deg C)

1982-2006 mean minus 1959-1981 mean

How reliable are the trends in ERA40 winds?

SST (deg C)


Slide 27

Comparison with ocean observations

392

Observations: Oceanic Climate Change and Sea Level Chapter 5

decrease during 1980 to 1983. The 0 to 700 m layer cooled at a rate of 1.2 W m–2 during this period. Most of this cooling occurred in the PaciÞc Ocean and may have been associated with the reversal in polarity of the PDO (Stephens et al., 2001; Levitus et al., 2005c, see also Section 3.6.3). Examination of the geographical distribution of the differences in 0 to 700 m heat content between the 1977–1981 and 1965–1969 pentads and the 1986–1990 and 1977–1981 pentads shows that the pattern of heat content change has spatial scales of entire ocean basins and is also found in similar analyses by Ishii et al. (2006). The PaciÞc Ocean dominates the decadal variations of global heat content during these two periods. The origin of this variability is not well understood.

Based on model experiments, it has been suggested that errors resulting from the highly inhomogeneous distribution of ocean observations in space and time (see Appendix 5.A.1) could lead to spurious variability in the analysis (e.g., Gregory et al., 2004, AchutaRao et al., 2006). As discussed in the appendix, even in periods with overall good coverage in the observing system, large regions in Southern Hemisphere (SH) are not well sampled, and their contribution to global heat content variability is less certain. However, the large-scale nature of heat content variability, the similarity of the Levitus et al. (2005a) and the Ishii et al. (2006) analyses and new results showing a decrease in the

global heat content in a period with much better data coverage (Lyman et al., 2006), gives conÞdence that there is substantial inter-decadal variability in global ocean heat content.

5.2.2.3 Implications for Earth’s Heat Balance

To place the changes of ocean heat content in perspective, Figure 5.4 provides updated estimates of the change in heat content of various components of the Earth’s climate system for the period 1961 to 2003 (Levitus et al., 2005a). This includes changes in heat content of the lithosphere (Beltrami et al., 2002), the atmosphere (e.g., Trenberth et al., 2001) and the total heat of fusion due to melting of i) glaciers, ice caps and the Antarctic and Greenland Ice Sheets (see Chapter 4) and ii) arctic sea ice (Hilmer and Lemke, 2000). The increase in ocean heat content is much larger than any other store of energy in the Earth’s heat balance over the two periods 1961 to 2003 and 1993 to 2003, and accounts for more than 90% of the possible increase in heat content of the Earth system during these periods. Ocean heat content variability is thus a critical variable for detecting the effects of the observed increase in greenhouse gases in the Earth’s atmosphere and for resolving the Earth’s overall energy balance. It is noteworthy that whereas ice melt from glaciers, ice caps and ice sheets is very important in the sea level budget

Figure 5.3. Linear trend (1955Ð2003) of zonally averaged temperature in the upper 1,500 m of the water column of the Atlantic, PaciÞc, Indian and World Oceans.The contour interval is 0.05¡C per decade, and the dark solid line is the zero contour. Red shading indicates values equal to or greater than 0.025¡C per decade and blue shading indicates values equal to or less than Ð0.025¡C per decade. Based on the work of Levitus et al. (2005a).

ORA-S3 IPCC-AR4 (LEVITUS)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


Slide 28

Comparison with ocean observations

392

Observations: Oceanic Climate Change and Sea Level Chapter 5

decrease during 1980 to 1983. The 0 to 700 m layer cooled at a rate of 1.2 W m–2 during this period. Most of this cooling occurred in the PaciÞc Ocean and may have been associated with the reversal in polarity of the PDO (Stephens et al., 2001; Levitus et al., 2005c, see also Section 3.6.3). Examination of the geographical distribution of the differences in 0 to 700 m heat content between the 1977–1981 and 1965–1969 pentads and the 1986–1990 and 1977–1981 pentads shows that the pattern of heat content change has spatial scales of entire ocean basins and is also found in similar analyses by Ishii et al. (2006). The PaciÞc Ocean dominates the decadal variations of global heat content during these two periods. The origin of this variability is not well understood.

Based on model experiments, it has been suggested that errors resulting from the highly inhomogeneous distribution of ocean observations in space and time (see Appendix 5.A.1) could lead to spurious variability in the analysis (e.g., Gregory et al., 2004, AchutaRao et al., 2006). As discussed in the appendix, even in periods with overall good coverage in the observing system, large regions in Southern Hemisphere (SH) are not well sampled, and their contribution to global heat content variability is less certain. However, the large-scale nature of heat content variability, the similarity of the Levitus et al. (2005a) and the Ishii et al. (2006) analyses and new results showing a decrease in the

global heat content in a period with much better data coverage (Lyman et al., 2006), gives conÞdence that there is substantial inter-decadal variability in global ocean heat content.

5.2.2.3 Implications for Earth’s Heat Balance

To place the changes of ocean heat content in perspective, Figure 5.4 provides updated estimates of the change in heat content of various components of the Earth’s climate system for the period 1961 to 2003 (Levitus et al., 2005a). This includes changes in heat content of the lithosphere (Beltrami et al., 2002), the atmosphere (e.g., Trenberth et al., 2001) and the total heat of fusion due to melting of i) glaciers, ice caps and the Antarctic and Greenland Ice Sheets (see Chapter 4) and ii) arctic sea ice (Hilmer and Lemke, 2000). The increase in ocean heat content is much larger than any other store of energy in the Earth’s heat balance over the two periods 1961 to 2003 and 1993 to 2003, and accounts for more than 90% of the possible increase in heat content of the Earth system during these periods. Ocean heat content variability is thus a critical variable for detecting the effects of the observed increase in greenhouse gases in the Earth’s atmosphere and for resolving the Earth’s overall energy balance. It is noteworthy that whereas ice melt from glaciers, ice caps and ice sheets is very important in the sea level budget

Figure 5.3. Linear trend (1955Ð2003) of zonally averaged temperature in the upper 1,500 m of the water column of the Atlantic, PaciÞc, Indian and World Oceans.The contour interval is 0.05¡C per decade, and the dark solid line is the zero contour. Red shading indicates values equal to or greater than 0.025¡C per decade and blue shading indicates values equal to or less than Ð0.025¡C per decade. Based on the work of Levitus et al. (2005a).

ORA-S3 IPCC-AR4 (LEVITUS) CI=0.05 deg/decade

Atlantic and Indian

Largest warming is in the Atlantic


Slide 29

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


Slide 30

Attribution of Sea Level Change (OSES)Steric height contribution to sea level rise

2002 2003 2004 2005 2006 2007Time

-0.020

-0.015

-0.010

-0.005

0.000

0.005

ORA-S3ORA-nobsNOSOLONOARGO

•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

Mass contribution to sea level rise

2002 2003 2004 2005 2006 2007Time

-0.01

0.00

0.01

0.02

0.03

ORA-S3ORA-nobsNOSOLONOARGOIPCC-AR4


Slide 31

Impact of data assimilation in the MOC

ORAS3

ORA-nobs

ORAS3

ORA-nobs

Bryden05

Cunningham07

•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


Slide 32

Salinity in ORA-S3

Global S300

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005Time

0.00

0.01

0.02

0.03

0.04ORAS3ORA-nobs

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

Large effect of ARGO

Large uncertainty in fresh water fluxes


Slide 33

SummaryState 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).


Slide 34

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

observing system experiments with ecwmf operational ocean analysis (ora-s3)

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