New ESA SSS Assimilation New ESA SSS Assimilation StudyStudy

To be ready to assimilate level 2 and 3 SMOS data in an To be ready to assimilate level 2 and 3 SMOS data in an optimal way, a dedicated assimilation effort needs to be optimal way, a dedicated assimilation effort needs to be started soon in support of the SMOS mission. started soon in support of the SMOS mission. Such an effort is necessary to make optimal use of of the Such an effort is necessary to make optimal use of of the SMOS data over the ocean for operational nowcasting, SMOS data over the ocean for operational nowcasting, climate studies and for SI forecasts.climate studies and for SI forecasts.Previous such efforts provided useful information but just Previous such efforts provided useful information but just “scratched the surface”. Serious work is still ahead of us and “scratched the surface”. Serious work is still ahead of us and needs to be addressed in a dedicated way in preparation and needs to be addressed in a dedicated way in preparation and support of the SMOS mission. support of the SMOS mission.

GoalsGoals

To understand the best use of new level 2 or level 3 SSS To understand the best use of new level 2 or level 3 SSS products for operational nowcasting efforts (filtering) products for operational nowcasting efforts (filtering)

To understand the impact of new SSS estimates for To understand the impact of new SSS estimates for dynamically consistent climate studies, including SI dynamically consistent climate studies, including SI forecasting. forecasting.

To understand the impact of new SSS data on estimates of To understand the impact of new SSS data on estimates of surface freshwater fluxes.surface freshwater fluxes.

To understand the complementarity of ARGO and SMOS To understand the complementarity of ARGO and SMOS data in ocean assimilation studies.data in ocean assimilation studies.

All problems were not considered in previous studies but All problems were not considered in previous studies but will determine the success of the data for ocean will determine the success of the data for ocean studies.studies.

SSS observations will serve in various ways SSS observations will serve in various ways for model and state estimation studies:for model and state estimation studies:

To provide improved information about a time-varying near To provide improved information about a time-varying near surface salinity field. Presently only the Levitus monthly SSS surface salinity field. Presently only the Levitus monthly SSS climatology is available with large data-void areas. Time-climatology is available with large data-void areas. Time-varying anomalies from the climatology on all space and varying anomalies from the climatology on all space and time-scales are not available in any systematic fashion.time-scales are not available in any systematic fashion.

Near-surface SSS has a profound impact on the evolution of the Near-surface SSS has a profound impact on the evolution of the surface mixed layer, its depth range as well as its temperature and surface mixed layer, its depth range as well as its temperature and air-sea coupling air-sea coupling (barrier layer, pressure gradient). Improved . Improved information about the near-surface stratification has thus impact on information about the near-surface stratification has thus impact on surface heat flux estimates (at least regionally) and on surface heat flux estimates (at least regionally) and on atmosphere-ocean gas exchanges.atmosphere-ocean gas exchanges.

SSS observations will serve in various ways SSS observations will serve in various ways for model and state estimation studies:for model and state estimation studies:

By constraining SSS in the estimation (assimilation) procedure (in By constraining SSS in the estimation (assimilation) procedure (in addition to SST) it can be expected that estimated surface fluxes addition to SST) it can be expected that estimated surface fluxes of heat and freshwater become much more consistent with ocean of heat and freshwater become much more consistent with ocean observations than feasible now. observations than feasible now.

With simple assimilation approaches: Impact on sea level and the use of altimetry in ocean models.

It is hoped that this route will lead to substantially improved It is hoped that this route will lead to substantially improved precip. information over the ocean which otherwise is extremely precip. information over the ocean which otherwise is extremely difficult to measure. difficult to measure.

Mean SSS fields from the NCAR model forced with the indicated precipitation fields, and the World Ocean Atlas (WOA98) field. Significant differences in response among the inputs are evident in all ocean basins, and with WOA98. Bill Large, NCAR (2002)

Differences in Precipitation Fields

Ocean models are forced by E-P

Comparison of different mean precipitation

products:

large differences

large errors for SSS

CMAP Precip.

The range of ΔS among the OGCM outputs with time intervals of 1 month, 3 months, 1 year and 3 years; shown as the ratio to the Aquarius measurement error.

Bill Large, NCAR, 2002

Fraction of the ocean where the difference in salinity change forced by various precipitation

data sets is likely to be detected.

Mean SSS from Levitus and the two different simulations

and differences

Note the large differences (> 0.4 psu)

at large scales => much larger than the expected SMOS noise

LEVITUSORCA Model/LODYC

(J.P. Boulanger, S. Masson)

Rms of SSS variations from ORCA simulations (LODYC)

Two reference simulations with different precipitation fields

(ECMWF reanalysis and CMAP)

Rms of differences = our today knowledge of SSS in the tropics. Errors range from 0.2 to 0.4 psu

Scales of variability are about 4° (e.g. Lagerloef/Delcroix,

2000). A these scales, SMOS noise should be below 0.05 psu

(i.e. improvement by a factor of 7)

= unique contribution of SMOS given the important role (and

impact) of SSS on ocean dynamics and climate in the tropics (e.g. barrier layer, buoyancy forcing, El Nino,

Indian ocean dipole, impact on sea level)

December 1997 SST Anomalies

The characteristic SST anomalies of the dipole mode are reinforced by 20% when salt is

involved in the vertical stratification

Reference exp. : SST anomaly regarding to the

December mean

SST difference between reference exp. and perturbation exp.

Min=-2,5°C Max=1,5°C Min=-0,5°C Max=0,3°C

Previous assimilation pilot studies: Previous assimilation pilot studies:

Mercator/Mersea OSSE Mercator/Mersea OSSE (supported by ESA)(supported by ESA)

ECCO Study in support of Aquarius ECCO Study in support of Aquarius (unsupported) (unsupported)

Elements of Operational OceanographyElements of Operational Oceanography

Research PlatformsEarth observing satellites

Autonomous in situobserving systems

Model simulations andassimilation on super computers

ObjectivesOriginally: conduct experiments to assimilate three different sets of

synthetic SSS level-3 products (SMOS, AQUARIUS and SMOS+AQUARIUS), and to compare their performances through so-called “Observing System Simulation Experiments”.

In practice: Sensitivity studies to level productsWhich level of SSS product is the most efficient for the new MERCATOR

Assimilation System SAM2?

Sensitivity studies to observation errorsAccuracy of SMOS level-2 products? Observation errors specification ?

Sensitivity studies to observing systemsRelative skills of SMOS and AQUARIUS products? Incremental benefit of

their combination?

MERCATOR/MERSEA OSSE Overview (impact of SSS data for operational nowcasting)

REFERENCE or CONTROL RUN• Hindcast experiment with in-situ, SST and altimeter data assimilation over 2003.• OPA model: MNATL(1/3°) covering North Atlantic from 20°S to 70°N, with an eddy-

permitting resolution (1/3°)• ECMWF daily forcing fluxes and a weak SST relaxation (40 W/m2)

DATA ASSIMILATION SCHEME: SAM2• Based on a SEEK filter : Reduced Order Kalman Filter (modal space)• 3D multivariate background error covariances: 140 seasonal 3D modes (ψ,T,S)

calculated from an hindcast exp. (7 years)• Innovation vector: FGAT method, observation operator for largest scales

TRUTH• The native sea surface salinity (SSS) located on the SMOS L2 data points • The native SSS comes from the North Atlantic and Mediterranean high resolution

(1/15°) MERCATOR OCEAN prototype named PSY2V1 re-sampled at a 1/3°

DIAGNOSTIC• Mean and variance of misfit between OSSEs and “truth” SSS (1/3°) located on

SMOS L2 data points.

OSSE Ingredients

Characteristics of simulated SSS data

0,1 - 1,5 Level 2 Aquarius (100kmx100km along tracks, 1 daily) – pixel scale

Aquarius L2P

0,2 - 2,5 Level 2 SMOS (40kmx40km along tracks , daily) – pixel scale

SMOS L2P

0,02 - 0,5 Level 3 SMOS (map of 200kmx200km, 10 days)

SMOS L3P

Observation error range (RMS in PSU)

Level (spatial and time resolution)

Simulated SSS products computed by CLS

SMOS L3 SMOS L2 Aquarius L2

Aquarius L2 vs SMOS L2Difference in variance (psu2)

SMOS L2 vs SMOS L3Difference in variance (psu2)

Conclusions from comparisons between simulations with or without simulated data :

SMOS L2 cover have a more important impact for our operational system than Aquarius L2 cover

L2 products are more suitable for our operational system than L3 products

OSSE : main resultsV

aria

nce

of m

isfit

Summary/ConclusionsL2 vs L3/SMOS vs Aquarius

0,31040,39450,4859

RMS difference (PSU) between experiments and “truth” overall the domain

(year 2003)

SMOS L2

SMOS L3

REFERENCEor

Control run

Experiments with simulated SSS data

0,30770,4353

SMOS L2+

AQUARIUS L2

AQUARIUS L2

1. The assimilation of native SMOS level-2 product is a better approach than the assimilation of gridded level-3 products, at least in the context of high-resolution models of the ocean circulation (to be expected)

2. In the case of the assimilation of Level 3 products, it should be suitable to have a “daily Level 3 products” for our assimilation system.

3. It should be better to assimilate SSS products with spatial resolution close to the ocean model resolution (dependent on assimilation approach).

4. The impact of the Aquarius L2 Products is weak compared to the SMOS L2 Products: it is quite equivalent to the SMOS L3 Products (depends on data resolution).

5. The combination of the two L2 Products has thus a small effect on final results (depends on approach).

6. Further studies are necessary to better understand the weak impact of the Aquarius L2 Products. Combining SMOS and Aquarius Products on the same time and space scales (should be done by model)

Discussions/ConclusionsObservation errors

6. The use of SMOS L2 products give satisfactory improvement in the model, since it provides a measurable impact of the quality of ocean estimates from operational systems.

7. The observation error variance as specified by CLS (Boone et al., 2005) is a minimum requirement to extract the best possible information from SSS measurements in the context of the MERCATOR ocean forecasting system available today.

0,28470,31040,41140,4859RMS difference (PSU) between experiments and “truth” overall the domain (year 2003)

SMOS L2_0.5(½ x obs. Error)

SMOS L2(native obs. Error)

SMOS L2_2(2 x obs. Error)

REFERENCEor

Control run

Experiments with Simulated SSS data

The Potential of SSS Observations The Potential of SSS Observations for Ocean State Estimatefor Ocean State Estimate

The goal of ocean state estimation is to combine all The goal of ocean state estimation is to combine all available and diverse ocean data with a numerical model to available and diverse ocean data with a numerical model to obtain a obtain a dynamically consistent estimatedynamically consistent estimate of the time- of the time-evolving flow field and its uncertainties. evolving flow field and its uncertainties.

Results can be used to study the ocean, its surface fluxes Results can be used to study the ocean, its surface fluxes (including run-off) and its impact on climate through heat (including run-off) and its impact on climate through heat and freshwater transports and related surface fluxes. and freshwater transports and related surface fluxes.

GECCO Data Assimilation

Method:Method:Synthesis of all available Synthesis of all available Observations to obtain Observations to obtain

dynamically consitent dynamically consitent description of ocean description of ocean circulation. Used to circulation. Used to

estimate climate change; estimate climate change; estimate oceans uptake of estimate oceans uptake of

CO2; initialize coupled CO2; initialize coupled models; now-casting of models; now-casting of

Ocean currents. Ocean currents.

MethodeMethode

The model can be imposed upon the objective function either by using Lagrange multipliers (constrained optimization), or in an unconstrained optimization form with a penalty-function type of formulation.

Model

Penalty-function type cost function

Cost Function

In the adjoint formalism a giant non-linear optimization In the adjoint formalism a giant non-linear optimization problem is solved by iteratively changing control parameters problem is solved by iteratively changing control parameters until a statistical minimum is reached. until a statistical minimum is reached.

Ongoing optimizations are run over 50 years now in one Ongoing optimizations are run over 50 years now in one sweep.sweep.

In compressed form: the forward model leads to a measure In compressed form: the forward model leads to a measure of the mode-data misfit. of the mode-data misfit.

This misfit is input to the adjoint model (running backward This misfit is input to the adjoint model (running backward in time) which in turn provides the sensitivity of the function in time) which in turn provides the sensitivity of the function to control parameters. Fed into a descent algorithm a new to control parameters. Fed into a descent algorithm a new control state is determined, etc.control state is determined, etc.

time

Smoothed Estimate: x(t+1)=Ax(t)+Gu(t)

Filtered Estimate: x(t+1)=Ax(t)+Gu(t)+(t)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).

(I. Fukumori)

Control terms usually include the initial model conditions as well as Control terms usually include the initial model conditions as well as surface forcing of momentum, heat and freshwater. Thereby surface forcing of momentum, heat and freshwater. Thereby center provided meteorological forcing fields are adjusted to best center provided meteorological forcing fields are adjusted to best fit ocean observations. An essential element is the existence of fit ocean observations. An essential element is the existence of prior error estimates of the data, the meteorological fields and the prior error estimates of the data, the meteorological fields and the model itself. model itself.

Uncertainties of forcing fields are very poorly known. Especially Uncertainties of forcing fields are very poorly known. Especially precipitation over the ocean is almost entirely unmeasured and precipitation over the ocean is almost entirely unmeasured and center provided estimates are having accordingly huge error bars, center provided estimates are having accordingly huge error bars, presumably ranging from 0.5 to 1 STD of the time varying field.presumably ranging from 0.5 to 1 STD of the time varying field.

Here especially much improvement is expected from SMOS Here especially much improvement is expected from SMOS data. data.

The Mean Ocean Circulation, globalThe Mean Ocean Circulation, global

Maximenko, Niiler et al.

Time-meanSSH;1992-2003

Surface Heat Flux EstimatesSurface Heat Flux Estimates

100 W/m^2

-100

100

-100

Estimates of un-observables: Estimates of un-observables: Global Ocean Heat and Freshwater Global Ocean Heat and Freshwater

TransportsTransports

o

oo

G&W

o

Interannual Variations

Freshwater FluxesFreshwater Fluxes

OGCM experiment assimilating an OGCM experiment assimilating an artificial SSS field to infer E-P forcing.artificial SSS field to infer E-P forcing.

In this study we performed an ocean optimization by In this study we performed an ocean optimization by assimilating ocean observations into an OGCM to estimate assimilating ocean observations into an OGCM to estimate

the ocean state over time and simultaneously calculates the ocean state over time and simultaneously calculates the necessary surface flux adjustments needed to match the necessary surface flux adjustments needed to match

the forcing fields to the ocean state (Stammer et al., 2002). the forcing fields to the ocean state (Stammer et al., 2002).

The experiment began with the optimized state of the ocean The experiment began with the optimized state of the ocean model that had initial conditions and surface fluxes adjusted model that had initial conditions and surface fluxes adjusted to match ocean observations. to match ocean observations.

All of the conditions where then held fixed, except that the E-All of the conditions where then held fixed, except that the E-P temporal variability in the flux adjustments was enhanced by P temporal variability in the flux adjustments was enhanced by a factor of 4 while the mean E-P adjustment remained the a factor of 4 while the mean E-P adjustment remained the same. same.

The model was then run forward with the E-P variability The model was then run forward with the E-P variability enhancement and the new surface and subsurface salinity enhancement and the new surface and subsurface salinity fields were computed and retrieved. These output fields fields were computed and retrieved. These output fields constituted an artificial data set for the final step of the constituted an artificial data set for the final step of the experiment. experiment.

In the final step, a new ocean optimization was computed In the final step, a new ocean optimization was computed treating the artificial SSS and subsurface fields as treating the artificial SSS and subsurface fields as "observations" in the assimilation. The goal was to ascertain "observations" in the assimilation. The goal was to ascertain how well the artificially enhanced surface E-P flux how well the artificially enhanced surface E-P flux adjustments could be reproduced. adjustments could be reproduced.

In principle, the mean adjustment should remain constant, In principle, the mean adjustment should remain constant, and the variability should match the factor of four and the variability should match the factor of four enhancement used to generate the artificial salinity. enhancement used to generate the artificial salinity.

Differences in time-mean SSSDifferences in time-mean SSS

Differences in time-mean E-PDifferences in time-mean E-P

Main results:Main results:1.1. The results were not as anticipated and are not yet fully The results were not as anticipated and are not yet fully

understood. understood.

2.2. Increasing the E-P variability changes the time-mean Increasing the E-P variability changes the time-mean state of SSS (through changes convection).state of SSS (through changes convection).

3.3. In the optimization the main change was therefore In the optimization the main change was therefore projected on the time-mean E-M, not the “weather”. projected on the time-mean E-M, not the “weather”.

4.4. It has to be investigated if this is a general result or if – as It has to be investigated if this is a general result or if – as expected – ocean state estimation can improve our expected – ocean state estimation can improve our estimates of surface fluxes.estimates of surface fluxes.

The experiment demonstrates the power of ocean state The experiment demonstrates the power of ocean state estimation with salinity data for constraining E-P fluxes. estimation with salinity data for constraining E-P fluxes. It is also successful in pointing out potential problems in It is also successful in pointing out potential problems in this type of calculation that need to be understood prior this type of calculation that need to be understood prior to the launch of SMOS. to the launch of SMOS.

A more comprehensive analysis of the results needs to be carried A more comprehensive analysis of the results needs to be carried out, but also a more comprehensive understanding of how out, but also a more comprehensive understanding of how SMOS and Aquarius data can be assimilated. SMOS and Aquarius data can be assimilated.

The finding that E-P variability leads to changes in the mean state The finding that E-P variability leads to changes in the mean state is scientifically very interesting as well, and may lead to new is scientifically very interesting as well, and may lead to new insights about how the ocean responds to an amplified water insights about how the ocean responds to an amplified water cycle as we proceed with follow-on investigations.cycle as we proceed with follow-on investigations.

We have to investigate to what extend the complementarity of We have to investigate to what extend the complementarity of satellite and in situ data can help reaching SMOS goals. satellite and in situ data can help reaching SMOS goals.

DeltaS = 0.1 corresponds to ~ 10cm/yr uncertainties in (E-P) - a DeltaS = 0.1 corresponds to ~ 10cm/yr uncertainties in (E-P) - a value much lower than present 0.5 - 1 m/yr error estimates. value much lower than present 0.5 - 1 m/yr error estimates. Precipitation fields yield errors on modeled SSS of up to 0.2 to 0.4 psu to be compared to a 0.1 psu accuracy (or better depending on scales) that could be achieved with SMOS.

Uncertainties for SSS should be as low as 0.01 psu. Presently Uncertainties for SSS should be as low as 0.01 psu. Presently variations, e.g. of the initial state are +- 0.1 psu and SSS variations, e.g. of the initial state are +- 0.1 psu and SSS observations with similar uncertainties would only be of marginal observations with similar uncertainties would only be of marginal value for constraining S0. Even with 0.1 psu accuracy, SSS value for constraining S0. Even with 0.1 psu accuracy, SSS observations will have an impact on the estimation of E-P and thus observations will have an impact on the estimation of E-P and thus P. P. Very positive impact of SMOS in the equatorial/tropical regions is expected. Better representation of the ocean state (currents, sea level), model improvements and possibly better seasonal forecasts. This has to be quantified with data assimilation studies.

Improving level 3 SSS data baseImproving level 3 SSS data base Use the multi-mission concept Use the multi-mission concept Intercalibration Intercalibration Assimilation of SMOS/Aquarius combined products Assimilation of SMOS/Aquarius combined products

on the same time and space scales as the SMOS L2 on the same time and space scales as the SMOS L2 Products.Products.

Assimilation in a global model (low and high Assimilation in a global model (low and high resolution; e.g., what is the expected impact in the resolution; e.g., what is the expected impact in the Pacific region?)Pacific region?)

Improving DA schemes for now casting and Improving DA schemes for now casting and hind castinghind casting

Control of fluxes in the assimilation scheme Control of fluxes in the assimilation scheme Combine SMOS and Aquarius with ARGO dataCombine SMOS and Aquarius with ARGO data Improve prior error description: Handling of Improve prior error description: Handling of

correlated data errors and biasses. correlated data errors and biasses. Test potential of TB assimilation.Test potential of TB assimilation.

PERSPECTIVES for new ESA Study (1)

Improving DA resultsImproving DA results Use twin-experiments to test optimal Use twin-experiments to test optimal

assimilation strategy for operational now- assimilation strategy for operational now- hind casting and initialization of coupled hind casting and initialization of coupled models. models.

Improving Surface Flux EstimatesImproving Surface Flux Estimates Use SSS in combination with other Use SSS in combination with other

estimates of surface net E-P freshwater estimates of surface net E-P freshwater fluxes to improve our understanding of net fluxes to improve our understanding of net precip. Over the ocean. precip. Over the ocean.

PERSPECTIVES for new ESA Study (2)

Thank you for your supportThank you for your support

CLIPPER SIMULATIONS - CONCLUSIONS / IMPACT ON SMOS (2)

The SSS variability is larger than 0.2 psu and 0.1 psu in, respectively, 40% and 70% of the Atlantic Ocean. The SSS variability at length scales larger than 300 km (that can potentially be retrieved by SMOS) represent more than 70% of the total variability.

The small scale SSS (length scales < 300 km) will add a noise to SMOS measurements generally below 0.1 psu and in some places larger than 0.5 psu. This noise will remain (much) smaller than the instrumental noise (larger than 1 psu) and will not affect much the estimation of mean (200 km x 200 km x 10 days) SSS fields from SMOS.

In-situ estimations of SSS (e.g. Argo) will be much more affected by the small scale SSS. The estimations of mean fields from sparse in-situ data are thus likely to be (much) less accurate than the ones derived from SMOS (but in-situ data should allow a precise calibration of SMOS).

The sampling requirements to resolve the mesoscale signals should be about 10 days and less than 100 km. This means that the effective (i.e. at these scales) noise from SMOS will be probably larger than 0.2 or 0.3 psu. Only areas of large mesoscale variability should thus benefit from SMOS observations.

The large scales SSS variations are the ones that will be best observed from SMOS. Variations are typically between 0.05 and 0.5 psu rms. With a noise of 0.1 psu for 2°x2°x10d boxes, a signal of 0.1 psu at scales of 1000 km “should be observable” with a 20% error, while the error drops to below 10% for the seasonal signal (which is much better than our present knowledge of the SSS seasonal cycle) and better for the interannual signals.

SSS measurements with a 0.1/0.2 psu accuracy over 2°x2°x10 days = large improvement on our present

knowledge of SSS variations

Main issues for a data assimilation study

Impact of SSS observations in ocean models via data assimilation

• from simple methods (relaxation) to more sophisticated ones (4D-VAR, EnKF).

• Effects of model biases and model errors. • Joint assimilation of in-situ and SMOS satellite data• Assimilation of averaged SSS fields at different space and time scales.

Assimilation of TB or SSS. • Impact on ocean state estimation, thermohaline circulation, on

seasonal forecasts for different scenarii.