Optimizing and Enhancing the Integrated

Atlantic Ocean Observing System

Observing System Design in Atlantos (Task 1.3)

Observing System Design in Atlantos (Task 1.3)

Deliver objective guidelines to improve existing elements and/or implement new components of the Atlantic observing system.

Approach based on Observing System Simulation Experiments (OSSEs) using modelling and data assimilation systems and statistical techniques.

Task 1.3 partnersD. Ford, F. Gasparin, M. Palmer, E. Rémy, A. Blaker, J.M. Brankart, P. Brasseur, F, Garry, M.

Gehlen, C. Germinaud, R. Ghosh, S. Guinehut, J. Jungclaus, R. King, P.Y. Le Traon, K. Lohmann,

C. Mao, M. Martin, S. Masina, M. Vrac, M. Mayer, I. Mirouze, A. New, A. Sommer, R. Wood, H. Zuo

Task 1.3 overview

Subtask 1.3.1: Toward an improved design of the in-situ observing system for oceanreanalysis, analysis and forecasting: physical variables (lead : Mercator Ocean,participants: Mercator Ocean, Met Office, CMCC, ECMWF, CLS) (lead: E. Remy)

Subtask 1.3.2 : Toward an improved design of the in-situ observing system for oceanreanalysis, analysis and forecasting: biogeochemistry (lead: CNRS/IGE, participants:CNRS/IGE, Met Office) (lead: P. Brasseur)

Subtask 1.3.3: Use of statistical techniques for identifying an optimal observationalnetwork for enabling ocean carbon system estimates (Lead: CNRS/LSCE, participants:CNRS/LSCE, University of Exeter) (lead : M. Gehlen)

Subtask 1.3.4: Design of the Atlantic Observing System to support climate prediction anddetection of change (Lead : Met Office, participants: Met Office, NOC) (lead: M. Palmer)

Task 1.3 was carried out over a 3.5 year time period (April 2015 – December 2018). Fourworkshops were organized to discuss the design of OSSEs, first, intermediate and final results.Workshops involved Atlantos WP2&WP3 network experts and Task 1.3 experts.

Four global eddy-

permitting systems

Assimilation of the

same synthetic in situ

data sets

Inter-comparison of

extra-observation

impacts on T/S fields

Subtask 1.3.1 Toward an improved design of the in-situ observing system for ocean reanalysis, analysis and forecasting: physical variables

Building

a Multi-System

OSSE framework

A “REALISTIC” NATURE RUN

The mean and climatology is similar with

Argo (Roemmich and Gilson, 2009)

Temperature long-term trends in the deep ocean are

regionally consistently regionally with observations

Gasparin, F., and Coauthors, 2018: A large-

scale view of oceanic variability from 2007 to

2015 in the global high resolution monitoring

and forecasting system at Mercator Ocean.

Journal of Marine Systems, 187, 260–276. The interannual variability of the upper branch of the

MOC is remarkably well-reproduced by PSY4.

Multi-System Intercomparison

Gasparin et al (2019) Requirements for an integrated in situ Atlantic Ocean Observing System from

coordinated Observing System Simulation Experiments. Frontier in Marine Sciences, accepted

1 ensemble composed of 4 members from Mercator-Ocean, CLS, CMCC and MetOffice

The four groups show improvement of the T/S representation below 2000m. The maximum

improvement is for salinity (up to 60%), but temperature is also significantly better.

Temperature and salinity profiles of error reduction of the DEEP exp.

as compared with the BACKBONE experiment, relative to the Nature

Run fields, area-averaged in (a,c) the Atlantic ocean and (b,d) the GS

The black line is the ensemble mean. Gray indicates the

standard deviation of the four members. Unit is

percentage of mean square error reduction.

Impact of the different

observing system components

1. No data

2. Satellite only: multiple altimetry,

SST

3. Backbone: Satellite + Argo +

Moorings

4. Backbone + Argo x 2 (WBC,

tropics)

5. Backbone – Moorings

6. Backbone + 0-150 m T profiles

7. Backbone + Deep Argo (4000 m)

8. Backbone + Deep Argo (6000 m)

Single experiments (Mercator Ocean)

Correlation of the depth of the 20°C isotherm anomaly with different

observing system designs and the Nature Run (F. Gasparin et al.)

1 2 3

4 5 6

2010 Ocean Heat Content error in

the deep and abyssal oceans for

the Backbone, DEEP4000 and

DEEP6000 experiments

Impact of deep-Argo is evident on

the 2010 mean in the 2000-4000m

layer, the Southern Ocean remains

undersampled

Compared with DEEP4000,

DEEP6000 significantly reduces

biases in the 4000-6000m layer

THE EXTENSION OF ARGO INTO THE DEEP/ABYSSAL OCEAN

Gasparin et al., submitted to J. Climate

8

7

3

Perform OSSEs to assess different BGC-Argo deployment strategies

Subtask 1.3.2 : Toward an improved design of the in-situ observing system for ocean reanalysis, analysis and forecasting: biogeochemistry (CNRS/IGE, Met Office)

Model runs (global ¼° NEMO-MEDUSA)

Nature run (used to generate “ocean colour” and “BGC-Argo”)

OSSE 1: Assimilate “ocean colour” into perturbed run

OSSE 2: Assimilate “ocean colour” and ¼ “BGC-Argo” into perturbed run

OSSE 3: Assimilate “ocean colour” and full “BGC-Argo” into perturbed run

Simulated observations

Ocean colour: daily surface chlorophyll

BGC-Argo: profiles of chlorophyll,nitrate, oxygen, pH

Ford et al., 2019

Subtask 1.3.3. Use of statistical techniques for identifying an optimal observational network for enabling ocean carbon system estimates (LSCE/CNRS/IPSL) (M. Gehlen, A. Sommer)

Use a neural network method to reconstruct a surface ocean pCO2 over the Atlantic Ocean based on a

feed-forward neural network (FFNN) (pCO2=f(SSS,SST,SSH,CHL,MLD,xCO2,lat,lon)

Standard deviation of difference between reconstructed and modelled pCO2 (NEMO-PISCES).

Pseudo-observations used for training the neuralnetwork:a) SOCAT data set (2001-2010)b) SOCAT + OceanSITES (2008-2010)c) Argo data (2008-2010);d) SOCAT + Argoe) SOCAT and 25% of Argof) SOCAT and 10% of Argo

Potential major contribution of Argo merged with SOCAT and OceanSITESμatm

Time series of the discrepancy

between estimates of full ocean

heat content change and the

subsampled heat content change

for four CMIP5 models under

RCP8.5. Coloured lines and

shaded regions correspond to

monitoring of OHC in different

vertical depth layers and

geographic domains. Results

highlight the need to observe

the ocean below 2000m,

particularly in the Atlantic and

Southern Ocean sectors in

order to accurately monitor the

magnitude of Earth’s energy

imbalance (Garry et al., 2018).

Subtask 1.3.4: Design of the Atlantic Observing System to support climate prediction and detection of change

Task 1.3 achievements

For the first time, coordinated multi-model OSSEs have been carried out to assess thepotential of a range of observing elements to better estimate the physical state of theAtlantic Ocean. Common protocols (e.g. sharing the same nature run, common diagnostics)were used and this contributed to the development of best practices and standards forobserving system design.

Work carried out in Task 1.3 evidenced the complementary nature of satellite and in-situobserving systems to constrain modelling and data assimilation systems for physics. Thepresent backbone system (satellites and in-situ) can be effectively assimilated in oceanmodels and allow constraining efficiently upper ocean fields.

Task 1.3 main results

Major impact of a deep Argo array to constrain modelling data assimilation systems atdepths below 2000 m. A deep Argo array would lead to substantial improvement indeep temperature and salinity estimates (20 to 40% error reduction).

Increased Argo density in western boundary currents and along the equator results inimproved estimates of T&S. The improvements are particularly noticeable in the 300-2000m depth range, with a higher impact on salinity.

A hypothetical extension of drifter data to 150m depth with thermistor chains resultsin a significant improvement to the representation of the near-surface layers.

The present tropical mooring array provides invaluable data for evaluation of modelsand assimilation systems. Assimilation into current ocean model systems has animpact primarily in the region of the moorings.

Task 1.3 main results

The existing ship-of-opportunity and mooring network, enhanced by BGC-Argosampling in the South Atlantic should allow a large improvement of the pCO2 fieldthere (and thus of CO2 air/sea fluxes and pH). Sampling these regions could be apriority for a BGC Argo pilot experiment in the tropical Atlantic and South Atlantic.

Assimilation of BGC-Argo data complements satellite surface colour data byimproving model estimates of oxygen, nutrients, carbon and Chl-a throughout thewater column. Inclusion of BGC sensors on roughly one quarter of the current Argoarray (around 1000 floats) provides clear improvements.

Data assimilation techniques and OSSEs are (much) less mature for BGCobservations. Further improvements to ocean state estimates and BGC OSSEsrequire development of more advanced data assimilation schemes.

Recommendations for the evolution of the Atlantic observing system

Continuity of the present backbone long term ocean observing system including satelliteobservations and in-situ observing system.

Implement extensions of Argo for physics (T&S). Deep Argo is a strong priority. Theimpact will be large (long term evolution of the deep ocean) and data can be readilyused in modelling and data assimilation systems and for climate studies.

Implement BGC Argo. Impact will be large both for surface carbon studies andbiogeochemistry. A priority could be to start a pilot study in the tropical and southAtlantic. Need in parallel to develop further the assimilation of BGC data in models.

Continue monitoring heat transport at both subpolar/subtropical latitudes.

Use new techniques (e.g. drifters, saildrones) to provide a higher sampling of uppertemperature and salinity fields.

Glider observations are essential for BCs, coastal and shelf areas. Impact not assessed aspart of Atlantos task 1.3 but should be a priority for future work.

Recommendations for future observing design activities

Coordination between modelling/data assimilation experts andobservation/network experts is essential for a proper design andinterpretation of OSSEs, especially to extract compelling messages on theability of the ocean observing system to resolve processes having differenttemporal and spatial scales.

OSSEs require dedicated infrastructures. It is essential to consolidate andstrengthen the capabilities of operational and climate centres to assess theimpact of present and future observations (in particular for BGC EOVs) toguide observing system agencies but also to improve the use of observationsin models.

These activities should be consolidated in Europe, in particular, as part of apartnership between EOOS, Atlantos and the Copernicus Marine Service.Cooperation with international partners (e.g. GOV/OceanPredict) is essential(OceanObs19).

AtlantOS Task 1.3 acknowledged publications

1. Hughes, C. W., Williams, J., Blaker, A., Coward, A., & Stepanov, V. (2018). A window on the deep ocean: The special value of ocean bottom pressure for monitoring the large-scale, deep-ocean circulation. Prog. Oceanogr., 161, 19-46. doi:10.1016/j.pocean.2018.01.011

2. Hedemann, C., Mauritsen, T., Jungclaus, J., and Marotzke, J. (2017). The subtle origins of surface-warming hiatuses. Nature Climate Change, 7, 336-339, doi:10.1038/nclimate3274.

3. Gasparin, F., Greiner, E., Lellouche, J.-M., Legalloudec, O., Garric, G., Drillet, Y., Bourdalle-Badie, R., Le Traon, P.-Y., Remy,E., and Drevillon, M. (2018). A large-scaleview of oceanic variability from 2007 to 2015 in the global high resolution monitoring and forecasting system at Mercator-Ocean, J. Marine Syst., 187, 260–276, https://doi.org/10.1016/j.jmarsys.2018.06.015.

4. Denvil-Sommer, A., Gehlen, M., Vrac, M., and Mejia, C. (2018). FFNN-LSCE: A two-step neural network model for the reconstruction of surface ocean pCO2 over the Global Ocean, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-247

5. Garry F.K. E. L. McDonagh A. T. Blaker C. D. Roberts D. G. Desbruyères E. Frajka‐Williams B. A. King, (2019) . Model derived uncertainties in deep oceantemperature trends between 1990‐2010, JGR-Oceans, https://doi.org/10.1029/2018JC014225.

6. Gasparin F., S. Guinehut C., Mao, I. Mirouze, E. Remy, R. King, M. Hamon, R. Reid., A. Storto, P.Y. Le Traon, M. Martin and S. Masina (2019). Requirements for an integrated in situ Atlantic Ocean Observing System from coordinated Observing System Simulation Experiments, Frontiers in Marine Sciences, Accepted.

7. Fujii, Y., Remy, F., Zuo, H., et al. (2019). OSE dased on Ocean Data Assimilation and Prediction Systems: On-going Challenges and Future Vision for Designing/Supporting Ocean Observational Networks, Frontiers in Marine Sciences. Accepted.

8. Gasparin F., Hamon, M., Remy, E., P.Y. Le Traon (2019). Towards robust estimations of the deep ocean variability with deep Argo. J. Climate, submitted. 9. Germineaud C., Brankart J.M. and Brasseur P. (2019). An ensemble-based probabilistic score approach to compare observation scenarios: an application to

biogeochemical-Argo deployments, Monthly Weather Review, submitted.

AtlantOS Task 1.3 publications in preparation10. Allison, L.C., C.D. Roberts, M.D. Palmer, R. Killick, L. Hermanson, N.A. Rayner and D.M. Smith (2019). Towards quantifying uncertainty in ocean heat content changes

using synthetic profiles. 11. Garry, F. K., Roberts, C. D., Frajka-Williams, E., McDonagh, E., Blaker, A. T., and King, B. A. (2019). Where do we need deep ocean observations to estimate planetary

energy imbalance from ocean heat content? .12. Ghosh, R., Jungclaus, J., Lohmann, K., Matei, D. (2019). Disentangling the effect of the global warming from the internal variability in the North Atlantic heat

transport. 13. Ford, D. et al. (2019). Description of Met Office biogeochemical OSSEs14. Mao, C., R. King, R. Reid, M. Martin and S. Good (2019). Impact of in situ observations in FOAM: an OSSE study. 15. Zuo, H., Balmaseda, M.A., Tietsche, S., Mogensen, K., Mayer, M. (2019). The ECMWF operational ensemble reanalysis-analysis system for ocean and sea-ice: a

description of the system and assessment. Ocean Science. 16. Guinehut, S. et al. (2019) Impact of Argo observing system enhancements in a multi observations ocean state estimate.