Data assimilation in global ocean analysis and forecasting system, for Marine applications :

focus on the Tropical Atlantic

Marie Drévillon, Elisabeth Rémy, Eric Greiner, Charly Régnier, Jean-Michel Lellouche

and the Mercator Ocean team

Overview

➢ Short description of Mercator Ocean’s global (re)analyses system

➢Main strengths and limitations in the Tropical Oceans

➢… with some illustrations

➢More information on Copernicus Marine Service CMEMS, GODAE and OSEs to follow in tomorrow’s presentation

pace

Satellite observations (surface, repetitive) In-situ observations (description at depth, sparse)Models (3D, assimilating all observations)

The past (long data time series)The present (current oceanic conditions)The future (forecast)

Blue ocean (physics : currents, T and S …)White ocean (sea ice)Green ocean (chlorophyll, CO2, oxygen, pH, …)

Monitoring the Marine Environment is INTEGRATING:

Cliquez et modifiez le titre

Global (re)analysis system in short

Use of NEMO ORCA025 (1/4°) 75L + LIM

Forced with ERA-interim reanalysis (large scalecorrection of precipitations and radiative forcing) and climatological runoffs

Multivariate assimilation of SST, SLA, in situ T/S profiles (and monovariate for Sea Ice)

3D T and S 3DVAR large scale bias correction

restoring to climatology at Gibraltar strait, Bab el Mandeb strait, and south of 60°S below 2000m

Focus on the altimetry era: 1992-now

Evaluation protocole from GODAE/GSOP/ORA-IP

Ocean reanalysis « GLORYS2V4 » 1993-2016 Ocean analyses 2007-now

Use of NEMO ORCA12 (1/12°) 50L + LIM

Forced with ECMWF IFS analyses and climatologicalrunoffs

Multivariate assimilation of SST, SLA, in situ T/S profiles (and monovariate for Sea Ice)

3D T and S 3DVAR large scale bias correction

Weak assimilation of EN4 climatology below 2000m

Adaptive observation errors for SLA and SST

Phased with homogeneous HR atmospheric forcing availability 2007-now

Evaluation protocole from GODAE/MyOcean/CMEMS

Cliquez et modifiez le titre

Global (re)analysis system in short

Use of NEMO ORCA12 (1/12°) 50 L+ LIM

Forced with ERA-interim reanalysis (large scalecorrection of precipitations and radiative forcing) and climatological runoffs

Multivariate assimilation of SST, SLA, in situ T/S profiles (and monovariate for Sea Ice)

3D T and S 3DVAR large scale bias correction

Weak assimilation of EN4 climatology below 2000m

Adaptive observation errors for SLA and SST

Focus on the altimetry era: 1992-now

Evaluation protocole from GODAE/GSOP/ORA-IP

Ocean reanalysis « GLORYS12V1 » 1993-2016 Ocean analyses 2007-now

Use of NEMO ORCA12 (1/12°) 50L + LIM

Forced with ECMWF IFS analyses and climatologicalrunoffs

Multivariate assimilation of SST, SLA, in situ T/S profiles (and monovariate for Sea Ice)

3D T and S 3DVAR large scale bias correction

Weak assimilation of EN4 climatology below 2000m

Adaptive observation errors for SLA and SST

Phased with homogeneous HR atmospheric forcing availability 2007-now

Evaluation protocole from GODAE/MyOcean/CMEMS

New in 2018: HR reanalysis GLORYS12, increased consistency

between reanalysis and NRT analysis

A very BIG dataset!

Tropical ocean in Mercator Ocean analyses

➢ Tropical oceans are a key area for ocean atmosphere interaction : Météo-France seasonal forecasting system oceanic initial conditions are derivedfrom Mercator Ocean analyses (cf Magdalena Balmaseda)

➢ Many other Marine applications require high quality high resolutionoceanic information in the tropical oceans: ▪ Defense▪ Fisheries, marine resources…▪ maritime safety, commercial ships routing▪ Marine renewable energies

pace

Coastal & marine

environment

Maritime safety

Marine

resources

Weather, climate & seasonal forecasting

Areas of benefit

Tropical ocean in Mercator Ocean analyses

• Data assimilation gives a compromise in between a model solution (first guess) and all sources of observations. The analysis is close to available observations on average -> the observing system is at the center, the more (QC) observations, the better

• Currently, scales smaller than ~¼° and ~1 day are not constrained -> in progress

• Errors cumulate where there are less observations/constraint: at depth, salinity

• Tropical oceans specificities: ➢ larger zonal correlations scales than in higher latitudes -> taken into

account➢ rapid wave propagation and strong vertical shear -> more difficult to

constrain➢ Issues with constraining equatorial dynamics with altimetry, MDT errors ->

large errors in currents

Sea surface temperature variability

SST variability is well represented from the weekly to the interannual scale. It isconstrained by atmospheric forcings and assimilated SST (OSTIA in real time, NOAA ¼° analyses in reanalyses)

Monthly SST average anomaly (°C, black line and color shading) in the nino3.4 boxDashed line is NOAA CPC nino3.4 index-> see CMEMS Ocean State Report, JOO 2017 -> Ocean Monitoring Indicators to appear on CMEMS catalogue in 2018, including Tropical Atlantic Boxes

Map of SST biases (NRT analyses)

NRT analyses are

too warm (~0.5°C) on

average with respect

to assimilated OSTIA

But bias with respect to in situ is

different ->

inconsistencies (foundation SST, in

situ depth etc…)

Lack of Trop atl in situ observations

Surface salinity

Surface salinity biases are reduced in the HR reanalysis with respect to ¼°reanalyses

GREP product: ensemble mean and standard deviation from 4 reanalysesORAS5, CGLORS, GLOSEA5, GLORYS2V4

Interannual variability of SSS

Hovmuller 2010 – 2015 of Pacific Ocean surface salinity 2°N-2°S for reanalysis ¼ ° (left) , and NRT analyses (right)seasonal cycle is removed

-> Influence of atmospheric forcings

Sea Level

RMS errors are very small on average (< 4 cm)

Significant biases persist in the Tropical Pacific.

RMS errors are large in highly variable areas. In the Tropical Atlantic RMS errors are large in the North Brazil Current.

Surface Currents climatology

Surface currents position and variability are well captured thanks to altimetry

GLORYS12V1 average zonal velocity 1993-2014 CMEMS INS TAC drifters average zonal velocity 1993-2014

m/s m/s

Surface Currents climatology

GLORYS12V1 average meridional velocity 1993-2014 CMEMS INS TAC drifters average merid. velocity 1993-2014

m/s m/s

Surface currents bias

U drift innovation in 2008-2013 (psy4v3r1)

m/s

Surface currents are not constrained directly by observations, and errors in winds or vertical physics, or MDT can induce large errors in currents -> equatorial divergence issues

Lack of observations for validation (here 5 years of drifters velocities are needed to produce an error map with global coverage)

Currents at depth

(from ARGO parking depth)

GLORYS12V1

Currents at depth

reanalysis

NRT analysis

Pirata O°E 23°W

Transports

From Mignac et al, OSD 2018

Spread in transports estimates -> linked withlack of near coastalmeasurements + DA tunings near the coasts

Green Ocean

BGC models with data assimilation of ocean colour:Encouraging results but still a long way to goFirst evaluations/calibrations with bio argoplanned in 2018

NO ASSIM ASSIM

DATA

Year 1995: Annual mean of chlorophyll concentration

GLORYS2V3 : Chlorophyll after 3 years

Assimilation of tropical moorings’ data

Tropical moorings have high temporal frequency but low spatial sampling -> underdetermined estimation problem for fast tropical waves, -> need of filtering of the data model misfits to remove unresolved scales.

Mean and RMS observation-analysis error to in situ temperature observations in the Nino 3 region : with the

TAO assimilated in red, without in blue.

Continuous improvement of analysis system

for marine applications

Case study of the search for the AF447 wreckageDrevillon et al, 2013 Clim. Dyn.-> under-observed conditions

With current NRT analysesWith NRT analyses available in 2010

Wreck was found near « ACARS » point

Ensembles of forward trajectories initiated from all points

inside the search area

Short distance score = minimum distance from all debris

is found for trajectories starting from those points

Conclusions

Strength and limitations of ocean analyses for marine applications:

• temperature and salinity variability and state are well captured, especially near the surface

• Surface currents variability is well captured

• Equatorial currents are too strong, especially at depth

• Only large scale information is extracted from current observation system

need for observations:

• All observations are valuable -> towards the use of high resolution observations

• HF moorings are essential for validation/calibration, and useful for DA

• More observations at depth will help reduce system biases and improve the capacity to capture trends

• More coastal observations + better taken into account could improve circulation

Perspectives

Perspectives:

• Data assimilation improves the average accuracy, but can induce spurious high frequency phenomena (gravity waves, recirculation cells) -> need for more process oriented validation of experiments with and without data assimilation

• Small scales (<1 day and < ¼°) are unconstrained -> improvements expected first from HR SST assimilation using “4D” approach, HF mooring observations impact will be evaluated

Part of this work is planned in Atlantos project and/or GODAE OSE-val TT

On the longer term

Ocean-Atmospheric Boundary Layer-waves coupling at high resolution (1/36°)

Ensemble runs -> uncertainty estimates, ensemble DA