data assimilation in global ocean analysis and forecasting ... marine application_mdrevill… ·...
TRANSCRIPT
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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
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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
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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:
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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
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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!
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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
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pace
Coastal & marine
environment
Maritime safety
Marine
resources
Weather, climate & seasonal forecasting
Areas of benefit
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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
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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
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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
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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
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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
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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.
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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
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Surface Currents climatology
GLORYS12V1 average meridional velocity 1993-2014 CMEMS INS TAC drifters average merid. velocity 1993-2014
m/s m/s
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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)
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Currents at depth
(from ARGO parking depth)
GLORYS12V1
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Currents at depth
reanalysis
NRT analysis
Pirata O°E 23°W
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Transports
From Mignac et al, OSD 2018
Spread in transports estimates -> linked withlack of near coastalmeasurements + DA tunings near the coasts
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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
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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.
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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
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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
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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