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MEsoSCale dynamical Analysis through combined model, satellite and in situ data PI: Bruno Buongiorno Nardelli 1 Co-PI: Ananda Pascual 2 & Marie-Hélène Rio 3 Participants: F.Bignami 1, S. Guinehut 3, G. Larnicol 3, S. Mulet 3, S.Ruiz 2, J. Tintorè 2 External expert: Y. Drillet 4 2009 MyOcean CALL for R&D proposals 123 4

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MESCLA concept Main objective and approach Scientific motivation Main activities and expected results Project organization Workpackage Breakdown Time schedule (GANTT) and required effort Scientific meetings First results Outline

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MESCLA approach includes: the diagnosis and comparison of vertical velocity fields from MyOcean model output and synthetic 3D fields obtained by combining in situ and satellite data (e.g. MyOcean ARMOR- 3D products) the adaptation of ARMOR-3D processing chain to ingest higher resolution MyOcean SST L4 products and the development of new extrapolation methodologies to obtain experimental higher resolution 3D re-analyses (requiring the development and test of a high resolution SSS L4 product). the analysis of the interannual variability of the vertical velocity fields MESCLA project is focused on the estimation and analysis of the vertical exchanges associated to mesoscale dynamics and of their interannual variability, concentrating on selected areas of the global oceans. Main objective and approach

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MESCLA approach includes: the diagnosis and comparison of vertical velocity fields from MyOcean model output and synthetic 3D fields obtained by combining in situ and satellite data (e.g. MyOcean ARMOR- 3D products) the adaptation of ARMOR-3D processing chain to ingest higher resolution MyOcean SST L4 products and the development of new extrapolation methodologies to obtain experimental higher resolution 3D re-analyses (requiring the development and test of a high resolution SSS L4 product). the analysis of the interannual variability of the vertical velocity fields MESCLA will thus also contribute to the scientific improvement of MyOcean products, developing and testing experimental techniques that could be used in the long-term to build the next generation of operational products. MESCLA project is focused on the estimation and analysis of the vertical exchanges associated to mesoscale dynamics and of their interannual variability, concentrating on selected areas of the global oceans. Main objective and approach FOCUS ON OBSERVATIONAL APPROACH NEW PRODUCTS DEVELOPMENT/TEST MODEL COMPARISON/VALIDATION

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Why mesoscale dynamics ? Scientific motivation

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Why mesoscale dynamics ? Scientific motivation Mesoscale dynamics modulates biological responses:

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Why mesoscale dynamics ? Scientific motivation Mesoscale dynamics modulates biological responses: Vertical displacements and mixing influence biology: upward transport of nutrients can lead to enhanced algal biomass in the photic zone… downward motions transport the biomass produced at the surface to deeper layers where it is remineralized, releasing once again nutrients into the dissolved phase... Mesoscale variability may have indirect consequences on ecosystem functioning and carbon cycle/climate Figures extracted from: Lévy, M, Klein, P. and A.-M. Treguier (2001). Impacts of sub-mesoscale physics on phytoplankton production and subduction, J. Mar. Res., 59,535-565 doi: 10.1357/002224001762842181

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Why mesoscale dynamics ? Scientific motivation Mesoscale dynamics modulates biological responses:

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Why mesoscale dynamics ? Scientific motivation The dynamical balance in key areas of the global oceans is mantained by mesoscale

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Why mesoscale dynamics ? Scientific motivation The dynamical balance in key areas of the global oceans is mantained by mesoscale EXAMPLE: ACC frontal instabilities at mesoscale carry momentum downward and both heat and mass poleward, counterbalancing the wind-driven upwelling along Antarctica A typical SSH gradient ( α surface geostrophic velocity) field south of Australia and New Zealand (3 Jul 2002) From Sokolov and Rintoul, 2007

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Why mesoscale dynamics ? Scientific motivation The dynamical balance in key areas of the global oceans is mantained by mesoscale EXAMPLE: ACC frontal instabilities at mesoscale carry momentum downward and both heat and mass poleward, counterbalancing the wind-driven upwelling along Antarctica Mesoscale has a primary role in the global heat, freshwater, carbon, and nutrient budgets impact on global climate system net effect under debate A typical SSH gradient (surface geostrophic velocity) field south of Australia and New Zealand (3 Jul 2002) From Sokolov and Rintoul, 2007

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Scientific motivation Effects of mesoscale on Climate and Biology Climate models have low resolution (computational limitations) Effects of mesoscale on property diffusion/transport need to be parameterized (e.g. through Eddy diffusivity) data assimilation in operational models allows more accurate analyses of vertical exchanges, but results can be significantly influenced by specific model configurations (e.g. forcing, parameterization of smaller scale processes and spatial resolution) Low resolution Medium resolution High resolution What is missing?…in terms of understanding Vertical velocities and space –time resolution are key factors to understand mesoscale processes and their impact at global scale

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Scientific motivation Effects of mesoscale on Climate and Biology Climate models have low resolution (computational limitations) Effects of mesoscale on property diffusion/transport need to be parameterized (e.g. through Eddy diffusivity) data assimilation in operational models allows more accurate analyses of vertical exchanges, but results can be significantly influenced by specific model configurations (e.g. forcing, parameterization of smaller scale processes and spatial resolution) What is missing?…in terms of understanding Low resolution Medium resolution High resolution New observational approaches can help to better understand the vertical exchanges associated with mesoscale processes, analyse their impact on ocean circulation and ecosystem and eventually help to define new parameterizations of mixing for climate models (far beyond MESCLA...)

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What is missing?…in terms of parameters Scientific motivation Vertical velocity cannot be directly measured indirect methods require 3D density and velocity fields at high resolution (10 km, at least daily)

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Scientific motivation Vertical velocity cannot be directly measured indirect methods require 3D density and velocity fields at high resolution (10 km, at least daily, down to the bottom) Vertical structure of the ocean is directly measured only by in situ observations sparse in space and time Vertical profiles of temperature collected from ships in 1998 Daily coverage by autonomous density profilers What is missing?…in terms of parameters

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Scientific motivation Vertical velocity cannot be directly measured indirect methods require 3D density and velocity fields at high resolution (10 km, at least daily) Vertical structure of the ocean is directly measured only by in situ observations: sparse in space and time Satellite sensors only provide measurements of surface parameters different resolution/coverage depending on sensor ALTIMETER surface level, surface (geostrophic) currents only along track, low spatial coverage/repetitiveness PASSIVE INFRARED/MICROWAVE SENSORS Sea surface temperature Up to <10 km, daily only provide surface measurements What is missing?…in terms of parameters

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Which improvements within MESCLA? Improve existing observational 3D fields (MyOcean ARMOR-3D) testing other multivariate techniques, merging in situ and satellite data and improving resolution Taking advantage of: higher resolution of satellite Sea Surface Temperature measurements historical in situ dataset cover a high number of the possible states of the system different parameters covariance Specific tasks/expected results HIGHER RESOLUTION DATA MyO SST L4 New SSS L4 NEW VERTICAL EXTRAPOLATION TECHNIQUE Multivariate EOF Reconstruction High resolution 3D fields Temperature Salinity Density

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Which improvements within MESCLA? High resolution 3D velocity fields will be estimated from synthetic fields and model output through a diagnostic numerical model These 3D velocity fields will be compared with estimates from MyOcean numerical model output Specific tasks/expected results Q-vector formulation of the OMEGA equation High resolution 3D fields Temperature Salinity Density High resolution 3D velocity fields w Vertical velocity U,V Horizontal geostrophic velocities

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WP 1. Vertical velocity estimation from MyOcean products Task 1.1 Adaptation of the Omega equation model to MyOcean products Task 1.2 Application of the Omega equation model and comparison between different vertical velocity estimates WP2: Development of 3D-fields experimental products from observations Task 2.1 Ingestion of MyOcean SST L4 products in ARMOR Task 2.2 Development of a high resolution SSS L4 product Task 2.3 Test of a multivariate EOF reconstruction for the extrapolation of vertical profiles from surface measurements WP3: Analysis of the interannual variability of the vertical exchanges associated to mesoscale dynamics Task 3.1 Re-analysis of vertical velocities Task 3.2 Analysis of the vertical velocities variability Workpackage breakdown

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Project schedule (GANTT) and required effort Task 1.1 Adaptation of the Omega equation model to MyOcean products Task 1.2 Application of the Omega equation model and comparison between different vertical velocity estimates Task 2.1 Ingestion of MyOcean SST L4 products in ARMOR Task 2.2 Development of a high resolution SSS L4 product Task 2.3 Test of a multivariate EOF reconstruction for the extrapolation of vertical profiles from surface measurements Task 3.1 Re-analysis of vertical velocities Task 3.2 Analysis of the vertical velocities variability

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Project schedule (GANTT) and required effort Task 1.1 Adaptation of the Omega equation model to MyOcean products Task 1.2 Application of the Omega equation model and comparison between different vertical velocity estimates Task 2.1 Ingestion of MyOcean SST L4 products in ARMOR Task 2.2 Development of a high resolution SSS L4 product Task 2.3 Test of a multivariate EOF reconstruction for the extrapolation of vertical profiles from surface measurements Task 3.1 Re-analysis of vertical velocities Task 3.2 Analysis of the vertical velocities variability MyO SDM

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WP 1. Vertical velocity estimation from MyOcean products Task 1.1 Adaptation of the Omega equation model to MyOcean products Task 1.2 Application of the Omega equation model and comparison between different vertical velocity estimates WP2: Development of 3D-fields experimental products from observations Task 2.1 Ingestion of MyOcean SST L4 products in ARMOR Task 2.2 Development of a high resolution SSS L4 product Task 2.3 Test of a multivariate EOF reconstruction for the extrapolation of vertical profiles from surface measurements WP3: Analysis of the interannual variability of the vertical exchanges associated to mesoscale dynamics Task 3.1 Re-analysis of vertical velocities Task 3.2 Analysis of the vertical velocities variability Workpackage breakdown completed started

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Task 1.1 Adaptation of the Omega equation to MyOcean products (resp. IMEDEA) Selection of input data: – PSY3V2R2: 1/4º resolution on mercator grid. 50 vertical levels – PSY2V3R1: 1/12º resolution on mercator grid. 50 vertical levels – ARMOR3D – V1 (v5e1 and v5e2) – Domain: NW Atlantic. Gulf Stream – Date test: 10/10/2007 Computation of geostrophic velocities (Dynamic Heights): – Sensitivity to the reference level: 1000 m surface SSH Vertical interpolation: – 10:10:1000 m Horizontal interpolation: – regular grid (1/4º, 1/12º) Conversion of data files to be ingested into the fortran code for the estimation of vector Q and vertical velocity Estimation of vector Q and vertical velocity Original profile Interpolated profile Example of a density profile

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Task 1.2 PSY2V3R1 1/12º – ref level 1000 m Applicaton of the Omega equation model and comparison between different vertical velocity estimates (resp. IMEDEA) w-Omega (1/12°) w-model (1/12°) SST DH

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Task 1.2 PSY2V3R1 1/12º – ref surf SSH Applicaton of the Omega equation model and comparison between different vertical velocity estimates (resp. IMEDEA) w-Omega (1/12°) w-model (1/12°) SST DH

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Task 1.2 PSY2V3R1 1/12º – ref level 1000 m - zoom Applicaton of the Omega equation model and comparison between different vertical velocity estimates (resp. IMEDEA) w-Omega w-model

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Task 1.2 PSY2V3R1 1/12º – ref level surf SSH - zoom Applicaton of the Omega equation model and comparison between different vertical velocity estimates (resp. IMEDEA) w-Omega w-model

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Task 1.2 PSY3V2R2 ¼º – ref level 1000 m Applicaton of the Omega equation model and comparison between different vertical velocity estimates (resp. IMEDEA) w-Omega (1/4°) w-model (1/4°) SST DH

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Task 1.2 PSY3V2R2 ¼º – ref surf SSH Applicaton of the Omega equation model and comparison between different vertical velocity estimates (resp. IMEDEA) w-Omega (1/4°) w-model (1/4°) SST DH

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Task 1.2 ARMOR-3D V1 projection of SLA and SST onto 24 Levitus vertical levels Applicaton of the Omega equation model and comparison between different vertical velocity estimates (resp. IMEDEA) w-Omega (1/4°) SST DH

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Task 1.2 ARMOR-3D V1 projection of SLA and SST onto 24 Levitus vertical levels + combination with in situ profiles Applicaton of the Omega equation model and comparison between different vertical velocity estimates (resp. IMEDEA) w-Omega (1/4°) SST DH

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Task 1.2 ARMOR-3D V1 projection of SLA and SST onto 24 Levitus vertical levels + combination with in situ profiles Applicaton of the Omega equation model and comparison between different vertical velocity estimates (resp. IMEDEA) w-Omega (1/4°) w-model (1/12°) SST DH

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Task 1.2 ARMOR-3D V1 projection of SLA and SST onto 24 Levitus vertical levels + combination with in situ profiles Applicaton of the Omega equation model and comparison between different vertical velocity estimates (resp. IMEDEA) w-Omega (1/4°) w-model (1/4°) SST DH

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Ingestion of MyOcean SST L4 product in Armor3D (Resp. CLS) Task 2.1 Armor3D: 2-step method based on observations and statistical method Step1: Vertical projection of satellite SLA+SST using a multiple linear regression method Step2: Combination of synthetic fields and in-situ T/S profiles using an optimal interpolation method Historically, Armor3D-v0 uses Reynolds OI 1° SST Armor3D-v1 uses Reynolds AVHRR-AMSR 1/4° SST (real-time and reanalysis) Several global L4 SST products exists (MyOcean, other data centres): How do they compare? What is the impact on Armor3D? What are there performances compared to independent Argo data set?

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Ingestion of MyOcean SST L4 product in Armor3D (Resp. CLS) Task 2.1 Comparison of different SST products

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Ingestion of MyOcean SST L4 product in Armor3D (Resp. CLS) Task 2.1 Comparison of different SST product increments weeklydaily

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Ingestion of MyOcean SST L4 product in Armor3D (Resp. CLS) Task 2.1 Synthetic T at 30m SST Synthetic T at 50m Synthetic T at 100m Reynolds 1°Reynolds 1/4° 27/12/2006 Impact of higher resolution on Armor3D

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Development of a high resolution Sea Surface Salinity (SSS) L4 product (Resp. CNR) Task 2.2 SSS possibly needed by new 3D reconstruction methods new product potentially useful in combination with SMOS data Hypothesis: high correlation between sea surface temperature (SST) and sea surface salinity (SSS) variations can be expected (in the open ocean) at scales significantly smaller than the ones dominating atmospheric variability similar correlation found by Jones et al. (1998) between SSH and SST analogous results recently obtained looking at SST and SSS variability by Aretxabaleta et al. (2009, communication at EGU) and Ballabrera-Poy et al. (2009). Proposed technique: optimal interpolation (Bretherton-like) algorithm that includes satellite SST differences in the covariance estimation

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Test strategy and input datasets First tests: calculations performed on a single day (17th October 2007), limiting to a portion of the North Atlantic Ocean Covariance function parameters (i.e. spatial (L), temporal (τ) and thermal (T) decorrelation scales) determined empirically minimizing errors vs independent surface observations Salinity Observations (input data): MyOcean INSITU-TAC obs. from profiling floats, CTD and XCTD, referenced to 5 m depth SST observations: ODYSSEA L4 SST different filtering applied to remove the large-scale variability associated to air-sea interactions, trying to keep only the signals associated with the different water masses and related to mesoscale structures original SST values high-pass filtered data (keeping the scales smaller than 500 and 1000 km respectively). Background field: MyOcean CORIOLIS SSS objectively analyzed maps (covering the ½° MERCATOR grid) Validation data: thermosalinograph obs. provided by the Global Ocean Surface Underway Data project (GOSUD) and by the French Sea Surface Salinity Observation Service (at LEGOS) Task 2.2

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Test datasets in situ SSS Red dots (input) 30 days window, centered on interpolation day Blue dots (validation) (only for interpolation day) Task 2.2

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in situ SSS Red dots (input) 30 days window, centered on interpolation day Blue dots (validation) (only for interpolation day) Background SSS Test Datasets Task 2.2

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in situ SSS Red dots (input) 30 days window, centered on interpolation day Blue dots (validation) (only for interpolation day) Background ODYSSEA L4 SST Test datasets Task 2.2

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Empirical determination of OI parameters: RMSE (black contour) and MBE (red) vs validation data (expressed as % with respect to CORIOLIS –SSS L4 errors), as a function of the spatial and thermal decorrelation scales. High-pass filtered SST (<1000 km) different values of noise-to-signal ratio different values of temporal decorrelation scale (τ) (L) (T) Task 2.2

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Best configuration and quantitative validation lowest errors RMSE ~0.11 psu ~70% of CORIOLIS error MBE ~0.01 psu ~30% of CORIOLIS error L= 400 km τ=6 days T=2.75 °C signal-to-noise=0.01 High-pass filter: signals <1000 Km RMSE (black contour) and MBE (red) vs validation data (expressed as % with respect to CORIOLIS–SSS L4 errors) Task 2.2

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Qualitative results: Background MESCLA MERCATOR MESCLA high resolution SSS field and derived SSS gradient reveal more realistic and smaller scale structures than those visible in CORIOLIS-SSS product. MERCATOR 1/12° displays even smaller scales… Task 2.2

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Conclusions and future work First results promising: Omega gives accurate results at 1/12° (quantitative validation/analysis to be completed) ARMOR3D being improved in terms of resolution: soon getting the first 1/12° estimates (with standard 3D extrapolation) Test of multivariate 3D extrapolation will start soon (using combinations of SSH, SST, SSS as input) Method for SSS L4 estimate promising

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THANKS! Thanks for listening and for funding MESCLA!

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OS3.2 From physical to biogeochemical processes: ocean mesoscale and sub-mesoscale impact on marine ecosystem and climate variability Convener: Ananda Pascual Co-Conveners: John Allen, Bruno Buongiorno Nardelli, Marina Lévy …This session will provide a forum to properly address the new scientific challenges associated with mesoscale and sub-mesoscale variability (between 1 km and 300 km), based on observations (both in situ and satellite and multi-sensor approaches), theory, and numerical simulations. …. IMPORTANT: Deadline for receipt of abstracts: 10 January 2011

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