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Sensitivity to Wind Stress Formulation in a Coupled Wave-Atmosphere Model

This study investigates the sensitivity of a coupled wave-atmosphere model to wind stress formulation and evaluates the model's performance against various wind observations. Results show the need for a more accurate parameterization to account for wave influence on wind stress.

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Sensitivity to Wind Stress Formulation in a Coupled Wave-Atmosphere Model

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  1. Sensitivity to wind stress formulation in a coupled wave-atmosphere modelHigh Winds Workshop, Exeter, 2016/11/15-17 Lucia Pineau-Guillou1, Fabrice Ardhuin2, Marie-Noëlle Bouin3, Jean-Luc Redelsperger2, Bertrand Chapron1, Jean Bidlot4 1 Ifremer/LOPS (Laboratory of Physical and Spatial Oceanography), Brest, France 2 CNRS/LOPS 3 Météo-France/LOPS 4ECMWF, Reading, UK

  2. 1. Introduction 2. Case study 3. Model & Observations 4. Results 5. Conclusion & Outlook

  3. 1. Introduction • Motivations • Could be due to underestimation of high winds in atmosphere models ? • Inappropriate representation of wind stress in numerical models ? Wind Sea levels Wave breaking Waves Wave set-up (surge due to wave breaking) Surges Currents Wave-induced current Underestimation of large wave heights in wave models Focus on ocean response to extreme wind eventsChallenges in coastal areas, flooding, submersion Tide Underestimation of storm surges in hydrodynamic models • Objective: define a wind stress parameterization taking into account the waveinfluence, along a more physic approach • Comparison with observations: scatterrometers, radiometers, altimeters, buoys, platforms Preliminary results, undergoing work

  4. 2. Case study • Mid latitude storms in North East Atlantic • Analysis ofERA-Interim database over the last 10 years (2005-2014) • Criteria on wind speed (>32 m/s) and surface pressure (<975 hPa) • Typical storm over North Atlantic, Kaat and Lilli from 23rd to 28th of January 2014 Kaat storm track Principal tracks in North Atlantic (dotted line) Lilli storm track Kaat and Lilli storm tracks on January, 2014 (data every 6 hours). In black dotted line, schematic of principal tracks for lower tropospheric storm track activity (Hoskins & Hodges, 2002)

  5. 2. Model & Observations Model • ECMWF atmosphere model IFS coupled with wave model ECWAM • All simulations on ECMWF Cray(simulations on about 2600 nodes), without data assimilation • Configuration: IFS atmosphere model: 16 km, 137 levels ECWAM wave model: 28 km, 36 directions, 36 frequencies5-day simulation from 23rd to 27th January 2014 Charnock parameter (exchanged between WAM/IFS) Wave effect taken into account through viscosity sea state

  6. 2. Model & Observations Wave age parameterization: too strong dragcompared with observationsExperiment 6-18 m/s MFWAM parameterization: too strong dragCould be adjusted, particularly with parameter TAUWSHELTERwhich contrains wave-induced stress Charnock parameter for different parameterizations Drag for different parameterizations Empirically-derived Charnock parameterization: allows reducing drag and variability compared to ECMWF default parameterization To select optimal parameterization, comparison with observed winds

  7. 2. Model & Observations Winds observations Platforms Buoys Satellites

  8. Winds on January 26, 2014 from satellites ASCAT ascending ASCAT descending SMOS AMSR2 ascending AMSR2 descending WindSat ascending WindSat descending JASON-2

  9. ECMWF default parameterization Empirically-derived Charnock parameterization 4. Results Uncoupling Charnock z0 Cd Wind stress Wind MSLP A largerCharnock parameter leads to larger roughness length, higher drag coefficient, higher wind stress, and then lowerwindspeed and higher surface pressure in the storm center. Developped parameterization reduces Charnock parameter and drag coefficient at high winds, leading to higherwindspeed.

  10. 4. Results Comparison with observations • Focus on North East Atlantic 30°W 10°E 30°N 65°N • Simulations from 23rd to 27th of January, for 5 parameterizations • Model extracted on a 0.125° grid • For more coherence, observations have been averaged if different points in the grid cell • Colocation criteria: time difference < 15’ • Correlations and biases have been computed Study area Observations and model grid

  11. Correlations between observations and ECMWF winds (x-axis)(ECMWF default parameterization) 4. Results ASCAT AMSR2 JASON-2 Loss sensitivityGood correlation (0.95) SMOS WindSat Quite noisy (0.79) Buoys (green)Platforms (blue) Underestimation of modelled high winds compared with observations (except for buoys and ASCAT)But bias depends on type of observation

  12. 4. Results Bias for each parameterization Platforms Buoys ASCAT Bias (model-obs) Uncoupling: positif biasECMWF default param.: bias~0 Uncoupling: no biasECMWF default param.: bias <0Modified Charnock : reduce bias ECMWF default param.: bias <0Modified Charnock : reduce bias AMSR2 WindSat SMOS 1) Comparison with all data (except buoys) show that strong winds are underestimated with ECMWF default parameterization 2) Developped parameterization allows reducing the bias Limitation : interpret carefully last values, few points

  13. 4. Results Bias for each type of observation(ECMWF default parameterization) ASCAT consistent with buoys but bias ASCAT/other satellites~ 4 m/s Bias buoys/platforms~ 4 m/s Satellites (except ASCAT) consistent with platforms 1) Underestimation of modelled high winds 2) Where is the truth betweeen observations ? 5-20 m/sgood agreement 20-40 m/sunderestimation

  14. 4. Results Results would have been different, depending on data provider Bias ~6 m/s ASCATKNMI ASCAT RSS winds higher than ASCAT KNMI winds Bias ASCAT RSS AMSR2SOLab AMSR2 RSS winds higher than SOLab winds Contribution from OceanDataLab AMSR2 RSS

  15. 4. Results • Going further on bias between buoys and platforms… • Buoys could underestimate high winds as affected by high sea state, vertical buoy motions and sheltering effect of the waves (Zeng & Brown 1998) PDF Platforms Buoys But wind probability different depending on areas • Platform measurements between 80 and 150 m • Methodology to reduce to 10 m ? (reduction coef.) • Flow distorsion due to structure ? Quite similar for high winds

  16. 5. Conclusion & outlook Motivation : Underestimation of large wave heights and surges in hydrodynamic and wave models • Could be due to underestimation of high winds in atmospheric models ? • Inappropriate representation of wind stress in numerical models ? 1)Possible underestimation of high winds in atmospheric models, bias ~5m/s at 30 m/s / satellites 2) Developped parameterization • Reduce bias at high winds. However, further validation is needed. • Has been implemented by ECMWF, currently under test, first results show improvements. • More work is needed to understand which physical mechanism(s) it represents that are not currently modelled by the default parameterizations 3) Observations • ASCAT and buoys strong winds tend to be underestimated, compared with other observations • Clear bias between buoys and platforms 4) Another storm 2015/12/07 • Similarresults • SMAP consistent with WindSat and SMOS Outlook: ocean response, impact of different parameterizations on storm surges

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