The impact of assimilating SST, Argo and SLA data into an eddy-resolving tidally driven model for the Brazil Current region
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1 Oceanographic Modeling and Observation Network (REMO) The impact of assimilating SST, Argo and SLA data into an eddy-resolving tidally driven model for the Brazil Current region Rafael Santana Filipe Costa, Davi Mignac, Alex Santana and Clemente Tanajura
2 1 Introduction Brazil Current (BC) >> The BC has a wellmarked mesoscale activity downstream Vitória-Trindade ridge >> The BC intensely meanders and generates Vitória, Cape São Tomé and Cape Frio eddies
3 1 Introduction Ocean eddies SLA and Vel. >> The BC has a wellmarked mesoscale activity downstream Vitória-Trindade ridge >> The BC intensely meanders and generates Vitória, Cape São Tomé and Cape Frio eddies Chl a >> Ex: CSTE >> Eddies are formed by barotropic and baroclinic instabilities
4 1 Introduction BC Eddies >> Eddies are formed due to barotropic and baroclinic instabilities IWBC >> Simulation of observed eddies needs: high-resolution modeling and data assimilation (Xie et al., 2011 and Fragoso et al. 2016) DWBC >> Tides interact with mesoscale features (Davies and Lawrence, 1995; Simmons et al., 2004; Moon, 2005 and Xie et al., 2011)
5 1 Introduction BC Eddies What are the impacts of DA into a tidal model to simulate the BC eddies? IWBC How DA and tides impact the model performance on simulating BC eddies? DWBC
6 1.1 Goal Aiming to answer the aforementioned questions, the study goal is to: Identify and quantify the impact of data assimilation and tides on the BC eddies simulation. Implement and evaluate our DA scheme into the tidal model Objectively validate the model performance on simulating observed BC eddies
7 2 Methods REMO nested model system >> To properly simulate the BC magnitude and its variability Nested model system: >> HYCOM 2.2 >> 1/4º > 1/12º > 1/24º >> 21 sigma-theta layers >> Tides included in 1/24º >> ETOPO2 + Nautical charts >> CFSR-NCEP
8 2 Methods REMO Ocean Data Assimilation System (RODAS) REMO Ocean Data Assimilation System (RODAS) (Tanajura et al., 2014, Mignac et at., 2015) Ensemble Optimal Interpolation (EnOI) (Evensen 2003; Oke et al., 2005; Xie and Zhu, 2010) Ensemble >> 126 seasonal members (free run) 60 days Year Year 6 At=t0
9 2 Methods REMO Ocean Data Assimilation System (RODAS) Assimilation cycle: 3 days Assimlated observations: SST from OSTIA T/S from Argo SLA (along-track 7km) (ATOBA-CLS) Localization radius: 150 km Observational error:
10 2 Methods RODAS 3 Scheme follows the Xie et al. (2011) strategy for DA with tides.
11 2 Methods RODAS Filter: Daily mean > SST T/S Tidal harmonic prediction > SLA 3 Scheme follows the Xie et al. (2011) strategy for DA with tides.
12 2.1 Methods Set of experiments CONTROL Free run with tides A_SST Assimilates SST data from OSTIA (with tides) A_TS Assimilates T/S profiles from Argo (with tides) OSE A_SLA - Assimilates SLA data from ATOBA (with tides) A_ALL Assimilates all observations above (with tides) Validation: 24h predicted (hindcast) variables in 2010 and 2011
13 3 RESULTS > SST RMSD ( ) OSTIA CONTROL 1.41 ºC A_SST 0.53 ºC A_TS 1.09 ºC A_SLA 1.07 ºC A_ALL 0.52 ºC 63%
14 3 RESULTS > T RMSD PROFILE 0-500m 1.43 ºC 0.92 ºC
15 3 RESULTS > T RMSD PROFILE 0-500m 1.43 ºC 0.92 ºC 1.07 ºC
16 3 RESULTS > T RMSD PROFILE 0-500m 1.43 ºC 0.92 ºC 1.07 ºC 0.81 ºC
17 3 RESULTS > T RMSD PROFILE 0-500m 1.43 ºC 0.92 ºC 1.07 ºC 0.81 ºC 0.79 ºC 45%
18 3 RESULTS > S RMSD PROFILE 0-500m %
19 3 RESULTS - SSH STD AND CORR. ( ) Black contour = 0.7 correlation AVISO CONTROL 0.33 A_SST 0.46 A_TS 0.42 A_SLA 0.60 A_ALL %
20 3 RESULTS > EDDY SIMULATION Eddy validation experiments A_SLA - Assimilates SLA data from ATOBA (with tides) A_ALL Assimilates all observations above (with tides) A_ALL_NOTIDES Assimilates all observations (without tides) EDDIES A_ALL_008 HYCOM 1/12 assimilates all observations
21 3 RESULTS > EDDY SIMULATION Objectively eddy validation >> Previous validation of simulated eddies used monthly averages of SSH and currents (Xie et al., 2011) or few snapshots from one eddy (Fragoso et al., 2016). A_ALL TIDES Mill et al. (2015) >> Daily fields using an objective method and database >> Chelton et al. (2007) and Okubo-Weiss parameter (manually assisted) >> Faghmous et al. (2014) algorithm works without any human assistance.
22 3 RESULTS > EDDY SIMULATION Comparison between eddy tracking algorithms Mill et al. (2015) Faghmous et al. (2014) A_ALL TIDES SLA contours of 3 cm and Okubo- Weiss parameter (Chelton et al., 2007) (Manually assisted). Simulated eddies were valid with at least 50 km distance to AVISO eddies.
23 3 RESULTS > EDDY SIMULATION 2011/04/22: Eddy SLA amplitude AVISO A_ALL A_ALL NOTIDES A_SLA
24 3 RESULTS > EDDY SIMULATION 2011/04/22: Eddy SLA amplitude TIDES AVISO A_ALL A_ALL NOTIDES A_SLA
25 3 RESULTS > EDDY SIMULATION 2011/04/22: Eddy SLA amplitude TIDES AVISO A_ALL A_ALL NOTIDES A_ALL_008
26 3 RESULTS > EDDY SIMULATION 2011/05/12: Eddy migration AVISO A_ALL A_ALL NOTIDES A_SLA
27 3 RESULTS > EDDY SIMULATION 2011/05/12: Eddy migration DA: TS AVISO A_ALL A_ALL NOTIDES A_SLA
28 3 RESULTS > EDDY SIMULATION 2011/05/12: Eddy migration DA: TS AVISO A_ALL A_ALL NOTIDES A_ALL_008
29 3 RESULTS > EDDY SIMULATION 2011/03/06 07/01: Eddy distance
30 3 RESULTS > EDDY SIMULATION : Eddies simulation
31 3 RESULTS > EDDY SIMULATION : Eddies simulation
32 4 SUMMARY Assimilation reduced the SST, T and S errors in 63%, 45% and 26% respectively and increased the SSH correlation 81%. The tide appears to provide more energy to the system and correct the eddy SLA amplitude as well as improve the eddy temporal representation. The assimilation of TS profiles was important to correct the thermohaline structure which allowed the correct eddy migration. The increase in model resolution and the inclusion of tides almost doubled the eddy representativeness.
33 Foto: cortesia de Antonio Nobre Thanks for your attention!
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